BCA

Workplace Project Portfolio

Angela Jacques

University of Sydney

Preface

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Introduction

This portfolio represents two projects carried out as part of duties pertaining to my

current employment at the Women and Infants Research Foundation (WIRF).

WIRF is one of Western Australia's leading community-based research

organisations dedicated to the fields of Obstetrics, Gynaecology and Newborn

Medicine. WIRF has a close working relationship with King Edward Memorial

Hospital for Women, which is the sole tertiary level perinatal centre for Western

Australia, and the School of Women’s and Infants’ Health at the University of

Western Australia (UWA). WIRF conducts research independently as well as in

partnership with other organisations to fund, support and advocate for high quality

scientific studies. Many fields are under investigation including the prevention of

preterm birth, care of sick newborns, the establishment of a human milk bank and

the management of menopause. Both projects presented in this portfolio cover two

different areas of women’s and infants’ health and are examples of the type of work

undertaken by the foundation on a daily basis.

This project work was carried out under the direct supervision of Associate

Professor Dorota Doherty, (Head Biostatistician, Biostatistics and Research Design

Unit at WIRF and Adjunct Associate Professor at the School of Womens’ and

Infant’s Health, UWA). The role of the unit is to provide biostatistical consultation

and collaboration in the design, conduct, analysis, interpretation and reporting of

research conducted at King Edward Memorial Hospital and affiliated institutions. I

am employed as a biostatistician within this unit and report to A/Prof Doherty.

Project 1

Depression in the antenatal or postpartum period is a potentially serious illness

which can be triggered by pregnancy. Detecting and treating mental health disorders

in the perinatal period is critical. There is clear evidence that if left untreated, there

can be serious consequences for the mothers, their infants and families such as

problems with bonding and issues with child development.

This study was done as part of the beyondblue National Postnatal Depression

Program and utilised the Western Australian data collected for this initiative. It

consists of a prospective cohort study designed to examine the course of depressive

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symptomatology, and the timing of prevention and intervention strategies, across the

perinatal period in 4838 women at four obstetric services in Perth.

Part of the work presented in Project 1 (“Tailoring screening protocols for perinatal

depression: prevalence of high risk across obstetric services in Western Australia”)

has been published in The Archives of Women’s Mental Health, 2009 and presented

at the Perinatal Society of Australia and New Zealand (PSANZ) 2006 Annual

Conference. The manuscript forms part of this project portfolio.

Project 2

Sepsis is a serious morbidity affecting survival of very preterm (less than 30 weeks

gestational age) infants. The study presented in this project is a retrospective cohort

study examining the association of chorioamnionitis (inflammation of the fetal

membranes due to bacterial infection) with early-onset sepsis (sepsis onset within 3

days postnatally) and late-onset sepsis (sepsis onset after 3 days postnatally), in

particular, Coagulase-negative staphylococcal late-onset sepsis. The cohort

consisted of 838 very preterm infants that were admitted to the neonatal intensive

care unit at KEMH between 2001 and 2007. This study was conducted in

collaboration with Dr David Burgner, Associate Professor in Paediatrics, School of

Pediatrics and Child Health, UWA, and Dr Tobias Strunk, Neonatal Fellow,

Department of Neonatal Paediatrics, King Edward Memorial Hospital. This work

has been presented at the European Society for Paediatric Research (ESPR)

Conference in October 2009, and at the European Society for Paediatric Infectious

Diseases (ESPID) Conference in May 2010. A manuscript is currently under

preparation for submission to the journal Paediatrics.

Student’s Role

Project 1 details work I performed as part of a team working on the beyondblue

project under the direct supervision of A/Prof Doherty. Due to the scope of the

project, analysis of the antenatal and postnatal outcomes were conducted

concurrently. I was responsible for the analysis which involved detailed evaluation

of the postnatal outcomes. There was extensive collaboration undertaken with

psychologists from the Department of Psychiatry at KEMH (Dr Delphin Swalm) and

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from Edith Cowan University (Professor Craig Speelman and Dr Janette Brooks)

while undergoing the process of writing the manuscript.

I conducted all the statistical analyses associated with Project 2, under the direct

supervision of A/Prof Doherty and in collaboration with neonatal and paediatric

physicians A/Prof David Burgner and Dr Tobias Strunk. Considerable time was

spent on data preparation associated with converting data extracted from medical

databases into an analytic dataset. Constructing the analytica dataset was done in an

interactive consultation with the physicians in order to create the appropriate

variables suitable for analysis of the outcomes.

Reflections on Learning

Along with data preparation, in the process of working on this project I learnt much

about managing data, syntax and presentation of results. It became rapidly apparent

that there is a vast difference in the amount of data analysis that is actually

performed compared with the final version submitted in a manuscript. Extensive

preliminary analysis has to be performed in order to acquaint researchers with

outcomes in order further proceed with inferential analysis.

As mentioned previously, in Project 2 considerable time was spent on transforming

data. There is an inherent problem extracting data from medical relational databases

designed to maximise data storage, when the requirements of statistical analysis

necessitate the creation of an analytic dataset. There was frequent consultation and

discussion on a continual basis with the two clinicians due to (i) the low frequency

of some of the outcomes of interest creating the possibility of overfitting the data

and (ii) large proportions of missing data in some of the outcome variables creating

the possibility of bias. All of that information had to be related to the clinicians in

order to discuss the implications of how the final analysis is performed. Work on

this project had to be done in accordance with deadlines for conference presentations

and manuscript preparations.

Apart from the statistical aspects of modeling the data, the practical ways of

acceptable data presentation was highly relevant to both of these projects. The

ability to present results in a concise yet easy to understand manner was a constant

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challenge and became obvious when the data was presented to clinicians without

statistical training. I learnt that the practice of following the presentation methods of

related manuscripts is a fairly important starting point when considering the best

way of presenting results. I have noticed that, on occasion, the presenting of optimal

statistical analyses can generate poor reviewer comments, or may not be acceptable

or understandable to the clinical fraternity. It is often prudent to keep in mind the

audience that the analysis is being presented to.

Apart from spending a surprisingly large amount of time in data preparation, another

significant difference between applying biostatistics in the workplace compared to

material covered in the BCA units is that the choice of appropriate models is more

often a compromise because the assumptions associated with different modeling

techniques are often not satisfied. Confidence with what models are best used under

given circumstances increases with practice, although learning the theoretical

aspects through the BCA course has helped me to form a more solid understanding

of the actual work that is undertaken.

The reality of using data that has been collected routinely by the hospital for

monitoring purposes has implications for data processing and transformations before

the analysis can even begin. Integrity checks have to be conducted as the data

cannot be guaranteed to be in a state acceptable for analysis. Non-parametric data

descriptions enable better lend themselves to data summaries as they enable easy

identification of outliers or erroneously coded information so are used as a matter of

course.

Software used in the course of these projects included SPSS version 15.0, SAS

version 9.1, Stata IC version 10 and Stat/Transfer for data transfer. SPSS has

generally been used for descriptive statistics, data summaries and basic data

manipulation. The ability to copy SPSS output directly into documents allows for

the streamlining of interim reports. More complex models were performed using

SAS and Stata. SPSS and SAS are the packages usually utilized in our workplace

although, having learnt to use Stata during the course of doing the BCA, I try and

incorporate its use when I can as the potential for greater programming flexibility

exists in Stata, especially with graph production. I found it easier to set up the

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logistic regression models in SPSS but Stata was far superior for survival analysis,

especially for the competing risks analysis which cannot easily be done in SPSS.

SAS is generally used for mixed models, although none have been done in the

course of these projects, and an existing SAS program was used to generate

expected birthweight norms in Project 2. Analysis methods used in both projects

mainly incorporated material covered in BCA units Linear Models and Regression,

Categorical Data Analysis, Clinical Biostatistics and Survival Analysis.

Ethical considerations

Both projects were approved by the King Edward Memorial Hospital Ethics

Committee. There were no confidentiality issues as datasets were de-indentified for

analysis.

Project 1 Report

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Tailoring screening protocols for perinatal depression: prevalence of high risk

across obstetric services in Western Australia.

Location and Date

This work was conducted at the Women and Infants Research Foundation, King

Edward Memorial Hospital as part of the beyondblue National Postnatal Depression

Program and utilised the Western Australian data collected for this initiative. The

recruitment period for the study was from July 2002 to December 2004. The

analysis and manuscript preparation was performed between 2005 and 2010. The

manuscript presented in this portfolio was submitted in July 2008 and published in

February 2009. In addition to the manuscript, prediction models developed using a

subset of the WA data are presented as an addendum.

Contextual Summary

The beyondblue National Postnatal Depression program is an Australian public

health initiative developed and implemented over four years, with a scope that

makes it unique on a worldwide level. It has tackled an important health problem

that affects the lives of 13-15% of women and their families. The Program raised

community awareness of perinatal depression, investigated early identification and

intervention strategies and took the lead in training health professionals in the

diagnosis and management of mood disorders during pregnancy and early

parenthood. Understanding the particular needs of male partners, multiple-birth

families, Indigenous women and women from culturally and linguistically diverse

backgrounds was also a major focus. The Program focused on bringing about

change in healthcare for women with perinatal depression, to improve outcomes for

them and their families, in order to reduce the potentially devastating consequences

on current and future generations.

The high rates of depression and identification of key psychosocial risk factors

highlight the importance of perinatal universal depression screening. When

depression is identified through screening, when otherwise it might not have been,

and women are supported into seeking help, the long term benefits to these women

and their families can be quite profound.

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Aim

This study was designed to describe the sample of Western Australian women,

measure the prevalence of depression in the sample and to identify the optimal time

to administer depression risk screening to women during pregnancy and after

childbirth, based on screening test results.

Contribution of Student

I performed this work as part of a team working on the beyondblue project under the

direct supervision of A/Prof Doherty. Due to the scope of the project, analysis of the

antenatal and postnatal outcomes were conducted concurrently. I was responsible

for the analysis which involved detailed evaluation of the postnatal outcomes. There

was extensive collaboration undertaken with psychologists from the Department of

Psychiatry at KEMH (Dr Delphin Swalm) and from Edith Cowan University

(Professor Craig Speelman and Dr Janette Brooks) while undergoing the process of

writing the manuscript.

Statistical issues

This project involved extensive data preparation to create the final analytic dataset

of 4838 pregnant women across four different hospitals in Perth Western Australia.

Analysis included descriptive statistics to examine demographic characteristics and

obstetric and psychological profiles of women and logistic regression to examine

how changing score trends are associated with timing of screening for risk of

postnatal depression.

Student Declaration

This project is my own work, and to the extent that any part of this work is not my

own work, I have indicated by acknowledging those parts of the work.

Signed:

Angela Jacques

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Supervisor Statement

In the interest of meeting the deadlines this project involved two parallel

components. Angela has worked mainly independently on all the aspects of

postnatal comparisons. She also collaborated with another biostatistician who was

responsible for the antenatal analysis of the data. Only minimal

assistance/supervision was required of me in order to complete the project. My

supervision was mainly related to the data manipulation and preparation and that

was due to the fact that Angela wasn’t aware of the efforts required for the data

preparation that resulted from our institution collecting the raw data.

Signed:

Dr Dorota Doherty 28 June 2010

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Statistical Appendix

Aim

This study was designed to describe the sample of Western Australian women, measure

the prevalence of depression in the sample and to identify the optimal time to administer

depression risk screening to women during pregnancy and after childbirth, based on

screening test results.

Ethics

The study in Western Australia was approved by the Ethics Committees at King Edward

Memorial Hospital, Edith Cowan University, Osborne Park Hospital and Mercy Hospital.

The Family Birth Centre was included in the King Edward memorial Hospital ethics

submission. KEMH was one of the few hospitals that did universal EPDS screening at

the time of the study.

Data management and collection

4838 women were recruited antenatally from four obstetric sites. The Psychosocial Risk

Factors Questionnaire, that covered key demographic and psychosocial information, was

administered only at recruitment. Participants answered questions designed to elicit

psychosocial, obstetric and demographic data. The 34-item, self-report questionnaire

included questions on both demographic and psychosocial variables. Some questions

were phrased to elicit categorical responses while responses to other questions were

either arranged on Likert-type scales or were binary. The top sheet of the Psychosocial

Risk Factors Questionnaire, with all of the patient’s identifying data, was removed. This

removed sheet was known as the Demographics Questionnaire.

Participants also completed the Edinburgh Postnatal Depression Scale. This self-rated

instrument comprises 10 statements relating to symptoms of depression and anxiety with

four possible responses for each. The EPDS was developed specifically to screen for

symptoms of postnatal depression: it cannot provide a diagnosis. It has been validated for

antenatal use and is deliberately brief, simple and non-reliant on somatic symptoms. The

Edinburgh Postnatal Depression Scale (EPDS) was administered antenatally and received

postnatally for each participant. Postnatal EPDS were routinely done if and when a

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woman saw her Child Health Nurse, otherwise by direct phone call to the woman.

Ideally, the postnatal EPDS was completed approximately 6-12 weeks postpartum. The

Psychosocial Risk Factors Questionnaire and antenatal and postnatal EPDS questionnaire

data were scanned and then exported into SAS.

The data subsequently received for analysis was then converted into SPSS format. See

Appendix for copies of the Demographics Questionnaire and the EPDS.

Bias

It is unlikely that self-selection bias has affected the WA sample as all women complete

an EPDS as part of routine care in three of the four recruitment sites. Mercy Hospital

does not routinely administer the EPDS but, as participation rates were high in this

centre, it seems unlikely that self selection bias was a factor. With such an unbiased

sampling method utilised for screening the rates appear to have been unaffected by

women agreeing to participate due to an inherent desire for additional attention/support.

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Statistical Analysis

Variables in the dataset

There is a considerable body of literature on risk factors for antenatal and postnatal

depression and an extensive list of variables significantly associated with postnatal

depressive symptoms. (Milgrom et al). These encompass a wide range of socio-

demographic, psychiatric, biological, medical and personal factors. The main risk factors

of interest are as follows:

• Demographic and socio-economic factors: Maternal age, lower socio-economic

status and lower educational attainment.

• Psychological and psychiatric factors: History of depression, depression/anxiety

in pregnancy, personality and psychological factors .

• Stressful life events: High scores on “current life events” scales , negative life

events and stressful events associated with pregnancy and childbirth. Two or more

stressful life events in the year prior to pregnancy predict depression in early

pregnancy and the postpartum.

• Social support: Low levels of both antenatal and postnatal social support are

significant risk factors.

• Obstetric and biological factors: History of miscarriage and pregnancy

termination.

Demographic, obstetric and psychological profile data obtained from the psychosocial

risk factor questionnaire, which were used in the Western Australian analysis, include the

following variables listed below. These variables encompass all of the documented risk

factors mentioned above.

• Demographic characteristic variables:

Maternal Age (continuous); marital status; country of birth; main language

spoken; occupation; partners’ occupation; level of education; family income level,

number of children (continuous).

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• Obstetric variables:

Gravidity (continuous); multiple birth, Pregnancy problems (varicose veins,

haemorrhoids, excessive vomiting, bladder/kidney infection, diabetes,

bleeding/threatened miscarriage, high blood pressure), Emotional problems during

pregnancy (depression, anxiety, eating disorder, difficulty accepting pregnancy, received

counselling/psychological therapy, antidepressants, other therapies); Alcohol intake.

• Psychological profile variables:

History of depression (Interfered with work/relationships, sought professional help);

Current emotional/psychological conditions (mild/major depression, diagnosis of

depression, anxiety); Relationship with mother and partner (mother supportive

when growing up, partner emotionally supportive); Level of daily hassles; Hoping for

boy/girl; Major distressing life events in last 12 months (Domestic Violence,

Financial Difficulties, Separation, Death of someone close, Physical Illness, Miscarriage,

Unemployment, Alcohol/Drug Addiction, Moving house, Eating disorder); Personality

traits (Order in life , Worrier, Pessimistic, Optimistic, Achiever, Guilt Prone,

Perfectionist); Emotional/practical support (level of support from Partner, Parents,

Parents-in-law, Sibling, Friend, Other); Management of baby; History of Child Abuse

(emotional, sexual, physical).

Recoding of variables

Frequencies were initially run on all variables in the sample in order to get an overall

overview of the data before any recoding commenced. Based on the frequency results

continuous variables such as maternal age, number of children and gravidity were

recoded into categorical variables based on usual cutoffs used within obstetric research.

For example the maternal ages are spilt into teenage mother (<20), older mother (>35)

and ‘normal’ age range 20-35. There are psychological issues associated with teenage

mothers and medical, obstetric and neonatal issues associated with women with maternal

age over 35, which makes it important to isolate these age subgroups. A medium to high

number of children (3 and over) are considered a risk factor but women with no children

are also at risk in comparison to those women with one or two children. Similarly with

gravidity (number of pregnancies) there are particular stresses associated with a first

pregnancy and also with numerous pregnancies. The continuous EPDS scores have been

categorized according to standard use to group women according to whether they are at

low, moderate or high risk of postnatal depression. Further, the high risk group is the

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main outcome of interest so the scores have also been binarised for high risk (yes/no).

Women who have an EPDS result that puts them in the high risk category are

automatically followed up by hospital psychologists. Gestational age at antenatal EPDS

and age at postnatal EPDS were categorized in order to try and narrow down a particular

time period for screening. Categories were selected based on clinically relevant time

points in the gestational and postpartum periods. Other variables with a large number of

categories such as country of birth, language, education, occupation, and alcohol intake,

have been recoded to make them more manageable and relevant. Gestational age at time

of antenatal EPDS was recalculated using estimated due date and date of EPDS and

infant age at time of postnatal EPDS was calculated based on date of birth and date of

EPDS.

Recoded variables are described below:

Original variable Recoded categorical variable

Maternal age (continuous),

1. 20-35 2. <20 3. >35

Marital status: 1. never married, 2. divorced, 3. widowed, 4. separated, 5. married/de facto

1. never married, 2.divorced/widowed/separated 3. married/de facto

Country of birth: 1. 1.Australia 2. New Zealand/Oceania 3. North-West Europe 4. Southern/Eastern Europe 5. North Africa/Middle East 6. Central,Western & Southern Africa 7. Southern & Central Asia 8. North America 9. South America

1. Australia 2. North-West Europe 3. Southern & Central Asia 4. New Zealand/Oceania 5. Other

Language spoken at home: 7 categories

English / Other

Occupation/partner’s occupation:

1. Manager or Administrator 2. Professional Health, Building,

Finance, Other 3. Semi-Professional Health, Business,

Other

1. Professional 2. Semi-professional 3. Trade/labour 4. Home/student/

unemployed

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4. Tradesperson or Related Worker 5. Secretarial, Administrative or

Financial Worker 6. Specialist Clerical or Sales Worker 7. Plant, Machinery or Vehicle

Operator 8. General Clerical, Sales, Security,

Personal Service 9. Laborer or Related Worker 10. Home Duties, Student, Unemployed Education: 1. Did not finish school 2. Cert Level 3. Adv Diploma/Diploma 4. Bachelor Degree 5. Grad Diploma/Grad Cert 6. Postgrad Degree 7. Apprenticeship 8. High School (yr7-12) 9. Other

1. High school 2. Not finished high school 3. Tertiary education 4. Other

Number of children (continuous) 1. No children 2. 1-2 children 3. 3 or more children

Gravidity (continuous) 1. 1st pregnancy 2. 2nd or 3rd pregnancy 3. 4th or more pregnancy

Multiple birth: 1. Twins 2. Triplet

Single birth/ Multiple birth

Diagnosis of: 1. Mild Depression 2. Major Depression 3. Anxiety 4. Other

Diagnosis of depression

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Alcohol intake:

1. None 2. Less than 1 drink/day 3. 1 drink/day 4. 2 drinks/day 5. 3 drinks/day 6. 4 drinks/day 7. More than 5 drinks/day

1. No alcohol 2. Less than 1 drink/day 3. 1 drink/day 4. More than 1 drink/day

Gestational age at antenatal EPDS (continuous)

1. Between 16 and 38 weeks 2. 16 weeks or less 3. 38 weeks or more

Antenatal EPDS score (continuous) 1. 0-8 (low risk) 2. 9-11 (moderate risk) 3. 12-30 (high risk)

Antenatal EPDS <12 Antenatal EPDS ≥ 12 (high risk)

Age at postnatal EPDS (continuous) 1. 0-6 wks 2. 6-12 wks 3. >12 wks

Postnatal EPDS score (continuous) 1. 0-8 (low risk) 2. 9-12 (moderate risk) 3. 13-30 (high risk)

Postnatal EPDS <13 Postnatal EPDS ≥ 13 (high risk)

Description of the sample

Continuous demographic, obstetric and psychological profile data variables were

described using non-parametric descriptive statistics, stratifying by hospital site. (Non-

parametric analyses better lend themselves to data summaries as they enable easy

identification of outliers or erroneously coded information so are used as a matter of

course.) Frequency distributions were used to summarise demographic, obstetric and

psychological profile categorical data, stratifying by hospital site. The variables were

univariately compared across sites using Kruskal-Wallis tests for the continuous variables

and Pearson Chi-squared tests for the categorical data variables. The descriptions of key

demographic, obstetric and psychological variables which were significantly different

across hospital sites and their p-values are shown in Tables 1 and 2 in the manuscript.

Although tables containing the descriptions and comparisons for every variable were

originally generated, in the interests of space the authors decided to only use the subset of

significantly different (p<0.05) variables in these tables for the manuscript.

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Depression during pregnancy, a major risk factor, was found to be significantly higher at

the tertiary and secondary obstetric hospitals compared to the private hospital and the

Family Birth Centre. The reason for this is that as the only tertiary obstetric hospital in

the state, KEMH receives the all the high risk obstetric cases as well as a

demographically wide range of women giving birth, whereas the strict selection criteria

of the family birth centre and the affordability (or lack thereof) of the private hospital

system automatically excludes a number of women.

Trends across time for high risk of depression

In the antenatal period, women with an EPDS score of ≥ 12 were classified at high risk of

developing postnatal depression while women with a postnatal EPDS score of > 12 were

classified at high risk of developing depression, according to EPDS classifications and

hospital policy. The EPDS raw scores in the data were dichotomised to create binary

indicators for women at high risk of depression in the antenatal or postnatal periods.

Frequency distributions were used to obtain prevalence of high risk in the antenatal and

postnatal periods and Pearson’s chi-squared tests compared the differences across

gestational and infant age categories. Prevalence of high risk in the antenatal period was

compared across three gestational age categories (≤ 16 weeks, 16 to 38 weeks and ≥ 38

weeks) and prevalence of high risk in the postnatal period was compared across three

infant age categories (< 6 weeks, 6 to 12 weeks and ≥ 13 weeks). The comparisons

showed a significant difference between these categories with prevalence of high risk in

the antenatal period decreasing with increasing gestational age (p<0.001) and prevalence

of high risk in the postnatal period increasing with increasing infant age (p<0.001) (Table

1a).

Repeat EPDS

Indicator variables were created based on whether women had paired EPDS data (ie they

had completed a second EPDS) for either the antenatal or postnatal periods. 989 women

completed a second antenatal EPDS and 469 women completed a second postnatal EPDS.

Paired data were compared with single observation data for differences in demographic

and psychosocial characteristics using Chi- square and Mann Whitney U tests for

categorical and continuous data respectively for both antenatal and postnatal periods.

Non-parametric descriptive statistics were used to describe continuous variables, with

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frequency distributions used to summarise categorical data. Univariate comparisons of

the paired and single data showed no differences in demographic and psychosocial

characteristics, with the exception of an increased proportion of diabetics in the paired

group compared to the single group during the antenatal period (p=0.003). This result

was expected in this group because the EPDS questionnaires were taken

opportunistically. Women with diabetes constituted a high risk pregnancy group, and

were subsequently more likely to complete a second EPDS due to more frequent visits to

the antenatal clinic. The diabetic group were no more likely than the non diabetic group

to have completed a second EPDS in the postnatal period (p=0.506) (Table 2a).

Analysis of paired data

Given that the overall trend for high risk scores for the sample showed a significantly

different decrease with increase in gestational age for first antenatal EPDS scores and a

significantly different increase with infant age for first postnatal scores, the questions

were then asked: (i) whether any subsequent EPDS scores would show a decrease from

the first score in the antenatal period and whether the time interval between first and

subsequent measures was important, and (ii) whether any subsequent EPDS scores would

show an increase in the postnatal period and whether timing of second score was

important. If the changes in scores could be associated with time then optimal screening

times could potentially be established for both the antenatal and postnatal periods.

Time differences in weeks between the first and second antenatal and postnatal EPDS

questionnaires were calculated. Initially, linear regressions were run to examine any

association between the magnitude of the score differences and the time difference

between scores, adjusting for gestational age at first antenatal EPDS, or infant age at first

postnatal EPDS. These regression analyses showed evidence to support decreasing trend

in antenatal EPDS scores as gestational age interval increased (p=0.003) and increasing

trend for postnatal EPDS scores with increasing infant age postnatal score differences

(p=0.011) for the groups identified as high risk on EPDS 1 in the antenatal and postnatal

periods but there were problems with these analyses due to unequal error variances

between groups and non-normality of residual distribution. Consequently it was decided

that, although there would be a slight loss of power, logistic regression would be the

simplest way to examine the likelihood of change in a second EPDS measured later in

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gestation (decrease), for the antenatal EPDS, or later postpartum (increase), for the

postnatal EPDS.

Antenatal analysis variable selection

A binary dependent variable was created for the paired antenatal data representing a

decreased EPDS score (yes/no) (decrease = 1 and no change/increase = 0), and analysed

using logistic regression. EPDS 1 risk (high versus low/moderate), gestational age at

EPDS 1, the time interval between EPDS assessments and their interactions were

considered in the regression model. Gestational age at EPDS 1 was selected as a covariate

to adjust for as it was important to ascertain whether the timing of the first EPDS affected

the outcome and results were stratified on EPDS 1 high risk. In addition, other potential

confounding factors related to the outcome such as history of depression and diagnosis of

depression were assessed.

Postnatal analysis variable selection

A dependent binary variable for the postnatal paired data was created to represent an

increase in EPDS score (yes/no) (increase = 1 and no change/decrease = 0), in order to

test whether there was a significant change in EPDS score from 12 weeks onwards after

birth (a binary variable indicating EPDS 2 was assessed after 12 weeks was created).

Covariates included for consideration were antenatal high risk, postnatal EPDS 1 high

risk, infant age at EPDS 1, whether EPDS 2 was assessed after 12 weeks and other

potential confounding factors related to the outcome such as history of depression and

diagnosis of depression. Antenatal and postnatal EPDS 1 high risk and timing of first

postnatal EPDS were included as they were potentially correlated with the outcome.

Model building

For the antenatal model building process univariate logistic regression between the binary

outcome of decrease in score and the continuous variable of time between EPDS scores

was implemented with the model showing a significant association between increasing

time and decrease in score. After then adjusting for high risk and gestational age at first

antenatal EPDS, other potential confounding factors related to the outcome such as

history of depression and diagnosis of depression were then sequentially tested for

inclusion in the model.

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For the postnatal model building process univariate logistic regression between the binary

outcome of increase in score and the binary variable indicating EPDS 2 after 12 weeks

was implemented with the model showing a significant association between timing of

second EPDS and increase in score. After adjusting for antenatal high risk and high risk

and infant age at first postnatal EPDS, other potential confounding factors related to the

outcome such as history of depression and diagnosis of depression were then sequentially

tested for inclusion in the model.

Univariately none of the covariates were significantly associated with the outcome in

either antenatal or postnatal models but multivariable models using forward stepwise

methods to enter all other covariates were run to check whether significance levels had

changed. Forward stepwise methods start with a model that doesn't include any of the

covariates and then at each step, the covariate with the largest score statistic whose

significance value is less than a specified value (in this case 0.05) is added to the model.

In other words, in one iteration, the variable that contributes the largest amount with its

presence in the model is added. The covariates left out of the analysis at the last step have

significance values larger than 0.05, so are not added. The covariates chosen by the

forward stepwise method should all have significant changes in -2 log-likelihood which is

more reliable than the Wald statistic. As a further check, the models were built using

backward stepwise methods. Backward methods start with a model that includes all of the

covariates. At each step, the covariate that contributes the least is removed from the

model, until all of the covariates in the model are significant. Covariates are removed if

they have a significance level of more than 0.1. Finally the Enter method was used to

force all the covariates into full multivariable models. Still none of the covariates had a

significant effect in any of the multivariable models, and significance levels remained the

same in all so the (unadjusted) univariate models were ultimately used for both the

antenatal and the postnatal regression analyses, with the antenatal model stratifying for

high risk.

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Antenatal logistic regression results

Probabilities of lower scores for their subsequent EPDS were calculated from the logistic

equation. The odds ratios for the high risk group compared to the low risk group were

then calculated by dividing the probability for the high risk group by the probability for

the low risk group for each time interval.

Women categorised as high risk, based on their first EPDS, were significantly more likely

to show a decrease in EPDS score as the time interval in gestation increased, compared

with women in the low/moderate risk group. Probability of lower scores with advancing

gestational age ranged from 0.61 to 0.89 for the high risk group, compared to 0.41 to 0.45

for the low risk group (Table 4 and Figure 2 of the manuscript). The gestational age at

which the first EPDS was completed did not affect the final probabilities (p>0.05),

subsequently these time intervals may be applied appropriately to any gestational age.

Postnatal logistic regression results

None of the covariates had a significant effect in any of the multivariable models, and

significance levels remained the same for all methods so the (unadjusted) univariate

model was ultimately used. The results showing odds ratios, 95% confidence intervals

and p values of the main predictor (EPDS completed >12 weeks postpartum) and other

covariates in the multivariable model are shown below.

OR 95% CI p-value

EPDS completed >12 wks 1.83 1.15-2.89 0.010

High risk antenatal score 1.25 0.61-2.56 0.547

Infant age at first postnatal EPDS 0.81 0.63-1.04 0.105

History of depression 1.19 0.68-2.10 0.543

Diagnosis of depression 1.40 0.75-2.63 0.295

High risk first postnatal EPDS 0.38 0.14-1.04 0.060

The univariate model resulted in OR 1.55 95% CI 1.04-2.29, p=0.029 for the main risk

factor. This analysis indicated that timing of the second antenatal EPDS was significantly

associated with higher second score, with women 55% more likely to have a higher

37

EPDS score when a second antenatal EPDS was completed after 12 weeks postpartum

compared with women who completed their second EPDS 12 weeks or less postpartum.

Hosmer-Lemeshow goodness-of-fit tests were used to assess whether the antenatal and

postnatal models adequately described the data. No evidence of poor fit for either was

indicated by the Hosmer-Lemeshow statistic (p>0.05). This statistic was used as it is

considered the most reliable test of model fit for binary logistic regression.

SPSS 15.0 statistical software was used to analyse the data. All p-values were two-sided

and p values <0.05 were considered statistically significant.

38

Table 1a Percentages of high risk EPDS scores over the perinatal period. High risk during pregnancy – EPDS score ≥12 and during postnatal period EPDS score ≥13.

Data represented as number and frequency (%).

Table 2a Demographic and psychosocial characteristics of women who completed a single EPDS compared with repeated EPDS assessments. Demographic & Psychosocial Characteristics

Single Antenatal EPDS

Repeat Antenatal EPDS

p-value

Single Postnatal EPDS

Repeat Postnatal EPDS

p-value

Maternal Age* 29 (24-32) 29 (24-32) 0.861 29 (25-33) 29 (24-33) 0.863 Marital Status:

Never married No longer married Married/defacto

586 (16%) 148 (4%) 2948 (80%)

168 (17%) 37 (4%) 774 (79%)

0.621

505 (15%) 122 (4%) 2727 (81%)

72 (15%) 14 (3%) 381 (82%)

0.775

English Language 3519 (95%) 939 (95%) 0.665 3204 (95%) 451 (96%) 0.160 Aboriginal/TSI 164 (4%) 35 (4%) 0.227 107 (3%) 9 (2%) 0.140 Education Level:

Not finished school High school Tertiary Other education

456 (12%) 2036 (55%) 986 (27%) 242 (7%)

112 (11%) 550 (56%) 262 (27%) 65 (7%)

0.878

386 (11%) 1865 (55%) 921 (27%) 214 (6%)

39 (8%) 263 (56%) 137 (29%) 30 (6%)

0.237

Number of Children: 0 children 1-2 children 3 or more children

1721 (47%) 1664 (45%) 311 (8%)

446 (45%) 446 (45%) 90 (9%)

0.685

1596 (48%) 1493 (44%) 273 (8%)

206 (44%) 231 (49%) 31 (7%)

0.109

History of Depression 972 (27%) 267 (27%) 0.580 889 (27%) 129 (28%) 0.567 Gravidity:

1st pregnancy 2nd or 3rd pregnancy 4th or more pregnancy

1354 (37%) 1686 (46%) 646 (18%)

373 (38%) 426 (43%) 186 (19%)

0.343

1279 (38%) 1502 (45%) 577 (17%)

159 (34%) 228 (49%) 79 (17%)

0.190

Multiple Birth 100 (3%) 21 (2%) 0.318 89 (3%) 10 (2%) 0.524 Pregnancy problems† 2047 (55%) 547 (55%) 0.881 1846 (55%) 259 (55%) 0.779 Diabetes ‡ 182 (5%) 72 (7%) 0.003 176 (5%) 21 (5%) 0.506

Gestational age (in weeks) Antenatal EPDS Score ≤ 16 16 - 38 ≥ 38 All p-value EPDS 1 High risk 68 (14%) 392 (10%) 12 (5%) 472 (10%) <0.001 N 492 3961 253 4706

Infants’ age (weeks) Postnatal EPDS Score ≤ 6 7 - 12 ≥ 13 EPDS 1 High risk 49 (6%) 112 (5%) 75 (9%) 236 (6%) <0.001 N 778 2263 812 3853

39

Table 2a continued Demographic & Psychosocial Characteristics

Single Antenatal EPDS

Repeat Antenatal EPDS

p-value

Single Postnatal EPDS

Repeat Postnatal EPDS

p-value

Diagnosis of Psychological Condition

971 (26%)

244 (25%)

0.358

849 (25%)

128 (27%)

0.302

Depression this pregnancy

466 (13%)

121 (12%)

0.802

390 (12%)

57 (12%)

0.689

Anxiety this pregnancy 476 (13%) 123 (12%) 0.761 432 (13%) 61 (13%) 0.882 Difficulty accepting pregnancy

143 (4%)

44 (4%)

0.387

128 (4%)

13 (3%)

0.275

Data represented as number and frequency (%), unless otherwise indicated. * indicates median (IQR) † pregnancy problems included excessive vomiting, high blood pressure, varicose veins, diabetes, bladder/kidney infection, bleeding/threatened miscarriage. ‡ EPDS questionnaires were taken opportunistically. Women with diabetes constituted a high risk pregnancy group, and were subsequently more likely to complete a second EPDS due to their more frequent visits to the antenatal clinic.

40

Appendix

1. Demographics Questionnaire 2. Edinburgh Postnatal Depression Scale (EPDS)

41

1. Demographics Questionnaire

42

43

44

45

46

2. Edinburgh Postnatal Depression Scale (EPDS)

47

Project 2 Report

48

49

A retrospective study examining the association between Chorioamnionitis and

early or late-onset sepsis

Location and Date

This project was undertaken by the Department of Neonatal Pediatrics, King Edward

Memorial Hospital, the School of Paediatrics and Child Health and the School of

Women’s and Infants’ Health, University of Western Australia and the Women and

Infants Research Foundation, King Edward Memorial Hospital between January 2008

and June 2010.

Contextual summary

Late-onset septicemia (sepsis onset after 3 days postnatally) is one of the key challenges

for very premature (less than 30 weeks gestational age) infants and Coagulase-negative

staphylococcal (CoNS) is the most common neonatal invasive organism. Impaired innate

immune responses are likely to be involved in the susceptibility of the premature neonate.

Inflammation of the placenta and/or fetal membranes (chorioamnionitis) is often

implicated in spontaneous preterm labour and is a known risk factor for early-onset sepsis

(sepsis onset within 3 days postnatally). This retrospective cohort study comprised

records of 838 preterm infants of less than 30 weeks gestation born at King Edward

Memorial Hospital between 2001 and 2007. Available clinical neonatal data was

extracted from hospital databases. This work has been presented at the European Society

for Paediatric Research (ESPR) Conference in October 2009, and at the European Society

for Paediatric Infectious Diseases (ESPID) Conference in May 2010. A manuscript is

currently under preparation for submission to the journal Paediatrics.

Statistical issues

This project involved extensive and complex data preparation to create the final analytic

dataset of 838 preterm infants. Analysis included:

• Descriptive statistics to compare differences between maternal demographic,

delivery and neonatal characteristics in infants with or without sepsis

50

• Logistic regression to examine the effect of chorioamnionitis on outcomes early

onset sepsis, necrotizing enterocolitis and perventricular leukomalacia

• Kaplan-Meier survival curves and Cox proportional hazard regression analysis to

examine the effect of chorioamnionitis on outcomes late-onset sepsis and death.

• Competing risks analysis to examine the effect of multiple cause late-onset sepsis

on survival

Student Declaration

I worked on all the statistical analysis associated with this project under the direct

supervision of A/Prof Doherty. Neonatal and paediatric physicians A/Prof David

Burgner and Dr Tobias Strunk provided the clinical input and direction in the

interpretation of results.

Signed:

Angela Jacques

51

Supervisor Statement

The project completion involved data preparation, generation of analytic datasets and

meeting with clinical collaborators. Angela proposed and performed appropriate analysis

and communicated the results with minimal supervision. The majority of data

preparation and analysis was performed by Angela.

Signed:

Dr Dorota Doherty 28 June 2010

52

53

A retrospective study examining the association between Chorioamnionitis and

early or late-onset sepsis

Background and rationale

Chorioamnionitis is an inflammation of the fetal membranes (amnion and chorion) due to

bacterial infection. It typically results from bacteria ascending into the uterus from the

vagina and is most often associated with prolonged labour. Inflammation of the placenta

and membranes are often involved in the onset of preterm labour and chorioamnionitis is

a known risk factor for early onset sepsis (EOS) [1]. Early-onset sepsis is defined as onset

of sepsis within 3 days postnatally, late-onset sepsis is onset of sepsis after 3 days

postnatally.

Previous studies have shown that chorioamnionitis is associated with preterm delivery,

fetal pulmonary injury (bronchopulmonary dysplasia), or chronic lung disease, and

neurologic injury (periventricular leukomalacia) [2], [3] [4], although findings have been

inconsistent, possibly due to variations in definitions of histologic chorioamnionitis and

differences in populations studied [5]. Neonatal sepsis, in particular Coagulase-negative

staphylococcal late-onset sepsis (CoNS LOS), is strongly associated with chronic lung

disease (CLD) [6]. Lahra and Jeffery [7] state that histologic chorioamnionitis is inversely

related to gestational age and that intrauterine exposure to infection may effect a

secondary fetal inflammatory response with a resulting increase in fetal cortisol

production, which facilitates fetal lung maturation with a subsequent reduction in

respiratory distress syndrome (RDS), and consequently an increase in neonatal survival.

In addition, both maternal and fetal inflammatory responses to chorioamnionitis have

been shown to be protective for respiratory distress syndrome [6]. The use of antenatal

corticosteroids in women with intrauterine inflammation/infection has been associated

with significant reduction in respiratory distress syndrome [8].

Late-onset sepsis is an important problem in very premature (less than 30 weeks

gestational age) and very low birth weight infants [9] [10], with the risk of late-onset sepsis

increasing with decreasing birthweight and/or gestational age.

54

Aim

Very premature infants are at greatly increased risk of acquiring late-onset sepsis, most

commonly with Coagulase-negative staphylococci (CoNS), but the impact of

chorioamnionitis on the risk of CoNS infection has not been studied. This study

examines the impact of chorioamnionitis and other factors on the risk of CoNS infection.

More specifically, it examines the effect of chorioamnionitis on early-onset sepsis in

infants, the influence of CoNS and chorioamnionitis on incidence of necrotizing

enterocolitis and periventricular leukomalacia and the influence of chorioamnionitis on

the incidence of, and time to, CoNS and other late-onset sepsis.

Study design

A single-centre retrospective cohort study using the records of all Western Australian

babies born between 2001 and 2007 at less than 30 weeks gestational age, with available

placental histology data. All very premature babies are admitted to the neonatal intensive

care unit at King Edward Memorial Hospital, whether inborn (at KEMH) or not.

Available clinical neonatal data was extracted from linked hospital databases.

Sepsis and placental histology

Sepsis was defined as: clinical signs of sepsis and blood culture positive with a single

isolate organism and intention to treat with at least 5 days of antibiotic therapy. Clinical

signs of sepsis in neonates are notoriously unreliable and have a poor predictive value,

especially in isolation, but some of the possible signs include abnormal temperature (high

or low), apneas, poor colour, feeding intolerance, respiratory distress and increased

ventilator or oxygen requirements.

Placental histology was scored in accordance with published guidelines [11]. Maternal

inflammation was defined as neutrophils in decidua either extending into the chorion in

the extraplacental membranes or from the maternal blood space into the chorionic plate.

55

Fetal inflammation was defined as neutrophils marginating either from the umbilical

vessels into the cord or from the fetal vessels of the chorionic plate towards the amnion.

Ethical issues

Full ethics approval from The King Edward Memorial Hospital Ethics Committee was

obtained before commencement of this study.

Data management

Data sources

Data was sourced from two institutional databases:

(i) The hospital database consisting of cases of neonatal admissions to either the

neonatal intensive care unit or special care nurseries, and

(ii) The midwives notification system database. This is data that has been

collected specifically to meet the reporting requirements by hospitals that

provide birth services. The data is under the custodianship of the Midwifery

and Nursing Director of the Obstetrics and Gynaecology Clinical Care Unit.

Babies born at less than 30 weeks gestational age between 2001 and 2007 were identified

on the neonatal admission database and their identification numbers used to extract the

corresponding maternal data from the midwives notification system database.

Data preparation

The dataset initially consisted of 869 cases of very preterm infants (<30 weeks

gestational age) born between 2001 and 2007, and admitted at King Edward Memorial

Hospital. Of these, 31 infants had a major congenital anomaly (eg trisomy 21), with a

corresponding increased risk of infection, so were excluded. Therefore the final

analyzable dataset consisted of 838 cases.

Data recorded in hospital databases is patient centred in that it exists mainly for the

purposes of the creation of patient discharge summaries, rather than for research

purposes. Data integrity had to be checked as there were numerous complementary data

56

variables in both databases that caused duplications. There were many complex issues

centred around the creation of new variables to be used in the analysis. For example, the

use of a central venous line (CVL) was considered an important risk factor to adjust for in

the analysis. Indicator variables for CVL had to be constructed based on the start and end

dates of the CVL and comparing these dates to the dates of infection in order to ascertain

timing of CVL use with respect to timing of infection. There were also about 30 different

individually coded organisms found in blood cultures. These had to be validated and

recoded in order to correctly group cases into subsets based on infection type.

Ideally neonatologists use a measure of neonatal wellbeing called a CRIB II score which

is a composite score made up of different components such as gestational age, sex,

birthweight, temperature and base excess. Due to large numbers of missing values in

some of the variables required in the calculation of the CRIB II score birthweight was

used as a surrogate measure. Standardised birthweight z-scores were calculated based on

Australian norms using birthweight, sex and gestational age data. These standardised

norms also facilitated the production of expected birthweight and IUGR status variables

[12]. The parameters used in the calculations of the standardised birthweight z-scores are

shown below.

Males Females

GA

Mean

BWT

SD

BWT SGA LGA

Mean

BWT

SD

BWT SGA LGA

22 495 80 <400 >600 485 85 <400 >600

23 607 92 <500 >710 591 103 <470 >740

24 690 129 <520 >860 661 95 <540 >780

25 791 132 <620 >980 760 116 <620 >900

26 921 158 <720 >1130 865 158 <680 >1040

27 1017 209 <740 >1280 944 183 <730 >1080

28 1157 240 <850 >1440 1060 228 <760 >1340

29 1316 261 <950 >1640 1233 247 <890 >1510

57

Statistical Analysis

Main outcome and predictor variables

Main outcome variables:

• Early-onset sepsis (EOS)

• First episode of coagulase-negative staphylococci late-onset sepsis (CoNS LOS)

• First episode of other late-onset sepsis (Other LOS)

• First episode of any late-onset sepsis (All LOS)

• Death

Other outcome variables were necrotising enterocolitis (NEC) and periventricular

Leukomalacia (PVL)

Main histological predictor variables were:

• Maternal chorioamnionitis (maternal intrauterine inflammatory response)

• Fetal chorioamnionitis (fetal histologic response to chorioamnionitis)

• Maternal or fetal chorioamnionitis

• Funisitis (inflammation of the connective tissues of the umbilical cord)

• Fetal reaction (Fetal chorioamnionitis and/or funisitis)

• Positive histology (Maternal chorioamnionitis or fetal reaction)

Other important covariates considered were:

• Gestational age

• Birthweight

• Central venous line

Infant characteristics which have a well known risk of infection were adjusted for. These

are characteristics such as use of a central venous line, low gestational age and low

birthweight which is a risk factor for poor growth and development.

58

Descriptive statistics

Non-parametric descriptive statistics include medians, inter-quartile ranges (IQR) and

ranges for continuous data. Frequency distributions were used to summarise

categorical data. Univariate analysis was performed using Wilcoxon Rank Sum Test,

Kruskal-Wallis and Chi-square or Fisher Exact Test for continuous and categorical

data, as appropriate. Characteristics of premature infants with nil sepsis, early onset

sepsis, CoNS late-onset sepsis and other late-onset sepsis were compared, with CoNS

late-onset sepsis categorised by the presence or absence of maternal and fetal

chorioamnionitis. Characteristics of premature infants were compared for groups of

infants with or without CoNS late-onset sepsis; infants with or without other late-

onset sepsis; infants with or without any late-onset sepsis (CoNS and/or other LOS)

and infants with or without early onset sepsis.

Kaplan-Meier Survival Curves

Kaplan-Meier survival curves were produced to examine the effect of chorioamnionitis

on survival of infants with late-onset septis. The extensive duration of time to late-onset

sepsis or death events makes this type of analysis suitable for these outcomes. Kaplan-

Meier survival curves were constructed to show: (i) proportions of all infants (including

where deaths occurred) without CoNS late-onset sepsis, stratifying by maternal

chorioamnionitis and adjusting for gestational age, and live infants (where no deaths

occurred) without CoNS late-onset sepsis, stratifying by maternal chorioamnionitis and

adjusting for gestational age and, (ii) proportions of all infants (including where deaths

occurred) without any late-onset sepsis, stratifying by maternal chorioamnionitis and

adjusting for gestational age, and live infants (where no deaths occurred) without any

late-onset sepsis, stratifying by maternal chorioamnionitis and adjusting for gestational

age, and (iii) proportions of all infants (including where deaths occurred) without any

late-onset sepsis, stratifying by fetal chorioamnionitis and adjusting for gestational age,

and live infants (where no deaths occurred) without any late-onset sepsis, stratifying by

fetal chorioamnionitis and adjusting for gestational age. Log-rank test statistics were

used to compare strata.

59

Logistic Regression

Logistic regression was used to evaluate early-onset septis as there were only 25 cases

and because the relatively short duration of this type of sepsis (up to 3 days) also made it

an unsuitable candidate for time to event analysis. Each predictor was evaluated

univariately and then adjusted for the covariates gestational age, birthweight z-score and

sex. Due to the small sample size, univariate analysis has been included for comparison

with the adjusted analysis. All babies were included in the analysis, including those

without early-onset septis that died within 3 days. Overfitting of the multivariable model

did occur due to the small sample size but it has been included for completeness.

In addition, logistic regression was also used to examine the influence of CoNS late-onset

sepsis and chorioamnionitis on the incidence of necrotising enterocolitis (NEC) and

periventricular leukomalacia (PVL). NEC is a condition seen in premature infants,

possibly caused by an infectious agent, where portions of the bowel undergo necrosis and

PVL is reflective of inflammation in the brain. The evaluation of the effect of CoNS LOS

(and chorioamnionitis) on NEC and on PVL could not be accurately evaluated due to

insufficient statistical power as NEC occurred after CoNS LOS in only 13 cases (1.5%)

and PVL in only 18 cases (2%).

Cox Proportional Hazards Regression

Time to sepsis and time to death variables naturally lend themselves to time to event

survival analysis. Cox proportional hazard models were implemented for all

outcomes except early-onset septis, with the influence of chorioamnionitis on the

incidence of early-onset septis examined using logistic regression analysis.

The influence of chorioamnionitis on the incidence of, and time to, CoNS and other

late-onset septis was examined using Cox proportional hazard regression analysis,

with the time to event variable selected as being the time to first episode of sepsis

only. Cases were censored on time to death or time to discharge as appropriate. Each

predictor was evaluated initially, adjusting for gestational age, and then

60

multivariably, adjusting for covariates gestational age, birthweight z-score and central

venous line (CVL). Analysis of residuals was used to examine the assumption of

proportionality of hazards. The results of the Cox proportional hazard regressions

were summarised using hazard ratios, 95% confidence intervals and p-values.

Competing Risks Analysis

Competing risks analysis was undertaken to examine the effect of multiple cause late-

onset sepsis on survival of premature neonates. This analysis was performed only to

fulfill the requirements of this WPP. Clinical colleagues were reluctant to use it in the

manuscript as this form of analysis is not normally utilised in neonatal medical literature.

This analysis can potentially provide a way to address the issue of different types of

events, in this case CoNS late-onset sepsis, other late-onset sepsis and death. As these

were unordered failure events of different types a marginal model was considered. Each

failure event could only occur once per subject and all subjects were equally at risk for

other events so when a subject experienced one of the events, they still remained at risk

for the other events. As there were three possible events each subject appeared 3 times in

the dataset (once per event type). Time to events were time to first CoNS late-onset

sepsis, time to first other late-onset sepsis and time to death. Cases were censored if the

event did not occur and time to discharge was used. Times are measured from date of

birth in days.

61

Figure 1: Competing risks situation in late-onset sepsis in neonates

The only way in which the stratified Cox model allows for heterogeneity in terms of the risk of

different events is through different baseline hazards so the baseline hazard function was

allowed to vary by failure type (event). This was accomplished by stratifying on event,

thereby allowing each stratum to have its own unique baseline hazard function, but

restricting the coefficients to be the same across event type[13]. The id variable was used

to cluster the related observations when estimating the Cox model.

Each predictor was evaluated initially, adjusting for gestational age, and then

multivariably, adjusting for covariates gestational age, birthweight z-score and central

venous line (CVL). Each model was repeated for every predictor variable. The results of

these stratified Cox proportional hazard regressions were summarized using hazard ratios,

95% confidence intervals and p-values.

Birth

CONS LOS

Other LOS

Death

CONS LOS

Death

Discharge (censored)

Other LOS

Death

Discharge (censored)

62

Results

Descriptive Statistics

Sample description

There were 838 cases with gestational age less than 30 weeks with median gestational

age 27.0 weeks (range 22.0-29.0 weeks) and of these 391 (47%) were females. There

were 538 cases with no sepsis (median gestational age 26.0), with 69 deaths (13%) and

300 cases with sepsis (median gestational age 27.4), including 27 deaths (9%).

25 infants (3%) developed early-onset sepsis (median gestational age 26.7 weeks) with 9

deaths (36%). 218 infants (26%) developed CoNS late-onset sepsis (median gestational

age 26.0 weeks) with 15 deaths (7%). There were 68 cases of other late-onset sepsis,

(median gestational age 25.9 weeks) with 3 deaths (4%) and 276 cases of any late-onset

sepsis, (median gestational age 26.1 weeks) with 18 deaths (8%).

There were 287 cases (34%) with maternal chorioamnionitis, 148 cases (18%) with fetal

chorioamnionitis, 303 cases (36%) with either maternal or fetal chorioamnionitis, 199

cases (24%) with funisitis, 261 cases (31%) with fetal reaction and 384 cases (46%) with

positive histology.

Figure 2: Critical path of babies entered into study

838 babies

GA<30w

Discharge n=200*CONS

LOS n=218 Other LOS

n=3

Death n=15

Discharge n=65

Other LOS n=68

CONS LOS n=7

Death n=3

No sepsis

n=538

Death n=69

Discharge n=468**

Sepsis n=300

Discharge n=16

EOS n=25

CONS LOS n=1

Death n=9

NEC n=13

PVL n=18

*3 cases missing data

**1 case missing data

838 babies

GA<30w

Discharge n=200*CONS

LOS n=218 Other LOS

n=3

Death n=15

Discharge n=65

Other LOS n=68

CONS LOS n=7

Death n=3

Discharge n=65

Other LOS n=68

CONS LOS n=7

Death n=3

No sepsis

n=538

Death n=69

Discharge n=468**

No sepsis

n=538

Death n=69

Discharge n=468**

Sepsis n=300

Discharge n=16

EOS n=25

CONS LOS n=1

Death n=9

Discharge n=16

EOS n=25

CONS LOS n=1

Death n=9

NEC n=13

PVL n=18

*3 cases missing data

**1 case missing data

63

Table A: Summaries of times to discharge and death with numbers of cases Total

n Discharged

n Time to

discharge (days)

Deaths n

Time to death (days)

EOS 25 16# 94 (71, 115) [34-206]

9 1 (1, 9) [0-16]

CoNS LOS 218* 200#

n=3 missing 91

(70, 112) [28-397]

15 25 (19, 53) [14-504]

Other LOS 68 65 96

(75, 121) [14-332]

3 23 (9, 53) [9-53]

Any LOS 276* 255#

n=3 missing 92

(70, 110) [16-397]

18 24 (19, 53) [9-504]

Any Infection 300 270 92

(70, 112) [16-397]

27 19 (6, 32) [0-504]

Nil infection 538 468

n=1 missing 69

(58, 90) [19-345]

69 3 (1, 9)

[0-214] *Total includes one (male) case that had EOS at 0 days and CONS LOS at 6 days. Infant

was discharged at 97 days.

#Both discharge frequencies include overlap case as detailed in *

Note:

Ten cases had both CoNS LOS and Other LOS (8 male, 2 female). For these cases,

median (IQR) [R] of age at infection for CoNS LOS: 20 (10, 46) [5-53] and Other LOS:

12 (6-24) [5-39]. 3 out of 10 cases had CoNS LOS prior to other LOS. All the infants

were discharged with median (IQR) [R] of age at discharge for these cases: 12 (88, 155)

[32-174]

64

Early-onset sepsis

Table 2 details univariate comparisons between babies with and without EOS. These

comparisons showed that significantly more babies with early-onset sepsis had low apgar

scores at 1 minute (p=0.029) and 5 minutes (p=0.009) and more required antibiotics

(p=0.022), indicating sicker babies. (Apgar scoring is a method of evaluating the

physical condition of a newborn infant shortly after delivery). Incidence of maternal

chorioamnionitis was significantly higher in cases with early-onset sepsis (p=0.020). Of

the cases with early-onset sepsis, one also developed CoNS late-onset sepsis.

CoNS late-onset sepsis

Table 1 details univariate comparisons between babies with and without CoNS LOS.

These comparisons showed that babies with CoNS LOS had significantly lower

gestational age (p<0.001) and birthweight (p<0.001). Apgar scores showed no significant

differences but use of antibiotics was significantly higher in babies with CoNS LOS

(p=0.023). Ventilators were used more frequently in CoNS LOS babies (p=0.001) and

median duration of ventilation was also significantly higher in these babies (p<0.001).

48% of CoNS LOS babies had chronic lung disease compared to 30% of other babies

(p<0.001). CoNS LOS babies also had a higher incidence of central venous line use

(p=0.010), higher incidence of parenteral (intravenous) nutrition (p=0.017), higher

incidence of enteral (tube) nutrition (p=0.005) and higher incidence of necrotising

enterocolitis (p=0.029). There were no differences in incidence of maternal or fetal

chorioamnionitis between groups. Of the cases with CoNS LOS, 10 cases also developed

other late-onset sepsis.

Other late-onset sepsis

Table 1 details univariate comparisons between babies with and without other LOS.

These comparisons showed that babies with other LOS had significantly lower

gestational age (p<0.001) and birthweight (p<0.001). 57% of other LOS babies had

chronic lung disease compared to 33% of other babies (p<0.001). Central venous lines

were used more often in babies with other LOS (p=0.001) and there was a higher

65

incidence of necrotising enterocolitis in these babies (p=0.021). Incidence of fetal

chorioamnionitis was lower in cases with other LOS (p=0.046).

Any late-onset sepsis

Table 2 details univariate comparisons between babies with and without any LOS.

These comparisons showed that babies with any LOS had significantly lower gestational

age (p<0.001) and birthweight (p<0.001). Antenatal steroid use occurred in 95% of cases

with any LOS compared to 53% of cases with no LOS (p=0.002) and postnatal antibiotics

were used more often in the sepsis cases (p=0.002). Ventilators were used more

frequently in LOS babies (p<0.001) and median duration of ventilation was also

significantly higher in these babies (p<0.001). 49% of LOS babies had chronic lung

disease compared to 28% of other babies (p<0.001). LOS babies also had a higher

incidence of central venous line use (p<0.001), higher incidence of parenteral

(intravenous) nutrition (p=0.013), higher incidence of enteral (tube) nutrition (p=0.004)

and higher incidence of necrotising enterocolitis (p=0.004). There were no differences in

incidence of maternal or fetal chorioamnionitis between groups. (Table 2)

Tables 1 and 2 show all the characteristics and outcomes available for the babies although

not all of these were used in the regression analyses.

66

Table 1 Descriptive statistics of babies with CoNS late-onset sepsis and other late-onset sepsis

Characteristics and outcomes

CoNS LOS

n=218 (26%)

No CoNS LOS

n=620(74%)

p-value

Other LOS

n=68 (8%)

No Other LOS

n=770 (92%)

p-value

Gestational Age#

26.0 (24.8-27.5) [22.0-29.9]

27.3 (25.9-28.7) [22.6-29.9]

<0.001 25.9 (24.5-27.6) [22.7-29.4]

27.1 (25.6-28.6) [22.0-29.9]

<0.001

Birthweight #

815 (680-1005) [410-1830]

960 (740-1174) [395-1920]

<0.001 765 (626-949) [470-1355]

945 (730-1146) [395-1920]

<0.001

Birthweight z score#

-0.09 (-0.58– 0.41) [-3.09 – 2.26]

-0.01 (-0.58 – 0.55) [-3.21 – 3.29]

0.808 -0.02 (-0.58 – 0.54) [-3.21 – 3.29]

0.079

Sex (Female) 98 (45%) 293 (47%) 0.558 32 (47%) 359 (47%) 0.945

Ethnicity:

Caucasian 117 (54%) 282 (46%) 0.066 30 (44%) 369 (48%) 0.225

Aboriginal/TSI 19 (9%) 59 (10%) 6 (9%) 72 (9%)

Other 14 (6%) 28 (5%) 7 (10%) 35 (5%)

AN steroids

144 (95%) 335 (90%) 0.100 43 (100%) 436 (91%) 0.012

Premature rupture of membranes

37 (17%) 102 (17%)

0.045 17 (25%)

122 (16%) 0.127

67

Table 1 (continued)

Characteristics and outcomes

CoNS LOS

n=218 (26%)

No CoNS LOS

n=620(74%)

p-value

Other LOS

n=68 (8%)

No Other LOS

n=770 (92%)

p-value

Delivery mode:

Vaginal 68 (31%) 171 (28%) 0.183 23 (34%) 216 (28%) 0.712

Emergency CS 36 (17%) 90 (15%) 11 (16%) 115 (15%)

Elective CS 47 (22%) 111 (18%) 9 (13%) 149 (19%)

Inborn 213 (98%) 589 (95%) 0.090 67 (99%) 735 (96%) 0.231

Apgar @ 1 <7 145 (67%) 409 (67%) 0.824 49 (72%) 505 (66%) 0.339

Apgar @ 5 <7 44 (20%) 126 (21%) 0.946 9 (13%) 161 (21%) 0.120

Antibiotics given to infant

149 (68%) 370 (60%) 0.023 43 (63%) 476 (62%) 0.818

Maternal chorioamnionitis

71 (33%) 216 (35%) 0.544 24 (35%) 263 (34%) 0.850

Fetal chorioamnionitis

40 (18%) 108 (17%) 0.764 6 (9%) 142 (19%) 0.046

Funisitis 52 (24%) 147 (24%) 0.906 15 (22%) 184 (24%) 0.712

Fetal reaction# 68 (31%) 193 (31%) 0.986 19 (28%) 242 (31%) 0.552

Positive histology 109 (50%) 275 (44%) 0.150 31 (46%) 353 (46%) 0.968

Ventilator 146 (67%) 335 (54%) 0.001 43 (63%) 438 (57%) 0.310

68

Table 1 (continued)

Characteristics and outcomes

CoNS LOS

n=218 (26%)

No CoNS LOS

n=620(74%)

p-value

Other LOS

n=68 (8%)

No Other LOS

n=770 (92%)

p-value

Ventilator* (hrs) #

285 (69-652) [1-1502]

49 (16-233) [1-2415]

<0.001 360 (45-770) [1-1464]

75 (20-347) [1-2415]

0.002

CPAP 143 (66%) 344 (56%) 0.009 41 (60%) 446 (58%) 0.704

CPAP (hrs) # 748 (432-1039) [4-1601]

584 (158-944) [1-1856]

0.001 640 (335-1019) [71-1856]

666 (193-979) [1-1827]

0.617

Any ventilation support

151 (69%) 370 (60%) 0.012 43 (63%) 478 (62%) 0.850

BPD (CLD) 104 (48%) 188 (30%) <0.001 39 (57%) 253 (33%) <0.001

CVL 64 (29%) 129 (21%) 0.010 27 (40%) 166 (22%) 0.001

Multiple CVL episodes

10 (5%) 15 (2%) 0.106 4 (6%) 21 (3%) 0.143

LOS with current CVL

34 (16%) - <0.001 11 (16%) - <0.001

LOS post CVL 11 (5%) - <0.001 16 (24%) - <0.001

Parenteral (intravenous) nutrition

151 (69%) 373 (60%) 0.017 43 (63%) 481 (63%) 0.900

Total parenteral nutrition days

16 (12-24) [3-56]

10 {6-16) [1-56]

<0.001 15 (8-30) [3-56]

12 (7-19) [1-56]

0.003

69

Table 1 (continued)

Characteristics and outcomes

CoNS LOS

n=218 (26%)

No CoNS LOS

n=620(74%)

p-value

Other LOS

n=68 (8%)

No Other LOS

n=770 (92%)

p-value

Multiple parenteral episodes

33 (15%) 34(6%) <0.001 7 (10%) 60 (8%) 0.466

Enteral (tube) feeds

143 (66%) 339 (55%) 0.005 40 (59%) 442 (57%) 0.820

Time to full enteral feeds#

20 (14-28) [6-133]

13 (9-18) [4-59]

<0.001 21 (12-32) [8-58]

15 (10-21) [4-133]

0.004

NEC 16 (7%) 23 (4%) 0.029 7 (10%) 32 (4%) 0.021

Death 15 (7%) 81 (13%) 0.014 3 (4%) 93 (12%) 0.057

Age at death #

25 (19-53) [14-504]

3 (1-9)

[0-214]

<0.001 23 (9-53) [9-53]

4 (1-16) [0-504]

0.085

Infection to death interval (d)#

15 (10-37) [0-499]

3 (1-12) [0-30]

0.009 18 (3-30) [3-30]

11 (2-23) [0-499]

0.614

Frequencies expressed as n(%) except where indicated #: median, (interquartile range),[range]

70

Table 2 Descriptive statistics of babies with no infection, early-onset sepsis and all late-onset sepsis

Characteristics and outcomes

No infection n=538 (64%)

EOS n=25 (3%)

No EOS n=813 (97%)

p-value LOS (Any) n=276 (33%)

No LOS n=562 (67%)

p-value

Gestational Age#

27.4 (26.0-28.8) [22.6-29.9]

26.7 (25.1-27.8) [23.3-29.1]

27.0 (25.4-28.5) [22.0-29.9]

0.153 26.1 (24.7-27.6) [22.0-29.9]

27.4 (26.0-28.7) [22.6-29.9]

<0.001

Birthweight #

980 (755-1191) [395-1920]

825 (620-1083) [505-1410]

930 (723-1140) [395-1920]

0.404 808 (674-994) [410-1830]

975 (750-1190) [395-1920]

<0.001

Birthweight z-score#

0 (-0.58 – 0.56) [-3.21 – 3.29]

0.11 (-0.90 – 0.66) [-2.13 – 2.49]

-0.04 (-0.58 – 0.47) [-3.21 – 3.29]

0.774 -0.11 (-0.58 – 0.38) [-3.09 – 2.26]

0 (-0.58 – 0.57) [-3.21 – 3.29]

0.283

Sex (Female) 250 (47%) 13 (52%) 378 (47%) 0.587 128 (46%) 263 (47%) 0.909

Ethnicity:

Caucasian 248 (46%) 8 (32%) 391 (48%) 0.144 143 (52%) 256 (46%) 0.010

Aboriginal/TSI 53 (10%) 1 (4%) 77 (10%) 24 (9%) 54 (10%)

Other 20 (4%) 1 (4%) 41 (5%) 21 (8%) 21 (4%)

AN steroids

289 (89%) 8 (80%) 471 (92%) 0.423 180 (95%) 297 (53%) 0.002

Premature rupture of membranes

85 (16%) 4 (16%) 135 (17%) 0.045 50 (18%) 89 (16%) 0.045

71

Table 2 (continued)

Characteristics and outcomes

No infection n=538 (64%)

EOS n=25 (3%)

No EOS n=813 (97%)

p-value LOS (Any) n=276 (33%)

No LOS n=562 (67%)

p-value

Delivery mode:

Vaginal 213 (42%) 7 (28%) 232 (29%) 0.125 89 (32%) 150 (27%) 0.133

Emergency CS 81 (25%) 1 (4%) 125 (15%) 44 (16%) 82 (15%)

Elective CS 100 (31%) 2 (8%) 156 (19%) 56 (20%) 102 (18%)

Inborn 509 (95%) 24 (96%) 778 (96%) 0.708 270 (98%) 532 (95%) 0.034

Apgar @ 1 <7 347 (65%) 21 (88%) 533 (66%) 0.029 186 (68%) 368 (66%) 0.576

Apgar @ 5 <7 108 (20%) 10 (42%) 160 (20%) 0.009 52 (19%) 118 (21%) 0.436

Antibiotics given to infant

322 (60%) 10 (40%) 509 (63%) 0.022 187 (68%) 332 (59%) 0.015

Maternal chorioamnionitis

184 (34%) 14 (56%) 273 (34%) 0.020 90 (33%) 197 (35%) 0.483

Fetal chorioamnionitis

97 (18%) 7 (28%) 141 (17%) 0.135 45 (16%) 103 (18%) 0.464

Funisitis 127 (24%) 10 (40%) 189 (23%) 0.055 63 (23%) 136 (24%) 0.701

Fetal reaction# 167 (31%) 13 (52%) 248 (31%) 0.022 82 (30%) 179 (32%) 0.529

Positive histology 235 (44%) 17 (68%) 367 (45%) 0.024 133 (48%) 251 (45%) 0.336

Ventilator 287 (53%) 10 (40%) 471 (58%) 0.074 184 (67%) 297 (53%) <0.001

72

Table 2 (continued) Characteristics and outcomes

No infection n=538 (64%)

EOS n=25 (3%)

No EOS n=813 (97%)

p-value LOS (Any) n=276 (33%)

No LOS n=562 (67%)

p-value

Ventilator* (hrs) #

41 (16-148) [1-2415]

229 (104-366) [1-1294]

83 (20-397) [1-2415]

0.443 297 (66-657) [1-1502]

44 (16-170) [1-2415]

<0.001

CPAP 301 (56%) 7 (28%) 480 (59%) 0.002 179 (65%) 308 (55%) 0.006

CPAP (hrs) # 574 (142-944) [1-1827]

703 (439-1033) [318-1064]

662 (207-980) [1-1856]

0.581 736 (380-1029) [4-1856]

575 (151-949) [1-1827]

0.001

Any ventilation support

322 (60%) 10 (40%) 511 (63%) 0.020 189 (69%) 332 (59%) 0.008

BPD (CLD) 150 (28%) 8 (32%) 284 (35%) 0.762 135 (49%) 157 (28%) <0.001

CVL 103 (19%) 2 (8%) 191 (24%) 0.070 88 (32%) 105 (19%) <0.001

Multiple CVL episodes

12 (12%) - 25 (13%) 0.583 13 (15%) 12 (11%) 0.491

Parenteral (intravenous) nutrition

325 (60%) 10 (40%) 514 (63%) 0.018 189 (69%) 335 (60%) 0.013

Total parenteral nutrition days

10 (6-15) [1-54]

10 (6-16) [3-22]

12 (7-20) [1-56]

0.369 16 (11-24) [3-56]

10 (6-15) [1-54]

<0.001

Multiple parenteral episodes

29 (9%) 1 (4%) 66 (8%) 0.790 37 (20%) 30 (9%) <0.001

73

Table 2 (continued) Characteristics and outcomes

No infection n=538 (64%)

EOS n=25 (3%)

No EOS n=813 (97%)

p-value LOS (Any) n=276 (33%)

No LOS n=562 (67%)

p-value

Enteral (tube) feeds

298 (55%) 6 (24%) 476 (59%) 0.001 178 (65%) 304 (54%) 0.004

Time to full enteral feeds#

13 (9-18) [4-59]

15 (9-18) [5-19]

15 (10-22) [4-133]

0.487 20 (13-28) [6-133]

13 (9-18) [4-59]

<0.001

NEC 17 (3%) 1 (4%) 38 (5%) 0.875 21 (8%) 18 (3%) 0.004

Death 69 (13%) 9 (36%) 87 (11%) 0.001 18 (8%) 78 (14%) 0.002

Age at death #

3 (1-9)

[0-214]

1 (1-9) [0-16]

5 (2-20) [0-504]

0.140 24 (19-53) [9-504]

3 (1-9)

[0-214]

<0.001

Infection to death interval (d)#

- 1 (1-8) [0-13]

17 (9-34) [0-499]

0.002 17 (9-34) [0-499]

1 (1-8) [0-13]

0.002

Frequencies expressed as n(%) except where indicated #: median, (interquartile range),[range]

Figure 3: Kaplan-Meier survival curves, adjusted for gestational age Onset of CoNS LOS

Figure 3a: All infants Figure 3b: Live infants

Onset of any LOS stratified by maternal chorioamnionitis Figure 3c: All infants Figure 3d: Live infants

Onset of any LOS stratified by fetal chorioamnionitis Figure 3e: All infants Figure 3f: Live infants

Maternal chorioamnionitis No maternal chorioamnionitis

75

Kaplan-Meier survival curves for onset of CoNS late-onset sepsis, stratified by maternal

chorioamnionitis, in Figures 3a and 3b showed no significant differences for all infants

(p=0.741) and live infants (p=0.818). Survival curves for onset of any LOS show no

significant differences whether stratified by maternal or fetal chorioamnionitis for all

infants (maternal: p=0.735, fetal: p=0.628) and live infants (maternal: p=0.789, fetal:

p=0.776), as shown in Figures 3c to 3f.

Logistic Regression

Early-onset sepsis

Median age at onset of early-onset sepsis was 0 days (birth) (interquartile range 0-3 days;

range 0-3 days). Univariately maternal chorioamnionitis (OR 2.52 95%CI 1.13-5.362,

p=0.024) was significantly associated with early-onset sepsis, but not after adjusting for

gestational age, birthweight and sex (Table 3).

Table 3 Logistic Regression for early-onset sepsis

Univariate Adjusted*

Predictors OR 95%CI p

value OR 95%CI

p

value

Maternal chorioamnionitis

2.52 1.13-5.62 0.024 2.29 0.99-5.28 0.053

Fetal chorioamnionitis

1.85 0.76-4.51 0.176 1.62 0.65-4.04 0.305

Maternal/fetal chorioamnionitis

2.31 1.03-5.15 0.041 2.08 0.90-4.80 0.085

Funisitis 2.18 0.97-4.94 0.061 2.02 0.89-4.63 0.094 Fetal reaction 2.47 1.11-5.49 0.027 2.23 0.98-5.09 0.056 Positive histology

2.58 1.10-6.05 0.029 2.38 0.96-5.87 0.060

*Adjusted for GA, BWZ, Sex

76

Cox Proportional Hazard Models

CoNS late-onset sepsis

Median age at the onset of first CoNS LOS was 10 days (interquartile range 7-14 days;

range 4-75 days). Maternal chorioamnionitis was the only predictor significantly

associated with CoNS LOS after adjusting for gestational age (HR 0.73 95%CI 0.55-0.98,

p=0.038) (Table 4).

Table 4 Cox Proportional Hazards Regression for CoNS late-onset sepsis

Adjusted#

Multivariable*

Predictors HR 95%CI p value HR 95%CI p

value Maternal chorioamnionitis

0.73 0.55-0.98 0.038 0.78 0.58-1.04 0.095

Fetal chorioamnionitis

0.82 0.58-1.17 0.270 0.86 0.60-1.21 0.374

Maternal/fetal chorioamnionitis

0.77 0.58-1.02 0.071 0.82 0.62-1.10 0.185

Funisitis 0.90 0.66-1.23 0.490 0.89 0.65-1.22 0.453 Fetal reaction 0.83 0.62-1.11 0.209 0.86 0.64-1.15 0.304 Positive histology

0.95 0.72-1.26 0.740 1.00 0.76-1.33 0.982 # Adjusted for GA *Adjusted for GA, BWZ, CVL

Other late-onset sepsis

Median age at onset of first other LOS was 13 days (interquartile range 9-22 days; range

4-130 days). Fetal chorioamnionitis was the only predictor significantly associated with

other LOS after adjusting for gestational age (HR 0.31 95%CI 0.13-0.72, p=0.006). It

remained significant in the multivariable analysis which adjusted for gestational age,

birthweight and CVL (HR 0.37 95%CI 0.16-0.87, p=0.022) (Table 5).

77

Table 5 Cox Proportional Hazards Regression for other late-onset sepsis

Adjusted#

Multivariable*

Predictor variables HR 95%CI p

value HR 95%CI p value

Maternal chorioamnionitis

0.81 0.48-1.35 0.417 0.89 0.53-1.59 0.646

Fetal chorioamnionitis

0.31 0.13-0.72 0.006 0.37 0.16-0.87 0.022

Maternal or fetal chorioamnionitis

0.74 0.44-1.23 0.243 0.82 0.49-1.37 0.447

Funisitis 0.75 0.42-1.33 0.328 0.86 0.48-1.53 0.600 Fetal reaction 0.66 0.38-1.13 0.131 0.77 0.44-1.33 0.345 Positive histology

0.75 0.46-1.24 0.265 0.90 0.54-1.50 0.694 # Adjusted for GA *Adjusted for GA, BWZ, CVL

Any late-onset sepsis

Median age at onset of first LOS (any) was 10 days (interquartile range 7-15 days; range

4-130 days). Maternal chorioamnionitis (HR 0.73 95%CI 0.56-0.94, p=0.016) and fetal

chorioamnionitis (HR: 0.67 95%CI 0.49-0.93, p=0.018) were both significantly

associated with all LOS after adjusting for gestational age. They remained significant in

the multivariable analysis which adjusted for gestational age, birthweight and CVL.

Maternal chorioamnionitis (HR 0.74 95%CI 0.57-0.96, p=0.024); fetal chorioamnionitis

(HR 0.67 95%CI 0.48-0.93, p=0.016 ) (Table 6).

Table 6 Cox Proportional Hazards Regression for any late-onset sepsis Adjusted#

Multivariable*

Predictor variables HR 95%CI p

value HR 95%CI p value

Maternal chorioamnionitis

0.73 0.56-0.94 0.016 0.74 0.57-0.96 0.024

Fetal chorioamnionitis

0.67 0.49-0.93 0.018 0.70 0.50-0.97 0.031

Maternal or fetal chorioamnionitis

0.74 0.57-0.95 0.020 0.75 0.58-0.97 0.031

Funisitis 0.82 0.62-1.09 0.173 0.84 0.63-1.12 0.234 Fetal reaction 0.75 0.57-0.97 0.030 0.77 0.59-1.00 0.051 Positive histology 0.87 0.68-1.12 0.280 0.91 0.71-1.17 0.447 # Adjusted for GA *Adjusted for GA, BWZ, CVL

78

Death

96 babies (12%) died with median age at death 4.5 days (interquartile range 1-16.5 days;

range 0-504 days). 9 babies (9%) who died had early-onset sepsis prior to death. 15

babies (16%) who died had CoNS late-onset sepsis prior to death and 3 babies (16%) who

died had other late-onset sepsis prior to death. There was no significant association

between positive histology for inflammation (chorioamnionitis) and death (HR 0.84

95%CI 0.56-1.28, p=0.423) (Table 7).

Table 7 Cox Proportional Hazards Regression for infant death

Adjusted#

Multivariable*

Predictor variables

HR 95%CI p value

HR 95%CI p value

Maternal chorioamnionitis

0.80 0.52-1.23 0.314 0.90 0.58-1.38 0.620

Fetal chorioamnionitis

0.68 0.40-1.17 0.136 0.81 0.47-1.39 0.453

Maternal or fetal chorioamnionitis

0.78 0.51-1.19 0.255 0.86 0.56-1.33 0.507

Funisitis 0.75 0.46-1.22 0.240 0.80 0.49-1.29 0.354 Fetal reaction 0.95 0.62-1.46 0.818 1.03 0.67-1.58 0.891 Positive histology 0.84 0.56-1.28 0.423 0.92 0.60-1.41 0.696 # Adjusted for GA *Adjusted for GA, BWZ, CVL

79

Cox Proportional Hazard Model: competing risks

The competing risks analyses showed that maternal chorioamnionitis (HR 0.79, 95%CI

0.65-0.98, p=0.033) and fetal chorioamnionitis (HR 0.67, 95%CI 0.52-0.88, p=0.004)

were protective for CoNS late-onset sepsis and other late-onset sepsis or death, taking

into account the possibility of other risk events. (Table 8)

Table 8: Cox Proportional Hazards Regression: competing risks

Adjusted# Multivariable*

Predictor variables HR 95%CI p

value

HR 95%CI p

value

Maternal chorioamnionitis 0.77 0.62-0.95 0.013 0.79 0.65-0.98 0.033

Fetal chorioamnionitis 0.66 0.51-0.86 0.002 0.67 0.52-0.88 0.004

Maternal or fetal

chorioamnionitis

0.76 0.62-0.94 0.010 0.80 0.65-0.98 0.029

Funisitis 0.83 0.66-1.05 0.115 0.83 0.67-1.04 0.110

Fetal reaction (fetal

chorioamnionitis/funisitis)

0.82 0.66-1.01 0.059 0.84 0.68-1.03 0.097

Positive histology (maternal

chorioamnionitis/fetal reaction)

0.86 0.71-1.06 0.159 0.91 0.74-1.12 0.372

# Adjusted for GA *Adjusted for GA, BWZ, CVL

80

Discussion

Late-onset sepsis is acquired post delivery in the neonatal intensive care unit. There are

well established risk factors known to be associated with late onset sepsis including

prematurity, CVL use, mechanical ventilation and prolonged hospitalisation. It is thought

that late-onset septicemia is acquired from the hospital environment, with vectors for

colonization including intravascular catheters, contact from caregivers with bacterial

colonization, exposure to antibiotics or contaminated equipment/enteral solutions. In

particular, prevalence of CoNS late-onset sepsis in premature infants has been thought to

be related to several intrinsic properties of the Staphylococcus epidermidis organism, the

causal mechanism of CoNS late-onset sepsis, that allow it to readily adhere to the plastic

mediums found in intravascular catheters and intraventricular shunts. [14, 15].

Although the association between chorioamnionitis and early-onset septicemia is well

documented, there is a paucity of information on the effects of chorioamnionitis and late-

onset septicemia. Prior to this study there has been no other study that has specifically

looked at this association. In the opinion of the clinical investigators this is a robust

dataset which provides clinically strong evidence of a negative association of

chorioamnionitis and risk of late-onset septicemia. More specifically, as shown in the

results section:

• Any late-onset sepsis

Maternal and fetal chorioamnionitis is associated with a strong decreased risk of any

late-onset sepsis, both after adjusting for gestational age or multivariably (adjusting

for gestational age, birthweight z-scores and central venous line use prior to onset of

sepsis) (HR 0.75 95%CI: 0.58-0.97). Separating out maternal and fetal

chorioamnionitis as individual risk factors showed that both of them individually had

a significant association with decreased risk of any late-onset sepsis in the

multivariable analysis: maternal fetal chorioamnionitis (HR 0.74 95%CI 0.57-0.96);

fetal chorioamnionitis (HR 0.67 95%CI 0.48-0.93). Lahra, et al (2009) [16]have

examined the association between chorioamnionitis and chronic lung disease in

infants born at < 30 weeks gestation with neonatal sepsis, which is strongly associated

81

with chronic lung disease, and have found that fetal chorioamnionitis (OR 0.20

95%CI 0.14-0.95) or maternal chorioamnionitis (OR 0.47 95%CI 0.30-0.74) have

reduced the odds of chronic lung disease in these infants, implying a protective

mechanism associated with chorioamnionitis. The effect sizes shown in the Lahra

study are clinically comparable to our results.

Teasing out the results shows that maternal chorioamnionitis is more likely to be

associated with CoNS late-onset sepsis, while fetal chorioamnionitis is more likely to

be associated with other late-onset sepsis:

• CoNS late-onset sepsis

Maternal chorioamnionitis only was significantly associated with a decreased risk of

CoNS late-onset sepsis after adjustment for gestational age, (HR 0.73 95%CI 0.55-

0.98, p=0.038). This association was not shown to be significant in the multivariable

analysis and there was no significant association shown between fetal

chorioamnionitis and decreased risk of CoNS late-onset sepsis. In addition, maternal

chorioamnionitis was also shown to be significantly associated with early-onset sepsis

(OR 2.52 95%CI 1.13-5.362, p=0.024), although this result was not replicated in

multivariable analysis.

• Other late-onset sepsis

Fetal chorioamnionitis was significantly associated with a decreased risk of other late-

onset sepsis infections after adjustment for gestational age and in the multivariable

analysis (HR 0.31 95%CI 0.13-0.72, p=0.006). There was no significant association

shown between maternal chorioamnionitis and decreased risk of other late-onset

sepsis.

Therefore the results of this study, in relation to late-onset sepsis, suggest that perinatal

inflammation (chorioamnionitis) might enhance the maturation of the neonatal innate

immune system and thus lower the risk of acquisition of late-onset sepsis in premature

infants.

82

In addition to late-onset sepsis, other outcomes such as early-onset sepsis and death and

their association with chorioamnionitis showed the following:

• Early-onset sepsis

Univariately maternal chorioamnionitis was significantly associated with early-onset

sepsis (OR 2.52 95%CI 1.13-5.362, p=0.024). These results are commensurate with

well established findings that early onset sepsis syndrome is known to be associated

with acquisition of microorganisms from the mother and is similar to the findings of

Escobar et al[17] and Yancey et al[18], whose multivariable logistic models showed an

increased risk of early-onset sepsis in infants of mothers with maternal

chorioamnionitis (OR 2.40 95% CI 1.15–5.00) and (OR 4.4, 95% CI 1.2–16.1),

respectively.

• Death

There was no evidence for an association between chorioamnionitis and death in

infants with any type of late-onset sepsis (HR 0.78 95%CI 0.51-1.19, p=0.255).

Rates of death in infants with either CoNS or other late-onset sepsis in this study were

16%, which are comparable to other published rates which range between 10 and

20% in infants with late-onset sepsis. (Salen and Eppes, 1997) [19]

Strengths and limitations of the study

A major strength of the study was the fact that it took place at a single centre with

consistent clinical practice. The reason that this was a single-site study was due to the fact

that King Edward Memorial Hospital is the only tertiary obstetric hospital in the state and

all very premature babies are admitted at this centre as a matter of course. There were

suitably large numbers of records available which allowed statistical control of some

known confounders. On the other hand, the fact that the study did take place in a single

centre could also be seen as a limitation: although the sample size was adequate for

multivariable analysis it was not suitably powered to examine neurological outcomes

such as PVL and IVH, which may have been possible with additional sites taking part.

83

Future studies involving multiple sites may better power similar analyses so that these

results can be corroborated and extended to include neurological outcomes.

The fact that the dataset consists only of infants with available histology may be a

potential source of bias in the study, although it is difficult to speculate as to whether this

would result in an over estimation or underestimation of the effect of chorioamnionitis on

late-onset sepsis. This study has included only babies with histological scoring for

chorioamnionitis, as clinical diagnosis alone is considered to be unreliable. As the

dataset used in this study is a consecutive patient series at King Edward Memorial

Hospital, excluding babies outborn or with major congenital anomalies (which are both

major risk factors for infection), and guidelines for very premature babies indicate that

placental histology should always be done, it is likely that if on the very rare occasion

placental histology was not done, it would have been for logistical rather than clinical

reasons.

Although logistic regression is a less powerful form of analysis than Cox regression, time

to event analysis was not considered feasible for the early-onset sepsis cases in this study

due to the small sample size and the fact that onset in most cases was clustered around

Day 0. Certain covariates were unable to be adjusted for in the multivariable Cox

regressions for late-onset sepsis due to missing or incomplete data.

84

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