using routine data to measure recurrence in head and neck cancer zi wei liu matt williams adam...
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Using routine datato measure recurrence
in Head and Neck Cancer
Zi Wei LiuMatt WilliamsAdam GibsonKate Ricketts
Heather Fitzke
[email protected] E-oncology Conference 2015
Defining the problem
Head and neck cancer
– ~6000 new diagnoses of head and neck cancer a year
– Strongly related to smoking
– Increase in incidence recently due to HPV related H+N cancer
– ~60% present at an advanced stage and require multi-modality treatment-surgery, radiotherapy, chemo.
Defining the problem
Recurrence rates in H&N cancer are important
For staff (efficacy) For patients (prognosis) For service planning (costs)
Not well measured in routine care population
Relies on patchy manual data entry (9th DAHNO 12% reported)
What is 'routine data'?
Nationally collected patient data
Uniform coding scheme Some linked to payments for activity mandatory data collection
Examples:
HES (hospital episodes statistic) SACT (Systemic anti-cancer therapy) RTDS (radiotherapy database) DBS(Demographic batch service) Cancer registry data
Hospital episodes statistic
Patient demographics Inpatient (and now outpatient) attendances Diagnosis & Procedures Co-morbidities
SACT & RTDS
SACT & RTDS cancer databases have a minimum dataset which usually contains the following:
Patient demographics: e.g NHS number, DOB, post code, consultant code
Primary diagnosis: ICD-10 code, staging, morphology Regimen, intention of treatment, height and weight,
PS Start and end date of treatment, intended and actual
treatment delivered Date of death
Aims and importance of our study
Can we determine recurrence rates and survival times from routine data ?
How closely do they match manually-measured rates & times ?
Pilot study
assess feasibility and possible problems Follow-up study
larger sample size, problems with scaling
Methods
Pilot study: 20 patients with head and neck identified from local MDT lists
Received radical treatment Weighted towards those diagnosed at UCH Weighted towards advanced disease
Paired datasets generated-'manual' and 'routine'
Tests of correlation performed on key clinical outcome indicators such as overall survival, progression survival and recurrence events.
Ref: Liu ZW, Fitzke H, Williams M. Using routine data to estimate survival and recurrence in head and neck cancer: our preliminary experience in twenty patients. (2013) Clinical Otolaryngology, 38(4):334-9.
Methods
Second expanded study 122 patients Paired datasets generated-'manual' and 'routine' Optimization strategies including backdating, time interval
optimization Survival curves
Ref: Ricketts K, Williams M, Liu ZW, Gibson A. (2014). Automated estimation of disease recurrence in head and neck cancer using routine healthcare data. Computer Methods and Programs in Biomedicine. 7(3):412-24.
Methods
Methods
Methods
Date & Site of first head and neck cancer diagnosis code Radical treatment Collect HES, RTDS and SACT data (incl. Dates) If further major surgical resection or palliative
chemotherapy, or palliative RT, assume recurrence
No intention on RT, so used a 3/12 cut-off for differentiating adjuvant vs. radical salvage RT
Results
Pilot study:
20 patients
13 male
9 primary oropharynx
15 LAHNSCC
Median OS 24.4 months
Median PFS 9.6 months
Results
Follow-up Study:
122 patients
82% locally advanced disease
51 oropharynx
26 larynx
Median OS 88% (1 year), 77% (2 years)
Median PFS 75% (1 year), 66% (2 years)
Results
Optimization strategies
– Backdating
– Optimizing time intervals between primary and secondary treatment
Results
Conditions
No. patients out
of bounds for
routine OS
No. patients
out of bounds for routine PFS
Diagnosis dates in
agreement{n = 122}
±1 week / ±1 month
Recurrence dates in agreement
{n = 40} ±1 week / ±1 month
No. of recurrence events correctly identified
No. of recurre
nce events falsely identifi
ed
No. of recurren
ce events missed
Initial approach
7 25 1 week (62)1 month (97)
1 week (1)1 month (4)
21 5 19
Backdating alone
3 23 1 week (61)1 month
(101)
1 week (5)1 month (7)
21 5 19
Backdating + optimised time intervals
3 21 1 week (61)1 month
(102)
1 week (7)1 month (9)
21 2 19
Results
Results
Results
Pilot study (n=20) Follow up study (n=122)
OS 95% good agreement 98% good agreement
PFS 80% good agreement 82% good agreement
Recurrence events 10/11 correctly identified 21/40 correctly identified
Discussion
Selected sample
LAHNSCC Radical treatment only
Reasonable agreement between routine and manual data
Used national-level data, possible to automate, adds to existing knowledge
Potentially inaccurate, esp. in palliative patients
Discussion
Further optimisation work HES density data looking at ratio of inpatient to
outpatient attendances to predict recurrence Measurement of non-OS outcomes
– In addition to recurrence:
– PEG dependency rates
– Tracheostomy dependency rates
Future directions
Phase III study using national cancer data under way Develop software to automate data handling and analysis
Experiments to optimise algorithm and utilise modelling to improve accuracy of predictions
Incorporate registry data First comprehensive automated analysis of national cancer
dataset in the UK Different subsites- head and neck and breast will be pilot sites
In collaboration with NCIN and Public Health England
Summary
2 studies using routine data validated against manually collected data demonstrating potential of analysing national databases for clinically relevant outcomes
Can be automated and less resource-intensive than audit Algorithms can be tailored for other cancer subsites
(GBM study under way) Third phase study
Questions?