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Examination of the effects of low-dose intraoperative fentanyl on postoperative respiratory complication rate. A pre-specified, retrospective analysis

- SUPPLEMENTAL DIGITAL CONTENT -

SUPPLEMENTAL SECTION 1. DATA SOURCES .......................................................................... 3

SUPPLEMENTAL SECTION 2. EVALUATION OF THE CONFOUNDER MODEL ................ 4

MULTIVARIABLE FRACTIONAL POLYNOMIAL MODELLING........................................................................4CHOICE OF CONFOUNDER FORMAT...........................................................................................................4

SUPPLEMENTAL SECTION 3. EXPLORATORY ANALYSES .................................................... 5

SUPPLEMENTAL SECTION 4. INTERACTION TERM ANALYSES ......................................... 6

SUPPLEMENTAL SECTION 5. SENSITIVITY ANALYSES ......................................................... 7

PREOPERATIVE OPIOID PRESCRIPTION.......................................................................................................7INTRAOPERATIVE LONG-ACTING OPIOID DOSE..........................................................................................7POSTOPERATIVE PAIN MANAGEMENT........................................................................................................7PROPENSITY-SCORE MATCHING................................................................................................................8DURATION OF SURGERY............................................................................................................................9SURGICAL COMPLEXITY............................................................................................................................9INDIVIDUAL ANAESTHESIOLOGIST PREFERENCES......................................................................................9YEAR OF SURGERY....................................................................................................................................9EXCLUSION OF MULTIPLE PROCEDURES WITHIN FOUR WEEKS................................................................10PRESPECIFIED RISK GROUPS....................................................................................................................10MULTIPLE IMPUTATION OF MISSING DATA..............................................................................................10

SUPPLEMENTAL SECTION 6. SUPPLEMENTAL TABLES ...................................................... 11

TABLE S1: DEFINITION OF OUTCOME..................................................................................................11TABLE S2. ASSOCIATION OF INTRAOPERATIVE FENTANYL DOSE AND POSTOPERATIVE RESPIRATORY COMPLICATIONS ACROSS FENTANYL-DOSE DECILES..................................................13TABLE S3. RESULTS OF EXPLORATORY OUTCOMES ACROSS FENTANYL DOSE QUARTILES.............14TABLE S4. SENSITIVITY ANALYSES......................................................................................................16TABLE S5. INTERACTION ANALYSIS - BODY MASS INDEX..................................................................19TABLE S6. CHARACTERISTICS OF STUDY POPULATION BY INTRAOPERATIVE FENTANYL DOSE – AFTER PROPENSITY MATCHING OF 1ST AND 4TH QUARTILE..................................................................20

SUPPLEMENTAL SECTION 7. SUPPLEMENTAL FIGURES .................................................... 22

FIGURE S1A. AREA UNDER THE RECEIVER OPERATING CHARACTERISTICS CURVE FOR THE PRIMARY REGRESSION MODEL INDEPENDENT OF FENTANYL DOSE....................................................22FIGURE S1B. RELIABILITY PLOT FOR THE PRIMARY REGRESSION MODEL.......................................22FIGURE S2. COMPOSITE AND INDIVIDUAL RESPIRATORY COMPLICATIONS BY YEAR OF SURGERY.23

REFERENCES ..................................................................................................................................... 24

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Supplemental section 1. Data sources

We retrieved data from three different databases. From the Anaesthesia Information Management System (AIMS), we collected information regarding intraoperative management as well as baseline patient characteristics. Data on preoperative medications, comorbidities, and postoperative outcomes were collected from the Research Patient Data Registry (RPDR), a centralized registry dedicated to providing data for research purposes. From 2007 to 2014, comorbidities and outcomes were coded according to the International Classification of Diseases and Related Health Problems, Ninth Revision (ICD9), while we used the Tenth Revision (ICD10) for diagnostic codes from the beginning of 2015. Additionally, we obtained information on hospital admission and discharge as well as hospital length of stay and hospital costs from Enterprise Performance Systems Inc. (EPSi), a system for performance improvement and financial planning.

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Supplemental section 2. Evaluation of the confounder model

We tested all continuous variables in the primary regression model for linearity. Due to non-linearity of the coefficients, the following variables were categorized into equally-sized quintiles: intraoperative vasopressor dose, duration of surgery, intraoperative fluid volume, work relative value units and inspiratory O2-fraction. The following confounder variables were categorized according to clinically reasonable cut-off points: body mass index (BMI)1, American Society of Anesthesiologists (ASA) physical status classification (0-2, 3-5), Score for Prediction of Postoperative Respiratory Complication (SPORC),2 Score for Prediction of Obstructive Sleep Apnoea (SPOSA),3 and intraoperative hypotensive minutes (0-10, 11-19, ≥ 20) as well as units of packed red blood cells (0, 1, 2, ≥ 3).

Multivariable fractional polynomial modelling

In our primary model, five covariates not underlying the linearity assumption were categorized into equally sized quintiles. In-order to adjust for potential insufficient consideration of non-linearity in these continuous covariates, we performed a sensitivity analysis utilizing multivariable fractional polynomial modelling.4

In the resulting model, intraoperative vasopressor dose was treated as polynomials with the power -2, duration of surgery with the power -1 and intraoperative fluid volume with -0.5 accordingly, while for the variable work relative value units fractional polynomials with the power of 0.5 were determined and of 2 for inspiratory O2-fraction.The sensitivity analysis utilizing multivariable fractional polynomial modelling confirmed our primary findings: a significant association of low intraoperative fentanyl dose and lower odds of PRCs (Q1 vs Q4: aOR 0.80, CI 0.76-0.84, P<0.001; Table S4 F).

Choice of confounder format

In the primary model, dose of volatile anaesthetics was expressed as average MAC (minimum alveolar concentration at 1 atmosphere that prevents movement in 50% of patients exposed to a surgical incision) of the case as calculated from the mean end-tidal concentration of the respective gas throughout the case and was adjusted by an anaesthetic-specific age-factor. Fraction of inspired oxygen (FiO2) was defined as the median FiO2 during the case. In response to concerns in peer-review, a sensitivity analysis was performed utilizing the format MAC-hours and FiO2-hours to account for dose of volatile anaesthetics and fraction of inspired oxygen over the course of the procedure.

Utilizing alternative methods to express the confounders dose of volatile anaesthetics and fraction of inspired oxygen, demonstrated the robustness of our findings: Q1 vs Q4 - aOR 0.81, CI 0.77-0.86, P<0.001.

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Supplemental section 3. Exploratory analyses

With an exploratory intent, we evaluated the association between fentanyl dose and length of hospital stay, total costs of hospital care, incidence of surgical site infection within 30 days after surgery, postoperative ICU admission and perioperative naloxone use as well as post-extubation desaturation, mortality within 30 days after surgery, non-invasive ventilation, constipation and delirium within 3 days after surgery. Costs were defined as the sum of direct costs, based on patient services, and indirect costs, the latter representing costs not directly linked to patient care (e.g. costs from the financial services department).

Low intraoperative fentanyl dose was significantly associated with a lower risk of ICU admission during the index hospital stay, surgical site infection within 30 days after surgery and lower perioperative naloxone use. Measures of healthcare utilization (i.e., hospital costs and length of hospital stay) were significantly lower among patients receiving low intraoperative fentanyl doses (Table S3).

Low fentanyl dose was significantly associated with a lower risk of post-extubation desaturation below 80%, but not below 90%. There was no significant association between intraoperative fentanyl dose and risk of non-invasive ventilation, constipation or delirium within three days after surgery or mortality within 30 days after surgery (Table S3).

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Supplemental section 4. Interaction term analyses

Methods

To identify vulnerable subcohorts of patients, we examined interaction terms concerning patient characteristics (“fentanyl dose x age”, “fentanyl dose x BMI”), surgery location (“fentanyl dose x thoracic surgery”, “fentanyl dose x abdominal surgery”), and other intraoperative medications (“fentanyl dose x mean alveolar concentration [MAC] of inhalational anaesthetics”, “fentanyl dose x neuromuscular blocking agents [NMBA] dose”) by integrating the interaction terms separately into the primary model.

In order to determine clinically meaningful differences in risk, we estimated the adjusted absolute risk difference (aARD) between low and high fentanyl dose (Q1 vs Q4), i.e. the risk of PRCs attributable to intraoperative fentanyl dose, in the respective subgroups with documented significant effect modification. For continuous variables, we defined equally-sized quartiles adopting the same methodology as for the exposure variable.

Results

While abdominal surgery did not modify the examined association (interaction term “fentanyl dose x abdominal surgery”: p for interaction = 0.81), thoracic surgery modified the association of low fentanyl dose and lower odds of PRCs compared with high fentanyl dose towards a more substantial difference in risk among patients undergoing thoracic surgery: -6.2% (Q1 vs Q4, CI -9.7 to -2.5%; P for interaction <0.001, OR 1.21, CI 1.17-1.25) (Table S4A)..

Depth of anaesthesia (i.e. MAC of volatile anaesthetics) also modified the association between intraoperative fentanyl dose and PRCs significantly (interaction term “fentanyl dose x MAC”: P for interaction =0.016, OR 1.03, CI 1.01-1.06). Although the estimated risk of PRCs, independent of fentanyl dose, was lower among patients receiving high doses of volatile anaesthetics (5.1-7.3%), the difference in risk of PRCs attributable to low fentanyl dose was more substantial: -2.2% (Q1 vs Q4, CI -2.9 to -1.5%; Table S4 B). Similarly, patients receiving high doses of NMBA demonstrated a greater difference in risk of PRCs attributable to low-dose fentanyl: -3.4% (Q1 vs Q4, CI -4.6 to -2.3%; interaction term “fentanyl dose x NMBA dose”: P for interaction <0.001, OR 0.99, CI 0.99-0.99; Table S4 C). Among patients receiving high doses of both, volatile anaesthetics and NMBA, the difference in risk of PRCs attributable to low-dose fentanyl in comparison to high-dose fentanyl added up to -4.1% (Q1 vs Q4, CI -5.8 to -2.3%; Table S4 D).

Age did not modify the examined association significantly (interaction term “fentanyl dose x age”: P for interaction = 0.20). The interaction term “fentanyl dose x BMI” showed significance (p for interaction < 0.001, OR 1.00, CI 1.00-1.01), but the difference in risk of PRCs attributable to low fentanyl dose of -1.5% (Q1 vs Q4, CI -2.5 to -0.4%) among severely obese patients (BMI ≥ 35 kg m-2) was not meaningfully different from that among patients with a lower BMI: -1.5% (Q1 vs Q4, CI -1.9 to -1.1%) (Table S5).

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Supplemental section 5. Sensitivity analyses

Preoperative opioid prescription

In our primary model, we controlled for preoperative opioid prescription. As it is challenging to capture the actual use of opioids, in a sensitivity analysis we utilized alternative classifications to control for preoperative opioid use (categorization in accordance to World Health Organization [WHO] strength classifications5 and total number of unique preoperative opioid compounds). Additionally, we re-examined the primary hypothesis among patients without prescription of any opioid within 90 days prior to surgery.

In our study cohort, 11% of all patients were prescribed any opioid medication within three months prior to surgery according to medical records. Use of two or more different opioid compounds within three months preoperatively was recorded in 3.8% of all patients and use of three or more different compounds was recorded in 1.2% of patients. According to WHO strength classification, 8.8% of patients received a prescription for a ‘strong’ opioid, 2.2% for only ‘weak’ compounds. When controlling for number of opioid compounds or for strength of prescribed preoperative opioid in the primary model, the primary findings were confirmed. Among the 129,725 patients without preoperative opioid prescription, the association of high intraoperative fentanyl dose and PRCs risk remained robust (Q1 vs Q4: aOR 0.79, CI 0.75-0.84, P<0.001; Table S4 C).

Intraoperative long-acting opioid dose

In the primary model, we adjusted for intraoperative dose of long-acting opioids such as methadone, morphine, hydromorphone and sufentanil (morphine equivalent). As a sensitivity measure, we tested if intraoperative long-acting opioid dose would modify the association of high intraoperative fentanyl dose and PRCs risk by integrating the corresponding interaction term into the primary regression model.

There was no significant interaction by intraoperative long-acting opioid dose: interaction term “fentanyl dose x long-acting opioid dose” – P for interaction =0.099.

In addition, in order to account for residual confounding due to combination of equivalent doses of opioids with different pharmacological characteristics, we performed a sensitivity analysis among 48,769 patients who did not receive any long-acting opioid intraoperatively. Our results remained robust in this subset of patients: Q1 vs Q4 – aOR 0.87, CI 0.79-0.95, P=0.002 (Table S4 D).

Postoperative pain management

In sensitivity analysis, we a) captured the number of different opioid compounds a patient was prescribed postoperatively on the day of discharge and b) alternatively categorized the compounds based on the WHO strength classification. We then included postoperative opioid prescription categorized according to a) and b) separately in the primary model as additional confounders. Additionally, we performed a sensitivity analysis in a subgroup of patients that were prescribed any opioids postoperatively.

Similarly, we also categorized patients based on prescriptions of non-opioid analgesics on discharge and performed sensitivity analyses in the following subgroups: patients that were

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not prescribed any pain medication (opioids or non-opioid analgesics) postoperatively and patients with an non-opioid analgesics prescription.

Of the 77,106 patients with postoperative opioid prescription, 88.3% received prescriptions for at least one ‘strong’ opioid compound and 10.8% were prescribed two or more different compounds. Adjustment for postoperative opioid prescription demonstrated the robustness of our findings: a) number of different compounds - Q1 vs Q4: aOR 0.79, CI 0.75-0.84, P<0.001; b) strength classification – Q1 vs Q4: aOR 0.79, CI 0.75-0.83, P<0.001. The association of low intraoperative fentanyl dose and lower odds of PRC remained significant among patients with a postoperative opioid prescription (n=77,106, Q1 vs Q4: aOR 0.74, CI 0.68-0.80, P<0.001, Table S4 E)

Low intraoperative fentanyl dose was significantly associated with lower odds of PRC was significant among patients with prescription of a non-opioid analgesic postoperatively (n=71,570, Q1 vs Q4: aOR 0.76, CI 0.70-0.82, P<0.001, Table S4 E) as well as among the 56,675 (38.9%) patients that were not prescribed any pain medication on discharge (Q1 vs Q4: aOR 0.82, CI 0.75-0.89, P<0.001; Table S4 E).

All patients underwent surgery with general anaesthesia. In our primary confounder model, we adjusted for the intraoperative use of neuraxial anaesthesia in addition to general anaesthesia. In a sensitivity analysis, we account for the intraoperative use of peripheral nerve blockage as additional confounder in our model. In this analysis the association of low intraoperative fentanyl dose and lower PRC rate remained robust: Q1 vs Q4 - aOR 0.79, CI 0.75-0.83, P<0.001 (Table S4 G).

Propensity-score matching

In order to account for unmeasured confounding, we aimed to address underlying bias leading to patients receiving high fentanyl doses with propensity-score matching. The following variables were utilized to estimate the propensity for receiving a high intraoperative fentanyl dose (fourth quartile, Q4): year of surgery, gender, age, body mass index, admission type , ASA physical status classification, Score for Prediction of Postoperative Respiratory Complication (SPORC)2, including high risk surgical service and emergency surgery status; Score for Prediction of Obstructive Sleep Apnoea (SPOSA)3, including history of hypertension, atrial fibrillation, chronic pulmonary disease, congestive heart failure, diabetes, dyslipidaemia, hemi/paraplegia, liver disease, pulmonary hypertension and coronary artery disease within one year prior to surgery; code status, prescription of any opioid within 90 days prior to surgery, use of neuraxial anaesthesia, duration of surgery, work relative value units, neuromuscular blocking agent dose, vasopressor dose, dose of long-acting opioids such as methadone, morphine, hydromorphone and sufentanil, dose of volatile anaesthetics, units of packed red blood cells, intravenous fluid volume, protective ventilator settings (driving pressure ≤ 15 mmHg), inspiratory O2-fraction and intraoperative hypotension. All of these factors were forced into a logistic regression model to build the propensity score.

In a sensitivity analysis, we re-examined the hypothesis that patients receiving low fentanyl doses (first quartile, Q1) would have lower odds of PRC than patients receiving high fentanyl doses (Q4) in a subset of patients matched by their propensity to receive a high intraoperative fentanyl dose. Matching was performed 1:1 with a calliper set to 0.10 using nearest neighbour and sampling without replacement.

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A total 39,774 of 73,107 (54.4%) patients from Q1 and Q4 were matched with a 1:1 ratio (Table S6). Re-examination of the primary finding in the matched cohort confirmed the association of low fentanyl dose and lower odds of PRC: OR 0.75, CI 0.71-0.79, P<0.001.

The following variables retained residual imbalances in the matched sample and were therefore forced into the regression model: gender, body mass index, duration of surgery, work relative value units, neuromuscular blocking dose and use of neuraxial anaesthesia (Table S6). This analysis confirmed our primary results as well (aOR 0.84, CI 0.79-0.89, P<0.001).

Duration of surgery

In the primary analysis, duration of surgery was included as a covariate. Additionally, we examined the interaction term “fentanyl dose x duration of surgery” by integrating it into the primary regression model. In case of significant effect modification by duration of surgery, subsequent sensitivity analyses in subgroups of short (under two hours) and long procedural duration (≥ two hours) were performed.

The interaction term “fentanyl dose x duration of surgery” demonstrated significant effect modification (p for interaction 0.005, OR 1.003, CI 1.001-1.005). Inclusion of this term in the primary regression analysis revealed a robust association of low fentanyl dose and lower PRC rates (Q1 vs Q4: aOR 0.83, CI 0.78-0.88, P<0.001; Table S4 H). Subsequent subgroup analyses of procedural duration confirmed our primary findings (Table S4 J).

Surgical complexity

The following variables were included in the primary model to adjust for surgical complexity: high risk surgical service, emergency surgery status, duration of surgery, work relative value units as well as administered vasopressor dose, units of packed red blood cell units and intravenous fluid administration volume. To address remaining bias due to surgical complexity, we performed a mixed effects logistic regression analysis adjusting for the primary surgical procedure. Clustering within the surgical procedure was set as random effect while all a priori defined covariates were included as fixed effects.

Information on the primary surgical procedures was available for 130,420 (89.5%) cases, including 3353 different primary procedures. Clustering within the primary surgical procedure in mixed effects analysis confirmed a robust association of low intraoperative fentanyl dose and lower odds of PRC (Q1 vs Q4: aOR 0.88, CI 0.83-0.94, P<0.001; Table S4 J).

Individual anaesthesiologist preferences

We performed mixed effects logistic regression analysis to account for differences in fentanyl use between anaesthesiologists. The random effect was clustering within anaesthesia provider and fixed effects were all predefined confounder variables.

Identification of the anaesthesia provider was possible in 145,725 (99.99%) surgical cases under the care of 751 individual anaesthesiologists. The primary association remained stable when accounting for differences in fentanyl use between anaesthesiologists in mixed effects regression analysis (Q1 vs Q4: aOR 0.79, CI 0.75-0.83, P<0.001) (Table S4 K).

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Year of surgery

Our study encompasses non-cardiac surgical cases from 2007 to 2015. During this eight-year study period standards of care and institutional practices may have undergone significant changes. Figure S2 shows the rate of the composite outcome and the individual postoperative respiratory complications within 3 days after surgery by year of procedure. In a sensitivity analysis, we added year of surgery as a categorical variable to the primary model.

Adjusting for year of surgery did not alter our primary findings and demonstrated lower odds of PRC with low fentanyl doses (Q1 vs Q4: aOR 0.77, CI 0.73-0.81, P<0.001; Table S4 L).

Exclusion of multiple procedures within four weeks

As our study period spans nine years, some patients are included multiple times for index surgeries at different dates within the study period. In a sensitivity analysis, we re-examined the primary hypothesis after exclusion of cases in which the same patient had undergone surgery within four weeks prior to the index surgery.

In this sensitivity analysis, we re-examined the primary hypothesis in a total of 136,696 cases. The association of low intraoperative fentanyl dose (Q1 vs Q4) and lower PRC rates was maintained: aOR 0.79 (CI 0.75-0.84, P<0.001; Table S4 M).

Prespecified risk groups

To further test the robustness of our findings, we performed subgroup analyses in different pre-specified risk groups characterized by Charlson Comorbidity Index (CCI)6 (<3, ≥3), ASA status classification (0-2, 3-5), high risk vs. low risk surgery (based on surgical service)2 as well as patients with and without pre-existing respiratory comorbidities, defined as a diagnosis of obstructive sleep apnoea (OSA), asthma, chronic obstructive pulmonary disease (COPD), or pulmonary hypertension within one year prior to surgery.

The association of low intraoperative fentanyl dose and lower odds of PRCs remained robust in all different pre-specified risk groups (Table S4 N-Q).

On reviewer suggestion, an additional sensitivity analysis was performed examining the robustness of our findings in subgroups of patients undergoing ambulatory or inpatient procedures.

The association of low fentanyl doses and lower odds of PRCs retained significance among both patients undergoing ambulatory procedures (Q1 vs Q4: aOR 0.68, CI 0.56-0.82, P<0.001) and inpatients (Q1 vs Q4: aOR 0.80, CI 0.76-0.85, P<0.001; Table S4 R).

Multiple imputation of missing data

Our primary analysis was performed in a complete case cohort. To account for bias due to pattern of missingness, we performed multiple imputation by chained equations using STATA. After five imputations with five iterations each, the primary analysis was repeated in the imputed dataset.

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A total of 11 confounder variables had missing data, with SPOSA and protective ventilation being the variables missing most frequently (9,687 [5.8%] and 9,072 [5.4%] cases, respectively). Re-evaluation of the primary hypothesis in the imputed study cohort showed consistency with our findings in the complete case cohort (Q1 vs Q4: aOR 0.81, CI 0.77-0.85, P<0.001; n=166,896; Table S4 S).

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Supplemental section 6. Supplemental tables

Table S1: Definition of outcome

Outcome Procedure Name Code Type* Code

Pneumonia

Pneumococcal pneumonia [Streptococcus pneumonia] ICD-9/ICD-10 481/J13

Pneumonia due to Klebsiella pneumoniae ICD-9/ICD-10 482.0/J15.0 Pneumonia due to Pseudomonas ICD-9/ICD-10 482.1/J15.1 Pneumonia due to Streptococcus, unspecified ICD-9/ICD-10 482.30/J15.4 Pneumonia due to Staphylococcus, unspecified ICD-9/ICD-10 482.40/J15.20 Pneumonia due to Staphylococcus aureus ICD-9/ICD-10 482.41/J15.211 Methicillin resistant pneumonia due to staphylococcus aureus ICD-9/ICD-10 482.42/J15.212

Pneumonia due to Escherichia coli [E. coli] ICD-9/ICD-10 482.82/J15.5 Pneumonia due to other gram-negative bacteria ICD-9/ICD-10 482.83/J15.6 Pneumonia due to other specified bacteria ICD-9/ICD-10 482.89/J15.8 Bacterial pneumonia, unspecified ICD-9/ICD-10 482.9/ J15.9 Pneumonia, organism unspecified ICD-9/ICD-10 486/J18.9 Pneumonia due to other specified organism ICD-9/ICD-10 483.8/J16.8 Pneumonia in aspergillosis ICD-9/ICD-10 484.6/B44.0 Bronchopneumonia, organism unspecified ICD-9/ICD-10 485/J18.0 Pneumonitis due to inhalation of food or vomitus ICD-9/ICD-10 507.0/J69.0

Pulmonary Oedema

Pulmonary congestion and hypostasis ICD-9/ICD-10 514/J81.1 Acute oedema of lung, unspecified ICD-9/ICD-10 518.4/J81.0 Congestive heart failure ICD-9/ICD-10 428.0/J50.9 Fluid overload ICD-9/ICD-10 276.61/E87.70 Other fluid overload ICD-9/ICD-10 276.69/E87.79

Reintubation

Intubation, endotracheal, emergency procedure CPT 31500Ventilation assist and management, initiation of pressure or volume preset ventilators for assisted or controlled breathing; hospital inpatient/observation, initial day

CPT 94002

Respiratory Failure

Pulmonary insufficiency following trauma and surgery ICD-9/ICD-10 518.5/J95.822

Acute respiratory failure following trauma and surgery ICD-9/ICD-10 518.51/J95.821

Other pulmonary insufficiency, not elsewhere classified, following trauma and surgery ICD-9/ICD-10 518.52/J95.3

Acute pulmonary insufficiency following thoracic surgery ICD-10 J95.1

Acute pulmonary insufficiency following non-thoracic surgery

ICD-10 J95.2

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Respiratory failure ICD-9/ICD-10 518.81/J96.90 Other pulmonary insufficiency, not elsewhere classified ICD-9/ICD-10 518.82/J80

Acute and chronic respiratory failure ICD-9/ICD-10 518.84/J96.20 Atelectasis Pulmonary collapse ICD-9/ICD-10 518.0/J89.19

Atelectasis ICD-10 J89.11* ICD-9: International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM); ICD-10: International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM); CPT: Current Procedural Terminology

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Table S2. Association of intraoperative fentanyl dose and postoperative respiratory complications across fentanyl-dose deciles

Fentanyl dose decile

1st Decile

(n=14,447)

2nd Decile

(n=14,592)

3rd Decile

(n=14,590)

4th Decile

(n=14,751)

5th Decile

(n=14,626)

6th Decile

(n=14,555)

7th Decile

(n=14,419)

8th Decile

(n=14,902)

9th Decile

(n=14,353)

10th Decile

(n=14,500)

Fentanyl dose*

(μg kg-1)

0 (0, 0)0.95

(0.77, 1.08)

1.33

(1.25, 1.41)

1.65

(1.56, 1.74)

2.00

(1.90, 2.08)

2.37

(2.27, 2.46)

2.74

(2.63, 2.81)

3.13

(3.02, 3.25)

3.67

(3.50, 3.85)

4.72

(4.31, 5.56)

Range of fentanyl dose(μg kg-1)

0-0 0.05-1.2 1.2-1.5 1.5-1.8 1.8-2.2 2.2-2.5 2.5-2.9 2.9-3.3 3.3-4.0 4.0-76.1

Postoperative Respiratory Complications Positive outcomes**

1,798(12.5%)

1,488(10.2%)

1,389(9.5%)

1,493(10.1%)

1,765(12.1%)

1,861(12.8%)

2,094 (14.5%)

2,238(15.0%)

2,178(15.2%)

2,535(17.5%)

adjusted OR [95% CI] 1

0.82[0.75-0.89]P<0.001

0.81 [0.74-0.88]P<0.001

0.86[0.80-0.94]

P=0.001

0.94 [0.87-1.02]

P=0.122

0.93[0.86-1.01]

P=0.085

1.05[0.97-1.13]

P=0.243

1.09[1.01-1.18]

P=0.029

1.08 [0.995-1.17]

P=0.065

1.15[1.07-1.25]P<0.001

adjusted OR [95% CI]

0.87[0.80-0.94]P<0.001

0.71[0.65-0.77]P<0.001

0.70[0.64-0.76]P<0.001

0.75[0.69-0.81]P<0.001

0.81 [0.75-0.88]P<0.001

0.81 [0.75-0.87]P<0.001

0.91 [0.84-0.98]

P=0.013

0.94 [0.88-1.02]

P=0.132

0.93 [0.87-1.00]

P=0.0631

Adjusted odds ratio (OR) and 95% confidence interval (CI) were derived from adjusted logistic regression analyses.*median (interquartile range)**Event rates of postoperative respiratory complications are shown as total numbers (percentages).

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Table S3. Results of exploratory outcomes across fentanyl dose quartiles

Fentanyl dose quartile 4th Quartile(n=36,095)

3rd Quartile(n=36,634)

2nd Quartile(n=35,994)

1st Quartile(n=37,012)

Fentanyl dose (μg kg-1) 3.85(3.42, 4.50)

2.63(2.42, 2.88)

1.74(1.54, 1.96)

0.80(0, 1.14)

Ileus within 3 daysPositive outcomes 1,620 (4.5%) 1,183 (3.2%) 915 (2.5%) 776 (2.1%)

aOR [95% CI] 1 0.98 [0.90-1.07]P=0.676

0.95 [0.87-1.04]P=0.248

0.88 [0.80-0.97]P=0.009

ICU admissionPositive outcomes 4,613 (12.8%)3,493 (9.5%) 2,531 (7.0%) 3,123 (8.4%)

aOR [95% CI] 1 0.85 [0.81-0.90]P=0.001

0.75 [0.71-0.80]P=0.001

0.86 [0.81-0.91]P<0.001

Wound infectionPositive outcomes 2,360 (6.6%) 2,406 (6.6%) 2,156 (6.0%) 2,173 (5.9%)

aOR [95% CI] 1 0.98 [0.92-1.05]P=0.590

0.96 [0.89-1.02]P=0.182

0.88 [0.82-0.94]P<0.001

Perioperative naloxone usePositive outcomes 707 (2.0%) 549 (1.5%) 383 (1.1%) 373 (1.0%)

aOR [95% CI] 1 0.81 [0.72-0.91]P=0.001

0.65 [0.56-0.74]p P<0.001

0.56 [0.48-0.64]P<0.001

Total hospital costs

aIRR [95% CI] 1 0.93 [0.92-0.94]P<0.001

0.90 [0.89-0.91]P<0.001

0.92 [0.91-0.93]P<0.001

Hospital length of stayMedian (IQR) in days 4 (2, 7) 3 (2, 6) 2 (1, 5) 2 (1, 5)

aIRR [95% CI] 1 0.92 [0.90-0.93]P<0.001

0.88 [0.87-0.90]P<0.001

0.92 [0.90-0.93]P<0.001

Post-extubation desaturation (<90 %) (n=125,680)Positive outcomes 1,400 (4.5%) 1,431 (4.6%) 1,395 (4.5%) 1,560 (4.8%)

aOR [95% CI] 1 0.89 [0.83-0.97]P=0.007

0.91 [0.84-0.99]P=0.035

0.92 [0.85-1.00]P=0.059

Post-extubation desaturation (<80 %) (n=125,680)Positive outcomes 359 (1.2%) 331 (1.1%) 303 (0.98%) 315 (0.98%)

aOR [95% CI] 1 0.88 [0.75-1.04]P=0.130

0.88 [0.75-1.05]P=0.149

0.84 [0.71-0.998]P=0.048

Non-invasive ventilationPositive outcomes 92 (0.25%) 182 (0.50%) 169 (0.47%) 157 (0.42%)

aOR [95% CI] 1 1.16 [0.89-1.52]P=0.279

1.11 [0.84-1.47]P=0.451

1.03 [0.77-1.38]P=0.832

30-day mortalityPositive outcomes 216 (0.6%) 171 (0.5%) 202 (0.6%) 291 (0.8%)

aOR [95% CI] 1 0.91 [0.74-1.13]P=0.384

1.00 [0.81-1.24]P=0.981

1.20 [0.98-1.47]P=0.083

Constipation within 3 days

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Positive outcomes 446 (1.2%) 339 (0.9%) 312 (0.9%) 316 (0.9%)

aOR [95% CI] 1 0.94 [0.81-1.10]P=0.450

0.94 [0.80-1.09]P=0.395

0.96 [0.82-1.12]P=0.596

Delirium within 3 daysPositive outcomes 646 (1.8%) 478 (1.3%) 387 (1.1%) 437 (1.2%)

aOR [95% CI] 1 0.89 [0.78-1.01]P=0.063

0.84 [0.73-0.96]P=0.013

0.91 [0.79-1.04]P=0.182

Event rates of postoperative respiratory complications are shown in total numbers (percentages). Adjusted odds ratio (aOR) or adjusted incident rate ratio (aIRR) and 95% confidence interval (95% CI) were estimated using adjusted logistic regression analyses, if not specified otherwise. If not further specified, the analyses were performed within the complete cohort.ICU = intensive care unit; IQR = interquartile range

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Table S4. Sensitivity analyses

Fentanyl dose quartile 4th Quartile(n = 36,095)

3rd Quartile(n = 36,634)

2nd Quartile(n = 35,994)

1st Quartile(n = 37,012)

A) Considering only major respiratory complications (reintubation or respiratory failure)Positive outcomes 1,705 (4.7%) 1,212 (3.3%) 801 (2.2%) 835 (2.3%)

aOR [95% CI] 1 0.85 [0.78-0.93]P<0.001

0.77 [0.70-0.85]P<0.001

0.84 [0.76-0.92]P<0.001

B) Considering only reintubationPositive outcomes 174 (0.5%) 131 (0.4%) 96 (0.3%) 105 (0.3%)

aOR [95% CI] 1 0.80 [0.64-1.01]P=0.063

0.65 [0.50-0.84]P=0.001

0.68 [0.53-0.87]P=0.002

C) Excluding cases with preoperative opioid prescription (n=129,725)Positive outcomes 5,282 (16.5%) 4,575 (14.0%) 3,527 (11.0%) 3,669 (11.1%)

aOR [95% CI] 1 0.87 [0.83-0.92]P<0.001

0.79 [0.74-0.83]P<0.001

0.79 [0.75-0.84]P<0.001

D) Excluding cases with intraoperative administration of long-acting opioids (n=48,769)Positive outcomes 2,733 (22.4%) 2,144 (17.7%) 1,637 (13.4%) 1,695 (13.8%)

aOR [95% CI] 1 0.92 (0.85-0.998)P=0.046

0.88 (0.80-0.95)P=0.003

0.87 (0.79-0.95)P=0.002

E) Postoperative pain managementNo prescription of pain medication on discharge (n=56,675)Positive outcomes 2,439 (17.3%) 2,144 (15.1%) 1,744 (12.3%) 1,720 (12.1%)

aOR [95% CI] 1 0.91 [0.84-0.98]P=0.011

0.84 [0.78-0.91]P<0.001

0.82 [0.75-0.89]P<0.001

Opioid prescription on discharge (n=77,106)Positive outcomes 2,812 (14.6%) 2,392 (12.4%) 1,722 (8.9%) 1,692 (8.8%)

aOR [95% CI] 1 0.89 [0.83-0.96]P=0.002

0.75 [0.69-0.81]P<0.001

0.74 [0.68-0.80]P<0.001

Prescription of non-opioid analgesic on discharge (n=71,570)Positive outcomes 2,871 (16.1%) 2,370 (13.3%) 1,904 (10.6%) 1,860 (10.4%)

aOR [95% CI] 1 0.86 [0.80-0.93]P<0.001

0.78 [0.72-0.84]P<0.001

0.76 [0.70-0.82] P<0.001

F) Fractional polynomial modelling

aOR [95% CI] 1 0.90 (0.86-0.94)P<0.001

0.81 (0.76-0.85)P<0.001

0.80 (0.76-0.84)P<0.001

G) Adjusting for intraoperative use of peripheral nerve blockade, additional confounder

aOR [95% CI] 1 0.89 [0.84-0.93]P<0.001

0.79 [0.75-0.83] P<0.001

0.79 [0.75-0.83] P<0.001

H) Interaction term “fentanyl dose x duration of surgery”

aOR [95% CI] 1 0.91 [0.86-0.96]P<0.001

0.81 [0.77-0.86]P<0.001

0.83 [0.78-0.88]P<0.001

I) Subgroups of short and long duration of surgeryProcedural duration under 120 minutes (n=57,025)Positive outcomes 1,509 (10.7%) 1,168 (8.1%) 984 (6.9%) 1,272 (8.9%)

aOR [95% CI] 1 0.82 [0.75-0.90]P<0.001

0.70 [0.64-0.78]P<0.001

0.76 [0.69-0.84]P<0.001

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Procedural duration at least 120 minutes (n=88,710)Positive outcomes 3,962 (17.9%) 3,619 (16.6%) 3,276 (14.8%) 3,049 (13.6%)

aOR [95% CI] 1 0.93 [0.88-0.99]P=0.022

0.82 [0.77-0.87]P<0.001

0.80 [0.75-0.85]P<0.001

J) Adjust for individual procedure performed – mixed effects logistic regression (n=130,420)Positive outcomes 5,156 (15.7%) 4,469 (13.4%) 3,318 (10.3%) 3,276 (10.2%)

aOR [95% CI] 1 0.90 [0.84-0.95]P<0.001

0.86 [0.81-0.92]P<0.001

0.88 [0.83-0.94]P<0.001

K) Preferences of individual anaesthesiologist - mixed effects logistic regression (n=145,725)Positive outcomes 5,848 (16.2%) 5,058 (13.8%) 3,891 (10.8%) 4,042 (10.9%)

aOR [95% CI] 1 0.86 [0.82-0.91]P<0.001

0.77 [0.73-0.81]P<0.001

0.79 [0.75-0.83]P<0.001

L) Adjusted for year of surgery, additional confounder

aOR [95% CI] 1 0.88 [0.84-0.92]P<0.001

0.77 [0.73-0.81]P<0.001

0.77 [0.73-0.81]P<0.001

M) Excluding cases with surgery within four weeks prior to index surgery (n=136,696)Positive outcomes 5,319 (15.6%) 4,587 (13.4%) 3,536 (10.4%) 3,583 (10.4%)

aOR [95% CI] 1 0.89 [0.84-0.93]P<0.001

0.78 [0.74-0.83]P<0.001

0.79 [0.75-0.84]P<0.001

N) Charlson Comorbidity Index5 (CCI)CCI < 3 (n=96,501)Positive outcomes 2,202 (9.2%) 1,892 (7.8%) 1,344 (5.6%) 1,605 (6.7%)

aOR [95% CI] 1 0.91 [0.84-0.98]P=0.011

0.77 [0.71-0.83]P<0.001

0.90 [0.83-0.98]P=0.011

CCI ≥ 3 (n=49,234)Positive outcomes 3,482 (28.5%) 3,242 (26.2%) 2,654 (21.5%) 2,418 (19.6%)

aOR [95% CI] 1 0.91 [0.85-0.98]P=0.007

0.82 [0.77-0.89]P<0.001

0.74 [0.69-0.80]P<0.001

O) ASA status classificationASA of 1-2 (n=99,591)Positive outcomes 2,449 (9.8%) 2,081 (8.5%) 1,493 (5.9%) 1,479 (5.9%)

aOR [95% CI] 1 0.87 [0.81-0.94]P<0.001

0.74 [0.69-0.80]P<0.001

0.78 [0.72-0.84] P<0.001

ASA of 3-5 (n=46,144)Positive outcomes 3,487 (30.2%) 2,992 (26.0%) 2,481 (21.5%) 2,377 (20.6%)

aOR [95% CI] 1 0.90 [0.84-0.97]P=0.003

0.84 [0.78-0.90]P<0.001

0.81 [0.75-0.88]P<0.001

P) Surgical service*

High-risk surgery (n=58,298)Positive outcomes 3,689 (25.4%) 3,365 (23.1%) 2,509 (17.2%) 2,333 (16.0%)

aOR [95% CI] 1 0.93 [0.87-0.99]P=0.043

0.73 [0.68-0.78]P<0.001

0.75 [0.69-0.80]P<0.001

Low-risk surgery (n=87,437)Positive outcomes 1,971 (9.1%) 1,737 (7.9%) 1,549 (7.1%) 1,686 (7.7%)

aOR [95% CI] 1 0.89 [0.82-0.96]P=0.003

0.92 [0.85-0.99]P=0.043

0.91 [0.84-0.99]P=0.032

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Q) Preoperative respiratory comorbidities**

Yes (n=30,681)Positive outcomes 2,088 (27.3%) 1,717 (22.5%) 1,422 (18.4%) 1,398 (18.2%)

aOR [95% CI] 1 0.90 [0.82-0.98]P=0.018

0.79 [0.72-0.87]P<0.001

0.79 [0.71-0.87]P<0.001

No (n=115,054)Positive outcomes 3,864 (13.5%) 3,309 (11.6%) 2.503 (8.7%) 2,538 (8.8%)

aOR [95% CI] 1 0.91 [0.86-0.97]P=0.002

0.81 [0.76-0.86]P<0.001

0.81 [0.76-0.87]P<0.001

R) Type of admissionAmbulatory (n=36,391)Positive outcomes 326 (3.6%) 303 (3.3%) 270 (3.0%) 334 (3.7%)

aOR [95% CI] 1 0.86 [0.72-1.02]P=0.077

0.70 [0.58-0.84]P<0.001

0.68 [0.56-0.82]P<0.001

Inpatient (n=109,344)Positive outcomes 4,998 (18.3%) 4,580 (16.8%) 3,993 (14.6%) 4,035 (14.7%)

aOR [95% CI] 1 0.92 [0.87-0.97]P=0.002

0.80 [0.76-0.85]P<0.001

0.80 [0.76-0.85]P<0.001

S) Multiple imputation (n=166,896)Positive outcomes 6,591 (16.2%) 5,896 (13.8%) 4,507 (10.8%) 4,662 (11.2%)

aOR [95% CI] 1 0.90 [0.86-0.94]P<0.001

0.81 [0.77-0.85]P<0.001

0.81 [0.77-0.85]P<0.001

Event rates of postoperative respiratory complications are shown in total numbers (percentages). Adjusted odds ratio (aOR) and 95% confidence interval (95% CI) were estimated using adjusted logistic regression analyses, if not specified otherwise.

*High-risk surgical service was defined as vascular, transplant, thoracic, general, neuro-, and burn surgery. Low-risk surgical service was defined as non-high-risk surgical service.**Preoperative respiratory comorbidities included chronic obstructive pulmonary disease, obstructive sleep apnoea, asthma, and pulmonary hypertension – defined as ICD-9/-10 diagnosis within one year prior to the index surgery.

ASA status classification = American Society of Anesthesiologist physical status classification.

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Table S5. Interaction analysis - Body Mass Index

Interaction term “fentanyl dose (continuous) x BMI (continuous)”: p<0.001, OR 1.004, CI 1.002-1.005)

Out-come

Lowest fentanyl dose quartile (Q1)*

Highest fentanyl dose quartile (Q4)*

Estimated Probability – Q1 (95% CI)**

Estimated Probability – Q4 (95% CI)**

Adjusted Absolute Risk Difference (95% CI)**

Adjusted OR (95% CI)**

P-value**

Patients with BMI < 35 kg m-2, n=124,345

PRCs 3,365/31,194(10.8%)

4,991/30,834(16.2%)

7.0% (6.7-7.3%)

8.4%(8.1-8.8%)

-1.5% (-1.9 to -1.1%)

0.81 (0.77-0.86)

<0.001

Severely obese patients (BMI ≥ 35 kg m-2), n=21,390

PRCs 606/5,353 (11.3%)

847/5,300 (16.0%)

8.0%(7.3-8.8%)

9.5%(8.7-10.3%)

-1.5% (-2.5 to -0.4%)

0.83(0.73-0.95)

0.006

*Observed event rates of postoperative respiratory complications are shown in total numbers (percentages). **Estimated probability (adjusted absolute risk), adjusted absolute risk difference and adjusted odds ratio (OR) including 95% confidence intervals (95% CI) were estimated using adjusted logistic regression analyses.BMI = body mass index

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Table S6. Characteristics of study population by intraoperative fentanyl dose – after propensity matching of 1st and 4th quartile

22Characteristics 1st Quartile

(n=19,887)4th Quartile(n=19,887)

Standardized mean differences

Fentanyl dose (μg kg-1) 0.65(0.00, 1.16)

3.70(3.36, 4.36)

PREOPERATIVE CHARACTERISTICSAge (years) 54.3 ± 16.9 53.8 ± 16.9 -0.028Gender (female) 8,880 (44.7%) 7,882 (39.6%) 0.102BMI (kg m-2) 27.3 ± 6.2 26.1 ± 5.3 -0.216ASA physical status classification 2.2 ± 0.6 2.2 ± 0.6 -0.002Ambulatory surgery 4247 (21.4%) 3729 (18.8%) 0.065Score for Prediction of Postoperative Respiratory Complications11 (SPORC) ≥ 7

813 (4.1%) 860 (4.3%)0.012

Emergency surgery 748 (3.8%) 993 (5.0%) 0.060High risk surgical service 7904 (39.7%) 8367 (42.1%) 0.047Score for Prediction of Obstructive Sleep Apnoea (SPOSA)12 ≥ 25

3799 (19.1%) 3268 (16.4%) -0.070

History of arterial hypertension 8097 (40.7%) 7833 (39.4%) -0.027History of atrial fibrillation 1668 (8.4%) 1315 (6.6%) -0.067History of congestive heart failure 1613 (8.1%) 1550 (7.8%) -0.012History of coronary artery disease 2352 (11.8%) 2298 (11.6%) -0.008History of chronic pulmonary disease 3318 (16.7%) 3588 (18.0%) 0.036History of pulmonary hypertension 299 (1.5%) 295 (1.5%) -0.002History of diabetes mellitus 2590 (13.0%) 2281 (11.5%) -0.047History of dyslipidaemia 6220 (31.3%) 5782 (29.1%) -0.048History of hemi- or paraplegia 595 (3.0%) 591 (3.0%) -0.001History of liver disease 2152 (10.8%) 2176 (10.9%) 0.004DNR code status (Do Not Resuscitate) 46 (0.23%) 47 (0.24%) 0.001Preoperative opioid subscription 2142 (10.8%) 2200 (11.1%) 0.009INTRAOPERATIVE CHARACTERISTICSDuration of surgery (h) 2.9 ± 1.8 3.3 ± 2.1 0.204Work relative value unit 15.0

(9.1, 21.9)15.8(10.5, 23.3) 0.157

Minimum alveolar concentration of inhalational anesthetics4 (age adjusted)

0.81 ± 0.36 0.83 ± 0.31 0.047

Other intraoperative opioid dose15

(oral morphine equivalent, mg kg-1)*17.0 (0.0, 30.0) 10.2 (0.0, 25.5) -0.048

Multiples of 95% effective NMBA dose5,

132.3 (0.0, 3.6) 2.7 (1.6, 4.1) 0.242

Neuraxial anaesthesia 1028 (5.2%) 1720 (8.6%) 0.138Vasopressor dose14

(mg norepinephrine equivalents)0.02 (0.00, 0.19) 0.03 (0.00,

0.24) 0.035

Duration of intraoperative hypotension (minutes)

0 (0, 1) 0 (0, 2) 0.050

Intraoperative fluid infusion (ml)** 1250 (900, 2000)

1500 (1000, 2400) 0.084

Units of packed red blood cells 0.0650 19172 (96.4%) 18919 (95.1%)1 331 (1.7%) 406 (2.0%)2 246 (1.2%) 353 (1.8%)

≥3 138 (0.7%) 209 (1.1%)Positive end expiratory pressure (cmH2O)

5 (3, 5) 5 (3, 5) 0.054

Plateau pressure (cmH2O) 18.4 ± 5.0 18.2 ± 4.4 -0.038Inspiratory O2-Fraction (%) 52 (39, 60) 53 (40, 60) -0.001Values are frequency (percentages), or median (interquartile range: 25th-, 75th-percentile);

23

24

Supplemental section 7. Supplemental Figures

Figure S1a. Area under the receiver operating characteristics curve for the primary regression model independent of fentanyl dose

The area under the receiver operating characteristics (ROC) curve of the primary regression model, independent of intraoperative fentanyl dose, was 0.823 (CI 0.820-0.827), indicating sufficient model discrimination.

Figure S1b. Reliability plot for the primary regression model

The reliability plot indicates good calibration of the primary regression model for postoperative respiratory complications.

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Figure S2. Composite and individual respiratory complications by year of surgery

In this figure, the rate of the composite outcome and the individual postoperative respiratory complications within 3 days after surgery by year of procedure is displayed.

26

References

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