A clinical prediction model for prolonged air leak after pulmonary resection

Abstract

Objective: Prolonged air leak increases costs and worsens outcomes after pulmonary resection. We aimed to develop a clinical prediction tool for prolonged air leak using pretreatment and intraoperative variables.

Methods: Patients who underwent pulmonary resection for lung cancer/nodules (from January 2009 to June 2014) were stratified by prolonged parenchymal air leak (>5 days). Using backward stepwise logistic regression with bootstrap resampling for internal validation, candidate variables were identified and a nomogram risk calculator was developed.

Results: A total of 2317 patients underwent pulmonary resection for lung cancer/nodules. Prolonged air leak (8.6%, n = 200) was associated with significantly longer hospital stay (median 10 vs 4 days; P < .001). Final model variables associated with increased risk included low percent forced expiratory volume in 1 second, smoking history, bilobectomy, higher annual surgeon caseload, previous chest surgery, Zubrod score >2, and interaction terms for right-sided thoracotomy and wedge resection by thoracotomy. Wedge resection, higher body mass index, and unmeasured percent forced expiratory volume in 1 second were protective. Derived nomogram discriminatory accuracy was 76% (95% confidence interval [CI], 0.72-0.79) and facilitated patient stratification into low-, intermediate- and high-risk groups with monotonic increase in observed prolonged air leaks (2.0%, 8.9%, and 19.2%, respectively; P < .001). Patients at intermediate and high risk were 4.80 times (95% CI, 2.86-8.07) and 11.86 times (95% CI, 7.21-19.52) more likely to have prolonged air leak compared with patients at low risk.

Conclusions: Using readily available candidate variables, our nomogram predicts increasing risk of prolonged air leak with good discriminatory ability. Risk stratification can support surgical decision making, and help initiate proactive, patient-specific surgical management.

Keywords: air leak; funnel plot; lung cancer; multivariable; persistent air leak; prolonged air leak; pulmonary resection; risk factors; risk stratification.

Conflict of interest statement

Conflicts of Interest: The authors have no conflicts of interest to disclose.

Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1. Stratified Cumulative Incidence of Hospital Discharge by Post-Operative Day

(95% confidence intervals indicated by black-capped spikes)

Figure 2. Bootstrap Reliability of Variables Associated with Prolonged Air Leak

Variables selected >50% of the time were considered for forming the final model.

Figure 3. Nomogram for Probability of Prolonged Air Leak

a.) To calculate the probability of prolonged air leak, sum points over all variables to a total point score with its corresponding probability. Example: A smoker (2.5 pts) with a BMI=32 (3 pts), %FEV1=65 (7.5 pts), and Zubrod Score=1 (0 pts) without prior chest operation (0 pts) or preoperative hospitalization ≥1 (6 pts) is having a right-sided open (6 pts) lobectomy (5 pts) by a surgeon who has an annual caseload of 50 (3 pts). Total points=33. Probability of PAL is around 15%. b.) We removed our institution specific variable annual surgeon caseload to create a more generalizable model that had a C-statistic=0.755 (95% CI, 0.722 to 0.788).

Figure 4. Funnel Plot of Prolonged Air Leak Rate of Operating Surgeons by Case Volume

95% and 99% control limits derived as follows: (θ± z*Sqrt(θ(1- θ)/p), where z is the z-score, θ the study’s average PAL rate, and p the total cases of individual surgeons.

Figure 4. Funnel Plot of Prolonged Air Leak Rate of Operating Surgeons by Case Volume

95% and 99% control limits derived as follows: (θ± z*Sqrt(θ(1- θ)/p), where z is the z-score, θ the study’s average PAL rate, and p the total cases of individual surgeons.