Development and Validation of a Nomogram for Predicting Postoperative Pulmonary Infection in Patients Undergoing Lung Surgery

Abstract

Objectives: To develop and validate a nomogram for predicting postoperative pulmonary infection (PPI) in patients undergoing lung surgery.

Design: Single-center retrospective cohort analysis.

Setting: A university-affiliated cancer hospital PARTICIPANTS: A total of 1,501 adult patients who underwent lung surgery from January 2018 to December 2020.

Interventions: Observation for PPI within 7 days after lung surgery.

Measurements and main results: A complete set of demographics, preoperative variables, and postoperative follow-up data was recorded. The primary outcome was PPI; a total of 125 (8.3%) out of 1,501 patients developed PPI. The variables with p < 0.1 in univariate logistic regression were included in the multivariate regression, and multivariate logistic regression analysis showed that surgical procedure, surgical duration, the inspired fraction of oxygen in one-lung ventilation, and postoperative pain were independent risk factors for PPI. A nomogram based on these factors was constructed in the development cohort (area under the curve: 0.794, 95% CI 0.744-0.845) and validated in the validation cohort (area under the curve: 0.849, 95% CI 0.786-0.912). The calibration slope was 1 in the development and validation cohorts. Decision curve analysis indicated that when the threshold probability was within a range of 0.02-to-0.58 and 0.02-to-0.42 for the development and validation cohorts, respectively, the nomogram model could provide a clinical net benefit.

Conclusions: The authors developed and validated a nomogram for predicting PPI in patients undergoing lung surgery. The prediction model can predict the development of PPI and identify high-risk groups.

Keywords: lung surgery; nomogram; postoperative pulmonary infection; prediction model.