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Identifying Risk Factors and Creating a Point-Based Risk Calculator for Postoperative Pneumonia in Thoracic Surgery

Zachary Petterson1, Sarah Cook2, Hayden Johnston3

  • 1CRNA at Kern Medical Hospital, Bakersfield, California.

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|April 9, 2026
PubMed
Summary
This summary is machine-generated.

A predictive model was developed to identify thoracic surgery patients at risk for postoperative pneumonia (POP). Nine key preoperative predictors were identified, enabling the creation of a risk-based calculator for improved patient care.

Keywords:
American College of SurgeonsNational Surgical Quality Improvement Programperioperative riskpostoperative pneumoniathoracic surgery

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Area of Science:

  • Thoracic Surgery
  • Surgical Quality Improvement
  • Predictive Analytics

Background:

  • Postoperative pneumonia (POP) is a significant complication following thoracic surgery.
  • Accurate identification of patients at high risk for POP is crucial for targeted interventions.
  • Existing risk stratification methods may require refinement for thoracic surgery populations.

Purpose of the Study:

  • To develop and validate a predictive model for identifying thoracic surgery patients at risk of postoperative pneumonia (POP).
  • To identify key preoperative predictors associated with POP in this patient cohort.
  • To create a risk-based calculator for clinical use.

Main Methods:

  • Secondary data analysis of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) dataset (2013-2022).
  • Development and validation of predictive models using logistic regression (Method 1) and Xtreme Gradient Boosting (Method 2).
  • Expert panel review (Method 3) and assessment of models using 10-fold cross-validation and Area Under the Receiver Operating Characteristic Curve (AUC ROC).

Main Results:

  • Logistic regression identified nine significant preoperative predictors of POP, including sepsis, systemic inflammatory response syndrome, and male gender, achieving a 10-fold cross-validated AUC ROC of 0.72.
  • Xtreme Gradient Boosting model achieved a 10-fold cross-validation AUC ROC of 0.75.
  • Expert panel assessment resulted in an AUC ROC of 0.6, indicating a poorer classification performance.

Conclusions:

  • A predictive model utilizing nine significant preoperative factors effectively identifies thoracic surgery patients at risk for postoperative pneumonia.
  • The identified predictors and developed risk calculator can aid clinicians in stratifying patient risk and implementing preventative strategies.
  • Further validation and implementation of the risk calculator are warranted to improve patient outcomes in thoracic surgery.