BACKGROUND Postoperative pulmonary complications (PPCs; unplanned reintubation, postoperative pneumonia, and failure to liberate from mechanical ventilation within 48 hours), contribute significantly to increased rates of morbidity and mortality. Procedure type… Click to show full abstract
BACKGROUND Postoperative pulmonary complications (PPCs; unplanned reintubation, postoperative pneumonia, and failure to liberate from mechanical ventilation within 48 hours), contribute significantly to increased rates of morbidity and mortality. Procedure type is an important factor that contributes risk in generalized PPC prediction models. The objective of this study was to develop and validate procedure-specific risk scores for the six procedures with highest rates of PPCs. STUDY DESIGN American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use File data (2005-2015) for patients undergoing pancreatectomy, hepatectomy, esophagectomy, abdominal aortic aneurysm repair, open aortoiliac repair, and lung resection were used for analysis. Multivariable logistic regression was used to develop pulmonary complications risk scores (PCRS) for each procedure. Youden indices were used to identify cutoff points within each PCRS and further validated using a random selection of the original NSQIP dataset collected. RESULTS Twenty-one variables were included in the initial analysis which yielded unique relative risk score models for each procedure. Within all the risk score models, long operative time (within the last quartile) was a strong predictor of PPCs. An increased rate of PPCs was associated with increasing PCRS values in both the training and validation samples for all procedures. CONCLUSION Important variables were identified for 6 common procedures that yield an increased risk of PPCs. These variables differed by procedure type, outlining the importance of procedure-specific risk scores. Each procedure-specific PCRS developed in this study can be used by healthcare professionals to better predict the risk of PPCs and to optimize patient outcomes.
               
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