Development and Validation of Procedure-Specific Risk Score for Predicting Postoperative Pulmonary Complication: A NSQIP Analysis
| Autor: | Irving L. Kron, R. Scott Jones, Min-Woong Sohn, Florence E. Turrentine, Eric J. Charles, Carrie A. Foster |
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| Rok vydání: | 2019 |
| Předmět: |
Lung Diseases
Male medicine.medical_specialty Databases Factual medicine.medical_treatment Specific risk Logistic regression Risk Assessment 03 medical and health sciences Postoperative Complications 0302 clinical medicine Risk Factors Clinical Decision Rules medicine Humans Aged Retrospective Studies Aged 80 and over Framingham Risk Score business.industry Retrospective cohort study Middle Aged Logistic Models Quartile Esophagectomy Surgical Procedures Operative 030220 oncology & carcinogenesis Relative risk Emergency medicine Female 030211 gastroenterology & hepatology Surgery business Risk assessment |
| Zdroj: | Journal of the American College of Surgeons. 229:355-365e3 |
| ISSN: | 1072-7515 |
| Popis: | 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 6 procedures with the highest rates of PPCs. Study Design American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use File data (2005 to 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 were 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. Conclusions 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 health care professionals to better predict the risk of PPCs and to optimize patient outcomes. |
| Databáze: | OpenAIRE |
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