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Title: A risk score for predicting respiratory complications after thoracic surgery
Authors: Tanyong Pipanmekaporn
Nutchanart Bunchungmongkol
Yodying Punjasawadwong
Worawut Lapisatepun
Apichat Tantraworasin
Surasak Saokaew
Keywords: Medicine
Issue Date: 1-May-2019
Abstract: © The Author(s) 2019. Background: Respiratory complications are some of the most common complications following thoracic surgery and can lead to higher perioperative morbidity and mortality. The purpose of this study was to develop a simple clinical score for prediction of respiratory complications after thoracic surgery, and determine the internal validity. Methods: In this retrospective cohort study, all consecutive patients were aged 18 years and over and undergoing non-cardiac thoracic surgery at a tertiary-care university hospital. Respiratory complications included bronchospasm, atelectasis, pneumonia, respiratory failure, and adult respiratory distress syndrome within 30 days of surgery or before discharge. Results: A total of 1488 patients were included over a 7-year period, and 15.8% (235 of 1488 patients) developed respiratory complications. The significant predictors of respiratory complications were chronic obstructive pulmonary disease, American Society of Anesthesiologist physical status ≥ 3, right-sided surgery, duration of surgery longer than 180 min, preoperative arterial oxygen saturation on room air < 96%, and open thoracotomy. The area under receiving operating characteristic curve was 0.78 (95% confidence interval: 0.75–0.82) and 0.76 (95% confidence interval: 0.70–0.83) for the derivation and validation cohorts, respectively. The model was well calibrated with a Hosmer-Lemeshow goodness-of-fit of 7.32 (p = 0.293). Conclusions: This study developed and internally validated a simple clinical risk score for prediction of respiratory complications following thoracic surgery. This score can be used to stratify high-risk patients, address modifiable risk factors for respiratory complications, and provide preventive strategies for improving postoperative outcomes.
ISSN: 18165370
Appears in Collections:CMUL: Journal Articles

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