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KMID : 0367020230350020085
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Journal of Korean Academic of Adult Nursing
2023 Volume.35 No. 2 p.85 ~ p.96
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Validation of Prediction Models for Postoperative Pulmonary Complications after Lung Resection: A Retrospective Study
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Jung Su-Ji
Hwang Sun-Kyung
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Abstract
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Purpose: This study aimed to verify the potential use of postoperative pulmonary complications prediction models in patients with lung resection.
Methods: In this retrospective study, 1,160 patients were selected among the admitted patients who underwent lung resection surgery. The predictive validity of the Assess Respiratory Risk in Surgical Patients in Catalonia Tool (ARISCAT) and the Pulmonary Complications Risk Score (PCRS)-lung resection model were assessed based on the sensitivity, specificity, positive and negative predictive values, and Area Under the receiver operating characteristic Curve (AUC).
Results: Of the patients, 420 (36.2%) developed postoperative pulmonary complications after lung resection surgery within 30 days. The sensitivity, specificity, positive predictive values, and negative predictive values were 52.2%, 70.6%, 49.0%, and 74.0%, respectively, for the ARISCAT (cut-off point of 47), and 53.8%, 78.5%, 15.3%, and 95.9%, respectively, for the PCRS-lung resection model (cut-off point of 147). The AUCs were 0.65 (ARISCAT) and 0.70 (PCRS-lung resection model).
Conclusion: The findings indicate that the predictive validity values of the ARISCAT was sufficient, and the PCRS-lung resection model was good. However, the clinical usefulness of the models should be verified in future studies.
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KEYWORD
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Postoperative complications, Pneumonectomy, Predictive value of tests
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