Postoperative pulmonary complications (PPCs) are significant sources of morbidity and mortality in surgical patients, contributing to prolonged hospital stays, increased healthcare costs, and decreased quality of life. Accurate prediction and early identification of patients at high risk for developing PPCs are crucial for implementing preventive measures, optimizing perioperative management, and improving patient outcomes [1].

External validation, the process of evaluating model performance on independent datasets from different populations or healthcare settings, is essential for assessing the robustness and reliability of predictive models. It provides valuable insights into model generalizability, potential biases, and limitations, thereby informing clinical decision-making and guiding the implementation of predictive tools into routine practice.

Through comprehensive external validation, this research endeavours to address critical gaps in the current literature, contribute to the evidence base supporting predictive modelling in perioperative care, and ultimately, improve patient outcomes by facilitating early identification and targeted interventions for individuals at high risk of developing postoperative pulmonary complications. The logistic regression model was developed from a departmental project (CTRI/2023/09/058028) and it consists of baseline lung ultrasound aeration score, total leucocyte count, INR, serum albumin and serum creatinine. 

Primary Objective

External validation of a logistic regression model comprised of clinical and lung USG parameters in adult patients undergoing elective or emergency surgery