Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients

Abstract

Objective: To identify independent risk factors of pulmonary infection in intensive care unit (ICU) patients, and to construct a prediction model. Methods: Medical data of 398 patients treated in the ICU of Jiaxing Hospital of Traditional Chinese Medicine from January 2019 to January 2023 were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for pulmonary infection in ICU patients. R software was used to construct a nomogram prediction model, and the prediction model was internally validated using computer simulation bootstrap method. Predictive value of the model was analyzed using the receiver operating characteristic (ROC) curve. Results: A total of 97 ICU patients (24.37%) developed pulmonary infection. Age, ICU stay time, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness were all identified as risk factors for pulmonary infection. The calibration curve of the constructed nomogram prediction model showed a good consistency between the predicted value of the model and the actual observed value. ROC curve analysis showed that the area under the curve (AUC) of the model was 0.784 (95% CI: 0.731-0.837), indicating a certain predictive value. Conclusions: Age, length of stay in ICU, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness are risk factors for pulmonary infection in ICU patients. The nomogram prediction model constructed based on the above risk factors has shown a good predictive value. doi: https://doi.org/10.12669/pjms.40.6.9307 How to cite this: Dai W, Zhong T, Chen F, Shen M, Zhu L. Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients. Pak J Med Sci. 2024;40(6):1129-1134. doi: https://doi.org/10.12669/pjms.40.6.9307 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.