====== Pneumonia in traumatic brain injury ======

{{rss>https://pubmed.ncbi.nlm.nih.gov/rss/search/1VONWHHyVP6t8cJgXyLdm6NPE78ODN8v7tqb79Rq1f5v_KT9Ux/?limit=15&utm_campaign=pubmed-2&fc=20231121063515}}
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The incidence of [[pneumonia]] in [[ICU]] patients with [[TBI]] is very high, seriously affecting the [[prognosis]]. 
A study aimed to construct a predictive model for pneumonia in ICU patients with TBI and provide help for the prevention of TBI-related pneumonia.
Clinical data of ICU patients with TBI were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and hospital data. Variables were screened by lasso and multivariate logistic regression to construct a predictive nomogram model, verified in internal validation cohort and external validation cohort by receiver operator characteristic (ROC) curve, calibration curve and [[decision curve analysis]] (DCA).A total of 1850 ICU patients with TBI were enrolled in the study from the MIMIC-IV database, including 1298 in the training cohort and 552 in internal validation cohort. The external validation cohort included 240 ICU patients with TBI from hospital data. Nine variables were selected from the training cohort by lasso regression and multivariate logistic regression, and a pneumonia prediction nomogram was constructed. This [[nomogram]] has a high discrimination in training, [[internal validation]] and [[external validation]] cohorts (AUC = 0.857, 0.877, 0.836). The [[calibration curve]] and DCA showed that this nomogram had a high calibration and better clinical decision-making efficiency.The nomogram showed excellent discrimination and clinical utility to predict pneumonia, and could identify pneumonia high-risk patients early, thus providing [[precision medicine]] strategies for ICU patients with TBI
((Geng X, Wu H, Liu C, Qi L, Ballah AK, Che W, Wu S, Fu T, Li N, Wei X, Cheng R, Pang Z, Ji H, Wang Y, Wang X. Construction and validation of a predictive model of pneumonia for ICU patients with traumatic brain injury (TBI). Neurosurg Rev. 2023 Nov 21;46(1):308. doi: 10.1007/s10143-023-02208-9. PMID: 37985473.))