@Article{作者信息:doi / 10.2196/25066 =”卡明斯,布兰登C和安萨里,Sardar Motyka,乔纳森•R和王关、Medlin Jr,理查德•P和克罗尼克史蒂文•L和辛格Karandeep和公园,波林K和纳波利塔诺,莉娜M和迪克森(Robert P和马修斯,迈克尔·R和Sjoding迈克尔•W和Admon安德鲁J和空白,罗斯和McSparron,雅克布我和沃德,凯文•R和Gillies克里斯托弗·E”,标题= "预测重症监护转移和其他不可预见的事件:，期刊="JMIR Med Inform"，年="2021"，月="Apr"，日="21"，卷="9"，数="4"，页数="e25066"，关键词="COVID-19;生物医学信息学;急救护理;机器学习;恶化;预测分析;信息学;预测;重症监护室; ICU; mortality", abstract="Background: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19. Objective: This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. Methods: The PICTURE model was trained and validated on a cohort of hospitalized non--COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non--COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions. Results: In non--COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95{\%} CI 0.805-0.834) per observation, compared to the EDI's AUROC of 0.763 (95{\%} CI 0.746-0.781; n=21,740; P<.001). In patients testing positive for COVID-19, PICTURE again outperformed the EDI with an AUROC of 0.849 (95{\%} CI 0.820-0.878) compared to the EDI's AUROC of 0.803 (95{\%} CI 0.772-0.838; n=607; P<.001). The most important variables influencing PICTURE predictions in the COVID-19 cohort were a rapid respiratory rate, a high level of oxygen support, low oxygen saturation, and impaired mental status (Glasgow Coma Scale). Conclusions: The PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering potential incipient waves of COVID-19 infections. The generalizability of the model will require testing in other health care systems for validation. ", issn="2291-9694", doi="10.2196/25066", url="https://medinform.www.mybigtv.com/2021/4/e25066", url="https://doi.org/10.2196/25066", url="http://www.ncbi.nlm.nih.gov/pubmed/33818393" }