S Lau, H P Shum, C C Y Chan, M Y Man, K B Tang, K K C Chan, A K H Leung, W W Yan
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Discrimination performance was assessed using area under the receiver operating characteristic curve (AUROC); calibration performance was evaluated using the Brier score and Hosmer-Lemeshow statistic.</p><p><strong>Results: </strong>In total, 14 503 patients were included, with 10% in the validation set and 90% in the ANN model development set. The ANN model (AUROC=0.88, 95% confidence interval [CI]=0.86-0.90, Brier score=0.10; P in Hosmer-Lemeshow test=0.37) outperformed the APACHE II model (AUROC=0.85, 95% CI=0.80-0.85, Brier score=0.14; P<0.001 for both comparisons of AUROCs and Brier scores) but showed performance similar to the APACHE IV model (AUROC=0.87, 95% CI=0.85-0.89, Brier score=0.11; P=0.34 for comparison of AUROCs, and P=0.05 for comparison of Brier scores). 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引用次数: 0
摘要
简介本研究比较了人工神经网络(ANN)模型与急性生理和慢性健康评估(APACHE)II和IV模型在预测香港重症患者住院死亡率方面的性能:这项回顾性分析包括2010年1月至2019年12月期间入住东区尤德夫人那打素医院深切治疗部的所有患者。使用与 APACHE IV 模型相同的参数构建了 ANN 模型。使用接收者操作特征曲线下面积(AUROC)评估识别性能;使用Brier评分和Hosmer-Lemeshow统计量评估校准性能:总共纳入了 14 503 名患者,其中 10% 属于验证集,90% 属于 ANN 模型开发集。ANN模型(AUROC=0.88,95%置信区间[CI]=0.86-0.90,Brier评分=0.10;Hosmer-Lemeshow检验中的P=0.37)优于APACHE II模型(AUROC=0.85,95% CI=0.80-0.85,Brier评分=0.14;PC结论:我们的ANN模型优于APACHE II模型:在预测香港医院死亡率方面,我们的人工神经网络模型优于 APACHE II 模型,但与 APACHE IV 模型相似。人工神经网络是一种有价值的工具,可提高实时预后预测能力。
Prediction of hospital mortality among critically ill patients in a single centre in Asia: comparison of artificial neural networks and logistic regression-based model.
Introduction: This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong Kong.
Methods: This retrospective analysis included all patients admitted to the intensive care unit of Pamela Youde Nethersole Eastern Hospital from January 2010 to December 2019. The ANN model was constructed using parameters identical to the APACHE IV model. Discrimination performance was assessed using area under the receiver operating characteristic curve (AUROC); calibration performance was evaluated using the Brier score and Hosmer-Lemeshow statistic.
Results: In total, 14 503 patients were included, with 10% in the validation set and 90% in the ANN model development set. The ANN model (AUROC=0.88, 95% confidence interval [CI]=0.86-0.90, Brier score=0.10; P in Hosmer-Lemeshow test=0.37) outperformed the APACHE II model (AUROC=0.85, 95% CI=0.80-0.85, Brier score=0.14; P<0.001 for both comparisons of AUROCs and Brier scores) but showed performance similar to the APACHE IV model (AUROC=0.87, 95% CI=0.85-0.89, Brier score=0.11; P=0.34 for comparison of AUROCs, and P=0.05 for comparison of Brier scores). The ANN model demonstrated better calibration than the APACHE II and APACHE IV models.
Conclusion: Our ANN model outperformed the APACHE II model but was similar to the APACHE IV model in terms of predicting hospital mortality in Hong Kong. Artificial neural networks are valuable tools that can enhance real-time prognostic prediction.
期刊介绍:
The HKMJ is a Hong Kong-based, peer-reviewed, general medical journal which is circulated to 6000 readers, including all members of the HKMA and Fellows of the HKAM. The HKMJ publishes original research papers, review articles, medical practice papers, case reports, editorials, commentaries, book reviews, and letters to the Editor. Topics of interest include all subjects that relate to clinical practice and research in all branches of medicine. The HKMJ welcomes manuscripts from authors, but usually solicits reviews. Proposals for review papers can be sent to the Managing Editor directly. Please refer to the contact information of the Editorial Office.