Institute for Global Health, University College London, London, UK.
Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK; Department of Clinical Surgery, University of Edinburgh, Edinburgh, UK.
Lancet Respir Med. 2021 Apr;9(4):349-359. doi: 10.1016/S2213-2600(20)30559-2. Epub 2021 Jan 11.
Prognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions.
We developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) among consecutively hospitalised adults with highly suspected or confirmed COVID-19 who were prospectively recruited to the International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) study across 260 hospitals in England, Scotland, and Wales. Candidate predictors that were specified a priori were considered for inclusion in the model on the basis of previous prognostic scores and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis. We used internal-external cross-validation to evaluate discrimination, calibration, and clinical utility across eight National Health Service (NHS) regions in the development cohort. We further validated the final model in held-out data from an additional NHS region (London).
74 944 participants (recruited between Feb 6 and Aug 26, 2020) were included, of whom 31 924 (43·2%) of 73 948 with available outcomes met the composite clinical deterioration outcome. In internal-external cross-validation in the development cohort of 66 705 participants, the selected model (comprising 11 predictors routinely measured at the point of hospital admission) showed consistent discrimination, calibration, and clinical utility across all eight NHS regions. In held-out data from London (n=8239), the model showed a similarly consistent performance (C-statistic 0·77 [95% CI 0·76 to 0·78]; calibration-in-the-large 0·00 [-0·05 to 0·05]); calibration slope 0·96 [0·91 to 1·01]), and greater net benefit than any other reproducible prognostic model.
The 4C Deterioration model has strong potential for clinical utility and generalisability to predict clinical deterioration and inform decision making among adults hospitalised with COVID-19.
National Institute for Health Research (NIHR), UK Medical Research Council, Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, EU Platform for European Preparedness Against (Re-)emerging Epidemics, NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, NIHR HPRU in Respiratory Infections at Imperial College London.
迫切需要预测急性 COVID-19 病例临床恶化风险的预后模型，以告知临床管理决策。
我们针对连续收治的高度疑似或确诊 COVID-19 成年患者，开发并验证了一个多变量逻辑回归模型，用于预测院内临床恶化（定义为任何需要通气支持或重症监护或死亡）。该模型来自于国际严重急性呼吸和新兴感染联盟冠状病毒临床特征联盟（ISARIC4C）研究，该研究在英格兰、苏格兰和威尔士的 260 家医院进行，前瞻性招募了这些患者。根据先前的预后评分和描述与 COVID-19 预后相关的常规测量生物标志物的新文献，我们预先指定了候选预测因子，以考虑将其纳入模型。我们使用内部-外部交叉验证在发展队列的 8 个国民保健服务（NHS）地区评估了模型的区分度、校准度和临床实用性。我们还在来自伦敦（London）的另一个 NHS 地区的保留数据中验证了最终模型。
共纳入 74944 名参与者（招募时间为 2020 年 2 月 6 日至 8 月 26 日），其中 73948 名有结局数据的患者中 31924 名（43.2%）符合复合临床恶化结局。在发展队列的内部-外部交叉验证中，包含 11 个入院时常规测量的预测因子的选定模型在所有 8 个 NHS 地区表现出一致的区分度、校准度和临床实用性。在伦敦的保留数据（n=8239）中，该模型的表现也较为一致（C 统计量 0.77[95%CI 0.76 至 0.78]；校准超大规模 0.00[-0.05 至 0.05]），并且优于任何其他可重复的预后模型。
4C 恶化模型具有很强的临床应用潜力和普遍性，可用于预测 COVID-19 住院患者的临床恶化，并为临床决策提供信息。
英国国民健康保险制度（NHS）、英国医学研究理事会（MRC）、惠康信托基金会（Wellcome Trust）、国际发展部（DFID）、欧盟新兴和重新出现传染病防备平台、利物浦大学新兴和动物传染病健康保护研究单位（NIHR HPRU）、伦敦帝国理工学院呼吸感染健康保护研究单位（NIHR HPRU）。
