Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care.
Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care.
复制标题
DOI：
10.1136/bmjopen-2021-049506
复制
发表时间：
2022-01-17
期刊：
影响因子：
2.9
通讯作者：
Nirantharakumar K
中科院分区：
文献类型：
--
作者：
Adderley NJ;Taverner T;Price MJ;Sainsbury C;Greenwood D;Chandan JS;Takwoingi Y;Haniffa R;Hosier I;Welch C;Parekh D;Gallier S;Gokhale K;Denniston AK;Sapey E;Nirantharakumar K
现有的英国COVID-19患者入院预后模型受到依赖合并症的限制，这些合并症在二级护理中记录不足，并且候选预测因子中缺乏成像数据。我们的目的是开发和外部验证英国二级保健不良结局（死亡和重症治疗病房（ITU）入院）的新型预后模型，并外部验证现有的4C评分。候选预测因素包括人口统计学变量、症状、生理指标、影像学和实验室检查。最终模型使用逐步选择的逻辑回归。模型开发是在伯明翰大学医院（UHB）的数据中进行的。在CovidCollab数据集中进行外部验证。纳入了2020年1月至8月在UHB住院的COVID-19患者。入院后28天内死亡和入院。1040名COVID-19患者被纳入推导队列; 288名（28%）死亡，183名（18%）在入院后28天内入住ITU。死亡率的受试者工作特征曲线下面积（AUROC）为0.791（95% CI 0.761 - 0.822），UHB和0.767（95% CI 0.754 - 0.780）; UHB和CovidCollab中ITU入院的AUROC分别为0.906（95% CI 0.883 - 0.929）和0.811（95% CI 0.795 - 0.828）。模型显示出良好的校准。将合并症添加到候选预测因子中并没有改善模型性能。UHB数据集中国际严重急性呼吸道和新发感染联盟4C评分的AUROC为0.753（95% CI 0.720 - 0.785）。新的预后模型在推导和外部验证数据集中显示出良好的区分和校准，并且仅使用常规收集的患者信息至少与现有的4C评分一样好。这些模型可以集成到电子医疗记录系统中，以计算每个患者在入院时的死亡概率或ITU入院概率。应评价模型的实施和临床效用。
Existing UK prognostic models for patients admitted to the hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death and intensive therapy unit (ITU) admission) in UK secondary care and externally validate the existing 4C score. Candidate predictors included demographic variables, symptoms, physiological measures, imaging and laboratory tests. Final models used logistic regression with stepwise selection. Model development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset. Patients with COVID-19 admitted to UHB January–August 2020 were included. Death and ITU admission within 28 days of admission. 1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating characteristic curve (AUROC) for mortality was 0.791 (95% CI 0.761 to 0.822) in UHB and 0.767 (95% CI 0.754 to 0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95% CI 0.883 to 0.929) in UHB and 0.811 (95% CI 0.795 to 0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the International Severe Acute Respiratory and Emerging Infection Consortium 4C score in the UHB dataset was 0.753 (95% CI 0.720 to 0.785). The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and performed at least as well as the existing 4C score using only routinely collected patient information. The models can be integrated into electronic medical records systems to calculate each individual patient’s probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.
登录
查看更多内容
COVID-19 国家早期预警评分 (NEWS2) 的评估和改进：一项多医院研究
影响因子：
9.3
作者：
Carr E;Bendayan R;Bean D;Stammers M;Wang W;Zhang H;Searle T;Kraljevic Z;Shek A;Phan HTT;Muruet W;Gupta RK;Shinton AJ;Wyatt M;Shi T;Zhang X;Pickles A;Stahl D;Zakeri R;Noursadeghi M;O'Gallagher K;Rogers M;Folarin A;Karwath A;Wickstrøm KE;Köhn-Luque A;Slater L;Cardoso VR;Bourdeaux C;Holten AR;Ball S;McWilliams C;Roguski L;Borca F;Batchelor J;Amundsen EK;Wu X;Gkoutos GV;Sun J;Pinto A;Guthrie B;Breen C;Douiri A;Wu H;Curcin V;Teo JT;Shah AM;Dobson RJB
通讯作者：
Dobson RJB
影响因子：
4.1
作者：
Li, Michael Hua-Gen;Hutchinson, Anastasia;Duke, Graeme
通讯作者：
Duke, Graeme
影响因子：
20.4
作者：
Mandrekar, Jayawant N.
通讯作者：
Mandrekar, Jayawant N.
影响因子：
6.7
作者：
Romero Aliberti, Marlon Juliano;Covinsky, Kenneth E.;Avelino-Silva, Thiago J.
通讯作者：
Avelino-Silva, Thiago J.
影响因子：
14.6
作者：
Rockwood, K;Song, XW;Mitnitski, A
通讯作者：
Mitnitski, A