建立COVID-19严重程度预后模型:冰岛一项基于人群的队列研究

Elias Eythorsson, Valgerdur Bjarnadottir, Hrafnhildur Linnet Runolfsdottir, Dadi Helgason, Ragnar Freyr Ingvarsson, Helgi K Bjornsson, Lovisa Bjork Olafsdottir, Solveig Bjarnadottir, Arnar Snaer Agustsson, Kristin Oskarsdottir, Hrafn Hliddal Thorvaldsson, Gudrun Kristjansdottir, Aron Hjalti Bjornsson, Arna R Emilsdottir, Brynja Armannsdottir, Olafur Gudlaugsson, Sif Hansdottir, Magnus Gottfredsson, Agnar Bjarnason, Martin I Sigurdsson, Olafur S Indridason, Runolfur Palsson
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引用次数: 1

摘要

背景:SARS-CoV-2感染的严重程度从无症状状态到严重呼吸衰竭不等,临床病程难以预测。该研究的目的是建立一种预后模型,以预测诊断时未接种疫苗的成年人COVID-19的严重程度。方法:冰岛所有sars - cov -2阳性成人在诊断时前瞻性地纳入远程医疗服务。对2020年2月27日至12月31日期间确诊并符合纳入标准的患者进行入组访谈,获得多变量比例-赔率logistic回归模型。结果是按顺序定义的:(1)随访期间不需要增加护理;(2)需要急诊访视的;(3)住院治疗;(4)入住重症监护病房(ICU)或死亡。利用链式方程对缺失数据进行多重输入,并利用自举技术对模型进行内部验证。进行决策曲线分析。结果:从4756例sars - cov -2阳性患者中获得预后模型。总共有375人(7.9%)只需要紧急护理,188人(4.0%)住院,50人(1.1%)因COVID-19并发症进入ICU或死亡。该模型包括年龄、性别、体重指数(BMI)、当前吸烟情况、潜在疾病、入组时的症状和临床严重程度评分。在内部验证中,乐观校正的Nagelkerke's R2为23.4% (95%CI, 22.7 ~ 24.2), c统计量为0.793 (95%CI, 0.789 ~ 0.797),校准斜率为0.97 (95%CI, 0.96 ~ 0.98)。结果特异性指标为急诊或更糟(校准截距-0.04 [95%CI, -0.06至-0.02],Emax 0.014 [95%CI, 0.008-0.020]),住院或更糟(校准截距-0.06 [95%CI, -0.12至-0.03],Emax 0.018 [95%CI, 0.010-0.027]), ICU入院或死亡(校准截距-0.10 [95%CI, -0.15至-0.04]和Emax 0.027 [95%CI, 0.013-0.041])。结论:我们的预后模型可以准确预测诊断时普通人群中未接种sars - cov -2疫苗的成年人的紧急门诊评估、住院、ICU入院和死亡情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland.

Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland.

Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland.

Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland.

Background: The severity of SARS-CoV-2 infection varies from asymptomatic state to severe respiratory failure and the clinical course is difficult to predict. The aim of the study was to develop a prognostic model to predict the severity of COVID-19 in unvaccinated adults at the time of diagnosis.

Methods: All SARS-CoV-2-positive adults in Iceland were prospectively enrolled into a telehealth service at diagnosis. A multivariable proportional-odds logistic regression model was derived from information obtained during the enrollment interview of those diagnosed between February 27 and December 31, 2020 who met the inclusion criteria. Outcomes were defined on an ordinal scale: (1) no need for escalation of care during follow-up; (2) need for urgent care visit; (3) hospitalization; and (4) admission to intensive care unit (ICU) or death. Missing data were multiply imputed using chained equations and the model was internally validated using bootstrapping techniques. Decision curve analysis was performed.

Results: The prognostic model was derived from 4756 SARS-CoV-2-positive persons. In total, 375 (7.9%) only required urgent care visits, 188 (4.0%) were hospitalized and 50 (1.1%) were either admitted to ICU or died due to complications of COVID-19. The model included age, sex, body mass index (BMI), current smoking, underlying conditions, and symptoms and clinical severity score at enrollment. On internal validation, the optimism-corrected Nagelkerke's R2 was 23.4% (95%CI, 22.7-24.2), the C-statistic was 0.793 (95%CI, 0.789-0.797) and the calibration slope was 0.97 (95%CI, 0.96-0.98). Outcome-specific indices were for urgent care visit or worse (calibration intercept -0.04 [95%CI, -0.06 to -0.02], Emax 0.014 [95%CI, 0.008-0.020]), hospitalization or worse (calibration intercept -0.06 [95%CI, -0.12 to -0.03], Emax 0.018 [95%CI, 0.010-0.027]), and ICU admission or death (calibration intercept -0.10 [95%CI, -0.15 to -0.04] and Emax 0.027 [95%CI, 0.013-0.041]).

Conclusion: Our prognostic model can accurately predict the later need for urgent outpatient evaluation, hospitalization, and ICU admission and death among unvaccinated SARS-CoV-2-positive adults in the general population at the time of diagnosis, using information obtained by telephone interview.

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