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利用来自意大利大量人群的新评分对发展为重症或致死性新冠肺炎的风险进行分层:一项基于人群的队列研究。

Stratification of the risk of developing severe or lethal Covid-19 using a new score from a large Italian population: a population-based cohort study.

作者信息

Corrao Giovanni, Rea Federico, Carle Flavia, Scondotto Salvatore, Allotta Alessandra, Lepore Vito, D'Ettorre Antonio, Tanzarella Cinzia, Vittori Patrizia, Abena Sabrina, Iommi Marica, Spazzafumo Liana, Ercolanoni Michele, Blaco Roberto, Carbone Simona, Giordani Cristina, Manfellotto Dario, Galli Massimo, Mancia Giuseppe

机构信息

Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy

National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.

出版信息

BMJ Open. 2021 Nov 18;11(11):e053281. doi: 10.1136/bmjopen-2021-053281.

Abstract

OBJECTIVES

To develop a population-based risk stratification model (COVID-19 Vulnerability Score) for predicting severe/fatal clinical manifestations of SARS-CoV-2 infection, using the multiple source information provided by the healthcare utilisation databases of the Italian National Health Service.

DESIGN

Retrospective observational cohort study.

SETTING

Population-based study using the healthcare utilisation database from five Italian regions.

PARTICIPANTS

Beneficiaries of the National Health Service, aged 18-79 years, who had the residentship in the five participating regions. Residents in a nursing home were not included. The model was built from the 7 655 502 residents of Lombardy region.

MAIN OUTCOME MEASURE

The score included gender, age and 29 conditions/diseases selected from a list of 61 conditions which independently predicted the primary outcome, that is, severe (intensive care unit admission) or fatal manifestation of COVID-19 experienced during the first epidemic wave (until June 2020). The score performance was validated by applying the model to several validation sets, that is, Lombardy population (second epidemic wave), and the other four Italian regions (entire 2020) for a total of about 15.4 million individuals and 7031 outcomes. Predictive performance was assessed by discrimination (areas under the receiver operating characteristic curve) and calibration (plot of observed vs predicted outcomes).

RESULTS

We observed a clear positive trend towards increasing outcome incidence as the score increased. The areas under the receiver operating characteristic curve of the COVID-19 Vulnerability Score ranged from 0.85 to 0.88, which compared favourably with the areas of generic scores such as the Charlson Comorbidity Score (0.60). A remarkable performance of the score on the calibration of observed and predicted outcome probability was also observed.

CONCLUSIONS

A score based on data used for public health management accurately predicted the occurrence of severe/fatal manifestations of COVID-19. Use of this score may help health decision-makers to more accurately identify high-risk citizens who need early preventive or treatment interventions.

摘要

目的

利用意大利国家医疗服务体系医疗利用数据库提供的多源信息,开发一种基于人群的风险分层模型(COVID-19易感性评分),以预测SARS-CoV-2感染的严重/致命临床表现。

设计

回顾性观察队列研究。

设置

基于人群的研究,使用来自意大利五个地区的医疗利用数据库。

参与者

年龄在18 - 79岁之间、在五个参与地区拥有居民身份的国家医疗服务体系受益人。养老院居民不包括在内。该模型基于伦巴第地区的7655502名居民构建。

主要结局指标

该评分包括性别、年龄以及从61种疾病列表中选出的29种疾病/状况,这些疾病/状况可独立预测主要结局,即在第一波疫情(截至2020年6月)期间经历的COVID-19的严重(入住重症监护病房)或致命表现。通过将该模型应用于多个验证集来验证评分性能,这些验证集包括伦巴第人群(第二波疫情)以及其他四个意大利地区(整个2020年),共计约1540万人和7031个结局。通过辨别力(受试者操作特征曲线下面积)和校准(观察到的与预测的结局对比图)评估预测性能。

结果

我们观察到随着评分增加,结局发生率呈明显的上升趋势。COVID-19易感性评分的受试者操作特征曲线下面积在0.85至0.88之间,与诸如查尔森合并症评分(0.60)等通用评分的面积相比更具优势。在观察到的和预测的结局概率校准方面,该评分也表现出色。

结论

基于用于公共卫生管理的数据得出的评分准确预测了COVID-19严重/致命表现的发生。使用该评分可能有助于卫生决策者更准确地识别需要早期预防或治疗干预的高危公民。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e20c/8602929/68032a4b3ce8/bmjopen-2021-053281f01.jpg

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