Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388971%3A_____%2F22%3A00560516" target="_blank" >RIV/61388971:_____/22:00560516 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11130/22:10465586

Výsledek na webu

<a href="https://www.bmj.com/content/378/bmj-2021-069881" target="_blank" >https://www.bmj.com/content/378/bmj-2021-069881</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1136/bmj-2021-069881" target="_blank" >10.1136/bmj-2021-069881</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis

Popis výsledku v původním jazyce

OBJECTIVEnTo externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19.nDESIGNnTwo stage individual participant data meta-analysis.nSETTINGnSecondary and tertiary care.nPARTICIPANTSn46 914 patients across 18 countries, admitted to a hospital. with polymerase chain reaction confirmed covid-19. nMODEL SELECTION AND ELIGIBILITY CRITERIAnPrognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor.nMETHODSnEight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. Main outcome measures 30 day mortality or in-hospital mortality.nRESULTSnDatasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28).n

Název v anglickém jazyce

Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis

Popis výsledku anglicky

OBJECTIVEnTo externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19.nDESIGNnTwo stage individual participant data meta-analysis.nSETTINGnSecondary and tertiary care.nPARTICIPANTSn46 914 patients across 18 countries, admitted to a hospital. with polymerase chain reaction confirmed covid-19. nMODEL SELECTION AND ELIGIBILITY CRITERIAnPrognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor.nMETHODSnEight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. Main outcome measures 30 day mortality or in-hospital mortality.nRESULTSnDatasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28).n

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30303 - Infectious Diseases

Projekt

<a href="/cs/project/LM2018131" target="_blank" >LM2018131: Česká národní infrastruktura pro biologická data</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

British Medical Journal

ISSN

0959-535X

e-ISSN

—

Svazek periodika

378

Číslo periodika v rámci svazku

JUL 12 2022

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

e069881

Kód UT WoS článku

000839395300002

EID výsledku v databázi Scopus

2-s2.0-85133913942