Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis
The result's identifiers
Result code in 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>
Alternative codes found
RIV/00216208:11130/22:10465586
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis
Original language description
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
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30303 - Infectious Diseases
Result continuities
Project
<a href="/en/project/LM2018131" target="_blank" >LM2018131: Czech National Infrastructure for Biological Data</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
British Medical Journal
ISSN
0959-535X
e-ISSN
—
Volume of the periodical
378
Issue of the periodical within the volume
JUL 12 2022
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
e069881
UT code for WoS article
000839395300002
EID of the result in the Scopus database
2-s2.0-85133913942