Article (Scientific journals)
Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.
Chatterjee, Avishek; Wu, Guangyao; Primakov, Sergey et al.
2021In PLoS ONE, 16 (4), p. 0249920
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Keywords :
Age Factors; Aged; Aged, 80 and over; Belgium/epidemiology; COVID-19/diagnosis/epidemiology/mortality; Cohort Studies; Communicable Disease Control; Comorbidity; Electronic Health Records; Female; Hospitalization; Humans; Male; Middle Aged; Netherlands/epidemiology; Prognosis; Risk Assessment; Risk Factors; SARS-CoV-2/isolation & purification
Abstract :
[en] OBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Chatterjee, Avishek
Wu, Guangyao
Primakov, Sergey
Oberije, Cary
Woodruff, Henry
Kubben, Pieter
Henry, Ronald
Aries, Marcel J. H.
Beudel, Martijn
Noordzij, Peter G.
Dormans, Tom
Gritters van den Oever, Niels C.
van den Bergh, Joop P.
Wyers, Caroline E.
Simsek, Suat
Douma, Renée
Reidinga, Auke C.
de Kruif, Martijn D.
GUIOT, Julien  ;  Centre Hospitalier Universitaire de Liège - CHU > Département de médecine interne > Service de pneumologie - allergologie
Frix, Anne-Noëlle ;  Centre Hospitalier Universitaire de Liège - CHU > Département de médecine interne > Service de pneumologie - allergologie
Louis, Renaud ;  Université de Liège - ULiège > Département des sciences cliniques > Pneumologie - Allergologie
Moutschen, Michel  ;  Université de Liège - ULiège > Département des sciences cliniques > Immunopath. - Maladies infect. et médec. interne gén.
LOVINFOSSE, Pierre ;  Centre Hospitalier Universitaire de Liège - CHU > Département de Physique Médicale > Service médical de médecine nucléaire et imagerie onco
Lambin, Philippe
More authors (14 more) Less
Language :
English
Title :
Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.
Publication date :
2021
Journal title :
PLoS ONE
eISSN :
1932-6203
Publisher :
Public Library of Science, United States - California
Volume :
16
Issue :
4
Pages :
e0249920
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 07 June 2021

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