Details

Autor(en) / Beteiligte

• Ottenhoff, Maarten C
• Ramos, Lucas A
• Potters, Wouter
• Janssen, Marcus L F
• Hubers, Deborah
• Hu, Shi
• Fridgeirsson, Egill A
• Piña-Fuentes, Dan
• Thomas, Rajat
• van der Horst, Iwan C C
• Herff, Christian
• Kubben, Pieter
• Elbers, Paul W G
• Marquering, Henk A
• Welling, Max
• Simsek, Suat
• de Kruif, Martijn D
• Dormans, Tom
• Fleuren, Lucas M
• Schinkel, Michiel
• Noordzij, Peter G
• van den Bergh, Joop P
• Wyers, Caroline E
• Buis, David T B
• Wiersinga, W Joost
• van den Hout, Ella H C
• Reidinga, Auke C
• Rusch, Daisy
• Sigaloff, Kim C E
• Douma, Renee A
• de Haan, Lianne
• Gritters van den Oever, Niels C
• Rennenberg, Roger J M W
• van Wingen, Guido A
• Aries, Marcel J H
• Beudel, Martijn

Titel

Predicting mortality of individual patients with COVID-19: a multicentre Dutch cohort

Ist Teil von

• BMJ open, 2021-07, Vol.11 (7), p.e047347-e047347

Ort / Verlag

England: BMJ Publishing Group LTD

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• ObjectiveDevelop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital.DesignRetrospective cohort study.SettingA multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020.ParticipantsSARS-CoV-2 positive patients (age ≥18) admitted to the hospital.Main outcome measures21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis.Results2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81).ConclusionBoth models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.