Development of a novel risk score for the prediction of critical illness amongst COVID‐19 patients
Abstract Objectives Coronavirus disease‐19 (COVID‐19) is associated with various clinical manifestations, ranging from asymptomatic infection to critical illness. The aim of this study is to evaluate the clinical and laboratory characteristics of hospitalised COVID‐19 patients and construct a predictive model for the discrimination of patients at risk of disease progression. Methods A single‐centre cohort study was conducted including consecutively patients with COVID‐19. Demographic, clinical and laboratory findings were prospectively collected at admission. The primary outcome of interest was the intensive care unit admission. A risk model was constructed by applying a Cox's proportional hazard's model with elastic net penalty. Its diagnostic performance was assessed by receiver operating characteristic analysis and was compared with conventional pneumonia severity scores. Results From a total of 67 patients 15 progressed to critical illness. The risk score included patients’ gender, presence of hypertension and diabetes mellitus, fever, shortness of breath, serum glucose, aspartate aminotransferase, lactate dehydrogenase, C‐reactive protein and fibrinogen. Its predictive accuracy was estimated to be high (area under the curve: 97.1%), performing better than CURB‐65, CRB‐65 and PSI/PORT scores. Its sensitivity and specificity were estimated to be 92.3% and 93.3%, respectively, at the optimal threshold of 1.6. Conclusions A10‐variable risk score was constructed based on clinical and laboratory characteristics in order to predict critical illness amongst hospitalised COVID‐19 patients, achieving better discrimination compared with traditional pneumonia severity scores. The proposed risk model should be externally validated in independent cohorts in order to ensure its prognostic efficacy..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:75 |
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| Enthalten in: |
International Journal of Clinical Practice - 75(2021), 4 |
| Beteiligte Personen: |
Bellos, Ioannis [VerfasserIn] |
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| BKL: |
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| Anmerkungen: |
Copyright © 2021 John Wiley & Sons Ltd |
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| Umfang: |
10 |
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| doi: |
10.1111/ijcp.13915 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Abstract Objectives Coronavirus disease‐19 (COVID‐19) is associated with various clinical manifestations, ranging from asymptomatic infection to critical illness. The aim of this study is to evaluate the clinical and laboratory characteristics of hospitalised COVID‐19 patients and construct a predictive model for the discrimination of patients at risk of disease progression. Methods A single‐centre cohort study was conducted including consecutively patients with COVID‐19. Demographic, clinical and laboratory findings were prospectively collected at admission. The primary outcome of interest was the intensive care unit admission. A risk model was constructed by applying a Cox's proportional hazard's model with elastic net penalty. Its diagnostic performance was assessed by receiver operating characteristic analysis and was compared with conventional pneumonia severity scores. Results From a total of 67 patients 15 progressed to critical illness. The risk score included patients’ gender, presence of hypertension and diabetes mellitus, fever, shortness of breath, serum glucose, aspartate aminotransferase, lactate dehydrogenase, C‐reactive protein and fibrinogen. Its predictive accuracy was estimated to be high (area under the curve: 97.1%), performing better than CURB‐65, CRB‐65 and PSI/PORT scores. Its sensitivity and specificity were estimated to be 92.3% and 93.3%, respectively, at the optimal threshold of 1.6. Conclusions A10‐variable risk score was constructed based on clinical and laboratory characteristics in order to predict critical illness amongst hospitalised COVID‐19 patients, achieving better discrimination compared with traditional pneumonia severity scores. The proposed risk model should be externally validated in independent cohorts in order to ensure its prognostic efficacy. | ||
| 700 | 1 | |a Lourida, Panagiota |4 aut | |
| 700 | 1 | |a Argyraki, Aikaterini |4 aut | |
| 700 | 1 | |a Korompoki, Eleni |4 aut | |
| 700 | 1 | |a Zirou, Christina |4 aut | |
| 700 | 1 | |a Kokkinaki, Ioanna |4 aut | |
| 700 | 1 | |a Pefanis, Angelos |4 aut | |
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