COVID-19 mortality risk assessment: An international multi-center study

Bertsimas, Dimitris; Lukin, Galit; Mingardi, Luca; Nohadani, Omid; Orfanoudaki, Agni; Stellato, Bartolomeo; Wiberg, Holly; González‐García, Sara; Calderón, Carlos; Robinson, Kenneth J.; Schneider, Michelle; Stein, Barry D.; Estirado, Alberto; Beccara, Lia a; Canino, Rosario; Bello, Martina Dal; Pezzetti, Federica; Pan, Angelo

doi:10.1371/journal.pone.0243262

Cited by 152 publications

(147 citation statements)

References 27 publications

(29 reference statements)

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“…As reported, the most frequently reported predictors were age, clinical status such as temperature, imaging results from chest radiography, and lymphocyte count. Recently, a study including 3927 patients from 33 hospitals developed the COVID-19 Mortality Risk (CMR) tool using the XGBoost algorithm 27 . This score is based on age, blood urea nitrogen, CRP, creatinine, glucose, AST, and platelet counts.…”

Section: Discussionmentioning

confidence: 99%

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Zhou

Lee²,

Wang³

et al. 2021

npj Digit. Med.

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Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

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Section: Discussionmentioning

confidence: 99%

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Zhou

Lee²,

Wang³

et al. 2021

npj Digit. Med.

View full text Add to dashboard Cite

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“…It has been reported that levels of CRP above 130 mg/l may be associated with increased mortality [ 3 ]. Albumin levels are significantly lower in severe COVID-19, but the change in albumin levels does not parallel the severity of hepatocellular damage.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, combined analysis of liver enzymes and platelet counts has provided important information on new biomarkers used to determine the severity of infectious and neoplastic diseases [ 1 , 2 ]. Moreover, thrombocytopenia and lymphopenia in severe COVID-19 patients are associated with an increased risk and even coagulopathy has poor prognosis [ 3 , 4 ]. Elevated aspartate aminotransferase lymphocyte ratio index (ALRI) levels prior to treatment seem to be indicators of poor prognosis in colorectal cancer [ 5 ].…”

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confidence: 99%

Predictive values of biochemical markers as early indicators for severe COVID-19 cases in admission

et al. 2021

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Aim: COVID-19 is a pandemic that causes high morbidity and mortality, especially in severe patients. In this study, we aimed to search and explain the relationship between biochemical markers, which are more common, easily available and applicable to diagnose and to stage the disease. Materials & methods: In this study, 609 patients were evaluated retrospectively. 11 biochemical parameters were included in analysis to explain the relationship with severity of disease. Results: Nearly, all the parameters that have been evaluated in this study were statistically valuable as a predictive parameter for severe disease. Areas under the curve of blood urea nitrogen (BUN)/albumin ratio (BAR), CALL score and lymphocyte/C-reactive protein ratio were 0.795, 0.778 and 0.770. The BUN/BAR and neutrophil/albumin ratios provide important prognostic information for decision-making in severe patients with COVID-19. Conclusion: High BUN/BAR and neutrophil/albumin ratios may be a better predictor of severity COVID-19 than other routinely used parameters in admission.

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“…There are several research reports using COVID-19 data sets, which focus on predicting the patients’ mortality or severity by mainly using regression modeling from labeled clinical records [ 12 - 17 ]. Further, in a multicenter study, using supervised machine learning, a personalized COVID-19 mortality risk score for hospitalized patients upon admission has been proposed [ 18 ]; however, in that study [ 18 ], the reason for choosing only a subset of the recorded clinical variables to build their model was not explained. Therefore, the algorithm might have been biased, even by the expert’s knowledge.…”

Section: Introductionmentioning

confidence: 99%

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

Benito‐León¹,

Castillo²,

Estirado³

et al. 2021

J Med Internet Res

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Background Early detection and intervention are the key factors for improving outcomes in patients with COVID-19. Objective The objective of this observational longitudinal study was to identify nonoverlapping severity subgroups (ie, clusters) among patients with COVID-19, based exclusively on clinical data and standard laboratory tests obtained during patient assessment in the emergency department. Methods We applied unsupervised machine learning to a data set of 853 patients with COVID-19 from the HM group of hospitals (HM Hospitales) in Madrid, Spain. Age and sex were not considered while building the clusters, as these variables could introduce biases in machine learning algorithms and raise ethical implications or enable discrimination in triage protocols. Results From 850 clinical and laboratory variables, four tests—the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH), C-reactive protein (CRP), and the number of neutrophils—were enough to segregate the entire patient pool into three separate clusters. Further, the percentage of monocytes and lymphocytes and the levels of alanine transaminase (ALT) distinguished cluster 3 patients from the other two clusters. The highest proportion of deceased patients; the highest levels of AST, ALT, LDH, and CRP; the highest number of neutrophils; and the lowest percentages of monocytes and lymphocytes characterized cluster 1. Cluster 2 included a lower proportion of deceased patients and intermediate levels of the previous laboratory tests. The lowest proportion of deceased patients; the lowest levels of AST, ALT, LDH, and CRP; the lowest number of neutrophils; and the highest percentages of monocytes and lymphocytes characterized cluster 3. Conclusions A few standard laboratory tests, deemed available in all emergency departments, have shown good discriminative power for the characterization of severity subgroups among patients with COVID-19.

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COVID-19 mortality risk assessment: An international multi-center study

Cited by 152 publications

References 27 publications

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Predictive values of biochemical markers as early indicators for severe COVID-19 cases in admission

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

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