Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87–0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88–0.95) on Seville patients, 0.87 (95% CI, 0.84–0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76–0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.

Background: Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. Methods: De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. Findings: The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. Interpretation: The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.

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.

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83–0.87), 0.76 (0.70–0.82), and 0.95 (0.92–0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.

Background. Spain is one of the European countries most affected by the COVID-19 pandemic. Epidemiologic studies are warranted to improve the disease understanding, evaluate the care procedure and prepare for futures waves. The aim of the study was to describe epidemiologic characteristics associated with hospitalized patients with COVID-19. Methods. This real-world, observational, multicenter and retrospective study screened all consecutive patients admitted to 8 Spanish private hospitals. Inclusion criteria: hospitalized adults (age≥18 years old) with clinically and radiologically findings compatible with COVID-19 disease from March 1st to April 5th, 2020. Exclusion criteria: patients presenting negative PCR for SARS-CoV-2 during the first 7 days from hospital admission, transfer to a hospital not belonging to the HM consortium, lack of data and discharge against medical advice in emergency departments. Results. One thousand and three hundred thirty-one COVID-19 patients (medium age 66.9 years old; males n= 841, medium length of hospital stayed 8 days, non-survivors n=233) were analyzed. One hundred and fifteen were admitted to intensive care unit (medium length of stay 16 days, invasive mechanical ventilation n= 95, septic shock n= 37 and renal replacement therapy n= 17). Age, male gender, leukocytes, platelets, oxygen saturation, chronic therapy with steroids and treatment with hydroxychloroquine/azithromycin were independent factors associated with mortality. The proportion of patients that survive and received tocilizumab and steroids were lesser and higher respectively than those that die, but their association was not significant. Conclusions. Overall crude mortality rate was 17.5%, rising up to 36.5% in the subgroup of patients that were admitted to the intensive care unit. Seven factors impact in hospital mortality. No immunomodulatory intervention were associated with in-hospital mortality.