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.

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BACKGROUND Early detection and intervention are the key factors for improving outcomes in COVID-19. OBJECTIVE To detect severity subgroups among COVID-19 patients, based only on clinical data and standard laboratory tests obtained during the assessment at the emergency department. METHODS We applied unsupervised machine learning to a dataset of 853 COVID-19 patients from HM hospitals in Spain. RESULTS From a total of 850 variables, four tests, the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH) and 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 the cluster 3 from the other two clusters. The cluster 1 was characterized by the higher mortality rate and higher levels of AST, ALT, LDH, CRP and number of neutrophils, and low percentage of monocytes and lymphocytes. The cluster 2 included patients with a moderate mortality rate and medium levels of the previous laboratory determinations. The cluster 3 was characterized by the lower mortality rate and lower levels of AST, ALT, LDH, CRP and number of neutrophils, and higher percentage of monocytes and lymphocytes. Age, sex, comorbidities, and vital signs did not allow us to separate the three clusters. An online cluster assignment tool can be found at https://g-nec.car.upm-csic.es/COVID19-severity-group-assessment/. CONCLUSIONS A few standard laboratory tests, deemed to be available in all emergency departments, have shown far discriminative power for characterization of severity subgroups among COVID-19 patients.

Evidence before this studyWe searched PubMed, BioRxiv, MedRxiv, arXiv, and SSRN for peer-reviewed articles, preprints, and research reports in English from inception to March 25 th , 2020 focusing on disease severity and mortality risk scores for patients that had been infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Earlier investigations showed promise at predicting COVID-19 disease severity using data at admission. However, existing work was limited by its data scope, either relying on a single center with rich clinical information or broader cohort with sparse clinical information. No analysis has leveraged Electronic Health Records data from an international multi-center cohort from both Europe and the United States. Added value of this studyWe present the first multi-center COVID-19 mortality risk study that uses Electronic Health Records data from 3,062 patients across four different countries, including Greece, Italy, Spain, and the United States, encompassing 33 hospitals. We employed state-of-the-art machine learning techniques to develop a personalized COVID-19 mortality risk (CMR) score for hospitalized patients upon admission based on clinical features including vitals, lab results, and comorbidities. The model validates clinical findings of mortality risk factors and exhibits strong performance, with AUCs ranging from 0.81 to 0.92 across external validation cohorts. The model identifies increased age as a primary mortality predictor, consistent with observed disease trends and subsequent public health guidelines. Additionally, among the vital and lab values collected at admission, decreased oxygen saturation (≤ 93%) and elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), blood creatinine (≥ 1.2 mg/dL), and blood glucose (≥180 mg/dL) are highlighted as key biomarkers of mortality risk. These findings corroborate previous studies that link COVID-19 severity to hypoxemia, impaired kidney function, and diabetes. These features are also consistent with risk factors used in severity risk scores for related respiratory conditions such as community-acquired pneumonia. Implications of all the available evidenceOur work presents the development and validation of a personalized mortality risk score. We take a data-driven approach to derive insights from Electronic Health Records data spanning Europe and the United States. While many existing papers on COVID-19 clinical characteristics and risk factors are based on Chinese hospital data, the similarities in our findings suggest consistency in the disease characteristics across international cohorts. Additionally, our machine learning model offers a novel approach to understanding the disease and its risk factors. By creating a single comprehensive risk score that integrates various admission data components, the calculator offers a streamlined way of evaluating COVID-19 patients upon admission to augment clinical expertise. The CMR model provides a valuable clinical decision support tool for patient t...