Personalized Prescription of ACEI/ARBs for Hypertensive COVID-19 Patients

The COVID-19 pandemic has prompted an international effort to develop and repurpose medications and procedures to effectively combat the disease. Several groups have focused on the potential treatment utility of angiotensin-converting-enzyme inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) for hypertensive COVID-19 patients, with inconclusive evidence thus far. We couple electronic medical record (EMR) and registry data of 3,643 patients from Spain, Italy, Germany, Ecuador, and the US with a machine learning framework to personalize the prescription of ACEIs and ARBs to hypertensive COVID-19 patients. Our approach leverages clinical and demographic information to identify hospitalized individuals whose probability of mortality or morbidity can decrease by prescribing this class of drugs. In particular, the algorithm proposes increasing ACEI/ARBs prescriptions for patients with cardiovascular disease and decreasing prescriptions for those with low oxygen saturation at admission. We show that personalized recommendations can improve patient outcomes by 1.0% compared to the standard of care when applied to external populations. We develop an interactive interface for our algorithm, providing physicians with an actionable tool to easily assess treatment alternatives and inform clinical decisions. This work offers the first personalized recommendation system to accurately evaluate the efficacy and risks of prescribing ACEIs and ARBs to hypertensive COVID-19 patients. Highlights- This paper introduces a data-driven approach for personalizing the prescription of ACE inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) for hypertensive COVID-19 patients. - Leveraging an international cohort of more than 3,500 patients, we identify clinical and demographic characteristics that may affect the effectiveness of ACEIs/ARBs for COVID-19 patients, such as low oxygen saturation at admission. - We developed a user-friendly online application that is available to physicians to facilitate interpretation and communication of the results of the algorithm.

COVID-19 Mortality Risk Assessment: An International Multi-Center Study

BackgroundTimely 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. MethodsDe-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. FindingsThe 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. InterpretationThe 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. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed, BioRxiv, MedRxiv, arXiv, and SSRN for peer-reviewed articles, preprints, and research reports in English from inception to March 25th, 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 triage and care management, improving risk estimation early within admission, that can significantly affect the daily practice of physicians.

From predictions to prescriptions: A data-drivenresponse to COVID-19

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemics spread and inform social distancing policies. Fourth, we propose an optimization model to reallocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and equitable vaccine distribution planning at a major pharmaceutical company, and have been integrated into the US Center for Disease Controls pandemic forecast. Significance StatementIn the midst of the COVID-19 pandemic, healthcare providers and policy makers are wrestling with unprecedented challenges. How to treat COVID-19 patients with equipment shortages? How to allocate resources to combat the disease? How to plan for the next stages of the pandemic? We present a data-driven approach to tackle these challenges. We gather comprehensive data from various sources, including clinical studies, electronic medical records, and census reports. We develop algorithms to understand the disease, predict its mortality, forecast its spread, inform social distancing policies, and re-distribute critical equipment. These algorithms provide decision support tools that have been deployed on our publicly available website, and are actively used by hospitals, companies, and policy makers around the globe.