ABSTRACT
Background: Sepsis patients with a concomitant Coronavirus (COVID-19) infection are related to a high morbidity and mortality rate. We investigated a large cohort of sepsis patients with a concomitant COVID-19 to determine clinical characteristics, laboratory and radiological findings, and predictors of mortality. We developed a risk score for the estimation of sepsis risk in patients with COVID-19.Methods: In the present study, we conducted a sub-analysis from the international Health Outcome Predictive Evaluation Registry for COVID-19 (HOPE-COVID-19-Registry). Out of 5,837 patients with COVID-19, 624 patients were diagnosed with sepsis according to the Sepsis-3 International Consensus.Findings: In multivariable analysis, the following risk factors were identified as independent predictors for developing sepsis: current smoking, tachypnoea (>22 breath per minute), haemoptysis, peripheral oxygen saturation (SpO2) < 92%, blood pressure (BP) (systolic BP< 90mmHg and diastolic BP <60mmHg), Glasgow coma scale (GCS) <15, elevated procalcitonin (PCT), elevated troponin I (TnI), and elevated Creatinine > 1.5 mg/dl. By assigning odds ratio weighted points to these variables, the following three risk categories were defined to develop sepsis during admission: low-risk group (probability of sepsis 3.1-11.8%); intermediate-risk group (24.8-53.8%); high-risk-group (58.3-100%). A score of 1 was assigned to current smoking, tachypnoea, decreased SpO2, decreased blood pressure, decreased GCS, elevated PCT, TnI, and creatinine, whereas a score of 2 was assigned to haemoptysis.Interpretation: The HOPE Sepsis Score including 9 parameters is useful in identifying high-risk COVID-19 patients to develop sepsis. Sepsis in COVID-19 is associated with a high mortality rate.Funding Statement: Non-conditioned grant (FUNDACIÓN INTERHOSPITALARIA PARA LA INVESTIGACIÓN CARDIOVASCULAR, FIC. Madrid, Spain)Declaration of Interests: We declare no competing interests.Ethics Approval Statement: The study was approved by the Ethics Committee in all involved centres.
Subject(s)
COVID-19 , Coma , HypotensionABSTRACT
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.