Development and Validation of a Multivariable Risk Prediction Model for COVID-19 Mortality in the Southern United States

Abstract

Objective: To evaluate clinical characteristics of patients admitted to the hospital with coronavirus disease 2019 in Southern United States and development as well as validation of a mortality risk prediction model. Patients and Methods: Southern Louisiana was an early hotspot during the pandemic, which provided a large collection of clinical data on inpatients with COVID-19. We designed a risk stratification model to assess the mortality risk for patients admitted to the hospital with COVID-19. Data from 1673 consecutive patients diagnosed with COVID-19 infection and hospitalized between March 1, 2020, and April 30, 2020, was used to create an 11-factor mortality risk model based on baseline comorbidity, organ injury, and laboratory results. The risk model was validated using a subsequent cohort of 2067 consecutive hospitalized patients admitted between June 1, 2020, and December 31, 2020.Results: The resultant model has an area under the curve of 0.783 (95% CI, 0.76 to 0.81), with an optimal sensitivity of 0.74 and specificity of 0.69 for predicting mortality. Validation of this model in a subsequent cohort of 2067 consecutively hospitalized patients yielded comparable prognostic performance. Conclusion:We have developed an easy-to-use, robust model for systematically evaluating patients presenting to acute care settings with COVID-19 infection.

When Outcomes Diverge: Age and Cardiovascular Risk as Determinants of Mortality and ICU Admission in COVID-19

“…Since the early reports on the COVID-19 pandemic, in-hospital mortality has been linked to the presence of a number of comorbidities. Predictors of mortality include respiratory pathologies (chronic obstructive pulmonary disease, asthma, smoking habits) metabolic diseases (diabetes, dyslipidemia, obesity), organ dysfunction (chronic renal failure, liver dysfunction), malignancies, and cardiovascular pathologies [ 1 , 2 , 3 ]. Among these, the most common are hypertension, coronary artery disease, valvular disease, heart failure, peripheral vascular disease, and cerebrovascular disease.…”

What, Where, When and How of COVID-19 Patents Landscape: A Bibliometrics Review

“…Psychotherapy patents were born (IN202041043167, US20200327977). In addition to timely interventions, on the other hand, to solve the pandemic issue, research on predicting the disease (CN112185560) is extremely vital for buying more time for clinical trials and conducive to the intervention and prevention of COVID-19 by departments at all levels (41,42).…”