The Low-Harm Score for Predicting Mortality in Patients Diagnosed With Covid-19: A Multicentric Validation Study

Abstract

- Importance: Many COVID-19 prognostic factors for disease severity have been identified and many scores have already been proposed to predict death and other outcomes. However, hospitals in developing countries often cannot measure some of the variables that have been reported as useful. - Objective: To assess the sensitivity, specificity, and predictive values of the novel LOW-HARM score (Lymphopenia, Oxygen saturation, White blood cells, Hypertension, Age, Renal injury, and Myocardial injury). - Design: Demographic and clinical data from patients with known clinical outcomes (death or discharge) was obtained. Patients were grouped according to their outcome. The LOW-HARM score was calculated for each patient and its distribution, potential cut-off values and demographic data were compared. - Setting: Twelve hospitals in ten different cities in Mexico. - Participants: Data from 438 patients was collected. A total of 400 (200 per group) was included in the analysis. - Exposure: All patients had an infection with SARS-CoV-2 confirmed by PCR. - Main Outcome: The sensitivity, specificity, and predictive values of different cut-offs of the LOW-HARM score to predict death. - Results: Mean scores at admission and their distributions were significantly lower in patients who were discharged compared to those who died during their hospitalization 10 (SD: 17) vs 70 (SD: 28). The overall AUC of the model was 95%. A cut-off > 65 points had a specificity of 98% and a positive predictive value of 96%. More than a third of the cases (36%) in the sample had a LOW-HARM score > 65 points. - Conclusions and relevance: The LOW-HARM score measured at admission is highly specific and useful for predicting mortality. It is easy to calculate and can be updated with individual clinical progression.

The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19

“…Several studies have reported risk factors associated with death in patients with COVID-19, although very few propose reliable prediction models, which should be constructed using an adequate sample size and a standardized methodology to avoid significant bias [ 5 , 11 , 12 , 13 , 14 ].…”

Development and validation of the patient history COVID-19 (PH-Covid19) scoring system: a multivariable prediction model of death in Mexican patients with COVID-19

“…Three prognostic COVID-19 models have been developed in Mexican patients. The LOW-HARM model [35] is a 100-point scoring system calculated by inputting patient history and laboratory values, in which 65 points was set as the cut-off value to predict death (AUC: 0.80, 95% CI 0.77–0.84), similar to the PH-Covid19 scoring system (AUC: 0.80, 95% CI 0.796–0.804) which advantageously only requires patient history predictors. Another scoring system uses age (cut-off 65 years), comorbidities and pneumonia to predict death [36].…”

Oxygen and mortality in COVID-19 pneumonia: a comparative analysis of supplemental oxygen policies and health outcomes across 26 countries

“…In a UK cohort, during the initial surge of infections, and during oxygen rationing, approximately 62% of patients presented hypoxic, and of those that died 75% (60/80) of patients presented hypoxic (defined broadly in this study as SpO2 <95%)[22]. In a cohort from Mexico, 96% of the patients who died presented with SpO2 under 88%[23].…”