The low‐harm score for predicting mortality in patients diagnosed with COVID‐19: A multicentric validation study

Abstract

Objective We sought to determine the accuracy of the LOW‐HARM score (Lymphopenia, Oxygen saturation, White blood cells, Hypertension, Age, Renal injury, and Myocardial injury) for predicting death from coronavirus disease 2019) COVID‐19. Methods We derived the score as a concatenated Fagan's nomogram for Bayes theorem using data from published cohorts of patients with COVID‐19. We validated the score on 400 consecutive COVID‐19 hospital admissions (200 deaths and 200 survivors) from 12 hospitals in Mexico. We determined the sensitivity, specificity, and predictive values of LOW‐HARM for predicting hospital death. Results LOW‐HARM scores and their distributions were significantly lower in patients who were discharged compared to those who died during their hospitalization 5 (SD: 14) versus 70 (SD: 28). The overall area under the curve for the LOW‐HARM score was 0.96, (95% confidence interval: 0.94–0.98). A cutoff > 65 points had a specificity of 97.5% and a positive predictive value of 96%. Conclusions The LOW‐HARM score measured at hospital admission is highly specific and clinically useful for predicting mortality in patients with COVID‐19.

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].…”

Usefulness of Easy-to-Use Risk Scoring Systems Rated in the Emergency Department to Predict Major Adverse Outcomes in Hospitalized COVID-19 Patients

“…Each of the parameters dichotomously scores as present/absent according to pre-established cutoff points, except for age, which scores differently according to the decade of age to which the patient corresponds. According to the optimal cutoff point, the LOW-HARM score may reach 63% sensitivity and 97.5% specificity to predict death [4]. Online calculator: https://lowharmcalc.com/ (accessed on 26 April 2021).…”

“…Although many of these risk scoring systems are based on clinical and laboratory criteria, it is essential to consider the more affordable techniques in the Emergency Department as an added value for guiding therapeutic strategies in any hospital, including those in low-income countries. Among the most user-friendly scores are those that consider a history of various diseases such as diabetes, obesity, hypertension, and chronic kidney disease especially important [3][4][5][6][7]. On the other hand, as a result of the hyperinflammation underlying severe COVID-19, the efficacy of some calculators that assess the extent of inflammation under other medical conditions is under comprehensive evaluation [8][9][10][11].…”

Prospective predictive performance comparison between Clinical Gestalt and validated COVID-19 mortality scores

“…Many prediction models have been developed for COVID-19 (1)(2)(3)(4)(5) and their applications in healthcare range from bed-side counseling to triage systems (6). However, most have been developed within specific clinical contexts (1,2) or validated with data from the early months of the pandemic (4,5).…”

“…Many prediction models have been developed for COVID-19 (1)(2)(3)(4)(5) and their applications in healthcare range from bed-side counseling to triage systems (6). However, most have been developed within specific clinical contexts (1,2) or validated with data from the early months of the pandemic (4,5). Since then, health systems have implemented protocols and adaptations to cope with a surge in hospitalization rates (7), and now, clinicians have more knowledge and experience for managing these patients.…”