The low‐harm score for predicting mortality in patients diagnosed with COVID‐19: A multicentric validation study
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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.
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SciScore for 10.1101/2020.05.26.20111120: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Ethical approval: This study was assessed and approved by the Ethics Committee of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán on April 29th, 2020 (Reg. No. DMC-3369-20-20-1). Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All calculations were performed using Microsoft Excel® and STATA® v12 software. STATA®suggested: (Stata, RRID:SCR_012763)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Result…SciScore for 10.1101/2020.05.26.20111120: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Ethical approval: This study was assessed and approved by the Ethics Committee of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán on April 29th, 2020 (Reg. No. DMC-3369-20-20-1). Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All calculations were performed using Microsoft Excel® and STATA® v12 software. STATA®suggested: (Stata, RRID:SCR_012763)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Finally, scores usually have practical and logistical limitations that preclude their applicability and adoption in real-world settings. As a part of this study, we have created a free digital tool where the calculation of the LOW-HARM score can be automatized, allowing quick, frequent (even daily), and reproducible predictions as the patient’s status evolves20. Choosing a cut-off: A numerical score is useful for making comparisons or tracking clinical evolution on their own, however, having a cut-off value could be useful for decision making. The largest AUROC was observed using a cut-off score of 25 (0.90, 95% CI 0.87-0.93). However, when predicting mortality, and particularly when resources might be allocated based on this prediction, it is preferable to avoid false positive errors (predicting a patient will die when they will survive). Therefore, we propose 65 points as a more clinically useful cut-off, since it has a more than five times lower rate of false positive results if compared against the 25-point cut-off (2% vs 11 %). It should be emphasized that, regardless of its diagnostic accuracy, proposing a score cut-off is as useful as the proportion of times this cut-off is met. In this case, 137/400 (36%) patients had a score above 65 which means it is possible to predict mortality with a specificity of 98% and a positive predictive value of 96% in more than a third of the patients at the time of admission. It is also worth mentioning that, compared with other countri...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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