SOFA score performs worse than age for predicting mortality in patients with COVID-19.

The use of the Sequential Organ Failure Assessment (SOFA) score, originally developed to describe disease morbidity, is commonly used to predict in-hospital mortality. During the COVID-19 pandemic, many protocols for crisis standards of care used the SOFA score to select patients to be deprioritized due to a low likelihood of survival. A prior study found that age outperformed the SOFA score for mortality prediction in patients with COVID-19, but was limited to a small cohort of intensive care unit (ICU) patients and did not address whether their findings were unique to patients with COVID-19. Moreover, it is not known how well these measures perform across races. In this retrospective study, we compare the performance of age and SOFA score in predicting in-hospital mortality across two cohorts: a cohort of 2,648 consecutive adult patients diagnosed with COVID-19 who were admitted to a large academic health system in the northeastern United States over a 4-month period in 2020 and a cohort of 75,601 patients admitted to one of 335 ICUs in the eICU database between 2014 and 2015. We used age and the maximum SOFA score as predictor variables in separate univariate logistic regression models for in-hospital mortality and calculated area under the receiver operator characteristic curves (AU-ROCs) and area under precision-recall curves (AU-PRCs) for each predictor in both cohorts. Among the COVID-19 cohort, age (AU-ROC 0.795, 95% CI 0.762, 0.828) had a significantly better discrimination than SOFA score (AU-ROC 0.679, 95% CI 0.638, 0.721) for mortality prediction. Conversely, age (AU-ROC 0.628 95% CI 0.608, 0.628) underperformed compared to SOFA score (AU-ROC 0.735, 95% CI 0.726, 0.745) in non-COVID-19 ICU patients in the eICU database. There was no difference between Black and White COVID-19 patients in performance of either age or SOFA Score. Our findings bring into question the utility of SOFA score-based resource allocation in COVID-19 crisis standards of care.

Effects of correction rate for severe hyponatremia in the intensive care unit on patient outcomes.

PURPOSE: Limited evidence exists regarding outcomes associated with different correction rates of severe hyponatremia. MATERIALS AND METHODS: This retrospective cohort analysis employed a multi-center ICU database to identify patients with sodium ≤120 mEq/L during ICU admission. We determined correction rates over the first 24 h and categorized them as rapid (> 8 mEq/L/day) or slow (≤ 8 mEq/L/day). The primary outcome was in-hospital mortality. Secondary outcomes included hospital-free days, ICU-free days, and neurological complications. We used inverse probability weighting for confounder adjustment. RESULTS: Our cohort included 1024 patients; 451 rapid and 573 slow correctors. Rapid correction was associated with lower in-hospital mortality (absolute difference: -4.37%; 95% CI, -8.47 to -0.26%), longer hospital-free days (1.80 days; 95% CI, 0.82 to 2.79 days), and longer ICU-free days (1.16 days; 95% CI, 0.15 to 2.17 days). There was no significant difference in neurological complications (2.31%; 95% CI, -0.77 to 5.40%). CONCLUSION: Rapid correction (>8 mEq/L/day) of severe hyponatremia within the first 24 h was associated with lower in-hospital mortality and longer ICU and hospital-free days without an increase in neurological complication. Despite major limitations, including the inability to identify the chronicity of hyponatremia, the results have important implications and warrant prospective studies.

Association Between ICU-Acquired Hypernatremia and In-Hospital Mortality: Data From the Medical Information Mart for Intensive Care III and the Electronic ICU Collaborative Research Database.

OBJECTIVES: Describe the relationship between ICU-acquired hypernatremia and in-hospital mortality and investigate the optimal hypernatremia correction rate. DESIGN SETTING PARTICIPANTS AND MEASUREMENTS: Observational study including two individual ICU cohorts. We used the Medical Information Mart for Intensive Care III v. 1.4 database consists of all ICU patients admitted to the Beth Israel Deaconess Medical Center in Boston from 2001 to 2012 (n = 46,476). The electronic ICU v. 2.0 database consists of all ICU patients admitted to 208 distinct hospitals across the United States from 2014 to 2015 (n = 200,859). We included all adult patients admitted to an ICU with two consecutive sodium samples within normal range (135-145 mmol/L) and without two consecutive hyponatremic samples (< 135 mmol/L) during the ICU stay. RESULTS: Of 23,445 patients identified in Medical Information Mart for Intensive Care III, 9% (n = 2,172) developed hypernatremia during their ICU stay. In electronic ICU, 88,160 patients were identified and 7% (n = 5,790) developed hypernatremia. In both cohorts, patients with hypernatremia had a higher mortality (Medical Information Mart for Intensive Care III: 20% vs 42%; p < 0.01 and electronic ICU: 6% vs 22%; p < 0.01), with hypernatremia increasing the risk of in-hospital mortality (Medical Information Mart for Intensive Care III: odds ratio, 1.15; 95% CI, 1.13-1.17 and electronic ICU: odds ratio, 1.11; 95% CI, 1.10-1.12) and over time using a Cox regression. Rapid sodium correction rate (> 0.5 mmol/L/hr) was associated with an increased in-hospital mortality in both cohorts (Medical Information Mart for Intensive Care III: odds ratio, 1.08; 95% CI, 1.03-1.13 and electronic ICU: odds ratio, 1.10; 95% CI, 1.06-1.13). In the electronic ICU cohort, rapid correction rates were associated with a significant difference in in-hospital mortality, but there was no statistically significant association in the Medical Information Mart for Intensive Care III cohort. CONCLUSIONS: ICU-acquired hypernatremia is associated with increased in-hospital mortality. Furthermore, a rapid sodium correction rates may be harmful. This suggests it is important to both prevent ICU-acquired hypernatremia and to avoid rapid correction rates if a patient becomes hypernatremic.

Disaster Metrics: Evaluation of de Boer's Disaster Severity Scale (DSS) Applied to Earthquakes.

INTRODUCTION: Quantitative measurement of the medical severity following multiple-casualty events (MCEs) is an important goal in disaster medicine. In 1990, de Boer proposed a 13-point, 7-parameter scale called the Disaster Severity Scale (DSS). Parameters include cause, duration, radius, number of casualties, nature of injuries, rescue time, and effect on surrounding community. Hypothesis This study aimed to examine the reliability and dimensionality (number of salient themes) of de Boer's DSS scale through its application to 144 discrete earthquake events. METHODS: A search for earthquake events was conducted via National Oceanic and Atmospheric Administration (NOAA) and US Geological Survey (USGS) databases. Two experts in the field of disaster medicine independently reviewed and assigned scores for parameters that had no data readily available (nature of injuries, rescue time, and effect on surrounding community), and differences were reconciled via consensus. Principle Component Analysis was performed using SPSS Statistics for Windows Version 22.0 (IBM Corp; Armonk, New York USA) to evaluate the reliability and dimensionality of the DSS. RESULTS: A total of 144 individual earthquakes from 2003 through 2013 were identified and scored. Of 13 points possible, the mean score was 6.04, the mode = 5, minimum = 4, maximum = 11, and standard deviation = 2.23. Three parameters in the DSS had zero variance (ie, the parameter received the same score in all 144 earthquakes). Because of the zero contribution to variance, these three parameters (cause, duration, and radius) were removed to run the statistical analysis. Cronbach's alpha score, a coefficient of internal consistency, for the remaining four parameters was found to be robust at 0.89. Principle Component Analysis showed uni-dimensional characteristics with only one component having an eigenvalue greater than one at 3.17. The 4-parameter DSS, however, suffered from restriction of scoring range on both parameter and scale levels. CONCLUSION: Jan de Boer's DSS in its 7-parameter format fails to hold statistically in a dataset of 144 earthquakes subjected to analysis. A modified 4-parameter scale was found to quantitatively assess medical severity more directly, but remains flawed due to range restriction on both individual parameter and scale levels. Further research is needed in the field of disaster metrics to develop a scale that is reliable in its complete set of parameters, capable of better fine discrimination, and uni-dimensional in measurement of the medical severity of MCEs.