Accuracy of the Sequential Organ Failure Assessment Score for In-Hospital Mortality by Race and Relevance to Crisis Standards of Care.

Importance: Crisis Standards of Care (CSC) are guidelines for rationing health care resources during public health emergencies. The CSC adopted by US states ration intensive care unit (ICU) admission using the Sequential Organ Failure Assessment (SOFA) score, which is used to compare expected in-hospital mortality among eligible patients. However, it is unknown if Black and White patients with equivalent SOFA scores have equivalent in-hospital mortality. Objective: To investigate whether reliance on SOFA is associated with bias against Black patients in CSC. Design, Setting, and Participants: This cohort study was conducted using data from the eICU Collaborative Research Database of patients admitted to 233 US ICUs in 2014 to 2015. Included individuals were Black and White adult patients in the ICU, who were followed up to hospital discharge. Data were analyzed from May 2020 through April 2021. Exposure: SOFA scores at ICU admission. Main Outcomes and Measures: Hierarchical logistic regression with hospital fixed effects was used to measure the interaction between race and SOFA as a factor associated with in-hospital mortality, as well as the odds of death among Black and White patients with equivalent priority for resource allocation according to the SOFA-based ranking rules of 3 statewide CSC (denoted A, B, and C) under shortage conditions that were severe (ie, only patients with the highest priority would be eligible for allocation), intermediate (ie, patients in the highest 2 tiers would be eligible for allocation), or low (ie, only patients with the lowest priority would be at risk of exclusion). Results: Among 111 885 ICU encounters representing 95 549 patients, there were 16 688 encounters with Black patients (14.9%) and 51 464 (46.0%) encounters with women and the mean (SD) age was 63.3 (16.9) years. The median (interquartile range) SOFA score was not statistically significantly different between Black and White patients (4 [2-6] for both groups; P = .19), but mortality was lower among Black individuals compared with White individuals with equivalent SOFA scores (odds ratio [OR], 0.98; 95% CI, 0.97-0.99; P < .001). This was associated with lower mortality among Black patients compared with White patients prioritized for resource allocation in 3 CSC under shortage conditions that were severe (system A: OR, 0.65; 95% CI, 0.58-0.74; P < .001; system B: OR, 0.70; 95% CI, 0.64-0.78; P < .001; system C: OR, 0.73; 95% CI, 0.67-0.80; P < .001), intermediate (system A: OR, 0.73; 95% CI, 0.67-0.80; P < .001; system B: OR, 0.83; 95% CI, 0.77-0.89; P < .001; system C: OR, 0.82; 95% CI, 0.77-0.89; P < .001), and low (system A: OR, 0.83; 95% CI, 0.77-0.89; P < .001; system C: OR, 0.86; 95% CI, 0.81-0.92; P < .001; not applicable for system B, which had fewer tiers). When SOFA-based ranking rules were adjusted for Black patients to simulate equitable allocation based on observed mortality, the proportion upgraded to higher priority ranged from 379 Black patient encounters (2.3%) in low shortage conditions to 2601 Black patient encounters (15.6%) in severe shortage conditions. Conclusions and Relevance: This study found that SOFA scores were associated with overestimated mortality among Black patients compared with White patients, and this was associated with a structural disadvantage for Black patients in CSC allocation systems. These findings suggest that guidelines should be revised to correct this inequity and alternative methods should be developed for more equitable triage.

Equitably Allocating Resources during Crises: Racial Differences in Mortality Prediction Models.

Rationale: Crisis standards of care (CSCs) guide critical care resource allocation during crises. Most recommend ranking patients on the basis of their expected in-hospital mortality using the Sequential Organ Failure Assessment (SOFA) score, but it is unknown how SOFA or other acuity scores perform among patients of different races. Objectives: To test the prognostic accuracy of the SOFA score and version 2 of the Laboratory-based Acute Physiology Score (LAPS2) among Black and white patients. Methods: We included Black and white patients admitted for sepsis or acute respiratory failure at 27 hospitals. We calculated the discrimination and calibration for in-hospital mortality of SOFA, LAPS2, and modified versions of each, including categorical SOFA groups recommended in a popular CSC and a SOFA score without creatinine to reduce the influence of race. Measurements and Main Results: Of 113,158 patients, 27,644 (24.4%) identified as Black. The LAPS2 demonstrated higher discrimination (area under the receiver operating characteristic curve [AUC], 0.76; 95% confidence interval [CI], 0.76-0.77) than the SOFA score (AUC, 0.68; 95% CI, 0.68-0.69). The LAPS2 was also better calibrated than the SOFA score, but both underestimated in-hospital mortality for white patients and overestimated in-hospital mortality for Black patients. Thus, in a simulation using observed mortality, 81.6% of Black patients were included in lower-priority CSC categories, and 9.4% of all Black patients were erroneously excluded from receiving the highest prioritization. The SOFA score without creatinine reduced racial miscalibration. Conclusions: Using SOFA in CSCs may lead to racial disparities in resource allocation. More equitable mortality prediction scores are needed.

Clinicians can independently predict 30-day hospital readmissions as well as the LACE index.

BACKGROUND: Significant effort has been directed at developing prediction tools to identify patients at high risk of unplanned hospital readmission, but it is unclear what these tools add to clinicians' judgment. In our study, we assess clinicians' abilities to independently predict 30-day hospital readmissions, and we compare their abilities with a common prediction tool, the LACE index. METHODS: Over a period of 50 days, we asked attendings, residents, and nurses to predict the likelihood of 30-day hospital readmission on a scale of 0-100% for 359 patients discharged from a General Medicine Service. For readmitted versus non-readmitted patients, we compared the mean and standard deviation of the clinician predictions and the LACE index. We compared receiver operating characteristic (ROC) curves for clinician predictions and for the LACE index. RESULTS: For readmitted versus non-readmitted patients, attendings predicted a risk of 48.1% versus 31.1% (p < 0.001), residents predicted 45.5% versus 34.6% (p 0.002), and nurses predicted 40.2% versus 30.6% (p 0.011), respectively. The LACE index for readmitted patients was 11.3, versus 10.1 for non-readmitted patients (p 0.003). The area under the curve (AUC) derived from the ROC curves was 0.689 for attendings, 0.641 for residents, 0.628 for nurses, and 0.620 for the LACE index. Logistic regression analysis suggested that the LACE index only added predictive value to resident predictions, but not attending or nurse predictions (p < 0.05). CONCLUSIONS: Attendings, residents, and nurses were able to independently predict readmissions as well as the LACE index. Improvements in prediction tools are still needed to effectively predict hospital readmissions.