Validation of a simplified comorbidity evaluation predicting clinical outcomes among patients with coronavirus disease 2019 - A multicenter retrospective observation study.

OBJECTIVES: We compared and validated the performance accuracy of simplified comorbidity evaluation compared to the Charlson Comorbidity Index (CCI) predicting COVID-19 severity. In addition, we also determined whether risk prediction of COVID-19 severity changed during different COVID-19 pandemic outbreaks. METHODS: We enrolled all patients whose SARS-CoV-2 PCR tests were performed at six different hospital Emergency Departments in 2020. Patients were divided into three groups based on the various COVID-19 outbreaks in the US (first wave: March-May 2020, second wave: June-September 2020, and third wave: October-December 2020). A simplified comorbidity evaluation was used as an independent risk factor to predict clinical outcomes using multivariate logistic regressions. RESULTS: A total of 22,248 patients were included, for which 7023 (32%) patients tested COVID-19 positive. Higher percentages of COVID-19 patients with more than three chronic conditions had worse clinical outcomes (i.e., hospital and intensive care unit admissions, receiving invasive mechanical ventilations, and in-hospital mortality) during all three COVID-19 outbreak waves. CONCLUSIONS: This simplified comorbidity evaluation was validated to be associated with COVID clinical outcomes. Such evaluation did not perform worse when compared with CCI to predict in-hospital mortality.

Role of ED crowding relative to trauma quality care in a Level 1 Trauma Center.

OBJECTIVE: Trauma Quality Improvement Program participation among all trauma centers has shown to improve patient outcomes. We aim to identify trauma quality events occurring during the Emergency Department (ED) phase of care. METHODS: This is a single-center observational study using consecutively registered data in local trauma registry (Jan 1, 2016-Jun 30, 2017). Four ED crowding scores as determined by four different crowding estimation tools were assigned to each enrolled patient upon arrival to the ED. Patient related (age, gender, race, severity of illness, ED disposition), system related (crowding, night shift, ED LOS), and provider related risk factors were analyzed in a multivariate logistic regression model to determine associations relative to ED quality events. RESULTS: Total 5160 cases were enrolled among which, 605 cases were deemed ED quality improvement (QI) cases and 457 cases were ED provider related. Similar percentages of ED QI cases (10-12%) occurred across the ED crowding status range. No significant difference was appreciated in terms of predictability of ED QI cases relative to different crowding status after adjustment for potential confounders. However, an adjusted odds ratio of 1.64 (95% CI, 1.17-2.30, p < 0.01) regarding ED LOS ≥2 h predictive of ED related quality issues was noted when analyzed using multivariate logistic regression. CONCLUSION: Provider related issues are a common contributor to undesirable outcomes in trauma care. ED crowding lacks significant association with poor trauma quality care. Prolonged ED LOS (≥2 h) appears to be linked with unfavorable outcomes in ED trauma care.

Common step-wise interventions improved primary care clinic visits and reduced emergency department discharge failures: a large-scale retrospective observational study.

BACKGROUND: It is critical to understand whether providing health insurance coverage, assigning a dedicated Primary Care Physician (PCP), and arranging timely post-Emergency Department (ED) clinic follow-up can improve compliance with clinic visits and reduce ED discharge failures. We aim to determine the benefits of providing these common step-wise interventions and further investigate the necessity of urgent PCP referrals on behalf of ED discharged patients. METHODS: This is a single-center retrospective observational study. All patients discharged from the ED over the period Jan 1, 2015 through Dec 31, 2017 were included in the study population. Step-wise interventions included providing charity health insurance, assigning a dedicated PCP, and providing ED follow-up clinics. PCP clinic compliance and ED discharge failures were measured and compared among groups receiving different interventions. RESULT: A total of 227,627 patients were included. Fifty-eight percent of patients receiving charity insurance had PCP visits in comparison to 23% of patients without charity insurance (p < 0.001). Seventy-seven percent of patients with charity insurance and PCP assignments completed post-ED discharge PCP visits in comparison to only 4.5% of those with neither charity insurance nor PCP assignments (p < 0.001). CONCLUSIONS: Step-wise interventions increased patient clinic follow-up compliance while simultaneously reducing ED discharge failures. Such interventions might benefit communities with similar patient populations.

What explains differences in average wait time in the emergency department among different racial and ethnic populations: A linear decomposition approach.

Objective: Non-Hispanic Black (NHB) and Hispanic/Latino (Hispanic) patients wait longer in the emergency department (ED) to see practitioners when compared with non-Hispanic White (NHW) patients. We investigate factors contributing to longer wait times for NHB and Hispanic patients using a linear decomposition approach. Methods: This retrospective observational study included patients presenting to one tertiary hospital ED from 2019 to 2021. Median wait times among NHW, NHB, and Hispanic were calculated with multivariable linear regressions. The extent to which demographic, clinical, and hospital factors explained the differences in average wait time among the three groups were analyzed with Blinder‒Oaxaca post-linear decomposition model. Results: There were 310,253 total patients including 34.7% of NHW, 34.7% of NHB, and 30.6% of Hispanic patients. The median wait time in NHW was 9 min (interquartile range [IQR] 4‒47 min), in NHB was 13 min (IQR 4‒59 min), and in Hispanic was 19 min (IQR 5‒78 min, p < 0.001). The top two contributors of average wait time difference were mode of arrival and triage acuity level. Post-linear decomposition analysis showed that 72.96% of the NHB‒NHW and 87.77% of the Hispanic‒NHW average wait time difference were explained by variables analyzed. Conclusion: Compared to NHW patients, NHB and Hispanic patients typically experience longer ED wait times, primarily influenced by their mode of arrival and triaged acuity levels. Despite these recognized factors, there remains 12%‒27% unexplained factors at work, such as social determinants of health (including implicit bias and systemic racism) and many other unmeasured confounders, yet to be discovered.

A Simplified Comorbidity Evaluation Predicting Clinical Outcomes Among Patients With Coronavirus Disease 2019.

BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) have shown a range of clinical outcomes. Previous studies have reported that patient comorbidities are predictive of worse clinical outcomes, especially when patients have multiple chronic diseases. We aim to: 1) derive a simplified comorbidity evaluation and determine its accuracy of predicting clinical outcomes (i.e., hospital admission, intensive care unit (ICU) admission, ventilation, and in-hospital mortality); and 2) determine its performance accuracy in comparison to well-established comorbidity indexes. METHODS: This was a single-center retrospective observational study. We enrolled all emergency department (ED) patients with COVID-19 from March 1, 2020, to December 31, 2020. A simplified comorbidity evaluation (COVID-related high-risk chronic condition (CCC)) was derived to predict different clinical outcomes using multivariate logistic regressions. In addition, chronic diseases included in the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) were scored, and its accuracy of predicting COVID-19 clinical outcomes was also compared with the CCC. RESULTS: Data were retrieved from 90,549 ED patient visits during the study period, among which 3,864 patients were COVID-19 positive. Forty-seven point nine percent (1,851/3,864) were admitted to the hospital, 9.4% (364) patients were admitted to the ICU, 6.2% (238) received invasive mechanical ventilation, and 4.6% (177) patients died in the hospital. The CCC evaluation correlated well with the four studied clinical outcomes. The adjusted odds ratios of predicting in-hospital death from CCC was 2.84 (95% confidence interval (CI): 1.81 - 4.45, P < 0.001). C-statistics of CCC predicting in-hospital all-cause mortality was 0.73 (0.69 - 0.76), similar to those of the CCI's (0.72) and ECI's (0.71, P = 0.0513). CONCLUSIONS: CCC can accurately predict clinical outcomes among patients with COVID-19. Its performance accuracies for such predictions are not inferior to those of the CCI or ECI's.

Causal Effect Analysis of Demographic Concordance of Physician Trust and Respect in an Emergency Care Setting.

OBJECTIVE: Patient perceptions of physician trust and respect are important factors for patient satisfaction evaluations. However, perceptions are subjective by nature and can be affected by patient and physician demographic characteristics. We aim to determine the causal effect on patient-physician demographic concordance and patient perceptions of physician trust and respect in an emergency care setting. METHODS: We performed a causal effect analysis in an observational study setting. A near-real-time patient satisfaction survey was sent via telephone to patients within 72 h of discharge from an emergency department (ED). Patient-trust-physician (PTP) and physician-show-respect (PSR) scores were measured. Patient and physician demographics (age, gender, race, and ethnicity) were matched. Causal effect was analyzed to determine the direct effect of patient-physician demographic concordance on PTP/PSR scores. RESULTS: We enrolled 1815 patients. The treatment effect of patient-physician age concordance on PTP scores was -0.119 (p = 0.036). Other treatment effect of patient-physician demographic concordance on patient perception of physician trust and respect ranged from -0.02 to -0.2 (p > 0.05). CONCLUSION: Patient-physician age concordance may cause a negative effect on patient perception of physician trust. Otherwise, patient-physician demographic concordance has no effect on patient perceptions of physician trust and respect.

Mortality association between obesity and pneumonia using a dual restricted cohort model.

BACKGROUND: An obesity survival paradox has been reported among obese patients with pneumonia. AIMS: To determine the impact of obesity on pneumonia outcomes and analyze the correlation between in-hospital all-cause mortality and obesity among patients with pneumonia. METHODS: The United States Nationwide Readmissions Database (NRD) was retrospectively analyzed for patients with pneumonia from 2013 to 2014. We used a step-wise restricted and propensity score matching cohort model (dual model) to compare mortality rates and other outcomes among pneumonia patients based on BMI. Mortality was calculated by a Cox proportional hazard model, adjusted for potential confounders with propensity score matched analysis. RESULTS: A total of 70,886,775 patients were registered in NRD during the study period. Of these, 7,786,913 patients (11.0%) were considered obese and 1,652,456 patients (2.3%) were admitted to the hospital with pneumonia. Based on the step-wise restricted cohort model, the hazard ratio comparing the mortality rates among obese pneumonia patients to mortality rates among normal BMI pneumonia patients was 0.75 (95% CI 0.60-0.94). The propensity score matched analysis estimated a hazard rate of 0.84 (95% CI 0.79-0.90) and the hazard ratio estimated from the dual model was 0.82 (95% CI 0.63-1.07). CONCLUSIONS: With the application of a dual model, there appears to be no significant difference in mortality of obese patients with pneumonia compared to normal BMI patients with pneumonia.

Identifying diverse concepts of discharge failure patients at emergency department in the USA: a large-scale retrospective observational study.

OBJECTIVES: Identifying patients who are at high risk for discharge failure allows for implementation of interventions to improve their care. However, discharge failure is currently defined in literature with great variability, making targeted interventions more difficult. We aim to derive a screening tool based on the existing diverse discharge failure models. DESIGN, SETTING AND PARTICIPANTS: This is a single-centre retrospective cohort study in the USA. Data from all patients discharged from the emergency department were collected from 1 January 2015 through 31 December 2017 and followed up within 30 days. METHODS: Scoring systems were derived using modified Framingham methods. Sensitivity, specificity and area under the receiver operational characteristic (AUC) were calculated and compared using both the broad and restricted discharge failure models. RESULTS: A total of 227 627 patients were included. The Screening for Healthcare fOllow-Up Tool (SHOUT) scoring system was derived based on the broad and restricted discharge failure models and applied back to the entire study cohort. A sensitivity of 80% and a specificity of 71% were found in SHOUT scores to identify patients with broad discharge failure with AUC of 0.83 (95% CI 0.83 to 0.84). When applied to a 3-day restricted discharge failure model, a sensitivity of 86% and a specificity of 60% were found to identify patients with AUC of 0.79 (95% CI 0.78 to 0.80). CONCLUSION: The SHOUT scoring system was derived and used to screen and identify patients that would ultimately become discharge failures, especially when using broad definitions of discharge failure. The SHOUT tool was internally validated and can be used to identify patients across a wide spectrum of discharge failure definitions.

Mortality association between obesity and pneumonia using a dual restricted cohort model.

BACKGROUND: An obesity survival paradox has been reported among obese patients with pneumonia. AIMS: To determine the impact of obesity on pneumonia outcomes and analyze the correlation between in-hospital all-cause mortality and obesity among patients with pneumonia. METHODS: The United States Nationwide Readmissions Database (NRD) was retrospectively analyzed for patients with pneumonia from 2013 to 2014. We used a step-wise restricted and propensity score matching cohort model (dual model) to compare mortality rates and other outcomes among pneumonia patients based on BMI. Mortality was calculated by a Cox proportional hazard model, adjusted for potential confounders with propensity score matched analysis. RESULTS: A total of 70,886,775 patients were registered in NRD during the study period. Of these, 7,786,913 patients (11.0%) were considered obese and 1,652,456 patients (2.3%) were admitted to the hospital with pneumonia. Based on the step-wise restricted cohort model, the hazard ratio comparing the mortality rates among obese pneumonia patients to mortality rates among normal BMI pneumonia patients was 0.75 (95% CI 0.60-0.94). The propensity score matched analysis estimated a hazard rate of 0.84 (95% CI 0.79-0.90) and the hazard ratio estimated from the dual model was 0.82 (95% CI 0.63-1.07). CONCLUSIONS: With the application of a dual model, there appears to be no significant difference in mortality of obese patients with pneumonia compared to normal BMI patients with pneumonia.