Recent trends in the prescription of opioids in the emergency department in patients with urolithiasis.

PURPOSE: Pain management is central in the treatment of urolithiasis. We aimed to estimate the impact of the 2017 Department of Health and Human Services declaration of an opioid crisis on prescribing patterns of opioids and NSAIDs in emergency department visits for urolithiasis. METHODS: The National Health Ambulatory Medical Care Survey (NHAMCS) was queried for emergency department visits of adults with a diagnosis of urolithiasis. The association between urolithiasis and narcotic and NSAIDs prescription patterns was evaluated and compared at pre-declaration (2014-2016) to post-declaration (2017-2018) periods. RESULTS: Opioids were prescribed in about 211 million (41.1%) out of 513 million emergency department visits, over a 5-year period. Diagnosis of urolithiasis accounted for 1.9% of the visits (6.0 million). The use of opioids was higher in urolithiasis (82.7%) compared to non-urolithiasis diagnosis (40.3%), as well as the use of multiple opioids per visit (p < 0.01 for all). There was an overall decrease in opioid prescriptions in the post-declaration period, - 4.3% for urolithiasis (p = 0.254) and - 5.6% for non-urolithiasis visits (p < 0.05). A decrease in the use of hydromorphone (- 47.5%. p < 0.001), an increase in the use of morphine (+ 59.7% p = 0.006), and an increase of 'other' opioids (+ 98.8%, p < 0.041), were observed. Opioids combined with NSAIDs comprised 72.6% of the opioid prescriptions and 62.3% of all analgesic prescriptions in visits with urolithiasis diagnosis. CONCLUSIONS: The use of opioids when managing urolithiasis decreased 4.3% after the crisis declaration; however, statistically are not different from pre-declaration numbers. Most often, opioids were prescribed with NSAIDs in urolithiasis patients.

External validation of the COVID-19 4C mortality score in an urban United States cohort.

BACKGROUND: Identifying patients at risk for mortality from COVID-19 is crucial to triage, clinical decision-making, and the allocation of scarce hospital resources. The 4C Mortality Score effectively predicts COVID-19 mortality, but it has not been validated in a United States (U.S.) population. The purpose of this study is to determine whether the 4C Mortality Score accurately predicts COVID-19 mortality in an urban U.S. adult inpatient population. METHODS: This retrospective cohort study included adult patients admitted to a single-center, tertiary care hospital (Philadelphia, PA) with a positive SARS-CoV-2 PCR from 3/01/2020 to 6/06/2020. Variables were extracted through a combination of automated export and manual chart review. The outcome of interest was mortality during hospital admission or within 30 days of discharge. RESULTS: This study included 426 patients; mean age was 64.4 years, 43.4% were female, and 54.5% self-identified as Black or African American. All-cause mortality was observed in 71 patients (16.7%). The area under the receiver operator characteristic curve of the 4C Mortality Score was 0.85 (95% confidence interval, 0.79-0.89). CONCLUSIONS: Clinicians may use the 4C Mortality Score in an urban, majority Black, U.S. inpatient population. The derivation and validation cohorts were treated in the pre-vaccine era so the 4C Score may over-predict mortality in current patient populations. With stubbornly high inpatient mortality rates, however, the 4C Score remains one of the best tools available to date to inform thoughtful triage and treatment allocation.

Association of race/ethnicity and socioeconomic status with COVID-19 30-day mortality at a Philadelphia medical center using a retrospective cohort study.

COVID-19 has disproportionately affected low-income communities and people of color. Previous studies demonstrated that race/ethnicity and socioeconomic status (SES) are not independently correlated with COVID-19 mortality. The purpose of our study is to determine the effect of race/ethnicity and SES on COVID-19 30-day mortality in a diverse, Philadelphian population. This is a retrospective cohort study in a single-center tertiary care hospital in Philadelphia, PA. The study includes adult patients hospitalized with polymerase-chain-reaction-confirmed COVID-19 between March 1, 2020 and June 6, 2020. The primary outcome was a composite of COVID-19 death or hospice discharge within 30 days of discharge. The secondary outcome was intensive care unit (ICU) admission. The study included 426 patients: 16.7% died, 3.3% were discharged to hospice, and 20.0% were admitted to the ICU. Using multivariable analysis, race/ethnicity was not associated with the primary nor secondary outcome. In Model 4, age greater than 75 (odds ratio [OR]: 11.01; 95% confidence interval [CI]: 1.96-61.97) and renal disease (OR: 2.78; 95% CI: 1.31-5.90) were associated with higher odds of the composite primary outcome. Living in a "very-low-income area" (OR: 0.29; 95% CI: 0.12-0.71) and body mass index (BMI) 30-35 (OR: 0.24; 95% CI: 0.08-0.69) were associated with lower odds of the primary outcome. When controlling for demographics, SES, and comorbidities, race/ethnicity was not independently associated with the composite primary outcome. Very-low SES, as extrapolated from census-tract-level income data, was associated with lower odds of the composite primary outcome.

Comparing reliability of ICD-10-based COVID-19 comorbidity data to manual chart review, a retrospective cross-sectional study.

International Statistical Classification of Disease and Related Health Problems, 10th Revision codes (ICD-10) are used to characterize cohort comorbidities. Recent literature does not demonstrate standardized extraction methods. OBJECTIVE: Compare COVID-19 cohort manual-chart-review and ICD-10-based comorbidity data; characterize the accuracy of different methods of extracting ICD-10-code-based comorbidity, including the temporal accuracy with respect to critical time points such as day of admission. DESIGN: Retrospective cross-sectional study. MEASUREMENTS: ICD-10-based-data performance characteristics relative to manual-chart-review. RESULTS: Discharge billing diagnoses had a sensitivity of 0.82 (95% confidence interval [CI]: 0.79-0.85; comorbidity range: 0.35-0.96). The past medical history table had a sensitivity of 0.72 (95% CI: 0.69-0.76; range: 0.44-0.87). The active problem list had a sensitivity of 0.67 (95% CI: 0.63-0.71; range: 0.47-0.71). On day of admission, the active problem list had a sensitivity of 0.58 (95% CI: 0.54-0.63; range: 0.30-0.68)and past medical history table had a sensitivity of 0.48 (95% CI: 0.43-0.53; range: 0.30-0.56). CONCLUSIONS AND RELEVANCE: ICD-10-based comorbidity data performance varies depending on comorbidity, data source, and time of retrieval; there are notable opportunities for improvement. Future researchers should clearly outline comorbidity data source and validate against manual-chart-review.

Public exposure and attitudes about homelessness.

In this study, we conducted a survey among a large sample of U.S. adults to assess attitudes and beliefs about the causes of homelessness, policies to address homelessness, and programs for homeless individuals. In 2016, we surveyed a national sample of 541 adults from 47 different U.S. states using Amazon Mechanical Turk. Of the total sample, 78% reported that homelessness was a problem in their communities and 60% believed homelessness would increase in the next 5 years. The majority expressed compassion for homeless individuals and endorsed structural, intrinsic, and health factors as causes of homelessness. Most participants (73%-88%) believed the federal government should dedicate more funds and policies for homeless individuals. These attitudes were substantially more likely to be reported by participants who were female, lower income, Democrat, and personally exposed to homelessness. Most Americans care about homelessness as a major problem but there are divergent perspectives on solutions to address homelessness based on gender, income level, and political affiliation.