Clinical Characteristics and Laboratory Biomarkers for Patients with Suspected COVID-19 Infection Within HCA Healthcare.

Background: The coronavirus infection (COVID-19), also known as the Severe Acute Respiratory Syndrome Virus 2 (SARS-CoV-2), caused significant illness and a worldwide pandemic beginning in 2020. Early case reports showed common patient characteristics, clinical variables and laboratory values in these patients. We compared a large population of American COVID-19 patients to see if they had similar findings to these smaller reports. In addition, we examined our population to identify any differences between mild or severe COVID-19 infections. Methods: We retrospectively accessed a de-identified, multi-hospital database managed by HCA Healthcare to identify all adult emergency department (ED) patients that were tested for COVID-19 from January 1st, 2020-April 30th, 2020. We collected clinical variables, comorbidities and laboratory values to identify any differences in those with or without a SARS-CoV-2 infection. Results: We identified 44,807 patients who were tested for SARS-CoV-2. Of those patients, 6,158 were positive for COVID-19. Male patients were more likely to test positive than female ones (15.0% vs. 12.6%, p < 0.001). The most frequently positive tests occurred in age groups 40-49, 50-59 and 60-69 (16.9%, 15.3% and 14.1% respectively). Both African Americans (20.2%) and Hispanics (20.8%) were more likely to test positive than Caucasians (8.3%, p < 0.001). Hypertension and diabetes were more common in those with positive tests, and multiple laboratory biomarkers showed significant differences in severe infections. Conclusions: This broad cohort of American COVID-19 patients showed similar trends in gender, age groups and race/ethnicity as previously reported. Severe COVID-19 disease was also associated with many positive laboratory biomarkers.

Analysis of Emergency Department Utilization in Medicaid Expansion and Non-expansion States.

Introduction The Affordable Care Act has been debated since its initial enactment over a decade ago. One of the primary topics for discussion has been Medicaid expansion, which has created a schism across the United States. The effects of Medicaid expansion largely remain unclear. The purpose of this report is to elucidate how Medicaid expansion has impacted emergency department (ED) utilization by analyzing Medicaid expansion and non-expansion states to determine who visited the ED and the reason for the visit. Methods We conducted a retrospective analysis using de-identified electronic medical record (EMR) data from 56,423 patients and 33 different hospitals (18 Medicaid non-expansion and 15 Medicaid expansion) who visited the ED in 2019. We used geographical demographics and insurance status to categorize patients who visited the ED and ambulatory care sensitive conditions (ACSC) to identify the reasons for the visit. Logistic regression and chi-square analysis were used to analyze the data. Results We observed a significant relationship between Medicaid expansion and geographic region such that patients living in rural or semirural regions likely resided in Medicaid non-expansion states. Patients using self-pay were more likely to live in a Medicaid non-expansion state than a Medicaid expansion state (32.3% vs. 21.5%, p-value < 0.0001). Finally, there were no significant differences between the top five ACSC for Medicaid expansion and Medicaid non-expansion states but living in an expansion state was significantly (p < 0.01) related to being diagnosed with an ACSC (OR, 1.056; 95% CI, 1.013-1.100). Conclusion In conclusion, Medicaid expansion was associated with differences in the use of medical resources. Patients using Medicaid insurance who reside in Medicaid expansion states preferentially use the ED. Geographical location does play a role in ED utilization and ambulatory care sensitive condition diagnoses in patients. Despite these findings, the full effects of Medicaid expansion on ED utilization require further investigation. However, our research indicates that Medicaid expansion is not the singular solution in decreasing ED utilization and healthcare costs.

Effect of initiation of medications for opioid use disorder on hospitalization outcomes for endocarditis and osteomyelitis in a large private hospital system in the United States, 2014-18.

BACKGROUND AND AIMS: Opioid use disorder (OUD) has led to not only increases in overdose deaths, but also increases in endocarditis and osteomyelitis secondary to injection drug use (IDU). We studied the association between initiation of medications for opioid use disorder (MOUD) and treatment outcomes for people with infectious sequelae of IDU and OUD. DESIGN AND SETTING: This is a retrospective cohort study reviewing encounters at 143 HCA Healthcare hospitals across 21 states of the United States from 2014 to 2018. PARTICIPANTS: Adults aged 18-65 with the ICD diagnosis code for OUD and endocarditis or osteomyelitis (n = 1407). MEASUREMENTS: Main exposure was the initiation of MOUD, defined as either methadone or buprenorphine at any dosage started during hospitalization. Primary outcomes were defined as patient-directed discharge (PDD), 30-day re-admission and days of intravenous antibiotic treatment. Covariates included biological sex, age, ethnicity, other co-occurring substance use disorders, and insurance status. FINDINGS: MOUD was initiated among 269 (19.1%) patients during hospitalization. Initiation of MOUD was not associated with decreased odds of PDD. Initiation of MOUD did not impact 30-day re-admission. Patients who received MOUD, on average, had 5.7 additional days of gold-standard intravenous antibiotic treatment compared with those who did not [ß = 5.678, 95% confidence interval (CI) = 3.563, 7.794), P < 0.05]. CONCLUSION: For people with opioid use disorder hospitalized with endocarditis or osteomyelitis, initiation of methadone or buprenorphine appears to be associated with improved receipt of gold-standard therapy, as quantified by increased days on intravenous antibiotic treatment.

Introduction to Research Statistical Analysis: An Overview of the Basics.

Description This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.

Demographics and Length of Stay for Osteomyelitis in Opioid Drug Users: A Unique Population with High Healthcare Costs.

Osteomyelitis is an infection of the bone. Risk factors include, but are not limited to, diabetes and intravenous drug use. The hypotheses for the primary objectives of this study were that the opioid epidemic would cause a younger population of patients to be seen with osteomyelitis and the treatment for this population has special considerations including longer hospitalization for proper intravenous antibiotics. This retrospective chart review compared 2,150 cases of osteomyelitis in the Hospital Corporation of America (HCA Healthcare) West Florida Division. A sample group of osteomyelitis with diabetes was compared to a group with reported opioid use. The results showed a significantly younger age at which the osteomyelitis was occurring in opioid drug users and a significantly longer hospitalization for the treatment. With the rising costs of healthcare and the continuing growth of drug abuse, the 11.501-year younger age difference (95% confidence interval [9.204, 13.799], p-value <0.001) and 4.992-day longer hospitalization (95% confidence interval [3.053, 6.931], p-value <.001) can raise awareness on an additional impact of drug abuse on healthcare costs.