Opioid use is associated with unintentional and intentional overdose and is one of the leading causes of emergency room visits and accidental deaths. However, the association between opioid abuse/dependence and outcomes in hospitalized patients has not been well studied. Congestive heart failure (HF) is the fourth most common cause of hospitalization in the United States. The purpose of this study was to examine the effect of opioid abuse/dependence on outcomes in patients hospitalized with HF. We queried the 2002-2010 Nationwide Inpatient Sample databases to identify all patients aged 18 years and older admitted with the primary diagnosis of HF. Multivariate logistic regression analysis was used to compare the frequency of hospital-acquired conditions (HACs) and in-hospital mortality between patients with and without a history of opioid abuse/dependence. Of 9,993,240 patients with HF, 29,014 had a history of opioid abuse or dependence. Opioid abusers/dependents were likely to be younger men of poor socioeconomic background with self pay or Medicaid as their primary payer. They had a lower prevalence of dyslipidemia, diabetes mellitus, coronary artery disease, prior myocardial infarction, and peripheral vascular disease (P < 0.001 for all). They were more likely to be smokers and have chronic pulmonary disease, depression, liver disease, and obesity (P < 0.001 for all). Patients with a history of opioid abuse/dependence had lower incidence of HACs (14.8% vs. 16.5%, adjusted odds ratio: 0.71, P < 0.001) and lower in-hospital mortality (1.3% vs. 3.6%, adjusted odds ratio: 0.64, P < 0.001) as compared with patients without prior opioid abuse/dependence. In conclusion, among adult patients aged 18 years and older hospitalized with HF, opioid abuse/dependence was associated with lower frequency of HACs and lower in-hospital mortality.

No abstract

IntroductionThe association of chest pain versus dyspnea with demographics, coronary angiographic findings, and outcomes of patients undergoing coronary angiography is unknown.Material and methodsWe studied 1,053 patients who had coronary angiography to investigate the association of chest pain versus dyspnea with demographics, coronary angiographic findings, and outcomes.ResultsOf 1,053 patients, 654 (62%) had chest pain, 229 (22%) had dyspnea, and 117 (11%) had chest pain and dyspnea. Patients with dyspnea were older (p < 0.0001) and had higher serum creatinine (p = 0.0011), lower left ventricular ejection fraction (LVEF) (p < 0.0001), more cardiogenic shock (p = 0.0004), less obstructive coronary artery disease (CAD) (p < 0.0001), less percutaneous coronary intervention (p < 0.0001), and similar 2-year mortality. Stepwise Cox regression analysis showed no significant difference in mortality between chest pain and dyspnea. Significant risk factors for time to death were age (hazard ratio (HR) = 1.07, p < 0.0001), serum creatinine (HR = 1.5, p < 0.0001), body mass index (HR = 0.93, p = 0.005), and obstructive CAD graft (HR = 3.2, p = 0.011).ConclusionsPatients undergoing coronary angiography presenting with dyspnea were older and had higher serum creatinine, lower LVEF, more frequent cardiogenic shock, less obstructive CAD, and less percutaneous coronary intervention compared to patients presenting with chest pain but similar 2-year mortality.

BACKGROUND: Although many predictive models have been developed to risk assess medical intensive care unit (MICU) readmissions, they tend to be cumbersome with complex calculations that are not efficient for a clinician planning a MICU discharge. OBJECTIVE: To develop a simple scoring tool that comprehensively takes into account not only patient factors but also system and process factors in a single model to predict MICU readmissions. DESIGN: Retrospective chart review. PARTICIPANTS: We included all patients admitted to the MICU of Robert Wood Johnson University Hospital, a tertiary care center, between June 2016 and May 2017 except those who were < 18 years of age, pregnant, or planned for hospice care at discharge. MAIN MEASURES: Logistic regression models and a scoring tool for MICU readmissions were developed on a training set of 409 patients, and validated in an independent set of 474 patients. KEY RESULTS: Readmission rate in the training and validation sets were 8.8% and 9.1% respectively. The scoring tool derived from the training dataset included the following variables: MICU admission diagnosis of sepsis, intubation during MICU stay, duration of mechanical ventilation, tracheostomy during MICU stay, non-emergency department admission source to MICU, weekend MICU discharge, and length of stay in the MICU. The area under the curve of the scoring tool on the validation dataset was 0.76 (95% CI, 0.68-0.84), and the model fit the data well (Hosmer-Lemeshow p = 0.644). Readmission rate was 3.95% among cases in the lowest scoring range and 50% in the highest scoring range. CONCLUSION: We developed a simple seven-variable scoring tool that can be used by clinicians at MICU discharge to efficiently assess a patient's risk of MICU readmission. Additionally, this is one of the first studies to show an association between MICU admission diagnosis of sepsis and MICU readmissions.