Sleep Disordered Breathing in Hospitalized African-Americans

Factors impacting sleep center no-show rates after hospital discharge using geospatial coding in Appalachia

STUDY OBJECTIVES

Screening for early detection of sleep-disordered breathing in hospitalized patients has been shown to reduce readmission rates. However, postdischarge polysomnography for confirmation of diagnosis is required. We analyzed factors for "no-shows" using geospatial techniques.

METHODS

Data were obtained between September 2019 and September 2023. The outcome for the study was patients' no-show rate (nonadherent for polysomnography) after hospital discharge. Predictors included the patient's age, sex, body mass index, health literacy, Distressed Communities Index score, and distance to a sleep center for the patient's zip code of residence. Logistic regression was applied to estimate odds of patients' adherence at the patient level using a geospatial mapping technique. Geographically weighted logistic regression was applied to estimate the odds of a zip code's including adherent patients.

RESULTS

Of the 1,318 hospitalized patients established as high-risk for sleep-disordered breathing and referred for an overnight sleep study who were able to be geocoded, 228 were adherent and 1,130 were nonadherent. In nonspatial regression analyses, health literacy (adjusted odds ratio = 1.06; 95% confidence interval = 1.03, 1.09), age (adjusted odds ratio = 0.99; 95% confidence interval = 0.98, 0.99), and drive time (adjusted odds ratio = 0.95; 95% confidence interval = 0.92, 0.97) were identified as statistically significant predictors of patients' adherence. Spatial regression analyses identified areas that had high and low predictive probability of patients' adherence, as well as which community-level factors were co-occurring in those areas.

CONCLUSIONS

The findings suggest that both patient-level factors and the community where patients live may impact no-show rates. Health literacy was identified as a key modifiable predictor at the patient level. At the community level, we found that predicted probability of patient adherence varied throughout the state. Efforts should focus on enhancing patients' education at the individual level and understanding geographical factors to improve adherence.

CITATION

Sharma S, Stansbury R, Rojas E, et al. Factors impacting sleep center no-show rates after hospital discharge using geospatial coding in Appalachia. J Clin Sleep Med. 2025;21(4):667-674.

The Emergence of Inpatient Sleep Medicine: Screening for Sleep Disordered Breathing to Reduce the Burden of Cardiovascular Disease

Purpose of Review

Treatment of obstructive sleep apnea (OSA) has historically been centered on outpatients given sleep testing is performed on an outpatient basis. Much of this practice originates from insurers only covering sleep testing on an outpatient basis. Over the last decade, there have been innovations made in the portability of sleep monitors which have allowed sleep testing on inpatients to be facilitated. There is also emerging data that inpatient sleep testing may reduce readmissions and healthcare costs in certain cardiovascular conditions. Accordingly, this review aims to provide comprehensive coverage of recent advances in the practice of inpatient sleep medicine and its effect on reducing the burden of cardiovascular disease.

Recent Findings

Chief cardiovascular diseases that intersect with OSA in inpatients are stroke, atrial fibrillation, and heart failure. There is data from the National Inpatient Sample comparing arrhythmia burdens in patients with OSA and HFpEF showing that OSA patients have higher mortality rates, hospital durations, and medical costs. Also, OSA is associated with higher burdens of arrhythmia. It is currently unknown whether treatment of inpatients with PAP therapy lowers the occurrence of arrhythmias. Recent data suggests that costs for heart failure patients with OSA that are readmitted are higher than those for heart failure patients without OSA. A recent analysis of patients with HFpEF (heart failure with preserved ejection fraction) and OSA showed that the PAP adherent patients had fewer healthcare related costs, lower readmission rates, and fewer emergency room visits than those that were nonadherent. In broader terms, rapid initiation of PAP therapy in a large administration database query of 23 million Medicare patients appears to reduce annual healthcare costs and reduce readmissions although further study is required.

Summary

OSA is globally underdiagnosed, with an estimated one billion individuals affected. OSA's pathogenesis involves a combination of risk factors, such as obesity, age, and increased neck circumference that contribute to fragmented sleep patterns and in turn, numerous cardiovascular comorbidities, such as stroke, atrial fibrillation, and coronary artery disease. Recently, inpatient sleep medicine programs have emerged as a promising avenue for improving diagnosis, patient safety, and potentially reducing readmissions. Integrating inpatient sleep medicine into healthcare systems to address the significant health and economic burden associated with undiagnosed OSA. Improved coverage of inpatient sleep testing and services will be a key driver of addressing inpatient gaps in sleep medicine care. The current research findings provide a bedrock from which further investigations may proceed in a prospective and randomized, controlled fashion to further clarify the effects of treatment of OSA on cardiovascular outcomes of inpatients.

Disease-dependent variations in the timing and causes of readmissions in Germany: A claims data analysis for six different conditions

BACKGROUND

Hospital readmissions place a major burden on patients and health care systems worldwide, but little is known about patterns and timing of readmissions in Germany.

METHODS

We used German health insurance claims (AOK, 2011-2016) of patients ≥ 65 years hospitalized for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack, or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type 2 diabetes mellitus, or osteoporosis to identify hospital readmissions within 30 or 90 days. Readmissions were classified into all-cause, specific, and non-specific and their characteristics were analyzed.

RESULTS

Within 30 and 90 days, about 14-22% and 27-41% index admissions were readmitted for any reason, respectively. HF and S/AF contributed most index cases, and HF and COPD accounted for most all-cause readmissions. Distributions and ratios of specific to non-specific readmissions were disease-specific with highest specific readmissions rates among COPD and AMI.

CONCLUSION

German claims are well-suited to investigate readmission causes if longer periods than 30 days are evaluated. Conditions closely related with the primary disease are the most frequent readmission causes, but multiple comorbidities among readmitted cases suggest that a multidisciplinary care approach should be implemented vigorously addressing comorbidities already during the index hospitalization.