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

Treatment Patterns, Outcomes, and Persistence to Newly Started Heart Failure Medications in Patients with Worsening Heart Failure: A Cohort Study from the United States and Germany

BACKGROUND

Data are limited regarding guideline-directed medical therapy (GDMT) treatment patterns in patients with worsening heart failure (HF).

METHODS

We used administrative claims databases in Germany and the USA to conduct a retrospective cohort study of patients with worsening HF. Two cohorts of patients with prevalent HF and a HF hospitalization (HFH) from 2016 to 2019, alive at discharge (N = 75,140 USA; N = 47,003 Germany) were identified. Index date was the first HFH during the study period. One-year HF rehospitalization and mortality rates were calculated and a composite endpoint of both outcomes assessed using Kaplan-Meier estimation. We evaluated HF medication patterns in the 6 months before and after the index date. New users of a HF medication (at discharge/after index HFH) were followed for 1 year to evaluate persistence (no treatment gaps > 2 months) RESULTS: One-year HF rehospitalization rates were 36.2% (USA) and 47.7% (Germany). One year mortality rates were 30.0% (USA) and 23.0% (Germany), and the composite endpoint (mortality/HF rehospitalization) was reached in 55.1 % (USA) and 56.6% (Germany). Kaplan-Meier plots showed the risk for the composite endpoint was high in the early post discharge period. Comparison of patterns pre- and postindex HFH showed some increase in use of mineralocorticoid receptor antagonists (MRAs), angiotensin receptor-neprilysin inhibitor (ARNI), and triple therapy; use of angiotensin-converting enzyme (ACE) inhibitor/ angiotensin receptor blocker (ARB) plus beta-blockers remained constant/slightly declined; < 20% patients received triple therapy (ACE inhibitor/ARB plus beta-blocker plus MRA). A third of patients were new users; 1 year persistence rates were often low.

CONCLUSIONS

Morbidity, mortality, and rehospitalization risk is high among patients with worsening HF; uptake and continuation of GDMT is suboptimal.

Designing and Implementation of a Digitalized Intersectoral Discharge Management System and Its Effect on Readmissions: Mixed Methods Approach

BACKGROUND

Digital transformation offers new opportunities to improve the exchange of information between different health care providers, including inpatient, outpatient and care facilities. As information is especially at risk of being lost when a patient is discharged from a hospital, digital transformation offers great opportunities to improve intersectoral discharge management. However, most strategies for improvement have focused on structures within the hospital.

OBJECTIVE

This study aims to evaluate the implementation of a digitalized discharge management system, the project "Optimizing instersectoral discharge management" (SEKMA, derived from the German Sektorübergreifende Optimierung des Entlassmanagements), and its impact on the readmission rate.

METHODS

A mixed methods design was used to evaluate the implementation of a digitalized discharge management system and its impact on the readmission rate. After the implementation, the congruence between the planned (logic model) and the actual intervention was evaluated using a fidelity analysis. Finally, bivariate and multivariate analyses were used to evaluate the effectiveness of the implementation on the readmission rate. For this purpose, a difference-in-difference approach was adopted based on routine data of hospital admissions between April 2019 and August 2019 and between April 2022 and August 2022. The department of vascular surgery served as the intervention group, in which the optimized discharge management was implemented in April 2022. The departments of internal medicine and cardiology formed the control group.

RESULTS

Overall, 26 interviews were conducted, and we explored 21 determinants, which can be categorized into 3 groups: "optimization potential," "barriers," and "enablers." On the basis of these results, 19 strategies were developed to address the determinants, including a lack of networking among health care providers, digital information transmission, and user-unfriendliness. On the basis of these strategies, which were prioritized by 11 hospital physicians, a logic model was formulated. Of the 19 strategies, 7 (37%; eg, electronic discharge letter, providing mobile devices to the hospital's social service, and generating individual medication plans in the format of the national medication plan) have been implemented in SEKMA. A survey on the fidelity of the application of the implemented strategies showed that 3 of these strategies were not yet widely applied. No significant effect of SEKMA on readmissions was observed in the routine data of 14,854 hospital admissions (P=.20).

CONCLUSIONS

This study demonstrates the potential of optimizing intersectoral collaboration for patient care. Although a significant effect of SEKMA on readmissions has not yet been observed, creating a digital ecosystem that connects different health care providers seems to be a promising approach to ensure secure and fast networking of the sectors. The described intersectoral optimization of discharge management provides a structured template for the implementation of a similar local digital care networking infrastructure in other care regions in Germany and other countries with a similarly fragmented health care system.

Oral anticoagulation in heart failure complicated by atrial fibrillation: A nationwide routine data study

BACKGROUND

This nationwide routine data analysis evaluates if oral anticoagulant (OAC) use in patients with heart failure (HF) and atrial fibrillation (AF) leads to a lower mortality and reduced readmission rate. Superiority of new oral anticoagulants (NOACs), compared to vitamin K antagonists (VKA), was analyzed for these endpoints.

METHODS

Anonymous data of patients with a health insurance at the Allgemeine Ortskrankenkasse and a claims record for hospitalization with the main diagnosis of HF and secondary diagnosis of AF (2017-2019) were included. A hospital stay in the previous year was an exclusion criterion. Mortality and readmission for all-cause and stroke/intracranial bleeding (ICB) were analyzed 91-365 days after the index hospitalization. Kaplan-Meier survival curves and multivariable Cox regression models were used to evaluate the impact of medication on outcome.

RESULTS

180,316 cases were included [81 years (IQR 76-86), 55.6% female, CHA2DS2-VASc score ≥ 2 (96.81%)]. In 80.6%, OACs were prescribed (VKA: 21.7%; direct factor Xa inhibitors (FXaI): 60.0%; direct thrombin inhibitors (DTI): 3.4%; with multiple prescriptions per patient included). Mortality rate was 19.1%, readmission rate was 29.9% and stroke/ICB occurred in 1.9%. Risk of death was lower with any OAC (HR 0.77, 95% CI [0.75-0.79]) but without significant differences in OAC type (VKA: HR 0.73, [0.71-0.76]; FXaI: HR 0.77, [0.75-0.78]; DTI: HR 0.71, [0.66-0.77]). The total readmission rate (HR 0.97, [0.94 to 0.99]) and readmission for stroke/ICB (HR 0.71, [0.65-0.77]) was lower with OAC.

CONCLUSIONS

Nationwide data confirm a reduction in mortality and readmission rate in HF-AF patients taking OACs, without NOAC superiority.

Measuring transitional patient safety: Adaptation and validation of the German version of the Care Transitions Measure

INTRODUCTION

The transition between different care contexts, especially during discharge from inpatient treatment to home, is associated with risks for patient safety. Internationally established, the Care Transitions Measure (CTM) is used to assess the quality and safety of this transition from the patients' perspective. A systematic and standardized assessment of quality and safety in the discharge process from the patients' perspective has not been possible in German-speaking countries due to the lack of a German adaptation and validation of the CTM. This study aims to translate, adapt, and validate the CTM for use in German-speaking countries METHODS: The German version of the CTM was developed based on internationally accepted recommendations for translating and adapting questionnaires. Patients of all departments (except pediatric departments) of a German university hospital who were discharged home after at least three days of inpatient treatment received the questionnaire by mail between May and August 2022. A total of 806 patients participated in the survey. The validity of the CTM was tested by factor analyses. For this purpose, different factor models were compared. In addition, the measurement invariance of the instrument was examined.

RESULTS

The construct validity of the long version of the CTM (15items) with a two-factorial model structure was confirmed with good model fit indices. The two subscales had excellent internal consistency. In addition, the one short version with four items achieved excellent model fit indices and high internal consistency. For the long version of the CTM, measurement invariance was confirmed for all sociodemographic, care-related, and survey response characteristics examined. The measurement invariance of the short version was only partially confirmed.

DISCUSSION

The validity and reliability of the German version of the CTM were confirmed. In its long version, the instrument is measurement invariant across various characteristics and thus allows valid interpretation of group differences. The short version is partially measurement invariant and is suitable as a screening instrument for assessing the quality and safety of discharge processes due to its high validity and reliability.

CONCLUSIONS

With a validated and standardized German version of the CTM, an instrument is now available to assess the quality and safety of the discharge process from the patients' perspective. Thus, this study provides an essential tool for systematically investigating and optimizing patient safety in the discharge process.

[Interventions to reduce hospital readmission rates in patients with COPD: a systematic review]

BACKGROUND

Patients with chronic obstructive pulmonary disease (COPD) have a high risk of hospital and intensive care unit readmission. Readmissions are a severe burden on patients, families, and the health care system. The aim of this study is to identify pedagogical-counseling interventions to reduce readmissions and other parameters in COPD patients.

METHODS

A systematic literature search was performed in March 2022 in the databases Medline, Cochrane Library, CINAHL, and LIVIVO. German‑, English‑, Arabic-, and French-language (cluster-) randomized, controlled studies were included.

RESULTS

In all, 21 studies with a total of 3894 COPD patients were included. The quality of included studies was moderate to good. Interventions were self-management programs, telemedical, and educational interventions. Five out of seven studies found a significant reduction in readmissions for self-management programs (p = 0.02-0.49). A positive effect of telemedicine interventions on outcome parameters was only reported in two studies (p < 0.05) and no significant effect in four studies. Educational interventions were examined in six studies: four found no difference between the study groups and two found a significant difference in favor of the intervention group (p = 0.01). Special care programs also showed a significant effect in two studies.

CONCLUSION

Association between Metabolic Obesity Phenotypes and the Burden of Hospitalized Postmenopausal Patients Concomitant with Osteoporosis: A Retrospective Cohort Study Based on the National Readmission Database

BACKGROUND

The present definition of obesity based on body mass index (BMI) is not accurate and effective enough to identify hospitalized patients with a heavier burden, especially for postmenopausal hospitalized patients concomitant with osteoporosis. The link between common concomitant disorders of major chronic diseases such as osteoporosis, obesity, and metabolic syndrome (MS) remains unclear. Here, we aim to evaluate the impact of different metabolic obesity phenotypes on the burden of postmenopausal hospitalized patients concomitant with osteoporosis in view of unplanned readmissions.

METHODS

Data was acquired from the National Readmission Database 2018. The study population was classified into metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) patients. We estimated the associations between metabolic obesity phenotypes and 30- and 90-day unplanned readmissions. A multivariate Cox Proportional Hazards (PH) model was used to assess the effect of factors on endpoints, with results expressed as HR and 95% CI.

RESULTS

The 30-day and 90-day readmission rates for the MUNO and MUO phenotypes were higher than that of the MHNO group (all p < 0.05), whereas no significant difference was found between the MHNO and MHO groups. For 30-day readmissions, MUNO raised the risk mildly (hazard ratio [HR] = 1.110, p < 0.001), MHO had a higher risk (HR = 1.145, p = 0.002), and MUO further elevated this risk (HR = 1.238, p < 0.001). As for 90-day readmissions, both MUNO and MHO raised the risk slightly (HR = 1.134, p < 0.001; HR = 1.093, p = 0.014, respectively), and MUO had the highest risk (HR = 1.263, p < 0.001).

CONCLUSIONS

Metabolic abnormalities were associated with elevated rates and risks of 30- or 90-day readmission among postmenopausal hospitalized women complicated with osteoporosis, whereas obesity did not seem to be innocent, and the combination of these factors led to an additional burden on healthcare systems and individuals. These findings indicate that clinicians and researchers should focus not only on weight management but also metabolism intervention among patients with postmenopausal osteoporosis.

A prediction model for nonpersistence or nonadherence to direct oral anticoagulants in hospitalized patients with atrial fibrillation

BACKGROUND: Medication adherence and persistence is fundamental for drug effectiveness, which is also true for the prevention of strokes in patients with atrial fibrillation (AF). Adherence to direct oral anticoagulants (DOACs) as first-line agents is often high in the early posthospital period. However, adherence often sharply declines (or eventually leads to nonpersistence) in the post-discharge ambulatory period, rendering stroke prevention ineffective. If patients at high risk of nonpersistence or nonadherence could be identified early, they could be offered early intervention measures to improve adherence and/or persistence. OBJECTIVE: To develop and internally validate a predictive model for medication nonadherence and nonpersistence to DOAC treatment in patients with AF after discharge using health insurance claims data. METHODS: We selected health insurance claims data between 2011 and 2016 from 8,055 patients with AF who were newly treated with rivaroxaban or apixaban after a hospital admission for stroke, transient ischemic attack, or AF. In the post-discharge ambulatory period, medication adherence was derived as the proportion of days covered, calculated from drug dispensation data. A maximum permissible 90-day gap between the end of a prescription and the next dispensation was used to estimate persistence. Candidate predictors were either derived from the index hospital admission or summarized from the previous year (eg, comorbidities or medication adherence to long-term treatments, such as ß-blockers, renin-angiotensin system inhibitors, statins, and thyroid hormones). A regularized logistic regression model was fitted using the least absolute shrinkage and selection operator in a split-sample approach (66.7% training data; 33.3% test data) to predict a composite of medication nonadherence/nonpersistence. Discrimination performance was assessed using the area under the receiver operating characteristic curve, the maximum sensitivity/specificity, and the scaled Brier score. A calibration curve fitted by linear regression was used to evaluate model calibration. RESULTS: The average age of the study participants was 79.7 years, 62% were female, and 3,515 patients (44%) were adherent and persistent (median follow-up of 185 days). Medication adherence to previous long-term treatments showed strong predictive properties. The developed model discriminated well (concordance statistic: 0.755), was well calibrated, and showed a scaled Brier score of 0.202 for identification of patients at risk. CONCLUSIONS: The model successfully predicted medication non-adherence/nonpersistence to DOAC treatment after discharge. Such a model could help ensure that targeted interventions are already in place at the time of hospital discharge, potentially preventing strokes and reducing costs. DISCLOSURES: Mr Wirbka is funded by the German Innovation Funds according to § 92a (2) Volume V of the Social Insurance Code (§ 92a Abs. 2, SGBV-Fünftes Buch Sozialgesetzbuch), grant number: 01VSF18019. Dr Haefeli received financial support from Daiichi-Sankyo, app development (https://www.easydoac.de/), and Bayer. He also received personal speaker fees from Bristol Myers-Squibb and Daiichi-Sankyo Online Seminar. Dr Meid is funded by the Physician-Scientist Programme of the Medical Faculty of Heidelberg University.

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

BACKGROUND

 Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks.

OBJECTIVES

 To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data.

METHODS

 In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions 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 (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined ("stacked") to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics.

RESULTS

 While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66-0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64-0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63-0.66]), HF (c-statistics: 0.61 [95% CI: 0.60-0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56-0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47-0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63-0.64].

CONCLUSION

 PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.