Thirty-day rehospitalizations among elderly patients with acute myocardial infarction: Impact of postdischarge ambulatory care

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.

Hospitalization for physical child abuse: Associated medical factors and medical history since birth

BACKGROUND

Physical abuse often begins at a very young age and sometimes results in serious or fatal injuries. It is crucial to diagnose physical abuse as early as possible to protect this vulnerable population.

OBJECTIVE

To study the factors associated with the first hospitalization for physical abuse from birth to the infant's first birthday in France.

PARTICIPANTS AND SETTING

We included all singleton children born in a hospital setting in France between 2009 and 2013, who were identified from the French national information system database (SNDS).

METHODS

To study factors associated with the first hospitalization for physical abuse during the first year after birth, we used the Fine and Gray regression model. Factors included in the multivariate model were the infant's sex, prematurity, neonatal conditions, the number of hospitalizations (at least two), medical consultations and complementary universal health insurance (proxy for family precariousness and socio-economic vulnerability).

RESULTS

Over the 2009-2013 period, among 3,432,921 newborn singletons, 903 (0.026 %) were hospitalized for physical abuse in the year following birth. Among the factors associated with physical abuse, such as prematurity (aHR = 2.2[1.8-2.7]), male sex (aHR = 1.3[1.2-1.5]), or having had at least two hospitalizations (aHR = 1.7[1.4-2.1]), we found that complementary universal health insurance coverage was the factor most associated (aHR = 4.1[3.5-4.7]) with being hospitalized for physical abuse.

CONCLUSION

These findings could help introduce preventative measures for infant protection in certain groups, such as parents of infants born prematurely, especially if they are in a precarious situation. This study also suggests that particular attention should be paid to infants who have been hospitalized at least two times in the first year of life, whatever the reason.

Rates and predictors of 30-day hospital readmissions in adults for drug-induced acute pancreatitis: A retrospective study from the United States National Readmission Database

BACKGROUND AND AIM

Drug-induced acute pancreatitis (DIAP) linked to several medications is a diagnosis of exclusion and is associated with significant morbidity and mortality, contributing to the US healthcare cost burden. Existing studies on DIAP focus on the drug classes that can cause acute pancreatitis. Hence, our retrospective study aims to determine the rates and predictors for 30-day readmissions (30-DR) in patients with index hospitalization for DIAP.

METHODS

From the Nationwide Readmissions Database, we followed adults admitted for DIAP who were discharged alive for 30 days. During 30-DR, we evaluated the rates, predictors, and outcomes of DIAP.

RESULTS

Of the 4457 DIAP patients surviving at discharge, 12.5% were readmitted at 30 days. During readmissions, the predictors of 30-DR for DIAP were young age, the Charlson-Deyo Comorbidity Index of 2 and 3, protein-energy malnutrition, and dyslipidemia. During 30-DR, DIAP had a higher mortality rate (2.4% vs. 0.7%; P < 0.020), extended hospital stays (5.6 days vs. 4 days, 0.000), and higher hospital charges ($12 983.6 vs. $8 255.6; P 0.000).

CONCLUSIONS

DIAP has high 30-DR rates and poorer outcomes.

Frequency and Risk Factors of Re-hospitalization in Geriatric Inpatient Wards: A Multicenter Retrospective Analysis

PURPOSE

We aimed to evaluate frequency and risk factors of re-hospitalization which are not stated in comorbidity indexes in geriatric wards.

METHODS

A total of 585 patients who were admitted to tertiary care geriatric inpatient clinics at least once between 1 September 2017 and 1 September 2018 and who survived to discharge during initial hospitalization were included in this cross-sectional retrospective multicenter study.

RESULTS

Overall, 507(86.7%) patients were hospitalized once for treatment during the study period, while re-hospitalization occurred in 78(13.3%) patients. Rates of previous surgery (10.3 vs. 3.0%, p = .006), urinary incontinence (UI) (50.0 vs. 36.3%, p = .021), controlled hypertension (64.1 vs. 46.4%, p = .024), malnutrition (55.1 vs. 29.6%, p = .014) were significantly higher in re-hospitalized patients. Re-hospitalized patients were younger (mean ± SD 76.4 ± 8.3 vs. 79.6 ± 7.9 years, p = .002) than once-hospitalized patients. Multivariate logistic regression analysis revealed the younger patient age (OR, 0.942, 95% CI 0.910 to 0.976, p = .001), higher Modified Charlson Comorbidity Index (MCCI) score (OR, 1.368, 95% CI 1.170 to 1.600, p < .001) to significantly predict the increased risk of re-hospitalization.

CONCLUSIONS

Our findings showed that previous history of surgery and geriatric syndromes such as UI, malnutrition were determined to significantly predict the increased risk of re-hospitalization. We suggest that these risk factors be added to prognostic tools designed for elderly patients.

In-hospital mortality and readmission after ST-elevation myocardial infarction in nonagenarians: A nationwide analysis from the United States

OBJECTIVES

To assess readmission rates in nonagenarians (age ≥ 90 years) with ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (pPCI) versus no pPCI.

BACKGROUND

There are limited data exploring readmissions following STEMI in nonagenarians undergoing pPCI versus no pPCI.

METHODS

We retrospectively analyzed the Nationwide Readmissions Database to identify nonagenarians hospitalized with STEMI. We divided the cohort into two groups based on pPCI status. We compared mortality during index hospitalization and during 30-day readmission, readmission rates, and causes of readmissions.

RESULTS

We identified 58,231 nonagenarian STEMI hospitalizations between 2010 and 2018, of which 18,809 (32.3%) included pPCI, and 39,422 (67.7%) had no pPCI. Unadjusted unplanned 30-day readmission was higher in pPCI cohort (21.0% vs. 15.4%, p < 0.001). However, mortality during index hospitalization and during 30-day readmission were significantly lower in pPCI cohort (15.8% vs. 32.2%, p < 0.001; 7.4% vs. 14.2%, p < 0.001, respectively). After adjusting for baseline characteristics, hospitalizations that included pPCI had 25% greater odds of unplanned 30-day readmission (adjusted odds ratio [aOR]: 1.25, 95% confidence interval [CI]: 1.12-1.39, p < 0.001) and 49% lower odds of in-hospital mortality during index hospitalization (aOR: 0.51, 95% CI: 0.46-0.56, p < 0.001). Heart failure was the most common cause of readmission in both cohorts followed by myocardial infarction.

CONCLUSIONS

In nonagenarians with STEMI, pPCI is associated with slightly higher 30-day readmission but significantly lower mortality during index hospitalization and during 30-day readmission than no pPCI. Given the overwhelming mortality benefit with pPCI, further research is necessary to optimize the utilization of pPCI while reducing readmissions following STEMI in nonagenarians.

Factors affecting hospital readmission rates following an acute coronary syndrome: A systematic review

Aim

To synthesise quantitative evidence on factors that impact hospital readmission rates following ACS with comorbidities.

Design

Systematic review and narrative synthesis.

Data sources

A search of eight electronic databases, including Embase, Medline, PsycINFO, Web of Science, CINAHL, Cochrane Library, Scopus and the Joanna Briggs Institute (JBI).

Review methods

The search strategy included keywords and MeSH terms to identify English language studies published between 2001 and 2020. The quality of included studies was assessed by two independent reviewers, using Joanna Briggs Institute (JBI) critical appraisal tools.

Results

Twenty‐four articles were included in the review. All cause 30‐day readmission rate was most frequently reported and ranged from 4.2% to 81%. Reported factors that were associated with readmission varied across studies from socio‐demographic, behavioural factors, comorbidity factors and cardiac factors. Findings from some of the studies were limited by data source, study designs and small sample size.

Conclusion

Strategies that integrate comprehensive discharge planning and individualised care planning to enhance behavioural support are related to a reduction in readmission rates. It is recommended that nurses are supported to influence discharge planning and lead the development of nurse‐led interventions to ensure discharge planning is both coordinated and person‐centred.

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.

Readmission of older acutely admitted medical patients after short-term admissions in Denmark: a nationwide cohort study

Background

Knowledge of unplanned readmission rates and prognostic factors for readmission among older people after early discharge from emergency departments is sparse. The aims of this study were to examine the unplanned readmission rate among older patients after short-term admission, and to examine risk factors for readmission including demographic factors, comorbidity and admission diagnoses.

Methods

This cohort study included all medical patients aged ≥65 years acutely admitted to Danish hospitals between 1 January 2013 and 30 June 2014 and surviving a hospital stay of ≤24 h. Data on readmission within 30 days, comorbidity, demographic factors, discharge diagnoses and mortality were obtained from the Danish National Registry of Patients and the Danish Civil Registration System. We examined risk factors for readmission using a multivariable Cox regression to estimate adjusted hazard ratios (aHR) with 95% confidence intervals (CI) for readmission.

Results

A total of 93,306 patients with a median age of 75 years were acutely admitted and discharged within 24 h, and 18,958 (20.3%; 95% CI 20.1 - 20.6%) were readmitted with a median time to readmission of 8 days (IQR 3 - 16 days). The majority were readmitted with a new diagnosis. Male sex (aHR 1.15; 1.11 - 1.18) and a Charlson Comorbidity Index ≥3 (aHR 2.28; 2.20 - 2.37) were associated with an increased risk of readmission. Discharge diagnoses associated with increased risk of readmission were heart failure (aHR 1.26; 1.12 - 1.41), chronic obstructive pulmonary disease (aHR 1.33; 1.25 - 1.43), dehydration (aHR 1.28; 1.17 - 1.39), constipation (aHR 1.26; 1.14 - 1.39), anemia (aHR 1.45; 1.38 - 1.54), pneumonia (aHR 1.15; 1.06 - 1.25), urinary tract infection (aHR 1.15; 1.07 - 1.24), suspicion of malignancy (aHR 1.51; 1.37 - 1.66), fever (aHR 1.52; 1.33 - 1.73) and abdominal pain (aHR 1.12; 1.05 - 1.19).

Conclusions

One fifth of acutely admitted medical patients aged ≥65 were readmitted within 30 days after early discharge. Male gender, the burden of comorbidity and several primary discharge diagnoses were risk factors for readmission.

Spatial distribution of in- and out-of-hospital mortality one year after acute myocardial infarction in France

OBJECTIVE

To describe the spatial distribution of acute myocardial infarction (AMI) mortality in France in association with the socio-economic characteristics of the patient's place of residence.

METHODS

In this population-based study, we included patients hospitalized for AMI identified according to ICD-10 codes, using data from the national health insurance database from January 1, 2013 to December 31, 2014. In- and out-of-hospital deaths were identified over a period of 1 year following the first hospital stay for AMI.An exploratory analysis was performed to classify area profiles. The spatial analysis of AMI mortality was performed using a principal component analysis followed by an ascending hierarchical classification taking into account socio-economic data, access-time by road to coronary angiography, standardized in-hospital prevalence, and 1 year mortality.

RESULTS

Over the 2 years, 115,418 patients were hospitalized with a diagnosis of AMI. Patients were a mean of 68 ​± ​15 years and most were men (68.5%). The overall mortality rate was 12.2% after 1 year. More than half of patients (65.5%) underwent an early revascularization procedure. The map of standardized 1 year mortality showed a geographic area of high mortality extending diagonally from north-east to south-west France. We identified 6 different area profiles with standardized mortality varying from 15.9 to 54.4 per 100,000 inhabitants. The spatial distribution of higher mortality was associated with lower socioeconomic levels. These findings were not associated with a lower access to coronary angiography.

CONCLUSION

There are considerable geographical differences in the prevalence of AMI and 1 year mortality. The spatial distribution of lower healthcare indicators follows the distribution of social inequalities. This study highlights the importance of focusing national policies on universally accessible prevention programs such as the promotion cardiac rehabilitation and healthy lifestyles.

Hierarchical analysis of factors associated with hospital readmissions for coronary heart disease: A case-control study

AIMS AND OBJECTIVES

To analyse, hierarchically, factors associated with hospital readmissions for acute coronary syndrome.

BACKGROUND

Hospital readmissions have risen, especially in patients with multiple comorbidities, which are most often chronic. The leading causes of hospital readmission include acute coronary syndrome, which is costly and often preventable. Determining clinical and nonclinical variables that increase the chances of readmission is important to assess and evaluate patients hospitalised for coronary heart diseases.

DESIGN

A case-control study whose dependent variable was hospital readmission for acute coronary syndrome.

METHODS

The study included 277 inpatients, of whom 132 were in their first hospitalisation and 145 had already been hospitalised for acute coronary syndrome. The independent variables for this hierarchical model were sociodemographic conditions, life habits, access to health services and physical health measures. Data were obtained by interviews, anthropometric measurements and patient records. Logistic regression analysis was performed using the stepwise technique, with Microsoft Excel and R version 3.2.3. The research was reported via strengthening the reporting of observational studies in epidemiology (STROBE).

RESULTS

In the final hierarchical logistic model, the following risk factors were associated with readmission for acute coronary syndrome: inadequate drug therapy adherence, stress, history of smoking for 30 years or more, and the lack of use of primary healthcare services.

CONCLUSIONS

Clinical and nonclinical variables are related to hospital readmission for acute coronary syndrome and can increase the chance of readmission by up to six times.

RELEVANCE TO CLINICAL PRACTICE

The predictive model can be used to avoid readmission for acute coronary syndrome, and it represents an advance in the prediction of the occurrence of the outcome. This implies the need for a reorientation of the network for postdischarge care in the first hospitalisation for acute coronary syndrome.

The functional status, rehospitalization, and hospital cost reduction in geriatric patients after the implementation of the universal health coverage program in the national referral hospital Indonesia

BACKGROUND Universal health coverage program (UHCP) might implicate negatively toward geriatric care with its impact on higher cost. The evaluation had to be made, especially in functional status, rehospitalization, and cost-effectiveness. METHODS Retrospective cohort study with historical control was done. Seventy two geriatric inpatients in the pre-UHCP group and 86 in the UHCP group were recruited from Cipto Mangunkusumo Hospital, Jakarta, Indonesia. Subjects with geriatric syndromes admitted from July to December 2013 (pre-UHCP era) and January to June 2014 (UHCP era). Functional status changes, rehospitalization, and process indicators were observed. Cost reduction was calculated using the incremental cost-effectiveness ratio (ICER), whereby costs, functional status changes, and rehospitalization of both groups were used to identify the differences. RESULTS Proportions of functional status increase were 35.3% and 34.8% in the pre- UHCP and UHCP groups, respectively (p = 1.00) and the decrease were 5.9% and 4.5% in the pre-UHCP and UHCP group, respectively (p = 1.00). Rehospitalization rates were 21.7% and 18.1% (p = 0.603) in the pre-UHCP and UHCP groups, respectively. Mean hospital expenses were between 17.1 million IDR (1,221 USD; 1 USD = 14,000 IDR) for the pre-UHCP group and 20.8 million IDR (1,486 USD) for the UHCP group. ICER showed that hospitalization cost was 3.7 million IDR (264 USD) higher to increase 1 activity of daily living score in the UHCP era. As for rehospitalization, the cost was 600,000 IDR (43 USD) less, with 3.6% smaller in readmission. CONCLUSIONS There was no changes in patients’ functional status after the UHCP implementation. There was a reduction in rehospitalization with lower cost in the UHCP era.

The prevalence of 30-day readmission after acute myocardial infarction: A systematic review and meta-analysis

Objective

The 30‐day readmission is associated with increased medical costs, which has become an important quality metric in several medical institutions. This current study is aimed at clarifying the prevalence, the underlying risk factors, and reasons of the 30‐day readmission after acute myocardial infarction (AMI).

Methods

PubMed, Cochrane Library, and EMBASE were systematically searched to identify eligible studies. Random‐effect models were employed to perform pooled analyses. Means and 95% confidence intervals (CIs) were used to estimate prevalence and reasons for 30‐day readmission. We also used Odds ratios (ORs) to explore the potential significant predictors of risk factors of 30‐day readmission after AMI. Potential publication bias was assessed using funnel plot and Begg'test.

Results

A total of 14 relevant studies were included in this systematic review and meta‐analysis. The pooled 30‐day readmission rate of AMI was 12% (95% CI 0.11‐0.14). Acute coronary syndrome (ACS), angina and acute ischemic heart disease, and heart failure (HF) were the principal cardiovascular reasons of 30‐day readmission. Meanwhile, non‐specific chest pain was regarded as the significant cause among non‐cardiovascular reasons. The common co‐morbidities kidney disease, HF and diabetes mellitus were significant risk factors for 30‐day readmission. No significant publication bias was found by funnel plot and statistical tests.

Conclusions

The 30‐day readmission rate of post‐AMI ranged from 11% to 14% and can be mainly attributed to cardiovascular and non‐cardiovascular events. The common co‐morbidities, such as kidney disease, HF, and diabetes mellitus were significant risk factors for 30‐day readmission.