Predicting readmission or death after acute ST-elevation myocardial infarction

Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study

Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.

Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach

BACKGROUND

Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explainability of machine learning algorithms in predicting unplanned readmission and death in cardiovascular patients at 30 days and 180 days from discharge.

METHODS

Gradient boosting machines were trained and evaluated using data from hospital electronic medical records linked to hospital administrative and mortality data for 39,255 patients admitted to four hospitals in New South Wales, Australia between 2017 and 2021. Sociodemographic variables, admission history, and clinical information were used as potential predictors. The performance was compared to LASSO regression, as well as the HOSPITAL and LACE risk score indices. Important risk factors identified by the gradient-boosting machine model were explored using Shapley values.

RESULTS

The models performed well, especially for the mortality outcomes. Area under the receiver operating characteristic curve values were 0.70 for readmission and 0.87-0.90 for mortality using the full gradient boosting machine algorithms. Among the top predictors for 30-day and 180-day readmission were increased red cell distribution width, old age (especially above 80 years), high measured troponin and urea levels, not being married or in a relationship, and low albumin levels. For mortality, these included increased red cell distribution width, old age (especially older than 70 years), high measured troponin and urea levels, high neutrophil and monocyte counts, and low eosinophil and lymphocyte counts. The Shapley values gave clear insight into the dynamics of decision-tree-based models.

CONCLUSIONS

We demonstrated an explainable predictive algorithm to identify cardiovascular patients who are at high risk of readmission or death at discharge from the hospital and identified key risk factors.

Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI

To identify risk factors and develop a risk-prediction nomogram model for 1-year readmission due to major adverse cardiovascular events (MACEs) in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PCI). This was a single-center, retrospective cohort study. A total of 526 eligible participants were enrolled, which included 456 non-readmitted and 70 readmitted patients. Multivariate logistical regressions were performed to identify the independent risk factors for readmission, and a prediction nomogram model was developed based on the results of the regression analysis. The receiver operating characteristic curve, Hosmer-Lemeshow test, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Female (OR = 2.426; 95% CI: 1.395-4.218), hypertension (OR = 1.898; 95% CI: 1.100-3.275), 3-vessel disease (OR = 2.632; 95% CI: 1.332-5.201), in-hospital Ventricular arrhythmias (VA) (OR = 3.143; 95% CI: 1.305-7.574), peak cTnI (OR = 1.003; 95% CI: 1.001-1.004) and baseline NT-proBNP (OR = 1.001; 95% CI: 1.000-1.002) were independent risk factors for readmission (all P < 0.05). The nomogram exhibited good discrimination with the area under the curve (AUC) of 0.723, calibration (Hosmer-Lemeshow test; χ2 = 15.396, P = 0.052), and clinical usefulness. Female gender, hypertension, in-hospital VA, 3-vessel disease, baseline NT-proBNP, and peak cTnI were independent risk factors for readmission. The nomogram helped clinicians to identify the patients at risk of readmission before their hospital discharge, which may help reduce readmission rates.

Patient-Reported Quality of Hospital Discharge Transitions: Results from the SILVER-AMI Study

BACKGROUND

Transitions from hospital to home in older patients are a high-risk period for adverse outcomes in a population that may have more challenges navigating the healthcare system. There is little information about the association of patient-reported quality of hospital discharge processes with clinical outcomes.

OBJECTIVES

We evaluated whether patient-reported quality of hospital discharge processes was associated with emergency department utilization and rehospitalization within 30 days of discharge after hospitalization for acute myocardial infarction (AMI) in older adults.

DESIGN

Multi-center, prospective cohort study.

PATIENTS

The ComprehenSIVe Evaluation of Risk Factors in Older Patients with Acute Myocardial Infarction (SILVER-AMI) study was a longitudinal study of 3006 adults age 75 and older hospitalized with AMI recruited from 94 academic and community hospitals from across the USA.

INTERVENTION

N/A MAIN MEASURES: Patients answered a subset of questions from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. Readmissions and emergency department utilization within 30 days of discharge were ascertained through medical record review.

KEY RESULTS

A total of 2132 patients were included in the study. Patients' median age was 81 years and the response rate to the survey of discharge quality was 87%. Patients who reported being asked about having the help they needed at home were significantly less likely to have emergency room utilization within 30 days of discharge in both the unadjusted (0.65, 95% CI 0.43-0.99) and adjusted (0.65, 95% CI 0.42-0.997) models, though there was no significant association with readmission.

CONCLUSION

Report of an assessment of help needed at home during hospitalization was associated with lower post-discharge emergency department utilization. Efforts to improve outcomes after hospital discharge in older patients may benefit from greater focus on assessing need of help at home.

Sociodemographic, labour market marginalisation and medical characteristics as risk factors for reinfarction and mortality within 1 year after a first acute myocardial infarction: a register-based cohort study of a working age population in Sweden

OBJECTIVES

Research covering a wide range of risk factors related to the prognosis during the first year after an acute myocardial infarction (AMI) is insufficient. This study aimed to investigate whether sociodemographic, labour market marginalisation and medical characteristics before/at AMI were associated with subsequent reinfarction and all-cause mortality.

DESIGN

Population-based cohort study.

PARTICIPANTS

The cohort included 15 069 individuals aged 25-64 years who had a first AMI during 2008-2010.

PRIMARY AND SECONDARY OUTCOME MEASURES

The outcome measures consisted of reinfarction and all-cause mortality within 1 year following an AMI, which were estimated by univariate and multivariable HRs and 95% CIs by Cox regression.

RESULTS

Sociodemographic characteristics such as lower education showed a 1.1-fold and 1.3-fold higher risk for reinfarction and mortality, respectively. Older age was associated with a higher risk of mortality while being born in non-European countries showed a lower risk of mortality. Labour market marginalisation such as previous long-term work disability was associated with a twofold higher risk of mortality. Regarding medical characteristics, ST-elevation myocardial infarction was predictive for reinfarction (HR: 1.14, 95% CI: 1.07 to 1.21) and all-cause mortality (HR: 3.80, 95% CI: 3.08 to 4.68). Moreover, diabetes mellitus, renal insufficiency, stroke, cancer and mental disorders were associated with a higher risk of mortality (range of HRs: 1.24-2.59).

CONCLUSIONS

Sociodemographic and medical risk factors were identified as risk factors for mortality and reinfarction after AMI, including older age, immigration status, somatic and mental comorbidities. Previous long-term work disability and infarction type provide useful information for predicting adverse outcomes after AMI during the first year, particularly for mortality.

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.

Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance

BACKGROUND

Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown.

METHODS AND RESULTS

We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models.

CONCLUSIONS

Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions.

Predicting need for advanced illness or palliative care in a primary care population using electronic health record data

Timely outreach to individuals in an advanced stage of illness offers opportunities to exercise decision control over health care. Predictive models built using Electronic health record (EHR) data are being explored as a way to anticipate such need with enough lead time for patient engagement. Prior studies have focused on hospitalized patients, who typically have more data available for predicting care needs. It is unclear if prediction driven outreach is feasible in the primary care setting. In this study, we apply predictive modeling to the primary care population of a large, regional health system and systematically examine the impact of technical choices, such as requiring a minimum number of health care encounters (data density requirements) and aggregating diagnosis codes using Clinical Classifications Software (CCS) groupings to reduce dimensionality, on model performance in terms of discrimination and positive predictive value. We assembled a cohort of 349,667 primary care patients between 65 and 90 years of age who sought care from Sutter Health between July 1, 2011 and June 30, 2014, of whom 2.1% died during the study period. EHR data comprising demographics, encounters, orders, and diagnoses for each patient from a 12 month observation window prior to the point when a prediction is made were extracted. L1 regularized logistic regression and gradient boosted tree models were fit to training data and tuned by cross validation. Model performance in predicting one year mortality was assessed using held-out test patients. Our experiments systematically varied three factors: model type, diagnosis coding, and data density requirements. We found substantial, consistent benefit from using gradient boosting vs logistic regression (mean AUROC over all other technical choices of 84.8% vs 80.7% respectively). There was no benefit from aggregation of ICD codes into CCS code groups (mean AUROC over all other technical choices of 82.9% vs 82.6% respectively). Likewise increasing data density requirements did not affect discrimination (mean AUROC over other technical choices ranged from 82.5% to 83%). We also examine model performance as a function of lead time, which is the interval between death and when a prediction was made. In subgroup analysis by lead time, mean AUROC over all other choices ranged from 87.9% for patients who died within 0 to 3 months to 83.6% for those who died 9 to 12 months after prediction time.

Efficacy of Zofenopril Compared With Placebo and Other Angiotensin-converting Enzyme Inhibitors in Patients With Acute Myocardial Infarction and Previous Cardiovascular Risk Factors: A Pooled Individual Data Analysis of 4 Randomized, Double-blind, Controlled, Prospective Studies

In the Survival of Myocardial Infarction Long-term Evaluation (SMILE) 1, 3, and 4 studies, early administration of zofenopril in acute myocardial infarction showed to be prognostically beneficial versus placebo or ramipril. The SMILE-2 showed that both zofenopril and lisinopril are safe and showed no significant differences in the incidence of major cardiovascular (CV) complications. In this pooled analysis of individual data of the SMILE studies, we evaluated whether the superior efficacy of zofenopril is maintained also in patients with ≥1 CV risk factor (CV+, n = 2962) as compared to CV− (n = 668). The primary study end point was set to 1-year combined occurrence of death or hospitalization for CV causes. The risk of CV events was significantly reduced with zofenopril versus placebo either in the CV+ (−37%; hazard ratio: 0.63; 95% confidence interval: 0.51–0.78; P = 0.0001) or in the CV− group (−55%; hazard ratio: 0.45; 0.26–0.78; P = 0.004). Also, the other angiotensin-converting enzyme inhibitors reduced the risk of major CV outcomes, though the reduction was not statistically significant versus placebo (CV+: 0.78; 0.58–1.05; P = 0.107; CV−: 0.71; 0.36–1.41; P = 0.334). The benefit was larger in patients treated with zofenopril than other angiotensin-converting enzyme inhibitors, with a statistically significant difference for CV+ (0.79; 0.63–0.99; P = 0.039) versus CV− (0.62; 0.37–1.06; P = 0.081). In conclusion, zofenopril administered to patients after acute myocardial infarction has a positive impact on prognosis, regardless of the patient's CV risk profile.

Predictors of Posthospital Transitions of Care in Patients With Advanced Cancer

Purpose Patients with advanced cancer experience potentially burdensome transitions of care after hospitalizations. We examined predictors of discharge location and assessed the relationship between discharge location and survival in this population. Methods We conducted a prospective study of 932 patients with advanced cancer who experienced an unplanned hospitalization between September 2014 and March 2016. Upon admission, we assessed patients' physical symptoms (Edmonton Symptom Assessment System) and psychological distress (Patient Health Questionnaire-4). The primary outcome was discharge location (home without hospice, postacute care [PAC], or hospice [any setting]). The secondary outcome was survival. Results Of 932 patients, 726 (77.9%) were discharged home without hospice, 118 (12.7%) were discharged to PAC, and 88 (9.4%) to hospice. Those discharged to PAC and hospice reported high rates of severe symptoms, including dyspnea, constipation, low appetite, fatigue, depression, and anxiety. Using logistic regression, patients discharged to PAC or hospice versus home without hospice were more likely to be older (odds ratio [OR], 1.03; 95% CI, 1.02 to 1.05; P < .001), live alone (OR, 1.95; 95% CI, 1.25 to 3.02; P < .003), have impaired mobility (OR, 5.08; 95% CI, 3.46 to 7.45; P < .001), longer hospital stays (OR, 1.15; 95% CI, 1.11 to 1.20; P < .001), higher Edmonton Symptom Assessment System physical symptoms (OR, 1.02; 95% CI, 1.003 to 1.032; P < .017), and higher Patient Health Questionnaire-4 depression symptoms (OR, 1.13; 95% CI, 1.01 to 1.25; P < .027). Patients discharged to hospice rather than PAC were more likely to receive palliative care consultation (OR, 4.44; 95% CI, 2.12 to 9.29; P < .001) and have shorter hospital stays (OR, 0.84; 95% CI, 0.77 to 0.91; P < .001). Patients discharged to PAC versus home had lower survival (hazard ratio, 1.53; 95% CI, 1.22 to 1.93; P < .001). Conclusion Patients with advanced cancer who were discharged to PAC facilities and hospice had substantial physical and psychological symptom burden, impaired physical function, and inferior survival compared with those discharged to home. These patients may benefit from interventions to enhance their quality of life and care.

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk

BACKGROUND

Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions.

OBJECTIVES

To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses.

MATERIALS AND METHODS

Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed.

RESULTS

C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g., $1720 for all-cause readmissions based on a published cost of readmission of $11,862.

CONCLUSIONS

Choice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention with intervention cost limits as a result.

The Association of Transfer Rate From Hospitals Without Revascularization Capabilities and Mortality Risk for Older Non-ST-Segment Elevation Myocardial Infarction Patients

BACKGROUND

Interhospital transfer invasive management patterns and implications for older non-ST-segment elevation myocardial infarction (NSTEMI) patients initially presenting to non-revascularization-capable hospitals have not been explored.

HYPOTHESIS

Patients admitted to hospitals with a higher transfer proportion have lower risk of long-term mortality.

METHODS

We linked CRUSADE Registry data on 5678 patients age ≥65 years from 65 United States non-revascularization-capable hospitals (2003-2006) with inpatient Medicare longitudinal claims. Hospitals were categorized according to hospital-level patient transfer-out rates, low (≤40%) vs high (>40%). The associations between transfer-out rates and 30-day, 6-month, and 3-year mortality risk were evaluated using Cox proportional hazard models.

RESULTS

Hospital-level transfer-out rates varied widely (median, 43%; interquartile range, 31%-54%). Compared with patients from low-transfer-out hospitals (n = 2715), patients from high-transfer-out hospitals (n = 2963) were more likely to be male, less likely to have renal insufficiency and prior heart failure, and had lower long-term CRUSADE mortality risk scores. These patients also more commonly received evidence-based acute medications before transfer and underwent subsequent revascularization after transfer. The adjusted risks of mortality at various time intervals were similar for those from high- vs low-transfer-out hospitals: 30 days (hazard ratio: 0.95, 95% confidence interval: 0.79-1.14), 6 months (0.97, 0.84-1.12), and 3 years (1.01, 0.91-1.11).

CONCLUSIONS

Transfer rates for older NSTEMI patients vary widely among non-revascularization-capable hospitals. Despite lower predicted mortality risk and higher rates of post-transfer revascularization, patients from high-transfer-out hospitals had a similar risk for short- and long-term mortality compared with those from low-transfer-out hospitals.

Development and implementation of a real-time 30-day readmission predictive model

Hospitals are under great pressure to reduce readmissions of patients. Being able to reliably predict patients at increased risk for rehospitalization would allow for tailored interventions to be offered to them. This requires the creation of a functional predictive model specifically designed to support real-time clinical operations. A predictive model for readmissions within 30 days of discharge was developed using retrospective data from 45,924 MGH admissions between 2/1/2012 and 1/31/2013 only including factors that would be available by the day after admission. It was then validated prospectively in a real-time implementation for 3,074 MGH admissions between 10/1/2013 and 10/31/2013. The model developed retrospectively had an AUC of 0.705 with good calibration. The real-time implementation had an AUC of 0.671 although the model was overestimating readmission risk. A moderately discriminative real-time 30-day readmission predictive model can be developed and implemented in a large academic hospital.

Is there a relationship between patient satisfaction and favorable outcomes?

OBJECTIVE

Patient satisfaction with the health care experience has become a top priority for Centers for Medicare and Medicaid Services. With resources and efforts directed at patient satisfaction, we evaluated whether high patient satisfaction measured by HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) surveys correlates with favorable outcomes.

METHODS

Medical centers were identified from the University HealthSystem Consortium database from 2011 to 2012. Variables included hospital characteristics, process measure compliance, and surgical outcomes. Chi-squared analysis was used to evaluate for variables associated with high patient satisfaction (defined as hospitals that scored above the 50th percentile of top box scores).

RESULTS

We identified 171 hospitals with complete data. The following variables were significantly associated with high overall patient satisfaction: large hospitals, high surgical volume, and low mortality (P < 0.001). Compliance with process measures and patient safety indicators, as well as length of stay, did not correlate with overall satisfaction. The presence of complications (P = 0.491) or increased rate of readmission (P = 0.056) were not found to affect patient satisfaction. Low mortality index was consistently found to be associated with high satisfaction across 9 of 10 HCAHPS domains.

CONCLUSIONS

We found that hospital size, surgical volume, and low mortality were associated with high overall patient satisfaction. However, with the exception of low mortality, favorable surgical outcomes were not consistently associated with high HCAHPS scores. With existing satisfaction surveys, we conclude that factors outside of surgical outcomes appear to influence patients' perceptions of their care.

Effects of a nurse-based case management compared to usual care among aged patients with myocardial infarction: results from the randomized controlled KORINNA study

Background

Transition from hospital to home is a critical period for older persons with acute myocardial infarction (AMI). Home-based secondary prevention programs led by nurses have been proposed to facilitate the patients’ adjustment to AMI after discharge. The objective of this study was to evaluate the effects of a nurse-based case management for elderly patients discharged after an AMI from a tertiary care hospital.

Methods

In a single-centre randomized two-armed parallel group trial of patients aged 65 years and older hospitalized with an AMI between September 2008 and May 2010 in the Hospital of Augsburg, Germany, patients were randomly assigned to a case management or a control group receiving usual care. The case-management intervention consisted of a nurse-based follow-up for one year including home visits and telephone calls. Key elements of the intervention were to detect problems or risks and to give advice regarding a wide range of aspects of disease management (e.g. nutrition, medication). Primary study endpoint was time to first unplanned readmission or death. Block randomization per telephone call to a biostatistical center, where the randomization list was kept, was performed. Persons who assessed one-year outcomes and validated readmission data were blinded. Statistical analysis was based on the intention-to-treat approach and included Cox Proportional Hazards models.

Results

Three hundred forty patients were allocated to receive case-management (n=168) or usual care (n=172). The analysis is based on 329 patients (intervention group: n=161; control group: n=168). Of these, 62% were men, mean age was 75.4 years, and 47.1% had at least either diabetes or chronic heart failure as a major comorbidity. The mean follow-up time for the intervention group was 273.6 days, and for the control group it was 320.6 days. During one year, in the intervention group there were 57 first unplanned readmissions and 5 deaths, while the control group had 75 first unplanned readmissions and 3 deaths. With respect to the endpoint there was no significant effect of the case management program after one year (Hazard Ratio 1.01, 95% confidence interval 0.72-1.41). This was also the case among subgroups according to sex, diabetes, living alone, and comorbidities.

Conclusions

A nurse-based management among elderly patients with AMI had no significant influence on the rate of first unplanned readmissions or death during a one-year follow-up. A possible long-term influence should be investigated by further studies.

Clinical trial registration

ISRCTN02893746