Strategies to Reduce 30-Day Readmissions in Older Patients Hospitalized with Heart Failure and Acute Myocardial Infarction

Development and psychometric properties of a short version of the Patient Continuity of Care Questionnaire

INTRODUCTION

Hospitalization due to cardiac conditions is increasing worldwide, and follow-up after hospitalization usually occurs in a different healthcare setting than the one providing treatment during hospitalization. This leads to a risk of fragmented care and increases the need for coordination and continuity of care after hospitalization. Furthermore, international reports highlight the importance of improving continuity of care and state that it is an essential indicator of the quality of care. Patients' perceptions of continuity of care can be evaluated using the Patient Continuity of Care Questionnaire (PCCQ). However, the original version is extensive and may prove burdensome to complete; therefore, we aimed to develop and evaluate a short version of the PCCQ.

METHODS

This was a psychometric validation study. Content validity was evaluated among user groups, including patients (n = 7), healthcare personnel (n = 15), and researchers (n = 7). Based on the results of the content validity and conceptual discussions among the authors, 12 items were included in the short version. Data from patients were collected using a consecutive sampling procedure involving patients 6 weeks after hospitalization due to cardiac conditions. Rasch analysis was used to evaluate the psychometric properties of the short version of the PCCQ.

RESULTS

A total of 1000 patients were included [mean age 72 (SD = 10), 66% males]. The PCCQ-12 presented a satisfactory overall model fit and a person separation index of 0.79 (Cronbach's α: .91, ordinal α: .94). However, three items presented individual item misfits. No evidence of multidimensionality was found, meaning that a total score can be calculated. A total of four items presented evidence of response dependence but, according to the analysis, this did not seem to affect the measurement properties or reliability of the PCCQ-12. We found that the first two response options were disordered in all items. However, the reliability remained the same when these response options were amended. In future research, the benefits of the four response options could be evaluated.

CONCLUSION

The PCCQ-12 has sound psychometric properties and is ready to be used in clinical and research settings to measure patients' perceptions of continuity of care after hospitalization.

PATIENT OR PUBLIC CONTRIBUTION

Patients, healthcare personnel and researchers were involved in the study because they were invited to select items relevant to the short version of the questionnaire.

Social disparities in unplanned 30-day readmission rates after hospital discharge in patients with chronic health conditions: A retrospective cohort study using patient level hospital administrative data linked to the population census in Switzerland

Unplanned readmissions shortly after discharge from hospital are common in chronic diseases. The risk of readmission has been shown to be related both to hospital care, e.g., medical complications, and to patients’ resources and abilities to manage the chronic disease at home and to make appropriate use of outpatient medical care. Despite a growing body of evidence on social determinants of health and health behaviour, little is known about the impact of social and contextual factors on readmission rates. The objective of this study was to analyse possible effects of educational, financial and social resources of patients with different chronic health conditions on unplanned 30 day-readmission risks. The study made use of nationwide inpatient hospital data that was linked with Swiss census data. The sample included n = 62,109 patients aged 25 and older, hospitalized between 2012 and 2016 for one of 12 selected chronic conditions. Multivariate logistic regressions analysis was performed. Our results point to a significant association between social factors and readmission rates for patients with chronic conditions. Patients with upper secondary education (OR = 1.26, 95% CI: 1.11, 1.44) and compulsory education (OR = 1.51, 95% CI: 1.31, 1.74) had higher readmission rates than those with tertiary education when taking into account demographic, social and health status factors. Having private or semi-private hospital insurance was associated with a lower risk for 30-day readmission compared to patients with mandatory insurance (OR = 0.81, 95% CI: 0.73, 0.90). We did not find a general effect of social resources, measured by living with others in a household, on readmission rates. The risk of readmission for patients with chronic conditions was also strongly predicted by type of chronic condition and by factors related to health status, such as previous hospitalizations before the index hospitalization (+77%), number of comorbidities (+15% higher probability per additional comorbidity) as well as particularly long hospitalizations (+64%). Stratified analysis by type of chronic condition revealed differential effects of social factors on readmissions risks. Compulsory education was most strongly associated with higher odds for readmission among patients with lung cancer (+142%), congestive heart failure (+63%) and back problems (+53%). We assume that low socioeconomic status among patients with chronic conditions increases the risk of unplanned 30-day readmission after hospitalisation due to factors related to their social situation (e.g., low health literacy, material deprivation, high social burden), which may negatively affect cooperation with care providers and adherence to recommended therapies as well as hamper active participation in the medical process and the development of a shared understanding of the disease and its cure. Higher levels of comorbidity in socially disadvantaged patients can also make appropriate self-management and use of outpatient care more difficult. Our findings suggest a need for increased preventive measures for disadvantaged populations groups to promote early detection of diseases and to remove financial or knowledge-based barriers to medical care. Socially disadvantaged patients should also be strengthened more in their individual and social resources for coping with illness.

Outcomes and Resource Utilization in Patients Hospitalized with Gastrointestinal Bleeding Complicated by Types 1 and 2 Myocardial Infarction

BACKGROUND

Types 1 and 2 myocardial infarction (MI) may occur in the setting of gastrointestinal bleeding (GIB). There is a paucity of data pertinent to the contemporary prevalence and impact of types 1 and 2 MI following GIB. We examined clinical profiles and the prognostic impact of both MI types on outcomes of patients hospitalized with GIB.

METHODS

The 2018 Nationwide Readmission Database was queried for patients hospitalized for the primary diagnosis of GIB and had concomitant diagnoses of type 1 or type 2 MI. Baseline characteristics, in-hospital mortality, resource utilization, and 30-day all-cause readmissions were compared among groups.

RESULTS

Of 381,867 primary GIB hospitalizations, 2902 (0.75%) had type 1 MI and 3963 (1.0%) had type 2 MI. GIB patients with type 1 and type 2 MI had significantly higher in-hospital mortality compared to their counterparts without MI (adjusted odds ratios [aOR]: 4.72, 95% confidence interval [CI] 3.43-6.48; and aOR: 2.17, 95% CI 1.48-3.16, respectively). Both types 1 and 2 MI were associated with higher rates of discharge to a nursing facility (aOR of type 1 vs. no MI: 1.65, 95% CI 1.45-1.89, and aOR of type 2 vs no MI: 1.37, 95% CI 1.22-1.54), longer length of stay, higher hospital costs, and more 30-day all-cause readmissions (aOR of type 1 vs no MI: 1.22, 95% CI 1.08-1.38; aOR of type 2 vs no MI: 1.17, 95% CI 1.05-1.30).

CONCLUSION

Types 1 and 2 MI are associated with higher in-hospital mortality and resource utilization among patients hospitalized with GIB in the United States.

Morbidity and mortality in elderly patients with heart failure managed with a comprehensive care model vs. usual care: The UMIPIC program

BACKGROUND

Elderly patients with heart failure (HF) have a high degree of comorbidity which leads to fragmented care, with frequent hospitalizations and high mortality. This study evaluated the benefit of a comprehensive continuous care model (UMIPIC program) in elderly HF patients.

METHODS AND RESULTS

We prospectively analyzed data from the RICA registry on 2862 patients with HF treated in internal medicine departments. They were divided into two groups: one monitored in the UMIPIC program (UMIPIC group, n: 809) and another which received conventional care (RICA group, n: 2.053). We evaluated HF readmissions during 12 months of follow-up and total mortality after episodes of HF hospitalization. UMIPIC patients were older with higher rates of comorbidity and preserved ejection fraction than the RICA group. However, the UMIPIC group had a lower rate of HF readmissions (17% vs. 26%, p < .001) and mortality (16% vs. 27%, respectively; p < .001). In addition, we selected 370 propensity score-matched patients from each group and the differences in HF readmissions (15% UMIPIC vs. 30% RICA; hazard ratio [HR] = 0.44; 95% confidence interval [CI] 0.32-0.60; p < .001) and mortality (17% UMIPIC vs. 28% RICA; hazard ratio = 0.58; 95% CI 0.42-0.79; p = .001) were maintained.

CONCLUSIONS

The implementation of the UMIPIC program, based on comprehensive continuous care of elderly patients with HF and high comorbidity, markedly reduce HF readmissions and total mortality.

Feasibility of patient-reported outcome research in acute geriatric medicine: an approach to the 'post-hospital syndrome'

BACKGROUND

A patient's self-reported health-related quality of life (HRQoL) can be quantified by a patient-reported outcome measure (PROM). A patient's HRQoL can provide another avenue to understand the 'post-hospital syndrome', a period after hospital discharge that a patient remains vulnerable to subsequent re-admission. The purpose of the study was to establish the feasibility of collecting HRQoL of older inpatients treated for acute illnesses on medical ward. Feasibility of the PROM would be qualitatively judged upon completion time, response rate and sensitivity to change in HRQoL over time.

METHODS

A prospective observational cohort of consecutively admitted patients to a step-down medical ward over 1 year. The COOP/WONCA chart was the PROM. Patients were interviewed by the author face-to-face within 48 hours of admission and then 2 weeks after discharge by telephone.

RESULTS

From the 300 patients admitted, 182 were excluded. Of the remaining 118, median age was 78 years (interquartile range, IQR, 64-86 years), and 71 (60.2%) were female. Proxies were used for 26 (22%) patients. Ninety-two (78%) completed follow-up. The participants were contacted at a median of 14 days (IQR, 13-16) after discharge. Exploratory analyses found that the COOP/WONCA had test-retest responsiveness, that is detected change in HRQoL over time.

CONCLUSION

The completion time of 3 minutes, high response rate (78%) and test-retest responsiveness are evidence that collecting PROs from acutely unwell elderly patients using the COOP/WONCA is feasible. PRO research could become fundamental to the understanding of the 'post-hospital syndrome'.

Potentially preventable hospitalizations-The 'pre-hospital syndrome': Retrospective observations from the MonashWatch self-reported health journey study in Victoria, Australia

RATIONALE, AIMS, AND OBJECTIVES

HealthLinks: Chronic Care is a state-wide public hospital initiative designed to improve care for cohorts at-risk of potentially preventable hospitalizations at no extra cost. MonashWatch (MW) is an hospital outreach service designed to optimize admissions in an at-risk cohort. Telehealth operators make regular phone calls (≥weekly) using the Patient Journey Record System (PaJR). PaJR generates flags based on patient self-report, alerting to a risk of admission or emergency department attendance. 'Total flags' of global health represent concerns about self-reported general health, medication, and wellness. 'Red flags' represent significant disease/symptoms concerns, likely to lead to hospitalization.

METHODS

A time series analysis of PaJR phone calls to MW patients with ≥1 acute non-surgical admissions in a 20-day time window (10 days pre-admission and 10 days post-discharge) between 23 December 2016 and 11 October 2017. Pettitt's hypothesis-testing homogeneity measure was deployed to analyse Victorian Admitted Episode/Emergency Minimum Datasets and PaJR data.

FINDINGS

A MW cohort of 103 patients (mean age 74 ± 15 years; with 59% males) had 263 admissions was identified. Bed days ranged from <1 to 37.3 (mean 5.8 ± 5.8; median 4.1). The MW cohort had 7.6 calls on average in the 20-day pre- and post-hospital period. Most patients reported significantly increased flags 'pre-hospital' admission: medication issues increased on day 7.0 to 8.5; total flags day 3, worse general health days 2.5 to 1.8; and red flags of disease symptoms increased on day 1. These flags persisted following discharge.

DISCUSSION/CONCLUSION

This study identified a 'pre-hospital syndrome' similar to a post-hospital phase aka the well-documented 'post-hospital syndrome'. There is evidence of a 10-day 'pre-hospital' window for interventions to possibly prevent or shorten an acute admission in this MW cohort. Further validation in a larger diverse sample is needed.

180-day readmission risk model for older adults with acute myocardial infarction: the SILVER-AMI study

OBJECTIVE

To develop a 180-day readmission risk model for older adults with acute myocardial infarction (AMI) that considered a broad range of clinical, demographic and age-related functional domains.

METHODS

We used data from ComprehenSIVe Evaluation of Risk in Older Adults with AMI (SILVER-AMI), a prospective cohort study that enrolled participants aged ≥75 years with AMI from 94 US hospitals. Participants underwent an in-hospital assessment of functional impairments, including cognition, vision, hearing and mobility. Clinical variables previously shown to be associated with readmission risk were also evaluated. The outcome was 180-day readmission. From an initial list of 72 variables, we used backward selection and Bayesian model averaging to derive a risk model (N=2004) that was subsequently internally validated (N=1002).

RESULTS

Of the 3006 SILVER-AMI participants discharged alive, mean age was 81.5 years, 44.4% were women and 10.5% were non-white. Within 180 days, 1222 participants (40.7%) were readmitted. The final risk model included 10 variables: history of chronic obstructive pulmonary disease, history of heart failure, initial heart rate, first diastolic blood pressure, ischaemic ECG changes, initial haemoglobin, ejection fraction, length of stay, self-reported health status and functional mobility. Model discrimination was moderate (0.68 derivation cohort, 0.65 validation cohort), with good calibration. The predicted readmission rate (derivation cohort) was 23.0% in the lowest quintile and 65.4% in the highest quintile.

CONCLUSIONS

Over 40% of participants in our sample experienced hospital readmission within 180 days of AMI. Our final readmission risk model included a broad range of characteristics, including functional mobility and self-reported health status, neither of which have been previously considered in 180-day risk models.

Persistent Heart Failure Symptoms at Hospital Discharge Predicts 30-Day Clinical Events

Background

The relationship between heart failure (HF) symptoms at hospital discharge and 30-day clinical events is unknown. Variability in HF symptom assessment may affect ability to predict readmission risk.

Objective

The aim of this study was to describe HF symptom profiles and burden at hospital discharge. A secondary aim was to examine the relationship between symptom burden at discharge and 30-day clinical events.

Methods

An exploratory descriptive design was used. Patients with HF (n = 186) were enrolled 24 to 48 hours pre hospital discharge. The HF Somatic Perception Scale quantified 18 HF physical signs and symptoms. Scores were divided into tertiles (0-10, 11-19, and 20 and higher). The Patient Health Questionnaire-9 quantified depressive symptoms. Self-assessed health, comorbid illnesses, and 30-day clinical events were documented. Chi-square and logistic regression were used to examine clinical events.

Results

The sample (n = 186) was predominantly White (87.6%), male (59.1%), elderly (mean [SD], 74.2 [12.5]), and symptomatic (92.5%) at discharge. Heart Failure Somatic Perception Scale scores ranged from 0 to 53, with a mean (SD) of 13.7 (10.1). Symptoms reported most frequently were fatigue (67%), nocturia (62%), need to rest (53%), and inability to do usual activities due to shortness of breath (52%). Thirty-day event rate was 28%, with significant differences between Heart Failure Somatic Perception Scale tertiles (9.4% vs 37.7% in the second and third tertiles, respectively; [chi]22(N = 186) = 16.73, P < .001). Heart Failure Somatic Perception Scale tertile 2 or 3 (odds ratio [OR], 5.7; P = .003; and OR, 4.3; P = .021), self-assessed health (OR, 2.6; P = .029), and being in a relationship predicted clinical events.

Conclusions

Heart failure symptom burden at discharge predicted 30-day clinical events. Comprehensive symptom assessment is important when determining readmission risk.

In-hospital interventions for reducing readmissions to acute care for adults aged 65 and over: An umbrella review

PURPOSE

The aim of this umbrella review was to synthesize existing systematic review evidence on the effectiveness of in-hospital interventions to prevent or reduce avoidable hospital readmissions in older people (≥65 years old).

DATA SOURCES

A comprehensive database search was conducted in May 2019 through MEDLINE, EMBASE, CINAHL, the JBI Database of Systematic Reviews, DARE and Epistemonikos.

STUDY SELECTION

Systematic reviews and other research syntheses, including meta-analyses, exploring the effectiveness of hospital-based interventions to reduce readmissions for people aged 65 and older, irrespective of gender or clinical condition, were included for review. If a review did not exclusively focus on this age group, but data for this group could be extracted, then it was considered for inclusion. Only reviews in English were included.

DATA EXTRACTION

Data extracted for each review included the review objective, participant details, setting and context, type of studies, intervention type, comparator and findings.

RESULTS OF DATA SYNTHESIS

Twenty-nine reviews were included for analysis. Within these reviews, 11 intervention types were examined: in-hospital medication review, discharge planning, comprehensive geriatric assessment, early recovery after surgery, transitional care, interdisciplinary team care, in-hospital nutrition therapy, acute care geriatric units, in-hospital exercise, postfall interventions for people with dementia and emergency department-based palliative care. Except for discharge planning and transitional care, none of the interventions significantly reduced readmissions among older adults.

CONCLUSION

There is limited evidence to support the effectiveness of existing hospital-based interventions to reduce readmissions for people aged 65 and older.

Patient Continuity of Care Questionnaire in a cardiac sample: A Confirmatory Factor Analysis

OBJECTIVE

Even though continuity is essential after discharge, there is a lack of reliable questionnaires to measure and assess patients' perceptions of continuity of care. The Patient Continuity of Care Questionnaire (PCCQ) addresses the period before and after discharge from hospital. However, previous studies show that the factor structure needs to be confirmed and validated in larger samples, and the aim of this study was to evaluate the psychometric properties of the PCCQ with focus on factor structure, internal consistency and stability.

DESIGN

A psychometric evaluation study. The questionnaire was translated into Swedish using a forward-backward technique and culturally adapted through cognitive interviews (n=12) and reviewed by researchers (n=8).

SETTING

Data were collected in four healthcare settings in two Swedish counties.

PARTICIPANTS

A consecutive sampling procedure included 725 patients discharged after hospitalisation due to angina, acute myocardial infarction, heart failure or atrial fibrillation.

MEASUREMENT

To evaluate the factor structure, confirmatory factor analyses based on polychoric correlations were performed (n=721). Internal consistency was evaluated by ordinal alpha. Test-retest reliability (n=289) was assessed with intraclass correlation coefficient (ICC).

RESULTS

The original six-factor structure was overall confirmed, but minor refinements were required to reach satisfactory model fit. The standardised factor loadings ranged between 0.68 and 0.94, and ordinal alpha ranged between 0.82 and 0.95. All subscales demonstrated satisfactory test-retest reliability (ICC=0.76-0.94).

CONCLUSION

The revised version of the PCCQ showed sound psychometric properties and is ready to be used to measure perceptions of continuity of care. High ordinal alpha in some subscales indicates that a shorter version of the questionnaire can be developed.

Thirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction

BACKGROUND

Early readmissions among older adults hospitalized for acute myocardial infarction (AMI) are costly and difficult to predict. Aging-related functional impairments may inform risk prediction but are unavailable in most studies. Our objective was to, therefore, develop and validate an AMI readmission risk model for older patients who considered functional impairments and was suitable for use before hospital discharge.

METHODS AND RESULTS

SILVER-AMI (Comprehensive Evaluation of Risk in Older Adults with AMI) is a prospective cohort study of 3006 patients of age ≥75 years hospitalized with AMI at 94 US hospitals. Participants underwent in-hospital assessment of functional impairments including cognition, vision, hearing, and mobility. Other variables plausibly associated with readmissions were also collected. The outcome was all-cause readmission at 30 days. We used backward selection and Bayesian model averaging to derive (N=2004) a risk model that was subsequently validated (N=1002). Mean age was 81.5 years, 44.4% were women, and 10.5% were nonwhite. Within 30 days, 547 participants (18.2%) were readmitted. Readmitted participants were older, had more comorbidities, and had a higher prevalence of functional impairments, including activities of daily living disability (17.0% versus 13.0%; P=0.013) and impaired functional mobility (72.5% versus 53.6%; P<0.001). The final risk model included 8 variables: functional mobility, ejection fraction, chronic obstructive pulmonary disease, arrhythmia, acute kidney injury, first diastolic blood pressure, P2Y12 inhibitor use, and general health status. Functional mobility was the only functional impairment variable retained but was the strongest predictor. The model was well calibrated (Hosmer-Lemeshow P value >0.05) with moderate discrimination (C statistics: 0.65 derivation cohort and 0.63 validation cohort). Functional mobility significantly improved performance of the risk model (net reclassification improvement index =20%; P<0.001).

CONCLUSIONS

In our final risk model, functional mobility, previously not included in readmission risk models, was the strongest predictor of 30-day readmission among older adults after AMI. The modest discrimination indicates that much of the variability in readmission risk among this population remains unexplained by patient-level factors.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov. Unique identifier: NCT01755052.

Gait Speed at Discharge and Risk for Readmission or Death: A Prospective Study of an Emergency Ward Population

Background

There has been a growing interest in measuring gait speed for assessing long-term mortality and risk for hospital readmission in different populations.

Objective

We studied the association between a 10-meter gait speed test at hospital discharge and the risk for 30- and 90-day hospital readmission or death in a mixed population of patients hospitalized for emergency care.

Patients and Methods

Patients were prospectively included from 5 wards at the Karolinska University Hospital. The 10-meter gait speed test was measured on the day of discharge. Statistical analysis was performed using logistic regression.

Results

A total of 344 patients were included. Forty-one patients (n=41) were readmitted to hospital or died within 30 days, and 81 were readmitted or died within 90 days after discharge. Readmitted patients were older and had more comorbidities. A 0.1 m/s reduction in gait speed was associated with a 13% greater odds of readmission or death within 30 days (OR 1.13 [95% CI 1.00–1.26]). The area under the receiver operating characteristic curve (AUC) was 0.59 (95% CI 0.51–0.68). The results were similar for 90-day readmission or death where a 0.1 m/s decrement in gait speed was associated with an OR of 1.13 (95% CI 1.04–1.24). When age, eGFR, hemoglobin concentration, and active cancer, which all were univariate predictors of 30-day readmissions, were added to the model it yielded an AUC of 0.68 (95% CI 0.60 to 0.77).

Conclusion

In a mixed population of patients hospitalized for emergency care, low gait speed at discharge was associated with an increased risk of 30- and 90-day readmission or death. However, the test did not discriminate well between those who were readmitted or died and those who did not; therefore we do not recommend its use as a stand-alone test in this population.

Suitability of elderly adult hospital readmission rates for profiling readmissions in younger adult and pediatric populations

OBJECTIVE

To determine the correlation between hospital 30-day risk-standardized readmission rates (RSRRs) in elderly adults and those in nonelderly adults and children.

DATA SOURCES/STUDY SETTING

US hospitals (n = 1760 hospitals admitting adult patients and 235 hospitals admitting both adult and pediatric patients) in the 2013-2014 Nationwide Readmissions Database.

STUDY DESIGN

Cross-sectional analysis comparing 30-day RSRRs for elderly adult (≥65 years), middle-aged adult (40-64 years), young adult (18-39 years), and pediatric (1-17 years) patients.

PRINCIPAL FINDINGS

Hospital elderly adult RSRRs were strongly correlated with middle-aged adult RSRRs (Pearson R² .69 [95% confidence interval (CI) 0.66-0.71]), moderately correlated with young adult RSRRs (Pearson R² .44 [95% CI 0.40-0.47]), and weakly correlated with pediatric RSRRs (Pearson R² .28 [95% CI 0.17-0.38]). Nearly identical findings were observed with measures of interquartile agreement and Kappa statistics. This stepwise relationship between age and strength of correlation was consistent across every hospital characteristic.

CONCLUSIONS

Hospital readmission rates in elderly adults, which are currently used for public reporting and hospital comparisons, may reflect broader hospital readmission performance in middle-aged and young adult populations; however, they are not reflective of hospital performance in pediatric populations.

Post-Discharge Services for Different Diagnoses Than Index Hospitalization Predict Decreased 30-Day Readmissions Among Medicare Beneficiaries

BACKGROUND

Efforts to reduce hospital readmissions include post-discharge interventions related to the illness treated during the index hospitalization (IH). These efforts may be inadequate because readmissions are precipitated by a wide range of health conditions unrelated to the primary diagnosis of the IH.

OBJECTIVE

To investigate the relationship between post-discharge health services utilization for the same or a different diagnosis than the IH and unplanned 30-day readmission.

DESIGN AND PARTICIPANTS

The study sample included 583,199 all-cause IHs among 2014 Medicare fee-for-service beneficiaries. For all-cause IH, as well as individually for heart failure, myocardial infarction, and pneumonia IH, we used multivariable logistic regressions to investigate the association between post-discharge services utilization and readmission.

MAIN MEASURES

The outcome was unplanned 30-day readmission. Primary independent variables were post-discharge services utilization, including institutional outpatient, office-based primary care, office-based specialist, office-based non-physician practitioner, emergency department, home health care, and skilled nursing facility providers.

KEY RESULTS

Among all-cause IH, 11.7% resulted in unplanned 30-day readmissions, and only 18.1% of readmissions occurred for the same primary diagnosis as IH. A substantial majority of post-discharge health services were utilized for a primary diagnosis differing from IH. Compared with no visit, institutional outpatient visits for the same primary diagnosis as IH (odds ratio [OR], 0.33; 95% confidence interval [CI], 0.31-0.34) and for a different primary diagnosis than IH (OR, 0.36; 95% CI, 0.35-0.37) were similarly strongly associated with decreased unplanned 30-day readmission. Primary care physician, specialist, non-physician practitioner, and home health care showed similar patterns. IH for heart failure, myocardial infarction, and pneumonia manifested similar patterns to all-cause IH both in terms of post-discharge services utilization and in terms of its impact on readmission.

CONCLUSIONS

To reduce unplanned 30-day readmission more effectively, discharge planning should include post-discharge services to address health conditions beyond the primary cause of the IH.

Association of US Centers for Medicare and Medicaid Services Hospital 30-Day Risk-Standardized Readmission Metric With Care Quality and Outcomes After Acute Myocardial Infarction: Findings From the National Cardiovascular Data Registry/Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines

Importance

The US Centers for Medicare and Medicaid Services Hospital Readmissions Reduction Program penalizes hospitals with higher-than-expected risk-adjusted 30-day readmission rates (excess readmission ratio [ERR] > 1) after acute myocardial infarction (MI). However, the association of ERR with MI care processes and outcomes are not well established.

Objective

To evaluate the association between ERR for MI with in-hospital process of care measures and 1-year clinical outcomes.

Design, Setting, and Participants

Observational analysis of hospitalized patients with MI from National Cardiovascular Data Registry/Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines centers subject to the first cycle of the Hospital Readmissions Reduction Program between July 1, 2008, and June 30, 2011.

Exposures

The ERR for MI (MI-ERR) in 2011.

Main Outcomes and Measures

Adherence to process of care measures during index hospitalization in the overall study population and risk of the composite outcome of mortality or all-cause readmission within 1 year of discharge and its individual components among participants with available Centers for Medicare and Medicaid Services-linked data.

Results

The median ages of patients in the MI-ERR greater than 1 and tertiles 1, 2, and 3 of the MI-ERR greater than 1 groups were 64, 63, 64, and 63 years, respectively. Among 380 hospitals that treated a total of 176 644 patients with MI during the study period, 43% had MI-ERR greater than 1. The proportions of patients of black race, those with heart failure signs at admission, and bleeding complications increased with higher MI-ERR. There was no significant association between adherence to MI performance measures and MI-ERR (adjusted odds ratio, 0.94; 95% CI, 0.81-1.08, per 0.1-unit increase in MI-ERR for overall defect-free care). Among the 51 453 patients with 1-year outcomes data available, higher MI-ERR was associated with higher adjusted risk of the composite outcome and all-cause readmission within 1 year of discharge. This association was largely driven by readmissions early after discharge and was not significant in landmark analyses beginning 30 days after discharge. The MI-ERR was not associated with risk for mortality within 1 year of discharge in the overall and 30-day landmark analyses.

Conclusions and Relevance

During the first cycle of the Hospital Readmissions Reduction Program, participating hospitals' risk-adjusted 30-day readmission rates following MI were not associated with in-hospital quality of MI care or clinical outcomes occurring after the first 30 days after discharge.

Readmission and mortality in patients ≥70 years with acute myocardial infarction or heart failure in the Netherlands: a retrospective cohort study of incidences and changes in risk factors over time

Objectives

To determine the risk of first unplanned all-cause readmission and mortality of patients ≥70 years with acute myocardial infarction (AMI) or heart failure (HF) and to explore which effects of baseline risk factors vary over time.

Methods

A retrospective cohort study was performed on hospital and mortality data (2008) from Statistics Netherlands including 5,175 (AMI) and 9,837 (HF) patients. We calculated cumulative weekly incidences for first unplanned all-cause readmission and mortality during 6 months post-discharge and explored patient characteristics associated with these events.

Results

At 6 months, 20.4% and 9.9% (AMI) and 24.6% and 22.4% (HF) of patients had been readmitted or had died, respectively. The highest incidences were found in week 1. An increased risk for 14-day mortality after AMI was observed in patients who lived alone (hazard ratio (HR) 1.57, 95% confidence interval (CI) 1.01–2.44) and within 30 and 42 days in patients with a Charlson Comorbidity Index ≥3. In HF patients, increased risks for readmissions within 7, 30 and 42 days were found for a Charlson Comorbidity Index ≥3 and within 42 days for patients with an admission in the previous 6 months (HR 1.42, 95% CI 1.12–1.80). Non-native Dutch HF patients had an increased risk of 14-day mortality (HR 1.74, 95% CI 1.09–2.78).

Conclusion

The risk of unplanned readmission and mortality in older AMI and HF patients was highest in the 1st week post-discharge, and the effect of some risk factors changed over time. Transitional care interventions need to be provided as soon as possible to prevent early readmission and mortality.

Electronic supplementary material

The online version of this article (10.1007/s12471-019-1227-4) contains supplementary material, which is available to authorized users.

What matters in "multimorbidity"? Arguably resilience and personal health experience are central to quality of life and optimizing survival

RATIONALE

Much is written about "multimorbidity" as it is a difficult problem for health systems, as it reflects a complex phenomenon unique to each individual health journey and health service context. This paper proposes the adoption of 2 constructs or knowledge streams into mainstream "multimorbidity" care which are arguably most important to person-centered care-personal health perceptions and resilience.

ANALYSIS

"Multimorbidity" is the manifestation of multiple nonlinear physical, psychosocial, and environmental phenomena in an individual health journey. Multimorbidity encompasses very stable states for the most part together with highly unstable phases that are difficult to manage. Averting or controlling the underlying loss of resilience in instability can be challenging without early warning signals pointing towards tipping points. Monitoring resilience and early warning signals for tipping points is new to health care. Yet what should we monitor in the complexity of multimorbidity? There are multiple and competing health service features and biometrics that can be measured. However, an expanding of literature endorses importance of simply asking a person about their self-rated health in order to provide predictions of their resilience and survival. Interoception, exemplified as self-rated health, arises from internal neurocognitive self-monitoring functions of different internal and external phenomena. Interoception is being to be recognized as predictors and barometers of resilience and survival.

CONCLUSIONS

Two phenomena of human systems-interoception and resilience-can guide care in the complex nature of multimorbidity in unstable health journeys and should be incorporated into clinical practice.

Continuity and utilization of health and community care in elderly patients with heart failure before and after hospitalization

BACKGROUND

The period after hospitalization due to deteriorated heart failure (HF) is characterized as a time of high generalized risk. The transition from hospital to home is often problematic due to insufficient coordination of care, leading to a fragmentation of care rather than a seamless continuum of care. The aim was to describe health and community care utilization prior to and 30 days after hospitalization, and the continuity of care in patients hospitalized due to de novo or deteriorated HF from the patients' perspective and from a medical chart review.

METHODS

This was a cross-sectional study with consecutive inclusion of patients hospitalized at a county hospital in Sweden due to deteriorated HF during 2014. Data were collected by structured telephone interviews and medical chart review and analyzed with the Spearman's rank correlation coefficient and Chi square. A P value of 0.05 was considered significant.

RESULTS

A total of 121 patients were included in the study, mean age 82.5 (±6.8) and 49% were women. Half of the patients had not visited any health care facility during the month prior to the index hospital admission, and 79% of the patients visited the emergency room (ER) without a referral. Among these elderly patients, a total of 40% received assistance at home prior to hospitalization and 52% after discharge. A total of 86% received written discharge information, one third felt insecure after hospitalization and lacked knowledge of which health care provider to consult with and contact in the event of deterioration or complications. Health care utilization increased significantly after hospitalization.

CONCLUSION

Most patients had not visited any health care facility within 30 days before hospitalization. Health care utilization increased significantly after hospitalization. Flaws in the continuity of care were found; even though most patients received written information at discharge, one third of the patients lacked knowledge about which health care provider to contact in the event of deterioration and felt insecure at home after discharge.

Predictive biomarkers for death and rehospitalization in comorbid frail elderly heart failure patients

Background

Heart failure (HF) is associated with a high rate of readmissions within 30 days post-discharge and in the following year, especially in frail elderly patients. Biomarker data are scarce in this high-risk population. This study assessed the value of early post-discharge circulating levels of ST2, NT-proBNP, CA125, and hs-TnI for predicting 30-day and 1-year outcomes in comorbid frail elderly patients with HF with mainly preserved ejection fraction (HFpEF).

Methods

Blood samples were obtained at the first visit shortly after discharge (4.9 ± 2 days). The primary endpoint was the composite of all-cause mortality or HF-related rehospitalization at 30 days and at 1 year. All-cause mortality alone at one year was also a major endpoint. HF-related rehospitalizations alone were secondary end-points.

Results

From February 2014 to November 2016, 522 consecutive patients attending the STOP-HF Clinic were included (57.1% women, age 82 ± 8.7 years, mean Barthel index 70 ± 25, mean Charlson comorbidity index 5.6 ± 2.2). The composite endpoint occurred in 8.6% patients at 30 days and in 38.5% at 1 year. In multivariable analysis, ST2 [hazard ratio (HR) 1.53; 95% CI 1.19–1.97; p = 0.001] was the only predictive biomarker at 30 days; at 1 year, both ST2 (HR 1.34; 95% CI 1.15–1.56; p < 0.001) and NT-proBNP (HR 1.19; 95% CI 1.02–1.40; p = 0.03) remained significant. The addition of ST2 and NT-proBNP into a clinical predictive model increased the AUC from 0.70 to 0.75 at 30 days (p = 0.02) and from 0.71 to 0.74 at 1 year (p < 0.05). For all-cause death at 1 year, ST2 (HR 1.50; 95% CI 1.26–1.80; p < 0.001), and CA125 (HR 1.41; 95% CI 1.21–1.63; p < 0.001) remained independent predictors in multivariable analysis. The addition of ST2 and CA125 into a clinical predictive model increased the AUC from 0.74 to 0.78 (p = 0.03). For HF-related hospitalizations, ST2 was the only predictive biomarker in multivariable analyses, both at 30 days and at 1 year.

Conclusions

In a comorbid frail elderly population with HFpEF, ST2 outperformed NT-proBNP for predicting the risk of all-cause mortality or HF-related rehospitalization. ST2, a surrogate marker of inflammation and fibrosis, may be a better predictive marker in high-risk HFpEF.

Electronic supplementary material

The online version of this article (10.1186/s12877-018-0807-2) contains supplementary material, which is available to authorized users.

Applicability of Publicly Reported Hospital Readmission Measures to Unreported Conditions and Other Patient Populations: A Cross-sectional All-Payer Study

BACKGROUND

Readmission rates after hospitalizations for heart failure (HF), acute myocardial infarction (AMI), and pneumonia among Medicare beneficiaries are used to assess quality and determine reimbursement. Whether these measures reflect readmission rates for other conditions or insurance groups is unknown.

OBJECTIVE

To investigate whether hospital-level 30-day readmission measures for publicly reported conditions (HF, AMI, and pneumonia) among Medicare patients reflect those for Medicare patients hospitalized for unreported conditions or non-Medicare patients hospitalized with HF, AMI, or pneumonia.

DESIGN

Cross-sectional.

SETTING

Population-based.

PARTICIPANTS

Hospitals in the all-payer Nationwide Readmissions Database in 2013 and 2014.

MEASUREMENTS

Hospital-level 30-day all-cause risk-standardized excess readmission ratios (ERRs) were compared for 3 groups of patients: Medicare beneficiaries admitted for HF, AMI, or pneumonia (Medicare reported group); Medicare beneficiaries admitted for other conditions (Medicare unreported group); and non-Medicare beneficiaries admitted for HF, AMI, or pneumonia (non-Medicare group).

RESULTS

Within-hospital differences in ERRs varied widely among groups. Medicare reported ratios differed from Medicare unreported ratios by more than 0.1 for 29% of hospitals and from non-Medicare ratios by more than 0.1 for 46% of hospitals. Among hospitals with higher readmission ratios, ERRs for the Medicare reported group tended to overestimate ERRs for the non-Medicare group but underestimate those for the Medicare unreported group.

LIMITATION

Medicare groups and risk adjustment differed slightly from those used by the Centers for Medicare & Medicaid Services.

CONCLUSION

Hospital ERRs, as estimated by Medicare to determine financial penalties, have poor agreement with corresponding measures for populations and conditions not tied to financial penalties. Current publicly reported measures may not be good surrogates for overall hospital quality related to 30-day readmissions.

PRIMARY FUNDING SOURCE

Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology.

Impact of Post-Hospital Syndrome on Outcomes Following Elective, Ambulatory Surgery

OBJECTIVE

The aim of this study was to investigate whether post-hospital syndrome (PHS) places patients undergoing elective hernia repair at increased risk for adverse postoperative events.

SUMMARY OF BACKGROUND DATA

PHS is a transient period of health vulnerability following inpatient hospitalization for acute illness. PHS has been well studied in nonsurgical populations, but its effect on surgical outcomes is unclear.

METHODS

State-specific datasets for California in 2011 available through the Healthcare Cost and Utilization Project (HCUP) were linked. Patients older than 18 years who underwent elective hernia repair were included. The primary exposure variable was PHS, defined as any inpatient admission within 90 days of an elective hernia repair performed in an ambulatory surgery center. The primary outcome was an adverse event, defined as any unplanned emergency department visit or inpatient admission within 30 days postoperatively. Mixed-effects logistic models were used for multivariable analyses.

RESULTS

A total of 57,988 patients met inclusion criteria. The 30-day risk-adjusted adverse event rate was significantly higher for PHS patients versus non-PHS patients (11.8% vs 5.8%, P < 0.001). PHS patients were more likely than non-PHS patients to experience postoperative complications (odds ratio 2.2, 95% confidence interval 1.6-3.0). Adverse events attributable to PHS cost an additional $63,533.46 per 100 cases in California. The risk of adverse events due to PHS remained elevated throughout the 90-day window between hospitalization and surgery.

CONCLUSIONS

Patients hospitalized within 90 days of an elective surgery are at increased risk of adverse events postoperatively. The impact of PHS on outcomes is independent of baseline patient characteristics, medical comorbidities, quality of center performing the surgery, and reason for hospitalization before elective surgery. Adverse events owing to PHS are costly and represent a quality improvement target.

Early Postdischarge STOP-HF-Clinic Reduces 30-day Readmissions in Old and Frail Patients With Heart Failure

INTRODUCTION AND OBJECTIVES

Heart failure (HF) is associated with a high rate of readmissions within 30 days postdischarge. Strategies to lower readmission rates generally show modest results. To reduce readmission rates, we developed a STructured multidisciplinary outpatient clinic for Old and frail Postdischarge patients hospitalized for HF (STOP-HF-Clinic).

METHODS

This prospective all-comers study enrolled patients discharged from internal medicine or geriatric wards after HF hospitalization. The intervention involved a face-to-face early visit (within 7 days), HF nurse education, treatment titration, and intravenous medication when needed. Thirty-day readmission risk was calculated using the CORE-HF risk score. We also studied the impact of 30-day readmission burden on regional health care by comparing the readmission rate in the STOP-HF-Clinic Referral Area (∼250000 people) with that of the rest of the Catalan Health Service (CatSalut) (∼7.5 million people) during the pre-STOP-HF-Clinic (2012-2013) and post-STOP-HF-Clinic (2014-2015) time periods.

RESULTS

From February 2014 to June 2016, 518 consecutive patients were included (age, 82 years; Barthel score, 70; Charlson index, 5.6, CORE-HF 30-day readmission risk, 26.5%). The observed all-cause 30-day readmission rate was 13.9% (47.5% relative risk reduction) and the observed HF-related 30-day readmission rate was 7.5%. The CatSalut registry included 65131 index HF admissions, with 9267 all-cause and 6686 HF-related 30-day readmissions. The 30-day readmission rate was significantly reduced in the STOP-HF-Clinic Referral Area in 2014-2015 compared with 2012-2013 (P < .001), mainly driven by fewer HF-related readmissions.

CONCLUSIONS

The STOP-HF-Clinic, an approach that could be promptly implemented elsewhere, is a valuable intervention for reducing the global burden of early readmissions among elder and vulnerable patients with HF.

Hospital Disease-Specific Care Certification Programs and Quality of Care: A Narrative Review

BACKGROUND

Disease-specific care certification (DSCC) programs have been developed to improve the quality and performance of programs or services that may be based within or associated with a hospital or other health care organization. A comprehensive summary of evidence for DSCC programs and their reported effect on the quality of care was prepared in a narrative review, the first of its kind on this topic.

METHODS

A systematic search was performed to identify articles that reported about DSCC. Any article that reported DSCC and certifications, published between 2003 and August 2015 (with an update in March 2016), and conducted in the United States was included. Databases searched included PubMed, MEDLINE, and CINAHL.

RESULTS

The articles were reviewed in terms of four topics: early development of DSCC, the journey toward DSCC, the relationship between DSCC and organizing process of care, and the relationship between DSCC and outcomes of care. Fifteen articles noted a positive relationship between DSCC programs and quality of care, only 6 of which reported empirical data. Therefore, a systematic review and meta-analysis were not warranted. Only 3 articles involved use of sophisticated statistical modeling with adequate control variables to investigate the effect of DSCC, which makes it difficult to conclude that the change in hospitals' or patients' outcomes were related to the certification.

CONCLUSIONS

The majority (13) of the articles focused on Joint Commission DSCC, with the remaining assessing Society of Cardiovascular Patient Care "accreditation" (certification). Only two studies, each study using a cross-sectional design, that empirically examined the relationship between DSCC and outcomes of care-mortality of care and readmission. More research studies are needed to evaluate the effectiveness of DSCC programs in improving outcomes of care, particularly patient-centered outcome measures, such as patient satisfaction and self-care.

Association Between Hospital Readmission and Acute and Sustained Delays in Functional Recovery During 18 Months After Elective Surgery: The Successful Aging after Elective Surgery Study

OBJECTIVES

To examine the effect of hospital readmission on functional recovery after elective surgery in older adults.

DESIGN

Prospective cohort of individuals aged 70 and older undergoing elective surgery, enrolled from June 2010 to August 2013.

SETTING

Two academic medical centers.

PARTICIPANTS

Community-dwelling older adults (N = 566; mean age ± standard deviation 77 ± 5) undergoing major elective surgery and expected to be admitted for at least 3 days.

MEASUREMENTS

Readmission was assessed in multiple interviews with participants and family members over 18 months and validated against medical record review. Physical function was assessed according to ability to perform instrumental activities of daily living (IADLs) and activities of daily living (ADL), Medical Outcomes Study 12-item Short-Form Survey Physical Component Summary score, and a standardized functional composite.

RESULTS

Two hundred fifty-five (45%) participants experienced 503 readmissions. Readmissions were associated with delays in functional recovery in all measures of physical function. Having two or more readmissions over 18 months was associated with persistent and significantly greater risk of IADL dependence (relative risk (RR) = 1.8, 95% confidence interval (CI) = 1.5-2.3) and ADL dependence (RR = 3.3, 95% CI = 1.7-6.4). Degree of functional impairment increased progressively with number of readmissions. Readmissions within 2 months resulted in delayed functional recovery to baseline by 18 months, and readmissions between 12 and 18 months after surgery resulted in loss of functional recovery previously achieved.

CONCLUSION

Readmission after elective surgery may contribute to delays in functional recovery and persistent functional deficits in older adults.

Analysis of Machine Learning Techniques for Heart Failure Readmissions

BACKGROUND

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions.

METHODS AND RESULTS

Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively).

CONCLUSIONS

Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates.

Higher Hospital Spending on Occupational Therapy Is Associated With Lower Readmission Rates

Hospital executives are under continual pressure to control spending and improve quality. While prior studies have focused on the relationship between overall hospital spending and quality, the relationship between spending on specific services and quality has received minimal attention. The literature thus provides executives limited guidance regarding how they should allocate scarce resources. Using Medicare claims and cost report data, we examined the association between hospital spending for specific services and 30-day readmission rates for heart failure, pneumonia, and acute myocardial infarction. We found that occupational therapy is the only spending category where additional spending has a statistically significant association with lower readmission rates for all three medical conditions. One possible explanation is that occupational therapy places a unique and immediate focus on patients’ functional and social needs, which can be important drivers of readmission if left unaddressed.

Geographic Variation in Trends and Disparities in Acute Myocardial Infarction Hospitalization and Mortality by Income Levels, 1999-2013

IMPORTANCE

During the past decade, the incidence and mortality associated with acute myocardial infarction (AMI) in the United States have decreased substantially. However, it is unknown whether these improvements were consistent across communities of different economic status and geographic regions since efforts to improve cardiovascular disease prevention and management may have had variable impact.

OBJECTIVE

To determine whether trends in US county-level, risk-standardized AMI hospitalization and mortality rates varied by county-based median income level.

DESIGN, SETTING, AND PARTICIPANTS

In this observational study, county-level risk-standardized (age, sex, and race) hospitalization and 1-year mortality rates for AMI from January 1, 1999, to December 31, 2013, were measured for Medicare beneficiaries 65 years or older. Data analysis was performed from June 2 through December 1, 2015. Counties were stratified by median income percentile using 1999 US Census Bureau data adjusted for inflation: low- (<25th), average- (25th-75th), or high- (>75th) income groups.

MAIN OUTCOMES AND MEASURES

The effect of income on the slope of AMI hospitalizations and mortality, measured as differences in the rate of change in AMI hospitalizations and mortality by county income and by the 4 US geographic regions, and a possible lag effect among low-income counties.

RESULTS

In the 15-year study period, AMI risk-standardized hospitalization and mortality rates decreased significantly for all 3 county income groups. Mean hospitalization rates were significantly higher among low-income counties compared with high-income counties in 1999 (1353 vs 1123 per 100 000 person-years, respectively) and in 2013 (853 vs 648 per 100 000 person-years, respectively). One-year mortality rates after hospitalization for AMI were similar across county income groups, decreasing from 1999 (31.5%, 31.4%, and 31.1%, for high-, average-, and low-income counties, respectively) to 2013 (26.2%, 26.1%, and 25.4%, respectively). Income was associated with county-level, risk-standardized AMI hospitalization rates but not mortality rates. Increasing 1 interquartile range of median county consumer price index-adjusted income ($12 000) was associated with a decline in 46 and 37 hospitalizations per 100 000 person-years for 1999 and 2013, respectively; interaction between income and time was 0.56. The rate of decline in AMI hospitalizations was similar for all county income groups; however, low-income counties lagged behind high-income counties by 4.3 (95% CI, 3.1-5.9) years. There were no significant differences in trends across geographic regions.

CONCLUSIONS AND RELEVANCE

Hospitalization and mortality rates of AMI declined among counties of all income levels, although hospitalization rates among low-income counties lag behind those of the higher income groups. These findings lend support for a more targeted, community-based approach to AMI prevention.

Iron deficiency and risk of early readmission following a hospitalization for acute heart failure

AIMS

Early rehospitalization after an episode of acute heart failure (AHF) remains excessively high and its prediction a contemporary challenge. Iron deficiency (ID) is a frequent finding in AHF, but its prognostic implications remain unclear. We sought to evaluate the association between ID and risk of 30-day readmission in an unselected cohort of patients discharged for AHF.

METHODS AND RESULTS

Serum ferritin and transferrin saturation (TSAT) were measured before discharge in 626 consecutive patients with AHF in a single teaching centre. ID was defined as serum ferritin <100 µg/L (absolute ID) or ferritin 100-299 µg/L with a TSAT <20% (functional ID). Cox regression adapted for competing events was used to determine the association between ID and the risk of 30-day readmissions. Mean age was 73.4 ± 10.4 years, 48% were females, and 52.1% showed an LVEF >50%. ID was identified in 463 patients (74%): 302 (48.2%) as absolute ID and 161 (25.7%) as functional ID. At 30-day post-discharge, 20 (3.2%) patients died and 103 (16.5%) were readmitted. Patients with absolute ID showed an increased rate of readmission compared with those with functional ID and no ID (19.9, 13, and 13.5%, respectively, P = 0.005). In a multivariate setting, absolute ID remained associated with higher risk of readmission [hazard ratio (HR) 1.72; 95% confidence interval (CI) 1.13-2.60, P = 0.011]. Compared with patients without ID, functional ID was not related to the risk of readmission (HR 0.87; 95% CI 0.46-1.62, P = 0.652).

CONCLUSION

In patients with AHF, absolute ID, but not functional ID, was associated with an increased risk of early readmission.

Multimorbidity in Older Adults with Heart Failure

Multimorbidity is common among older adults with heart failure and creates diagnostic and management challenges. Diagnosis of heart failure may be difficult, as many conditions commonly found in older persons produce dyspnea, exercise intolerance, fatigue, and weakness; no singular pathognomonic finding or diagnostic test differentiates them from one another. Treatment may also be complicated, as multimorbidity creates high potential for drug-disease and drug-drug interactions in settings of polypharmacy. The authors suggest that management of multimorbid older persons with heart failure be patient, rather than disease-focused, to best meet patients' unique health goals and minimize risk from excessive or poorly-coordinated treatments.

Predicting readmission of heart failure patients using automated follow-up calls

BACKGROUND

Readmission rates for patients with heart failure (HF) remain high. Many efforts to identify patients at high risk for readmission focus on patient demographics or on measures taken in the hospital. We evaluated a method for risk assessment that depends on patient self-report following discharge from the hospital.

METHODS

In this study, we investigated whether automated calls could be used to identify patients who are at a higher risk of readmission within 30 days. An automated multi-call follow-up program was deployed with 1095 discharged HF patients. During each call, the patient reported his or her general health status. Patients were grouped by the trend of their responses over the two calls, and their unadjusted 30-day readmission rates were compared. Pearson's chi-square test was used to evaluate whether readmission risk was independent of response trend.

RESULTS

Of the 1095 patients participating in the program, 837 (76%) responded to the general status question in at least one of the calls and 515 (47%) patients responded to the general status question in both calls. Out of the 89 patients exhibiting a negative response trend, 37% were readmitted. By contrast, the 97 patients showing a positive trend and the 329 patients showing a neutral trend were readmitted at rates of 16% and 14% respectively. The dependence of readmission on trend group was statistically significant (P < 0.0001).

CONCLUSIONS

Patients at an elevated risk of readmission can be identified based on the trend of their responses to automated follow-up calls. This presents a simple method for risk stratification based on patient self-assessment.