Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan

Depression treatment and healthcare expenditures among elderly Medicare beneficiaries with newly diagnosed depression and incident breast, colorectal, or prostate cancer

OBJECTIVES

Depression is associated with high healthcare expenditures, and depression treatment may reduce healthcare expenditures. However, to date, there have not been any studies on the effect of depression treatment on healthcare expenditures among cancer survivors. Therefore, this study examined the association between depression treatment and healthcare expenditures among elderly with depression and incident cancer.

METHODS

The current study used a retrospective longitudinal study design, the linked Surveillance, Epidemiology, and End Results-Medicare database. Elderly (≥66 years) fee-for-service Medicare beneficiaries with newly diagnosed depression and incident breast, colorectal, or prostate cancer (N = 1502) were followed for a period of 12 months after depression diagnosis. Healthcare expenditures were measured every month for a period of 12-month follow-up period. Depression treatment was identified during the 6-month follow-up period. The adjusted associations between depression treatment and healthcare expenditures were analyzed with generalized linear mixed model regressions with gamma distribution and log link after controlling for other factors.

RESULTS

The average 1-year total healthcare expenditures after depression diagnosis were $38 219 for those who did not receive depression treatment; $42 090 for those treated with antidepressants only; $46 913 for those treated with psychotherapy only; and $51 008 for those treated with a combination of antidepressants and psychotherapy. As compared to no depression treatment, those who received antidepressants only, psychotherapy only, or a combination of antidepressants and psychotherapy had higher healthcare expenditures. However, second-year expenditures did not significantly differ among depression treatment categories.

CONCLUSIONS

Among cancer survivors with newly diagnosed depression, depression treatment did not have a significant effect on expenditures in the long term.

Review of early hospitalisation after percutaneous coronary intervention

BACKGROUND

Percutaneous coronary intervention (PCI) is the most common modality of revascularization in patients with coronary artery disease. Understanding the readmission rates and reasons for readmission after PCI is important because readmissions are a quality of care indicator, in addition to being a burden to patients and healthcare services.

METHODS

A literature review was performed. Relevant studies are described by narrative synthesis with the use of tables to summarize study results.

RESULTS

Data suggests that 30-day readmissions are not uncommon. The rate of readmission after PCI is highly influenced by the cohort and the healthcare system studied, with 30-day readmission rates reported to be between 4.7-% and 15.6%. Studies consistently report that a majority of readmissions within 30days are due to a cardiac-related disorders or complication-related disorders. Female sex, peripheral vascular disease, diabetes mellitus, renal failure and non-elective PCI are predictive of readmission. Studies also suggest that there is greater risk of mortality among patients who are readmitted compared to those who are not readmitted.

CONCLUSION

Readmission after PCI is common and its rate is highly influenced by the type of cohort studied. There is clear evidence that majority of readmissions within 30days are cardiac related. While there are many predictors of readmission following PCI, it is not known whether targeting patients with modifiable predictors could prevent or reduce the rates of readmission.

Discharge medication complexity and 30-day heart failure readmissions

BACKGROUND

Limited research exists regarding Medication Regimen Complexity Index (MRCI) and its utility in identifying patients at risk for hospital readmission.

OBJECTIVE

This study evaluates the association between discharge MRCI and 30-day rehospitalization in patients with heart failure (HF) after discharge.

METHODS

The study involved a retrospective, cohort study at a large tertiary teaching facility from the University HealthSystem Consortium. The consortium database was used to identify HF patients hospitalized from January 2011 to December 2013. A 30-day readmission was defined as being readmitted to the same hospital within 30 days of discharge with a principal discharge diagnosis of HF. Index hospitalizations was defined as the first hospitalization, and readmission was the subsequent hospitalization for HF. A pilot analysis was conducted to compare manual MRCI collection tool and a computerized scoring MRCI system. Multivariable logistic regression was used to examine the association of computerized MRCI (≥15) and 30-day rehospitalization after controlling for other variables.

RESULTS

A total of 1,452 patients were included in the study with 81 patients (5.9%) readmitted within 30 days of discharge. The manual and computerized MRCIs were correlated with an R of 0.74 and R² of 0.55. The multivariate logistic regression analysis found computerized MRCI ≥15 (OR: 1.62; 95% CI: 1.01-2.59) was associated with 30-day rehospitalization after controlling for other factors. Patients prescribed angiotensin-converting-enzyme inhibitors or angiotensin receptor blockers, were less likely (OR: 0.54; CI: 0.33-0.89) to be readmitted 30 days after discharge, and patients with coronary artery disease were more likely (OR: 1.76; CI: 1.03-3.00) to be readmitted 30 days after discharge.

CONCLUSIONS

The computerized MRCI score was moderately correlated with manual MRCI score. A significant association was found between computerized MRCI and 30-day HF readmission. Such predictive tools may allow pharmacists to prioritize patient care and optimize patient outcomes through medication therapy management.

Patterns of scheduled follow-up appointments following hospitalization for heart failure: insights from an urban medical center in the United States

Objectives

Although postdischarge outpatient follow-up appointments after a hospitalization for heart failure represent a potentially effective strategy to prevent heart failure readmissions, patterns of scheduled follow-up appointments upon discharge are poorly described. We aimed to characterize real-world patterns of scheduled follow-up appointments among adult patients with heart failure upon hospital discharge.

Patients and methods

This was a retrospective cohort study performed at a large urban academic center in the United States among adults hospitalized with a principal diagnosis of congestive heart failure between January 1, 2013, and December 31, 2014. Patient demographics, administrative data, clinical parameters, echocardiographic indices, and scheduled postdischarge outpatient follow-up appointments were collected.

Results

Of the 796 patients hospitalized for heart failure, just over half of the cohort had a scheduled follow-up appointment upon discharge. Follow-up appointments were less likely among patients who were white and had heart failure with preserved ejection fraction and more likely among patients with Medicaid and chronic obstructive pulmonary disease. In an adjusted multivariable regression model, age ≥65 years was inversely associated with a scheduled follow-up appointment upon hospital discharge, despite higher rates of several cardiovascular and noncardiovascular comorbidities.

Conclusion

Just half of the patients discharged home following a hospitalization for heart failure had a follow-up appointment scheduled, representing a missed opportunity to provide a recommended care transition intervention. Despite a greater burden of both cardiovascular and noncardiovascular comorbidities, older adults (age ≥65 years) were less likely to have a follow-up appointment scheduled upon discharge compared with younger adults, revealing a disparity that warrants further investigation.

Risk Factors for Readmission in Patients With Cancer Comanaged by Hospitalists

Little is known about which variables put patients with cancer at risk for 30-day hospital readmission. Comanagement of this often complex patient population by specialists and hospitalists has become increasingly common. This retrospective study examined inpatients with cancer comanaged by hospitalists, hematologists, and oncologists to determine the rate of readmission and factors associated with readmission. Patients in this cohort had a readmission rate of 23%. Patients who were discharged to a skilled nursing facility (odds ratio [OR] = 0.34) or hospice (OR = 0.11) were less likely to have 30-day readmissions, whereas patients who had surgery (OR = 3.16) during their index admission were more likely. Other factors, including patient demographics, cancer types, and hospitalization interventions and events, did not differ between patients who were readmitted and those who were not. These findings contribute to a growing body of literature identifying risk factors for readmission in medical oncology and hematology patients.

Evaluation of prediction strategy and care coordination for COPD readmissions

OBJECTIVES

Factors that influence the likelihood of readmission for chronic obstructive pulmonary disease (COPD) patients and the impact of posthospital care coordination remain uncertain. LACE index (L = length of stay, A = Acuity of admission; C = Charlson comorbidity index; E = No. of emergency department (ED) visits in last 6 months) is a validated tool for predicting 30-days readmissions for general medicine patients. We aimed to identify variables predictive of COPD readmissions including LACE index and determine the impact of a novel care management process on 30-day all-cause readmission rate.

METHODS

In a case-control design, potential readmission predictors including LACE index were analyzed using multivariable logistic regression for 461 COPD patients between January-October 2013. Patients with a high LACE index at discharge began receiving care coordination in July 2013. We tested for association between readmission and receipt of care coordination between July-October 2013. Care coordination consists of a telephone call from the care manager who: 1) reviews discharge instructions and medication reconciliation; 2) emphasizes importance of medication adherence; 3) makes a follow-up appointment with primary care physician within 1-2 weeks and; 4) makes an emergency back-up plan.

RESULTS

COPD readmission rate was 16.5%. An adjusted LACE index of ≥ 13 was not associated with readmission (p = 0.186). Significant predictors included female gender (odds ratio [OR] 0.51, 95% confidence interval [CI] 0.29-0.91, p = 0.021); discharge to skilled nursing facility (OR 3.03, 95% CI 1.36-6.75, p = 0.007); 4-6 comorbid illnesses (OR 9.21, 95% CI 1.17-76.62, p = 0.035) and ≥ 4 ED visits in previous 6 months (OR 6.40, 95% CI 1.25-32.87, p = 0.026). Out of 119 patients discharged between July-October 2013, 41% received the care coordination. The readmission rate in the intervention group was 14.3% compared to 18.6% in controls (p = 0.62).

CONCLUSIONS

Factors influencing COPD readmissions are complex and poorly understood. LACE index did not predict 30-days all-cause COPD readmissions. Posthospital care coordination for transition of care from hospital to the community showed a 4.3% reduction in the 30-days all-cause readmission rate which did not reach statistical significance (p = 0.62).

Hospital Transfers of Skilled Nursing Facility (SNF) Patients Within 48 Hours and 30 Days After SNF Admission

BACKGROUND

Close to 1 in 5 patients admitted to a skilled nursing facility (SNF) are readmitted to the acute hospital within 30 days, and a substantial percentage are readmitted within 2 days of the SNF admission. These rapid returns to the hospital may provide insights for improving care transitions between the acute hospital and the SNF.

OBJECTIVES

To describe the characteristics of SNF to hospital transfers that occur within 48 hours and 30 days of SNF admission based on root cause analyses (RCAs) performed by SNF staff, and identify potential areas of focus for improving transitions between hospitals and SNFs.

DESIGN

Trained staff from SNFs enrolled in a randomized, controlled clinical trial of the INTERACT (Interventions to Reduce Acute Care Transfers) quality improvement program performed retrospective RCAs on hospital transfers during a 12-month implementation period.

SETTING

SNFs from across the United States.

PARTICIPANTS

64 of 88 SNFs randomized to the intervention group submitted RCAs.

INTERVENTIONS

SNFs were implementing the INTERACT quality improvement program.

MEASURES

Data were abstracted from the INTERACT Quality Improvement (QI) tool, a structured, retrospective RCA on hospital transfers.

RESULTS

Among 4658 transfers for which data on the time between SNF admission and hospital transfer were available, 353 (8%) occurred within 48 hours of SNF admission, 524 (11%) 3 to 6 days after SNF admission, 1450 (31%) 7 to 29 days after SNF admission, and 2331 (50%) occurred 30 days or longer after admission. Comparisons between transfers that occurred within 48 hours and within 30 days of SNF admission to transfers that occurred 30 days or longer after SNF admission revealed several statistically significant differences between patient risk factors for transfer, symptoms and signs precipitating the transfers, and other characteristics of the transfers. Hospitalization in the last 30 days and year was significantly more common among those with rapid returns to the hospital. Shortness of breath was significantly more common among those transferred within 48 hours or 30 days, and falls, functional decline, suspected respiratory infection, and new urinary incontinence less common. SNF staff rated a higher proportion of transfers within 30 days versus 30 days or longer as potentially preventable (25.1% vs 21.5%, P = .005). Case descriptions derived from the QI tools of transfers back to the hospital within 48 hours of SNF admission illustrate several factors underlying these rapid returns to the hospital.

CONCLUSION

RCAs on transfers back to the hospital shortly after SNF admission provide insights into strategies that both hospitals and SNFs can consider in collaborative efforts to reduce potentially avoidable hospital readmissions.

Factors Associated with Inpatient Hospital (Re)admissions in Medicare Beneficiaries in Need of Food Assistance

Little is known about pathways underlying inpatient hospital (re)admissions in older adults unable to meet basic needs. This study examined the factors associated with (re)admissions in a sample of low-income older Medicare beneficiaries in need of food assistance in Georgia in 2008 (N = 892, mean age 75.4 ± 8.8 years, 30.3% Black, 68.5% female). About 35.3% of the sample experienced 1 + hospital (re)admissions. (Re)admissions were significantly more likely in individuals who requested Older Americans Act Nutrition Program Home Delivered Meals services (OR 2.3; 95% CI 1.4, 3.8), had more outpatient emergency room visits (1 visit: OR 2.1; 95% CI 1.4, 3.1; 2+ visits: OR 3.6; 95% CI 2.4, 5.4), and experienced greater multimorbidity (OR 1.6; 95% CI 1.4, 3.1). Support for home and community-based services may be critical in reducing potentially avoidable inpatient hospital (re)admissions.

Food Anxiety Is Associated with Poor Health Status Among Recently Hospital-Discharged Older Adults

Older adults returning home from the hospital may encounter health issues that cause anxiety about their ability to obtain enough food. Home-delivered meal (HDM) programs support nutritional needs and improve food security of those who cannot provide for themselves. A study conducted in six states examined feelings of anxiety about getting enough food in older adults (aged 60 years and older), comparing three time points: prior to hospitalization, at hospitalization (n = 566) and after receiving HDMs for two months posthospitalization (n = 377). Food anxiety during hospitalization was significantly higher among Hispanic ethnicity, current and former smokers, diabetics, and those who eat alone or have difficulty shopping. Food anxiety was significantly lower from baseline to two months follow-up (P < 0.0001), and participants showed improvements in certain coping strategies they used to get their meals. Indicators of food anxiety can help the health care system and community nutrition programs target those at highest risk of negative health outcomes.

Risk Factors for the Discontinuation of Home Medical Care among Low-functioning Older Patients

OBJECTIVES

Older patients receiving home medical care often have declining functional status and multiple disease conditions. It is important to identify the risk factors for care transition events in this population in order to avoid preventable transitions. In the present study, therefore, we investigated the factors associated with discontinuation of home medical care as a potentially preventable care transition event in older patients.

METHODS

Baseline data for participants in the Observational study of Nagoya Elderly with HOme MEdical (ONEHOME) study and data on the mortality, institutionalization, or hospitalisation of the study participants during a 2-year follow-up period were used. Discontinuation of home care was defined as admission to a hospital for any reason, institutionalization, or death. Univariate and multivariate Cox hazard models were used to assess the association of each of the factors with the discontinuation of home care during the observational period. The covariates included in the multivariate analysis were those significantly associated with the discontinuation of home care at the level of P<0.05 in the univariate analysis.

RESULTS

The univariate Cox hazard model revealed that a low hemoglobin level (< 11g/dL), low serum albumin level (< 3g/dL), higher Charlson Comorbidity Index score, and low Mini Nutritional Assessment Short Form score (< 7) were significantly associated with the discontinuation of home care. A multivariate Cox hazard model including these four factors demonstrated that all four were independently associated with home-care discontinuation.

CONCLUSIONS

The present results demonstrated that anemia, hypoalbuminemia, malnourishment, and the presence of serious comorbidities were associated with the discontinuation of home medical care among low-functioning older patients.

Readmissions of medical patients: an external validation of two existing prediction scores

BACKGROUND

Hospital readmissions are increasingly used as a quality indicator with a belief that they are a marker of poor care and have led to financial penalties in UK and USA. Risk scoring systems, such as LACE and HOSPITAL, have been proposed as tools for identifying patients at high risk of readmission but have not been validated in international populations.

AIM

To perform an external independent validation of the HOSPITAL and LACE scores.

DESIGN

An unplanned secondary cohort study.

METHODS

Patients admitted to the medical admission unit at the Hospital of South West Jutland (10/2008-2/2009; 2/2010-5/2010) and the Odense University Hospital (6/2009-8/2011) were analysed. Validation of the scores using 30 day readmissions as the endpoint was performed.

RESULTS

A total of 19 277 patients fulfilled the inclusion criteria. Median age was 67 (range 18-107) years and 8977 (46.6%) were female. The LACE score had a discriminatory power of 0.648 with poor calibration and the HOSPITAL score had a discriminatory power of 0.661 with poor calibration. The HOSPITAL score was significantly better than the LACE score for identifying patients at risk of 30 day readmission (P < 0.001). The discriminatory power of both scores decreased with increasing age.

CONCLUSION

Readmissions are a complex phenomenon with not only medical conditions contributing but also system, cultural and environmental factors exerting a significant influence. It is possible that the heterogeneity of the population and health care systems may prohibit the creation of a simple prediction tool that can be used internationally.

Health Literacy and Time Spent With a Hand Surgeon

PURPOSE

To characterize the relationship between health literacy and duration of new hand surgery office visits.

METHODS

Using a stopwatch from outside the room, we measured the duration of the visit (minutes of face-to-face contact between attending surgeon and patient) for 224 new patients presenting to 1 of 5 orthopedic hand surgeons (D.R.). Directly after the visit, patients were asked to complete the Newest Vital Sign (NVS) health literacy test, a sociodemographic survey, and 3 Patient-Reported Outcomes Measurement Information System-based questionnaires: Pain Interference, Upper Extremity Function, and Depression. The Newest Vital Sign scores were divided into limited (0-3) and adequate (4-6) health literacy. Medical records were reviewed to collect data on diagnosis, visit type, management, and whether patients were first seen by a resident/fellow. Multiple linear regression modeling was used to characterize the association between health literacy and duration of visit while controlling for the effect of other patient and visit characteristics.

RESULTS

The unadjusted mean visit duration was 1.9 minutes shorter in patients with limited health literacy (9.4 minutes) than in patients with adequate health literacy (11.3 minutes), and this difference persisted after adjustment for a broad range of patient and visit characteristics. Greater magnitude of disability was associated with longer visits, as were second-opinion appointments, a diagnosis of nonspecific arm pain or compression neuropathy, and appointments in which operative management was chosen. Visits in which a resident/fellow saw the patient first were shorter than visits without resident/fellow assistance.

CONCLUSIONS

The finding that limited health literacy correlated with shorter visits may suggest that patients who may stand to benefit the most from detailed health education and counseling received less.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prognostic II.

Medication review in hospitalised patients to reduce morbidity and mortality

BACKGROUND

Pharmacotherapy in the elderly population is complicated by several factors that increase the risk of drug-related harms and less favourable effectiveness. The concept of medication review is a key element in improving the quality of prescribing and in preventing adverse drug events. Although there is no generally accepted definition of medication review, it can be broadly defined as a systematic assessment of pharmacotherapy for an individual patient that aims to optimise patient medication by providing a recommendation or by making a direct change. Medication review performed in adult hospitalised patients may lead to better patient outcomes.

OBJECTIVES

We examined whether delivery of a medication review by a physician, pharmacist or other healthcare professional leads to improvement in health outcomes of hospitalised adult patients compared with standard care.

SEARCH METHODS

We searched the Specialised Register of the Cochrane Effective Practice and Organisation of Care (EPOC) Group; the Cochrane Central Register of Controlled Trials (CENTRAL); MEDLINE; EMBASE; and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) to November 2014, as well as International Pharmaceutical Abstracts and Web of Science to May 2015. In addition, we searched reference lists of included trials and relevant reviews. We searched trials registries and contacted experts to identify additional published and unpublished trials. We applied no language restrictions.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) of medication review in hospitalised adult patients. We excluded trials of outclinic and paediatric patients. Our primary outcome was all-cause mortality, and secondary outcomes included hospital readmissions, emergency department contacts and adverse drug events.

DATA COLLECTION AND ANALYSIS

Two review authors independently included trials, extracted data and assessed trials for risk of bias. We contacted trial authors for clarification of data and for additional unpublished data. We calculated risk ratios for dichotomous data and mean differences for continuous data (with 95% confidence intervals (CIs)). The GRADE (Grades of Recommendation, Assessment, Development and Evaluation) approach was used to assess the overall certainty of evidence for the most important outcomes.

MAIN RESULTS

We identified 6600 references (4647 references in our initial review) and included 10 trials (3575 participants). Follow-up ranged from 30 days to one year. Nine trials provided mortality data (3218 participants, 466 events), with a risk ratio of 1.02 (95% CI 0.87 to 1.19) (low-certainty evidence). Seven trials provided hospital readmission data (2843 participants, 1043 events) with a risk ratio of 0.95 (95% CI 0.87 to 1.04) (high-certainty evidence). Four trials provided emergency department contact data (1442 participants, 244 events) with a risk ratio of 0.73 (95% CI 0.52 to 1.03) (low-certainty evidence). The estimated reduction in emergency department contacts of 27% (with a CI ranging from 48% reduction to 3% increase in contacts) corresponds to a number needed to treat for an additional beneficial outcome of 37 for a low-risk population and 12 for a high-risk population over one year. Subgroup and sensitivity analyses did not significantly alter our results.

AUTHORS' CONCLUSIONS

We found no evidence that medication review reduces mortality or hospital readmissions, although we did find evidence that medication review may reduce emergency department contacts. However, because of short follow-up ranging from 30 days to one year, important treatment effects may have been overlooked. High-quality trials with long-term follow-up (i.e. at least up to a year) are needed to provide more definitive evidence for the effect of medication review on clinically important outcomes such as mortality, readmissions and emergency department contacts, and on outcomes such as adverse events. Therefore, if used in clinical practice, medication reviews should be undertaken as part of a clinical trial with long-term follow-up.

Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data

INTRODUCTION

Unplanned 30-day hospital readmission account for roughly $17 billion in annual Medicare spending. Many factors contribute to unplanned hospital readmissions and multiple models have been developed over the years to predict them. Most researchers have used insurance claims or administrative data to train and operationalize their Readmission Risk Prediction Models (RRPMs). Some RRPM developers have also used electronic health records data; however, using health informatics exchange data has been uncommon among such predictive models and can be beneficial in its ability to provide real-time alerts to providers at the point of care.

METHODS

We conducted a semi-systematic review of readmission predictive factors published prior to March 2013. Then, we extracted and merged all significant variables listed in those articles for RRPMs. Finally, we matched these variables with common HL7 messages transmitted by a sample of health information exchange organizations (HIO).

RESULTS

The semi-systematic review resulted in identification of 32 articles and 297 predictive variables. The mapping of these variables with common HL7 segments resulted in an 89.2% total coverage, with the DG1 (diagnosis) segment having the highest coverage of 39.4%. The PID (patient identification) and OBX (observation results) segments cover 13.9% and 9.1% of the variables. Evaluating the same coverage in three sample HIOs showed data incompleteness.

DISCUSSION

HIOs can utilize HL7 messages to develop unique RRPMs for their stakeholders; however, data completeness of exchanged messages should meet certain thresholds. If data quality standards are met by stakeholders, HIOs would be able to provide real-time RRPMs that not only predict intra-hospital readmissions but also inter-hospital cases.

CONCLUSION

A RRPM derived using HIO data exchanged through may prove to be a useful method to prevent unplanned hospital readmissions. In order for the RRPM derived from HIO data to be effective, hospitals must actively exchange clinical information through the HIO and develop actionable methods that integrate into the workflow of providers to ensure that patients at high-risk for readmission receive the care they need.

Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach

INTRODUCTION

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare".

BACKGROUND

Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners.

OBJECTIVES

Explore the use of conditional logistic regression to increase the prediction accuracy.

METHODS

We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models.

RESULTS

The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures.

CONCLUSIONS

It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.

Contextual, organizational and ecological effects on the variations in hospital readmissions of rural Medicare beneficiaries in eight southeastern states

The enactment of the Patient Protection and Affordable Care Act (ACA) has been expected to improve the coverage of health insurance, particularly as related to the coordination of seamless care and the continuity of elder care among Medicare beneficiaries. The analysis of longitudinal data (2007 through 2013) in rural areas offers a unique opportunity to examine trends and patterns of rural disparities in hospital readmissions within 30 days of discharge among Medicare beneficiaries served by rural health clinics (RHCs) in the eight southeastern states of the Department of Health & Human Services (DHHS) Region 4. The purpose of this study is twofold: first, to examine rural trends and patterns of hospital readmission rates by state and year (before and after the ACA enactment); and second, to investigate how contextual (county characteristic), organizational (clinic characteristic) and ecological (aggregate patient characteristic) factors may influence the variations in repeat hospitalizations. The unit of analysis is the RHC. We used administrative data compiled from multiple sources for the Centers of Medicare and Medicaid Services for a period of seven years. From 2007 to 2008, risk-adjusted readmission rates increased slightly among Medicare beneficiaries served by RHCs. However, the rate declined in 2009 through 2013. A generalized estimating equation of sixteen predictors was analyzed for the variability in risk-adjusted readmission rates. Nine predictors were statistically associated with the variability in risk-adjusted readmission rates of the RHCs pooled from 2007 through 2013 together. The declined rates were associated with by the ACA effect, Georgia, North Carolina, South Carolina, and the percentage of elderly population in a county where RHC is located. However, the increase of risk-adjusted rates was associated with the percentage of African Americans in a county, the percentage of dually eligible patients, the average age of patients, and the average clinical visits by African American patients. The sixteen predictors accounted for 21.52 % of the total variability in readmissions. This study contributes to the literature in health disparities research from the contextual, organizational and ecological perspectives in the analysis of longitudinal data. The synergism of multiple contextual, organizational and ecological factors, as shown in this study, should be considered in the design and implementation of intervention studies to address the problem of hospital readmissions through prevention and enhancement of disease management of rural Medicare beneficiaries.

Unplanned 30-Day Readmissions in a General Internal Medicine Hospitalist Service at a Comprehensive Cancer Center

PURPOSE

Hospital readmissions are considered by the Centers for Medicare and Medicaid as a metric for quality of health care delivery. Robust data on the readmission profile of patients with cancer are currently insufficient to determine whether this measure is applicable to cancer hospitals as well. To address this knowledge gap, we estimated the unplanned readmission rate and identified factors influencing unplanned readmissions in a hospitalist service at a comprehensive cancer center.

METHODS

We retrospectively analyzed unplanned 30-day readmission of patients discharged from the General Internal Medicine Hospitalist Service at a comprehensive cancer center between April 1, 2012, and September 30, 2012. Multiple independent variables were studied using univariable and multivariable logistic regression models, with generalized estimating equations to identify risk factors associated with readmissions.

RESULTS

We observed a readmission rate of 22.6% in our cohort. The median time to unplanned readmission was 10 days. Unplanned readmission was more likely in patients with metastatic cancer and those with three or more comorbidities. Patients discharged to hospice were less likely to be readmitted (all P values < .01).

CONCLUSION

We observed a high unplanned readmission rate among our population of patients with cancer. The risk factors identified appear to be related to severity of illness and open up opportunities for improving coordination with primary care physicians, oncologists, and other specialists to manage comorbidities, or perhaps transition appropriate patients to palliative care. Our findings will be instrumental for developing targeted interventions to help reduce readmissions at our hospital. Our data also provide direction for appropriate application of readmission quality measures in cancer hospitals.

Geriatric assessment as predictors of hospital readmission in older adults with cancer

BACKGROUND

Hospital readmission is a common, costly problem. Little is known regarding risk factors for readmission in older adults with cancer. This study aims to identify factors associated with 30-day readmission in a cohort of older medical oncology patients.

SETTING/PARTICIPANTS

Adults age 65 and over hospitalized to an Oncology Acute Care for Elders Unit at Barnes-Jewish Hospital.

MEASUREMENTS

Standard geriatric screening tests were administered in routine clinical care. Clinical data and 30-day readmission status were obtained through medical record review.

RESULTS

677 patients met the inclusion criteria. 77% were white and 53% were male. Thoracic (32%), hematologic (20%), and gastrointestinal (18%) malignancies were most common. The 30-day unplanned readmission rate was 35.2%. Multivariable analyses identified complete dependence in feeding (odds ratio [OR], 3.70; 95% confidence interval [CI], 1.29-10.65), and some dependence (1.58, 1.04-2.41) and complete dependence (2.64, 1.70-4.12) in housekeeping, prior to admission, as associated with higher odds of readmission. Age<75 (1.49, 1.04-2.14), African-American race (1.59, 1.06-2.39), potentially inappropriate medications (1.36, 0.94-1.99), and higher-risk reasons for index admission (1.93, 1.34-2.78) also increased odds of readmission. These factors were organized into a prognostic index.

CONCLUSION

Hospital readmission was common and higher than previously reported rates in general medical populations. We identified several previously unrecognized factors associated with increased risk for readmission, including some geriatric assessment parameters, and developed a practical tool that can be used by clinicians to assess risk of 30-day readmission.

Assessing Community Quality of Health Care

OBJECTIVE

To determine the agreement of measures of care in different settings-hospitals, nursing homes (NHs), and home health agencies (HHAs)-and identify communities with high-quality care in all settings.

DATA SOURCES/STUDY SETTING

Publicly available quality measures for hospitals, NHs, and HHAs, linked to hospital service areas (HSAs).

STUDY DESIGN

We constructed composite quality measures for hospitals, HHAs, and nursing homes. We used these measures to identify HSAs with exceptionally high- or low-quality of care across all settings, or only high hospital quality, and compared these with respect to sociodemographic and health system factors.

PRINCIPAL FINDINGS

We identified three dimensions of hospital quality, four HHA dimensions, and two NH dimensions; these were poorly correlated across the three care settings. HSAs that ranked high on all dimensions had more general practitioners per capita, and fewer specialists per capita, than HSAs that ranked highly on only the hospital measures.

CONCLUSION

Higher quality hospital, HHA, and NH care are not correlated at the regional level; regions where all dimensions of care are high differ systematically from regions which score well on only hospital measures and from those which score well on none.

Patient-reported geriatric symptoms as risk factors for hospitalization and emergency department visits

There is an urgent need to identify predictors of adverse outcomes and increased health care utilization in the elderly. The Mayo Ambulatory Geriatric Evaluation (MAGE) is a symptom questionnaire that was completed by patients aged 65 years and older during office visits to Primary Care Internal Medicine at Mayo Clinic in Rochester, MN. It was introduced to improve screening for geriatric conditions. We conducted this study to explore the relationship between self-reported geriatric symptoms and hospitalization and emergency department (ED) visits within 1 year of completing the survey. This was a retrospective cohort study of patients who completed the MAGE from April 2008 to December 2010. The primary outcome was an ED visit or hospitalization within 1 year. Predictors included responses to individual questions in the MAGE. Data were obtained from the electronic medical record and administrative records. Logistic regression analyses were performed from significant univariate factors to determine predictors in a multivariable setting. A weighted scoring system was created based upon the odds ratios derived from a bootstrap process. The sensitivity, specificity, and AUC were calculated using this scoring system. The MAGE survey was completed by 7738 patients. The average age was 76.2 ± 7.68 years and 57% were women. Advanced age, a self-report of worse health, history of 2 or more falls, weight loss, and depressed mood were significantly associated with hospitalization or ED visits within 1 year. A score equal to or greater than 2 had a sensitivity of 0.74 and specificity of 0.45. The calculated AUC was 0.60. The MAGE questionnaire, which was completed by patients at an outpatient visit to screen for common geriatric issues, could also be used to assess risk for ED visits and hospitalization within 1 year.

Health literacy in hand surgery patients: a cross-sectional survey

PURPOSE

To determine the prevalence of and factors associated with limited health literacy among outpatients presenting to an urban academic hospital-based hand surgeon.

METHODS

A cohort of 200 English- and Spanish-speaking patients completed the Newest Vital Sign (NVS) health literacy assessment tool, a sociodemographic survey, and 2 Patient-Reported Outcomes Measurement Information System-based computerized adaptive testing questionnaires: Patient-Reported Outcomes Measurement Information System Pain Interference and Upper-Extremity Function. The NVS scores were divided into limited (0-3) and adequate (4-6) health literacy. Multivariable regression modeling was used to identify independent predictors of limited health literacy.

RESULTS

A total of 86 patients (43%) had limited health literacy (English-speaking: 33%; Spanish-speaking: 100%). Factors associated with limited health literacy were advanced age, lower income, and being publicly insured or uninsured. Increasing years of education was a protective factor. Primary language was not included in the logistic regression model because all Spanish-speaking patients had limited health literacy. When evaluating health literacy on a continuum, primary language was the factor that most influenced the NVS scores, accounting for 14% of the variability.

CONCLUSIONS

Limited health literacy was commonplace among patients seeing a hand surgeon, more so in elderly and disadvantaged individuals. We hope our study raises awareness of this issue among hand surgeons and encourages providers to simplify messages and improve communication strategies.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prognostic II.

The association of acute kidney injury in the critically ill and postdischarge outcomes: a cohort study*

OBJECTIVE

Hospital readmissions contribute significantly to the cost of inpatient care and are targeted as a marker for quality of care. Little is known about risk factors associated with hospital readmission in survivors of critical illness. We hypothesized that acute kidney injury in patients who survived critical care would be associated with increased risk of 30-day postdischarge hospital readmission, postdischarge mortality, and progression to end-stage renal disease.

DESIGN

Two center observational cohort study.

SETTING

Medical and surgical ICUs at the Brigham and Women's Hospital and the Massachusetts General Hospital in Boston, Massachusetts.

PATIENTS

We studied 62,096 patients, 18 years old and older, who received critical care between 1997 and 2012 and survived hospitalization.

INTERVENTIONS

None

MEASUREMENTS AND MAIN RESULTS

: All data was obtained from the Research Patient Data Registry at Partners HealthCare. The exposure of interest was acute kidney injury defined as meeting Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease Risk, Injury or Failure criteria occurring 3 days prior to 7 days after critical care initiation. The primary outcome was hospital readmission in the 30 days following hospital discharge. The secondary outcome was mortality in the 30 days following hospital discharge. Adjusted odds ratios were estimated by multivariable logistic regression models with inclusion of covariate terms thought to plausibly interact with both acute kidney injury and readmission status. Adjustment included age, race (white vs nonwhite), gender, Deyo-Charlson Index, patient type (medical vs surgical) and sepsis. Additionally, long-term progression to End Stage Renal Disease in patients with acute kidney injury was analyzed with a risk-adjusted Cox proportional hazards regression model. The absolute risk of 30-day readmission was 12.3%, 19.0%, 21.2%, and 21.1% in patients with No Acute Kidney Injury, Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease class Risk, Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease class Injury, and Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease class Failure, respectively. In patients who received critical care and survived hospitalization, acute kidney injury was a robust predictor of hospital readmission and post-discharge mortality and remained so following multivariable adjustment. The odds of 30-day post-discharge hospital readmission in patients with Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease class Risk, Injury, or Failure fully adjusted were 1.44 (95% CI, 1.25-1.66), 1.98 (95% CI, 1.66-2.36), and 1.55 (95% CI, 1.26-1.91) respectively, relative to patients without acute kidney injury. Further, the odds of 30-day post-discharge mortality in patients with Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease class Risk, Injury, or Failure fully adjusted per our primary analysis were 1.39 (95% CI, 1.28-1.51), 1.46 (95% CI, 1.30-1.64), and 1.42 (95% CI, 1.26-1.61) respectively, relative to patients without acute kidney injury. The addition of the propensity score to the multivariable model did not change the point estimates significantly. Finally, taking into account age, gender, race, Deyo-Charlson Index, and patient type, we observed a relationship between acute kidney injury and development of end-stage renal disease. Patients with Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease class Risk, Injury, Failure experienced a significantly higher risk of end-stage renal disease during follow-up than patients without acute kidney injury (hazard ratio, 2.03; 95% CI, 1.56-2.65; hazard ratio, 3.99; 95% CI, 3.04-5.23; hazard ratio, 10.40; 95% CI, 8.54-12.69, respectively).

CONCLUSIONS

Patients who suffer acute kidney injury are among a high-risk group of ICU survivors for adverse outcomes. In patients treated with critical care who survive hospitalization, acute kidney injury is a robust predictor of subsequent unplanned hospital readmission. In critical illness survivors, acute kidney injury is also associated with the odds of 30-day postdischarge mortality and the risk of subsequent end-stage renal disease.

Determination of risk factors for drug-related problems: a multidisciplinary triangulation process

INTRODUCTION AND OBJECTIVES

Drug-related problems (DRPs) constitute a frequent safety issue among hospitalised patients leading to patient harm and increased healthcare costs. Because many DRPs are preventable, the specific risk factors that facilitate their occurrence are of considerable interest. The objective of our study was to assess risk factors for the occurrence of DRPs with the intention to identify patients at risk for DRPs to guide and target preventive measures where they are needed most in patients.

DESIGN

Triangulation process using a mixed methods approach.

METHODS

We conducted an expert panel, using the nominal group technique (NGT) and a qualitative analysis, to gather risk factors for DRPs. The expert panel consisted of two consultant hospital physicians (internal medicine and geriatrics), one emergency physician, one independent general practitioner, one clinical pharmacologist, one clinical pharmacist, one registered nurse, one home care nurse and two independent community pharmacists. The literature was searched for additional risk factors. Gathered factors from the literature search and the NGT were assembled and validated in a two-round Delphi questionnaire.

RESULTS

The NGT resulted in the identification of 33 items with 13 additional risk factors from the qualitative analysis of the discussion. The literature search delivered another 39 risk factors. The 85 risk factors were refined to produce 42 statements for the Delphi online questionnaire. Of these, 27 risk factors were judged to be 'important' or 'rather important'.

CONCLUSIONS

The gathered risk factors may help to characterise and identify patients at risk for DRPs and may enable clinical pharmacists to guide and target preventive measures in order to limit the occurrence of DRPs. As a further step, these risk factors will serve as the basis for a screening tool to identify patients at risk for DRPs.

A tool for prediction of risk of rehospitalisation and mortality in the hospitalised elderly: secondary analysis of clinical trial data

OBJECTIVES

To construct and internally validate a risk score, the '80+ score', for revisits to hospital and mortality for older patients, incorporating aspects of pharmacotherapy. Our secondary aim was to compare the discriminatory ability of the score with that of three validated tools for measuring inappropriate prescribing: Screening Tool of Older Person's Prescriptions (STOPP), Screening Tool to Alert doctors to Right Treatment (START) and Medication Appropriateness Index (MAI).

SETTING

Two acute internal medicine wards at Uppsala University hospital. Patient data were used from a randomised controlled trial investigating the effects of a comprehensive clinical pharmacist intervention.

PARTICIPANTS

Data from 368 patients, aged 80 years and older, admitted to one of the study wards.

PRIMARY OUTCOME MEASURE

Time to rehospitalisation or death during the year after discharge from hospital. Candidate variables were selected among a large number of clinical and drug-specific variables. After a selection process, a score for risk estimation was constructed. The 80+ score was internally validated, and the discriminatory ability of the score and of STOPP, START and MAI was assessed using C-statistics.

RESULTS

Seven variables were selected. Impaired renal function, pulmonary disease, malignant disease, living in a nursing home, being prescribed an opioid or being prescribed a drug for peptic ulcer or gastroesophageal reflux disease were associated with an increased risk, while being prescribed an antidepressant drug (tricyclic antidepressants not included) was linked to a lower risk of the outcome. These variables made up the components of the 80+ score. The C-statistics were 0.71 (80+), 0.57 (STOPP), 0.54 (START) and 0.63 (MAI).

CONCLUSIONS

We developed and internally validated a score for prediction of risk of rehospitalisation and mortality in hospitalised older people. The score discriminated risk better than available tools for inappropriate prescribing. Pending external validation, this score can aid in clinical identification of high-risk patients and targeting of interventions.

Barriers to discharge in an acute care medical teaching unit: a qualitative analysis of health providers' perceptions

BACKGROUND

The complex process of discharging patients from acute care to community care requires a multifaceted interaction between all health care providers and patients. Poor communication in a patient's discharge can result in post hospital adverse events, readmission, and mortality. Because of the gravity of these problems, discharge planning has been emphasized as a potential solution. The purpose of this paper is to identify communication barriers to effective discharge planning in an acute care unit of a tertiary care center and to suggest solutions to these barriers.

METHODS

Health care providers provided comments to a single open-ended question: "What are the communication barriers between the different health care providers that limit an effective discharge of patients from Unit 36?" We conducted qualitative thematic analysis by identifying themes related to communication barriers affecting a successful discharge process.

RESULTS

Three broad themes related to barriers to the discharge process were identified: communication, lack of role clarity and lack of resources. We also identified two themes for opportunities for improvement, ie, structure and function of the medical team and need for leadership.

CONCLUSION

While it was evident that poor communication was an overarching barrier identified by health care providers, other themes emerged. In an effort to increase inter-team communication, "bullet rounds", a condensed form of discharge rounds, were introduced to the medical teaching unit and occurred on a daily basis between the multidisciplinary team. To help facilitate provider-patient communication, electronic transfer of care summaries were suggested as a potential solution. To help role clarity, a discharge coordinator and/or liaison was suggested. Communication can be enhanced through use of electronic discharge summaries, bullet rounds, and implementation of a discharge coordinator(s). The findings from this study can be used to aid future researchers in devising appropriate discharging strategies that are focused around the patient and inter-health care provider communication.

Reflective Practice: A Tool for Readmission Reduction

Factors intrinsic to local practice, but not captured by the medical record, contribute to readmissions. Frontline providers familiar with their practice systems can identify these. The objective was to decrease 30-day hospital readmissions. The intervention involved retrospective review by hospitalists of their own patients' readmissions, using reflective practice guided by a chart review tool. Subjects were patients discharged by hospitalists and readmitted to a tertiary care academic medical center. Hospitalists reviewed 193 readmissions of 170 patients. Factors contributing to readmission were grouped under patient characteristics, operational factors, and care transition. After reflection, physicians scheduled earlier follow-up appointments while nurse practitioners and physician assistants improved discharge instructions. Readmissions decreased during the review period, and the decrease sustained for one year after the review period. Hospitalists reflected on and identified local practice factors that contributed to their own patients' 30-day readmissions. Reflective practice may be an effective strategy to decrease hospital readmissions.

Community factors and hospital readmission rates

OBJECTIVE

To examine the relationship between community factors and hospital readmission rates.

DATA SOURCES/STUDY SETTING

We examined all hospitals with publicly reported 30-day readmission rates for patients discharged during July 1, 2007, to June 30, 2010, with acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PN). We linked these to publicly available county data from the Area Resource File, the Census, Nursing Home Compare, and the Neilsen PopFacts datasets.

STUDY DESIGN

We used hierarchical linear models to assess the effect of county demographic, access to care, and nursing home quality characteristics on the pooled 30-day risk-standardized readmission rate.

DATA COLLECTION/EXTRACTION METHODS

Not applicable.

PRINCIPAL FINDINGS

The study sample included 4,073 hospitals. Fifty-eight percent of national variation in hospital readmission rates was explained by the county in which the hospital was located. In multivariable analysis, a number of county characteristics were found to be independently associated with higher readmission rates, the strongest associations being for measures of access to care. These county characteristics explained almost half of the total variation across counties.

CONCLUSIONS

Community factors, as measured by county characteristics, explain a substantial amount of variation in hospital readmission rates.

Quality of care factors associated with unplanned readmissions of older medical patients: a case-control study

BACKGROUND

Unplanned readmissions befall up to 25% of acutely hospitalised older patients, and many may be potentially preventable.

AIM

To assess the type and prevalence of quality of care factors associated with potentially preventable readmissions to a tertiary hospital general medicine service.

METHODS

A retrospective case-control study was undertaken of hospital records of patients 65 years or older admitted acutely between 1 January 2005 and 31 December 2010. Readmissions up to 30 days postdischarge (cases) were purposively sampled according to frequencies of primary discharge diagnoses coded during the study period. Non-readmitted patients (controls), matched according to age, sex and primary discharge diagnosis on index admission, were selected in a 1.7:1 ratio.

RESULTS

One hundred and thirteen cases and 198 controls were analysed, the former demonstrating a significantly higher comorbidity burden (mean (±standard deviation) comorbidity score 6.6 (±2.2) vs 5.6 (±2.4), P = 0.003) and a higher proportion of individuals with one or more hospitalisations over the preceding 6 months (55.7% vs 8.1%, P < 0.001). Among readmitted patients, 50 (44.3%) were associated with one or more quality factors versus 23 (11.6%) controls (P < 0.001). The most common were: failure to develop/activate an advance care plan (18, 15.9% vs 2, 1.0%; P < 0.001); suboptimal management of presenting illness (13, 11.4% vs 0, 0%; P < 0.001); inadequate assessment of functional limitations (11, 9.7% vs 0, 0%; P < 0.001); and potentially preventable complication of therapy (8, 7.1% vs 1, 0.5%, P = 0.002).

CONCLUSIONS

Quality of care factors are more common among readmitted than among non-readmitted older patients suggesting potential for remedial strategies. Such strategies may still have limited effects as older, frail patients with advanced diseases and multimorbidity will likely retain a high propensity for readmission despite optimal care.

Feasibility of "Standardized Clinician" Methodology for Patient Training on Hospital-to-Home Transitions

INTRODUCTION

Patient engagement in health care is increasingly recognized as essential for promoting the health of individuals and populations. This study pilot tested the standardized clinician (SC) methodology, a novel adaptation of standardized patient methodology, for teaching patient engagement skills for the complex health care situation of transitioning from a hospital back to home.

METHODS

Sixty-seven participants at heightened risk for hospitalization were randomly assigned to either simulation exposure-only or full-intervention group. Both groups participated in simulation scenarios with "standardized clinicians" around tasks related to hospital discharge and follow-up. The full-intervention group was also debriefed after scenario sets and learned about tools for actively participating in hospital-to-home transitions. Measures included changes in observed behaviors at baseline and follow-up and an overall program evaluation.

RESULTS

The full-intervention group showed increases in observed tool possession (P = 0.014) and expression of their preferences and values (P = 0.043). The simulation exposure-only group showed improvement in worksheet scores (P = 0.002) and fewer engagement skills (P = 0.021). Both groups showed a decrease in telling an SC about their hospital admission (P < 0.05). Open-ended comments from the program evaluation were largely positive.

CONCLUSIONS

Both groups benefited from exposure to the SC intervention. Program evaluation data suggest that simulation training is feasible and may provide a useful methodology for teaching patient skills for active engagement in health care. Future studies are warranted to determine if this methodology can be used to assess overall patient engagement and whether new patient learning transfers to health care encounters.

Patterns and predictors of unplanned hospitalization in a population-based cohort of elderly patients with GI cancer

PURPOSE

Hospitalizations among patients with cancer are common and costly and, if unplanned, may interrupt oncologic treatment. The rate of unplanned hospitalizations in the population of elderly patients with cancer is unknown. We sought to describe and quantify patterns and risk factors for early unplanned hospitalization among elderly patients with GI cancer.

PATIENTS AND METHODS

We conducted a retrospective cohort study using linked Texas Cancer Registry and Medicare claims data from 2001 to 2009. Texas residents age 66 years or older initially diagnosed with GI cancer between 2001 and 2007 were included in the study. The unplanned hospitalization rate was estimated, and reasons for unplanned hospitalization were evaluated. Risk factors were identified using adjusted Cox proportional hazards modeling.

RESULTS

Thirty thousand one hundred ninety-nine patients were included in our study, 59% of whom had one or more unplanned hospitalizations. Of 60,837 inpatient claims, 58% were unplanned. The rate of unplanned hospitalization was 93 events per 100 person-years. The most common reasons for unplanned hospitalization were fluid and electrolyte disorders, intestinal obstruction, and pneumonia. Multivariable analysis showed that black race; residing in census tracts with poverty levels greater than 13.3%; esophageal, gastric, and pancreatic cancer; advanced disease stage; high Charlson comorbidity index score; and dual eligibility for Medicare and Medicaid increased the risk for unplanned hospitalization (all P values < .05).

CONCLUSION

Unplanned hospitalizations among elderly patients with GI cancer are common. Some of the top reasons for unplanned hospitalization are potentially preventable, suggesting that comorbidity management and close coordination among involved health care providers should be promoted.

Dose-response relationship between depressive symptoms and hospital readmission

BACKGROUND

Evidence suggests depression increases hospital readmission risk.

OBJECTIVE

Determine whether depressive symptoms are associated with unplanned readmission within 30 days of discharge of general medical patients.

DESIGN

Secondary analysis of the Project Re-Engineered Discharge (RED) randomized controlled trials.

SETTING

Urban academic safety-net hospital.

PATIENTS

A total of 1418 hospitalized adult English-speaking patients.

INTERVENTION

The 9-Item Patient Health Questionnaire (PHQ-9) was used to screen patients for depressive symptoms.

MEASUREMENTS

Hospital readmission within 30 days of discharge. Poisson regression was used to control for confounding variables.

RESULTS

There were 225 (16%) patients who screened positive for mild depressive symptoms (5 ≤PHQ-9 ≤ 9) and 336 (24%) for moderate or severe depressive symptoms (PHQ-9 ≥ 10). After controlling for confounders, a higher rate of readmission was observed in subjects with mild depressive symptoms compared to subjects with PHQ-9 <5, incidence rate ratio (IRR) 1.49 (95% confidence interval [CI]: 1.11-2.00). The adjusted IRR of readmission for those with moderate-to-severe symptoms was 1.96 (95% CI: 1.51-2.49) compared to those with no depression.

CONCLUSIONS

Screening positive for mild and moderate-to-severe depressive symptoms during a hospitalization on a general medical service is associated with an increased dose-dependent readmission rate within 30 days of discharge in an urban, academic, safety-net hospital. Further research is needed to determine whether treatments targeting the reduction of depressive symptoms reduce the risk of readmission.

Medication Regimen Complexity and Unplanned Hospital Readmissions in Older People

BACKGROUND

Medication-related problems and adverse drug events are leading causes of preventable hospitalizations. Few previous studies have investigated the possible association between medication regimen complexity and unplanned rehospitalization.

OBJECTIVE

To investigate the association between discharge medication regimen complexity and unplanned rehospitalization over a 12-month period.

METHOD

The prospective study comprised patients aged ≥70 years old consecutively admitted to a Geriatrics Evaluation and Management (GEM) unit between October 2010 and December 2011. Medication regimen complexity at discharge was calculated using the 65-item validated Medication Regimen Complexity Index (MRCI). Cox proportional-hazards regression was used to compute unadjusted and adjusted hazard ratios (HRs) with 95% CIs for factors associated with rehospitalization over a 12-month follow-up period.

RESULT

Of 163 eligible patients, 99 patients had one or more unplanned hospital readmissions. When adjusting for age, sex, activities of daily living, depression, comorbidity, cognitive status, and discharge destination, MRCI (HR = 1.01; 95% CI = 0.81-1.26), number of discharge medications (HR = 1.01; 95% CI = 0.94-1.08), and polypharmacy (≥9 medications; HR = 1.12; 95% CI = 0.69-1.80) were not associated with rehospitalization. In patients discharged to nonhome settings, there was an association between rehospitalization and the number of discharge medications (HR = 1.12; 95% CI = 1.01-1.25) and polypharmacy (HR = 2.24; 95% CI = 1.02-4.94) but not between rehospitalization and MRCI (HR = 1.32; 95% CI = 0.98-1.78).

CONCLUSION

Medication regimen complexity was not associated with unplanned hospital readmission in older people. However, in patients discharged to nonhome settings, the number of discharge medications and polypharmacy predicted rehospitalization. A patient's discharge destination is an important factor in unplanned medication-related readmissions.

Identifying risk of readmission in hospitalized elderly adults through inpatient medication exposure

OBJECTIVE

To use electronic health record (EHR) data to examine the association between inpatient medication exposure and risk of hospital readmission.

DESIGN

Retrospective, observational study.

SETTING

Tertiary and quaternary care academic health system in Durham, North Carolina.

PARTICIPANTS

All individuals aged 60 and older who were residents of Durham County, North Carolina and were hospitalized and discharged alive from Duke University Hospital between 2007 and 2009 (N = 4,627).

MEASUREMENTS

Independent variables were inpatient exposure to individual medication classes. Primary outcome was readmission to a Duke Health System hospital within 30 days.

RESULTS

Readmission rate was 21% (n = 955). In adjusted models, exposure to anticonvulsants (odds ratio OR 1.26, 95% confidence interval (CI) = 1.08-1.48), benzodiazepines (OR = 1.23, 95% CI = 1.04-1.44), corticosteroids (OR = 1.26, 95% CI = 1.07-1.50), and opioids (OR = 1.25, 95% CI = 1.06-1.47) was associated with greater likelihood of readmission. Exposure to antidepressants (OR = 1.85, 95% CI = 1.16-2.96) and opioids on the cardiology service (OR = 1.76, 95% CI = 1.01-3.07) and exposure to opioids on the medicine service (OR = 1.94, 95% CI = 1.17-3.22) were associated with greater odds of readmission than for individuals on the surgery service.

CONCLUSION

Exposure of hospitalized elderly adults to certain medication classes was associated with greater likelihood of readmission. Incorporating medication data from EHRs may improve the performance of hospital readmission prediction models.

Comprehension deficits among older patients in a quick diagnostic unit

Background

Higher prevalence of multiple illnesses and cognitive impairment among older patients pose a risk of comprehension difficulties, potentially leading to medication errors. Therefore, the objective of this study was to investigate comprehension of discharge instructions among older patients admitted to a Quick Diagnostic Unit (QDU).

Methods

One hundred and two patients discharged from the QDU answered a questionnaire covering understanding of their hospitalization and discharge plan. Patients’ ability to recall discharge instructions and awareness of comprehension deficits, ie, ability to identify the misconceived information, were evaluated by comparing the questionnaires with the discharge letters. The population was divided into an older group (age ≥65 years) and a younger group.

Results

The older group (n=40) was less able to recall correct medication instructions when compared to the younger group (54% versus 78%, respectively; P=0.02). In multiple logistic regression analysis, correct recall of medication instructions was 4.2 times higher for the younger group compared to the older group (odds ratio 4.2, 95% confidence interval 1.5–11.9, P=0.007) when adjusted for sex and education. The older patients were less aware of their own comprehension deficits, and in respect to medication instructions awareness decreased 6.1% for each additional year of age (odds ratio 0.939, 95% confidence interval 0.904–0.98, P=0.001) when adjusted for sex and education.

Conclusion

Older patients were less able to recall correct medication instructions and less aware of their comprehension deficits after discharge from a QDU. The findings of the present study emphasize the importance of thorough communication and follow-up when treating older patients.

Risk factors for early hospital readmission in low-income elderly adults

OBJECTIVES

To identify risk factors for early hospital readmission in low-income community-dwelling older adults.

DESIGN

Prospective cohort study.

SETTING

University-affiliated urban safety-net healthcare system in Indianapolis, Indiana.

PARTICIPANTS

Community-dwelling adults aged 65 and older with annual income less than 200% of the federal poverty level and enrolled in the Geriatric Resources for Assessment and Care of Elders (GRACE) randomized controlled trial (N = 951).

MEASUREMENTS

Participant health and functional status at baseline and 6, 12, 18, and 24 months. Early readmission was defined as a repeat hospitalization occurring within 30 days of a prior hospital discharge. Candidate risk factors included sociodemographic characteristics, health and functional status, prior care, lifestyle, and satisfaction with care.

RESULTS

Of 457 index admissions in 328 participants, 85 (19%) were followed by an early readmission. The independent risk factors for early readmission identified according to regression analysis were living alone (odds ratio (OR) = 1.71, 95% confidence interval (CI) = 1.02-2.87), fair or poor satisfaction with primary care physician (OR = 2.12, 95% CI = 1.01-4.46), not having Medicaid (OR = 1.80, 95% CI = 1.05-3.11), receiving a new assistive device in the past 6 months (OR = 2.26, 95% CI = 1.26-4.05), and staying in a nursing home in the past 6 months (OR = 5.08, 95% CI = 1.56-16.53). Age, race, sex, education, and chronic diseases were not associated with early readmission.

CONCLUSION

A broad range of nonmedical risk factors played a greater role than previously recognized in early hospital readmission of low-income seniors.

Depressive symptoms and hospital readmission in older adults

OBJECTIVES

To quantify the risk of 30-day unplanned hospital readmission in adults aged 65 and older with depressive symptoms.

DESIGN

Prospective cohort study.

SETTING

University of Maryland Medical Center.

PARTICIPANTS

Individuals aged 65 and older admitted between July 1, 2011, and August 9, 2012, to the general medical and surgical units and followed for 31 days after hospital discharge (N = 750).

MEASUREMENTS

Primary exposure was depressive symptoms at admission, defined as a score of 6 or more on the 15-item Geriatric Depression Scale. Primary outcome was unplanned 30-day hospital readmission, defined as an unscheduled overnight stay at any inpatient facility not occurring in the emergency department.

RESULTS

Prevalence of depressive symptoms was 19% and incidence of 30-day unplanned hospital readmission was 19%. Depressive symptoms were not significantly associated with hospital readmission (relative risk (RR) = 1.20, 95% confidence interval (CI) = 0.83-1.72). Age, Charlson Comorbidity Index score, and number of hospitalizations within the past 6 months were significant predictors of unplanned 30-day hospital readmission.

CONCLUSION

Although not associated with hospital readmission, depressive symptoms were associated with other poor outcomes and may be underdiagnosed in hospitalized older adults. Hospitals interested in reducing readmission should focus on older adults with more comorbid illness and recent hospitalizations.

Patient activation and 30-day post-discharge hospital utilization

BACKGROUND

Patient activation is linked to better health outcomes and lower rates of health service utilization. The role of patient activation in the rate of hospital readmission within 30 days of hospital discharge has not been examined.

METHODS

A secondary analysis using data from the Project RED-LIT randomized controlled trial conducted at an urban safety net hospital. Data from 695 English-speaking general medical inpatient subjects were analyzed. We used an adapted, eight-item version of the validated Patient Activation Measure (PAM). Total scores were categorized, according to standardized methods, as one of four PAM levels of activation: Level 1 (lowest activation) through Level 4 (highest activation). The primary outcome measure was total 30-day post-discharge hospital utilization, defined as total emergency department (ED) visits plus hospital readmissions including observation stays. Poisson regression was used to control for confounding.

RESULTS

Of the 695 subjects, 67 (9.6 %) were PAM Level 1, 123 (17.7 %) were Level 2, 193 (27.8 %) were Level 3, and 312 (44.9 %) were Level 4. Compared with highly activated patients (PAM Level 4), a higher rate of 30-day post-discharge hospital utilization was observed for patients at lower levels of activation (PAM Level 1, incident rate ratio [IRR] 1.75, 95 % CI,1.18 to 2.60) and (PAM Level 2, IRR 1.50, 95 % CI 1.06 to 2.13). The rate of returning to the hospital among patients at PAM Level 3 was not statistically different than patients with PAM Level 4 (IRR 1.30, 95 % CI, 0.94 to 1.80). The rate ratio for PAM Level 1 was also higher compared with Level 4 for ED use alone (1.68(1.07 to 2.63)) and for hospital readmissions alone (1.93 [1.22 to 3.06]).

CONCLUSION

Hospitalized adult medical patients in an urban academic safety net hospital with lower levels of Patient Activation had a higher rate of post-discharge 30-day hospital utilization.

Restarting the cycle: incidence and predictors of first acute care use after nursing home discharge

OBJECTIVES

To describe the time to first acute care use (e.g., emergency department (ED) use without hospitalization or rehospitalization) for older adults discharged to home after receiving postacute care in skilled nursing facilities (SNFs); to identify predictors of first acute care use.

DESIGN

Retrospective cohort study using administrative claims data.

SETTING

SNFs providing postacute care for patients in North and South Carolina (N = 1,474).

PARTICIPANTS

A cohort of Medicare beneficiaries aged 65 and older (N = 55,980) who were hospitalized and then transferred to a SNF for postacute care and subsequently discharged home (January 1, 2010, to August 31, 2011).

MEASUREMENTS

Medicare institutional claims data (Parts A and B) and Medicare enrollment data were used; facility-level variables were obtained from CMS Nursing Home Compare. Survival from SNF discharge to first acute care use was explored. Cox proportional hazards regression models were used to describe individual-, home care-, and nursing facility-level predictors.

RESULTS

After discharge from SNF to home, 22.1% of older adults had an episode of acute care use within 30 days, including 7.2% with an ED visit without hospitalization and 14.8% with a rehospitalization; 37.5% of older adults had their first acute care use within 90 days. Male sex, dual eligibility status, higher Charlson comorbidity score, certain primary diagnoses at index hospitalization (neoplasms and respiratory disease), and care in SNFs with for-profit ownership or fewer licensed practical nurses hours per patient-day were associated with greater likelihood of acute care use.

CONCLUSION

Medicare beneficiaries have a high use of acute care services after discharge from SNFs, and several factors associated with acute care use are potentially modifiable. Findings suggest the need for interventions to support beneficiaries as they transition from SNFs to home.

The effect of serious mental illness on the risk of rehospitalization among patients with diabetes

BACKGROUND

Medical-surgical rehospitalizations within a month after discharge among patients with diabetes result in tremendous costs to the US health care system.

OBJECTIVE

The study's aim was to examine whether co-morbid serious mental illness diagnoses (bipolar disorder, schizophrenia, or other psychotic disorders) among patients with diabetes are independently associated with medical-surgical rehospitalization within a month of discharge after an initial hospitalization.

METHODS

This cohort study of all community hospitals in Washington state evaluated data from 82,060 adults discharged in the state of Washington with any International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis indicating diabetes mellitus between 2010 and 2011. Data on medical-surgical hospitalizations were obtained from the Washington State Comprehensive Hospital Abstract Reporting System. Co-morbid serious mental illness diagnoses were identified based on International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes indicating bipolar disorder, schizophrenia, or other psychotic disorders. Logistic regression analyses identified factors independently associated with rehospitalization within a month of discharge. Cox proportional hazard analyses estimated time to rehospitalization for the entire study period.

RESULTS

After adjusting for demographics, medical co-morbidity, and characteristics of the index hospitalization, co-morbid serious mental illness diagnosis was independently associated with increased odds of rehospitalization within 1 month among patients with diabetes who had a medical-surgical hospitalization (odds ratio: 1.24, 95% confidence interval: 1.07, 1.44). This increased risk of rehospitalization persisted throughout the study period (up to 24 mo).

CONCLUSIONS

Co-morbid serious mental illness in patients with diabetes is independently associated with greater risk of early medical-surgical rehospitalization. Future research is needed to define and specify targets for interventions at points of care transition for this vulnerable patient population.

Early rehospitalization after kidney transplantation: assessing preventability and prognosis

Early rehospitalization after kidney transplantation (KT) is common and may predict future adverse outcomes. Previous studies using claims data have been limited in identifying preventable rehospitalizations. We assembled a cohort of 753 adults at our institution undergoing KT from January 1, 2003 to December 31, 2007. Two physicians independently reviewed medical records of 237 patients (32%) with early rehospitalization and identified (1) primary reason for and (2) preventability of rehospitalization. Mortality and graft failure were ascertained through linkage to the Scientific Registry of Transplant Recipients. Leading reasons for rehospitalization included surgical complications (15%), rejection (14%), volume shifts (11%) and systemic and surgical wound infections (11% and 2.5%). Reviewer agreement on primary reason (85% of cases) was strong (kappa = 0.78). Only 19 rehospitalizations (8%) met preventability criteria. Using logistic regression, weekend discharge (odds ratio [OR] 1.59, p = 0.01), waitlist time (OR 1.10, p = 0.04) and longer initial length of stay (OR 1.42, p = 0.03) were associated with early rehospitalization. Using Cox regression, early rehospitalization was associated with mortality (hazard ratio [HR] 1.55; p = 0.03) but not graft loss (HR 1.33; p = 0.09). Early rehospitalization has diverse causes and presents challenges as a quality metric after KT. These results should be validated prospectively at multiple centers to identify vulnerable patients and modifiable processes-of-care.

Medication regimen complexity and hospital readmission for an adverse drug event

BACKGROUND

Adverse drug events (ADEs) are costly, dangerous, and often preventable. Little is known about the link between medication regimen complexity and rehospitalization as a result of an ADE.

OBJECTIVE

The objective of this study was to compare admission and discharge medication regimen complexity in 2 cohorts: patients readmitted for an ADE within 30 days and patients not readmitted for an ADE.

METHODS

The study used a retrospective parallel-group case-control design. Participants from 4 urban acute care hospitals were included in the revisit cohort if they were rehospitalized within 30 days as a result of an adverse event coded as accidental poisoning. The no-revisit cohort was formed by randomly sampling patients with the same disease classification codes as the revisit group but without history of a readmission within 30 days. Complexity of medication regimens at the initial admission and discharge was quantified with the medication regimen complexity index (MRCI).

RESULTS

The revisit group comprised 92 individuals and the no-revisit group, 228. The revisit group had a significantly higher MRCI score at admission and discharge than the no-revisit group (all P < .005). Receiver operating characteristic curves, used to determine a potential MRCI cutoff score for risk of an ADE, revealed MRCI scores of 8 or greater to optimally predict increased risk for readmission caused by an ADE.

CONCLUSIONS

Complex medication regimens at hospital admission are predictive of rehospitalizations for ADEs. This finding suggests that medication regimen complexity be considered as a target for interventions to decrease the risk for readmission.