Hospital readmission in general medicine patients: a prediction model

Identifying predictors of hospital readmission following congenital heart surgery through analysis of a multiinstitutional administrative Database

BACKGROUND

Despite resource burdens associated with hospital readmission, there remains little multiinstitutional data available to identify children at risk for readmission following congenital heart surgery.

METHODS AND RESULTS

Children undergoing congenital heart surgery and discharged home between January of 2011 and December 2012 were identified within the Pediatric Health Information System database, a multiinstitutional collection of clinical and administrative data. Patient discharges were assigned to derivation and validation cohorts for the purposes of predictive model design, with 17 871 discharges meeting inclusion criteria. Readmission within 30 days was noted following 956 (11%) of discharges within the derivation cohort (n = 9104), with a median time to readmission of 9 days (interquartile range [IQR] 5-18 days). Readmissions resulted in a rehospitalization length of stay of 4 days (IQR 2-8 days) and were associated with an intensive care unit (ICU) admission in 36% of cases. Independent perioperative predictors of readmission included Risk Adjustment in Congenital Heart Surgery score of 6 (odds ratio [OR] 2.6, 95% confidence interval [CI] 1.8-3.7, P < .001) and ICU length of stay of at least 7 days (OR 1.9 95% CI 1.6-2.2, P < .001). Demographic predictors included Hispanic ethnicity (OR 1.2, 95% CI 1.1-1.4, P = .014) and government payor status (OR 1.2, 95% CI 1.1-1.4, P = .007). Predictive model performance was modest among validation cohort (c statistic 0.68, 95% CI 0.66-0.69, P < .001).

CONCLUSIONS

Readmissions following congenital heart surgery are common and associated with significant resource consumption. While we describe independent predictors that may identify patients at risk for readmission prior to hospital discharge, there likely remains other unreported factors that may contribute to readmission following congenital heart surgery.

Impact of adaptive functioning on readmission to alcohol detoxification among Alaska Native People

BACKGROUND

This study examined predictors associated with readmission to detoxification in a sample of adult Alaska Native patients admitted to inpatient alcohol detoxification. Even though Alaska Native people diagnosed with alcoholism have been identified as frequent utilizers of the health care system and at elevated risk of death, little is known about factors associated with readmission to detoxification for this group.

METHODS

We sought to predict readmission using a retrospective cohort study. The sample included 383 adult Alaska Native patients admitted to an inpatient detoxification unit and diagnosed with alcohol withdrawal during 2006 and 2007. Cox proportional hazard modeling was used to estimate unadjusted and adjusted associations with time to readmission within one year.

RESULTS

Forty-two percent of the patients were readmitted within one year. Global Assessment Functioning (GAF; Axis V in the multi-axial diagnostic system of the Diagnostic and Statistical Manual of Mental Disorders [DSM IV]) score measured at the time of intake was associated with readmission. A one point increase in the GAF score (HR=.96, 95% CL=.94, .99, P=.002) was associated with a four percent decrease in readmission. The results also indicated that the GAF mediated the relationship between readmission and: employment and housing status.

CONCLUSIONS

The GAF measures both illness severity and adaptive functioning, is part of standard behavioral health assessments, and is easy to score. Readmission rates potentially could be decreased by creating clinical protocols that account for differences in adaptive functioning and illness severity during detoxification treatment and aftercare.

Does travel distance influence length of stay in elective pancreatic surgery?

BACKGROUND

Length of stay (LoS) following elective surgery is being reported as an outcomes quality measure. Regional referral centres may care for patients travelling significant distances. The effect of travel distance on LoS in pancreatic surgery patients was examined.

METHODS

National Surgical Quality Improvement Program data on pancreatic surgery patients, operated during the period from 2005 to 2011, were reviewed. Demographics, surgical variables and distance travelled were analysed relative to LoS. The LoS was log-transformed in general linear models to achieve normality.

RESULTS

Of the 243 patients, 53% were male. The mean ± standard deviation (SD) age of the total patient sample was 60.6 ± 14 years. The mean ± SD distance travelled was 203 ± 319 miles (326.7 ± 513.4 km) [median: 132 miles (212.4 km); range: 3-3006 miles (4.8-4837.7 km)], and the mean ± SD LoS was 10.5 ± 7 days (range: 1-46 days). Univariate analysis showed a near significant increase in LoS with increased distance travelled (P = 0.05). Significant variables related to LoS were: age (P = 0.002); relative value units (P < 0.001), and preoperative American Society of Anesthesiologists class (P = 0.005). In a general linear model, for every 100 miles (160.9 km) travelled there is an associated 2% increase in LoS (P = 0.031). When the distance travelled is increased by 500 miles (804.7 km), LoS increases by 10.5%.

CONCLUSIONS

Increased travel distance from a patient's home to the hospital was independently associated with an increase in LoS. If LoS is a reportable quality measure in pancreatic surgery, travel distance should be considered in risk adjustments.

Predictors of Inpatient Utilization among Veterans with Dementia

Dementia is prevalent and costly, yet the predictors of inpatient hospitalization are not well understood. Logistic and negative binomial regressions were used to identify predictors of inpatient hospital utilization and the frequency of inpatient hospital utilization, respectively, among veterans. Variables significant at the P < 0.15 level were subsequently analyzed in a multivariate regression. This study of veterans with a diagnosis of dementia (n = 296) and their caregivers found marital status to predict hospitalization in the multivariate logistic model (B = 0.493, P = 0.029) and personal-care dependency to predict hospitalization and readmission in the multivariate logistic model and the multivariate negative binomial model (B = 1.048, P = 0.007, B = 0.040, and P = 0.035, resp.). Persons with dementia with personal-care dependency and spousal caregivers have more inpatient admissions; appropriate care environments should receive special care to reduce hospitalization. This study was part of a larger clinical trial; this trial is registered with ClinicalTrials.gov NCT00291161.

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study

Background

The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time.

Methods

Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak.

Results

The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%.

Conclusion

The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

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.

Factors contributing to emergency department care within 30 days of hospital discharge and potential ways to prevent it: differences in perspectives of patients, caregivers, and emergency physicians

BACKGROUND

Identifying needs in patients who utilize the emergency department (ED) soon after being discharged from inpatient care is essential for planning appropriate care-transition interventions.

OBJECTIVE

To examine differences in stakeholder perspectives on reasons for ED care soon after hospital discharge and interventions that could be useful to prevent these ED visits.

DESIGN AND MEASUREMENTS

A convenience sample of 135 patients who presented to an urban teaching hospital ED <30 days after last hospital discharge, their caregivers (when present), and emergency physicians were administered identical structured surveys. Concordance and agreement rates between patient-physician and patient-caregiver dyads were calculated.

RESULTS

Concordances between stakeholders were poor, with weighted kappas ranging from 0.02 to 0.34 for patient-physician dyads and 0.03 to 0.68 for patient-caregiver dyads. Emergency physicians and caregivers identified factors between 1% and 42% of the time the patients did not. Less than half of any stakeholder could identify an intervention to potentially prevent the ED visit.

CONCLUSIONS

Our findings suggest the difficulty in forming unified definitions for root cause of ED visits soon after hospital discharge and support the use of multiple stakeholders in identifying appropriate targets for care-transition interventions.

Risk factors for potentially avoidable readmissions due to end-of-life care issues

BACKGROUND

Repeated hospitalizations are frequent toward the end of life, where each admission should be an opportunity to initiate advance-care planning to high-risk patients.

OBJECTIVE

To identify the risk factors for having a 30-day potentially avoidable readmission due to end-of-life care issues among all medical patients.

DESIGN

Nested case-control study.

SETTING/PATIENTS

All 10,275 consecutive discharges from any medical service of an academic tertiary medical center in Boston, Massachusetts between July 1, 2009 and June 30, 2010.

MEASUREMENTS

A random sample of all the potentially avoidable 30-day readmissions was independently reviewed by 9 trained physicians to identify the ones due to end-of-life issues.

RESULTS

Among 534, 30-day potentially avoidable readmission cases reviewed, 80 (15%) were due to an end-of-life care issue. In multivariable analysis, the following risk factors were significantly associated with a 30-day potentially avoidable readmission due to end-of-life care issues: number of admissions in the previous 12 months (odds ratio [OR]: 1.10 per admission, 95% confidence interval [CI]: 1.02-1.20), neoplasm (OR: 5.60, 95% CI: 2.85-10.98), opiate medications at discharge (OR: 2.29, 95% CI: 1.29-4.07), Elixhauser comorbidity index (OR: 1.16 per 5-point increase, 95% CI: 1.10-1.22). The discrimination of the model (C statistic) was 0.85.

CONCLUSIONS

In a medical population, we identified 4 main risk factors that were significantly associated with 30-day potentially avoidable readmission due to end-of-life care issues, producing a model with very good to excellent discrimination. Patients with these risk factors might benefit from palliative care consultation prior to discharge in order to improve end-of-life care and possibly reduce unnecessary rehospitalizations.

Effect of health insurance on the utilisation of allied health services by people with chronic disease: a systematic review and meta-analysis

Allied health services benefit the management of many chronic diseases. The effects of health insurance on the utilisation of allied health services has not yet been established despite health insurance frequently being identified as a factor promoting utilisation of medical and hospital services among people with chronic disease. The objective of this systematic review and meta-analysis was to establish the effects of health insurance on the utilisation of allied health services by people with chronic disease. Medline (Ovid Medline 1948 to Present with Daily Update), EMBASE (1980 to 1 April 2011), CINAHL, PsychINFO and the Cochrane Central Register of Controlled Trials were searched to 12 April 2011 inclusive. Studies were eligible for inclusion if they were published in English, randomised controlled trials, quasi-experimental trials, quantitative observational studies and included people with one or more chronic diseases using allied health services and health insurance. A full-text review was performed independently by two reviewers. Meta-analyses were conducted. One hundred and fifty-eight citations were retrieved and seven articles were included in the meta-analyses. The pooled odds ratio (95% CI) of having insurance (versus no insurance) on the utilisation of allied health services among people with chronic disease was 1.33 (1.16-1.52; P<0.001). There was a significant effect of insurance on the utilisation of non-physiotherapy services, pooled odds ratio (95% CI) 4.80 (1.46-15.79; P=0.01) but having insurance compared with insurance of a lesser coverage was not significantly associated with an increase in physiotherapy utilisation, pooled odds ratio (95% CI) 1.53 (0.81-2.91; P=0.19). The presence of co-morbidity or functional limitation and higher levels of education increased utilisation whereas gender, race, marital status and income had a limited and variable effect, according to the study population. The review was limited by the considerable heterogeneity in the research questions being asked, sample sizes, study methodology (including allied health service), insurance type and dependent variables analysed. The presence of health insurance was generally associated with increased utilisation of allied health services; however, this varied depending on the population, provider type and insurance product.

Derivation and validation of a formula to estimate risk for 30-day readmission in medical patients

OBJECTIVE

To create a simple readmission risk-prediction tool that can be generated easily at the bedside by physicians, nurses, care coordinators and discharge planners.

DESIGN

Retrospective cohort study.

SETTING

Tertiary academic medical center.

PARTICIPANTS

Inpatients aged 18 and older on general internal medicine services.

MEASURES

Predictor variables included age, prior hospitalization, high-risk diagnoses, high-risk medications, polypharmacy, depression, use of palliative care and a cumulative score summing these factors (readmission risk score-RRS). The main outcome measure was 30-day readmission. Predictive values were calculated.

RESULTS

Readmission increased linearly from 4.9% of those whose RRS score was 0-37.5% of those with highest risk scores (P = 0.0002). We derived a simple formula for readmission risk as 8 and 4% more for each additional readmission risk factor. The positive predictive value for RRS >0 was low, while the negative predictive value for this cutoff was 95%.

CONCLUSIONS

An easily calculated 7-point score can be used to estimate readmission risk. This tool may be particularly useful for identifying lower risk patients who may not require intensive intervention, thus aiding in appropriate targeting of resources.

The effect of Massachusetts health reform on 30 day hospital readmissions: retrospective analysis of hospital episode statistics

OBJECTIVES

To analyse changes in overall readmission rates and disparities in such rates, among patients aged 18-64 (those most likely to have been affected by reform), using all payer inpatient discharge databases (hospital episode statistics) from Massachusetts and two control states (New York and New Jersey).

DESIGN

Difference in differences analysis to identify the post-reform change, adjusted for secular changes unrelated to reform.

SETTING

US hospitals in Massachusetts, New York, and New Jersey.

PARTICIPANTS

Adults aged 18-64 admitted for any cause, excluding obstetrical.

MAIN OUTCOME MEASURE

Readmissions at 30 days after an index admission.

RESULTS

After adjustment for known confounders, including age, sex, comorbidity, hospital ownership, teaching hospital status, and nurse to census ratio, the odds of all cause readmission in Massachusetts was slightly increased compared with control states post-reform (odds ratio 1.02, 95% confidence interval 1.01 to 1.04, P<0.05). Racial and ethnic disparities in all cause readmission rates did not change in Massachusetts compared with control states. In analyses limited to Massachusetts only, there were minimal overall differences in changes in readmission rates between counties with differing baseline uninsurance rates, but black people in counties with the highest uninsurance rates had decreased odds of readmission (0.91, 0.84 to 1.00) compared with black people in counties with lower uninsurance rates. Similarly, white people in counties with the highest uninsurance rates had decreased odds of readmission (0.96, 0.94 to 0.99) compared with white people in counties with lower uninsurance rates.

CONCLUSIONS

In the United States, and in Massachusetts in particular, extending health insurance coverage alone seems insufficient to improve readmission rates. Additional efforts are needed to reduce hospital readmissions and disparities in this outcome.

Mining high-dimensional administrative claims data to predict early hospital readmissions

BACKGROUND

Current readmission models use administrative data supplemented with clinical information. However, the majority of these result in poor predictive performance (area under the curve (AUC)<0.70).

OBJECTIVE

To develop an administrative claim-based algorithm to predict 30-day readmission using standardized billing codes and basic admission characteristics available before discharge.

MATERIALS AND METHODS

The algorithm works by exploiting high-dimensional information in administrative claims data and automatically selecting empirical risk factors. We applied the algorithm to index admissions in two types of hospitalized patient: (1) medical patients and (2) patients with chronic pancreatitis (CP). We trained the models on 26,091 medical admissions and 3218 CP admissions from The Johns Hopkins Hospital (a tertiary research medical center) and tested them on 16,194 medical admissions and 706 CP admissions from Johns Hopkins Bayview Medical Center (a hospital that serves a more general patient population), and vice versa. Performance metrics included AUC, sensitivity, specificity, positive predictive values, negative predictive values, and F-measure.

RESULTS

From a pool of up to 5665 International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnoses, 599 ICD-9-CM procedures, and 1815 Current Procedural Terminology codes observed, the algorithm learned a model consisting of 18 attributes from the medical patient cohort and five attributes from the CP cohort. Within-site and across-site validations had an AUC≥0.75 for the medical patient cohort and an AUC≥0.65 for the CP cohort.

CONCLUSIONS

We have created an algorithm that is widely applicable to various patient cohorts and portable across institutions. The algorithm performed similarly to state-of-the-art readmission models that require clinical data.

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.

Hospital length of stay and readmission: an early investigation

This article is an investigation into the relationship between length of stay and readmission within 30 days of discharge from an acute care hospitalization. We estimated probability models for heart attack and for heart failure patients using generalized estimating techniques applied to hospital administrative data from California for calendar year 2008. The key independent variable was length of stay in the initial hospitalization. We found negative associations between length of stay and readmission probability, particularly in the case of heart attack. Simulated values of predicted readmissions based on a 1-day increase in length of stay yielded estimated reductions in readmission rates in the 7% to 18% range for heart attack patients and the 1% to 8% range for heart failure patients. Increasing length of stay for some patients may be a means of improving quality of care by reducing readmission during the 30-day postdischarge period.

Institution specific risk factors for 30 day readmission at a community hospital: a retrospective observational study

BACKGROUND

As of October 1, 2012, hospitals in the United States with excess readmissions based on the Centers for Medicare and Medicaid Services (CMS) risk-adjusted ratio began being penalized. Given the impact of high readmission rates to hospitals nationally, it is important for individual hospitals to identify which patients may be at highest risk of readmission. The objective of this study was to assess the association of institution specific factors with 30-day readmission.

METHODS

The study is a retrospective observational study using administrative data from January 1, 2009 through December 31, 2010 conducted at a 257 bed community hospital in Massachusetts. The patients included inpatient medical discharges from the hospitalist service with the primary diagnoses of congestive heart failure, pneumonia or chronic obstructive pulmonary disease. The outcome was 30-day readmission rates. After adjusting for known factors that impact readmission, provider associated factors (i.e. hours worked and census on the day of discharge) and hospital associated factors (i.e. floor of discharge, season) were compared.

RESULTS

Over the study time period, there were 3774 discharges by hospitalists, with 637 30-day readmissions (17% readmission rate). By condition, readmission rates were 19.6% (448/2284) for congestive heart failure, 13.0% (141/1083) for pneumonia, and 14.7% (200/1358) for chronic obstructive lung disease. After adjusting for known risk factors (gender, age, length of stay, Elixhauser sum score, admission in the previous year, insurance, disposition, primary diagnosis), we found that patients discharged in the winter remained significantly more likely to be readmitted compared to the summer (OR 1.54, p = 0.0008). Patients discharged from the cardiac floor had a trend toward decreased readmission compared a medical/oncology floor (OR 0.85, p = 0.08). Hospitalist work flow factors (census and hours on the day of discharge) were not associated with readmission.

CONCLUSIONS

We found that 30 day hospital readmissions may be associated with institution specific risk factors, even after adjustment for patient factors. These institution specific risk factors may be targets for interventions to prevent readmissions.

Early hospital readmission in the trauma population: are the risk factors different?

INTRODUCTION

Hospital readmission rates will soon impact Medicare reimbursements. While risk factors for readmission have been described for medical and elective surgical patients, little is known about their predictive value specifically in trauma patients.

PATIENTS AND METHODS

We retrospectively identified all admissions after trauma resuscitation to our urban level 1 trauma centre from 1/1/2004 to 8/31/2010. All patients discharged alive were included. Data collected included demographics, Injury Severity Score (ISS), and length of stay (LOS). We analyzed these index admissions for the development of complications that have previously been shown to be associated with readmission. Readmissions that occurred within 30 days of index admission were identified. Univariable and multivariable analyses were performed. p<0.05 was considered significant.

RESULTS

We identified 10,306 index admissions, with 447 (4.3%) early (within 30 days) readmissions. Mean ISS was 11.1 (SD 10.4). On multivariable analysis, African-American race (OR 1.3, p=0.009), pre-existing chronic obstructive pulmonary disease (COPD) (OR 1.5, p=0.02), and diabetes mellitus (OR 1.8, p<0.001) were associated with readmission, along with higher ISS (OR 1.01, p<0.001), ICU admission (OR 2.1, p<0.001), and increased LOS (OR 1.01, p<0.001). Among many in-hospital complications examined, only the development of surgical site infection (SSI) (OR 1.9, p=0.02) was associated with increased risk of readmission.

CONCLUSIONS

Trauma patients have a low risk of readmission. In contrast to elective surgical patients, the only modifiable risk factor for readmission in our trauma population was SSI. Other risk factors may present clinicians with opportunities for targeted interventions, such as proactive follow up or early phone contact. With future changes to health care policy, clinicians may have even greater motivation to prevent readmission.

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.

The role of continuous care in reducing readmission for patients with heart failure

INTRODUCTION

About 20-50% of patients with heart failure are readmitted to hospitals in 14 day to 6 months of hospital discharge. Several supportive programs are developed to reduce post discharge hospital readmissions. The present study was performed to review the clinical trials conducted to determine the effect of post-discharge follow-up on readmission of patients with heart failure (HF). 

METHODS

Internet search was conducted to identify clinical trial studies that have been conducted on post-discharge follow-up care for patients with HF. Databases of Science direct, Pubmed, Iranmedex, SID and also the Google's search engine were searched for studies that have been published between the years 1995 and 2013. Keywords used in searching Persian databases were included readmission, heart failure, continuous care, and follow-up. Keywords used in searching English databases were included of heart failure, readmission, follow-up and home monitoring.

RESULTS

21 clinical trials were reviewed. 16 studies have shown that continuous care through patient education before discharge, home visits, and telephone follow up could significantly reduce the rate of post discharge readmissions of patients with HF. However, five studies did not show significant reductions in post-discharge readmissions.

CONCLUSION

Patient education and continuous post-discharge follow up interventions conducted by nurses could significantly reduce the rates of readmissions to the hospital or to the physicians' office. Considering limited health care resources, using one or a combination of follow-up methods, can reduce the number of readmissions of patients with HF.

Transitional Care Partners: a hospital-to-home support for older adults and their caregivers

PURPOSE

To describe the development, implementation, and preliminary results of the Transitional Care (TLC) Partners, a clinical demonstration program that supports the transition from hospital to home of older veterans.

DATA SOURCES

Hospital records of TLC patients to track their hospital and emergency department visits before and after the TLC Partners enrollment. Caregivers of patients completed Preparedness in Caregiving and the Short Form Zarit Burden Scale during the first week of the TLC Partners enrollment and on the week when the services ended.

CONCLUSIONS

The proportion of patients with one or more emergency department visits and rehospitalization is consistently lower among TLC patients compared to non-TLC patients at 30 and 60 days of hospital discharge. The mean preparedness and burden scores before and after the program essentially remained the same.

IMPLICATIONS FOR PRACTICE

The description of the implementation of the TLC Partners offers an example of how nurse practitioner-led interprofessional care models can be adapted to the needs of specific healthcare systems, and how they can be monitored to evaluate their reach, effectiveness, and fidelity to the core components of proved care models.

Does 30-Day Readmission Affect Long-term Outcome Among Glioblastoma Patients?

BACKGROUND:

Research on readmissions has focused mainly on the economic and resource burden it places on hospitals.

OBJECTIVE:

To evaluate the effect of 30-day readmission on overall survival among newly diagnosed glioblastoma multiforme (GBM) patients.

METHODS:

A nationwide cohort of GBM patients diagnosed between 1991 and 2007 was studied using the Surveillance, Epidemiology and End Results Medicare database. Multivariate models were used to determine factors associated with readmission and overall survival. Odds ratio, hazard ratio, 95% confidence interval, and P values were reported. Complete case and multiple imputation analyses were performed.

RESULTS:

Among the 2774 newly diagnosed GBM patients undergoing surgery at 442 hospitals nationwide, 437 (15.8%) were readmitted within 30 days of the index hospitalization. Although 63% of readmitted patients returned to the index hospital where surgery was performed, a significant portion (37%) were readmitted to nonindex hospitals. The median overall survival for readmitted patients (6.0 months) was significantly shorter than for nonreadmitted (7.6 months; P &lt; .001). In a confounder-adjusted imputed model, 30-day readmission increased the hazard of mortality by 30% (hazard ratio, 1.3; P &lt; .001). Neurological symptoms (30.2%), thromboembolic complications (19.7%), and infections (17.6%) were the leading reasons for readmission.

CONCLUSION:

Prior studies that have reported only the readmissions back to index hospitals are likely underestimating the true 30-day readmission rate. GBM patients who were readmitted within 30 days had significantly shorter survival than nonreadmitted patients. Future studies that attempt to decrease readmissions and evaluate the impact of reducing readmissions on patient outcomes are needed.

Improvement of adequate use of warfarin for the elderly using decision tree-based approaches

OBJECTIVES

Due to the narrow therapeutic range and high drug-to-drug interactions (DDIs), improving the adequate use of warfarin for the elderly is crucial in clinical practice. This study examines whether the effectiveness of using warfarin among elderly inpatients can be improved when machine learning techniques and data from the laboratory information system are incorporated.

METHODS

Having employed 288 validated clinical cases in the DDI group and 89 cases in the non-DDI group, we evaluate the prediction performance of seven classification techniques, with and without an Adaptive Boosting (AdaBoost) algorithm. Measures including accuracy, sensitivity, specificity and area under the curve are used to evaluate model performance.

RESULTS

Decision tree-based classifiers outperform other investigated classifiers in all evaluation measures. The classifiers supplemented with AdaBoost can generally improve the performance. In addition, weight, congestive heart failure, and gender are among the top three critical variables affecting prediction accuracy for the non-DDI group, while age, ALT, and warfarin doses are the most influential factors for the DDI group.

CONCLUSION

Medical decision support systems incorporating decision tree-based approaches improve predicting performance and thus may serve as a supplementary tool in clinical practice. Information from laboratory tests and inpatients' history should not be ignored because related variables are shown to be decisive in our prediction models, especially when the DDIs exist.

Identifying patients at increased risk for unplanned readmission

BACKGROUND

Reducing readmissions is a national priority, but many hospitals lack practical tools to identify patients at increased risk of unplanned readmission.

OBJECTIVE

To estimate the association between a composite measure of patient condition at discharge, the Rothman Index (RI), and unplanned readmission within 30 days of discharge.

SUBJECTS

Adult medical and surgical patients in a major teaching hospital in 2011.

MEASURES

The RI is a composite measure updated regularly from the electronic medical record based on changes in vital signs, nursing assessments, Braden score, cardiac rhythms, and laboratory test results. We developed 4 categories of RI and tested its association with readmission within 30 days, using logistic regression, adjusted for patient age, sex, insurance status, service assignment (medical or surgical), and primary discharge diagnosis.

RESULTS

Sixteen percent of the sample patients (N=2730) had an unplanned readmission within 30 days of discharge. The risk of readmission for a patient in the highest risk category (RI<70) was >1 in 5 while the risk of readmission for patients in the lowest risk category was about 1 in 10. In multivariable analysis, patients with an RI<70 (the highest risk category) or 70-79 (medium risk category) had 2.65 (95% confidence interval, 1.72-4.07) and 2.40 (95% confidence interval, 1.57-3.67) times higher odds of unplanned readmission, respectively, compared with patients in the lowest risk category.

CONCLUSION

Clinicians can use the RI to help target hospital programs and supports to patients at highest risk of readmission.

Risk Factors for Unplanned Hospital Readmission in Otolaryngology Patients

Objective

Identify the risk factors that predict 30-day unplanned readmission in hospitalized otolaryngology patients.

Study Design

Retrospective cohort study.

Setting

Single academic hospital.

Subjects and Methods

All otolaryngology admissions for the 1-year period between January 1, 2011, and December 31, 2011, at an academic hospital were reviewed. Univariate logistic regression and multivariate logistic regression, employing a backward elimination stepwise approach, were performed to identify risk factors for unplanned readmission to the hospital within 30 days of discharge from the otolaryngology service.

Results

There were 1058 patients that accounted for 1271 hospital admissions. The 30-day unplanned readmission rate for patients discharged from the otolaryngology service was 7.3% (93/1271). Significant predictors identified on univariate analysis were used to build a multivariable logistic regression model of risk factors for unplanned readmission. These risk factors included presence of a complication (odds ratio [OR] = 11.60, 95% confidence interval [CI], 7.11-18.93), new total laryngectomy (OR = 4.72, 95% CI, 1.58-14.10), discharge destination of skilled nursing facility (OR = 2.70, 95% CI, 1.21-6.02), severe coronary artery disease or chronic lung disease (OR = 2.33, 95% CI, 1.38-3.93), and current illicit drug use (OR = 2.60, 95% CI, 1.27-5.34). The discriminative ability of the multivariate regression model to predict unplanned readmissions, as measured by the c-statistic, was 0.85.

Conclusion

Otolaryngology patients have unique risk factors that predict unplanned readmission within 30 days of discharge. These data identify specific patient characteristics and care processes that can be targeted with quality improvement interventions to decrease unplanned readmissions.

Contribution of psychiatric illness and substance abuse to 30-day readmission risk

BACKGROUND

Little is known about the contribution of psychiatric illness to medical 30-day readmission risk.

OBJECTIVE

To determine the independent contribution of psychiatric illness and substance abuse to all-cause and potentially avoidable 30-day readmissions in medical patients.

DESIGN

Retrospective cohort study.

SETTING

Patients discharged from the medicine services at a large teaching hospital from July 1, 2009 to June 30, 2010.

MEASUREMENTS

The main outcome of interest was 30-day all-cause and potentially avoidable readmissions; the latter determined by a validated algorithm (SQLape) in both bivariate and multivariate analysis. Readmissions were captured at 3 hospitals where the majority of these patients are readmitted.

RESULTS

Of 6987 discharged patients, 1260 were readmitted within 30 days (18.0%); 388 readmissions were potentially avoidable (5.6%). In multivariate analysis, 2 or more prescribed outpatient psychiatric medications (odds ratio [OR]: 1.1, 95% confidence interval [CI]: 1.01-1.20) or any prescription of anxiolytics (OR: 1.16, 95% CI: 1.00-1.35) were associated with increased all-cause readmissions, whereas discharge diagnoses of anxiety (OR: 0.82, 95% CI: 0.68-0.99) or substance abuse (OR: 0.80, 96% CI: 0.65-0.99) were associated with fewer all-cause readmissions. These findings were not replicated as predictors of potentially avoidable readmissions; rather, patients with discharge diagnoses of depression (OR: 1.49, 95% CI: 1.09-2.04) and schizophrenia (OR: 2.63, 95% CI: 1.13-6.13) were at highest risk.

CONCLUSIONS

Our data suggest that patients treated during a hospitalization for depression and for schizophrenia are at higher risk for potentially avoidable 30-day readmissions, whereas those prescribed more psychiatric medications as outpatients are at increased risk for all-cause readmissions. These populations may represent fruitful targets for interventions to reduce readmission risk.

Validation of patient and nurse short forms of the Readiness for Hospital Discharge Scale and their relationship to return to the hospital

OBJECTIVE

To validate patient and nurse short forms for discharge readiness assessment and their associations with 30-day readmissions and emergency department (ED) visits.

DATA SOURCES/STUDY SETTING

A total of 254 adult medical-surgical patients and their discharging nurses from an Eastern US tertiary hospital between May and November, 2011.

STUDY DESIGN

Prospective longitudinal design, multinomial logistic regression analysis.

DATA COLLECTION/EXTRACTION METHODS

Nurses and patients independently completed an eight-item Readiness for Hospital Discharge Scale on the day of discharge. Patient characteristics, readmissions, and ED visits were electronically abstracted.

PRINCIPAL FINDINGS

Nurse assessment of low discharge readiness was associated with a six- to nine-fold increase in readmission risk. Patient self-assessment was not associated with readmission; neither was associated with ED visits.

CONCLUSIONS

Nurse discharge readiness assessment should be added to existing strategies for identifying readmission risk.

Early recognition of risk factors for adverse outcomes during hospitalization among Medicare patients: a prospective cohort study

Background

There is a persistently high incidence of adverse events during hospitalization among Medicare beneficiaries. Attributes of vulnerability are prevalent, readily apparent, and therefore potentially useful for recognizing those at greatest risk for hospital adverse events who may benefit most from preventive measures. We sought to identify patient characteristics associated with adverse events that are present early in a hospital stay.

Methods

An interprofessional panel selected characteristics thought to confer risk of hospital adverse events and measurable within the setting of acute illness. A convenience sample of 214 Medicare beneficiaries admitted to a large, academic medical center were included in a quality improvement project to develop risk assessment protocols. The data were subsequently analyzed as a prospective cohort study to test the association of risk factors, assessed within 24 hours of hospital admission, with falls, hospital-acquired pressure ulcers (HAPU) and infections (HAI), adverse drug reactions (ADE) and 30-day readmissions.

Results

Mean age = 75(±13.4) years. Risk factors with highest prevalence included >4 active comorbidities (73.8%), polypharmacy (51.7%), and anemia (48.1%). One or more adverse hospital outcomes occurred in 46 patients (21.5%); 56 patients (26.2%) were readmitted within 30 days. Cluster analysis described three adverse outcomes: 30-day readmission, and two groups of in-hospital outcomes. Distinct regression models were identified: Weight loss (OR = 3.83; 95% CI = 1.46, 10.08) and potentially inappropriate medications (OR = 3.05; 95% CI = 1.19, 7.83) were associated with falls, HAPU, procedural complications, or transfer to intensive care; cognitive impairment (OR = 2.32; 95% CI = 1.24, 4.37), anemia (OR = 1.87; 95% CI = 1.00, 3.51) and weight loss (OR = 2.89; 95% CI = 1.38, 6.07) were associated with HAI, ADE, or length of stay >7 days; hyponatremia (OR = 3.49; 95% CI = 1.30, 9.35), prior hospitalization within 30 days (OR = 2.66; 95% CI = 1.31, 5.43) and functional impairment (OR = 2.05; 95% CI = 1.02, 4.13) were associated with 30-day readmission.

Conclusions

Patient characteristics recognizable within 24 hours of admission can be used to identify increased risk for adverse events and 30-day readmission.

Development and validation of a continuous measure of patient condition using the Electronic Medical Record

Patient condition is a key element in communication between clinicians. However, there is no generally accepted definition of patient condition that is independent of diagnosis and that spans acuity levels. We report the development and validation of a continuous measure of general patient condition that is independent of diagnosis, and that can be used for medical-surgical as well as critical care patients. A survey of Electronic Medical Record data identified common, frequently collected non-static candidate variables as the basis for a general, continuously updated patient condition score. We used a new methodology to estimate in-hospital risk associated with each of these variables. A risk function for each candidate input was computed by comparing the final pre-discharge measurements with 1-year post-discharge mortality. Step-wise logistic regression of the variables against 1-year mortality was used to determine the importance of each variable. The final set of selected variables consisted of 26 clinical measurements from four categories: nursing assessments, vital signs, laboratory results and cardiac rhythms. We then constructed a heuristic model quantifying patient condition (overall risk) by summing the single-variable risks. The model's validity was assessed against outcomes from 170,000 medical-surgical and critical care patients, using data from three US hospitals. Outcome validation across hospitals yields an area under the receiver operating characteristic curve(AUC) of ≥0.92 when separating hospice/deceased from all other discharge categories, an AUC of ≥0.93 when predicting 24-h mortality and an AUC of 0.62 when predicting 30-day readmissions. Correspondence with outcomes reflective of patient condition across the acuity spectrum indicates utility in both medical-surgical units and critical care units. The model output, which we call the Rothman Index, may provide clinicians with a longitudinal view of patient condition to help address known challenges in caregiver communication, continuity of care, and earlier detection of acuity trends.

Racial, Income, and Marital Status Disparities in Hospital Readmissions Within a Veterans-Integrated Health Care Network

Hospital readmission is an important indicator of health care quality and currently used in determining hospital reimbursement rates by Centers for Medicare & Medicaid Services. Given the important policy implications, a better understanding of factors that influence readmission rates is needed. Racial disparities in readmission have been extensively studied, but income and marital status (a postdischarge care support indicator) disparities have received limited attention. By employing three Poisson regression models controlling for different confounders on 8,718 patients in a veterans-integrated health care network, this study assessed racial, income, and martial disparities in relation to total number of readmissions. In contrast to other studies, no racial and income disparities were found, but unmarried patients experienced significantly more readmissions: 16%, after controlling for the confounders. These findings render unique insight into health care policies aimed to improve race and income disparities, while challenging policy makers to reduce readmissions for those who lack family support.

Functional status impairment is associated with unplanned readmissions

OBJECTIVE

To determine whether functional status on admission to a Comprehensive Integrated Inpatient Rehabilitation Program (CIIRP) is associated with unplanned readmission to acute care.

DESIGN

Retrospective cohort study.

SETTING

Academic hospital-based CIIRP.

PARTICIPANTS

Consecutive patients (N=1515) admitted to a CIIRP between January 2009 and June 2012.

INTERVENTIONS

Patients' functional status, the primary exposure variable, was assessed using tertiles of the total FIM score at CIIRP admission, with secondary analyses using the FIM motor and cognitive domains. A propensity score, consisting of 25 relevant clinical and demographic variables, was used to adjust for confounding in the analysis.

MAIN OUTCOME MEASURES

Readmission to acute care was categorized as (1) readmission before planned discharge from the CIIRP, (2) readmission within 30 days of discharge from the CIIRP, and (3) total readmissions from both groups, with total readmissions being the a priori primary outcome.

RESULTS

Among the 1515 patients, there were 347 total readmissions. Total readmissions were significantly associated with FIM scores, with adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for the lowest and middle FIM tertiles versus the highest tertile (AOR=2.6; 95% CI, 1.9-3.7; P<.001 and AOR=1.7; 95% CI, 1.2-2.4; P=.002, respectively). There were similar findings for secondary analyses of readmission before planned discharge from the CIIRP (AOR=3.5; 95% CI, 2.2-5.8; P<.001 and AOR=2.1; 95% CI, 1.3-3.5l P=.002, respectively), and a weaker association for readmissions after discharge from the CIIRP (AOR=1.6; 95% CI, 1.0-2.4; P=.047 and AOR=1.3; 95% CI, 0.8-1.9; P=.28, respectively). The FIM motor domain score was more strongly associated with readmissions than the FIM cognitive score.

CONCLUSIONS

Functional status on admission to the CIIRP is strongly associated with readmission to acute care, particularly for motor aspects of functional status and readmission before planned discharge from the CIIRP. Efforts to reduce hospital readmissions should consider patient functional status as an important and potentially modifiable risk factor.

Predictors of rehospitalization among elderly patients admitted to a rehabilitation hospital: the role of polypharmacy, functional status, and length of stay

OBJECTIVES

Rehospitalizations for elderly patients are an increasing health care burden. Nonetheless, we have limited information on unplanned rehospitalizations and the related risk factors in elderly patients admitted to in-hospital rehabilitation facilities after an acute hospitalization.

SETTING

In-hospital rehabilitation and aged care unit.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Elderly patients 65 years or older admitted to an in-hospital rehabilitation hospital after an acute hospitalization between January 2004 and June 2011.

MEASUREMENTS

The rate of 30-day unplanned rehospitalization to hospitals was recorded. Risk factors for unplanned rehospitalization were evaluated at rehabilitation admission: age, comorbidity, serum albumin, number of drugs, decline in functional status, delirium, Mini Mental State Examination score, and length of stay in the acute hospital. A multivariable Cox proportional regression model was used to identify the effect of these risk factors for time to event within the 30-day follow-up.

RESULTS

Among 2735 patients, with a median age of 80 years (interquartile range 74-85), 98 (4%) were rehospitalized within 30 days. Independent predictors of 30-day unplanned rehospitalization were the use of 7 or more drugs (hazard ratio [HR], 3.94; 95% confidence interval, 1.62-9.54; P = .002) and a significant decline in functional status (56 points or more at the Barthel Index) compared with the month before hospital admission (HR 2.67, 95% CI: 1.35-5.27; P = .005). Additionally, a length of stay in the acute hospital of 13 days or more carried a twofold higher risk of rehospitalization (HR 2.67, 95% CI: 1.39-5.10); P = .003).

CONCLUSIONS

The rate of unplanned rehospitalization was low in this study. Polypharmacy, a significant worsening of functional status compared with the month before acute hospital admission, and hospital length of stay are important risk factors.

Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration

BACKGROUND

Statistical models that identify patients at elevated risk of death or hospitalization have focused on population subsets, such as those with a specific clinical condition or hospitalized patients. Most models have limitations for clinical use. Our objective was to develop models that identified high-risk primary care patients.

METHODS

Using the Primary Care Management Module in the Veterans Health Administration (VHA)'s Corporate Data Warehouse, we identified all patients who were enrolled and assigned to a VHA primary care provider on October 1, 2010. The outcome variable was the occurrence of hospitalization or death during the subsequent 90 days and 1 year. We extracted predictors from 6 categories: sociodemographics, medical conditions, vital signs, prior year use of health services, medications, and laboratory tests and then constructed multinomial logistic regression models to predict outcomes for over 4.6 million patients.

RESULTS

In the predicted 95th risk percentiles, observed 90-day event rates were 19.6%, 6.2%, and 22.6%, respectively, for hospitalization, death, and either hospitalization or death, compared with population averages of 2.7%, 0.7%, and 3.4%, respectively; 1-year event rates were 42.3%, 19.4%, and 51.3%, respectively, compared with population averages of 8.2%, 2.6%, and 10.8%, respectively. The C-statistics for 90-day outcomes were 0.83, 0.86, and 0.81, respectively, for hospitalization, death, and either hospitalization or death and were 0.81, 0.85, and 0.79, respectively, for 1-year outcomes.

CONCLUSIONS

Prediction models using electronic clinical data accurately identified patients with elevated risk for hospitalization or death. This information can enhance the coordination of care for patients with complex clinical conditions.

Influence of neighborhood household income on early death or urgent hospital readmission

BACKGROUND

The relationship of socioeconomic status (SES) with hospital readmissions is unclear.

METHODS

We used population-based administrative datasets to randomly select 40,827 adult Ontarians discharged from hospital to the community. Patient postal codes were linked to average neighborhood household-income quintiles. The association of this SES measure with 30-day death or urgent readmission was measured after controlling for outcome risk using a validated index, LACE+: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency-department use (E).

RESULTS

Within 1 month of discharge, 2638 (6.5%) people died or were urgently readmitted. Lower neighborhood income was significantly associated with both an increased outcome risk (P < 0.0001) and LACE+ score. After adjusting for LACE+ score, neighborhood income was no longer associated with 30-day death or urgent readmission (P = 0.21).

CONCLUSIONS

After accounting for known risk factors, early death or readmission is not more common in people from lower-income neighborhoods. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Association of self-reported hospital discharge handoffs with 30-day readmissions

IMPORTANCE

Poor health care provider communication across health care settings may lead to adverse outcomes.

OBJECTIVE

To determine the frequency with which inpatient providers report communicating directly with outpatient providers and whether direct communication was associated with 30-day readmissions.

DESIGN

We conducted a single-center prospective study of self-reported communication patterns by discharging health care providers on inpatient medical services from September 2010 to December 2011 at The Johns Hopkins Hospital.

SETTING

A 1000-bed urban, academic center.

PARTICIPANTS

There were 13 954 hospitalizations in this time period. Of those, 9719 were for initial visits. After additional exclusions, including patients whose outpatient health care provider was the inpatient attending physician, those who had planned or routine admissions, those without outpatient health care providers, those who died in the hospital, and those discharged to other healthcare facilities, we were left with 6635 hospitalizations for analysis.

INTERVENTIONS

Self-reported communication was captured from a mandatory electronic discharge worksheet field. Thirty-day readmissions, length of stay (LOS), and demographics were obtained from administrative databases.

DATA EXTRACTION

We used multivariable logistic regression models to examine, first, the association between direct communication and patient age, sex, LOS, race, payer, expected 30-day readmission rate based on diagnosis and illness severity, and physician type and, second, the association between 30-day readmission and direct communication, adjusting for patient and physician-level factors.

RESULTS

Of 6635 included hospitalizations, successful direct communication occurred in 2438 (36.7%). The most frequently reported reason for lack of direct communication was the health care provider's perception that the discharge summary was adequate. Predictors of direct communication, adjusting for all other variables, included patients cared for by hospitalists without house staff (odds ratio [OR], 1.81 [95% CI, 1.59-2.08]), high expected 30-day readmission rate (OR, 1.18 [95% CI, 1.10-1.28] per 10%), and insurance by Medicare (OR, 1.35 [95% CI, 1.16-1.56]) and private insurance companies (OR, 1.35 [95% CI, 1.18-1.56]) compared with Medicaid. Direct communication with the outpatient health care provider was not associated with readmissions (OR, 1.08 [95% CI, 0.92-1.26]) in adjusted analysis.

CONCLUSIONS AND RELEVANCE

Self-reported direct communication between inpatient and outpatient providers occurred at a low rate but was not associated with readmissions. This suggests that enhancing interprovider communication at hospital discharge may not, in isolation, prevent readmissions.

Development of an automated, real time surveillance tool for predicting readmissions at a community hospital

BACKGROUND

The Centers for Medicare and Medicaid Services' Readmissions Reduction Program adjusts payments to hospitals based on 30-day readmission rates for patients with acute myocardial infarction, heart failure, and pneumonia. This holds hospitals accountable for a complex phenomenon about which there is little evidence regarding effective interventions. Further study may benefit from a method for efficiently and inexpensively identifying patients at risk of readmission. Several models have been developed to assess this risk, many of which may not translate to a U.S. community hospital setting.

OBJECTIVE

To develop a real-time, automated tool to stratify risk of 30-day readmission at a semirural community hospital.

METHODS

A derivation cohort was created by extracting demographic and clinical variables from the data repository for adult discharges from calendar year 2010. Multivariate logistic regression identified variables that were significantly associated with 30-day hospital readmission. Those variables were incorporated into a formula to produce a Risk of Readmission Score (RRS). A validation cohort from 2011 assessed the predictive value of the RRS. A SQL stored procedure was created to calculate the RRS for any patient and publish its value, along with an estimate of readmission risk and other factors, to a secure intranet site.

RESULTS

Eleven variables were significantly associated with readmission in the multivariate analysis of each cohort. The RRS had an area under the receiver operating characteristic curve (c-statistic) of 0.74 (95% CI 0.73-0.75) in the derivation cohort and 0.70 (95% CI 0.69-0.71) in the validation cohort.

CONCLUSION

Clinical and administrative data available in a typical community hospital database can be used to create a validated, predictive scoring system that automatically assigns a probability of 30-day readmission to hospitalized patients. This does not require manual data extraction or manipulation and uses commonly available systems. Additional study is needed to refine and confirm the findings.

Red blood cell distribution width is a predictor of readmission in cardiac patients

BACKGROUND

Three-quarters of rehospitalizations ($44 billion yearly estimated cost) may be avoidable. A screening tool for the detection of potential readmission may facilitate more efficient case management.

HYPOTHESIS

An elevated red blood cell distribution width (RDW) is an independent predictor of hospital readmission in patients with unstable angina (UA) or non-ST-elevation myocardial infarction (NSTEMI).

METHODS

The study is a retrospective observational cohort analysis of adults admitted in 2007 with UA or NSTEMI. Data were gathered by review of inpatient medical records. The rate of 30-day nonelective readmission and time to nonelective readmission were recorded until November 1, 2011, and compared by RDW group using the 95th percentile (16.3%) as a cutoff.

RESULTS

The median follow-up time of the 503 subjects (average age, 65 ± 13 years; 56% male) was 3.8 years (interquartile range: 0.3-4.3 years). Those readmitted within 30 days were older, had more comorbidities and higher RDW and creatinine levels, and were more likely to have had an intervention. At 3.8 years of follow-up, subjects with high RDW (>16.3%) were more likely to be readmitted compared to those with normal RDW (≤16.3%) (72.28% vs 59.95%, P = 0.003). In multivariable analyses, high RDW was a statistically significant predictor of readmission in general (hazard ratio: 1.35 (95% confidence interval [CI]:1.02-1.79), P = 0.033) but not of 30-day rehospitalization (odds ratio: 1.34 (95% CI: 0.78-2.31), P = 0.292). Its area under the receiver operating characteristic curve was 0.54 (sensitivity 23% and specificity 85%).

CONCLUSIONS

An elevated RDW is an independent predictor of hospital readmission in patients with UA or NSTEMI.

Does travel distance influence length of stay in elective colorectal surgery?

BACKGROUND

Length of stay following elective colorectal surgery is being reported as a quality measure in surgical outcome registries, such as the National Surgical Quality Improvement Program. Regional referral centers with large geographic catchment areas attract patients from significant distances.

OBJECTIVE

The aim of this study was to examine the effect of patient distance traveled, from primary residence to a tertiary care hospital, on length of stay in elective colorectal surgery patients.

DESIGN

Retrospective population-based cohort study uses data obtained from the National Surgical Quality Improvement Program database.

SETTINGS

This study was conducted at a tertiary referral hospital.

PATIENTS

Data on 866 patients undergoing elective colorectal surgery from May 2003 to April 2011 were reviewed.

MAIN OUTCOME MEASURES

Demographics, surgery-related variables, and distance traveled were analyzed relative to the length of stay.

RESULTS

Of the 866 patients, 54% were men, mean age was 57 years, mean distance traveled was 145 miles (range, 2-2984 miles), and mean length of stay was 8.8 days. Univariate analysis showed a significant increase in length of stay with increased distance traveled (p = 0.02). Linear regression analysis revealed a significant association between increased length of stay and male sex (p = 0.006), increasing ASA score (p = 0.000), living alone (p = 0.009), and increased distance traveled (p = 0.028). For each incremental increase in log distance traveled, the length of stay increases by 2.5%.

LIMITATIONS

This is a retrospective review that uses National Surgical Quality Improvement Program data. It is not known how many patients left the hospital and did not return to their primary residence.

CONCLUSIONS

In a model that controlled for variables, increased travel distance from a patient's residence to the surgical hospital was associated with an increase in length of stay. If length of stay is a reportable quality measure in patients undergoing colorectal surgery, significant travel distance should be accounted for in the risk adjustment model calculations.

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 poorer adherence. The concept of medication review is a key element in improving the quality of prescribing and the prevention of adverse drug events. While no generally accepted definition of medication review exists, it can be defined as a systematic assessment of the pharmacotherapy of an individual patient that aims to evaluate and optimise patient medication by a change (or not) in prescription, either by a recommendation or by a direct change. Medication review performed in adult hospitalised patients may lead to better patient outcomes.

OBJECTIVES

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

SEARCH METHODS

We searched the Cochrane Effective Practice and Organisation of Care (EPOC) Group's Specialised Register (August 2011); The Cochrane Central Register of Controlled Trials (CENTRAL), The Cochrane Library 2011, Issue 8; MEDLINE (1946 to August 2011); EMBASE (1980 to August 2011); CINAHL (1980 to August 2011); International Pharmaceutical Abstracts (1970 to August 2011); and Web of Science (August 2011). 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 did not apply any 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 readmission, 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 additional unpublished data. We calculated relative risks for dichotomous data and mean differences for continuous data (with 95% confidence intervals (CIs)).

MAIN RESULTS

We identified 4647 references and included five trials (1186 participants). Follow-up ranged from 30 days to one year. We found no evidence of effect on all-cause mortality (risk ratio (RR) 0.98; 95% CI 0.78 to 1.23) and hospital readmissions (RR 1.01; 95% CI 0.88 to 1.16), but a 36% relative reduction in emergency department contacts (RR 0.64; 95% CI 0.46 to 0.89).

AUTHORS' CONCLUSIONS

It is uncertain whether medication review reduces mortality or hospital readmissions, but medication review seems to reduce emergency department contacts. However, the cost-effectiveness of this intervention is not known and due to the uncertainty of the estimates of mortality and readmissions and the short follow-up, important treatment effects may have been overlooked. Therefore, medication review should preferably be undertaken in the context of clinical trials. High quality trials with long follow-up are needed before medication review should be implemented.

Moving beyond readmission penalties: creating an ideal process to improve transitional care

Hospital readmissions are common and costly; this has resulted in their emergence as a key quality indicator in the current era of renewed focus on cost containment. However, many concerns remain about the use of readmissions as a hospital quality measure and about how to reduce hospital readmissions. These concerns stem in part from deficiencies in the state of the science of transitional care. A conceptualization of the "ideal" discharge process could help address these deficiencies and move the state of the science forward. We describe an ideal transition in care, explicate the key components, discuss its implications in the context of recent efforts to reduce readmissions, and suggest next steps for policymakers, researchers, healthcare administrators, practitioners, and educators.

[Hospital readmissions related to drug interactions: a retrospective study in a hospital setting]

OBJECTIVE

To examine the relationship between potential drug interactions and hospital readmissions.

METHODS

Retrospective study with 1,487 adult patients (> 18 years old) admitted to a general hospital in the city of Vitória da Conquista, Northeastern Brazil, from January to December 2007. Data were collected from Hospital Admission Authorization (AIH) forms in the Brazilian National Health System Hospital Database (SIH/SUS). Probabilistic linkage was used to combine multiple AIH forms from the same admission into a single record and to identify readmissions. Information on prescriptions was manually added to the SIH/SUS records. Logistic regression was used to quantitatively assess the impact of drug interactions on hospital readmissions. Cox regression was performed to test the impact of this variable on time to first readmission.

RESULTS

A total of 99 readmissions (7% of all patients) were identified. Potential drug interactions were found in 35% of all prescriptions evaluated. Patients with potential drug interactions in a prior admission were more likely to be readmitted. The adjusted odds ratio indicated a 2.4-fold increase in odds of being readmitted; and the adjusted hazard ratio showed that this risk was increased by 79% in patients with potential drug interactions (p < 0.01).

CONCLUSIONS

The study results suggest an association between prior drug interactions and increased risk of readmission. Health professionals should be aware of potential hazard of certain drug combinations and closely monitor high-risk patients such as elderly patients and those with renal impairment.

The effect of clinical interventions on hospital readmissions: a meta-review of published meta-analyses

BACKGROUND

The economic impact and ease of measurement of all-cause hospital readmission rates (HRR) have led to the current debate as to whether they are reducible, and whether they should be used as a publicly reported quality indicators of medical care.

OBJECTIVE

To assess the efficacy of broad clinical interventions in preventing HRR of patients with chronic diseases

METHOD

A meta-review of published systematic reviews of randomized controlled trials (RCTs) of clinical interventions that have included HRR among the patients' outcomes of interest.

MAIN FINDINGS

Meta-analyses of RCTs have consistently found that, in the community, disease management programs significantly reduced HRR in patients with heart failure, coronary heart disease and bronchial asthma, but not in patients with stroke and in unselected patients with chronic disorders. Inhospital interventions, such as discharge planning, pharmacological consultations and multidisciplinary care, and community interventions in patients with chronic obstructive pulmonary diseases had an inconsistent effect on HRR. MAIN STUDY LIMITATION: Despite their economic impact and ease of measurement, HRR are not the most important outcome of patient care, and efforts aimed at their reduction may compromise patients' health by reducing also justified re-admissions.

CONCLUSIONS

The efficacy of inhospital interventions in reducing HRR is in need of further study. In patients with heart diseases and bronchial asthma, HRR may be considered as a publicly reported quality indicator of community care, provided that future research confirms that efforts to reduce HRR do not adversely affect other patients' outcomes, such as mortality, functional capacity and quality of life. Future research should also focus on the reasons for the higher efficacy of community interventions in patients with heart diseases and bronchial asthma than in those with other chronic diseases.

Mobility after hospital discharge as a marker for 30-day readmission

BACKGROUND

Little is known about older patient's mobility soon after discharge home from an acute hospitalization. We examined daily postdischarge mobility levels as marker of overall health and response to in-hospital treatment in older medicine patients.

METHODS

One hundred and eleven ambulatory men and women aged 65 years and older hospitalized with an acute medical illness and discharged to home were studied. Patients received an ankle-worn accelerometer during hospitalization and wore it continually for up to 1 week after discharge. Total number of steps taken per day was assessed. The primary outcome was all-cause 30-day readmission.

RESULTS

Thirteen (11.7%) participants were readmitted within 30 days of discharge. There was a significant association between mean daily steps taken postdischarge and 30-day readmission (odds ratio = 0.85, 95% confidence interval = 0.72-0.99, and p = .04; odds ratio and confidence intervals were calculated for 500-step intervals). Though not statistically significant in the fully adjusted model (odds ratio = 0.83, 95% confidence interval = 0.71-1.02, and p = .08), mean daily steps was the strongest predictor among known readmission risk factors. The least active participants postdischarge were significantly more likely to be older (p = .02), be not married (p = .02), use a cane or walker prior to admission (p < .01), have longer lengths of hospital stay (p = .02), and be readmitted (p = .05).

CONCLUSIONS

Mobility level soon after discharge home shows promise as a simple physical biomarker of overall health and risk of 30-day readmission in older patients.

Risk factors for 30-day readmission in general medical patients admitted from the emergency department: a single centre study

BACKGROUND

Overcrowding in emergency departments (ED) around the world is an increasingly serious problem with an adverse impact on both patient flow and patient outcomes. A significant contributing factor to ED overcrowding is possibly due to readmission. Risk factors for readmission in patients admitted from ED are rarely studied, particularly in Asian countries where the length of stay is reportedly longer.

METHODS

A retrospective study of patients admitted to general medical wards from the ED of a referral centre in northern Taiwan from November 2009 to April 2010 was conducted. The primary outcome was 30-day hospital readmission and clinical characteristics were analysed for predictors of readmission.

RESULTS

Of the recruited 2698 patients, 451 (16.7%) were readmitted within 30 days after discharge. Age, gender, marital status and the activities of daily living (Barthel's score) were not associated with 30-day readmission. Higher Charlson score ((score 2-4) hazard ratio (HR): 1.42, 95% confidence interval (CI): 1.07-1.89; (score >4) HR: 1.93, 95% CI: 1.37-2.73), longer hospital stay ((8-14 days) HR: 1.51, 95% CI: 1.17-1.95; (15-28 days) HR: 1.64, 95% CI: 1.22-2.19; (>28 days) HR: 1.97, 95% CI: 1.43-2.71), and presence of underlying active malignancy (HR: 1.66, 95% CI: 1.27-2.16) and anaemia (HR: 1.26, 95% CI: 1.02-1.55) were independently associated with readmission.

CONCLUSION

Medical patients admitted from the ED of a referral centre have a 30-day readmission rate of 16.7%. Post-discharge care should focus on patients with higher Charlson score, longer hospitalisation, anaemia and underlying active malignancy, which are independent predictive factors for 30-day readmission.

Exploring the value of clinical data standards to predict hospitalization of home care patients

BACKGROUND

There is a critical need to reduce hospitalizations for Medicare patients and electronic health record (EHR) home care data provide new opportunities to evaluate risk of hospitalization for patients.

OBJECTIVES

The objectives of this study were to 1) develop a measure to predict risk of hospitalization among home care patients, the Hospitalization Risk Score (HRS), and 2) compare it with an existing severity of illness measure, the Charlson Index of Comorbidity (CIC).

METHODS

A convenience sample of clinical data from 14 home care agencies' EHRs, representing 1,643 home care patient episodes was used for the study. The development of the HRS was based on review of the literature, and expert panel evaluation to construct the HRS. Descriptive statistics and generalized linear models were used for comparative analysis; areas under curve (AUC) values were compared for receiver operating curves (ROC), and cut points predicting hospitalization were evaluated.

RESULTS

The HRS for this sample ranged from 0 to 5.6, with a median of 1.25. The CIC for this sample ranged from 0 to 9 and with a median of 0. Nearly three fourths of the sample was hospitalized at an HRS of 2, and a CIC of 1. AUC values for ROC were 0.63 for HRS and 0.59 for the CIC. The ROC curves were significantly different (t = -7.59, p <0.003).

CONCLUSIONS

This preliminary study demonstrates the potential value of the HRS using Omaha System EHR data. There was a statistically significant difference for predicting hospitalization of home care patients with the HRS versus the CIC; however the AUC values for both were low. Continued research is needed to further refine the HRS, determine whether it is more sensitive for particular subgroups of patients, and combine it with additional risk factors in understanding rehospitalization.