Prediction of early readmission in medical inpatients using the Probability of Repeated Admission instrument

Depressive symptoms and malnutrition are associated with other geriatric syndromes and increase risk for 30-Day readmission in hospitalized older adults: a prospective cohort study

BACKGROUND

Readmission in older adults is typically complex with multiple contributing factors. We aim to examine how two prevalent and potentially modifiable geriatric conditions - depressive symptoms and malnutrition - relate to other geriatric syndromes and 30-day readmission in hospitalized older adults.

METHODS

Consecutive admissions of patients ≥ 65 years to a general medical department were recruited over 16 months. Patients were screened for depression, malnutrition, delirium, cognitive impairment, and frailty at admission. Medical records were reviewed for poor oral intake and functional decline during hospitalization. Unplanned readmission within 30-days of discharge was tracked through the hospital's electronic health records and follow-up telephone interviews. We use directed acyclic graphs (DAGs) to depict the relationship of depressive symptoms and malnutrition with geriatric syndromes that constitute covariates of interest and 30-day readmission outcome. Multiple logistic regression was performed for the independent associations of depressive symptoms and malnutrition with 30-day readmission, adjusting for variables based on DAG-identified minimal adjustment set.

RESULTS

We recruited 1619 consecutive admissions, with mean age 76.4 (7.9) years and 51.3% females. 30-day readmission occurred in 331 (22.0%) of 1,507 patients with follow-up data. Depressive symptoms, malnutrition, higher comorbidity burden, hospitalization in the one-year preceding index admission, frailty, delirium, as well as functional decline and poor oral intake during the index admission, were more commonly observed among patients who were readmitted within 30 days of discharge (P < 0.05). Patients with active depressive symptoms were significantly more likely to be frail (OR = 1.62, 95% CI 1.22-2.16), had poor oral intake (OR = 1.35, 95% CI 1.02-1.79) and functional decline during admission (OR = 1.58, 95% CI 1.11-2.23). Malnutrition at admission was significantly associated with frailty (OR = 1.53, 95% CI 1.07-2.19), delirium (OR = 2.33, 95% CI 1.60-3.39) cognitive impairment (OR = 1.88, 95% CI 1.39-2.54) and poor oral intake during hospitalization (OR = 2.70, 95% CI 2.01-3.64). In minimal adjustment set identified by DAG, depressive symptoms (OR = 1.38, 95% CI 1.02-1.86) remained significantly associated with 30-day readmission. The association of malnutrition with 30-day readmission was no longer statistically significant after adjusting for age, ethnicity and depressive symptoms in the minimal adjustment set (OR = 1.40, 95% CI 0.99-1.98).

CONCLUSION

The observed causal associations support screening and targeted interventions for depressive symptoms and malnutrition during admission and in the post-acute period.

Exploration of risk factors for high-risk adverse events in elderly patients after discharge and comparison of discharge planning screening tools

OBJECTIVES

Discharge planning is an effective strategy to prevent adverse health events and reduce medical expenditures. The high-risk target populations of discharged elderly patients and important predictors for the occurrence of adverse events are still not clear. Therefore, the purposes of this study were to examine the validity of discharge planning screening tools in sufficiently identifying high-risk adverse events to health after discharge and to compare two screening tools with our study model.

DESIGN

We conducted a prospective study and recruited elderly patients who had had no hospitalization within 3 months before admission to 13 general wards of a medical center in northern Taiwan from November 2018 to May 2020.

METHODS

Elderly patients were randomly selected during the study period. Within 24 h of admission, patients were asked to consent to join this study. After the patient was discharged, the patient's health and hospitalization for the next year were tracked by telephone interviews.

RESULTS

In total, 300 participants were recruited for this study. Incidences of high-risk adverse events within 30 days, 60 days, and 12 months after discharge were 20.3%, 25.7%, and 48.7% respectively. A logistic regression showed that an increased age, physical or mental disabilities or a major illness, a low body-mass index, and having been hospitalized in the past year were significantly related to the occurrence of high-risk events among elderly discharge patients. The pooled sensitivity of the P_ra was 52% and the specificity was 72%; the pooled sensitivity of the LACE index was 67% and the specificity was 36%. The predictive model of this study had a higher discriminatory power than the P_ra and LACE index for high-risk events after discharge.

CONCLUSIONS

Elderly patients are more vulnerable to high-risk adverse events after discharge. Both the LACE index and P_ra are useful discharge planning screening tools to screen for high-risk adverse events after discharge. Elderly patients need more-active and complete continuity of care plans and discharge planning services to ensure that the overall quality of patient care can be improved and readmissions and mortality reduced.

CLINICAL RELEVANCE

The findings of this study can provide information for discharge planning managers to identify high-risk elderly patients during hospitalization and promptly offer care education or resources to improve care management.

Care setting and 30-day hospital readmissions among older adults: a population-based cohort study

BACKGROUND

Despite the fact that many older adults receive home or long-term care services, the effect of these care settings on hospital readmission is often overlooked. Efforts to reduce hospital readmissions, including capacity planning and targeting of interventions, require clear data on the frequency of and risk factors for readmission among different populations of older adults.

METHODS

We identified all adults older than 65 years discharged from an unplanned medical hospital stay in Ontario between April 2008 and December 2015. We defined 2 preadmission care settings (community, long-term care) and 3 discharge care settings (community, home care, long-term care) and used multinomial regression to estimate associations with 30-day readmission (and death as a competing risk).

RESULTS

We identified 701 527 individuals (mean age 78.4 yr), of whom 414 302 (59.1%) started in and returned to the community. Overall, 88 305 in dividuals (12.6%) were re admitted within 30 days, but this proportion varied by care setting combination. Relative to individuals returning to the community, those discharged to the community with home care (adjusted odds ratio [OR] 1.43, 95% confidence interval [CI] 1.39-1.46) and those returning to long-term care (adjusted OR 1.35, 95% CI 1.27-1.43) had a greater risk of readmission, whereas those newly admitted to long-term care had a lower risk of readmission (adjusted OR 0.68, 95% CI 0.63-0.72).

INTERPRETATION

In Ontario, about 40% of older people were discharged from hospital to either home care or long-term care. These discharge settings, as well as whether an individual was admitted to hospital from long-term care, have important implications for understanding 30-day readmission rates. System planning and efforts to reduce readmission among older adults should take into account care settings at both admission and discharge.

Predictive models for hospital readmission risk: A systematic review of methods

OBJECTIVES

Hospital readmission risk prediction facilitates the identification of patients potentially at high risk so that resources can be used more efficiently in terms of cost-benefit. In this context, several models for readmission risk prediction have been proposed in recent years. The goal of this review is to give an overview of prediction models for hospital readmission, describe the data analysis methods and algorithms used for building the models, and synthesize their results.

METHODS

Studies that reported the predictive performance of a model for hospital readmission risk were included. We defined the scope of the review and accordingly built a search query to select the candidate papers. This query string was used as input for the chosen search engines, namely PubMed and Google Scholar. For each study, we recorded the population, feature selection method, classification algorithm, sample size, readmission threshold, readmission rate and predictive performance of the model.

RESULTS

We identified 77 studies that met the inclusion criteria, out of 265 citations. In 68% of the studies (n = 52) logistic regression or other regression techniques were utilized as the main method. Ten (13%) studies used survival analysis for model construction, while 14 (18%) used machine learning techniques for classification, of which decision tree-based methods and SVM were the most utilized algorithms. Among these, only four studies reported the use of any class imbalance addressing technique, of which resampling is the most frequent (75%). The performance of the models varied significantly among studies, with Area Under the ROC Curve (AUC) values in the ranges between 0.54 and 0.92.

CONCLUSION

Logistic regression and survival analysis have been traditionally the most widely used techniques for model building. Nevertheless, machine learning techniques are becoming increasingly popular in recent years. Recent comparative studies suggest that machine learning techniques can improve prediction ability over traditional statistical approaches. Regardless, the lack of an appropriate benchmark dataset of hospital readmissions makes a comparison of models' performance across different studies difficult.

Predicting ICU readmission using grouped physiological and medication trends

BACKGROUND

Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmission prediction models are often built based on physiological variables. Intuitively, snapshot measurements, especially the last measurements, are effective predictors that are widely used by researchers. However, methods that only use snapshot measurements neglect predictive information contained in the trends of physiological and medication variables. Mean, maximum or minimum values take multiple time points into account and capture their summary statistics, however, these statistics are not able to catch the detailed picture of temporal trends. In this study, we find strong predictors with ability of capturing detailed temporal trends of variables for 30-day readmission risk and build prediction models with high accuracy.

METHODS

We study physiological measurements and medications from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) clinical dataset. Time series of each variable are converted into trend graphs with nodes being discretized measurements of each variable. Then we extract important temporal trends by applying frequent subgraph mining on the trend graphs. The frequency of a subgraph is a good cue to find important temporal trends since similar patients often share similar trends regarding their pathophysiological evolution under medical interventions. Important temporal trends are then grouped automatically by non-negative matrix factorization. The grouped trends could be considered as an approximate representation of patients' pathophysiological states and medication profiles. We train a logistic regression model to predict 30-day ICU readmission risk based on snapshot measurements, grouped physiological trends and medication trends.

RESULTS

Our dataset consists of 1170 patients who are alive 30 days after discharge from ICU and have at least 12 h of data. In the dataset, 860 patients were not readmitted and 310 were readmitted, within 30 days after discharge. Our model outperforms all comparison models, and shows an improvement in the area under the receiver operating characteristic curve (AUC) of almost 4% from the best comparison model.

CONCLUSIONS

Grouped physiological and medication trends carry predictive information for ICU readmission risk. In order to build predictive models with higher accuracy, we should add grouped physiological and medication trends as complementary features to snapshot measurements.

Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore

The LACE index (length of stay, acuity of admission, Charlson comorbidity index, CCI, and number of emergency department visits in preceding 6 months) derived in Canada is simple and may have clinical utility in Singapore to predict readmission risk. We compared the performance of the LACE index with a derived model in identifying 30-day readmissions from a population of general medicine patients in Singapore. Additional variables include patient demographics, comorbidities, clinical and laboratory variables during the index admission, and prior healthcare utilization in the preceding year. 5,862 patients were analysed and 572 patients (9.8%) were readmitted in the 30 days following discharge. Age, CCI, count of surgical procedures during index admission, white cell count, serum albumin, and number of emergency department visits in previous 6 months were significantly associated with 30-day readmission risk. The final logistic regression model had fair discriminative ability c-statistic of 0.650 while the LACE index achieved c-statistic of 0.628 in predicting 30-day readmissions. Our derived model has the advantage of being available early in the admission to identify patients at high risk of readmission for interventions. Additional factors predicting readmission risk and machine learning techniques should be considered to improve model performance.

Multimorbidity in risk stratification tools to predict negative outcomes in adult population

INTRODUCTION

Risk stratification tools were developed to assess risk of negative health outcomes. These tools assess a variety of variables and clinical factors and they can be used to identify targets of potential interventions and to develop care plans. The role of multimorbidity in these tools has never been assessed.

OBJECTIVES

To summarize validated risk stratification tools for predicting negative outcomes, with a specific focus on multimorbidity.

METHODS

MEDLINE, Cochrane Central Register of Controlled Trials and PubMed database were interrogated for studies concerning risk prediction models in medical populations. Review was conducted to identify prediction models tested with patients in both derivation and validation cohorts. A qualitative synthesis was performed focusing particularly on how multimorbidity is assessed by each algorithm and how much this weighs in the ability of discrimination.

RESULTS

Of 3674 citations reviewed, 36 articles met criteria. Of these, 29 had as outcome hospital admission/readmission. The most common multimorbidity measure employed in the models was the Charlson Comorbidity Index (12 articles). C-statistics ranged between 0.5 and 0.85 in predicting hospital admission/ readmission. The highest c-statistics was 0.83 in models with disability as outcome. For healthcare cost, models which used ACG-PM case mix explained better the variability of total costs.

CONCLUSIONS

This review suggests that predictive risk models which employ multimorbidity as predictor variable are more accurate; CHF, cerebro-vascular disease, COPD and diabetes were strong predictors in some of the reviewed models. However, the variability in the risk factors used in these models does not allow making assumptions.

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.

Influence of frailty-related diagnoses, high-risk prescribing in elderly adults, and primary care use on readmissions in fewer than 30 days for veterans aged 65 and older

OBJECTIVES

To determine the effect of two variables not previously studied in the readmissions literature (frailty-related diagnoses and high-risk medications in the elderly (HRME)) and one understudied variable (volume of primary care visits in the prior year).

DESIGN

Retrospective cohort study using data from a study designed to examine outcomes associated with inappropriate prescribing in elderly adults.

SETTING

All Veterans Affairs (VA) facilities with acute inpatient beds in fiscal year 2006 (FY06).

PARTICIPANTS

All veterans aged 65 and older by October 1, 2005, who received VA care at least once per year between October 1, 2004, and September 30, 2006, and were hospitalized at least once during FY06 on a medical or surgical unit.

MEASUREMENTS

A generalized linear interactive risk prediction model included demographic and clinical characteristics (mental health and chronic medical conditions, frailty-related diagnoses, number of medications) in FY05; incident HRME in FY06 before index hospitalization or readmission; chronic HRME in FY05; and FY05 emergency department (ED), hospital, geriatric, palliative, or primary care use. Facility-level variables were complexity, rural versus urban, and FY06 admission rate.

RESULTS

The mean adjusted readmission rate was 18.3%. The new frailty-related diagnoses variable is a risk factor for readmission in addition to Charlson comorbidity score. Incident HRME use was associated with lower rates of readmission, as were higher numbers of primary care visits in the prior year.

CONCLUSION

Frailty-related diagnoses may help to target individuals at higher risk of readmission to receive more-intensive care transition services. HRME use does not help in this targeting. A higher number of face-to-face primary care visits in the prior year, unlike ED and hospital use, correlates with fewer readmissions and may be another avenue for targeting prevention strategies.

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.

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.

Predictive model of readmission to internal medicine wards

OBJECTIVE

Prevention of readmission to hospital is considered an outstanding example of a cost-effective practice. Our aim was to derive and validate a clinically useful index to quantify the risk of readmission among patients discharged from Internal Medicine departments.

METHODS

We analysed hospital Basic Minimum Data Sets (BMDS) recorded between 2006 and 2008 to determine patterns of rehospitalization. Multivariate statistical analysis of routinely collected data was used to develop an algorithm ('SEMI INDEX') to identify patients predicted to have the highest risk of readmission in the 30 days following discharge. The algorithm was developed by using data from admissions in 2006-2007, for four age subgroups. Coefficients for the most powerful and statistically significant variables were applied against episodes recorded in 2008 to validate the findings of the algorithm developed from the first sample.

RESULTS

Of the 999,089 internal medicine admissions in Spain during 2006-2007, 12.4% were rehospitalized within 30 days. The key factors that predicted subsequent admission included male sex, length of stay, comorbidity of the patient, and some clinical conditions. There were small but relevant differences among the different age subgroups.

CONCLUSIONS

Readmissions to Internal Medicine departments are prevalent (12.4%). The SEMI INDEX can be used to assess accurately the risk of readmission within 30 days after discharge.

Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30)

OBJECTIVES

To develop an algorithm for identifying inpatients at high risk of re-admission to a National Health Service (NHS) hospital in England within 30 days of discharge using information that can either be obtained from hospital information systems or from the patient and their notes.

DESIGN

Multivariate statistical analysis of routinely collected hospital episode statistics (HES) data using logistic regression to build the predictive model. The model's performance was calculated using bootstrapping.

SETTING

HES data covering all NHS hospital admissions in England.

PARTICIPANTS

The NHS patients were admitted to hospital between April 2008 and March 2009 (10% sample of all admissions, n=576 868).

MAIN OUTCOME MEASURES

Area under the receiver operating characteristic curve for the algorithm, together with its positive predictive value and sensitivity for a range of risk score thresholds.

RESULTS

The algorithm produces a 'risk score' ranging (0-1) for each admitted patient, and the percentage of patients with a re-admission within 30 days and the mean re-admission costs of all patients are provided for 20 risk bands. At a risk score threshold of 0.5, the positive predictive value (ie, percentage of inpatients identified as high risk who were subsequently re-admitted within 30 days) was 59.2% (95% CI 58.0% to 60.5%); representing 5.4% (95% CI 5.2% to 5.6%) of all inpatients who would be re-admitted within 30 days (sensitivity). The area under the receiver operating characteristic curve was 0.70 (95% CI 0.69 to 0.70).

CONCLUSIONS

We have developed a method of identifying inpatients at high risk of unplanned re-admission to NHS hospitals within 30 days of discharge. Though the models had a low sensitivity, we show how to identify subgroups of patients that contain a high proportion of patients who will be re-admitted within 30 days. Additional work is necessary to validate the model in practice.

Risk prediction models for hospital readmission: a systematic review

CONTEXT

Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison.

OBJECTIVE

To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use.

DATA SOURCES AND STUDY SELECTION

The databases of MEDLINE, CINAHL, and the Cochrane Library were searched from inception through March 2011, the EMBASE database was searched through August 2011, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies published in the English language of prediction models tested with medical patients in both derivation and validation cohorts.

DATA EXTRACTION

Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection.

DATA SYNTHESIS

Of 7843 citations reviewed, 30 studies of 26 unique models met the inclusion criteria. The most common outcome used was 30-day readmission; only 1 model specifically addressed preventable readmissions. Fourteen models that relied on retrospective administrative data could be potentially used to risk-adjust readmission rates for hospital comparison; of these, 9 were tested in large US populations and had poor discriminative ability (c statistic range: 0.55-0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c statistic range: 0.56-0.72), and 5 could be used at hospital discharge (c statistic range: 0.68-0.83). Six studies compared different models in the same population and 2 of these found that functional and social variables improved model discrimination. Although most models incorporated variables for medical comorbidity and use of prior medical services, few examined variables associated with overall health and function, illness severity, or social determinants of health.

CONCLUSIONS

Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly. Although in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.

Variability in selecting patients to manage in the community: a service evaluation of community matron's case-finding strategies

BACKGROUND

Community-based case management of high-frequency health service users, also termed 'high-intensity users' may reduce the demand on secondary care. In the UK, experienced senior nurses 'community matrons' have been appointed to provide more care in the community and to case manage high-intensity users and prevent unnecessary hospital admission. Computerized scoring systems have been developed to help with case finding.

OBJECTIVE

To evaluate how community matrons select their caseload, in particular the impact of a case finding tool, and access to computer-based systems.

METHODS

We used direct observation and the think aloud protocol during case selection and a series of workshops. Analysis was based on the Framework Approach (familiarization, coding, charting, mapping and interpretation) using verbatim transcripts analysed by N-Vivo software.

RESULTS

Community matrons within the same locality have different and sub-optimal caseloads. Although common elements exist in case selection, processes are modified by individuals depending on the influence of their interpersonal relationships, accessibility of computer systems and personal experience and expertise. The supporting IT system often produced data too late and while predicting admission did not identify cases amenable to community intervention.

CONCLUSIONS

Inter-professional networks, personal experience and training influence the patients identified for case management. The combination of an improved case finding tool and a better defined role for community matrons could lead to more standardized and equitable case selection.

A comprehensive review of predictive and prognostic composite factors implicated in the heterogeneity of treatment response and outcome across disease areas

AIM

To assess and present the current body of evidence regarding composite measures associated with differential treatment response or outcome as a result of patient heterogeneity and to evaluate their consistency across disease areas.

METHODS

A comprehensive review of the literature from the last 10 years was performed using three databases (PubMed, Embase and Cochrane). All articles that met the inclusion/exclusion criteria were selected, abstracted and assessed using the NICE level-of-evidence criteria.

RESULTS

Forty-nine studies were identified in the data abstraction. Approximately one-third focused on existing composite measures, and the rest investigated emerging composite factors. The majority of studies targeted patients with cancer, cardiovascular disease or psychological disorders. As a whole, the composite measures were found to be disease-specific, but some composite elements, including age, gender, comorbidities and health status, showed consistency across disease areas. To complement these findings, common individual factors found in five previous independent disease-specific literature assessments were also summarised, including age, gender, treatment adherence and satisfaction, healthcare resource utilisation and health status.

CONCLUSIONS

Composite measures can play an important role in characterising heterogeneity of treatment response and outcome in patients suffering from various medical conditions. These measures can help clinicians to better distinguish between patients with high likelihood to respond well to treatment and patients with minimal chances of positive therapeutic outcomes. Herein, the individual factors identified can be used to develop novel predictive or prognostic composite measures that can be applicable across disease areas. Reflecting these cross-disease measures in clinical and public health decisions has the distinctive appeal to enable targeted treatment for patients suffering from multiple medical conditions, which may ultimately yield significant gains in individual outcomes, population health and cost-effective resource allocation.

Inability of providers to predict unplanned readmissions

BACKGROUND

Readmissions cause significant distress to patients and considerable financial costs. Identifying hospitalized patients at high risk for readmission is an important strategy in reducing readmissions. We aimed to evaluate how well physicians, case managers, and nurses can predict whether their older patients will be readmitted and to compare their predictions to a standardized risk tool (Probability of Repeat Admission, or P(ra)).

METHODS

Patients aged ≥ 65 discharged from the general medical service at University of California, San Francisco Medical Center, a 550-bed tertiary care academic medical center, were eligible for enrollment over a 5-week period. At the time of discharge, the inpatient team members caring for each patient estimated the chance of unscheduled readmission within 30 days and predicted the reason for potential readmission. We also calculated the P(ra) for each patient. We identified readmissions through electronic medical record (EMR) review and phone calls with patients/caregivers. Discrimination was determined by creating ROC curves for each provider group and the P(ra).

RESULTS

One hundred sixty-four patients were eligible for enrollment. Of these patients, five died during the 30-day period post-discharge. Of the remaining 159 patients, 52 patients (32.7%) were readmitted. Mean readmission predictions for the physician providers were closest to the actual readmission rate, while case managers, nurses, and the P(ra) all overestimated readmissions. The ability to discriminate between readmissions and non-readmissions was poor for all provider groups and the P(ra) (AUC from 0.50 for case managers to 0.59 for interns, 0.56 for P(ra)). None of the provider groups predicted the reason for readmission with accuracy.

CONCLUSIONS

This study found (1) overall readmission rates were higher than previously reported, possibly because we employed a more thorough follow-up methodology, and (2) neither providers nor a published algorithm were able to accurately predict which patients were at highest risk of readmission. Amid increasing pressure to reduce readmission rates, hospitals do not have accurate predictive tools to guide their efforts.

Improving disposition outcomes for patients in a geriatric skilled nursing facility

OBJECTIVES

To evaluate an intervention to improve discharge disposition from a skilled nursing unit (SNU).

DESIGN

Historical control comparison of discharge disposition before and after implementation.

SETTING

Fifty-bed SNU.

PARTICIPANTS

All patients admitted from acute care hospitals to a SNU between June 2008 and May 2010.

INTERVENTION

Physician admission procedures were standardized using a template, patients with three or more hospital admissions over the prior 6 months received palliative care consultations, and multidisciplinary root-cause analysis conferences for patients transferred back to the hospital acutely were conducted bimonthly to identify problems and improve processes of care.

MEASUREMENTS

Patients' discharge disposition (i.e., acute care, long-term care, home, or death) before and after implementation were compared.

RESULTS

Discharge dispositions were determined for all 1,725 patients admitted during the study; 862 patients before (June-May 2008) and 863 during (June 2009-May 2010) the intervention. Discharge dispositions were significantly differently distributed across the two periods (P=.03). Readmission to acute care declined (from 16.5% to 13.3%, a nearly 20% decline). Multivariable logistic regression, controlling for age, sex, and case-mix index and adjusting for clustering due to repeated admissions of individual patients, suggests that, during the intervention period, patients were more likely than during the baseline period to die on the unit in accordance with their wishes than to be transferred out to the hospital (odds ratio=2.45, 95% confidence interval=1.09-5.5).

CONCLUSION

Interventions such as the ones implemented can lead to fewer hospital transfers for SNUs.

Incidence and predictors of 30-day readmission among patients hospitalized for advanced liver disease

BACKGROUND & AIMS

The rate of readmission to the hospital 30 days after discharge (30-day readmission rate) is used as a quality measure for hospitalized patients, but it has not been studied adequately for patients with advanced liver disease. We investigated the incidence and factors that predict this rate and its relationship with mortality at 90 days.

METHODS

We analyzed data from patients with advanced liver disease who were hospitalized to an inpatient hepatology service at 2 large academic medical centers in 2008. Patients with elective admission and recipients of liver transplants were not included. During the study period, there were 447 patients and a total of 554 eligible admissions. Multivariate analyses were performed to identify variables associated with 30-day readmission and to examine its relationship with mortality at 90 days.

RESULTS

The 30-day readmission rate was 20%. After adjusting for multiple covariates, readmission within 30 days was associated independently with model for end-stage liver disease scores at discharge (odds ratio [OR], 1.06; 95% confidence interval [CI], 1.02-1.09; P = .002), the presence of diabetes (OR, 1.78; 95% CI, 1.07-2.95; P = .027), and male sex (OR, 1.73; 95% CI, 1.03-2.89; P = .038). After adjusting for age, sex, and model for end-stage liver disease score at discharge, the 90-day mortality rate was significantly higher among patients who were readmitted to the hospital within 30 days than those who were not (26.8% vs 9.8%; OR, 2.6; 95% CI, 1.36-5.02; P = .004).

CONCLUSIONS

Patients with advanced liver disease frequently are readmitted to the hospital within 30 days after discharge; these patients have a higher 90-day mortality rate than those who are not readmitted in 30 days. These data might be used to develop strategies to reduce early readmission of hospitalized patients with cirrhosis.

Determinants of preventable readmissions in the United States: a systematic review

BACKGROUND

Hospital readmissions are a leading topic of healthcare policy and practice reform because they are common, costly, and potentially avoidable events. Hospitals face the prospect of reduced or eliminated reimbursement for an increasing number of preventable readmissions under nationwide cost savings and quality improvement efforts. To meet the current changes and future expectations, organizations are looking for potential strategies to reduce readmissions. We undertook a systematic review of the literature to determine what factors are associated with preventable readmissions.

METHODS

We conducted a review of the English language medicine, health, and health services research literature (2000 to 2009) for research studies dealing with unplanned, avoidable, preventable, or early readmissions. Each of these modifying terms was included in keyword searches of readmissions or rehospitalizations in Medline, ISI, CINAHL, The Cochrane Library, ProQuest Health Management, and PAIS International. Results were limited to US adult populations.

RESULTS

The review included 37 studies with significant variation in index conditions, readmitting conditions, timeframe, and terminology. Studies of cardiovascular-related readmissions were most common, followed by all cause readmissions, other surgical procedures, and other specific-conditions. Patient-level indicators of general ill health or complexity were the commonly identified risk factors. While more than one study demonstrated preventable readmissions vary by hospital, identification of many specific organizational level characteristics was lacking.

CONCLUSIONS

The current literature on preventable readmissions in the US contains evidence from a variety of patient populations, geographical locations, healthcare settings, study designs, clinical and theoretical perspectives, and conditions. However, definitional variations, clear gaps, and methodological challenges limit translation of this literature into guidance for the operation and management of healthcare organizations. We recommend that those organizations that propose to reward reductions in preventable readmissions invest in additional research across multiple hospitals in order to fill this serious gap in knowledge of great potential value to payers, providers, and patients.

Hospital readmission in general medicine patients: a prediction model

BACKGROUND

Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models.

OBJECTIVE

To identify predictors of early hospital readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk.

DESIGN

Prospective observational cohort study.

PATIENTS

Participants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts.

MEASUREMENTS

We identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk.

RESULTS

Approximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, >or=1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of >or=25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively.

CONCLUSIONS

Select patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission.

Patient and physician perceptions after software-assisted hospital discharge: cluster randomized trial

BACKGROUND

Hospital discharge software potentially improves communication and clinical outcomes.

OBJECTIVE

To measure patient and physician perceptions after discharge with computerized physician order entry (CPOE) software.

DESIGN

Cluster randomized controlled trial.

SETTING

Tertiary care, teaching hospital in central Illinois.

PATIENTS

A total of 631 inpatients discharged to home with high risk for readmission.

INTERVENTION

A total of 70 internal medicine hospital physicians randomly assigned (allocation concealed) to discharge software vs. usual care, handwritten discharge.

MEASUREMENTS

Discharge perceptions from patients, outpatient primary care physicians, and hospital physicians.

RESULTS

One week after discharge, 92.4% (583/631) of patients answered interviews. For 78.6% (496/631) of patients, their outpatient physicians returned questionnaires 19 days (median) postdischarge. Generalized estimating equations gave intervention variable coefficients with 95% confidence intervals (CIs). When comparing patients assigned to discharge software vs. usual care, patient mean (standard deviation [SD]) scores for discharge preparedness were higher (17.7 [4.1] vs. 17.2 [4.0]; coefficient = 0.147; 95% CI = 0.005-0.289; P = 0.042), patient scores for satisfaction with medication information were unchanged (12.3 [4.8] vs. 12.1 [4.6]; coefficient = -0.212; 95% CI = -0.937-0.513; P = 0.567), and their outpatient physicians scored higher quality discharge (17.2 [3.8] vs. 16.5 [3.9]; coefficient = 0.133; 95% CI = 0.015-0.251; P = 0.027). Hospital physicians found mean effort to use discharge software was more difficult than the usual care (6.5 [1.9] vs. 7.9 [2.1]; P = 0.011).

CONCLUSIONS

Discharge software with CPOE caused small improvements in discharge perceptions by patients and their outpatient physicians. These small improvements might balance the difficulty perceived by hospital physicians who used discharge software.