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Smith AH, Doyle TP, Mettler BA, Bichell DP, Gay JC. Identifying predictors of hospital readmission following congenital heart surgery through analysis of a multiinstitutional administrative Database. CONGENIT HEART DIS 2014; 10:142-52. [PMID: 25130487 DOI: 10.1111/chd.12209] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/29/2014] [Indexed: 11/28/2022]
Abstract
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.
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Affiliation(s)
- Andrew H Smith
- Thomas P. Graham Jr. Division of Pediatric Cardiology, Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tenn, USA; Division of Pediatric Critical Care Medicine, Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tenn, USA
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Running Bear U, Anderson H, Manson SM, Shore JH, Prochazka AV, Novins DK. Impact of adaptive functioning on readmission to alcohol detoxification among Alaska Native People. Drug Alcohol Depend 2014; 140:168-74. [PMID: 24837583 PMCID: PMC4137759 DOI: 10.1016/j.drugalcdep.2014.04.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2013] [Revised: 04/16/2014] [Accepted: 04/16/2014] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Ursula Running Bear
- University of Colorado Anschutz Medical Campus, Centers for American Indian and Alaska Native Health, Colorado School of Public Health, 13055 East 17th Avenue, Aurora, CO 80045, United States of America.
| | - Heather Anderson
- University of Colorado Anschutz Medical Campus, Skaggs School of Pharmacy and Pharmaceutical Sciences, Department of Clinical Pharmacy, 12850 E. Montview Blvd, Mail Stop C238, Aurora, CO 80045, United States of America
| | - Spero M Manson
- University of Colorado Anschutz Medical Campus, Centers for American Indian and Alaska Native Health, Colorado School of Public Health, 13055 East 17th Avenue, Aurora, CO 80045, United States of America
| | - Jay H Shore
- University of Colorado Anschutz Medical Campus, Centers for American Indian and Alaska Native Health, Colorado School of Public Health, 13055 East 17th Avenue, Aurora, CO 80045, United States of America; University of Colorado Anschutz Medical Campus, Department of Psychiatry, 13055 East 17th Avenue, Aurora, CO 80045, United States of America
| | - Allan V Prochazka
- University of Colorado Anschutz Medical Campus, Department of Medicine, Academic Office One, 12631 E. 17th Avenue, Aurora, CO 80045, United States of America; Ambulatory Care 11B, Denver VA Medical Center, 1055 Clermont, Denver, CO 80220, United States of America
| | - Douglas K Novins
- University of Colorado Anschutz Medical Campus, Centers for American Indian and Alaska Native Health, Colorado School of Public Health, 13055 East 17th Avenue, Aurora, CO 80045, United States of America; University of Colorado Anschutz Medical Campus, Department of Psychiatry, 13055 East 17th Avenue, Aurora, CO 80045, United States of America
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203
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Jackson KL, Glasgow RE, Mone MC, Sheng X, Mulvihill SJ, Scaife CL. Does travel distance influence length of stay in elective pancreatic surgery? HPB (Oxford) 2014; 16:543-9. [PMID: 24245982 PMCID: PMC4048076 DOI: 10.1111/hpb.12180] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Accepted: 07/28/2013] [Indexed: 12/12/2022]
Abstract
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.
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Affiliation(s)
| | - Robert E Glasgow
- Department of Surgery, University of UtahSalt Lake City, UT, USA
| | - Mary C Mone
- Department of Surgery, University of UtahSalt Lake City, UT, USA
| | - Xiaoming Sheng
- Department of Paediatrics, University of UtahSalt Lake City, UT, USA
| | - Sean J Mulvihill
- Department of Surgery, University of UtahSalt Lake City, UT, USA
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Godwin KM, Morgan RO, Walder A, Bass DM, Judge KS, Wilson N, Snow AL, Kunik ME. Predictors of Inpatient Utilization among Veterans with Dementia. Curr Gerontol Geriatr Res 2014; 2014:861613. [PMID: 24982674 PMCID: PMC4058849 DOI: 10.1155/2014/861613] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Revised: 05/02/2014] [Accepted: 05/06/2014] [Indexed: 11/18/2022] Open
Abstract
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.
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Affiliation(s)
- Kyler M. Godwin
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, (MEDVAMC 152), 2002 Holcombe Boulevard, Houston, TX 77030, USA
- Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
- Memorial Hermann Hospital, Houston, 6411 Fannin, TX 77030, USA
| | - Robert O. Morgan
- The University of Texas School of Public Health, 1200 Herman Pressler, Rm. E-343, Houston, TX 77030, USA
| | - Annette Walder
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, (MEDVAMC 152), 2002 Holcombe Boulevard, Houston, TX 77030, USA
- Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - David M. Bass
- Margaret Blenkner Research Institute, Benjamin Rose Institute on Aging, 11890 Fairhill Road, Cleveland, OH 44120, USA
| | - Katherine S. Judge
- Cleveland State University, 2121 Euclid Avenue, Cleveland, OH 44115, USA
| | - Nancy Wilson
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, (MEDVAMC 152), 2002 Holcombe Boulevard, Houston, TX 77030, USA
- Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - A. Lynn Snow
- Center for Mental Health and Aging and Department of Psychology, The University of Alabama, P.O. Box 870315, Tuscaloosa, AL 35487, USA
- Tuscaloosa VA Medical Center, 3701 Loop Road, Tuscaloosa, AL 35404, USA
| | - Mark E. Kunik
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, (MEDVAMC 152), 2002 Holcombe Boulevard, Houston, TX 77030, USA
- Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
- VA South Central Mental Illness Research, Education and Clinical Center (A Virtual Center), USA
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AbdelRahman SE, Zhang M, Bray BE, Kawamoto K. A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study. BMC Med Inform Decis Mak 2014; 14:41. [PMID: 24886637 PMCID: PMC4074427 DOI: 10.1186/1472-6947-14-41] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Accepted: 05/06/2014] [Indexed: 11/23/2022] Open
Abstract
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.
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Affiliation(s)
- Samir E AbdelRahman
- Department of Biomedical Informatics, University of Utah, 615 Arapeen Way, Suite 208, Salt Lake City, UT 84092, USA.
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206
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Pavon JM, Zhao Y, McConnell E, Hastings SN. Identifying risk of readmission in hospitalized elderly adults through inpatient medication exposure. J Am Geriatr Soc 2014; 62:1116-21. [PMID: 24802165 DOI: 10.1111/jgs.12829] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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.
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Affiliation(s)
- Juliessa M Pavon
- Division of Geriatrics, Duke University Medical Center, Durham, North Carolina; Geriatric Research, Education and Clinical Center, Durham Veterans Affairs Medical Center, Durham, North Carolina
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207
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Suffoletto B, Hu J, Guyette M, Callaway C. 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. J Hosp Med 2014; 9:315-9. [PMID: 24497467 DOI: 10.1002/jhm.2167] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 01/09/2014] [Accepted: 01/15/2014] [Indexed: 11/07/2022]
Abstract
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.
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Affiliation(s)
- Brian Suffoletto
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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208
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Donzé J, Lipsitz S, Schnipper JL. Risk factors for potentially avoidable readmissions due to end-of-life care issues. J Hosp Med 2014; 9:310-4. [PMID: 24532224 DOI: 10.1002/jhm.2173] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Revised: 01/23/2014] [Accepted: 01/28/2014] [Indexed: 11/06/2022]
Abstract
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.
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Affiliation(s)
- Jacques Donzé
- Division of General Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
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209
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Skinner EH, Foster M, Mitchell G, Haynes M, O'Flaherty M, Haines TP. Effect of health insurance on the utilisation of allied health services by people with chronic disease: a systematic review and meta-analysis. Aust J Prim Health 2014; 20:9-19. [PMID: 24079301 DOI: 10.1071/py13092] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2013] [Accepted: 08/07/2013] [Indexed: 11/23/2022]
Abstract
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.
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Affiliation(s)
- Elizabeth H Skinner
- Department of Physiotherapy, Monash Health, 246 Clayton Road, Clayton, Melbourne, Vic. 3168, Australia
| | - Michele Foster
- Postgraduate Research Studies, School of Social Work and Human Services, The University of Queensland, Mansfield Place, St Lucia, Brisbane, Qld 4072, Australia
| | - Geoffrey Mitchell
- General Practice and Palliative Care, MBBS Program, The University of Queensland, Building 12, Ipswich Campus, Salisbury Road, Ipswich, Qld 4305, Australia
| | - Michele Haynes
- Institute for Social Science Research, General Purpose North Building 4, The University of Queensland, Mansfield Place, St Lucia, Brisbane, Qld 4072, Australia
| | - Martin O'Flaherty
- Institute for Social Science Research, General Purpose North Building 4, The University of Queensland, Mansfield Place, St Lucia, Brisbane, Qld 4072, Australia
| | - Terry P Haines
- Department of Physiotherapy, Monash Health, 246 Clayton Road, Clayton, Melbourne, Vic. 3168, Australia
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Taha M, Pal A, Mahnken JD, Rigler SK. Derivation and validation of a formula to estimate risk for 30-day readmission in medical patients. Int J Qual Health Care 2014; 26:271-7. [PMID: 24737834 DOI: 10.1093/intqhc/mzu038] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
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.
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Affiliation(s)
- Mohammad Taha
- Division of General and Geriatric Medicine, University of Kansas School of Medicine, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
| | - Aroop Pal
- Division of General and Geriatric Medicine, University of Kansas School of Medicine, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
| | - Jonathan D Mahnken
- Department of Biostatistics, University of Kansas School of Medicine, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
| | - Sally K Rigler
- Division of General and Geriatric Medicine, University of Kansas School of Medicine, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
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Lasser KE, Hanchate AD, McCormick D, Manze MG, Chu C, Kressin NR. The effect of Massachusetts health reform on 30 day hospital readmissions: retrospective analysis of hospital episode statistics. BMJ 2014; 348:g2329. [PMID: 24687184 PMCID: PMC3970763 DOI: 10.1136/bmj.g2329] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Karen E Lasser
- Section of General Internal Medicine, Boston University School of Medicine, Boston, MA 02118, USA
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He D, Mathews SC, Kalloo AN, Hutfless S. Mining high-dimensional administrative claims data to predict early hospital readmissions. J Am Med Inform Assoc 2014; 21:272-9. [PMID: 24076748 PMCID: PMC3932470 DOI: 10.1136/amiajnl-2013-002151] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Revised: 08/15/2013] [Accepted: 09/08/2013] [Indexed: 11/03/2022] Open
Abstract
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.
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Affiliation(s)
- Danning He
- Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Simon C Mathews
- Department of Medicine, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Anthony N Kalloo
- Division of Gastroenterology & Hepatology, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Susan Hutfless
- Division of Gastroenterology & Hepatology, The Johns Hopkins Hospital, Baltimore, Maryland, USA
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Iloabuchi TC, Mi D, Tu W, Counsell SR. Risk factors for early hospital readmission in low-income elderly adults. J Am Geriatr Soc 2014; 62:489-94. [PMID: 24576082 DOI: 10.1111/jgs.12688] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
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.
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Affiliation(s)
- Tochukwu C Iloabuchi
- Department of Medicine, School of Medicine, Indiana University, Indianapolis, Indiana
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Albrecht JS, Gruber-Baldini AL, Hirshon JM, Brown CH, Goldberg R, Rosenberg JH, Comer AC, Furuno JP. Depressive symptoms and hospital readmission in older adults. J Am Geriatr Soc 2014; 62:495-9. [PMID: 24512099 DOI: 10.1111/jgs.12686] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
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.
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Affiliation(s)
- Jennifer S Albrecht
- Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Mayland; Department of Pharmaceutical Health Services Research, School of Pharmacy, University of Maryland, Baltimore, Mayland
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Abstract
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.
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Affiliation(s)
- Kathleen Carey
- 1Boston University School of Public Health, Boston, MA, USA
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216
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Park L, Andrade D, Mastey A, Sun J, Hicks L. Institution specific risk factors for 30 day readmission at a community hospital: a retrospective observational study. BMC Health Serv Res 2014; 14:40. [PMID: 24467793 PMCID: PMC3916302 DOI: 10.1186/1472-6963-14-40] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Accepted: 01/21/2014] [Indexed: 11/10/2022] Open
Abstract
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.
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Affiliation(s)
- Lee Park
- Hospital Medicine Unit, Division of General Internal Medicine, Massachusetts General Hospital, 50 Staniford St, Suite 503B, Boston, MA 02114, USA.
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217
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Morris DS, Rohrbach J, Sundaram LMT, Sonnad S, Sarani B, Pascual J, Reilly P, Schwab CW, Sims C. Early hospital readmission in the trauma population: are the risk factors different? Injury 2014; 45:56-60. [PMID: 23726120 PMCID: PMC4149179 DOI: 10.1016/j.injury.2013.04.029] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2012] [Revised: 04/08/2013] [Accepted: 04/27/2013] [Indexed: 02/02/2023]
Abstract
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.
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Affiliation(s)
- David S. Morris
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Jeff Rohrbach
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Latha Mary Thanka Sundaram
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Seema Sonnad
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Babak Sarani
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Jose Pascual
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Patrick Reilly
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - C. William Schwab
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Carrie Sims
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
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Chwastiak LA, Davydow DS, McKibbin CL, Schur E, Burley M, McDonell MG, Roll J, Daratha KB. The effect of serious mental illness on the risk of rehospitalization among patients with diabetes. PSYCHOSOMATICS 2013; 55:134-43. [PMID: 24367898 DOI: 10.1016/j.psym.2013.08.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Revised: 08/28/2013] [Accepted: 08/29/2013] [Indexed: 12/20/2022]
Abstract
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.
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Affiliation(s)
- Lydia A Chwastiak
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA.
| | - Dimitry S Davydow
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA
| | | | - Ellen Schur
- Department of Medicine, University of Washington School of Medicine, Seattle, WA
| | - Mason Burley
- Washington Institute for Mental Health Research and Training, Washington State University, Spokane, WA
| | - Michael G McDonell
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA
| | - John Roll
- College of Nursing, Washington State University, Spokane, WA
| | - Kenn B Daratha
- College of Nursing, Washington State University, Spokane, WA; Providence Medical Research Center, Spokane, WA; Department of Medical Education & Biomedical Informatics, University of Washington School of Medicine, Seattle, WA
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219
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Adib-Hajbaghery M, Maghaminejad F, Abbasi A. The role of continuous care in reducing readmission for patients with heart failure. J Caring Sci 2013; 2:255-67. [PMID: 25276734 DOI: 10.5681/jcs.2013.031] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Accepted: 08/20/2013] [Indexed: 11/17/2022] Open
Abstract
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.
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Affiliation(s)
- Mohsen Adib-Hajbaghery
- Medical Surgical Nursing Department, Faculty of Nursing, Kashan University of Medical Sciences, Kashan, Iran
| | - Farzaneh Maghaminejad
- Medical Surgical Nursing Department, Faculty of Nursing, Kashan University of Medical Sciences, Kashan, Iran
| | - Ali Abbasi
- Department of Cardiology, Shahid Beheshti Hospital, Kashan University of Medical Sciences, Kashan, Iran
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Hendrix C, Tepfer S, Forest S, Ziegler K, Fox V, Stein J, McConnell ES, Hastings SN, Schmader K, Colon-Emeric C. Transitional Care Partners: a hospital-to-home support for older adults and their caregivers. J Am Assoc Nurse Pract 2013; 25:407-414. [PMID: 24170636 DOI: 10.1111/j.1745-7599.2012.00803.x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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.
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Affiliation(s)
- Cristina Hendrix
- (Nurse Investigator), (Social Worker), (Physician), Geriatrics Research, Education, and Clinical Center, Veterans Affairs Medical Center, Durham, North Carolina (Nurse Investigator), Duke University School of Nursing, Durham, North Carolina (Nurse Investigator), (Physician), Duke University Center for the Study of Aging and Human Development, Durham, North Carolina (Nurse Practitioner), (Occupational Therapist), (Physician), Ambulatory Care, Veterans Affairs Medical Center, Durham, North Carolina (Physician), Center for Health Services Research in Primary Care, Veterans Affairs Medical Center, Durham, North Carolina (Physician), Department of Medicine, Division of Geriatrics, Duke University, Durham, North Carolina
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Nuño M, Ly D, Ortega A, Sarmiento J, Mukherjee D, Black KL, Patil CG. Does 30-Day Readmission Affect Long-term Outcome Among Glioblastoma Patients? Neurosurgery 2013; 74:196-204; discussion 204-5. [DOI: 10.1227/neu.0000000000000243] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Abstract
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 < .001). In a confounder-adjusted imputed model, 30-day readmission increased the hazard of mortality by 30% (hazard ratio, 1.3; P < .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.
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Affiliation(s)
- Miriam Nuño
- Center for Neurosurgical Outcomes Research, Maxine Dunitz Neurosurgical Institute, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California
| | - Diana Ly
- Center for Neurosurgical Outcomes Research, Maxine Dunitz Neurosurgical Institute, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California
| | - Alicia Ortega
- Center for Neurosurgical Outcomes Research, Maxine Dunitz Neurosurgical Institute, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California
| | - J.Manuel Sarmiento
- Center for Neurosurgical Outcomes Research, Maxine Dunitz Neurosurgical Institute, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California
| | - Debraj Mukherjee
- Center for Neurosurgical Outcomes Research, Maxine Dunitz Neurosurgical Institute, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California
| | - Keith L. Black
- Center for Neurosurgical Outcomes Research, Maxine Dunitz Neurosurgical Institute, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California
| | - Chirag G. Patil
- Center for Neurosurgical Outcomes Research, Maxine Dunitz Neurosurgical Institute, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California
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Liu KE, Lo CL, Hu YH. Improvement of adequate use of warfarin for the elderly using decision tree-based approaches. Methods Inf Med 2013; 53:47-53. [PMID: 24136011 DOI: 10.3414/me13-01-0027] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 09/16/2013] [Indexed: 11/09/2022]
Abstract
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.
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Affiliation(s)
| | | | - Y-H Hu
- Ya-Han Hu, Department of Information Management and Graduate Institute of Healthcare Information Management, National Chung Cheng University, 168 University Road, Min-Hsiung Chia-Yi 62102, Taiwan, E-mail:
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Bradley EH, Yakusheva O, Horwitz LI, Sipsma H, Fletcher J. Identifying patients at increased risk for unplanned readmission. Med Care 2013; 51:761-6. [PMID: 23942218 PMCID: PMC3771868 DOI: 10.1097/mlr.0b013e3182a0f492] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
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.
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Affiliation(s)
- Elizabeth H Bradley
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT 06520, USA.
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Graboyes EM, Liou TN, Kallogjeri D, Nussenbaum B, Diaz JA. Risk Factors for Unplanned Hospital Readmission in Otolaryngology Patients. Otolaryngol Head Neck Surg 2013; 149:562-71. [DOI: 10.1177/0194599813500023] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Evan M. Graboyes
- Department of Otolaryngology–Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Tzyy-Nong Liou
- Department of Otolaryngology–Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Dorina Kallogjeri
- Department of Otolaryngology–Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Brian Nussenbaum
- Department of Otolaryngology–Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jason A. Diaz
- Department of Otolaryngology–Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
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225
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Burke RE, Donzé J, Schnipper JL. Contribution of psychiatric illness and substance abuse to 30-day readmission risk. J Hosp Med 2013; 8:450-5. [PMID: 23589474 DOI: 10.1002/jhm.2044] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2012] [Revised: 02/27/2013] [Accepted: 03/07/2013] [Indexed: 11/11/2022]
Abstract
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.
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Affiliation(s)
- Robert E Burke
- Hospital Medicine Section, Department of Veterans Affairs Medical Center, Eastern Colorado Health Care System, Denver, CO 80220, USA.
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226
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Weiss ME, Costa LL, Yakusheva O, Bobay KL. Validation of patient and nurse short forms of the Readiness for Hospital Discharge Scale and their relationship to return to the hospital. Health Serv Res 2013; 49:304-17. [PMID: 23855675 DOI: 10.1111/1475-6773.12092] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/11/2013] [Indexed: 11/30/2022] Open
Abstract
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.
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Affiliation(s)
- Marianne E Weiss
- Wheaton Franciscan Healthcare, St. Joseph/Sister, Rosalie Klein Professor of Women's Health, Marquette University College of Nursing, Milwaukee, WI, 53201-1881
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227
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Borenstein J, Aronow HU, Bolton LB, Choi J, Bresee C, Braunstein GD. Early recognition of risk factors for adverse outcomes during hospitalization among Medicare patients: a prospective cohort study. BMC Geriatr 2013; 13:72. [PMID: 23834816 PMCID: PMC3710470 DOI: 10.1186/1471-2318-13-72] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2012] [Accepted: 06/25/2013] [Indexed: 01/01/2023] Open
Abstract
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.
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Affiliation(s)
- Jeff Borenstein
- Applied Health Services Research, Cedars-Sinai Health System, Los Angeles, CA 90048, USA.
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228
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Rothman MJ, Rothman SI, Beals J. Development and validation of a continuous measure of patient condition using the Electronic Medical Record. J Biomed Inform 2013; 46:837-48. [PMID: 23831554 DOI: 10.1016/j.jbi.2013.06.011] [Citation(s) in RCA: 146] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2013] [Revised: 05/22/2013] [Accepted: 06/25/2013] [Indexed: 10/26/2022]
Abstract
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.
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Affiliation(s)
- Michael J Rothman
- PeraHealth, Inc., 1520 S. Boulevard, Suite 228, Charlotte, NC 28203, USA.
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229
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Moore CD, Gao K, Shulan M. Racial, Income, and Marital Status Disparities in Hospital Readmissions Within a Veterans-Integrated Health Care Network. Eval Health Prof 2013; 38:491-507. [PMID: 23811693 DOI: 10.1177/0163278713492982] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
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.
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Affiliation(s)
- Crystal Dea Moore
- Department of Social Work, Skidmore College, Saratoga Springs, NY, USA
| | - Kelly Gao
- Department of Social Work, Skidmore College, Saratoga Springs, NY, USA
| | - Mollie Shulan
- Geriatrics and Extended Care, Stratton VA Medical Center, Albany, NY, USA Albany Medical College, Albany, NY, USA
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230
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Hoyer EH, Needham DM, Miller J, Deutschendorf A, Friedman M, Brotman DJ. Functional status impairment is associated with unplanned readmissions. Arch Phys Med Rehabil 2013; 94:1951-8. [PMID: 23810355 DOI: 10.1016/j.apmr.2013.05.028] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2013] [Revised: 04/24/2013] [Accepted: 05/26/2013] [Indexed: 10/26/2022]
Abstract
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.
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Affiliation(s)
- Erik H Hoyer
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD.
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231
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Morandi A, Bellelli G, Vasilevskis EE, Turco R, Guerini F, Torpilliesi T, Speciale S, Emiliani V, Gentile S, Schnelle J, Trabucchi M. Predictors of rehospitalization among elderly patients admitted to a rehabilitation hospital: the role of polypharmacy, functional status, and length of stay. J Am Med Dir Assoc 2013; 14:761-7. [PMID: 23664484 DOI: 10.1016/j.jamda.2013.03.013] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2013] [Revised: 03/11/2013] [Accepted: 03/12/2013] [Indexed: 12/20/2022]
Abstract
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.
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Affiliation(s)
- Alessandro Morandi
- Department of Rehabilitation and Aged Care Hospital Ancelle, Cremona, Italy; Geriatric Research Group, Brescia, Italy; Center for Quality Aging, Department of Medicine, Vanderbilt University, Nashville, TN.
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232
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Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration. Med Care 2013; 51:368-73. [PMID: 23269113 DOI: 10.1097/mlr.0b013e31827da95a] [Citation(s) in RCA: 224] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
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.
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233
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van Walraven C, Wong J, Forster AJ. Influence of neighborhood household income on early death or urgent hospital readmission. J Hosp Med 2013; 8:261-6. [PMID: 23576329 DOI: 10.1002/jhm.2025] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2012] [Revised: 01/15/2013] [Accepted: 01/16/2013] [Indexed: 11/11/2022]
Abstract
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.
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Affiliation(s)
- Carl van Walraven
- Ottawa Hospital Research Institute and Institute for Clinical Evaluative Sciences, Ottawa, Canada.
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234
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Oduyebo I, Lehmann CU, Pollack CE, Durkin N, Miller JD, Mandell S, Ardolino M, Deutschendorf A, Brotman DJ. Association of self-reported hospital discharge handoffs with 30-day readmissions. JAMA Intern Med 2013; 173:624-9. [PMID: 23529278 PMCID: PMC3692004 DOI: 10.1001/jamainternmed.2013.3746] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
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.
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Affiliation(s)
- Ibironke Oduyebo
- Departments of Medicine, The Johns Hopkins University, Baltimore, MD 21287, USA
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235
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Hersh AM, Masoudi FA, Allen LA. Postdischarge environment following heart failure hospitalization: expanding the view of hospital readmission. J Am Heart Assoc 2013; 2:e000116. [PMID: 23580604 PMCID: PMC3647271 DOI: 10.1161/jaha.113.000116] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Affiliation(s)
- Andrew M Hersh
- Department of Internal Medicine, University of California, Davis, CA, USA
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236
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Gildersleeve R, Cooper P. Development of an automated, real time surveillance tool for predicting readmissions at a community hospital. Appl Clin Inform 2013; 4:153-69. [PMID: 23874355 DOI: 10.4338/aci-2012-12-ra-0058] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2012] [Accepted: 03/18/2013] [Indexed: 12/15/2022] Open
Abstract
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.
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Affiliation(s)
- R Gildersleeve
- Augusta Health, Information Technology, Fishersville, Virginia 22939, USA
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237
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Prediction of 30-day cardiac-related-emergency-readmissions using simple administrative hospital data. Int J Cardiol 2013; 164:193-200. [DOI: 10.1016/j.ijcard.2011.06.119] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2011] [Revised: 06/23/2011] [Accepted: 06/25/2011] [Indexed: 11/20/2022]
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238
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Variation in Risk-Adjusted Hospital Readmission After Treatment of Appendicitis at 38 Children's Hospitals. Ann Surg 2013; 257:758-65. [DOI: 10.1097/sla.0b013e318268a663] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Ephrem G. Red blood cell distribution width is a predictor of readmission in cardiac patients. Clin Cardiol 2013; 36:293-9. [PMID: 23553899 DOI: 10.1002/clc.22116] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2013] [Revised: 02/23/2013] [Indexed: 01/17/2023] Open
Abstract
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.
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Affiliation(s)
- Georges Ephrem
- Department of Cardiovascular Disease, Hofstra-North Shore-LIJ Health System, Manhasset, New York 11030, USA.
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Abstract
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.
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Abstract
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.
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Affiliation(s)
- Mikkel Christensen
- Department of Clinical Pharmacology, Bispebjerg Hospital, Copenhagen, Denmark.
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Burke RE, Kripalani S, Vasilevskis EE, Schnipper JL. Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med 2013; 8:102-9. [PMID: 23184714 PMCID: PMC3650641 DOI: 10.1002/jhm.1990] [Citation(s) in RCA: 126] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2012] [Revised: 08/31/2012] [Accepted: 09/19/2012] [Indexed: 11/08/2022]
Abstract
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.
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Affiliation(s)
- Robert E Burke
- Hospital Medicine Section, Department of Veterans Affairs Medical Center, Eastern Colorado Health Care System, Denver, CO 80220, USA.
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243
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Moura CSD, Tavares LS, Acurcio FDA. [Hospital readmissions related to drug interactions: a retrospective study in a hospital setting]. Rev Saude Publica 2013; 46:1082-9. [PMID: 23358622 DOI: 10.1590/s0034-89102013005000001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2012] [Accepted: 06/28/2012] [Indexed: 11/22/2022] Open
Abstract
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.
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Affiliation(s)
- Cristiano Soares de Moura
- Núcleo de Epidemiologia e Saúde Coletiva, Instituto Multidisciplinar em Saúde, Universidade Federal da Bahia, Vitória da Conquista, BA, Brasil.
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Benbassat J, Taragin MI. The effect of clinical interventions on hospital readmissions: a meta-review of published meta-analyses. Isr J Health Policy Res 2013; 2:1. [PMID: 23343012 PMCID: PMC3557155 DOI: 10.1186/2045-4015-2-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Accepted: 11/13/2012] [Indexed: 01/08/2023] Open
Abstract
UNLABELLED 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.
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Affiliation(s)
- Jochanan Benbassat
- JDC Brookdale Institute, Health Policy Research Program, PO Box 3886, Jerusalem, 91037, Israel
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246
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Mueller SK, Donzé J, Schnipper JL. Intern workload and discontinuity of care on 30-day readmission. Am J Med 2013; 126:81-8. [PMID: 23260505 DOI: 10.1016/j.amjmed.2012.09.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2012] [Accepted: 09/27/2012] [Indexed: 11/30/2022]
Affiliation(s)
- Stephanie K Mueller
- BWH Hospitalist Service and Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA 02120, USA.
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248
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Fisher SR, Kuo YF, Sharma G, Raji MA, Kumar A, Goodwin JS, Ostir GV, Ottenbacher KJ. Mobility after hospital discharge as a marker for 30-day readmission. J Gerontol A Biol Sci Med Sci 2012; 68:805-10. [PMID: 23254776 DOI: 10.1093/gerona/gls252] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
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.
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Affiliation(s)
- Steve R Fisher
- Department of Physical Therapy, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555-0460, USA.
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Shu CC, Lin YF, Hsu NC, Ko WJ. Risk factors for 30-day readmission in general medical patients admitted from the emergency department: a single centre study. Intern Med J 2012; 42:677-82. [PMID: 21790921 DOI: 10.1111/j.1445-5994.2011.02562.x] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
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.
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Affiliation(s)
- C-C Shu
- Department of Traumatology, National Taiwan University Hospital, Taipei, Taiwan
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Monsen KA, Swanberg HL, Oancea SC, Westra BL. Exploring the value of clinical data standards to predict hospitalization of home care patients. Appl Clin Inform 2012; 3:419-36. [PMID: 23646088 DOI: 10.4338/aci-2012-05-ra-0016] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2012] [Accepted: 11/09/2012] [Indexed: 11/23/2022] Open
Abstract
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.
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