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Describing the usefulness and efficacy of discharge interventions: predicting 30 day readmissions through application of the cumulative complexity model (protocol).Aaron L. Leppin, M.D.; Henry H. Ting, M.D., M.B.A.; Michael Gionfriddo, PharmD; Dr. Katie Gallacher; Prof. Frances Mair; Tanya Sylvester; M. Hassan Murad, M.D.; Victor M. Montori, M.D. Correspondence: Victor M. Montori, M.D.Mayo Clinic200 1st St SWRochester, MN 55905Montori.victor@mayo.eduBackgroundIntroductionHospital readmissions, particularly among elderly patients with chronic conditions, are common and costly ADDIN EN.CITE <EndNote><Cite><Author>Jencks</Author><Year>2009</Year><RecNum>20</RecNum><DisplayText>[1]</DisplayText><record><rec-number>20</rec-number><foreign-keys><key app="EN" db-id="fee2trrwmz0dwpea5dzx9weqv0x0rrfdd9px">20</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jencks, S. F.</author><author>Williams, M. V.</author><author>Coleman, E. A.</author></authors></contributors><auth-address>Division of Hospital Medicine, Northwestern University Feinberg School of Medicine, Chicago, USA.</auth-address><titles><title>Rehospitalizations among patients in the Medicare fee-for-service program</title><secondary-title>The New England journal of medicine</secondary-title><alt-title>N Engl J Med</alt-title></titles><periodical><full-title>The New England journal of medicine</full-title><abbr-1>N Engl J Med</abbr-1></periodical><alt-periodical><full-title>The New England journal of medicine</full-title><abbr-1>N Engl J Med</abbr-1></alt-periodical><pages>1418-28</pages><volume>360</volume><number>14</number><edition>2009/04/03</edition><keywords><keyword>Diagnosis-Related Groups</keyword><keyword>Fee-for-Service Plans/economics/*statistics &amp; numerical data</keyword><keyword>Humans</keyword><keyword>Length of Stay</keyword><keyword>Medicare/economics/*statistics &amp; numerical data</keyword><keyword>Mortality</keyword><keyword>Patient Readmission/economics/*statistics &amp; numerical data</keyword><keyword>United States/epidemiology</keyword></keywords><dates><year>2009</year><pub-dates><date>Apr 2</date></pub-dates></dates><isbn>1533-4406 (Electronic)&#xD;0028-4793 (Linking)</isbn><accession-num>19339721</accession-num><work-type>Research Support, Non-U.S. Gov&apos;t</work-type><urls><related-urls><url>;[1]. They are one of the most important causes of preventable health expense in the United States. Thus, policymakers and reimbursement strategists have established the reduction of hospital readmission rates as a national priorityPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NZWRpY2luZTwvQXV0aG9yPjxZZWFyPjIwMDY8L1llYXI+

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ADDIN EN.CITE.DATA [4, 5]. In an effort to improve care quality and avoid financial penalties, a number of predictive models have been developed to better understand the risk for readmission and a variety of interventions have been established to reduce their incidence. Predictive models have been devised to identify patients at high risk of readmission, but they do not often identify modifiable risk factors PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LYW5zYWdhcmE8L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFy

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ADDIN EN.CITE.DATA [6-8]. Most models focus on demographic and clinical variables that are routinely collected and available, but they may fail to fully consider the complexities of a patient’s experience, including socioeconomic concerns, the relative vulnerability of the patient, and the impact any intervention itself may impose. To the extent that a prediction model is accurate, it is only useful insofar as it can inform the orientation of effective strategies to reduce readmissions. Indeed, currently available readmission risk prediction models generally perform poorly and are often limited in applicability PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LYW5zYWdhcmE8L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFy

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ADDIN EN.CITE.DATA [6, 9, 10]. This may be explained by the models’ failure to account for important contextual issues and/or the use of interventions that are ineffective, inadequately deployed, or similarly misdirected. When patients have inadequate capacity to adopt, adapt, and integrate interventions into their lives or when the healthcare system is unable to implement the interventions effectively, even well-designed and efficacious interventions may fail.Many peridischarge interventions, however, have been found to successfully reduce the rates of hospital readmissions and health care costs in specific contexts and single-center trialsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5OYXlsb3I8L0F1dGhvcj48WWVhcj4xOTk5PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA [16, 17]. The characteristics of interventions that seem to bring value are still not fully known. A recent systematic review by Hansen, et al found no single intervention alone to be effective and suggested only bundled interventions have valuePEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYW5zZW48L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxS

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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYW5zZW48L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA [18]. The inconsistency in evidence is probably due to the complexity and variability of both patients and interventions, and the fact that most synthesized information is based on studies with significant heterogeneityPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5BbGk8L0F1dGhvcj48WWVhcj4yMDA0PC9ZZWFyPjxSZWNO

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ADDIN EN.CITE.DATA [17, 19-21]. In many cases, the lack of comparable studies has made firm conclusions impossible. The meta-analytic data that does exist is informed by rough categorizations of the activities that comprise interventions PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYW5zZW48L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA [18, 20]. However, intervention value may need to be assessed in other ways. No attempt has been made to assess the efficacy of interventions based on their patient-centeredness or their sensitivity to post-discharge patient vulnerability and context, despite considerable face validity.Because the post-discharge period has been described as a period of high physiological and psychological vulnerability ADDIN EN.CITE <EndNote><Cite><Author>Krumholz</Author><Year>2013</Year><RecNum>25</RecNum><DisplayText>[22]</DisplayText><record><rec-number>25</rec-number><foreign-keys><key app="EN" db-id="fee2trrwmz0dwpea5dzx9weqv0x0rrfdd9px">25</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Krumholz, H. M.</author></authors></contributors><auth-address>Section of Cardiovascular Medicine and the Robert Wood Johnson Clinical Scholars Program, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA.</auth-address><titles><title>Post-hospital syndrome--an acquired, transient condition of generalized risk</title><secondary-title>The New England journal of medicine</secondary-title><alt-title>N Engl J Med</alt-title></titles><periodical><full-title>The New England journal of medicine</full-title><abbr-1>N Engl J Med</abbr-1></periodical><alt-periodical><full-title>The New England journal of medicine</full-title><abbr-1>N Engl J Med</abbr-1></alt-periodical><pages>100-2</pages><volume>368</volume><number>2</number><edition>2013/01/11</edition><keywords><keyword>*Hospitalization</keyword><keyword>Humans</keyword><keyword>Inpatients/*psychology</keyword><keyword>Patient Readmission</keyword><keyword>Risk</keyword><keyword>*Stress Disorders, Post-Traumatic/etiology/prevention &amp; control</keyword></keywords><dates><year>2013</year><pub-dates><date>Jan 10</date></pub-dates></dates><isbn>1533-4406 (Electronic)&#xD;0028-4793 (Linking)</isbn><accession-num>23301730</accession-num><urls><related-urls><url>;[22], it seems possible that interventions in this context may be particularly disruptive to the patient and could result in decompensation when important factors (such as current patient capacity and workload) are not taken into consideration ADDIN EN.CITE <EndNote><Cite><Author>May</Author><Year>2009</Year><RecNum>29</RecNum><DisplayText>[23]</DisplayText><record><rec-number>29</rec-number><foreign-keys><key app="EN" db-id="fee2trrwmz0dwpea5dzx9weqv0x0rrfdd9px">29</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>May, C.</author><author>Montori, V. M.</author><author>Mair, F. S.</author></authors></contributors><auth-address>Institute of Health and Society, Newcastle University, Newcastle upon Tyne NE2 4AA. c.r.may@ncl.ac.uk</auth-address><titles><title>We need minimally disruptive medicine</title><secondary-title>BMJ</secondary-title><alt-title>Bmj</alt-title></titles><periodical><full-title>BMJ</full-title><abbr-1>Bmj</abbr-1></periodical><alt-periodical><full-title>BMJ</full-title><abbr-1>Bmj</abbr-1></alt-periodical><pages>b2803</pages><volume>339</volume><edition>2009/08/13</edition><keywords><keyword>Chronic Disease/*therapy</keyword><keyword>Cost of Illness</keyword><keyword>Humans</keyword><keyword>Patient Participation</keyword><keyword>Physician-Patient Relations</keyword><keyword>Treatment Refusal</keyword><keyword>Workload</keyword></keywords><dates><year>2009</year></dates><isbn>1756-1833 (Electronic)&#xD;0959-535X (Linking)</isbn><accession-num>19671932</accession-num><urls><related-urls><url>;[23]. Additionally, no attempts have been made to apply predictive models to interventions themselves.The Cumulative Complexity ModelThe cumulative complexity modelPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TaGlwcGVlPC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48

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ADDIN EN.CITE.DATA [24]is a functional, patient-centered tool that may have usefulness in improving the predictability of outcomes when applied to discharge interventions, even in the context of diverse patient and disease factors. The model may shed light on the essential characteristics of successful interventions.2611755609600027889201981835Figure SEQ Figure \* ARABIC 1: The Cumulative Complexity Model. Treatment burden and illness burden can adversely affect the balance of workload and capacity in a patient’s life. This may limit the patient’s ability to access and use care interventions in a way that will improve outcomes.00Figure SEQ Figure \* ARABIC 1: The Cumulative Complexity Model. Treatment burden and illness burden can adversely affect the balance of workload and capacity in a patient’s life. This may limit the patient’s ability to access and use care interventions in a way that will improve outcomes.The cumulative complexity model (Figure 1) can be summarized as a description of the patient experience that depends on the balance between workload and capacityPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TaGlwcGVlPC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48

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ADDIN EN.CITE.DATA [24]. Workload, in this context, includes all of the activities required from and responsibilities placed on a patient. Workload becomes overwhelming when it is too high and this may result in intervention failures. Capacity can be thought of as the ability of the patient to be well, empowered, and equipped and is affected by disease factors, social issues, matters of support, and education, among other things. Increasing a patient’s capacity is likely to increase the odds of therapeutic success.ObjectiveThe objective of this study and review is to systematically determine the usefulness of the cumulative complexity model as a tool for predicting the efficacy of various interventions targeted at reducing 30-day hospital readmission rates. If the model proves successful, it will provide much needed guidance and perspective to stakeholders interested in reducing readmissions, designing successful interventions, and improving and individualizing care. MethodsEligibility CriteriaStudies that will be considered eligible for review will be English or Spanish language studies that assess the effectiveness of any peridischarge intervention at reducing hospital readmission rates. The studies must evaluate these interventions in adults (age>18) acutely admitted to the hospital for any medical or surgical diagnosis for at least 24 hours. Eligible studies must also report outcomes as risks of all-cause readmission* to the hospital at 30 days (RR or OR) but may have longer follow-up. In order to increase the ability to assess magnitudes of intervention effect indirectly, comparators of any type will be considered acceptable. Eligible study designs will be restricted to randomized controlled trials. Studies that assess interventions not primarily targeted at the hospital to home interface will be considered ineligible, as will studies that assess the effect of disease specific treatment or diagnostic strategies on readmission rates. Studies published before 1990, that admitted children or any patients for obstetric or psychiatric diagnoses, or exclusively evaluated in-hospital (ICU) readmissions will also be excluded.*Amendment: At the start of full-text screening (May 2013), the protocol was changed to include studies reporting outcomes of unplanned readmissions in addition to all-cause readmissions. It was also decided to include studies reporting either of these outcomes as a composite variable with out of hospital deaths. Search StrategyElectronic searches of PubMed, Ovid MEDLINE, Ovid EMBASE, EBSCO CINAHL, and SCOPUS will be searched for relevant studies. Keywords and MESH terms will be determined by an expert research librarian in collaboration with the lead author. Hand searching references from recent reviews and included studies and consultation with experts will also be used to identify eligible studies.SelectionTwo reviewers will independently consider the potential eligibility of each RCT abstract and title that results from the search strategy outlined above; disagreements will justify full text retrieval for evaluation and consensus. Full text versions of all potentially eligible studies will also be requested for further evaluation. In full text screening, two reviewers will work independently and blindly to identify the final selection of eligible studies. Prior to this, they will calibrate their judgments using a small set of reports. Disagreements will be resolved by consensus and, if necessary, by arbitration. Agreement will be measured using the kappa or phi statistics, as appropriate (the latter is appropriate when the distribution of agreement is extreme).SubgroupsWe will conduct subgroup analyses based on the number and types of trials generated from the search. We anticipate analyses based on patient characteristics such as index diagnosis and age (CHF, elderly, general medical, etc). Other subgroup or sensitivity analyses based on study characteristics may be required (i.e. for risk of bias) but will not be prospectively planned. If possible, we will also analyze the difference in effect of targeting various interventions to specific populations (pharmacy consult applied only in populations with polypharmacy vs. all patients, etc). This analysis will be used to estimate the effect of relevance in intervention application.Intervention Categorization and RatingAuthors from included studies will be contacted via email if the intervention is not well described in the paper. They will be asked to provide more detailed descriptions of the interventions used in their studies. These descriptions will then be standardized in language and transcribed into a web-based survey system (SurveyMonkey, Google Forms, etc) so that the interventions can be blindly categorized. The intervention rating survey will be administered to two investigators familiar with the cumulative complexity model and the clinical application of discharge interventions. These raters must not have participated in study selection (thus they will be blinded to the study and the outcome associated with the intervention). Raters will work independently after calibrating their judgments of interventions. They will be asked to evaluate each intervention based on “the degree to which the intervention increases or decreases patient workload” and “the degree to which the intervention decreases or increases patient capacity.” A 9 point scale* (ratings from 1 to 9) will be used. If a rater feels the intervention resulted in “no change” from baseline in regards to workload or capacity, he or she will be asked to select a value of “5.” Ratings will be averaged between the two investigators.*Amendment: After piloting the initial survey form with the raters, it was agreed that a 7 point scale should be used for capacity and workload ratings. The average rating was taken and, to analyze appropriately, intervention ratings were categorized based on whether they resulted in increases or decreases in the variable.Data ExtractionData extractors, working independently, will collect primary data from the included trials through use of a web-based program (DistillerSR). The extracted data will include patient characteristics, primary outcomes (RR or OR of all-cause 30 day hospital readmission), interventions and controls, and factors associated with study quality. Data will also be extracted related to the number of people involved in intervention delivery, the number of contacts that occur, the number of components involved in the intervention, and the location of the intervention activity (inpatient vs outpatient). Discrepancies in data collection will be adjudicated by consensus. As a proxy for assessing the patient relevance of interventions, extractors will also be asked to determine whether the intervention is designed for specific application in the population studied. Quality*To assess the methodological quality of randomized trials we will determine how the randomization sequence was generated and how or if allocation was concealed. We will look for important imbalances at baseline and determine which groups, if any, were blinded. The number lost to follow up, whether analyses were by intention to treat, and how missing data were dealt with will all be considered. Specific efforts will be made to determine and assess the strategy for acquiring data on readmissions to hospitals outside of and beyond the center(s) where the study is conducted. If felt to be appropriate, study exclusion may be indicated on the basis of quality and/or sensitivity analyses may be conducted. *Amendment: In addition to the rating of internal validity, it was decided to assess studies on their external validity and general applicability as well. Efforts were specifically taken to assess for factors that might limit one’s ability to recommend any particular intervention for widespread implementation.AnalysisThe outcomes of the re-categorized interventions (relative risk of 30 day readmission) will be used to generate meta-analytic estimates of treatment effect when possible. This data will then be assessed for association with intervention ratings and characteristics. ADDIN EN.REFLIST 1.Jencks, S.F., M.V. Williams, and E.A. Coleman, Rehospitalizations among patients in the Medicare fee-for-service program. The New England journal of medicine, 2009. 360(14): p. 1418-28.2.Medicine, I.o., Rewarding Provider Performance: Aligning Incentives in Medicare, 2006: Washington, DC.mission., M.P.A., Report to the Congress: promoting greater efficiency in Medicare., 2007.4.Joynt, K.E. and A.K. Jha, A path forward on Medicare readmissions. The New England journal of medicine, 2013. 368(13): p. 1175-7.5.Halfon, P., et al., Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Medical care, 2006. 44(11): p. 972-81.6.Kansagara, D., et al., Risk prediction models for hospital readmission: a systematic review. JAMA : the journal of the American Medical Association, 2011. 306(15): p. 1688-98.7.Hasan, O., et al., Hospital readmission in general medicine patients: a prediction model. Journal of general internal medicine, 2010. 25(3): p. 211-9.8.Burke, R.E. and E.A. Coleman, Interventions to Decrease Hospital Readmissions: Keys for Cost-effectiveness. JAMA internal medicine, 2013: p. 1-4.9.Lichtman, J.H., et al., Predictors of hospital readmission after stroke: a systematic review. Stroke; a journal of cerebral circulation, 2010. 41(11): p. 2525-33.10.Ross, J.S., et al., Statistical models and patient predictors of readmission for heart failure: a systematic review. Archives of internal medicine, 2008. 168(13): p. 1371-86.11.Naylor, M.D., et al., Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. JAMA : the journal of the American Medical Association, 1999. 281(7): p. 613-20.12.Raven, M.C., et al., An intervention to improve care and reduce costs for high-risk patients with frequent hospital admissions: a pilot study. BMC health services research, 2011. 11: p. 270.13.Hesselink, G., et al., Improving patient handovers from hospital to primary care: a systematic review. Annals of internal medicine, 2012. 157(6): p. 417-28.14.Coleman, E.A., et al., The care transitions intervention: results of a randomized controlled trial. Archives of internal medicine, 2006. 166(17): p. 1822-8.15.Jack, B.W., et al., A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Annals of internal medicine, 2009. 150(3): p. 178-87.16.Scott, I.A., Preventing the rebound: improving care transition in hospital discharge processes. Australian health review : a publication of the Australian Hospital Association, 2010. 34(4): p. 445-51.17.Mistiaen, P., A.L. Francke, and E. Poot, Interventions aimed at reducing problems in adult patients discharged from hospital to home: a systematic meta-review. BMC health services research, 2007. 7: p. 47.18.Hansen, L.O., et al., Interventions to reduce 30-day rehospitalization: a systematic review. Annals of internal medicine, 2011. 155(8): p. 520-8.19.Ali, W.a.R., P., What is the evidence for the effectiveness of managing the hospital/community interface for older people? A critical appraisal of the literature, in NZHTA Report2004, New Zealand Health Technology Assessment: Aukland. p. 1-146.20.Parker, S.G., et al., A systematic review of discharge arrangements for older people. Health technology assessment, 2002. 6(4): p. 1-183.21.Marks, E., Complexity Science and the Readmission Dilemma: Comment on "Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients" and "Association of Self-reported Hospital Discharge Handoffs With 30-Day Readmissions". JAMA internal medicine, 2013: p. 1-2.22.Krumholz, H.M., Post-hospital syndrome--an acquired, transient condition of generalized risk. The New England journal of medicine, 2013. 368(2): p. 100-2.23.May, C., V.M. Montori, and F.S. Mair, We need minimally disruptive medicine. BMJ, 2009. 339: p. b2803.24.Shippee, N.D., et al., Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice. Journal of clinical epidemiology, 2012. 65(10): p. 1041-51. ................
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