Andrew M. Hersh|Frederick A. Masoudi|Larry A. Allen
Postdischarge Environment Following Heart Failure Hospitalization: Expanding the View of Hospital Readmission
David Grant USAF Medical Center|University of California, Davis|Outcomes Research Consortium|University of Colorado Anschutz Medical Campus|Outcomes Research Consortium|University of Colorado Anschutz Medical Campus
homejournal american heart associationvol 2 2postdischarge environment following heart failure hospitalization expanding view hospital readmission open accessreview articlepdfepubaboutview pdfview epubsections toolsadd favoritesdownload citationstrack citations shareshare onfacebooktwitterlinked inmendeleyreddit jump toopen accessreview articlepdfepubpostdischarge environment following heart failure hospitalization expanding view hospital readmission andrew hersh frederick masoudi larry allen andrew hershandrew hersh department internal medicine university california davis ca department internal medicine david grant usaf medical center travis afb ca frederick masoudifrederick masoudi colorado cardiovascular outcomes research consortium denver co division cardiology university colorado anschutz medical campus aurora co larry allenlarry allen colorado cardiovascular outcomes research consortium denver co division cardiology university colorado anschutz medical campus aurora co originally published11 apr 2013httpsdoiorg101161jaha113000116journal american heart association 20132e000116introductionreadmission hospitalization heart failure hf received increasing attention due significant burden places patients payers1 2 among medicare beneficiaries readmission within 30 days following heart failure hospitalization approaches 252 even adjusting case mix significant variation hospital readmission rates exists hospitallevel variation suggests many readmissions may preventable3 hf readmission rates adjusted risk using claimsbased model publicly reported measure institutional quality wwwhospitalcomparehhsgov october 2012 patient protection affordable care acts ppaca valuebased purchasing policies began reducing medicare payments hospitals excess hf readmissions offered new funding opportunities innovative approaches reduce hf readmissions4despite obvious value reducing unnecessary readmissions way forward clear policies might suggest increasing segment medical community voicing concern extent public reporting financial penalties positively influence institutional hf readmission rates5 valuebased purchasing may unfairly punish hospitals provide care socioeconomically disadvantaged patients incentivize avoidance highrisk patients6 7 8 9 due perceived inadequacies current risk standardization models10 addition effective interventions prevent unnecessary readmissions remain elusive11prior efforts identify risk factors hf readmission put inordinate priority convenience data collection vast majority existing risk models employ administrative billing inpatient clinical data single episode care designed fully elucidate breadth potential causes readmission notably missing factors reflecting patients postdischarge environment recent literature suggests social instabilitya term reflects relative lack social support education economic stability access care safety patients environmentis important mediator readmission risk12 13within context set 1 review known postdischarge environment relationship hf readmission 2 propose new conceptual model hf readmission integrates patient provider health system environmental factors potential improve predictive capacity hf readmission risk models thereby making quality measures fairer guide us improving transitions care ultimately leading toward reductions unnecessary readmissionsliterature searchthe concept postdischarge environment clearly defined domain current readmission literature therefore approach taken systematically identify readmission models manually extract factors perceived represent postdischarge environment systematic reviews literature regarding hf readmission risk models performed previously kansagara et al10 2011 ross et al14 2008 used published kansagara search alogrithm capture newer literature published november 15 2012 addition supplemented kansagara search algorithm additional search focusing specifically postdischarge environment using terms postdischarge environment environment social social instability education poverty economic socioeconomic combination readmission andor rehospitalization medical surgical condition reviewed abstracts included studies explored relationship readmission one aspects postdischarge environmentmodels identified searches included factor representing postdischarge environment summarized tabletable 1 selected heart failure general readmission risk models focusing patient system environmental level covariatesyearmodelpatientsystemenvironmentdemographic covariatesindicator frailty functional statuscomorbiditiesmarkers illness severityuse patternshospital characteristics postdischarge servicesreadiness discharge inpatient qualityfinances education stability supportpatient behavior1985predicting hospital readmissions medicare population15age sex racedisability statusnonenoneno discharges previous 60 days discharges dx past 60 days los hospital reimbursement admission chronic vs acute diagnosishospital based characteristics region countrynonedisability status supplemental medicaid coveragenone1988identifying factors associated health care use hospitalbased risk screening index16age 75dependent ambulation incontinence poor mental status terminal illness2 chronic condition terminal illness psychiatric diseasenoneemergency admission prior hospitalization within past 2 monthsnonenoneunmarried less subsistence level income lives alone snf dependent self care requires help adls unemployment receiving disability poor social supporthistory alcoholism1988postdischarge care readmission17age sex racenonenonebun pao2 wbc hemoglobiner visits previous 6 monthspost discharge care including rn calls mailings appointment reminder vs usual carenonenonenone1990risk factors early readmission among veterans18age sex race period military service county residencenonespinal cord injury number surgeries performed risk category admitting diagnosisnonelos unit type medical intermediate neurological surgical discharged medical adviceva auspices place type dispositionnonecompensationpension status distance hospital services marital status former pownone1991factors predicting readmission older general medicine patients19age racecognition mmsedepression diagnoses admission chf copdillness severity computerized severity illness indexemergent hospitalization hospitalizations last year days hospitalized last year los admitted homenonenoneincome level education marital status lives alone meeting adlsnovne1992contribution measure disease complexity complex prediction outcome charges among hospitalized patients20age sexnoneused metric comprised count significantly effected body systems well comorbidity severity scorenonenonenonenonenonenone1996does riskadjusted readmission rate provide valid information hospital quality19age sexnonecomplexity measured number pmcs presentpmc relative intensity scorelosnonenonenonenone1997correlates early hospital readmission death patients congestive heart failure21age sex racenonehistory mi hf vtvf dm charlson comorbidity indexef systolic bp respiratory rate serum sodium serum creatinine cardiomegaly admission cxr nsr admission ekg absence new stt changes admission ekgnonehas pcpsymptoms discharge laboratory abnormalities dischargeincome education single person home help medical carenone1999prediction hospital readmission heart failure development simple risk score administrative data22age sex racenonecharlson comorbidity index specific comorbid conditionssee use patternlos total hospital discharge dollars use icu procedural complication discharge snf transfer acute care hospital home health services discharge discharged ama cardiology service ptot specific noninvasive cardiology procedures echo telemetry monitoring est etc invasive cardiac procedures pci cabg etc critical care procedures pulmonary artery catheterization inotropic agents mechanic ventilator support hd etchospital location hospital typenoneinsurance typehistory drug alcohol abuse2000predicting nonelective hospital readmissions multisite study23age gender racesf36 score physical component summarysf36 mental component summarydisease specific severity markers eg insulin dependence home o2 use nyha class discharge lab values including bun hb wbcno ed visits previous 6 months admissions previous 6 months los patient satisfaction scores survey datanonenonemarital status highest grade completed distance vamc employment status service connectionnone2000predictors readmission among elderly survivors admission heart failure24age sex racedischarge mobilityspecific comorbid conditionspresences pnd orthopnea chest pain systolicdiastolic blood pressure respiratory rate pulmonary edema cxr lvef occurrence hospitalization major complication cardiac arrest shock mi stroke major procedure hospitalization cabg cardiac catheterization labs discharge including sodium bun creatinine buncr ratio ace inhibitor prescription digoxin prescriptionprevious admission within 1 year losnonenonenonenone2004posthospital care transitions patterns complications risk identification25age sexpremorbid functional status score selfrated general health visual impairment need assistance adlscharlson comorbidity index specific comorbid conditions alzheimers diseasenoneprevious admission average los previous 6 months number prior snf stays average los previous 6 monthsnonenonemedicaid status unmarriednone2004risk stratification hospitalization decompensated heart failure26age gender racenonespecific comorbid conditionsduration hf diagnosis hf etiology history pci presence peripheral edema s3 murmur ef nyha class jvd hjr rales heart rate systolic bp diastolic bp respiratory rate k bun cr na platelets hbnumber prior hf hospitalizations previous 12 monthsnonenonenonenone2006identifying patients high risk emergency hospital admissions logistic regression analysis27age sex ethnicity std admission rationonecharleson comorbidity index presence ambulatory care sensitive conditionnoneno ed visits past 365 days ed visits past 366 days 36 months number consultant episodes index spellnonenonearealevel lifestyle group arealevel deprivation source admissionnone2006validation potentially avoidable hospital readmission rate routine indicator quality hospital care28age sexnonecharlson comorbidity index number comorbidities diagnoses admissionnoneprevious admission past 6 monthsnonenonenonenone2007improving management care highcost medicaid patients29age sex raceethnicitynonenumber type comorbidities history mental illness history alcohol substance abusenonefrequency interval hospitalizations ed visits primary care visits specialist care visits previous 3 years use home health care personal care rehab services substance abuse services prescription medications inpatient spendingnonenonesocioeconomic status zip code residencenone2007prediction rehospitalization death severe heart failure physicians nurses escape trial30age sex race6 minute walk distancenonenyha class need high dose loop diuretic ischemic vs nonischemic hf systolic bp diastolic bp hr na cr bun ef required cpr required mechanical ventilationnonenonebeta blocker prescribed discharge ace inhibitor prescribed dischargenonenone2008hospital 30day heart failure readmission measure methodology31age sexprotein calorie malnutritionspecific comorbid conditionsnonenonenonenonenonedrug alcohol abuse2008risk factors 30day hospital readmission patients 65 years age32age sex raceethnicitynonespecific comorbid conditionsnoneservice type medical vs surgicaldischarge destinationnoneinsurance status distance hospital median income zip code residencenone2009using routine inpatient data identify patients risk hospital readmission33age sex indigenous statusnonespecific comorbid conditionsnoneprevious admission preceding 90 days 1 year 3 years previous emergency admission preceding 90 days 1 year 3 yearsnonenonemarital status socioeconomic status rurality geographic remotenessnone2010an automated model identify heart failure patients risk 30day readmission death using electronic medical record data13age sex racesee markers severitydepression anxietytabak mortality risk score derived albumin total bilirubin ck creatinine sodium bun pco2 wbc troponini glucose inr bnp ph temperature pulse diastolic bp systolic bpnumber prior admissions ed visits outpatient visits presentation ed 6 amto 6 pmnonenonesocioeconomic status single payment method use health system pharmacycocaine use history leaving ama missed outpatient appointments number home address changes2010derivation validation index predict early death unplanned readmission discharge hospital community34age sexdependent one adlcharlson comorbidity indexnonelos visit ed previous 6 months hospital admissions within previous 6 months medical vs surgical admission emergent admission number medications discharge number new medications discharge season discharge consultation number complications hospitalizedhas pcpnonelives alonenone2010hospital readmission general medicine patients prediction model35age sex raceethnicitysf12 physical component mmse presence functional limitationcharlson comorbidity index sf12 mental componentnonenumber admissions previous year los need extra day stay current admissionhas pcpnonehousehold income education primary insurance marital status lives alone someone available help carenone2011inability providers predict unplanned readmissions36age sexpoor selfrated general healthcad dm2noneadmission prior year 6 doctor visits prior yearnonenonenonenone2011incremental value clinical data beyond claims data predicting 30day outcomes heart failure hospitalization37age sexnonediagnoses admission including psychiatric diagnosesef heart rate hemoglobin serum creatinine serum sodium systolic blood pressure weightnonenonenonenonenone2011unplanned readmissions hospital discharge among patients identified high risk readmission using validated predictive algorithm38nonenonecharlson comorbidity indexnonelos number ed previous 6 months emergent admissionnonenonenonenonebolded covariates included final model nonbolded covariates proposed included los indicates length stay snf skilled nursing facility adl activities daily living wbc white blood cell mmse minimental state examination chf congestive heart failure hf heart failure vt ventricular tachycardia ef ejection fraction pcp primary care provider est exercise stress test pci percutaneous coronary intervention cabg coronary artery bypass graft hd hemodialysis hb hemoglobin lvef left ventricular ejection fraction ace angiotensinconverting enzyme ck creatine kinase inr international normalized ratio bnp brain natriuretic peptide cad coronary artery diseasebun blood urea nitrogen rn registered nurse va veterans administration pow prisoner war copd chronic obstructive pulmonary disease pmc patient management category mi myocardial infarction vf ventricular fibrillation dm diabetes mellitus bp blood pressure cxr chest xray nsr normal sinus rhythm ekg electrocardiogram stt st segment icu intensive care unit ptot physical therapyoccupational therapy sf36 short form36 nyha new york heart association ed emergency department vamc veterans affairs medical center pnd paroxysmal nocturnal dyspnea cr creatinine jvd jugular venous distention hjr hepatojugular reflux escape evaluation study congestive heart failure pulmonary artery catheterization effectiveness hr heart rate na sodium cpr cardiopulmonary resuscitationusing results attempted synthesize information conceptual model hf readmission figure paying special attention postdischarge environmental factorsdownload powerpointfigure 1 proposed conceptual model heart failure readmission emphasizing patient health care provider work within environmentthe state heart failure readmission risk modelingprediction models play vital role understanding interpretation reaction hf readmissions provide insight primary factors underlie readmission point toward new focused interventions furthermore understanding individual patient risk allows hospitals triage costly highintensity interventions patients likely derive benefit finally readmission rates adjusted patient factors used measure institutional quality care thus appropriate risk modeling vital creating applestoapples comparisons different institutions well within single institution timerisk factors associated prediction models hf readmission systematically described elsewhere10 14 although several hf readmission risk models validated published state risk prediction hf readmission remains crude ability discriminate patients readmitted significantly lower postdischarge mortality cindices hf readmission models rarely exceeding 07013 22 24 26 30 37 likewise ability providers predict hf readmission via clinical gestalt appears similarly limited36 reasons multiple first relatively high proportion readmissions may inherently stochastic events therefore models readmission ceiling predictive performance second variation readmission risk following adjustment patientlevel factors may partially attributable provider systemlevel differences care delivery ie differences quality third existing models might fail reliably predict readmissions missing key domains drive occurrenceexisting prediction models relied heavily data collected hospitalization typically inpatient clinical registries claimsbased administrative data table data first approach uses readily available data dictate hypotheses tested rather way around largely neglects difficulttomeasure logical domains include complex comorbid disease frailty subclinical depression anxiety substance abuse cognitive limitations lack formal informal education health literacy numeracy acculturation suboptimal patient adherence inability provide selfcare caregiver support social networksthe importance postdischarge environmentalthough easily captured measures patients postdischarge environment considered existing models eg income marital status insurance status table systematic approach domain largely absent hf readmission discussion new data emerging indicate stability postdischarge environment plays critical part hf readmissionamarasingham et al derived validated hf readmission risk model within large inner city safetynet hospital using wide range automated data gleaned electronic medical record13 multivariable analysis several factors emerged associated 30day readmission including single male using medicaid increased number address changes average income level zip code residence time presentation ed 6 6 pm markers social instability included group previously validated model 30day risk prediction improved markedly cstatistic 061 072 suggests social environmental factors important determinants readmission riska second study arbaje et al supports hypothesis12 using medicare claims data well current beneficiary survey group looked relationship socioeconomics postdischarge environment likelihood early hospital readmission range diseases including hf studys population unmarried living alone lacking selfmanagement skills unmet activity daily living lower level education put patient increased risk readmission interestingly adjusting factors direct relationship found income riska variety studies shown indigent populations tend higher rates hf readmission analysis national medicare data showed 30day hf readmission rates medicare beneficiaries higher among black patients white patients patients minorityserving hospitals higher readmission rates nonminorityserving hospitals39 even adjustment measured clinical factors medicaid populations higher hf readmission rates commercially insured counterparts40 portion differences may due inferior health care populations differences patient environmental factors captured existing models likely contribute well least among medicare population community measures explain far variance institutional hf readmission rates hospital process performance measures41recent analyses specifically collected data social factors captured traditional databases hypothesis first approach helped expand view mediators readmission peterson et al showed series papers derived prospective health survey information health literacy42 acculturation43 strong predictors adverse outcomes discharge among patients hospitalized hf tao et al44 suggest scoring system might used predict patients whose social situation place higher risk readmissionas evidence influence postdischarge environment readmission successful interventions effectively reduced readmissions generally done altering patients postdischarge environment patients ability manage hisher environment example comprehensive discharge planning including education patient family socialservice consultation intensive followup components earliest successful hf readmission interventions45 46 recently transition coaches go directly home environment support variety patient needs shown effective47 unlike successful interventions use trained personnel broadly support patients transition home unimodal interventions11 focused primarily physiology hf48 consistently failed reduce hf readmission ratesa new conceptual model hf readmissionhf readmission event occurs definition postdischarge environment reasonable surmise environment would act mediator based current understanding readmissions propose new explicit paradigm hf readmission positions patient health system factors within relevant environment figure patient interacts provider health system within context surrounding environment conceptualization moves postdischarge environment peripheral ignored role encompassing one changing conceptualization transforms view readmission biological hospitalbased event sociobiological process new model also helps reconcile patient factors providerhealth system factors relate postdischarge environment concretely reframing suggests new lines research postdischarge environment may lead improvements ability predict mitigate risk readmissionthe question postdischarge environment affects readmissions important readmission typically multifactorial process49 hypothesize increased stability postdischarge environment positively affect variety domains related readmission social stability potential improve dietary compliance fluid restriction increase medication adherence increase access health care improve compliance appointments raise levels exercise reduce tobacco alcohol use etc together factors may positively influence hf severity disease progression addition may decrease comorbidity number severity even help bolster patients physiologic reserve domains may remove barriers combine provider systemsbased factors synergistically influence rates readmissionenvironmental factors public policyit argued socioeconomic factors limited place risk modeling adjusting may excuse substandard care indigent impoverished populations5 6 7 8 9 50 contrary acknowledging patient health system reside within larger environment counters argument including environmental factors riskstandardization models public reporting valuebased purchasing recognizes unique challenges posed patients significant environmental instability addition perspective lends support incentives would foster development innovative transitional care programs order accommodate social instability directly enhance patients ability navigate postdischarge environment moving overly simplistic dichotomous patienthospital construct consideration patient clinician hospital members community reside promotes integrated approach health ultimately major improvements health patients chronic progressive diseases like hf require coordinated efforts among patients families providers health systems governmental agencies community organizations integrated approach properly incentivized sound public policyas centers medicare medicaid services scale performancebased payments must consider potential influence socioeconomic factors outcomes ensure hospital payment penalties exacerbate disparities care although outcome measures designed reduce unnecessary hospital readmissions may important step forward advancing quality respects failure incorporate environmental factors could influence hospitals ability willingness serve vulnerable populations51 stratifying institutional readmission results important environmental factors may one way level playing field assessing hospital performance encourage hospitals maintain access care vulnerable populationsfuture researchfactors related postdischarge environment need better explored measured integrated risk models interventions without comprehensive systematic analysis postdischarge environment unlikely realize reductions unnecessary hf readmissions approach would involve number steps including development definitions associated taxonomy around relevant factors postdischarge environment followed surveillance factors explicit mechanism52research ross et al14 arbaje et al12 provides example assess incremental value factors social instability assessing risk model performance inclusion factors meantime institutions