Risk Factors for All-Cause Hospital Readmission Within … · Vol. 20, No. 5 May 2013 JCOM 203...

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ABSTRACT• Objective:To develop a predictive model of 30-day

readmissionusingclinicalandadministrativedata.• Design: Retrospective cohort study. After dividing

dataintodevelopmentalandvalidationsets,multivari-ablelogisticregressionwasperformed.

• Participants:AdultswithdatainHealthFacts,adata-base composed of participating hospitals’ electronicmedical records. The index hospitalization was apatient’s first qualifying hospital admission between1 October 2008 and 31 August 2010.We excludedobservationstays,admissionswithlengthofstayof0days,obstetricstays,andpatientswhosepredominantcaresettingwasapsychiatricorrehabilitationunit.

• Measurements: Readmission within 30 days of livedischargefromtheindexhospitalization.

• Results:Therewere463,351indexadmissionsto91hospitals,with45,098(9.7%)patientsreadmitted. Inmultivariablemodeling,factorsassociatedwithread-missionincludedpriorhospitaladmission,lowhemo-globin, longerstays,and increasingCharlson index;arthroplasty procedures were associated with lowerriskofreadmission.Modeldiscriminationwasmodestindevelopmentaldata(c-statistic=0.668)andslightlylower(0.657)invalidationdata.

• Conclusions:Increasedcomorbidityandpriorhospitalexposureareassociatedwithunplannedreadmission.Despite the availability of many potentially relevantclinical variables, model performance was modestandfewclinicalvariableswereassociatedwithread-missioninamultivariablemodel.Focusingonspecificconditions with a narrower set of relevant variablesmay facilitate identifyingpatientsatparticularlyhighriskforreadmission.

Hospital readmission has gained increased at-tention both as a potential reflection of poor health care quality and as a cost driver. The

Medicare Payment Advisory Committee estimated that readmissions resulted in $15 billion in Medicare expendi-tures annually [1]. The Patient Protection and Affordable Care Act (ACA) includes payment reductions for hospi-tals with high readmission rates to help control Medicare expenditures [2]. To improve care and avoid financial penalties, hospitals and clinicians need to optimize dis-charge planning and care coordination. This is particu-larly salient as health care networks position themselves as accountable care organizations [3]. Better understanding risk factors for readmission can help achieve these aims.

Estimates of readmission rates vary widely, particularly by diagnosis and patient age. Among Medicare enrollees, 30-day readmission often exceeds 20%. For example, 25.7% of Medicare fee-for-service enrollees with heart failure were readmitted within 30 days [4] compared with 19.6% of patients in a general Medicare fee-for-service sample [5]. Readmission rates for Medicare fee-for-service patients with pneumonia, heart failure, or myocardial infarction all exceeded 21%, with African-American patients and patients in minority-serving hos-pitals readmitted more often [6]. Other populations also experience high readmission. In a general population of heart failure patients discharged from a single, urban hospital, 24.2% were readmitted within 30 days [7]. Readmission following radical cystectomy for bladder carcinoma was 19.7% in the first 30 days [8].

Given the magnitude and variation in readmission rates, substantial effort has been directed at determin-ing factors that are associated with readmission. A recent review of 26 models [9] reported that most performed poorly. Nine US studies of large population-based data-

Risk Factors for All-Cause Hospital Readmission Within 30 Days of Hospital DischargeRobin L. Kruse, PhD, Harlen D. Hays, MPH, Richard W. Madsen, PhD, Matthew F. Emons, MD, MBA, Douglas S. Wakefield, PhD, and David R. Mehr, MD, MS

ORIGINAL RESEARCH

From the University of Missouri School of Medicine, Columbia, MO (Drs. Kruse, Madsen, Wakefield, and Mehr) and Cerner Corporation, Kansas City, MO (Mr. Hays and Dr. Emons).

204 JCOM May 2013 Vol. 20, No. 5 www.jcomjournal.com

RISK FOR READMISSION

bases or multicenter studies, mostly involving Medicare data or older patients, had c-statistics of 0.55 to 0.65 (the c-statistic varies from 0.5 to 1.0, where 1.0 indicates perfect fit and 0.5 represents results no better than a coin flip). Ross and colleagues [10] reviewed 117 studies of re-admission following hospitalization for heart failure and found few patient characteristics consistently associated with readmission. Two models were reported, both with poor discrimination (c-statistic 0.60 for both).

Claims data have been extensively used to study readmission; they typically include demographic char-acteristics, diagnoses and procedures, and insurance information but lack other clinical data that might identify patients during a hospital stay who are at high risk of readmission. Factors such as the use of high-risk medications, critical care exposure, laboratory abnormali-ties, organ dysfunction, and severity of illness indicators might be more powerful predictors of 30-day readmis-sion than diagnosis. Though often unavailable in claims data, these variables are readily available in electronic health records. We analyzed the Health Facts database (Cerner Corporation, Kansas City, MO), electronic health data aggregated from numerous health systems, to provide insight into additional factors associated with hospital readmissions. Our overall objective was to create a predictive model for all causes of 30-day hospital read-mission using variables pertaining to the index hospital-ization available at the time of discharge. We considered characteristics of the entire index admission because our focus was risk of future readmission. By relying on data from electronic health records, the model could be refined and embedded in an electronic health record to inform discharge planning.

METHODS

Using a retrospective cohort design, we identified adults with an acute care hospitalization (index admission) and determined which patients had an inpatient readmis-sion within 30 days following discharge. We compared patients who were and were not re-hospitalized to deter-mine risk factors for readmission. The Health Sciences Institutional Review Board at the University of Missouri deemed the study exempt from review.

Health Facts DatabaseWe used Health Facts, a database assembled from partici-pating hospitals and health systems’ comprehensive clini-cal records. Health Facts has been used in several studies

of acute myocardial infarction (AMI) outcomes [11–14] as well as surveillance of meningococcal disease in chil-dren [15]. Billing and encounter data are integrated with clinical information relating to drug order/dispensing and the results of diagnostic testing. Data are submitted from diverse hospitals and outpatient clinics throughout the United States. Depending on the specific electronic health record components implemented in each facility, different data elements are contributed to Health Facts. Cerner Corporation has established Health Insurance Portability and Accountability Act (HIPAA)–compliant policies and procedures that use statistical methods to de-identify data prior to inclusion in Health Facts. Because patients are de-identified when hospitals contribute their data, readmissions can only be tracked within the same health system. However, in Medicare data, 78% of read-missions are to the same hospital [16].

Inclusion and Exclusion CriteriaWe included inpatient admissions with at least 1 of each of the following: diagnosis or procedure, medication order, and laboratory order. The index hospitalization was the first qualifying acute care hospital admission for a patient between 1 October 2008 and 31 August 2010. We included patients who were at least 18 years of age at admission and who were discharged alive. We excluded the following: (1) admissions with a length of stay of 0 days; (2) outpatient (observation) stays; (3) patients with primary or secondary diagnosis of preg-nancy or complications of pregnancy, childbirth and the puerperium (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] diagnosis codes 630–679) or who had primary or secondary proce-dures that were obstetrical (ICD-9-CM procedure codes 72–75); and (4) patients whose predominant care setting during the admission was a psychiatric or rehabilitation unit. After reviewing distributions of medication orders, we operationalized predominant care setting as a psy-chiatric or rehabilitation unit if more than 90% of the medication orders originated from that type of unit. We included patients with psychiatric conditions requiring temporary acute care not excluded by the above criterion, such as alcohol detoxification, drug detoxification, or stabilization following a suicide attempt. We did not con-sider elective admissions following the index admission as readmissions. Elective (planned) readmissions were those so designated by the admitting physician except when the patient was admitted through the emergency depart-


ment. Admissions within 24 hours of discharge from the index admission were combined with the index admission and treated as 1 admission (most were within 3 hours and not likely true discharges).

AnalysisWe used SAS for Windows, version 9.2 (SAS Institute Inc., Cary, NC) for all analyses. Potential risk factors were selected based on the literature, availability in Health Facts, and clinical judgment of the physician-investigators. Descriptive statistics including unadjusted odds ratios were calculated; the chi-square statistic was used to determine statistical association of each potential risk factor with 30-day readmission. Because there were nearly 400 candidate variables, they were grouped into categories for initial modeling: patient and hospital char-acteristics, medications, laboratory results, microbiology results, indicators of organ dysfunction, characteristics of the index admission, treatment, and diagnoses. A com-plete list of variables is available from the authors.

Missing DataMissing data were common among laboratory results. In general, we considered a missing value for a particular laboratory test as indicating that the care team felt there was no reason to order the test. Thus, missing values were assumed to lie within a test’s normal range. For example, 129,061 patients had hematocrit values and 334,290 had no hematocrit value. For the purposes of modeling, we assume that the 334,290 patients had normal hemato-crits. “Missing” medications and procedures were as-sumed not to have been ordered or performed. Hospital exposure in the year prior to the index admission was considered missing if Health Facts had no inpatient or outpatient encounters for the patient during this time.

Modeling ProcessWe developed logistic regression models and accounted for nesting of patients within hospitals with generalized estimating equations using the GENMOD procedure in SAS software. During model development and valida-tion we considered effect size, clinical relevance, and statistical significance. We used the c-statistic (area under the receiver operator characteristic [ROC] curve) to as-sess model discrimination. To assess model calibration, we divided the predicted probabilities into deciles and compared each decile’s median value with the observed proportion of 30-day readmission.

The data were randomly divided into developmental and validation data sets, setting aside 10% of the ob-servations for validation. The developmental data set was further divided into 20 random samples (without replacement). Using chi-square tests for categorical vari-ables and 2-sample t tests for continuous variables, we determined the relationship of each potential predictor to 30-day hospital readmission. Variables within a category that were significant at the 0.0001 level were included in a logistic regression with backward elimination that also used a 0.0001 level. We chose this level in view of the large sample size (about 20,000 observations per sample). Each laboratory result had up to 4 variables: baseline, nadir, peak, and discharge results. As a pre-liminary step, these 4 variables were compared; the one with the strongest association with readmission was used in the pool of potential predictors. Modeling proceeded on all 20 subsamples. Variables retained in models for at least 10 of the 20 samples were eligible for inclusion in the final model.

Within each developmental sample, the collection of category “winners” was used as potential predictors in a logistic regression model with backward elimination at the 0.005 level. The most common “winners” from the 20 developmental sets were estimated in the full develop-mental data set. To make sure that the modeling process did not exclude important variables, we tested whether including other variables improved model performance. We focused on categories of variables that were not repre-sented in the final model (eg, indicators of impaired im-mune function and medications) and variables that were dropped late in the process. Adding these other variables back to the model resulted in minimal improvement in the c-statistic (0.01), so we did not include variables be-yond the 5 originally selected.

Model ValidationThe variable coefficients from the final model were used to calculate readmission risk for patients in the validation sample. To visualize model discrimination and calibra-tion, we plotted an ROC curve and a calibration plot, respectively.

RESULTS

Characteristics of the included hospitals are shown in Table 1. There were 91 hospitals included, ranging in size from less than 5 to over 500 beds. All US census regions were represented, with the majority of hospitals

ORIGINAL RESEARCH



in the Northeast and South regions. Almost half (46.2%) were academic health centers.

Derivation of the study cohort is depicted in Figure 1. There were 463,351 index hospital stays, with 45,098 (9.7%) patients readmitted within 30 days of discharge from the index hospitalization. Mean patient age was 61 years (95% CI 61.0–61.1), with 27.3% of the population age 75 years or older. Over half (54.6%) were women. Most patients were Caucasian (78.8%) and 15.0% were African American. Patients age 65 years or older with other or unknown insurance were assumed to have Medicare. Almost half (45.1%) of patients had Medi-care, 14.2% had commercial insurance, and 4.9% had Medicaid. Insurance status was unknown for 28.3% of patients.

The unadjusted associations of individual risk factors with readmission are shown in Table 2. Patients with hospital exposure in the prior 12 months had higher odds of readmission than those with no or unknown hospital exposure. Readmission increased with age, with 12.2% of those age 85 years and older readmit-ted. Compared with commercial insurance, Medicare and Medicaid were also associated with more frequent readmission, while self-pay patients were less likely to

be readmitted. Relative to patients with a Charlson index [17] of 0, patients with scores above 5 were much more likely to be readmitted. Several diagnoses and conditions were associated with readmission, including cancer, end-stage renal disease, and major organ trans-plantation. Compared with those with lower values, the odds of readmission were more than double for patients with blood urea nitrogen ≥ 35 mg/dL or serum creati-nine levels of ≥ 2 mg/dL. Readmission increased with the number of medications ordered and dispensed dur-ing the index admission. In particular, high-dose oral corticosteroids and chemotherapy agents were strongly associated with readmission.

The multivariable model contains 5 independent variables (Table 3). The adjusted odds of readmission increased with length of stay, the Charlson index, prior hospitalization, and low hemoglobin (nadir of all values). In the model, which controls for comorbidities, patients hospitalized for arthroplasty were about half as likely to be readmitted as other patients. The c-statistic in the developmental data was 0.67.

The model performed almost as well in the validation data, with a c-statistic of 0.66 (Figure 2A). The calibra-tion curve (Figure 2B) indicates reasonable performance

Table 1.CharacteristicsofParticipatingHospitals

Characteristic

Hospitals, n (%)

n = 91

Patients, n (%)

n = 463,351

No.ofbeds

<100 29(31.9) 38,948(8.4)

100–199 16(17.6) 63,831(13.8)

200–299 19(20.9) 85,556(18.5)

300–399 17(18.7) 123,574(26.7)

≥500 10(11.0) 151,442(32.7)

Censusregion

Northeast 36(39.6) 198,177(42.8)

Midwest 16(17.6) 78,404(16.9)

South 33(36.3) 15,4813(33.4)

West 6(6.6) 31,957(6.9)

Location

Urban 89(97.8) 462,849(99.9)

Rural 2(2.2) 502(0.1)

Teachingstatus

Teaching 42(46.2) 329,871(71.2)

Non-teaching 49(53.8) 133,480(28.8)


across all levels of risk. The lowest and highest estimated individual risk of readmission were 1.9% and 49%, re-spectively. In the validation data, 3.1% and 21.8% of the patients in the lowest and highest deciles of risk were readmitted, respectively.

DISCUSSION

Our model identified increased comorbidity and length of stay, prior hospital exposure, and low hemoglobin as risk factors for all cause readmission in a general adult inpatient population; having a hip or knee arthroplasty

Figure 1. Flowchartshowinginclusionandexclusionofinpatientadmissionsintheanalyticcohort.*Morethan1exclusionreasoncanapplytoanadmission,thereforethesumoftheindividualexclusionsexceedsthetotaladmissionsexcluded.

ORIGINAL RESEARCH

981,581admissionsinHealthFacts•Submitteddiagnosisandprocedureinformation,

pharmacy,andlaboratorydata•Discharged10/01/08through9/30/10

202,074admissionswithpatients<18yearsoldatthetimeofadmission

23,339in-hospitaldeaths

2587admissionswith≥ 90%ofordersoriginatingfromarehabilitationunit

3659admissionswith≥ 90%ofordersoriginatingfromapsychiatricunit

99,884admissionswithprimaryorsecondarydiagnosispregnancy-relatedorobstetrical

666,519qualifyingadmissions*

463,362index(first)admissions•45,098readmittedwithin30days•418,264notreadmittedwithin30days



Table 2.UnadjustedBivariableAssociationofSelectedRiskFactorswith30-DayReadmissionFollowingHospitalDischarge

No. of Patients

Patients Who Were Readmitted

Number (%) Odds Ratio (95% CI)

Demographiccharacteristics

Age

18–44 88,747 6548(7.4) Reference

45–54 75,158 6402(8.5) 1.17(1.13–1.21)

55–64 88,409 8213(9.3) 1.29(1.24–1.33)

65–74 84,584 8917(10.5) 1.48(1.43–1.53)

75–84 80,996 9481(11.7) 1.66(1.61–1.72)

85–90 45,457 5537(12.2) 1.74(1.68–1.81)

Insurance

Commercial,other 78,325 6046(7.7) Reference

Medicare* 208,949 23,845(11.4) 1.54(1.50–1.59)

Medicaid 22,756 2562(11.3) 1.52(1.44–1.59)

Self-pay 22,373 1567(7.0) 0.90(0.85–0.95)

Unknown 130,948 11,078(8.5) 1.10(1.07–1.14)

Maritalstatus

Married/lifepartner 179,104 16,557(9.2) Reference

Divorced/separated 41,918 4496(10.7) 1.18(1.14–1.22)

Single 84,535 8022(9.5) 1.03(1.00–1.06)

Widowed 63,452 7588(12.0) 1.33(1.30–1.37)

Unknown 94,342 8435(8.9) 0.96(0.94–0.99)

Race/ethnicity

African-American 69,381 7467(10.8) 1.13(1.10–1.16)

Caucasian 365,039 35,243(9.6) Reference

Hispanic 10,620 932(8.8) 0.90(0.84–0.96)

Otherknown 13,795 1238(9.0) 0.92(0.87–0.98)

Unknown 4516 218(4.8) 0.48(0.41–0.54)

Sex(59unknown)

Female 252,927 23,600(9.3) Reference

Male 210,365 21,496(10.2) 1.11(1.08–1.13)

Indexhospitalization

Urgentoremergentadmission 338,271 34,887(10.3) 1.29(1.26–1.32)

Typeofadmission

Medical 286,997 30,386(10.6) 1.28(1.25–1.30)

Surgical 147,273 12,497(8.5) Reference

Unknown 27,981 2215(7.6) 0.89(0.85–0.93)

Admissionsource

Emergencydepartment 249,014 25,835(10.4) Reference

Hospital,otherfacility 25,458 2342(9.2) 0.88(0.84–0.92)

Skillednursingfacility,nursinghome 5527 797(14.4) 1.46(1.35–1.57)

Other 166,396 14,709(8.8) 0.84(0.82–0.86)

Unknown 16,956 1415(8.4) 0.79(0.74–0.83)


ORIGINAL RESEARCH

Table 2.UnadjustedBivariableAssociationofSelectedRiskFactorswith30-DayReadmissionFollowingHospitalDischarge(continued)

No. of Patients



Hospitallengthofstay

≤ 2days 91,800 5677(6.2) Reference

2.01–3days 83,752 6077(7.3) 1.19(1.14–1.23)

3.01–4.25days 99,038 8127(8.2) 1.36(1.31–1.40)

4.26–7days 96,108 10,750(11.2) 1.91(1.85–1.98)

Over7days 92,653 14,467(15.6) 2.81(2.72–2.90)

Priorhealthcareutilization

Nursinghomeexposureprior90days 5054 1038(20.5) 2.11(1.98–2.27)

Lefthospitalagainstmedicaladviceprior12months

4428 707(16.0) 1.54(1.42–1.67)

Hospitalexposureprior12months

No 231,399 21,885(9.46) Reference

Yes 67,492 11,087(16.4) 1.88(1.84–1.93)

Unknown 164,460 12,126(7.37) 0.76(0.74–0.78)

Diagnosesandconditions

Charlsonindex

0 188,254 11,939(6.3) Reference

1–5 251,160 28,310(11.3) 1.88(1.84–1.92)

6–10 23,584 4764(20.2) 3.74(3.60–3.88)

11–20 353 85(24.1) 4.69(3.67–5.99)

Organsystemdysfunctions

0 389,745 35,059(9.0) Reference

1 58,236 7498(12.9) 1.50(1.46–1.54)

2–5 15,370 2541(16.5) 2.00(1.92–2.09)

Blooddyscrasia 9531 1608(16.9) 1.91(1.81–2.02)

Cancer 38,321 6627(17.3) 2.10(2.04–2.16)

Coronaryarterydisease 73,989 8704(11.8) 1.29(1.26–1.33)

Diabetes 113,997 13,383(11.7) 1.33(1.30–1.36)

Endstagerenaldisease 10,553 2159(20.5) 2.45(2.34–2.58)

Fluidorelectrolyteimbalance 100,259 12,556(12.5) 1.45(1.42–1.49)

Heartfailure 55,420 8298(15.0) 1.78(1.73–2.06)

Majororgantransplantation 3855 650(16.7) 1.89(1.74–1.87)

Pneumonia 33,421 4145(12.4) 1.34(1.30–1.39)

Sepsis 17,652 2639(15.0) 1.67(1.60–1.74)

Laboratorystudies

Absolutelymphocytecount<800/µL 145,623 18,438(12.7) 1.58(1.55–1.61)

Bloodureanitrogen≥35mg/dL 64,069 10,670(16.7) 2.12(2.07–2.17)

Estimatedglomerularfiltrationrate 70,782 11,231(15.9) 2.00(1.95–2.04)

<40mL/min/1.73m2

Hemoglobin(nadir)†

<8g/dL 34,776 5667(16.3) 2.34(2.27–2.42)

8–11g/dL 161,479 18,924(11.7) 1.60(1.56–1.63)

>11g/dL 267,096 20,507(7.7) Reference



was associated with lower risk of readmission. Model performance was modest (c-statistic = 0.67).

Our model performed as well or better than most models based on claims data. In fact, it performed as well or better than most models that studied a single condition. A recent systematic review of readmission risk models [9] found 26 distinct models. Nine models from large, multicenter US studies reported c-statistics of 0.55–0.65. Of the 6 models with a reported c-statistic of 0.7 or above, 4 were based on European or Australian data and 1 US study was based on heart failure patients at a single center. Coleman’s [18] model using Medicare Current Beneficiary Survey data had good discrimina-tion (c = 0.77) when prior utilization and diagnoses were included; adding self-reported survey items on functional status and vision improved model performance (c = 0.83).

Stronger models can undoubtedly be created by includ-ing additional clinical and social factors or by restricting the population to more narrowly defined constellations of conditions. We were able to identify 25% of our sample with less than a 6% risk of readmission. In the validation data, 21.8% and 15.1% of the patients in the highest two risk deciles were readmitted. Although far from perfect, such data can help target interventions to prevent read-mission to higher risk individuals.

Studies of readmission vary widely in terms of case finding and the time period studied. Vest et al [19] con-ducted a systematic review of preventable readmissions; indicators of complexity or general ill health (eg, Charl-son index), increasing length of stay, and Medicare or Medicaid status were the most commonly identified risk factors. In a large, prospective cohort study of all-cause

Table 2.UnadjustedBivariableAssociationofSelectedRiskFactorswith30-DayReadmissionFollowingHospitalDischarge(continued)

No. of Patients



Plateletcount<100,000/µL 27,786 4560(16.4) 1.91(1.85–1.98)

Serumcreatinine≥2mg/dL 46,920 7961(17.0) 2.09(2.03–2.14)

Serumalbumin<3g/dL 101,657 15,129(14.9) 1.93(1.90–1.98)

Serumpotassium>5.5mmol/L 19,661 3302(16.8) 1.94(1.87–2.02)

Totalbilirubin≥2mg/dL 19,541 2944(15.1) 1.69(1.62–1.76)

Whitebloodcells<4,000/µL 27,595 4143(15.0) 1.70(1.65–1.76)

Treatmentsandprocedures

No.ofmedicationsdispensed

0 190,255 17,259(9.1) Reference

1–10 98,186 8243(8.4) 0.92(0.89–0.94)

11–20 112,771 11,192(9.9) 1.10(1.08–1.13)

21–30 44,761 5585(12.5) 1.43(1.38–1.48)

31ormore 17,378 2819(16.2) 1.94(1.86–2.03)

Amiodarone 9407 1478(15.7) 1.75(1.66–1.86)

Arthroplasty 26,083 1160(4.4) 0.42(0.39–0.44)

Beerscriteria,anyagent(age>74) 22,480 2813(12.5) 1.08(1.03–1.12)

Bloodtransfusion 8190 1304(15.9) 1.78(1.67–1.89)

Chemotherapyagent 2535 700(27.6) 3.58(3.28–3.91)

Hemodialysis 10,760 2148(20.0) 2.38(2.27–2.50)

High-doseoralcorticosteroid 14,228 2295(16.1) 1.83(1.74–1.91)

Note:Toprotectconfidentiality,theageofpatientsover90yearsoldwasresetto90.

*Includespatientsage65orolderwithotherorunknowninsurance.

†Patientswithunknownhemoglobin(3.5%)wereassumedtohavevaluesover11g/dL.


readmission in 11 Ontario hospitals, van Walraven [20] found that length of stay, acuity (emergent admission), comorbidity [17], and emergency department use in the prior 6 months were associated with death or readmis-sion. Prior hospital exposure is a consistent risk factor for readmission [5,7,18,21,22]. Other common risk factors include poor physical functioning [5,21–23] and social factors such as social instability and living alone [7,21–26]. While a large number of risk factors in our analysis had high odds ratios when considered in bivariable analy-ses, many occurred infrequently, making them less useful for patient-level prediction in a multivariable model. For example, the odds ratio for readmission was 3.58 among patients who received chemotherapy agents, but only 2535 patients (0.55%) received them. This suggests that analyzing a more homogeneous group of patients (eg, patients with cancer or heart failure) might help identify a more predictive set of risk factors.

Readmission rates in large patient samples are often 15% or higher, depending on the population and the definition of readmission [4,5,7,23,27,28]. Patients in our sample had a lower readmission rate than these studies, although van Walraven [20] found a similar rate among general patients in 11 Ontario hospitals (8.0% combined outcome of death

or readmission within 30 days). There are likely several reasons for our relatively low rate. First, in Medicare data, about a fifth of readmissions are to a different hospital [16,29]. In our data, we could only track readmissions to the same hospital system. Second, we restricted index admissions to the first hospitalization during a period. Because the first admission is less likely to have been pre-ceded by a prior admission than subsequent stays, and prior hospital utilization was associated with readmission, this likely reduced our rate. Consistent with this sugges-tion, individuals with prior hospital exposure had a much higher risk of readmission than the overall sample. We excluded elective admissions from the readmission count; many studies with high readmission rates included elec-tive readmissions [4,5,21,29]. Facilities that contributed data to Health Facts might be early adopters of electronic medical records, which could have affected care delivery. Finally, our data included adults in Medicare managed care plans who are typically excluded from analyses of Medicare claims because hospital claims for those individuals are not submitted. Because patients in managed care are often healthier, their readmission rate could be lower.

Although our c-statistic is modest, our model points the way towards several paths to clinically useful point-

ORIGINAL RESEARCH

Table 3.Multivariable(Adjusted)AssociationofIndependentRiskFactorswithAll-Cause30-DayReadmissionFollowinganAcuteHospitalization

Risk Factor Coefficient Odds Ratio (95% CI)

Charlsonindex 0.1335 1.14(1.14–1.15)

Lengthofstay

≤2days Reference

2–3days 0.1151 1.12(1.08–1.17)

3–4.25days 0.2631 1.30(1.25–1.35)

4.25–7days 0.4790 1.62(1.56–1.67)

>7days 0.7305 2.08(2.00–2.15)

Arthroplastyprocedure –0.8014 0.45(0.42–0.48)

Hemoglobin(nadir)

<8g/dL 0.1970 1.22(1.18–1.26)

8–11g/dL Reference

>11g/dL –0.2247 0.80(0.78–0.82)

Hospitalexposureprior12months

No Reference

Yes 0.3919 1.48(1.44–1.52)

Unknown –0.2252 0.80(0.78–0.82)

C-statistic 0.668



of-service rules. For example, other important variables such as functional data, support systems, and health literacy could be collected by hospital personnel dur-ing discharge planning to estimate readmission risk. Electronic systems could alert clinicians when patients had developed increased risk due to changes in labora-tory results or medication orders. A recent meta-analysis found that individualized discharge plans reduce read-missions over routine discharge care [30]. One example is the Re-Engineered Discharge (RED) program [31], an 11-component intervention that included patient educa-tion, organizing postdischarge appointments and ser-vices, confirming medications, a written discharge plan, and a postdischarge phone call. In a trial of 749 patients, RED reduced subsequent hospital utilization by 30%. An intervention that provided patients with communica-tion tools, encouraged them to take a more active role in their care, and provided visits and calls from a “transi-tion coach” reduced 30- and 90-day readmissions [32]. With better risk models, graded interventions would be possible, reserving proven but resource-intensive strate-gies for the highest risk individuals. Thus, our model may serve as a basis for developing more refined models that include social and functional information, allow-ing hospitals to focus resources on patients at highest risk.

Strengths and LimitationsWe analyzed a large, cross-sectional sample of hospital-ized adults from 91 hospitals. In addition to administra-tive variables, we included clinical information on labora-tory results, medications and treatments, and severity of illness. Using data that can be accessed in real time can support predicting readmission at the time of a patient’s discharge. Because we had data on patients regardless of insurance status, our sample included patients in Medicare managed care who are typically absent from Medicare claims data. Ideally, we would want to include information on functional limitations, social support, substance abuse, socioeconomic status, and social insta-bility such as number of address changes [7,22–24,26].We were unable to track readmissions to hospitals in dif-ferent health systems or hospitals not included in Health Facts, which undoubtedly reduced the readmission rate in our data. Patients admitted to a different hospital have some differences from those admitted to the same hospi-tal [16]. Nonetheless, because only about 20% of patients are admitted to a different hospital, that is unlikely to have a major effect on our findings. Participating institu-tions may not be representative of US hospitals in general. In particular, rural hospitals are underrepresented in our study. We were unable to identify discharge medications, which could be an important factor in readmissions.

Figure 2. Calibration(Panel A)andreceiveroperatorcharacteristic(Panel B)plotsforlogisticmodelofall-cause30-dayread-missionsinthevalidationdataset.

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CONCLUSION

Comorbidity and prior utilization are strong risk fac-tors for readmission in a general population of patients discharged from the hospital. Despite the availability of a large number of potentially relevant clinical variables, model performance was modest. Considering that the proportion of readmissions that are potentially prevent-able is likely under 25% [28,33], it will be difficult for hospitals to successfully identify and intervene with pa-tients with a high likelihood of readmission. Including information on social support and functional status will likely improve model performance. Specific laboratory abnormalities might be more strongly associated with readmission in relevant subpopulations of patients (eg, infection indicators in pneumonia patients). Using clini-cal data to predict readmission in more homogeneous groups of patients is worthy of further study.

Acknowledgements: The authors would like to acknowledge Jane Griffin, RPh, and Jeffrey Binkley, PharmD, for their facilitation of this project. Preliminary results from this study were presented at the Cerner Health Conference on 10 October 2011.

Corresponding author: Robin L. Kruse, PhD, MA306 Medi-cal Sciences Building, University of Missouri School of Medi-cine, Columbia, MO 65212, [email protected].

Funding/support: This study was partially supported by the Tiger Institute Research Group, a collaborative research effort between the University of Missouri and the Cerner Corpora-tion. The Tiger Institute, wholly owned by the University of Missouri, had no role in conduct of the study; management, analysis, or interpretation of the data; or the preparation, re-view, or approval of the manuscript.

Financial disclosures: Mr. Hays and Dr. Emons are employed by the Cerner Corporation.

Author contributions: conception and design, RLK, HDH, MFE, DSW, DRM; analysis and interpretation of data, RLK, HDH, RWM, MFE, DRM; drafting of article, RLK, MFE; critical revision of the article, RLK, RWM, MFE, DSW, DRM; statistical expertise, RWM; administrative or techni-cal support, DRM; collection and assembly of data, RLK, HDH, MFE.

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