Charles A. Dana Research Institute and Harvard Thorndike Laboratory of. Beth Israel ... Address correspondence and reprint requests to Dr. Phillips: Division.
Predicting Emergency Readmissions for Patients Discharged from the Medical Service of a Teaching Hospital RUSSELL S. PHILLIPS, MD, CHARLES SAFRAN, MD, MS, PAUL D. CLEARY, PhD, THOMAS L. DELBANCO, MD E m e r g e n c y readmissions a m o n g patients ~ a r g e d from the medical service of a n acute-care teaching hospital w e r e a n a l y z e d . Using the multivariate technique of recursive partitioning, the authors developed a n d validated a model to predict readmission based on diagnoses a n d other c/inic a / f a c t o r s . Of the 4 , ? 6 9 patients in the validation series, 19% w e r e readmiffed within 90 days. Twenty-six per cent of the readmlssions occurred with/n ten days of discharge, a n d 57% within 30 days. Readmitted patients w e r e older, had longer hospitalizations, a n d had greater hospital charges (p < 0.01). The discharge diagnoses of AIDS, renal disease, a n d c a n c e r w e r e associated with increased risks ot roadm/ss/on regardless oI patients" demographics or test results. The relative risks (95% confidence interval) associated with these diagnoses were: AIDS, 3.3 (1.4-7.8): renal d / s e a s e , 2.3 (1.7-3.0); cancer, 2 . 8 (2.4-3.4). Other patients at inc r e a s e d risk w e r e those with diabetes, a h e m / a , a n d elevated creatinine (2.1; 1.6-2.8) a n d those with heart Ia//ure a n d elevated an/on g a p s (2.2; 1.7-2,8). For patients without o n e ot these diagnoses, a normal a/bumin a n d no prior admission within 6 0 days identified patients at r e d u c e d risk for readmlssion (0.4; 0.3- 0.4). Thus, c o m m o n / y a v a / / a b l e clinical data identify patients at i n c r e a s e d risk for e m e r g e n c y roadmission. Risk/actor profi/es should alert p h y s / c i a n s to these patients, as intensive intervention may be appropri-
ate. Future studies should test the impacts of clinical interventions d e s / g n e d to reduce e m e r g e n c y readmissions. Key words: readmJssion; risk factors; recursJve partitioning. ] GEN INTERNMED 1 9 8 7 : 2 : 4 0 0 - 405.
READMISSIONS to acute care hospitals occur frequently, contribute significantly to the cost of health care, a n d h a v e a negative impact on morale a n d quality of life for patients a n d their families. Zook a n d associates suggest that r e p e a t e d hospitalizations for the s a m e d i s e a s e account for half of all admissions a n d 60% of all hospital charges, l"2 Illnesses such as cancer, chronic lung disease, heart failure, a n d renal failure account for a large proportion of these r e p e a t hospitalizations.
Received from the Division of General Medicine and Primary Care, Department of Medicine,Harvard MedicalSchool, Beth IsraelHospital; the Charles A. Dana ResearchInstitute and Harvard Thorndike Laboratory of Beth IsraelHospital; and the Center for ClinicalComputing, Department of Medidne. Harvard MedicalSchool. Beth Israel and Brigham and Women's Hospitals, Boston, Massachusetts. Presented in part at the meeting of the American Federation for Clinical Research, Washington. DC, May 1986. Supported in part by grants HS04928 from the National Center for Health Services Research and LM 04260 from the National Library of Medicine. Addresscorrespondenceand reprint requeststo Dr. Phillips: Division of General Medicine, Beth Israel Hospital, 330 Brookline Avenue, Boston, MA 02215.
400
Some readmissions m a y be predictable a n d avoidable; however, relatively few studies h a v e add r e s s e d the determinants of readmission, a n d these studies h a v e differed in both method a n d conclusions. Anderson a n d Steinberg suggested that age, being male, disability, a n d the a b s e n c e of a surgical procedure during the index admission were important risk factors for readmission, s Smith a n d associates found a diagnosis of c a n c e r w a s associated with readmission, a n d frequent e m e r g e n c y room visits in the six months preceding admission, elev a t e d blood u r e a nitrogen levels, leukocytosis, hypoxia, a n d a n e m i a were significant multivariate risk factors for nonelective readmission to a medical service. 4 Potentially harmful habits such as cigarette or alcohol use h a v e b e e n found to be more frequent a m o n g patients with r e p e a t hospitalizations. 1' z We sought to determine predictors of u n p l a n n e d readmissions. Therefore, we conducted a n analysis of e m e r g e n c y readmissions a m o n g patients disc h a r g e d from the medical service of a n acute-care teaching hospital during two years. Using d a t a from the hospital's clinical computing system, 5 we developed a n d validated a multivariate model to predict readmission.
METHODS Study Site
Boston's Beth Israel Hospital, a 450-bed, community-based teaching hospital, provides obstetrical services a n d medical a n d surgical care for adults. There a r e approximately 23,000 admissions annually, of which n e a r l y 30% are to the medical service. All patients a r e c a r e d for by attending physicians a n d housestaff. The medical service is divided into patient-care teams, a n d patients readmitted to the hospital within six months of a prior admission are generally c a r e d for by their original housestaff a n d primary nurse team. The hospital is served by a clinical computing system that stores d a t a on inpatient a n d outpatient admissions a n d registration, outpatient a n d operating room scheduling, medical records, utilization review, nursing personnel, intravenous medications, blood b a n k usage, surgical pathology, radiology, neurophysiology, a n d cardiology reports, a n d microbiology, chemistry, hematology, a n d blood g a s results. 5
JOURNALOFGENERALINTERNALMEDICINE, Volume 2
Study Population The study population w a s selected from patients admitted to the Beth Israel Hospital after September 30, 1984, a n d d i s c h a r g e d from the medical service before October 1, 1986. For the purposes of model development a n d validation, the study population w a s divided into two groups. Patients admitted a n d d i s c h a r g e d during the first y e a r were used to develop a model, a n d patients admitted a n d disc h a r g e d during the second y e a r were used to valid a t e the model. For e a c h year, we excluded patients who died during the initial admission, who h a d b e e n admitted for participation in a clinical r e s e a r c h protocol, a n d who were readmitted electively. Among the remaining patients, readmission w a s defined as a n y admission to Beth Israel Hospital within 90 d a y s of the d a t e of discharge. In the first y e a r (model development series), 5,171 patients were admitted to the medical service. Patients who died during their first admission during this time period (n = 307), were readmitted for clinical r e s e a r c h (n - 71), or were readmitted electively (n = 414) were excluded from the analysis, leaving 4,379 patients, of whom 805 (18%) h a d e m e r g e n c y readmissions. In the second y e a r (model validation series), 5,567 patients were admitted to the medical service. Patients who died during the initial admission (n = 266), were admitted for clinical r e s e a r c h (n = 74), or were readmitted electively (n = 458) were excluded, leaving 4,769 patients, of w h o m 894 (19%) h a d emerg e n c y readmissions.
Diagnostic Categories To g e n e r a t e a clinically relevant model, we initially identified d i a g n o s e s associated with readmission a n d determined predictors of readmission within these groups. Diseases were grouped b a s e d on the o r g a n system involved, the uniformity of the diagnosis, a n d the suspected risk of readmission. Using these criteria, the following categories w e r e c r e a t e d b a s e d on ICD9-CM (International Classification of Diseases, ninth edition, Clinical Modification) codesS: cancer, acquired immune deficiency s y n d r o m e (AIDS), cerebrovascular disease, asthma, chronic pulmonary disease, diabetes mellitus, gastrointestinal bleeding, other gastrointestin a l illness, ischemic h e a r t disease, heart failure a n d cardiomyopathy, psychiatric illness, a n d acute or chronic renal disease. (A listing of the specific ICD9CM d i a g n o s e s that comprise these diagnostic groups a r e available from the authors.) Diagnoses w e r e a s s i g n e d at the time of discharge by the attending physician a n d supplemented a n d translated into diagnosis codes by nurses specializing in utilization review. All diagnoses that accounted for more t h a n five admissions were represented within
(Nov/Dec), 1987
401
these categories. Each diagnostic grouping w a s tested as a univariate risk factor for readmission using the chi-square test. Ninety-five per cent confid e n c e intervals for the relative risk of readmission were calculated for patients in e a c h diagnostic category. High-risk patients, defined as those with a diagnosis associated with readmission (p58 41/245
17
0.9 ( 0 . 6 - 1.2)
Anion gap _>16 I181370
52
2.2 (1.7- 2.8)
Anion gap 0.2). For example, patients with d i a b e t e s h a d r e a d m i s s i o n rates of 24 a n d 23%, respectively (p>0.2). Patients with d i a b e t e s a n d a n e m i a h a d r e a d m i s s i o n r a t e s of 30 a n d 27%, respectively (p>0.2).
Risk of Readmission A m o n g patients with at least o n e of the high-risk diagnoses, the r e a d m i s s i o n rate w a s 28%, m o r e t h a n
E m e r g e n c y readmissions a m o n g patients disc h a r g e d from the m e d i c a l service a r e common. W e h a v e identified high-risk patients b y diagnostic, dem o g r a p h i c a n d l a b o r a t o r y variables. A d i s c h a r g e diagnosis of c a n c e r , h e a r t failure, diabetes, AIDS, or r e n a l d i s e a s e predicts readmission. A prior admission within 60 d a y s a n d abnormalities of c o m m o n l y o b t a i n e d test results (anion gap, albumin, creatinine, a n d hematocrit) further s e p a r a t e patients with a n d without t h e s e d i a g n o s e s into high- a n d low-risk groups. The r e a d m i s s i o n r a t e w e report is similar to that found in other studies in which patients w e r e selected differently. Analysis of a similar population b y Smith a n d a s s o c i a t e s s h o w e d a r e a d m i s s i o n r a t e of 17% a m o n g g e n e r a l m e d i c a l patients with non-elective readmissions; 18% of the readmissions o c c u r r e d within ten d a y s of discharge. 4 Interestingly, w e found e a r l y r e a d m i s s i o n to b e e v e n m o r e common; n e a r l y 26% of readmissions o c c u r r e d within ten d a y s of discharge, a n d 57% o c c u r r e d within 30 d a y s of discharge. The high-risk diagnostic c a t e g o r i e s w e identified a r e a s s o c i a t e d with significant morbidity and, therefore, it is not surprising that t h e s e d i a g n o s e s predict readmission. Our finding that a prior admission predicts r e a d m i s s i o n is in a g r e e m e n t with the work of others w h o h a v e s h o w n that prior utilization of health services predicts future utilization. 9 Elevation of creatinine p r e d i c t e d r e a d m i s s i o n for patients with d i a b e t e s or h e a r t failure a n d also s e e m s r e a s o n a b l e on clinical grounds. 1° An e l e v a t e d a n i o n g a p a m o n g patients with h e a r t failure m a y reflect lactate accumulation d u e to a low c a r d i a c output, which would suggest m o r e s e v e r e c a r d i a c dysfunction. Although t h e r e a r e few d a t a on the prognosis a s s o c i a t e d with low albumin, it m a y result from malnourishment, malabsorption, h e p a t i c disease, a n d nephrosis, n all of which m a y l e a d to readmissions. L a b o r a t o r y test results w e r e helpful in identifying patients with high-risk d i a g n o s e s w h o w e r e not at i n c r e a s e d risk for readmission. For example, for a patient with d i a b e t e s w h o h a d a n o r m a l creatinine
JOURNALOFGENERALINTERNALMEDICINE,VoIuI-fle2 (NovJDe£), 1987
a n d hematocrit, the risk of readmission w a s not inc r e a s e d (i.e., the confidence interval for the estimate of relative risk includes 1.0). Similarly, patients with heart failure whose anion g a p s were normal were not at increased risk of readmission. Our initial partition b y high-risk diagnoses w a s d e p e n d e n t upon the a c c u r a c y of c o d e d discharge diagnoses. Although some studies h a v e suggested that ceding error rates m a y be as high as 20- 40%, others h a v e found that coding practices h a v e improved since Medicare's prospective p a y m e n t w a s linked to diagnosis.~'~s Diagnoses m a y be under- or overreported, d e p e n d i n g on coding practices a n d the incentive for p a y m e n t . Our model w a s developed on patients discharged prior to the introduction of diagnosis-related p a y m e n t s a n d w a s validated on patients d i s c h a r g e d after the Medicare diagnosis-related group (DRG) p a y m e n t system went into effect in Massachusetts to account for bias introd u c e d by c h a n g i n g coding practices. ~8 Our initial partition is b a s e d upon diagnosis to facilitate clinical interpretation a n d generalizability of our results. As some laboratory variables m a y predict readmission for some diagnostic groups but not others, excluding diagnoses from the analysis m a y result in a model that reflects the prevalences of specific d i a g n o s e s a m o n g patients in our hospital, which might be less generalizable to others. However, despite these precautions, it is not clear whether this model could be generalized to patients c a r e d for in other hospitals. For example, differences in c a s e mix or severity of illness a m o n g patients hospitalized elsewhere could affect the ability of the model to discriminate patients into risk groups. Prior to g e n e r a l use, the model should be prospectively validated in other settings. In this study we utilized recursive partitioning, which h a s several a d v a n t a g e s over univariate analysis a n d other types of multivariate analyses. ~7 First, it demonstrates the interrelation b e t w e e n variables, Factors that do not a p p e a r in the multivariate model m a y nevertheless be important. For example, a factor m a y h a v e b e e n first runner-up at several s t a g e s in the analysis, or, given a n alternate starting point, it might h a v e e m e r g e d as a significant multivariate factor. Recursive partitioning also e n a b l e s one to identify easily the position of e a c h patient in a tree such as the one shown in Figure 1. Using this figure, one c a n predict the proportion of readmissions without complex formulas. Predictive risks a r e formulated a n d displayed easily, permitting straightforward validation of findings. Our analysis w a s not d e s i g n e d to define specific interventions that m a y prevent readmissions but rather to define patients at increased risk. Although the risk factors identified in the model a r e not subject to direct intervention, identifying factors that place patients at increased risk m a y improve the clini-
405
cian's ability to predict readmission. This process of identifying high-risk patients m a y be helpful for several reasons. First, if resources to provide services that might prevent readmission, such as more frequent outpatient follow-up or home services, are limited, it seems r e a s o n a b l e to target high-risk patients to receive them. In addition, identifying high-risk patients m a y aid in the testing of interventions. Interventions directed at high-risk patients would require fewer patients to demonstrate a significant reduction in the rate of readmission. Based on diagnoses a n d clinical factors, we identified a large group of patients at increased risk for readmission, while confirming that readmissions a r e both frequent a n d costly. Future studies of hospitalized patients should carefully e x a m i n e readmission as a n a d v e r s e outcome of hospitalization, with e m p h a s i s on developing intervention strategies that reduce readmissions. The authorsthank Pat WilkinsonandLisaH. Underhillfor assistingin the preparation of the manuscript.The helpfulcriticismsof Drs. E. FrancisCook,DouglasPorter, and Harvey Makadonare appreciated.
REFERENCES 1. Zook CJ, Savickis SF, Moore FD. Repeated hospitalization for the same disease: a multiplier of national health costs. Milbank Mere Fund Q 1980;58i454-71 2. Zook CJ, Moore FD. High cost users of medical care. N Engl J Med 1980;302:996-1002 3. Anderson GF, Steinberg EP. Hospital readmissions in the Medicare population. N Engl J Med 1984;311 : 1349-53 4. Smith DM, Norton JA, McDonald CJ. Nonelective readmissions of medical patients. J Chronic Dis 1985;38:213-24 5. Bleich HL, Beckley RF, Horowitz GL, et al. Clinical computing in a teaching hospital. N Engl J Med 1985;312:756-64 6. ICD9-CMVolume 1 diseasestabularlist, Commission on Professional and Hospital Activities. Ann Arbor. Mh Edwards Brothers, 1980 7. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Belmont, CA: Wadsworth International Group, 1984 8. Snedecor GW, Cochran WG. Statistical methods. Ames, IA: Iowa State University Press, 1980 9. Schroeder SA, Showstack JA, Roberts HE. Frequency and clinical description of high-cost patients in 17 acute-care hospitals. N Engl J Med 1979;300:1306-9 10. Ellenberg M, Rifkin H (eds.). Diabetes mellitus: theory and practice. 3rd ed. New Hyde Park, NY: Medical Examination Publishing Co., 1983 11. AIIman RM, Laprade CA, Noel LB, et al. Pressure sores among hospitalized patients. Ann Intern Med 1986; 105:337-42 12. Demio LK, Campbell PM, Brown SS. Reliability of information abstracted from patients' records. Med Care 1978; 16:995-1005 13. Lloyd SS, Rissing JP. Physician and coding errors in patient records. JAMA 1985;254:1330-6 14. Still S. The reliability of medical records. J Am Med Rec Assoc 1980;51:20-7 15. Corn RF. Quality control of hospital discharge data. Med Care 1980; 18:416-26 16. Stern RS, Epstein AM. Institutional responses to prospective payment based on diagnosis-related groups. Implications for cost, quality, and access. N Engl J Meal 1984;312:621-7 17. Cook EF, Goldman L. Empiric analysis of multivariate analytic techniques: advantages and disadvantages of recursive partitioning analysis. J Chronic Dis 1984;37:721-31
