Risk Factors for Nonelective Hospital Readmissions David M. Smith, MD, Barry P. Katz, PhD, Gertrude A. Huster, MHS, John F. Fitzgerald, MD, Douglas Ko Martin, MD, Jay A. Freedman, PhD

We p r e v i o u s l y r e p o r t e d a p r e d i c t i v e m o d e l t h a t i d e n t i f i e d po- t e n t i a l i y m o d i f i a b l e r i s k f a c t o r s for n o n e l e c t i v e r e s d m l s s i o n

t o a c o u n t y h o s p i t a l . T h e o b j e c t i v e s o f t h i s s t u d y w e r e to de- t e r m l n e i f t h o s e r i s k f a c t o r s w e r e g e n e r a l i z a b l e t o a d i f f erent p o p u l a t i o n . We f o u n d t h a t t h e p r e v i o u s l y r e p o r t e d r i sk fac- t o r s w e r e g e n e r a l i z a b l e , a n d o t h e r p o t e n t i a l l y m o d i f i a b l e r i sk

f a c t o r s w e r e i d e n t i f i e d in t h i s p o p u l a t i o n o f v e t e r a n s . How-

ever , fur ther r e s e a r c h i s n e e d e d t o e s t a b l i s h w h e t h e r or n o t t h e r i sk f a c t o r s c a n be m o d i f i e d a n d w h e t h e r or n o t m o d i f i c a - t i o n i m p r o v e s o u t c o m e s .

K E Y WORDS: p a t i e n t r e a d m i s s i o n ; p a t i e n t d i scharge ; hosp i - t a l i z a t i o n . J GEN INTERN MED 1 9 9 6 ; 1 1 : 7 6 2 - 7 6 4 .

or older; (5) had access to a telephone; and (6) were ex-

pected to live for more than 3 months.

We excluded 208 pat ients who were discharged be-

fore they could be recruited, 185 who declined to partici-

pate, 7 who enrolled in another study, and 6 women. This

left 662 pat ients (62% of eligible patients) for study, repre-

sent ing 14% of pat ients admit ted to the medical service.

The subsequen t annual readmiss ion rate for the s tudy

populat ion (N = 662) was 24% greater than the overall

mean (N = 4,245), indicating that a high-risk sample was

selected. The demographic character is t ics and subse-

quent annual readmiss ion rates for missed and noncon-

sent ing pat ients were similar to those for the participants.

A mong general medicine inpatients, 17% to 19% ex-

perience nonelective readmiss ions (those for urgent

or emergent reasons) within 90 days of discharge. 1-3 Cer-

ta in identified risk factors for readmiss ion are diagnostic

groups and difficult to modify2,4; other r isk factors are po-

tentially modifiable. 1 Our objective was to determine the

generalizability of the Smi th Index, a model previously de-

veloped and validated at a county hospital, 1,5 to pat ients

discharged from a Veterans Affairs Medical Center. We

also investigated whether other modifiable risk factors

could be identified.

METHODS

Setting and Patients

Data were collected during a trial to reduce nonelec-

tive readmiss ions among patients discharged from the

medical service of a Veterans Affairs Medical Center. 3 Eli-

gible pat ients (N = 1,068) (1) were discharged home be-

tween November 1, 1988, and October 31, 1990; (2) lived

within the pr imary service area; (3} were to receive pri-

mary care in the hospital 's clinics; (4) were aged 45 years

Received from The Richard L. Roudebush Veterans Affairs Medical Center (DMS, DKM, JAF), and the Divisions of General Internal Medicine (DMS, JFF, DKM, JAF) and Biostatistics (BPK, GAH) of the Department of Medicine, Indiana University School of Medicine, and The Regenstrief Institute for Health Care (DMS), Indianapolis.

Supported by Health Services and Research Development II-ISR&D) project IlR 87-137.1 from the Veterans Administration.

Address correspondence and reprint requests to Dr. Smith: Richard L. Roudebush VAMC, HSR&£) (11H), 1481 West Tenth St., Indianpolis, IN 46202.

762

Assessments

Within 3 days of discharge, research nurses obtained

informed consent and administered a quest ionnaire com-

posed of the Duke Older Americans Resources and Ser-

vices (OARS) instrument , including assessment of 24 un-

met medical, nursing, and social support needs with 0 =

no unmet needs6; the Short Portable Mental Sta tus Exam-

ination, 10 items, 10 = no impairmentT; the Interpersonal

Support Evaluation List (ISEL}, 40 items, 40 = perceived

good supportS; interviewer-rated functional s ta tus (5-point

scale, 1 = no impairment, 5 = total impairment}; the Mich-

igan Alcoholism Screening Test (MAST)9; and patient satis-

faction, 4 items on a 5-point scale, maximum satisfaction =

20. Additional information included sociodemographic

data, last laboratory test results, number of medications,

number of emergency depar tment visits in the past 6

months, and whether the current admission was elective

or nonelective. Research nurses had 95% concordance

with obtaining survey data. Health resource utilization

was obtained by interview 12 months after enrollment.

Response Variable

We confirmed elective and nonelective VA and non-

VA hospital admiss ions by having two investigators re-

view the records. Elective admiss ions were those in which

a delay in the intended inpat ient stay presented no imme-

diate r isk to the patient. The two investigators agreed on

the urgency of the admiss ions in 93% of the cases. If they

disagreed, a third investigator decided the urgency.

Statistical Analysis

To assess the performance of the Smith Index, we

computed diser iminant scores from the original five risk

variables: 1 number of emergency depar tment visits in pre-

vious 6 months, blood urea nitrogen (BUN) level, arterial

]GIM Volume 11, December 1996 763

PO2, whi te b lood cell c o u n t (WBC) of a t l ea s t 1 2 , 0 0 0 / m m 3

a n d h e m o g l o b i n less t h a n 12 g /dL . Of these , all b u t n u m -

b e r of e m e r g e n c y d e p a r t m e n t v is i t s in t he p rev ious 6

m o n t h s a re po ten t ia l ly modi f iab le pr ior to d i scharge . Y =

1.517 + 0 .444XI + 0.038X2 + 1.018X3 + 1.131X4 +

0.827X~. Here X~ = n u m b e r of e m e r g e n c y d e p a r t m e n t vis-

i ts in t he p r ev ious 6 m o n t h s ; X2 = BUN (or m e a n of 16.45

ff miss ing) ; Xa = 1 ff a r te r ia l P O 2 < 88 Inln Hg, = 0 o ther -

wise; X4 = 1 i f W B C >- 1 2 , 0 0 0 / r a m 3, = 0 o therwise ; X s =

1 ff h e m o g l o b i n < 12 g / d l (for males) , 0 ff o therwise . Pa-

t i en t s w i t h d i s c r i m i n a n t s co re s above 0 .2040 were pre-

d ic ted to be r eadmi t t ed .

We ident i f ied n e w r i sk fac tors u s i n g logistic regres -

sion. Initially, b ivar ia te c o m p a r i s o n s of va r i ab les f rom pa-

t i en t s no t r e a d m i t t e d to t hose r e a d m i t t e d were done by

S t u d e n t ' s t t e s t s or ×2 ana lyses . Var iab les were r e t a ined ff

s igni f icant a t p < .20. S tepwise , forward a n d b a c k w a r d lo-

gist ic r eg re s s ion was done. Pe r fo rmance w a s a s s e s s e d by

eva lua t ing t h e a r e a u n d e r t he r ece ive r -ope ra t ing cu rve

(ROC). B o o t s t r a p p i n g (10) was u s e d to s i m u l a t e t he per-

f o r m a n c e in a n e w p o p u l a t i o n a n d ca l cu la t e a n a d j u s t e d

a r e a u n d e r t he ROC.

RESULTS

Baseline Characteristics

C o m p a r e d wi th t he or iginal s a m p l e ) t he c u r r e n t

s a m p l e of v e t e r a n s h a d a s l ight ly h i g h e r none lec t ive read-

m i s s i o n ra te a t 90 days (20.1% vs 16.9%). The c u r r e n t

s a m p l e was older (65 vs 54 years) , m o r e l ikely to be m a l e

(100% vs 64%), a n d less l ikely to be Af r i can -Amer ican

(17% vs 59%). These cha rac t e r i s t i c s e s t ab l i shed t h a t o u r

c u r r e n t p o p u l a t i o n was dif ferent f rom the or iginal s a m p l e

a n d they were exposed to a di f ferent h e a l t h ca re sys tem.

Performance of the Smith Index in a Different Population

In t he p rev ious s t u d y ) t he sens i t iv i ty of t he m o d e l

w a s 55%, specif ici ty was 68%, a n d the posi t ive predic t ive

va lue (PPV) w a s 28%. In t he c u r r e n t s tudy , u s i n g the

s a m e r i sk fac tors a n d d i s c r i m i n a n t score, t he sens i t iv i ty

w a s 75%, the specif ici ty 50%, a n d the PPV 27.5%. Thus ,

t he va r i ab les in S m i t h Index pe r fo rmed very s imi lar ly in

th i s ve ry different popu la t ion , ind ica t ing t h a t t he r i sk fac-

tors m a y be appl icab le to th is d i f ferent popu la t ion . We

u s e d t h e s e s a m e va r i ab les in a logist ic r eg re s s ion model ,

a n d the a r e a u n d e r t he ROC w a s 0 .660.

Attempts to Improve the Model by Using Additional Variables

A tota l of 33 va r i ab le s were cons ide r ed for model ing ,

and 22 were e l iminated th rough bivariate compar i sons (p >

.2) or h a d s igni f icant m i s s i n g var iab les . The va r i ab le s

e l imina ted inc luded race, mar i t a l s ta tus , employment , ser-

v ice -connec ted disabil i ty, h e a l t h i n s u r a n c e , h e m o g l o b i n

be low 12 g /dL , WBC less t h a n 1 2 , 0 0 0 / m m 3, s e r u m crea t -

inine, a r te r ia l 02 less t h a n 80 m m Hg, s e r u m a lbumin ,

n u m b e r of medica t ions , d iagnoses of d iabe tes mel l i tus and

h e a r t d isease , base l ine a d m i s s i o n (elective vs nonelect ive) ,

phys ica l hea l t h ra t ing scale, act ivi t ies of daffy living, MAST

score, and pa t ien t satisfaction. The var iables inc luded in de-

veloping the mode l a re s h o w n in Table 1. The fmal m o d e l

{Table 2) u s e d 640 pa t i en t s or 97% of t hose enrol led. Of

t he s ix var iab les , two were in t he S m i t h Index: BUN a n d

WBC >- 1 2 , 0 0 0 / r a m 3. Also, of t he six var iab les , all b u t di-

agnos i s of h y p e r t e n s i o n a re po ten t ia l ly modi f iab le by ac-

t ions. After boo t s t r app ing , t he a r e a u n d e r t he ROC w a s

0 .690 wi th a 95% conf idence in te rva l of 0 .634 to 0 .746.

DISCUSSION

The r e su l t s d e m o n s t r a t e t h a t the S m i t h Index I h a d

s imi la r a c c u r a c y of p red ic t ion w h e n appl ied to pa t i en t s

wi th m a r k e d l y dif ferent d e m o g r a p h i c s a n d ca r ed for in a

di f ferent h e a l t h ca re sys tem. Val ida t ion of a mode l for

none lec t ive r e a d m i s s i o n s in a di f ferent p o p u l a t i o n a n d

h e a l t h ca re s y s t e m h a s no t p rev ious ly b e e n repor ted .

F o u r of t he va r i ab les (risk factors), e leva ted BUN, hypox-

emia , l eukocytos i s , a n d anemia , cou ld serve as indica-

t ions for i n t ense i n p a t i e n t e v a l u a t i o n s for poss ib le revers -

ible cond i t ions d u r i n g hospi ta l iza t ion .

Wai te a n d coworke r s u s e d the S m i t h Index to p red ic t

all r e a d m i s s i o n s (elective a n d nonelect ive) 6 m o n t h s af ter

Table I . Variables Used in Developing a New Predictive Model

Not Readmitted Readmitted

(n = 529) (n = 133)

Mean Mean p Characteristic or % SD or % SD Value

Age, years 64.3 7.7 65.4 7.7 .175 Systolic blood

pressure <- 115 m m Hg, % 26.0 34.1 .062

WBC -> 12 ,000/ ram s, % 10.2 18.3 .010

BUN, m g / d L 19.1 9.2 22.7 11.8 .002 Emergency dept.

visits in prior 6 mo 1.6 2.1 2.0 1.5 .032

Diagnosis of chronic obstructive pulmonary disease 32.3 43.6 .014 hypertension 60.3 75.2 .001

Interviewer-rated functional s ta tus 3.7 0.7 4.1 0.7 < .001

Mental s ta tus 9.1 1.2 8.8 1.7 .094 Interpersonal Suppor t

Evaluat ion List 31.9 6.7 30.4 6.8 .021 Service Assessment

Scale, unme t needs 0.18 0.49 0.43 0.99 .006

764 S m i t h e t al., Pat ient R e a d m i s s i o n s JGIM

Table 2. Predictive Model for Nonelective Readmissions at 90 Days

95% Standard Odds Confidence

Variable ~ Error Ratio Interval

WBC - 1 2 , 0 0 0 / m m 3 0 .822 0 ,294 2 .28 1.28, 4 .05

D i a g n o s i s of

h y p e r t e n s i o n 0 .696 0 .243 2.01 1.25, 3 .22

In t e rv iewer - ra t ed func t iona l s t a t u s

(1 ca tegory change) 0 .545 0 .158 1.73 1.27, 1.36

Systol ic b lood p r e s s u r e ---

i 15 m m Hg 0.511 0 .226 1.67 1.07, 2 .60

Service A s s e s s m e n t Scale, u n m e t n e e d s

(1 need change) 0 .507 0,171 1.66 1.19, 2 .32

BUN, m g / d L change) 0 .024 0 .010 1.27 1.04, 1.55

d i s c h a r g e a n d f o u n d n o r e l a t i o n o f t h e S m i t h I n d e x to re -

a d m i s s i o n s , t l B e c a u s e e lect ive r e a d m i s s i o n s a r e m o r e fre-

q u e n t fo r h e a l t h i e r p a t i e n t s , w e w o u l d e x p e c t n o r e l a t i o n

b e t w e e n e lec t ive h o s p i t a l i z a t i o n s a n d r i s k f a c t o r s for b u r -

d e n of d i s e a s e , x2 Also , r i s k f a c t o r s for r e a d m i s s i o n v a r y

w i t h t h e l e n g t h o f f o l l ow-up . ~3 T h u s , r i s k f a c t o r s for r e a d -

m i s s i o n s a t 9 0 d a y s m a y n o t b e g e n e r a l i z a b l e to 6 m o n t h s .

T h e S m i t h I n d e x a n d t h e n e w m o d e l (Table 2) i den t i -

f ied v a r i a b l e s t h a t a r e p o t e n t i a l l y m o d i f i a b l e a n d m a y b e

ob jec t ive m e a s u r e s of d e c r e a s e d r e a d i n e s s for d i s c h a r g e .

T h e s e r i s k f a c t o r s m a y h a v e u t i l i ty in t h i s e r a o f c a s e

m a n a g e m e n t . H o w e v e r , f u r t h e r r e s e a r c h i s n e e d e d to e s -

t a b l i s h w h e t h e r o r n o t t h e r i s k f a c t o r s c a n b e m o d i f i e d

a n d w h e t h e r o r n o t m o d i f i c a t i o n i m p r o v e s o u t c o m e s .

REFERENCES

I. Sndth DM, Norton JA, McDonald CJ. Non-elective readmissions of medical patients. J Chron Dis. 1985;38:213-24.

2. Phillips RS, Safran C, Cleary PD, Delbanco TL. Predicting emer- gency readmissions for patients discharged from the medical ser- vice of a teaching hospital. J Gen Intern Med. 1987;2:400-5.

3. Fitzgerald JF, Smith DM, Martin DK, Freedman JA, Katz BP. A case manager intervention to reduce readmissions. Arch Intern

Med. 1994;154:1721-9. 4. Burns R, Nichols IX). Factors predicting readmission of older gen-

eral medicine patients. J Gen Intern Med. 199 I;6:389-93. 5. Smith DM, Weinberger M, Katz BP, Moore PS. Postdischarge care

and readmissions. Med Care. 1988;26:699-708. 6. Duke University Center for the Study of Aging. Multi-dlmensional

Functional Assessment: The OARS Methodology. 2rid ed. Durham, NC: Duke University Press, 1978.

7. Pfeiffer E. A short portable mental s tatus questionnaire for the as- sessment of organic brain deficit in elderly patients. J Am Geriatr Soc. 1975;23:433-41.

8. Cohen S, Mermelstein R, Kamarck T, Hoberman M. Measuring the functional components of social service. In: Sarason [G, Sarason BR, eds. Social Support Theory, Research and Applications. Bos- ton, Mass: Martinus Nijhoff Publishers; 1985; 182-203.

9. Seizer ML. The Michigan alcoholism screening test: the quest for a new diagnostic instrument. Am J Psychiatry. 197 I;127:1653-8.

10. Efron B. The Jackknife, the Bootstrap, and Other Resampling Plans. Philadelphia, Pa: Society for Industrial and Applied Mathe- matics {SIAM); 1982.

1 I. Waite K, Oddone E, Weinberger M, Samsa G, Foy M, Henderson W. Lack of association between patients" measured burden of dis- ease and risk for hospital readmission. J Clin Epidemiol. 1994;47:1229-36.

12. Smith DM, Weinberger M, Katz BP. A controlled trial to increase office visits and reduce hospitalizations of diabetic patients. J Gen Intern IVied. 1987;2:231-7.

13. Murdey PH, Hyer LA. Demographic and clinical characteristics as predictors of readmission: a one-year follow-up. J Clin Psychol.

1978;34:833-7.

The authors thank Terry Adams, RN, Nancy Armstrong, RN, Bev- erly Musick, and Gayle Redmon for technical assistance and Morris Weinberger, PhD, and Lorrie A. Mamlin, MPH, for com- ments on drafts of this manuscript.