Point-of-Care Guides

Predicting Hospital Readmission

 


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Am Fam Physician. 2016 Aug 15;94(4):307-309.

Author disclosure: No relevant financial affiliations.

Clinical Question

What is the best way to predict unplanned readmissions or early death following a hospitalization?

Evidence Summary

In 2007, the Medicare Payment Advisory Committee reported that 18% of hospital admissions resulted in a readmission, of which 76% were potentially avoidable.1 The development of a clinical decision rule to identify patients at risk of readmission could aid in directing interventions and resources, potentially improving cost-effectiveness of care and reducing postdischarge mortality.

There are generalized clinical decision rules to help predict readmissions for all types of patients, and condition-specific clinical decision rules for patients with conditions such as heart failure or pneumonia. Two of the most widely used and validated generalized clinical decision rules for predicting readmissions are LACE (length of stay, acuity of admission, comorbidity, emergency department use within six months of admission)2 and HOSPITAL (low hemoglobin level, discharge from oncology, low sodium level, procedure during hospitalization, nonelective index admission type, number of hospital admissions during the previous year, length of stay).3,4

The LACE rule (Table 1) predicts 30-day early death or unplanned readmission after discharge from a hospital to the community.2 It was derived from a multicenter cohort of 4,812 patients from 11 hospitals in five cities in Ontario, Canada, and then it was externally validated using 1,000,000 patients selected from databases of all hospital admissions in Ontario.2

View/Print Table

Table 1.

LACE Rule to Predict Readmission or Death After Hospital Discharge

Component

Points

Length of stay (days)

< 1

0

1

1

2

2

3

3

4 to 6

4

7 to 13

5

≥ 14

7

Acute/emergent admission

No

0

Yes

3

Charlson Comorbidity Index score*

0

0

1

1

2

2

3

3

≥ 4

5

Emergency department visits in the past 6 months

0

0

1

1

2

2

3

3

≥ 4

4

Total points:

____

Score

Probability of readmission or death within 30 days of discharge (%)

0

2.0

1

2.5

2

3.0

3

3.5

4

4.3

5

5.1

6

6.1

7

7.3

8

8.7

9

10.3

10

12.2

11

14.4

12

17.0

13

19.8

14

23.0

15

26.6

16

30.4

17

34.6

18

39.1

19

43.7


*—The scale is available at http://www.pmidcalc.org/?sid=7722560.

Adapted with permission from van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):553, 555.

Table 1.

LACE Rule to Predict Readmission or Death After Hospital Discharge

Component

Points

Length of stay (days)

< 1

0

1

1

2

2

3

3

4 to 6

4

7 to 13

5

≥ 14

7

Acute/emergent admission

No

0

Yes

3

Charlson Comorbidity Index score*

0

0

1

1

2

2

3

3

≥ 4

5

Emergency department visits in the past 6 months

0

0

1

1

2

2

3

3

≥ 4

4

Total points:

____

Score

Probability of readmission or death within 30 days of discharge (%)

0

2.0

1

2.5

2

3.0

3

3.5

4

4.3

5

5.1

6

6.1

7

7.3

8

8.7

9

10.3

10

12.2

11

14.4

12

17.0

13

19.8

14

23.0

15

26.6

16

30.4

17

34.6

18

39.1

19

43.7


*—The scale is available at http://www.pmidcalc.org/?sid=7722560.

Adapted with permission from van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):553, 555.

The LACE rule uses administrative data readily available to clinicians, making it easy to use.2 Although there are many comorbidity indices published, the LACE rule uses the Charlson Comorbidity Index.5 The rule has a possible total score of 19. The validation study showed that for each one-point increase in the LACE score, the odds of an unplanned readmission increases by 18% (odds ratio = 1.18; 95% confidence interval [CI], 1.14 to 1.21), with a moderately predictive c-statistic of 0.68. A one-point increase in LACE score increases the odds of early death by 29%, with a c-statistic of 0.793.2

In another validation study, a five-point increase in the LACE score doubled the risk of readmissions or death (hazard ratio = 2.0; 95% CI, 1.7 to 2.3).6 It is important to note that although the LACE rule was originally derived and validated in Canada, this validation study was performed in a U.S. hospital, using 1,239 patients from the Vanderbilt Inpatient Cohort Study.

The HOSPITAL rule predicts potentially avoidable hospital readmissions and includes the following components and points3,4:

  • Low hemoglobin level at discharge (< 12 g per dL [120 g per L]) = 1 point

  • Discharge from oncology service = 2 points

  • Low sodium level at discharge (< 135 mEq per L [135 mmol per L]) = 1 point

  • Procedure during hospital stay = 1 point

  • Index admission type is nonelective = 1 point

  • Number of hospital admissions during the previous year: 0 = 0 points, 1 to 5 = 2 points, ≥ 6 = 5 points

  • Length of stay ≥ 5 days = 2 points

This HOSPITAL score was derived from a cohort of 9,212 patients discharged from the Brigham and Women's Hospital in Boston, Mass., and then it was externally validated using a cohort of 117,065 patients from nine large hospitals in four countries. Readmissions planned at the time of the index hospitalization and unforeseen readmissions for conditions unrelated to the original index h

Address correspondence to Seth Jelinek, MD, at sjelinek@uga.edu. Reprints are not available from the authors.

Author disclosure: No relevant financial affiliations.

REFERENCES

show all references

1. Medicare Payment Advisory Committee. Report to the Congress. Promoting greater efficiency in Medicare. June 2007. http://www.medpac.gov/documents/reports/Jun07_EntireReport.pdf. Accessed April 27, 2016....

2. Van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551–557.

3. Donzé JD, Williams MV, Robinson EJ, et al. International validity of the HOSPITAL score to predict 30-day potentially avoidable hospital readmissions. JAMA Intern Med. 2016;176(4):496–502.

4. Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632–638.

5. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1988;40(5):373–383.

6. Mixon AS, Goggins K, Bell SP, et al. Preparedness for hospital discharge and prediction of readmission [published ahead of print February 29, 2016]. J Hosp Med. http://onlinelibrary.wiley.com/doi/10.1002/jhm.2572/abstract;jsessionid=086CD05A663253B3A48ADA182539893B.f01t04. Accessed June 6, 2016.

7. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.

This guide is one in a series that offers evidence-based tools to assist family physicians in improving their decision-making at the point of care.

This series is coordinated by Mark H. Ebell, MD, MS, Deputy Editor.

A collection of Point-of-Care Guides published in AFP is available at http://www.aafp.org/afp/poc.



 

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