1. Development of a Locally Specific Risk Score to Identify Patients at High Risk for Readmission
John Malaty, MD
Peter J. Carek, MD, MS
Maribeth Porter, MD, MSCR
Frank Gonzalez, MD
Kimberly Lynch
Benjamin Rooks
Yang Yang, PhD
Arch G. Mainous III, PhD

2. Learning Objectives
Understand how to review and determine the characteristics of patients frequently readmitted to the hospital.
Create a plan to address the needs of patients at high risk for readmissions.
Develop a readmission risk score based upon characteristics of patients admitted to a specific health care system or hospital.
HEART (History, ECG, Age, Risk Factors, Troponin) Score – used in Pts with undifferentiated symptoms i.e. chest pain to determine risk they are/will have ACS - more specifically predictive of MI, need for revascularization, and all cause mortality within the following 6 weeks. It outperforms TIMI score or NSTEMI for low risk Pts.

3. Session Agenda Introduction and literature review
Open discussion regarding experience with addressing readmissions
Review process to develop risk score
Suggest patient characteristics associated with high risk for readmission
Questions and answers
30 minute presentation

4. Readmissions Readmissions after hospital discharge common
Up to 1 in 5 patients affected
Hospital Readmissions Reduction Program (HRRP)
Numerous attempts to reduce readmission rates described (Hansen, 2011)
Pre-discharge
Post-discharge
Bridging
HRRP – CMS program – penalties for readmission
Hansen study – pre vs post vs bridging – no one intervention identified – did all kinds of things i.e. case managers. Bridging from inpt to outpt

5. Readmissions Risk Factors Longer duration of hospital stay
Comorbidities
Higher Charleson index
Functional ability
More discharge medications
Social factors
Low socioeconomic status
Living situation
Social support/Marital status
Risky behaviors – smoking, cocaine use, etc
These studies have identified these risk factors for readmissions.
Garcia-Perez 2011, Calvillo-King 2012, Garrison 2013, Regenstein 2013, Logue 2016, Barnett 2015, Logue 2016

6. Readmissions Risk for readmission in Family Medicine Garrison, 2013*
More hospitalizations
More ED visits
Longer LOS
Marital status
More medications
More comorbidities
Logue, 2016*
Polypharmacy
Higher Charlson index at admission
These risk factors are specific for Family Medicine inpatient services
We have found polypharmacy to be a big factor
Charlson comorbidity index – predicts mortality for a Pt who has a range of comorbid conditions
*Family medicine inpatient service

7. Readmissions Readmission risk assessment/scores HOSPITAL SCORE
LACE Index
Attribute
Points
Length of stay
<1 day
1 day 1
2 days 2
3 days 3
4-6 days 4
7-13 days 5
>14 days 7
Acute or emergent admission
Charlson comorbidity index score
>4
Visits to emergency department past 6 months
HOSPITAL SCORE
Attribute
Points
Low hemoglobin at discharge (<12 g/dL) 1
Discharge from an Oncology service 2
Low sodium level at discharge (<135 mEq/dL)
Procedure during hospital stay (ICD coded)
Index admission type urgent or emergent
Number of hospital admissions during previous year
0-1
2-5
>5 5
Length of stay >5 days
C statistic: looks at area under the curve. >0.9 is excellent. Most readmission risk scores that are relatively good are considered to be
HOSPITAL – one that worked at multiple hospitals
LACE – worked at 5 hospitals in Montreal but not elsewhere. We looked at LACE at our hospital and essentially all our Pts were high risk using that score – wasn’t useful for us
C statistic= 0.68
C statistic= 0.7114

8. Readmissions
How has your department/ program/ clinic addressed readmissions?

9. Risk Score Development
Subjects
Individuals >18 years old readmitted at least three times within calendar year (3 year period used)
Readmission = admission occurring within 30 days of previous hospital admission in calendar year
Development of risk score
Demographic data
Gender
Age
Home zip code
Race
Ethnicity
Marital status
Insurance status

10. Risk Score Development
Development of risk score (cont’d)
Other data
LOS
Readmissions
Primary discharge diagnoses
Presence of psychiatric, substance abuse, or chronic pain diagnoses
Problem list (# of problems)
Medication list (# of medications)
Charlson comorbidity index (CCI)

11. Risk Score Development
Multivariate logistic regressions for predicting high frequency readmission
HfR vs. SA + LfR among all patients
Risk score developed to predict patients at high risk for MULTIPLE admission (>3 readmissions) based upon initial admission information (HfR)
Variables evaluated for inclusion in risk score
Significant predictors identified and odds ratios converted into whole numbers
Significance defined at p<0.05
This score is unique in that it is trying to predict risk for multiple readmissions (high utilizer for readmissions)

12. Risk Score Development
Single admissions (SA)
Low frequency
(1-2) readmissions (LfR)
High frequency (>3) readmissions (HfR) p
SA v LfR
LfR v HfR
SA v HfR
Patients
1410 
897 
314 
 NA NA
Age: mean (SD)
58.7 (15.9)
56.2 (18.2)
56.1 (16.0)
0.0099
0.63
0.019
Gender
Male: # (%)
555 (39.4)
361 (40.2)
153 (48.7)
0.70
0.011
0.0028
Female: # (%)
855 (60.6)
536 (59.8)
161 (51.3)
Race
White: # (%)
838 (59.4)
527 (58.8)
175 (55.7)
0.92 
0.40
0.33
Black: # (%)
513 (36.4)
330 (36.8)
128 (40.8)
Other: # (%)
59 (4.2)
40 (4.5)
11 (3.5)
Ethnicity
Hispanic: # (%)
41 (2.9)
32 (3.6)
13 (4.1)
0.45 
0.77
0.34
Non-Hispanic: # (%)
1369 (97.1)
865 (96.4)
301 (95.9)

13. Risk Score Development
Single admissions (SA)
Low frequency
(1-2) readmissions (LfR)
High frequency (>3) readmissions (HfR) p
SA v LfR
LfR v HfR
SA v HfR
Zip Code
32601, 32609, 32640, 32641,
461 (32.7)
277 (30.9)
111 (35.4)
0.056 
0.34
0.19
32603, 32607, 32608, 32653, 32656,
307 (21.8)
168 (18.7)
54 (17.2)
Other
642 (45.5)
452 (50.4)
149 (47.5)
Marital Status
Single: # (%)
681 (48.3)
466 (52.0)
183 (58.3)
0.23 
0.15
0.0059
Married: # (%)
561 (39.8)
332 (37.0)
100 (31.8)
Other: # (%)
168 (11.9)
99 (11)
31 (9.9)
Insurance Status
Medicare: # (%)
668 (47.4)
428 (47.7)
167 (53.2)
<0.001
0.021
Medicaid: # (%)
257 (18.2)
227 (25.3)
87 (27.7)
485 (34.4)
242 (27.0)
60 (19.1)

14. Risk Score Development
Single admissions (SA)
Low frequency
(1-2) readmissions (LfR)
High frequency (>3) readmissions (HfR) p
SA v LfR
LfR v HfR
SA v HfR
Length of stay: mean (± SD)
2.9 (2.9)
4.5 (2.9)
5.0 (3.5)
<0.001
Psychiatric diagnosis
Yes
602 (42.7)
413 (46.0)
174 (55.1)
0.61
0.0063 No
808 (57.3)
484 (54.0)
140 (44.9)
Substance abuse diagnosis
187 (13.0)
115 (12.7)
56 (19.1)
0.56
0.0071
0.0095
1223 (87.0)
782 (87.3)
258 (80.9)
Chronic pain diagnosis
102 (6.9)
71(7.8)
37 (12.9)
0.97
0.0089
0.0033
1308 (93.1)
826 (92.2)
277 (87.1)

15. Risk Score Development
Single admissions (SA)
Low frequency
(1-2) readmissions (LfR)
High frequency (>3) readmissions (HfR) p
SA v LfR
LfR v HfR
SA v HfR
Medication list: mean (± SD)
8.7 (5.7)
9.8 (7.2)
14.1 (7.4)
0.0052
<0.001
Problems list: mean (± SD)
12.7 (8.2)
13.7 (9.7)
19.5 (10.7)
0.11
Charlson index: mean (± SD)
2.0 (2.1)
3.0 (2.7)
4.6 (2.7)

16. Risk Score Development
Multivariate regressions for predicting high frequency readmissions (>3 readmissions in any given year) compared to single admission or low frequency readmission (1-2 readmissions in any given year) completed
Area under the curve (AUC) =
AUC at least as good as others, with different focus
2014
2015
2016 p
2014 v 2015
2015 v 2016
2014 v 2016
Length of stay: mean (± SD)
4.34
(±2.37 )
5.62
(± 5.07)
5.77
(± 6.47)
0.015
0.845
0.024
Psychiatric diagnosis
Yes
66 (52.4)
65 (57.0)
63 (52.9)
0.555
0.622
1.0 No
60 (47.6)
49 (43.0)
56 (47.1)
Substance abuse diagnosis
22 (17.5)
22 (19.3)
23 (18.5)
0.841
0.966
104 (82.5)
92 (80.7)
97 (81.5)
Chronic pain diagnosis
11 (8.7)
18 (15.8)
16 (13.4)
0.140
0.748
0.330
115 (91.3)
96 (84.2)
103 (86.6)
Medication list: mean (± SD)
13.06
(±7.39)
13.77 (±7.16)
15.24
(±7.48)
0.452
0.128
0.023
Problems list: mean (± SD)
18.17 (±11.19)
19.38 (±9.41)
19.57 (±10.91)
0.364
0.884
0.321
Charleson index: mean (± SD)
4.59
(±2.90)
4.43 (±2.77)
5.30
(±3.27)
0.873
0.030
0.042

17. Risk Score Development
Variable
HfR vs. SA + LfR p
Odds ratio (95% CI)
Intercept
< 0.001
Age (middle 40-59)
1.597 (1.200, 2.126)
Age (young 18-39)
2.769 (1.819, 4.193)
Charlson (medium 2-4)
3.582 (2.423, 5.421)
Charlson (high >5)
7.856 (5.085, )
Morse (medium 41-80)
1.840 (1.388, 2.455)
Sex (male)
1.319 (1.020, 1.706)
Marital status (single)
1.351 (1.036, 1.765)
Medication list (high >13)
0.0334
1.352 (1.023, 1.783)
Problem list (high>16)
1.362 (1.033, 1.792)
LOS (high >4)
1.784 (1.376, 2.311)
Points 2 3 4 8 1
After doing multivariate regression – Odds ratio calculated – converted to an integer for points system

18. Method Total points available: 27 Highest score: 19
Patients with score > 10 considered high risk for readmission
When risk score was developed our readmission rate was 18% - this correlated to total score of 10. Goal was to reduce readmission rate.

19. Risk Score Development
Limitations
Single family medicine inpatient service at one institution
Not all readmissions included (hospital-specific data obtained)
Score has not been validated
Other factors potentially present
Other Social Determinants of Health?
What other patient characteristics associated with high risk for multiple readmissions?
Ask what others see useful – other social determinants of Health or other Pt characteristics

20. Future Direction Integrated on daily rounds
Report in EPIC
At time of admission, RN Health Coach being notified of high risk patients (score>10) and discharge planning initiated
Local risk score to be compared to similar tool generated by EHR

21. Conclusion
Developed and implemented unique and locally created readmission risk score
Using data from a specific health care system/hospital
Identifies patients with risk of multiple (>3) admissions
Risk factors for readmission identified and may be targets for consideration

22. Questions?
Development of a Locally Specific Risk Score to Identify Patients at High Risk for Readmission