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

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.

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

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

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

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

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 0.7-0.8 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

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

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

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)

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)

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)

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)

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)

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)

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) = 0.7781 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

Risk Score Development Variable HfR vs. SA + LfR p Odds ratio (95% CI) Intercept < 0.001 Age (middle 40-59) 0.00131 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, 12.384) Morse (medium 41-80) 1.840 (1.388, 2.455) Sex (male) 0.03455 1.319 (1.020, 1.706) Marital status (single) 0.02676 1.351 (1.036, 1.765) Medication list (high >13) 0.0334 1.352 (1.023, 1.783) Problem list (high>16) 0.02795 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

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.

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

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

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

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