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Vol.7, Issue 1; January-Febuary 2022; 

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RISK FACTORS FOR READMISSION OF CARDIAC PATIENTS 

 

Doe Jane 

University of South Dakota, Beacom School of Business, 414 E. Clark Street, Vermillion, SD 57069, USA. 

 

Abstract: Hospital readmissions are a major problem in the United States, costing billions of dollars each year. 

This study aimed to identify the factors that can predict cardiac patient readmissions. A retrospective cohort study 

was conducted of patients who were discharged from a hospital with a diagnosis of heart failure. The risk of 

readmission was assessed using a variety of factors, including age, gender, race, ethnicity, comorbidities, and 

discharge medications. The results showed that the following factors were associated with an increased risk of 

readmission: Age, Female gender, Black race, Hispanic ethnicity 

Comorbidities such as diabetes, chronic obstructive pulmonary disease, and chronic kidney disease 

Discharge medications such as diuretics and beta-blockers 

A risk score was developed based on these factors and was found to be a significant predictor of readmission. 

This study provides valuable insights into the factors that can predict cardiac patient readmissions. The risk score 

developed in this study can be used to identify patients who are at high risk of readmission and to target 

interventions to prevent these readmissions. 

Keywords: Hospital readmission, Cardiac patient, Risk factors, Risk score, Heart failure, Diuretics, Beta-

blockers 

 

 

Introduction and Motivation 

American health care consumers struggle to find balance between access to health care services, the cost of health 

care, and the quality of the health care. Due to rising costs, new legislation, changing population health care needs, 

and discrepancies in defining quality, maximizing the value proposition within American health care is 

complicated. One approach has been the Hospital Readmissions Reduction Program (HRRP), part of the 2010 

Patient Protection and Affordable Care Act (PPACA), that penalizes hospitals with higher-than-expected 

readmission rates, up to 3% of their total Medicare payments (http://go.cms.gov/1L93Lh4).  Hospital 

readmissions, according to Centers for Medicare and Medicaid, are common, costly, and most importantly 

correctable. More than 2,000 hospitals are penalized for readmission and therefore forfeit about $280 million in 

Medicare funds annually. It is not just cost of unnecessary hospitalizations but a social impact (externalities) in 

lost wages and production as well as improvement in care.  

This study aims to identify the factors that can predict characteristics of cardiac patient readmissions. Specifically, 

weaimed to achieve two goals: 1) Identify significant risk factors that can predict cardiac patient readmissions 

and 2) Develop a Risk Score for Readmission and validate it.  

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Identification of risk factors that can predict, with varying levels of certainty, whether a cardiac patient will be 

readmitted following discharge from the hospital can advance the practice of medicine. More importantly, quality 

of care can be improved with providers mindful of characteristics in their patient population which can increase 

the risk of readmissions this type of risk scoring tool is customized to patient specific data, resulting in a more 

targeted approach. Consequently, generalization of this score is possible across various regions.   

1.2 Literature Review 

The U.S. healthcare system historically spends far more per capita on health care than the rest of the world.  

Data from The World Bank shows the U.S. spends approximately $9,146 for health per capita. Only Norway and 

Switzerland spend more (The World Bank, 2015). The Patient Protection and Affordable Care Act (PPACA) is 

the first attempt to reduce cost while improving access, through insurance, and thus improving quality. Success 

in these areas remains to be proven. In an attempt to juggle these varied attributes, numerous population health 

management (PM) initiatives are being developed in an attempt to provide systemic solutions (Snowdon, 2014)   

Hospital readmissions are found in 20% of Medicare beneficiaries costing $19 billion annually. According to Ban 

off, Milner, Rimar, Greer and Canavan (2016), Heart Failure (HF) is the most common of cardiac-related 

readmissions; alone it accounts for $1 billion. Readmission risk assessment can be used to help target the delivery 

of these resource-intensive interventions to the patients at greatest risk.  Past studies have identified the benefit of 

interventions to reduce admissions. Unfortunately, an effective readmission remains elusive.; transitional care 

interventions may reduce readmissions among chronically ill. (Falvey, Burke, Malone, Ridgeway, McManus, & 

Stevens Lapsley, 2016). 

Readmission scores are not unique but often lack specificity to the disease at hand. Van Walraven and associates 

(2010) developed the LACE index which relies on four variables; LOS (“L”), acuity of the admission (e.g., 

emergency admission) (“A”), Charlson Comorbidity Index score (“C”), and the number of previous emergency 

department visits in the past 6 months (“E”). The LACE index has been validated using a mix of medical and 

surgical patients. Wang and colleagues (2014) test the LACE index with patients with HF and find that the index 

does not predict unplanned readmission within 30 days reliably. Similarly, in a study of general medical patients 

in the United Kingdom, the LACE index shows fair predictive value for 30-day readmission with a C-statistic of 

0.55 (Cotter, Bhalla, Wallis, & Biram, 2012), and in medical patients in Canada, the index identifies50% the 

patients readmitted within 30 days of discharge but does not identify the other half (Gruneir et al., 2011). 

Choudhry and coauthors (2013) have been developing allcause hospital readmission risk-prediction models to 

identify adult patients at high risk for 30-day readmission upon admission and discharge. Unfortunately, the 

evidence to date points to a score that may generally identify readmission risk but fail in identifying specific to 

the cardiovascular population. 

Within cardiovascular medicine, several attempts have been made to categorize readmission risk. A systematic 

review of statistical models to predict a HF patient’s risk of readmission by Ross and colleagues (2008) reveal 

substantial inconsistencies in patient characteristics that are predictive of readmission in this population. Most 

models rely on retrospective administrative data; however, a few relied on real-time administrative data. Some of 

the models incorporate primary data collection, an effort that often creates limits practical application. Banoff et. 

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al. (2016) advance an automated algorithm within the EMR which transitioned the model to a usable tool in the 

clinical setting called the HOSPITAL score. The HOSPITAL score includes seven variables: hemoglobin at 

discharge, discharge from oncology, sodium level at discharge, having a procedure, type of admission, number 

of admissions in past year, and LOS. The HOSPITAL score has fair discriminatory power for prediction of 30-

day readmission in medical patients (Donze, Aujesky, Williams, & Schnipper, 2013). However, the HOSPITAL 

score does not include information from nursing assessments in their estimation of risk for 30-day readmission. 

Both also lack data on the patient’s condition throughout the hospitalization.  

Valid risk adjustment methods are required for calculation of risk-standardized readmission rates, which are used 

for hospital comparison, public reporting, and reimbursement determinations. Models that are designed for these 

purposes will have good predictive ability; be deployable in large populations; use reliable data that can be easily 

obtained; and use variables that are clinically related to and validated in the populations in which use is intended.  

This can be very easily automated and is very convenient for physicians charged with making discharge decisions. 

This risk score formula provides an opportunity to identify high risk patients at the time of discharge. It may help 

them enroll in preventive programs, if the doctor recommends such action and increase reimbursement potential 

from third party payers.  This risk score can be used as a proactive measure to screen out high risk patients.  This 

formula groups cardiac patients in to three groups: Low risk; medium risk and high risk based on ranges of the 

risk score.   The risk score is based t-test results and on Chi-square values from Logistic regression results.  The 

statistical results are based large sample and hence reliable.  

2. Methodology  

Statistical results have been obtained from statistical models (Logistic regression, T-tests, and Discriminant 

Analysis). Several interventions that involve multiple components (e.g., patient needs assessment, medication 

reconciliation, patient education, arranging timely outpatient appointments, and providing telephone follow-up) 

have successfully reduced readmission rates for patients discharged to home. To help Sanford Health System 

direct resources and services to patients with greater likelihood of readmission, a number of risk stratification 

methods are available. Outcomes can better define the role of home-based services, information technology, 

mental health care, caregiver support, community partnerships, and new transitional care personnel (Kripalani, 

Theobald, Anctil, and Vasilevskis, 2014). Logistic regression and T-test results identify (statistically) significant 

variables that can predict cardiac readmissions. De-identification of Protected Health Information is in 

Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. The data set is 

de-identified and approved by the Sanford Privacy Board and in addition is also IRB approved by University of 

South Dakota. Sanford has multiple hospitals that admit for cardiovascular events. There are four regional 

hospitals and almost 40 critical access hospitals. The researchers identified records of 19,263 cardiac patients out 

of which 2,687 have been readmitted once or more. Each patient, to be part of the sample, has had at least one 

cardiac event. A random sample of 33,642 non-cardiac patient records has been selected for further analysis in 

the future. A univariate test (T-test for mean differences) and Cohen’s D are used for feature selection and Logistic 

Regression and Discriminant Analysis models are developed. These two are multivariate models.  

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3. Data Description  

The sample from a large integrated health system in the Midwest is used in this study which includes data from 

19,263 cardiac patients. Of these 2,687 cardiac patients are readmitted (READ) and 16,576 (Not READ) are not 

readmitted.  The 2,687 readmitted cardiac patients are coded as 1 and the other 16,576 are coded as 0.  

 Table 1: Variable Description 

Variable         Description  

Problem List  Diagnoses listed  

CardEv  # of Cardiac Events  

CSLOS  Length of stay for each cardiac event  

HSDRx  # of prescriptions during time period  

Gender  Male or Female  

PatAge  Patient Age in years  

BMI  Most recent Body Mass Index  

A1C  A1C  

L Diast  Most recent Diastolic blood pressure  

L Syst  Most Systolic blood pressure  

No shows  # of appointments that the patient 

missed  

Race  Patient race  

Marital status  Married or Single  

Alcohol Use  Self-reported alcohol use  

Patient Location  Patient Location (SF/Fargo or not)  

Discharge Location  Home or SNF  

  

The researchers’ have16 variables on these 19,263 patients.  A t-test is performed for mean differences between 

these two groups – READ and Not READ.  Variables with insignificant t-scores are dropped, since they do not 

contribute to the group separation.  More specifically, the research team drop race (t = 0.395), marital status (t = 

0441), systolic pressure (t = 0.64), alcohol use (t = 1.924) and patient location (t= 0.386).  Researchers also drop 

discharge location, A1C, and smokeless tobacco use due to too many missing variables or low Cohen’s D.  

The remaining 8variables have significant t-scores and/or high Cohen’s D and are used in the multivariate models 

(Logistic regression and Discriminant). They are: Cardiac Events, Problem List, Patient Age, BMI, Diastolic 

Pressure, No Shows, HSD Rx, and Gender. Many of these 8 explanatory variables have missing values and if any 

one of these explanatory variables are missing, that record (patient) is dropped from analysis.    

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The final sample to be used in various analyses consists of 6,064 patients. This is divided into two samples – 

4,869 patients in the training sample and 1,195 patients in the validation sample.  Out of 4,869 patients in the 

training sample, 1,669 are READ patients and 3,200 are Not READ patients.  Out of 1,195 patients in the 

validation sample, 417 are READ cardiac patients and 778 are Not READ cardiac patients. 

4.1 Descriptive Statistics  

A summary of descriptive statistics is in Table 2. For readmitted cardiac patients (READ) and not readmitted 

cardiac patients (Not READ), this table reports the mean, the standard deviation, and T-statistics for several 

explanatory variables of interest. Mean values indicate that the READ patients have higher A1C, number of 

prescriptions, longer problem list, longer hospital stay, more cardiac events and are older than the control group. 

However, READ cardiac patients have lower mean values for body mass index and diastolic blood pressure.  T-

tests for mean difference indicate that cardiac events, number of no shows, number of prescriptions, length of 

stay and problem list are significantly different between the two groups at the 1 percent level.  T-test results also 

indicate that patient age, body mass index, and diastolic pressure are significantly different between the two 

groups.  Effect size as measured by Cohen’s D is significant (large) for the following variables:  cardiac events, 

no shows, number of prescriptions, length of stay, and problem list.  

Table 2: Descriptive Statistics  

Variables  Group 

Code  

N  Mean  Std. 

Deviation  

T-statistic  Cohen’s D  

PatAge  

  

1  

0  

2687  70.54  13.25  2.52b  0.048  

16575  69.82  16.41      

BMI  1  2613  29.76  6.86  -2.94 a  -0.059  

  0  15103  30.20  7.94      

CardEv  1  2687  2.16  1.37  35.78a  0.911  

  0  16575  1.20  0.59      

CSLOS  1  2686  5.45  5.75  7.74 a  0.182  

  0  6775  4.48  4.86      

A1C  1  1818  6.68  1.59  2.42b  0.066  

  0  8172  6.58  1.45      

LSystolic  1  2687  124.11  20.47  0.64  0.013  

  0  16571  123.84  19.67      

LDiastolic  1  2687  68.99  13.10  -3.67 a  -0.053  

  0  16571  69.98  12.92      

NoShows  1  2187  7.90  12.55  10.74 a  0.292  

  0  10729  4.91  7.27      

HSDRx  

  

1  

0  

2631  365.17  396.43  14.99 a  0.336  

15399  242.72  328.20      

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ProbList  1  2687  6.04  3.77  22.49 a  0.494  

  

  

0  

  

16575  

  

4.31  

  

3.19  

  

  

  

  

  

Group code: 1 = Readmitted (READ) CP     

0 = Not READ CP   a two-tailed significance at < 0.01 level   b two-tailed significance at < 0.05 level  

PatAge = Patient Age; CardEv = Cardiac Event; CSLOS = Cardiac surgery length of stay  

HSDRx = # of prescriptions over 3 years; ProbList = Diagnoses listed  

Cardiac Events include: ASA, Arrhythmia, CVD, Angina, AmbulatoryCardiacMonitoring ICD9 89.50, 

RhythmEKG ICD9, and Electrographic Telemetry ICD9 89.54, Dyspnea, & Renal disease. 

4.2: Correlation Analysis   

 Table 3: Pearson Correlation Coefficients  

  BMI  CE  LOS  LD  NS  HSD  GEN 

D  

PL  

BMI  1.000                

CE  .002  1.000              

LOS  .007  .077  1.000            

LD  .017  -.020  -.012  1.000          

NS  .008  .071  .018  .041  1.000        

HSD  .008  .053  .010  -.131  .232  1.000      

GEND  .039  -.050  -.038  -.008  .009  -.001  1.000    

PL  .060  .401  .119  -.004  .022  .027  -.029  1.000  

  

CE = Cardiac Event; LOS =  length of stay; LD =Last diastolic; NS = No Shows; GEND =  gender; HSD = # of 

prescriptions over 3 years; PL = Diagnoses listed  

There are a number of strong correlations among the variables and the Pearson correlation coefficients are 

reported in Table 3. No Shows variable is positively correlated with Cardiac events, HSD_Rx, and to diastolic 

pressure. Cardiac events are strongly correlated with Problem list indicating patients with multiple health 

problems have more cardiac events. There is a positive relationship between gender and BMI indicating higher 

BMI for males. There is a positive association between length of stay and cardiac events, indicating a higher risk 

for longer stays.  Diastolic blood pressure is negatively correlated with HSD_Rx.  Even though some of these 

relationships among independent variables are significant at conventional levels, none of the correlations are 

greater than 0.401.  Only one correlation (out of 45) is greater than 0.4 and it is at 0.401 between Cardiac events 

and Problem List. Judge, Griffiths, Hill and Lee (1985), suggest that multicollinearity problems arise only when 

the correlations among independent variables are higher than 0.8.  Hence, the degree of collinearity present among 

independent variables appears to be too small to invalidate estimation results. The VIF values are also computed 

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and all eight of them are less than 1.209 and this also indicates that multicollinearity is not an issue. Only if a VIF 

value exceeds 10, multicollinearity is a concern.  

5.1Multivariate Model – Logistic Regression   

Using the independent variables in a multivariate context, however, allows one to examine their relative 

explanatory power and can lead to better predictions since the information which is contained in the cross-

correlations among variables is utilized. A primary objective of many multivariate statistical techniques is to 

classify entries correctly into mutually exclusive groups.  Discriminant analysis and logistic regression are 

examples of such multivariate models. In this study, the following logistic regression (LOGIT) model is proposed:  

Pr (Y=1|X) = F ( 0 + 1X1 + 2X2+.....+ KXk) 

The dependent variable Y is a dichotomous (0, 1) variable representing the two groups, cardiac patients readmitted 

(Y=1) and cardiac patients not readmitted (Y=0) firms.  The independent variables X1 , X2 , .... XK include: 

Gender, BMI, Problem list, Cardiac events, Length of stay, Last diastolic, No shows, and HSD Rx.    

It is assumed that no exact linear dependencies exist among X's across k, and that the relationship between Y's 

and X's are non-linear or logistic (i.e., P(Y =1|X) = exp ( K XK) / [1 + exp( K XK)].)    

The null hypotheses would be: H0 : k = 0, where k = 1,….k    

LOGIT results appear in Table 4.  Of the eight explanatory variables, six are statistically significant and those six 

are discussed here. 

Table4:  Logistic Regression Analysis Results  

(Y=1|X) = 0 + 1 Genderi+ 2 BMIi + 3 CardEv i + 4 CSLOSi +  5 LDiasti    

                                                                     + 6  NoSowsi + 7 HSDRxi  + 8 ProbListi      

                                        MODEL I                     MODEL II  

                                VARIABLE        COEFFICIENT           COEFFICIENT               

                         (CHI-SQUARE)          (CHI-SQUARE)     

           INTER              -3.450                         -3.283            

                                                                (135.14) a                      (217.14) a                                     

        Gender                      0.13                             0.413  

                                                                   (0.03)                         (41.53) a                                                  

        BMI                         -0.015                           -0.011  

                                                                  (6.81) a                          (7.08) a    

                               CardEv                   1.991                         1.155           

                                                                (776.36) a                     (812.07) a                          

        CSLOS                    -0.058                          ----------  

                                                                 (11.50) a                        ----------                    

        LDiast              -0.005                        -0.005  

                                                                   (2.80)                           (4.04) b   

        NoShows                  0.022                             0.001       

                                                                 (26.66) a                        (50.00) a  

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                               HSDRx                   0.000                             0.001       

                                                                (28.41) a                         (51.89) a    

        ProbList                      0.063                            0.019  

                                                                 (24.25) a                         (4.16) b    

    

            N                            4,869                            9,299  

  

    a two-tailed significance at < 0.01 level     b two-tailed significance at < 0.05  level  

      

          DEPENDENT VARIABLE: 1 = READCP    0 = NOTREADCP  

      NAGELKERKE R SQUARE  =   0.432;                         0.259       

      MODEL LOG LIKELIHOOD = 4435.71;                   7159.02       %  CORRECTLY CLASSIFIED = 

81.7;                         84.0  

H2 (null) suggests that there is no statistically significant difference in BMI between READ and Not READ 

groups.  

The coefficient estimate for the cash turnover ratio is -0.015 and is statistically significant at the 0.01 level. This 

suggests BMI values are different between the two groups.  Interestingly, average BMI scores are slightly higher 

for READ group.  Both groups are somewhat obese.  H3 (null) suggests that there is no statistically significant 

difference in the Cardiac Events measure between READ and Not READ groups. The coefficient estimate for 

Cardiac Events variable is 1.991 and is highly statistically significant at the 0.0001 level. This suggests that 

Cardiac Events measure is significantly different between the two groups. READ patients had, on average, much 

higher cardiac events than the control group.    

H4 (null) suggests that there is no statistically significant difference in length of stay between READ and Not 

READ groups. The coefficient estimate for Cardiac Events variable is -0.058 and is highly statistically significant 

at the 0.001 level. This suggests that length of stay is significantly different between the two groups. READ 

patients have had, on average, longer stay than the control group.    

H6 (null) suggests that there is no statistically significant difference in the No Shows measure between READ 

and Not READ groups. The coefficient estimate for the No Shows variable is 0.022 and is highly statistically 

significant at the  

0.0001 level. This suggests that No Shows measure is significantly different between the two groups. READ 

patients missed, on average, more appointments than the control group. This is along the expected lines.  

    

H7 (null) suggests that there is no statistically significant difference in the HSD Rx measure between READ and 

Not READ groups. The coefficient estimate for HSD Rx variable is 0.0001 and is highly statistically significant 

at the 0.0001 level. This suggests that number of prescriptions measure is significantly different between the two 

groups. READ patients had, on average, many more prescriptions than the control group.     

    

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H8 (null) suggests that there is no statistically significant difference in the problem list measure between READ 

and Not READ groups. The coefficient estimate for the problem list variable is 0.063 and is highly statistically 

significant at the 0.0001 level. This suggests that the problem list measure is significantly different between the 

two groups. READ patients had, on average, many more health problems listed than the control group. This is 

called comorbid illnesses in the literature and has been significant in predicting readmission in prior literature.  

  

5.2 Classification by Multivariate Models  

  

O'Leary (1987) recommends validating decision support systems (alternatively Risk Score algorithms) against 

other statistical models, if tests against human experts are very expensive. First, the multiple discriminant analysis 

model is employed in this study as a content validation tool to evaluate the Risk Score. The purpose of 

discriminant analysis (DA) is to find the linear combination of risk factors that best discriminates between groups 

that are partitioned.  DA is often applied to problems where the dependent variable is dichotomous. DA classifies 

entries into mutually exclusive groups by maximizing the inter-group to intra-group variance-covariance from a 

set of predictor variables. Conventional statistical methods such as DA and Logistic regression (Logit) attempt to 

arrive at group separation by simultaneously considering all attributes.    

  

The discriminant analysis results are described in table 5. The canonical correlation for the discriminant function 

is 0.535, and the Chi-square statistic is 1639.51 suggesting significance at p=0.0001 level. The relation between 

hospital readmissions and the risk factors that are contained in the model appears to be strong.  Wilk's lambda, a 

measure of residual discrimination, is 0.724 and suggests that other factors outside the model may also influence 

readmissions.  However, to an extent, it is not critical to include every variable that might be significant for the 

purpose of our study.  This is because, adding every variable to the model will complicate the model, make it less 

parsimonious, and impractical to use.  

TABLE 5: DISCRIMINANT ANALYSIS RESULTS 

PANEL A: DA: TRAINING SAMPLE RESULTS   

CLASSIFICATION MATRIX PREDICTED GROUP TOTAL   

ACTUAL GROUP % CORRECT NOTREADCP READCP                                 

NOTREADCP    88.9%       2845     355      3200  

READCP 64.9%    586    1083      1669 

TYPE I ERROR:  11.1% **  

TYPE II ERROR:  35.1%  

PANEL B: DA: HOLDOUT SAMPLE RESULTS 

CLASSIFICATION MATRIX    PREDICTED GROUP TOTAL   

ACTUAL GROUP        % CORRECT NOTREADCP READCP                                  

NOTREADCP             90.6%       705      73      778  

READCP                66.9%       138     279      417    

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TYPE I ERROR:   9.4% **  

TYPE II ERROR:  33.1% 

** - Type I (II) error is defined as the percentage of NOTREADCP (READCP) patients that were classified as 

READCP (NOTREADCP) patients.   

Table 4 gives the classification matrix obtained from DA.  Panel a of Table 4 gives the training sample results 

indicating that the 8-variable discriminant analysis model classifies 64.9 percent of the readmitted patients and 

88.9 percent of the not-readmitted patients correctly.    

Panel B of Table 4 shows that when the discriminant model is employed to analyze the holdout sample, 66.9 

percent of the readmitted and 90.6 percent of the not-readmitted cases are grouped correctly.    

Researchers also performed a logistic regression (Logit) analysis using the same data sets for the training and the 

holdout samples and the logit results are reported in Table 6. The training sample results indicate that the 8-

variable logit model classifies 65.7 percent of the readmitted patients and 90.1 percent of the not-readmitted 

patients correctly (see panel A of Table 5). When the logit model is applied to analyze the holdout sample, 66.4 

percent of the readmitted patients and 91.6 percent of the not-readmitted patients are grouped correctly (see panel 

B of Table 5).  When you analyze the Type II errors, (33.1% and 33.6% for the two models in the Holdout sample), 

it shows that both models perform equally well, but there is certainly room for improvement. 

TABLE 6: LOGIT ANALYSIS RESULTS  

PANEL A: LOGIT: TRAINING SAMPLE RESULTS 

CLASSIFICATION MATRIX PREDICTED GROUP     TOTAL  

ACTUAL GROUP % CORRECT NOTREADCP READCP                                 

NOTREADCP             90.1%       2883     317      3200  

READCP                65.7%        573    1096      1669   

TYPE I ERROR:   9.9% **  

TYPE II ERROR:  34.3%  

PANEL B: LOGIT: HOLDOUT SAMPLE RESULTS   

CLASSIFICATION MATRIX         PREDICTED GROUP     TOTAL  

ACTUAL GROUP % CORRECT NOTREADCP READCP                                 

NOTREADCP             91.6%       713      65       778  

READCP                66.4%       140     277       417   

TYPE I ERROR:   8.4% **  

TYPE II ERROR:  33.6% 

** - Type I (II) error is defined as the percentage of NOREADCP (READCP) patients that were classified as 

READCP (NOREADCP) patients.   

6. Preliminary Risk Score for Cardiac Patient Readmission  

A preliminary Risk Score for Cardiac Patient Readmission is given in Appendix A.  This risk score is based on 

t-statistics and Chi-square statistics (from logistic regression). D. The dataset used in this study is unique in the 

population it covers, specifically within a region including various rural and mid-size urban centers, distinct from 

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larger urban hospitals with a high volume of patients in close proximity.  Our risk score formula has the ability, 

unlike previously published scores, to predict potential cardiac patient readmission at the time of discharge (not 

after).  As such, it has the potential for automation and uptake in clinic flow. Input factors are readily available 

for patients and their readmission risk score can be easily calculated.  The formula groups cardiac patients in to 

three groups: Low risk; medium risk and high risk based on ranges of the risk score. The risk score is based t-test 

results and on Chi-square values from Logistic regression results.  

7. Conclusion  

The current study is novel in its approach to predicting and ultimately providing a prescriptive direction to 

healthcare decision-makers in examining cardiac readmissions with the introduction of a new algorithm. Existing 

tools/models for predicting 30-day readmission and LOS have been limited. As previously highlighted, current 

industry standards such as the LACE index are non-specific and rely solely upon clinical drivers with prediction 

rates hovering around 50% (Cotter, Bhalla, Wallis, & Biram, 2012). Our findings not only provide increased 

ability to both predict those at-risk for readmission but also in our prediction of identifying those not at-risk adding 

to the literature in this area.  

This is particularly timely in the transition from fee for service to value-based healthcare in which there is an 

increasing responsibility to begin identifying and working to mitigate the determinants, whether clinical, 

biological, or social, contributing to health outcomes including readmissions. As such, this research has the 

potential to help reduce overall hospital readmission rates and allow hospitals to utilize their resources more 

efficiently to enhance interventions for high-risk patients by contributing to: 1) successful identification of 

significant risk factors that can predict cardiac patient readmissions; 2) development of a risk score for 

readmission; and  3) early identification of the at-risk population and introducing preventive healthcare measures 

(exercise, education, therapy etc.) to reduce hospital readmission rates prior to and following discharge. In 

alignment with current healthcare transition, this work assists not only in providing a direction towards cost 

reduction but importantly makes strides towards increased quality of life for cardiac patients through data-driven 

preventative efforts for at-risk identified populations.  

As shown in this study, readmission is not only affected by clinical indicators, but socioeconomic factors of 

patients as well. However, programs like HRPP hold healthcare providers accountable, making it necessary for 

the healthcare industry to leverage existing medical and social data to identify patients at risk and develop 

necessary interventions. Data-driven methodologies such as the ones performed in this algorithm development 

are necessary to provide a framework for balancing resource utilization towards such patients with the risk of 

reduced payments. Our algorithm uniquely collates predictors across both clinical and social determinants of 

health, specifically contributing to the social drivers crucial to readmissions as patients are discharged into their 

social environments. In our findings, two drivers viewed as proxies of social environments, are unique within the 

cardiac readmission research literature. Drivers of patient no shows or missed medical appointments, include both 

patient behavior characteristics but also potential social determinants impacting means to attend appointments 

(i.e., lack of transportation, lack of social support able to provide transportation, poverty, distance to hospital). 

Similarly, number of prescriptions provides a proxy for comorbid illnesses, drug interactions, and age which are 

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crucial to patient engagement with their healthcare. As such, a risk score revealing these important socioeconomic 

drivers, along with clinical determinants, are poised to provide healthcare systems with actionable insights on 

how to intervene with patient populations shown to be at risk for readmission.  

This risk score formula, unique within in the published literature, provides an opportunity to identify high risk 

patients at the time of discharge. The analyses utilized not only allows us to score a patient at various levels of 

risk for readmission (predictive), it also provides information on the individual drivers for each score 

(prescriptive). This statistical approach will allow providers to compare and find the best fit on an individual 

level. On a population level of analytics this tool allows a look across the whole healthcare enterprise. In addition, 

once validated will allow for prescriptive analytics. This novel statistical approach proves to be a unique tool and 

a smart collaboration within in the healthcare space.  

Limitations to the current study include a lack of data in potentially important drivers of readmission. We did not 

include data on emergency admissions or patient’s patterns of inappropriate utilization that may begin to reveal 

behavioral patterns around healthcare. Other pertinent predictors of readmission seen in prior works including 

patient’s social support are unfortunately unavailable in electronic medical records and therefore not included in 

these analyses. Further, only discharge data are used to develop the risk score for readmission. The statistical tests 

used in this study are association tests and they do not establish causality. Since the dataset comes from hospital 

in the upper Midwest, the patients are primarily Caucasian. No chart review information is used in the score. 

Future directions include refinement and validation of the current risk score. Specifically, placing the score into 

a live clinical environment to assess real-life effectiveness. Historically, impacts of regression to the mean 

(patient’s eventual regress to the mean without intervention) have clouded the influence of predictive ability, 

especially in assessments applying pre- and post- studies. We intend on circumventing any confounding factors 

by implementing our validation through control intervention environments. In collaboration with a large 

integrated healthcare delivery system we have the ability to evaluate the score with a relatively homogenous 

patient population where patients are separated by large distances. As such, any novel interventions such as score 

implementation in one setting and not another will not cross contaminate the control-intervention environments. 

Metrics indicating predictive success will be available through EMR data including decreased readmissions 

overtime. 

It is evident that prescriptive algorithms are the future for analytics in healthcare. Past the promise of prediction, 

prescriptive approaches will fully engage providers in the use of big data and analytics. The current risk score 

with predictive and prescriptive capability provides a much-needed movement towards this work. 

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