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Clinical Science

Clinical and Sociobehavioral Prediction Model of 30-Day Hospital Readmissions Among People With HIV and Substance Use Disorder: Beyond Electronic Health Record Data

Nijhawan, Ank E. MD, MPH, MSCS*; Metsch, Lisa R. PhD; Zhang, Song PhD; Feaster, Daniel J. PhD§; Gooden, Lauren PhD; Jain, Mamta K. MD, MPH*; Walker, Robrina PhD; Huffaker, Shannon MSN, NP; Mugavero, Michael J. MD, MHSc#; Jacobs, Petra MD, MHS**; Armstrong, Wendy S. MD††; Daar, Eric S. MD‡‡; Sullivan, Meg MD§§; del Rio, Carlos MD††,║║; Halm, Ethan A. MD, MPH¶¶

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: March 1, 2019 - Volume 80 - Issue 3 - p 330-341
doi: 10.1097/QAI.0000000000001925

Abstract

INTRODUCTION

Thirty-day hospital readmissions are a key quality metric under the Centers for Medicare and Medicaid Services, which limits reimbursements for hospitals with excess readmissions. Under this metric, expected readmission rates are solely adjusted for age, sex, discharge diagnosis, and recent medical diagnoses.1 Concerns have been raised that this limited adjustment unfairly penalizes hospitals serving a disproportionate number of socially disadvantaged patients.

We previously published a readmission prediction model for patients with HIV, which uses electronic health record (EHR) variables that are available in real time at the time of hospital admission. We found that a combination of variables that indicate medical illness (eg, CD4 count and abnormal creatinine) and socioeconomic status (eg, Medicaid insurance and homelessness) provided the best-performing prediction model.2 Similarly, various studies have found that social and behavioral factors such as number of address changes in the past year, residing in socioeconomically deprived areas, cocaine use, smoking, and being unmarried are associated with readmissions among different medical conditions, including congestive heart failure, pneumonia, cirrhosis, and traumatic injury.3–7 In response to these concerns, the National Academy of Medicine was commissioned to identify social risk factors that impact clinical outcomes and identified the following priority domains: socioeconomic position, race/ethnicity, gender, social relationships, and residential and community context.8,9 However, many of these factors are not readily available from the EHR, and further efforts are needed to determine how to effectively incorporate these factors and assess the impact of these adjustments on expected readmission rates.

In the context of a multicenter prospective study of inpatients with HIV, in which detailed clinical and social data were collected, we have a unique opportunity to evaluate the contribution of these factors to 30-day readmissions. Patients with HIV who were hospitalized and had a history of substance and/or heavy alcohol use were recruited from the inpatient setting and randomized to 1 of 3 groups: patient navigation, patient navigation with financial incentives, or treatment as usual. In addition to demographic and clinical variables, extensive data were collected on social and behavioral factors, including those at an individual level (self-efficacy, perceived health status, and psychological distress), interpersonal level (social support, intimate partner violence, and patient–provider relationship), and household/community level (housing stability and food insecurity), which may be associated with readmissions.

People living with HIV (PLWH) are at high risk of readmission within 30 days of hospital discharge (rates of 19%–25%),2,10,11 which may be related to a high level of social need, medical complexity, or both. HIV disproportionately affects racial/ethnic minorities and socially disadvantaged patients, and less than one-half of PLWH have an undetectable HIV viral load (VL).12 PLWH are frequently admitted to safety net hospitals in large urban centers, which are 30% more likely to have readmissions above the national average.13 By identifying key clinical and social risk factors involved in hospital readmissions among PLWH, we will not only improve our ability to predict 30-day readmissions, refining a clinical prediction tool, but also provide empirical evidence for more equitable adjustment of this national quality metric.14

In this study, we seek to: (1) externally validate a published, EHR-enabled readmission risk prediction model for PLWH2 (EHR-only model) in a new and multicenter sample; (2) assess the contributions of social and behavioral risk factors to 30-day readmissions in this cohort of hospitalized patients with HIV and substance use; and (3) refine and validate a best-performing and efficient prediction model (EHR-plus model) for 30-day readmissions among PLWH.

METHODS

This study analyzed data from a 3-arm randomized clinical trial in hospitalized patients with HIV and substance use. Participants were recruited from 11 hospitals across the United States (Atlanta, GA; Baltimore, MD; Boston, MA; Birmingham, AL; Chicago, IL; Dallas, TX; Los Angeles, CA; Miami, FL; New York, NY; and Philadelphia and Pittsburgh, PA) from July 2012 to January 2014. This study was approved by the institutional review boards at all participating sites. The checklist according to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis method (TRIPOD) statement15 was followed for this study.

Study Description

Participants were eligible if they were HIV-infected, hospitalized, 18 years or older, able to communicate in English, lived in the vicinity, provided detailed locator information, completed the baseline assessment, signed a medical record release, had a Karnofsky performance score of ≥60, reported or had medical records documenting opioid, stimulant (cocaine, ecstasy, or amphetamines), or heavy alcohol use as determined by the Alcohol Use Disorders Identification Test (AUDIT-C) within the past 12 months, and met ≥1 clinical criteria indicating uncontrolled HIV as described elsewhere.16 After enrollment, social/behavioral assessments and a blood draw were completed. Participants were then randomly assigned in a 1:1:1 ratio to receive (1) patient navigation, (2) patient navigation plus financial incentives, or (3) treatment as usual. Details about the interventions have been described previously.16 All participants enrolled in the main study were included in this ancillary study.

Measures

Variables collected at baseline include demographics (age, race, ethnicity, gender, and marital status) and socioeconomic variables (income, housing, employment, education, and prior incarceration). In addition, alcohol/substance use [Addiction Severity Index, Drug Abuse Screening Test (DAST-10), and AUDIT] data were collected. Severe substance use was defined as a DAST-10 score ≥6, and/or AUDIT ≥6 (for women) or AUDIT ≥7 (for men). Clinical variables (CD4 count, HIV VL, and hepatitis C antibody), HIV medication adherence (AIDS Clinical Trials Group questionnaire),17 primary diagnosis from index admission (using Clinical Classifications Software classification system),18 and health care utilization [clinic visits, emergency department (ED) use, and hospitalizations] were obtained. All remaining variables from the original EHR prediction model (eg, creatinine, anion gap, and history of AIDS-defining illness)19 were collected from the medical record for all participants. Additional sociobehavioral factors measured at baseline include: adherence self-efficacy,20 medical mistrust,21 patient–provider relationship,22 access to care,23 smoking,24 health literacy (3-item scale),25 HIV-related cognitive problems (HIV Dementia Scale),26 perceived health status (SF-12),27 food insecurity (Household Food Insecurity Access Scale),28 readiness for substance use treatment (readiness and negative attitudes scales),29 interpersonal violence,30 housing stability,31 psychological distress [Brief Symptom Inventory (BSI-18)],32 and social support (short social support and conflictual social interaction scales).33

Primary Outcome

The primary outcome for this study is 30-day readmission (binary variable of whether or not an individual was rehospitalized within 30 days of the initial hospital discharge date). Only the initial admission during which the patient was recruited and enrolled was considered as an index admission. A readmission could be to any hospital, and if the participant was admitted to a hospital different from the index hospitalization, medical records were requested and reviewed by the research team.

Data Analyses

Data analysis was conducted in 2 parts. The first part involved the application of an existing model2 using new data (“EHR-only”) to validate its performance, and the second part was to develop a new model (“EHR-plus”) using the data from this trial. The EHR-only model is an external validation in a new sample (HIV-positive substance users at 11 US hospitals) of our prior EHR prediction model of 30-day readmissions (developed in a single center using data from all HIV inpatients). All variables included in the initial published model were entered into the EHR-only model.

For the EHR-only model, the same variables were entered as in the original model, with previously estimated regression coefficients, including history of AIDS-defining illness, CD4 <92 cells/μL, absolute lymphocyte count ≤0.33 (×109/L), creatinine ≤0.55 (mg/dL), creatinine >1.77 (mg/dL), HCO3 ≤ 18 (mmol/L), alanine aminotransferase (ALT) or aspartate aminotransferase (AST) >35 (U/L), hematocrit ≤28.3 or >48.8 (%), pO2 57–133 (mm Hg), anion gap >12 or missing (mmol/L), Medicaid insurance, lives >13 miles from hospital (distance between individual and hospital zip codes using zipcityfunction in SAS), number of inpatient admissions in the past 6 months, number of ED visits in the past 12 months, and homelessness.

The second model, “EHR-plus,” is a de novo parsimonious prediction model that incorporates additional clinical and sociobehavioral variables available from the study. First, we examined the univariate relationship between readmission and each variable through logistic regression at threshold significance of P ≤ 0.20. Continuous variables were divided into categories based on published cutoff values or standard clinical thresholds. Variables with theoretic overlap were assessed for collinearity. Multivariate logistic regression using a stepwise variable selection procedure, with a threshold significance of P < 0.05 to remain in the model, was used to fit the final model. There were relatively few missing values except in 2 variables, hepatitis C antibody and patient–provider relationship (around 30%, with revised N indicated in the baseline characteristics table), neither of which was significantly associated with 30-day readmission in univariate analyses. Because these 2 variables were not included in the multivariate model, final variable selection was based on complete data.

Both models were validated using several approaches. Model discrimination was assessed by the C-statistic. Model calibration was conducted using the Hosmer–Lemeshow χ2 goodness-of-fit test. For the EHR-only model, a survival analysis of readmission-free time up to 30 days was performed comparing predicted readmission risk groups (high, medium, and low). In addition, 5 risk categories were created based on quintiles of predicted risk and graphically represented.

As the EHR-plus model was a de novo model, cross-validation was performed by randomly splitting the data set into 2/3 derivation and 1/3 validation data sets. The final model was refit on the derivation set and then the fitted model was applied to the validation set, obtaining a C-statistic, also known as a concordance statistic, which is equal to the area under the receiver operating curve. This operation was repeated 1000 times, and from these 1000 C-statistics, we obtained the mean and 95% confidence interval (CI) of C-statistics. A graph of the Hosmer–Lemeshow goodness-of-fit test result was created by comparing predicted versus observed risk of 30-day readmission. All statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC).

RESULTS

A total of 801 participants were included. The mean age was 44.1 years, and the majority of participants were males (67.4%), African-American (75.3%), never married (66.2%), and earned <$20,000/year (62.8%). In addition, 59.2% reported food insecurity and 77.6% had a history of incarceration. All participants had a history of alcohol use (58.5%) and/or substance use (97.4%), with 69.4% reporting stimulant use and 21.5% reporting opiate use in the past 12 months. There were low self-reported rates of medication adherence (17% reported taking >85% of pills in the past month), and high rates of psychological distress (53.8%) and interpersonal violence (67.7%) (Table 1).

T1
TABLE 1.:
Baseline Characteristics of Study Population
table1-a
TABLE 1-A.:
Baseline Characteristics of Study Population
table1-b
TABLE 1-B.:
Baseline Characteristics of Study Population

Overall, 140/801 (17.5%) individuals had a hospital readmission within 30 days of the initial admission. The mean time to readmission was 15.1 days. The most common diagnoses at the time of index admission were infections that were non–AIDS-defining (294, 36.8%), such as pneumonia, skin and soft-tissue infections, and bacteremia, although 70% (207/294) of this group met the clinical definition of AIDS (CD4 < 200). Other common diagnostic categories were pulmonary complaints (12.3%, including shortness of breath and chronic obstructive pulmonary disease), gastrointestinal (GI) illness (10.1%, including liver disease, GI bleeding, and abdominal pain), and AIDS-defining illnesses (9.5%, such as Candida esophagitis and pneumocystis pneumonia). Only 38.6% reported receiving antiretroviral therapy (ART) before admission, and 15.5% were started on ART during the index admission (Table 1).

External Validation of the EHR-Only Risk Prediction Model

The previously published prediction model using this new data set resulted in a C-statistic of 0.65 (95% CI: 0.60 to 0.70). Some but not all variables were significant in this model (Table 2). Survival analyses (Fig. 1A) show the difference in time to readmission in the first 30 days after discharge in the 3 risk groups—high, medium, and low—with the high-risk group experiencing higher numbers of readmissions sooner after discharge. Figure 1B further demonstrates the calibration of the EHR-only model by stratifying 30-day readmission rate by the risk group in quintiles, from very low risk group (8.8% readmitted) to very high risk group (28.8% readmitted).

T2
TABLE 2.:
External Validation of Multivariate Prediction Model for 30-Day Hospital Readmissions Among HIV-Infected Individuals
F1
FIGURE 1.:
A, Proportion of participants remaining readmission-free up to 30 days, by the EHR-only model predicted the readmission risk group. B, Thirty-day readmission rate by the EHR-only model predicted readmission risk category.

EHR-Plus Prediction Model

Variables that were associated with 30-day readmission in univariate analyses include HIV risk factor, low income, unemployment, Medicaid insurance, food insecurity, low readiness for substance use treatment, low health literacy, low perceived health status, interpersonal violence, depression, CD4 count, low Karnofsky score, high HIV dementia score, abnormal complete blood counts, abnormal kidney function, admission diagnoses related to GI and cardiovascular diseases, and prior ED visits and hospitalizations (Table 3).

T3
TABLE 3.:
Predictors of 30-Day Readmissions: Univariate Analyses
table3-a
TABLE 3-A.:
Predictors of 30-Day Readmissions: Univariate Analyses
table3-b
TABLE 3-B.:
Predictors of 30-Day Readmissions: Univariate Analyses

In multivariate modeling, significant independent predictors of 30-day readmissions included variables from multiple categories, including socioeconomic (food insecurity), sociobehavioral (readiness for substance use treatment), clinical illness severity [CD4 category (<50 compared with >200 cells/μL), elevated creatinine (>1.17 mg/dL), primary admitting diagnosis of cardiovascular or GI disease], and health care utilization [ED visits (≥2 in the past 12 months) and hospitalizations (number in the past 6 months)] (Table 4). Model discrimination of the EHR-plus as measured by the C-statistic was 0.74. Model calibration, using cross-validation, resulted in a C-statistic of 0.71 (95% CI: 0.64 to 0.77). The comparison of predicted versus observed readmission, as per the Hosmer–Lemeshow method, is graphed in Figure 1, Supplemental Digital Content, https://links.lww.com/QAI/B255.

T4
TABLE 4.:
Multivariate Model of Predictors of 30-Day Readmissions Among HIV-Positive Individuals, EHR-Plus Model

DISCUSSION

Our external validation of a published prediction model for 30-day readmissions among PLWH in a multicenter population of HIV-positive individuals with substance use disorder performed moderately well, with a C-statistic of 0.65 (95% CI: 0.60 to 070). In comparison with this EHR-only model, the EHR-plus model, which included detailed socioeconomic and sociobehavioral variables from patient interviews, resulted in a substantially stronger prediction model, with a C-statistic of 0.74 (0.71 with cross-validation, 95% CI: 0.64 to 0.77). The addition of 2 social predictors of readmission, food insecurity and readiness for substance use treatment, resulted in considerable improvement in prognostic capacity for 30-day readmission. The EHR-plus model also contained previously identified predictors of readmission (CD4 count, renal dysfunction, and prior acute care utilization), and highlighted the role of cardiovascular and GI diseases in readmissions among this population.

The independent predictive value of these sociobehavioral variables to 30-day readmissions, which are not typically available in the EHR, underscores the impact of key social determinants influencing readmission risk and the need to ask about and address them, especially in safety net populations. Frequently cited prediction models, such as LACE34 and HOSPITAL,35 do not include any social or behavioral predictors, which may limit their use in safety net populations (where LACE index had a C-statistic of 0.56 among congestive heart failure patients).36 In 2 different systematic reviews of readmission prediction models, very few models included variables involving social determinants of health.37,38 A 2011 review of 26 models (average C-statistic =0.66) cited 2 models that included social determinants of health,38 and a 2018 review (range of C-statistics was 0.21–0.88) identified a small proportion of models that included factors such as living arrangement, marital status, and substance use.37 Given mounting evidence that social and behavioral factors are strong predictors of readmissions (as well as morbidity and mortality), hospital systems will be incentivized to measure these domains as part of routine clinical care or to use tools such as natural language processing to extract key factors from the EHR.39 The National Academy of Medicine has identified social and behavioral domains related to health outcomes (including alcohol use and financial strain for acquiring food), with a focus on systematic and efficient incorporation of these factors into the EHR, creating an opportunity for future automated EHR-plus prediction models.40

Food insecurity and its role in HIV outcomes is well documented, as inadequate access to quality nutrition has been associated with decreased adherence to ART and lower virologic suppression rates.41–43 In addition, and particularly relevant to this study population, there may be synergistic effects of food insecurity and substance use that impede adherence to ART in people with HIV.44,45 These associations persist after adjusting for neighborhood poverty, transportation, and housing, indicating that food insecurity is uniquely associated with adherence. Similarly, we found that food insecurity was not collinear with low income or Medicaid status, indicating that these more commonly available variables may inadequately estimate the impact of food insecurity. In our study, food insecurity may follow similar pathways to those noted in other studies, leading to ART nonadherence that contributes to readmission. Food assistance for PLWH has been shown to have a positive impact on ART adherence,46 and others have proposed addressing food insecurity to reduce hospital readmissions in different populations.47 Access to safe and adequate nutrition is not routinely assessed during hospitalizations but may be a sensitive indicator of a patient's capacity to prioritize health care needs over other survival needs.

Readiness for substance use treatment was independently associated with decreased 30-day readmissions in this study population of individuals who were either heavy alcohol or substance users or both. Substance use disorders among hospitalized patients have been associated with increased subsequent acute care hospital utilization in other urban populations.48 In addition, engagement in substance use treatment from the inpatient setting may decrease ED utilization and increase ambulatory visits.49 Therefore, readiness for substance use treatment may indicate a willingness to change behavior, including adherence to medications or visits. Hospitalization itself may serve as a catalyst for behavior change among substance users. In a study assessing readiness to change substance use behaviors among inpatients, tested at baseline and every 3 days thereafter, 43.6% of subjects increased to a higher stage or remained in the action stage of behavior change.50 These findings and our own underscore the potential impact of direct linkage to substance use treatment services from the inpatient setting on subsequent health care utilization.

Our study has several implications for policies and providers. It provides empiric evidence for the importance of measuring and addressing social determinants of health during hospital admissions, specifically for assessing food insecurity and substance use treatment readiness. Our findings may inform current readmission metrics and their equitable application to different hospital settings including safety net hospitals. In addition, our findings have direct implications for interventions, such as rapid ART initiation for those with low CD4 counts, addressing cardiovascular and GI comorbidities, food assistance programs for the food insecure, and direct linkage to substance use services for those expressing readiness for treatment.

Several limitations are worth noting. First, the study population is composed of hospitalized HIV-positive substance users, a group that is majority non-white, socially vulnerable, and who mainly accesses care at safety net hospitals. Although this may appear to limit the generalizability of our findings, a large proportion of hospitalized PLWH in our current era share these characteristics10,11,51 and may benefit from readmission reduction interventions.52 Second, given the sample size, we did not adjust for study site in our analyses, and therefore were unable to adjust for hospital-specific effects, although the multicenter nature of the study does enhance its relevance to other US hospitals. Third, the study population is a select group who was willing to participate in a randomized trial and received extensive follow-up for intervention purposes (care navigation and financial incentives for 6 months in intervention arms) as well as ongoing contact and outreach for study retention, which may not be generalizable to the general population. Finally, we had a modest number of events, with only 140 of 801 participants experiencing 30-day readmissions; therefore, we may not be powered to detect weak predictors of readmissions in our study. However, our readmission rate (17.5%, allowing for only one readmission per individual) is comparable with this same metric in other published studies involving PLWH, 14.6% and 15%.10,53

CONCLUSIONS

In sum, we present the results of an external validation of an EHR-based readmission prediction model for HIV-positive individuals in a new multicenter population. In addition, by incorporating extensive social and behavioral factors into a new prediction model, we have improved our prediction model performance and have identified 2 additional independent sociobehavioral predictors of readmission: food insecurity and low readiness for substance use treatment. In addition to providing important potential targets for future interventions among PLWH, these findings have broader implications for health care systems. In an era of value-based care, where hospital systems need to optimize their use of medical informatics and are simultaneously compelled to recognize the critical (and costly) impact of social determinants on health outcomes, the collection and integration of social and behavioral variables into the EHR is imperative to improving outcomes.

ACKNOWLEDGMENTS

Support from the University of Miami Center for AIDS Research (CFAR) (P30AI073961; Dr. Savita Pahwa), the Emory University CFAR (P30AI050409; Drs. Carlos del Rio, James W. Curran, and Eric Hunter), the Atlanta Clinical and Translational Science Institute (UL1TR000454; Dr. David Stephens), and the HIV Center for Clinical and Behavioral Studies at the New York State Psychiatric Institute/Columbia University Medical Center (P30MH043520; Dr. Robert Remien) is also acknowledged.

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Keywords:

readmissions; social determinants; prediction model; EHR

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