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Predictors of Alcohol Withdrawal Readmissions

Predictors of Alcohol Withdrawal Readmissions Abstract Aims Hospital readmissions serve as a major benchmark for the quality of care and alcohol withdrawal (AW) may lead to multiple hospitalizations and readmissions. We sought to evaluate readmission rates and predictors of having AW-related readmissions in a nationally representative sample. Short summary In a nationally representative sample, AW readmission within 30 days and multiple readmissions during the year were high and were particularly predicted by discharge against medical advice (AMA), comorbid psychosis, comorbid depression, poor socioeconomic status, comorbid drug abuse and alcohol-related medical disease. Methods Subjects from the 2013 Nationwide Readmissions Database (NRD) with AW as a primary or secondary diagnosis. Cross-sectional and retrospective analyses were performed using regression methods appropriate for the NRD complex sampling design. The outcome measures were AW-related readmission, 30-day readmission and multiple readmissions. Results In 2013, 393,118 discharges involved ICD-9 coding for AW and 41.5% of these included AW as the primary discharge diagnosis. The rate of AW-related readmission in 2013, as estimated from first-quarter index events, was 58.8% (95% confidence interval (CI) 57.5–60.1), with an average of 1.8 readmissions (95% CI 1.7–1.9). The 30-day readmission rate, estimated from January–November index events, was 19.7% (95% CI 19.0–20.4). The strongest independent predictors of yearly, 30-day and multiple readmission were discharged AMA and comorbid psychotic disorder. Conclusion AW readmission within 30 days and multiple readmissions during the year were common and were particularly predicted by AMA discharge and comorbid psychotic disorder. While these and other factors can help identify high-risk patients, further study to determine causal mechanisms may aid efforts to improve both the outcomes and costs associated with acute AW treatment. BACKGROUND Approximately, 6.2% of the adult population in the USA or roughly 15 million individuals had an alcohol use disorder (AUD) in 2015 (National Institute on Alcohol Abuse and Alcoholism, 2015). Alcohol-related problems pose a massive economic burden on the healthcare system. For example, from 2002 to 2010, the rate of emergency department visits for alcohol-related diagnoses increased by 38% (Centers for Disease Control and Prevention, 2013). Alcohol dependence and associated conditions constitute major problems in acute care settings. Alcohol withdrawal (AW) is the most common alcohol-related illness that requires inpatient admission and is associated with adverse events such as uncontrolled agitation with the potential for over-sedation, generalized seizures and prolonged hospital stay (Clark et al., 2013). Hospital readmissions serve as a quality care benchmark and hospitalists may encounter repeated AW admissions for the same patient. Rates of readmission among patients presenting to detoxification programs within 1 year have ranged from 34% to 48% (Running Bear et al., 2014). Patients who reported alcohol as the primary preferred drug were more likely to be readmitted within 1 year (Li et al., 2008). In efforts to limit readmission, a number of single-centered studies have sought to identify readmission predictors (Callaghan, 2003; Carrier et al., 2011; Li et al., 2008; Mark et al., 2006). Some limitations of these studies include poor generalizability, inability to identify patients readmitted elsewhere, a focus on patients admitted to dedicated detoxification units or intensive care units, and limiting study to those with a primary AW diagnosis (Callaghan, 2003; Carrier et al., 2011; Clark et al., 2013; Li et al., 2008; Mark et al., 2006; Running Bear et al., 2014). In this study, we utilized a nationally representative sample of hospital discharges to estimate 30-day and yearly readmissions of AW patients, and to evaluate potential predictors of readmissions. METHODS We analyzed the 2013 Nationwide Readmissions Database (NRD), a subset of the Healthcare Cost and Utilization Project sponsored by the Agency for Healthcare Research and Quality (AHRQ), a nationally representative sample of acute care hospital discharges in the USA. The NRD is a large publicly available all-payer inpatient care database. Unweighted, the NRD contains data from ~14 million discharges each year, estimating roughly 36 million weighted discharges, with unique linkage numbers used to identify any additional hospital discharges for each subject. National estimates are produced using sampling weights provided by the sponsor and precision is estimated using stratification and clustering variables to account for the complex probability sampling scheme. The study was granted exemption from human subjects review as it involved de-identified and publically-available data. Identification of alcohol withdrawal and hospital readmissions The 2013 NRD provided up to 25 ICD-9 diagnoses for each hospital discharge. AW (ICD-9 code 291.0) and AW delirium (ICD-9 code 291.81) were combined to identify AW hospitalizations. Other ICD-9 diagnoses such as ‘alcohol abuse’ were not included. Primary AW included hospital discharges with withdrawal or withdrawal delirium as the primary diagnosis. Secondary AW included hospital discharges with other primary diagnoses. For estimating readmissions, an ‘index event’ was defined as having a hospital discharge that involved AW and did not result in death. Index events occurring in December were excluded from the 30-day readmission analyses, since readmissions may have extended into 2014. Index events occurring in the first 3 months of 2013 were used to approximate yearly readmissions. Potential predictors of readmission Potential predictors included demographic variables (age, sex and markers of socioeconomic status), tobacco abuse due to its association with alcoholism and adverse health events, alcohol-related medical disease, index admission through the emergency room (ER) since this may reflect severity, and factors identified in previous studies (mental health disorders, comorbid drug abuse). Age, sex and admission through ER were directly included in the NRD database. We did not evaluate ethnicity as this variable was not consistently recorded in the NRD. Alcohol-related medical disease was categorized as present or not present dependent on having an ICD-9 diagnosis that was specific for alcohol-related organ damage. In the ICD-9 system, this was limited to liver disease, cardiomyopathy, polyneuropathy and gastritis. We adopted a conservative approach and did not attempt to categorize other diagnoses as alcohol-related (e.g. an ICD-9 diagnosis of alcohol abuse during a hospitalization for pancreatitis or seizure). As a marker of socioeconomic status, low income was estimated from average income in the zip code of residence, with those living in the lowest quartile zip codes categorized as ‘low income’. Socioeconomic status was also estimated from health insurance records, with those having only Medicaid or relying on self-pay being compared with those with other forms of health insurance. Depression, psychosis and drug abuse were derived from the AHRQ comorbidity indices for these conditions that are directly provided in the NRD. Tobacco abuse was derived using ICD-9 codes. Statistical analysis All analyses adjusted for the NRD sampling design and were completed with SAS version 9.4 (SAS Institute, Cary, NC, USA). The total number of hospital discharges involving AW as a primary or secondary diagnosis was estimated using PROC SURVEYFREQ. The probability of readmission and average number of readmissions were estimated using PROC SURVEYMEANS. PROC SURVEYLOGISTIC was used to estimate unadjusted and adjusted risks for readmission associated with each covariate. PROC SURVEYLOGISTIC with a generalized logit link was used to estimate odds ratios for having one or multiple readmissions relative to no readmissions. In addition to the overall sample of AW discharges, we repeated analyses in the subset of patients who did not have a primary mental health diagnosis. This was done to reflect a medical-surgical population that may better represent patients cared for by hospitalists. RESULTS There were 393,118 discharges that involved ICD-9 coding for AW as a discharge diagnosis with 41.5% having AW as the primary discharge diagnosis. The yearly readmission rate, estimated from first-quarter index events, was 58.8% (95% CI 57.5–60.1), with an average of 1.8 readmissions per patient (95% CI 1.7–1.9). The 30-day readmission rate, estimated from January–November index events, was 19.7% (95% CI 19.0–20.4). Table 1 compares the characteristics of patients with AW diagnosis who required readmission for any reason within 30 days of discharge from an index admission. Male patients and those with poor socioeconomic status, comorbid depression, psychosis and other drug abuse were more likely to be readmitted, as were those with alcohol-related medical conditions to a lesser extent. Discharge against medical advice (AMA) had the strongest association with readmission. Age, tobacco abuse and admission through the emergency department were not associated with early readmission. Table 1. Characteristics of alcohol withdrawal index admissions stratified by 30-day readmission Variable No 30-day readmission (n = 284,744)a 30-Day readmission (n = 69,741)a P-value Mean age 50.4 50.1 0.053 Female (%) 25.7 23.0 <0.001 Primary alcohol withdrawal (%) 42.3 41.5 0.155 Alcohol-related disease (%) 25.5 26.9 0.015 Comorbid depression (%) 21.6 23.9 <0.001 Comorbid psychosis (%) 14.6 19.9 <0.001 Comorbid drug abuse (%) 8.9 10.3 <0.001 Tobacco use disorder (%) 45.2 45.9 0.332 ER admission (%) 80.8 83.1 0.132 Discharged against medical advice (%) 7.4 14.2 <0.001 Medicaid or self-pay (%) 41.9 48.8 <0.001 Low-income zip code (%) 30.5 33.9 <0.001 Variable No 30-day readmission (n = 284,744)a 30-Day readmission (n = 69,741)a P-value Mean age 50.4 50.1 0.053 Female (%) 25.7 23.0 <0.001 Primary alcohol withdrawal (%) 42.3 41.5 0.155 Alcohol-related disease (%) 25.5 26.9 0.015 Comorbid depression (%) 21.6 23.9 <0.001 Comorbid psychosis (%) 14.6 19.9 <0.001 Comorbid drug abuse (%) 8.9 10.3 <0.001 Tobacco use disorder (%) 45.2 45.9 0.332 ER admission (%) 80.8 83.1 0.132 Discharged against medical advice (%) 7.4 14.2 <0.001 Medicaid or self-pay (%) 41.9 48.8 <0.001 Low-income zip code (%) 30.5 33.9 <0.001 aNot equal to total AW discharges included in the text as 30-day readmission analysis did not include December discharges (n = 32,066), subjects who died during an index admission (n = 5,684) and subjects where missing data (n = 883) did not allow for readmission analysis. Table 1. Characteristics of alcohol withdrawal index admissions stratified by 30-day readmission Variable No 30-day readmission (n = 284,744)a 30-Day readmission (n = 69,741)a P-value Mean age 50.4 50.1 0.053 Female (%) 25.7 23.0 <0.001 Primary alcohol withdrawal (%) 42.3 41.5 0.155 Alcohol-related disease (%) 25.5 26.9 0.015 Comorbid depression (%) 21.6 23.9 <0.001 Comorbid psychosis (%) 14.6 19.9 <0.001 Comorbid drug abuse (%) 8.9 10.3 <0.001 Tobacco use disorder (%) 45.2 45.9 0.332 ER admission (%) 80.8 83.1 0.132 Discharged against medical advice (%) 7.4 14.2 <0.001 Medicaid or self-pay (%) 41.9 48.8 <0.001 Low-income zip code (%) 30.5 33.9 <0.001 Variable No 30-day readmission (n = 284,744)a 30-Day readmission (n = 69,741)a P-value Mean age 50.4 50.1 0.053 Female (%) 25.7 23.0 <0.001 Primary alcohol withdrawal (%) 42.3 41.5 0.155 Alcohol-related disease (%) 25.5 26.9 0.015 Comorbid depression (%) 21.6 23.9 <0.001 Comorbid psychosis (%) 14.6 19.9 <0.001 Comorbid drug abuse (%) 8.9 10.3 <0.001 Tobacco use disorder (%) 45.2 45.9 0.332 ER admission (%) 80.8 83.1 0.132 Discharged against medical advice (%) 7.4 14.2 <0.001 Medicaid or self-pay (%) 41.9 48.8 <0.001 Low-income zip code (%) 30.5 33.9 <0.001 aNot equal to total AW discharges included in the text as 30-day readmission analysis did not include December discharges (n = 32,066), subjects who died during an index admission (n = 5,684) and subjects where missing data (n = 883) did not allow for readmission analysis. In an adjusted analysis (Table 2), all factors associated with 30-day readmission in Table 1 remained statistically significant predictors. The strongest independent predictor was AMA discharge (OR 2.01, 95% CI 1.90–2.10) followed by comorbid psychosis (OR 1.54, 95% CI 1.48–1.60). As seen in Figure 1, except for gender, a similar pattern held for any readmission in the calendar year. AMA discharge, comorbid psychosis and health insurance status were the strongest predictors for having multiple readmissions. Table 2. Fully adjusteda odds ratios for 30-day readmission Variable Odds ratio (95% confidence interval) Female 0.84 (0.80–0.88) Alcohol-related medical disease 1.11 (1.05–1.17) Comorbid depression 1.28 (1.22–1.34) Comorbid psychosis 1.54 (1.48–1.60) Comorbid drug abuse 1.13 (1.07–1.20) Discharged against medical advice 2.01 (1.90–2.10) Medicaid or self-pay 1.25 (1.20–1.31) Low-income zip code 1.16 (1.06–1.18) Variable Odds ratio (95% confidence interval) Female 0.84 (0.80–0.88) Alcohol-related medical disease 1.11 (1.05–1.17) Comorbid depression 1.28 (1.22–1.34) Comorbid psychosis 1.54 (1.48–1.60) Comorbid drug abuse 1.13 (1.07–1.20) Discharged against medical advice 2.01 (1.90–2.10) Medicaid or self-pay 1.25 (1.20–1.31) Low-income zip code 1.16 (1.06–1.18) aEach variable adjusted for all other variables in the table. Table 2. Fully adjusteda odds ratios for 30-day readmission Variable Odds ratio (95% confidence interval) Female 0.84 (0.80–0.88) Alcohol-related medical disease 1.11 (1.05–1.17) Comorbid depression 1.28 (1.22–1.34) Comorbid psychosis 1.54 (1.48–1.60) Comorbid drug abuse 1.13 (1.07–1.20) Discharged against medical advice 2.01 (1.90–2.10) Medicaid or self-pay 1.25 (1.20–1.31) Low-income zip code 1.16 (1.06–1.18) Variable Odds ratio (95% confidence interval) Female 0.84 (0.80–0.88) Alcohol-related medical disease 1.11 (1.05–1.17) Comorbid depression 1.28 (1.22–1.34) Comorbid psychosis 1.54 (1.48–1.60) Comorbid drug abuse 1.13 (1.07–1.20) Discharged against medical advice 2.01 (1.90–2.10) Medicaid or self-pay 1.25 (1.20–1.31) Low-income zip code 1.16 (1.06–1.18) aEach variable adjusted for all other variables in the table. Fig. 1. View largeDownload slide Predictors of yearly readmission and multiple readmissions relative to no readmission. Fig. 1. View largeDownload slide Predictors of yearly readmission and multiple readmissions relative to no readmission. In the subset analysis stratified by primary mental health discharges, the 30-day readmission rate of AW discharges with and without primary mental health diagnosis were ~22% and 19%, respectively. Results in those without a primary mental health discharge diagnosis were in general similar to the overall sample. Minor differences included statistically significant increases in 30-day readmission for an index event involving the ER (odds ratio 1.26, P < 0.001) and for those with tobacco abuse (odds ratio 1.05, P = 0.037) and similar risks for yearly readmission. DISCUSSION We report data from a nationally representative database on the predictors of readmission following an AW discharge (1.3% of all adult discharges). Our results showed an approximate yearly readmission rate approaching 60%, a high prevalence of multiple readmissions and nearly 20% of subjects having an early readmission. This is higher than the previously reported readmission rates of 34–48% in similar studies (Ponzer et al., 2002; Callaghan, 2003; Li et al., 2008; Mark et al., 2006). Compared with these single-centered studies, ours being a nationally representative sample may have captured a more accurate number of readmissions. Several predictors of readmission have been identified, with the most important risk factors including discharge AMA and comorbid psychosis. The association between AMA discharge and readmission is particularly strong and could be related to incomplete detoxification with a resultant immediate return to heavy drinking following hospitalization. However, prior research has identified several correlates of AMA discharge, including young age, reporting opiates as the primary preferred drug, having HCV infection, welfare check issue period and lower education level (Li et al., 2008). Thus, the relationship between AMA discharge and readmission may be much more complex than incomplete detoxification. Though there is a downward annual trend observed in AMA discharges among the mental health and substance abuse population (Spooner et al., 2017), further studies are needed to characterize this at-risk population in order to achieve better patient outcomes and reduced costs. Other important predictors included markers of socioeconomic status and comorbid psychosis. As shown in previous studies (Mark et al., 2006; Carrier et al., 2011), we had similar findings of increased readmission in low-income patients and those with no insurance or having fee-for-service Medicaid. This may represent poor accessibility of outpatient care following hospitalization, or a more general lack of resources to support health. Our study shows the strong association of comorbid psychosis, and lower magnitude associations of depression and other drug abuse, with 30-day and multiple readmissions. The high prevalence of psychiatric comorbidity and other substance abuse in AUD patients is well-known (Lai et al., 2015). These conditions are particularly prevalent in hospitalized alcoholics (Stewart, 2007), and are associated with poor treatment compliance, worse quality of life and poorer outcomes in patients with AUD (Morojele et al., 2012). Similar to our findings in this nationally representative sample of hospitalized patients, it is well established that psychiatric comorbidity and other substance abuse are risk factors for increased readmissions to detoxification centers (Moos et al., 1994a, 1994b; Luchansky et al., 2000; Mertens et al., 2005; Walker et al., 1995; Tomasson and Vaglum, 1998; Ponzer et al., 2002). Recent studies have also confirmed this association of comorbid psychiatric condition and other substance abuse with increased readmissions in AW admissions (Booth and Blow, 1993; Clark et al., 2013), as is shown in our study. The high 30-day readmission rate in AW admissions without comorbid psychiatric condition is also worth mentioning as these two conditions often coexist. Contrary to previous studies (Luchansky et al., 2000; Mertens et al., 2005), our study is consistent with Larson et al. (2012) in that women were slightly less likely to have an early readmission. However, gender was not associated with later readmission in adjusted analyses and there was no gender association with multiple readmissions. Surprisingly, specifically-coded ICD-9 diagnoses for alcohol-related medical disease were only weakly associated with readmission. However, comorbid medical conditions in general may have a negative effect on abstinence and result in increased readmissions (Shih and Simon, 2008) and some studies have reported associations of comorbid medical disease with increased readmissions in AW patients (Larson et al., 2012; Clark et al., 2013). Admissions through the emergency department (representing a large majority of AW discharges), having a primary AW diagnosis and tobacco use disorder were not significantly associated with readmissions, although emergency department admission was a modest risk factor in discharges that did not include a primary mental health diagnosis. Our study complements prior research by extending findings to a nationally representative sample. We were also able to capture readmissions to different hospitals following an index admission. While these are some of the merits, our study has significant limitations. Data analyses are limited by the accuracy of ICD coding in hospital databases. In particular, it is difficult to adequately capture alcohol-related medical conditions and many discharges including ‘alcohol abuse’ may have included unrecorded AW. In our analysis, there were more ‘alcohol abuse’ primary discharge diagnoses compared with AW diagnosis. Any tendency to under-code alcohol-related conditions may bias association with readmission, and under-coding of AW would at a minimum underestimate AW prevalence. The follow-up period may not have been long enough to detect additional readmissions (for example, an index admission in March 2013 and readmission in January 2014) and, since we assumed index admission to be the first admission, these patients may have had prior admissions in the previous calendar year. However, the study design likely provided close estimates for readmission frequency and prior year AW discharges would not influence estimates on 30-day readmission. Finally, since the NRD is directly abstracted from clinical and administrative hospital records, other previously studied predictors of readmission such as unemployment, homelessness, race, severity of alcohol use, severity of AW, laboratory data such as liver function tests and urine drug screen, clinical case management and follow-up treatment post-discharge could not be studied (Booth and Blow, 1993; Ponzer et al., 2002; Callaghan, 2003; Mark et al., 2006; McLellan et al., 2005; Worner, 1996). In conclusion, major risk factors associated with 30-day readmission and multiple readmissions to the US hospitals following an AW discharge were discharge AMA, comorbid psychosis and to a lesser extent low socioeconomic status. Hospitalists can use these factors (e.g. prior AMA discharges, psychoses and poor insurance) to estimate risks for poor outcomes requiring re-hospitalization. However, while these are important markers of risk, further study to determine the underlying causal mechanisms may ultimately aid efforts to improve both the outcomes and costs associated with acute AW treatment. ACKNOWLEDGEMENTS None CONFLICT OF INTEREST STATEMENT None declared. REFERENCES Booth BM , Blow FC . ( 1993 ) The kindling hypothesis: further evidence from a U.S. national study of alcoholic men . Alcohol Alcohol 28 : 593 – 8 . Google Scholar PubMed Callaghan RC . ( 2003 ) Risk factors associated with dropout and readmission among First Nations individuals admitted to an inpatient alcohol and drug detoxification program . CMAJ 169 : 23 – 7 . Google Scholar PubMed Carrier E , McNeely J , Lobach I , et al. . ( 2011 ) Factors associated with frequent utilization of crisis substance use detoxification services . J Addict Dis 30 : 116 – 22 . Google Scholar CrossRef Search ADS PubMed Centers for Disease Control and Prevention . ( 2013 ). Morbidity and Mortality Weekly Report. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6235a9.htm. (25 September 2017, date last accessed). Clark BJ , Keniston A , Douglas IS , et al. . ( 2013 ) Healthcare utilization in medical intensive care unit survivors with alcohol withdrawal . Alcohol Clin Exp Res 37 : 1536 – 43 . Google Scholar CrossRef Search ADS PubMed Lai HM , Cleary M , Sitharthan T , et al. . 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( 2006 ) Factors affecting detoxification readmission: analysis of public sector data from three states . J Subst Abuse Treat 31 : 439 – 45 . Google Scholar CrossRef Search ADS PubMed McLellan AT , Weinstein RL , Shen Q , et al. . ( 2005 ) Improving continuity of care in a public addiction treatment system with clinical case management . Am J Addict 14 : 426 – 40 . Google Scholar CrossRef Search ADS PubMed Mertens JR , Weisner CM , Ray GT . ( 2005 ) Readmission among chemical dependency patients in private, outpatient treatment: patterns, correlates and role in long-term outcome . J Stud Alcohol 66 : 842 – 7 . Google Scholar CrossRef Search ADS PubMed Moos RH , Brennan PL , Mertens JR . ( 1994 a) Diagnostic subgroups and predictors of one-year re-admission among late-middle-aged and older substance abuse patients . J Stud Alcohol 55 : 173 – 83 . Google Scholar CrossRef Search ADS PubMed Moos RH , Mertens JR , Brennan PL . ( 1994 b) Rates and predictors of four-year readmission among late-middle-aged and older substance abuse patients . J Stud Alcohol 55 : 561 – 70 . Google Scholar CrossRef Search ADS PubMed Morojele NK , Saban A , Seedat S . ( 2012 ) Clinical presentations and diagnostic issues in dual diagnosis disorders . Curr Opin Psychiatry 25 : 181 – 6 . Google Scholar CrossRef Search ADS PubMed National Institute on Alcohol Abuse and Alcoholism . ( 2015 ). Alcohol Use Disorder. https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/alcohol-use-disorders. (25 September 2017, date last accessed). Ponzer S , Johansson SE , Bergman B . ( 2002 ) A four-year follow-up study of male alcoholics: factors affecting the risk of readmission . Alcohol 27 : 83 – 8 . Google Scholar CrossRef Search ADS PubMed Running Bear U , Anderson H , Manson SM , et al. . ( 2014 ) Impact of adaptive functioning on readmission to alcohol detoxification among Alaska Native People . Drug Alcohol Depend 140 : 168 – 74 . Google Scholar CrossRef Search ADS PubMed Shih M , Simon PA . ( 2008 ) Health-related quality of life among adults with serious psychological distress and chronic medical conditions . Qual Life Res 17 : 521 – 8 . Google Scholar CrossRef Search ADS PubMed Spooner KK , Salemi JL , Salihu HM , et al. . ( 2017 ) Discharge against medical advice in the United States, 2002–2011 . Mayo Clin Proc 92 : 525 – 35 . Google Scholar CrossRef Search ADS PubMed Stewart SH . ( 2007 ) Alcoholics in acute medical settings have increased risk for other drug, mood, and personality disorders . Int J Psychiatry Med 37 : 59 – 67 . Google Scholar CrossRef Search ADS PubMed Tomasson K , Vaglum P . ( 1998 ) The role of psychiatric comorbidity in the prediction of readmission for detoxification . Compr Psychiatry 39 : 129 – 36 . Google Scholar CrossRef Search ADS PubMed Walker RD , Howard MO , Anderson B , et al. . ( 1995 ) Diagnosis and hospital readmission rates of female veterans with substance-related disorders . Psychiatr Serv 46 : 932 – 7 . Google Scholar CrossRef Search ADS PubMed Worner TM . ( 1996 ) Relative kindling effect of readmissions in alcoholics . Alcohol Alcohol 31 : 375 – 80 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Medical Council on Alcohol and Oxford University Press. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Alcohol and Alcoholism Oxford University Press

Predictors of Alcohol Withdrawal Readmissions

Yedlapati, Siva Harsha; Stewart, Scott H
Alcohol and Alcoholism , Volume Advance Article (4) – Mar 30, 2018
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© The Author(s) 2018. Medical Council on Alcohol and Oxford University Press. All rights reserved.
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Abstract

Abstract Aims Hospital readmissions serve as a major benchmark for the quality of care and alcohol withdrawal (AW) may lead to multiple hospitalizations and readmissions. We sought to evaluate readmission rates and predictors of having AW-related readmissions in a nationally representative sample. Short summary In a nationally representative sample, AW readmission within 30 days and multiple readmissions during the year were high and were particularly predicted by discharge against medical advice (AMA), comorbid psychosis, comorbid depression, poor socioeconomic status, comorbid drug abuse and alcohol-related medical disease. Methods Subjects from the 2013 Nationwide Readmissions Database (NRD) with AW as a primary or secondary diagnosis. Cross-sectional and retrospective analyses were performed using regression methods appropriate for the NRD complex sampling design. The outcome measures were AW-related readmission, 30-day readmission and multiple readmissions. Results In 2013, 393,118 discharges involved ICD-9 coding for AW and 41.5% of these included AW as the primary discharge diagnosis. The rate of AW-related readmission in 2013, as estimated from first-quarter index events, was 58.8% (95% confidence interval (CI) 57.5–60.1), with an average of 1.8 readmissions (95% CI 1.7–1.9). The 30-day readmission rate, estimated from January–November index events, was 19.7% (95% CI 19.0–20.4). The strongest independent predictors of yearly, 30-day and multiple readmission were discharged AMA and comorbid psychotic disorder. Conclusion AW readmission within 30 days and multiple readmissions during the year were common and were particularly predicted by AMA discharge and comorbid psychotic disorder. While these and other factors can help identify high-risk patients, further study to determine causal mechanisms may aid efforts to improve both the outcomes and costs associated with acute AW treatment. BACKGROUND Approximately, 6.2% of the adult population in the USA or roughly 15 million individuals had an alcohol use disorder (AUD) in 2015 (National Institute on Alcohol Abuse and Alcoholism, 2015). Alcohol-related problems pose a massive economic burden on the healthcare system. For example, from 2002 to 2010, the rate of emergency department visits for alcohol-related diagnoses increased by 38% (Centers for Disease Control and Prevention, 2013). Alcohol dependence and associated conditions constitute major problems in acute care settings. Alcohol withdrawal (AW) is the most common alcohol-related illness that requires inpatient admission and is associated with adverse events such as uncontrolled agitation with the potential for over-sedation, generalized seizures and prolonged hospital stay (Clark et al., 2013). Hospital readmissions serve as a quality care benchmark and hospitalists may encounter repeated AW admissions for the same patient. Rates of readmission among patients presenting to detoxification programs within 1 year have ranged from 34% to 48% (Running Bear et al., 2014). Patients who reported alcohol as the primary preferred drug were more likely to be readmitted within 1 year (Li et al., 2008). In efforts to limit readmission, a number of single-centered studies have sought to identify readmission predictors (Callaghan, 2003; Carrier et al., 2011; Li et al., 2008; Mark et al., 2006). Some limitations of these studies include poor generalizability, inability to identify patients readmitted elsewhere, a focus on patients admitted to dedicated detoxification units or intensive care units, and limiting study to those with a primary AW diagnosis (Callaghan, 2003; Carrier et al., 2011; Clark et al., 2013; Li et al., 2008; Mark et al., 2006; Running Bear et al., 2014). In this study, we utilized a nationally representative sample of hospital discharges to estimate 30-day and yearly readmissions of AW patients, and to evaluate potential predictors of readmissions. METHODS We analyzed the 2013 Nationwide Readmissions Database (NRD), a subset of the Healthcare Cost and Utilization Project sponsored by the Agency for Healthcare Research and Quality (AHRQ), a nationally representative sample of acute care hospital discharges in the USA. The NRD is a large publicly available all-payer inpatient care database. Unweighted, the NRD contains data from ~14 million discharges each year, estimating roughly 36 million weighted discharges, with unique linkage numbers used to identify any additional hospital discharges for each subject. National estimates are produced using sampling weights provided by the sponsor and precision is estimated using stratification and clustering variables to account for the complex probability sampling scheme. The study was granted exemption from human subjects review as it involved de-identified and publically-available data. Identification of alcohol withdrawal and hospital readmissions The 2013 NRD provided up to 25 ICD-9 diagnoses for each hospital discharge. AW (ICD-9 code 291.0) and AW delirium (ICD-9 code 291.81) were combined to identify AW hospitalizations. Other ICD-9 diagnoses such as ‘alcohol abuse’ were not included. Primary AW included hospital discharges with withdrawal or withdrawal delirium as the primary diagnosis. Secondary AW included hospital discharges with other primary diagnoses. For estimating readmissions, an ‘index event’ was defined as having a hospital discharge that involved AW and did not result in death. Index events occurring in December were excluded from the 30-day readmission analyses, since readmissions may have extended into 2014. Index events occurring in the first 3 months of 2013 were used to approximate yearly readmissions. Potential predictors of readmission Potential predictors included demographic variables (age, sex and markers of socioeconomic status), tobacco abuse due to its association with alcoholism and adverse health events, alcohol-related medical disease, index admission through the emergency room (ER) since this may reflect severity, and factors identified in previous studies (mental health disorders, comorbid drug abuse). Age, sex and admission through ER were directly included in the NRD database. We did not evaluate ethnicity as this variable was not consistently recorded in the NRD. Alcohol-related medical disease was categorized as present or not present dependent on having an ICD-9 diagnosis that was specific for alcohol-related organ damage. In the ICD-9 system, this was limited to liver disease, cardiomyopathy, polyneuropathy and gastritis. We adopted a conservative approach and did not attempt to categorize other diagnoses as alcohol-related (e.g. an ICD-9 diagnosis of alcohol abuse during a hospitalization for pancreatitis or seizure). As a marker of socioeconomic status, low income was estimated from average income in the zip code of residence, with those living in the lowest quartile zip codes categorized as ‘low income’. Socioeconomic status was also estimated from health insurance records, with those having only Medicaid or relying on self-pay being compared with those with other forms of health insurance. Depression, psychosis and drug abuse were derived from the AHRQ comorbidity indices for these conditions that are directly provided in the NRD. Tobacco abuse was derived using ICD-9 codes. Statistical analysis All analyses adjusted for the NRD sampling design and were completed with SAS version 9.4 (SAS Institute, Cary, NC, USA). The total number of hospital discharges involving AW as a primary or secondary diagnosis was estimated using PROC SURVEYFREQ. The probability of readmission and average number of readmissions were estimated using PROC SURVEYMEANS. PROC SURVEYLOGISTIC was used to estimate unadjusted and adjusted risks for readmission associated with each covariate. PROC SURVEYLOGISTIC with a generalized logit link was used to estimate odds ratios for having one or multiple readmissions relative to no readmissions. In addition to the overall sample of AW discharges, we repeated analyses in the subset of patients who did not have a primary mental health diagnosis. This was done to reflect a medical-surgical population that may better represent patients cared for by hospitalists. RESULTS There were 393,118 discharges that involved ICD-9 coding for AW as a discharge diagnosis with 41.5% having AW as the primary discharge diagnosis. The yearly readmission rate, estimated from first-quarter index events, was 58.8% (95% CI 57.5–60.1), with an average of 1.8 readmissions per patient (95% CI 1.7–1.9). The 30-day readmission rate, estimated from January–November index events, was 19.7% (95% CI 19.0–20.4). Table 1 compares the characteristics of patients with AW diagnosis who required readmission for any reason within 30 days of discharge from an index admission. Male patients and those with poor socioeconomic status, comorbid depression, psychosis and other drug abuse were more likely to be readmitted, as were those with alcohol-related medical conditions to a lesser extent. Discharge against medical advice (AMA) had the strongest association with readmission. Age, tobacco abuse and admission through the emergency department were not associated with early readmission. Table 1. Characteristics of alcohol withdrawal index admissions stratified by 30-day readmission Variable No 30-day readmission (n = 284,744)a 30-Day readmission (n = 69,741)a P-value Mean age 50.4 50.1 0.053 Female (%) 25.7 23.0 <0.001 Primary alcohol withdrawal (%) 42.3 41.5 0.155 Alcohol-related disease (%) 25.5 26.9 0.015 Comorbid depression (%) 21.6 23.9 <0.001 Comorbid psychosis (%) 14.6 19.9 <0.001 Comorbid drug abuse (%) 8.9 10.3 <0.001 Tobacco use disorder (%) 45.2 45.9 0.332 ER admission (%) 80.8 83.1 0.132 Discharged against medical advice (%) 7.4 14.2 <0.001 Medicaid or self-pay (%) 41.9 48.8 <0.001 Low-income zip code (%) 30.5 33.9 <0.001 Variable No 30-day readmission (n = 284,744)a 30-Day readmission (n = 69,741)a P-value Mean age 50.4 50.1 0.053 Female (%) 25.7 23.0 <0.001 Primary alcohol withdrawal (%) 42.3 41.5 0.155 Alcohol-related disease (%) 25.5 26.9 0.015 Comorbid depression (%) 21.6 23.9 <0.001 Comorbid psychosis (%) 14.6 19.9 <0.001 Comorbid drug abuse (%) 8.9 10.3 <0.001 Tobacco use disorder (%) 45.2 45.9 0.332 ER admission (%) 80.8 83.1 0.132 Discharged against medical advice (%) 7.4 14.2 <0.001 Medicaid or self-pay (%) 41.9 48.8 <0.001 Low-income zip code (%) 30.5 33.9 <0.001 aNot equal to total AW discharges included in the text as 30-day readmission analysis did not include December discharges (n = 32,066), subjects who died during an index admission (n = 5,684) and subjects where missing data (n = 883) did not allow for readmission analysis. Table 1. Characteristics of alcohol withdrawal index admissions stratified by 30-day readmission Variable No 30-day readmission (n = 284,744)a 30-Day readmission (n = 69,741)a P-value Mean age 50.4 50.1 0.053 Female (%) 25.7 23.0 <0.001 Primary alcohol withdrawal (%) 42.3 41.5 0.155 Alcohol-related disease (%) 25.5 26.9 0.015 Comorbid depression (%) 21.6 23.9 <0.001 Comorbid psychosis (%) 14.6 19.9 <0.001 Comorbid drug abuse (%) 8.9 10.3 <0.001 Tobacco use disorder (%) 45.2 45.9 0.332 ER admission (%) 80.8 83.1 0.132 Discharged against medical advice (%) 7.4 14.2 <0.001 Medicaid or self-pay (%) 41.9 48.8 <0.001 Low-income zip code (%) 30.5 33.9 <0.001 Variable No 30-day readmission (n = 284,744)a 30-Day readmission (n = 69,741)a P-value Mean age 50.4 50.1 0.053 Female (%) 25.7 23.0 <0.001 Primary alcohol withdrawal (%) 42.3 41.5 0.155 Alcohol-related disease (%) 25.5 26.9 0.015 Comorbid depression (%) 21.6 23.9 <0.001 Comorbid psychosis (%) 14.6 19.9 <0.001 Comorbid drug abuse (%) 8.9 10.3 <0.001 Tobacco use disorder (%) 45.2 45.9 0.332 ER admission (%) 80.8 83.1 0.132 Discharged against medical advice (%) 7.4 14.2 <0.001 Medicaid or self-pay (%) 41.9 48.8 <0.001 Low-income zip code (%) 30.5 33.9 <0.001 aNot equal to total AW discharges included in the text as 30-day readmission analysis did not include December discharges (n = 32,066), subjects who died during an index admission (n = 5,684) and subjects where missing data (n = 883) did not allow for readmission analysis. In an adjusted analysis (Table 2), all factors associated with 30-day readmission in Table 1 remained statistically significant predictors. The strongest independent predictor was AMA discharge (OR 2.01, 95% CI 1.90–2.10) followed by comorbid psychosis (OR 1.54, 95% CI 1.48–1.60). As seen in Figure 1, except for gender, a similar pattern held for any readmission in the calendar year. AMA discharge, comorbid psychosis and health insurance status were the strongest predictors for having multiple readmissions. Table 2. Fully adjusteda odds ratios for 30-day readmission Variable Odds ratio (95% confidence interval) Female 0.84 (0.80–0.88) Alcohol-related medical disease 1.11 (1.05–1.17) Comorbid depression 1.28 (1.22–1.34) Comorbid psychosis 1.54 (1.48–1.60) Comorbid drug abuse 1.13 (1.07–1.20) Discharged against medical advice 2.01 (1.90–2.10) Medicaid or self-pay 1.25 (1.20–1.31) Low-income zip code 1.16 (1.06–1.18) Variable Odds ratio (95% confidence interval) Female 0.84 (0.80–0.88) Alcohol-related medical disease 1.11 (1.05–1.17) Comorbid depression 1.28 (1.22–1.34) Comorbid psychosis 1.54 (1.48–1.60) Comorbid drug abuse 1.13 (1.07–1.20) Discharged against medical advice 2.01 (1.90–2.10) Medicaid or self-pay 1.25 (1.20–1.31) Low-income zip code 1.16 (1.06–1.18) aEach variable adjusted for all other variables in the table. Table 2. Fully adjusteda odds ratios for 30-day readmission Variable Odds ratio (95% confidence interval) Female 0.84 (0.80–0.88) Alcohol-related medical disease 1.11 (1.05–1.17) Comorbid depression 1.28 (1.22–1.34) Comorbid psychosis 1.54 (1.48–1.60) Comorbid drug abuse 1.13 (1.07–1.20) Discharged against medical advice 2.01 (1.90–2.10) Medicaid or self-pay 1.25 (1.20–1.31) Low-income zip code 1.16 (1.06–1.18) Variable Odds ratio (95% confidence interval) Female 0.84 (0.80–0.88) Alcohol-related medical disease 1.11 (1.05–1.17) Comorbid depression 1.28 (1.22–1.34) Comorbid psychosis 1.54 (1.48–1.60) Comorbid drug abuse 1.13 (1.07–1.20) Discharged against medical advice 2.01 (1.90–2.10) Medicaid or self-pay 1.25 (1.20–1.31) Low-income zip code 1.16 (1.06–1.18) aEach variable adjusted for all other variables in the table. Fig. 1. View largeDownload slide Predictors of yearly readmission and multiple readmissions relative to no readmission. Fig. 1. View largeDownload slide Predictors of yearly readmission and multiple readmissions relative to no readmission. In the subset analysis stratified by primary mental health discharges, the 30-day readmission rate of AW discharges with and without primary mental health diagnosis were ~22% and 19%, respectively. Results in those without a primary mental health discharge diagnosis were in general similar to the overall sample. Minor differences included statistically significant increases in 30-day readmission for an index event involving the ER (odds ratio 1.26, P < 0.001) and for those with tobacco abuse (odds ratio 1.05, P = 0.037) and similar risks for yearly readmission. DISCUSSION We report data from a nationally representative database on the predictors of readmission following an AW discharge (1.3% of all adult discharges). Our results showed an approximate yearly readmission rate approaching 60%, a high prevalence of multiple readmissions and nearly 20% of subjects having an early readmission. This is higher than the previously reported readmission rates of 34–48% in similar studies (Ponzer et al., 2002; Callaghan, 2003; Li et al., 2008; Mark et al., 2006). Compared with these single-centered studies, ours being a nationally representative sample may have captured a more accurate number of readmissions. Several predictors of readmission have been identified, with the most important risk factors including discharge AMA and comorbid psychosis. The association between AMA discharge and readmission is particularly strong and could be related to incomplete detoxification with a resultant immediate return to heavy drinking following hospitalization. However, prior research has identified several correlates of AMA discharge, including young age, reporting opiates as the primary preferred drug, having HCV infection, welfare check issue period and lower education level (Li et al., 2008). Thus, the relationship between AMA discharge and readmission may be much more complex than incomplete detoxification. Though there is a downward annual trend observed in AMA discharges among the mental health and substance abuse population (Spooner et al., 2017), further studies are needed to characterize this at-risk population in order to achieve better patient outcomes and reduced costs. Other important predictors included markers of socioeconomic status and comorbid psychosis. As shown in previous studies (Mark et al., 2006; Carrier et al., 2011), we had similar findings of increased readmission in low-income patients and those with no insurance or having fee-for-service Medicaid. This may represent poor accessibility of outpatient care following hospitalization, or a more general lack of resources to support health. Our study shows the strong association of comorbid psychosis, and lower magnitude associations of depression and other drug abuse, with 30-day and multiple readmissions. The high prevalence of psychiatric comorbidity and other substance abuse in AUD patients is well-known (Lai et al., 2015). These conditions are particularly prevalent in hospitalized alcoholics (Stewart, 2007), and are associated with poor treatment compliance, worse quality of life and poorer outcomes in patients with AUD (Morojele et al., 2012). Similar to our findings in this nationally representative sample of hospitalized patients, it is well established that psychiatric comorbidity and other substance abuse are risk factors for increased readmissions to detoxification centers (Moos et al., 1994a, 1994b; Luchansky et al., 2000; Mertens et al., 2005; Walker et al., 1995; Tomasson and Vaglum, 1998; Ponzer et al., 2002). Recent studies have also confirmed this association of comorbid psychiatric condition and other substance abuse with increased readmissions in AW admissions (Booth and Blow, 1993; Clark et al., 2013), as is shown in our study. The high 30-day readmission rate in AW admissions without comorbid psychiatric condition is also worth mentioning as these two conditions often coexist. Contrary to previous studies (Luchansky et al., 2000; Mertens et al., 2005), our study is consistent with Larson et al. (2012) in that women were slightly less likely to have an early readmission. However, gender was not associated with later readmission in adjusted analyses and there was no gender association with multiple readmissions. Surprisingly, specifically-coded ICD-9 diagnoses for alcohol-related medical disease were only weakly associated with readmission. However, comorbid medical conditions in general may have a negative effect on abstinence and result in increased readmissions (Shih and Simon, 2008) and some studies have reported associations of comorbid medical disease with increased readmissions in AW patients (Larson et al., 2012; Clark et al., 2013). Admissions through the emergency department (representing a large majority of AW discharges), having a primary AW diagnosis and tobacco use disorder were not significantly associated with readmissions, although emergency department admission was a modest risk factor in discharges that did not include a primary mental health diagnosis. Our study complements prior research by extending findings to a nationally representative sample. We were also able to capture readmissions to different hospitals following an index admission. While these are some of the merits, our study has significant limitations. Data analyses are limited by the accuracy of ICD coding in hospital databases. In particular, it is difficult to adequately capture alcohol-related medical conditions and many discharges including ‘alcohol abuse’ may have included unrecorded AW. In our analysis, there were more ‘alcohol abuse’ primary discharge diagnoses compared with AW diagnosis. Any tendency to under-code alcohol-related conditions may bias association with readmission, and under-coding of AW would at a minimum underestimate AW prevalence. The follow-up period may not have been long enough to detect additional readmissions (for example, an index admission in March 2013 and readmission in January 2014) and, since we assumed index admission to be the first admission, these patients may have had prior admissions in the previous calendar year. However, the study design likely provided close estimates for readmission frequency and prior year AW discharges would not influence estimates on 30-day readmission. Finally, since the NRD is directly abstracted from clinical and administrative hospital records, other previously studied predictors of readmission such as unemployment, homelessness, race, severity of alcohol use, severity of AW, laboratory data such as liver function tests and urine drug screen, clinical case management and follow-up treatment post-discharge could not be studied (Booth and Blow, 1993; Ponzer et al., 2002; Callaghan, 2003; Mark et al., 2006; McLellan et al., 2005; Worner, 1996). In conclusion, major risk factors associated with 30-day readmission and multiple readmissions to the US hospitals following an AW discharge were discharge AMA, comorbid psychosis and to a lesser extent low socioeconomic status. Hospitalists can use these factors (e.g. prior AMA discharges, psychoses and poor insurance) to estimate risks for poor outcomes requiring re-hospitalization. However, while these are important markers of risk, further study to determine the underlying causal mechanisms may ultimately aid efforts to improve both the outcomes and costs associated with acute AW treatment. ACKNOWLEDGEMENTS None CONFLICT OF INTEREST STATEMENT None declared. REFERENCES Booth BM , Blow FC . ( 1993 ) The kindling hypothesis: further evidence from a U.S. national study of alcoholic men . Alcohol Alcohol 28 : 593 – 8 . Google Scholar PubMed Callaghan RC . 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Journal

Alcohol and Alcoholism – Oxford University Press

Published: Mar 30, 2018

References

  • The kindling hypothesis: further evidence from a U.S. national study of alcoholic men
    Booth
  • Risk factors associated with dropout and readmission among First Nations individuals admitted to an inpatient alcohol and drug detoxification program
    Callaghan
  • Factors associated with frequent utilization of crisis substance use detoxification services
    Carrier