Diagnosis-Specific Psychiatric Comorbidity in Heart Failure: Associations With Length of Stay, Costs, and Mortality in a National Cohort

Diagnosis-Specific Psychiatric Comorbidity in Heart Failure: Associations With Length of Stay, Costs, and Mortality in a National Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnosis-Specific Psychiatric Comorbidity in Heart Failure: Associations With Length of Stay, Costs, and Mortality in a National Cohort Austin Charles, Kyle Thurmann, Peter Ernst, Paul Kang, Michael White This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8745482/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Psychiatric comorbidities are common in patients hospitalized for heart failure (HF), but diagnosis-specific associations with hospital utilization and outcomes remain unclear, a core question in consultation-liaison (C-L) psychiatry. Objective To evaluate how specific psychiatric comorbidities relate to index length of stay (LOS), hospital costs, and mortality after HF hospitalization. Methods Retrospective cohort study using the 2016–2022 Nationwide Readmissions Database. Adults with a principal HF diagnosis were included. Comorbidities were depression, anxiety, bipolar disorder, schizophrenia/psychotic disorders, post-traumatic stress disorder (PTSD), and substance use disorder (SUD). Outcomes were index LOS, inflation-adjusted costs, and 30-day and 1-year in-hospital mortality during readmissions. Survey-weighted multivariable models adjusted for demographics, socioeconomic factors, hospital characteristics, discharge disposition, and comorbidity burden; p ≤ 0.001 was prespecified. Results Among 31,886,859 weighted HF hospitalizations, psychiatric comorbidity was common. Anxiety was associated with longer LOS (β = 0.88 days; p < 0.001) and higher costs (β= $ 2,779; p < 0.001) without differences in 30-day or 1-year mortality. Several diagnoses were associated with lower mortality, including depression (30-day OR = 0.86; 1-year OR = 0.86), bipolar disorder (0.66; 0.68), schizophrenia/psychotic disorders (0.68; 0.72), PTSD (0.73; 0.78), and SUD (0.87; 0.92) (all p < 0.001). Bipolar disorder showed the largest cost reduction (β=− $ 1,320; p < 0.001). Conclusions Psychiatric comorbidity in HF is heterogeneous. Anxiety is associated with increased hospital utilization without a mortality difference, whereas several other diagnoses are associated with lower observed mortality; costs were lower for depression and bipolar disorder. These diagnosis-specific patterns support targeted screening, early consultation, and integrated C-L care pathways during HF hospitalization. consultation-liaison psychiatry heart failure psychiatric comorbidity anxiety substance use disorder health care utilization mortality Figures Figure 1 INTRODUCTION Heart failure (HF) is a major cause of morbidity, mortality, and rising healthcare costs in the United States. As of 2024, HF was estimated to affect six million American adults aged 20 years or older, with that number expected to rise as the population ages [ 1 ]. In 2023, HF was cited on over 450,000 death certificates, accounting for nearly 15% of all U.S. deaths [ 2 ]. In 2012, the economic burden of HF was estimated at $ 30.7 billion, a figure projected to rise to $ 69.7 billion by 2030 [ 3 ]. HF therefore imposes a substantial and growing burden on the healthcare system and society in the United States. Psychiatric comorbidities are common in HF and contribute to symptom burden, functional impairment, and reduced quality of life. The intersection of cardiovascular disease and psychiatric illness is central to consultation-liaison (C-L) psychiatry, where psychiatric symptoms, diagnoses, and treatment decisions can influence hospital course, discharge planning, and follow-up care in medically complex patients. Meta-analytic data suggest that approximately 25% of patients with HF also meet criteria for depression [ 4 ]. However, the psychiatric burden in HF extends beyond depression, including substance use disorder (SUD), anxiety, post-traumatic stress disorder (PTSD), schizophrenia, and bipolar disorder [ 5 ]. These conditions may be under-recognized or undertreated in HF due to overlapping clinical presentations and competing medical priorities. When unaddressed, psychiatric comorbidities may contribute to worse HF-related outcomes through mechanisms such as reduced treatment adherence, functional decline, and increased healthcare utilization [ 6 , 7 ]. Most prior studies have linked psychiatric comorbidities in HF to adverse outcomes, including higher hospitalization rates and mortality [ 6 – 8 ]. However, existing evidence is limited by inconsistent measurement of psychiatric diagnoses, reliance on smaller or geographically restricted samples, and limited evaluation of diagnosis-specific effects. In addition, many studies evaluate psychiatric illness as a single exposure, which may obscure clinically important heterogeneity across diagnoses with distinct symptom profiles, treatment needs, and care pathways. Accordingly, we examined diagnosis-specific associations between psychiatric comorbidities and HF outcomes, including index hospital length of stay (LOS), inflation-adjusted hospital costs, and 30-day and 1-year mortality, using the large, nationally representative Nationwide Readmissions Database (NRD). METHODS Data Source We performed a retrospective cohort study using the 2016–2022 NRD, a component of the Healthcare Cost and Utilization Project (HCUP) that captures all-payer inpatient discharges from 30 states in the United States, representing approximately 60% of national hospitalizations [ 9 ]. Each annual NRD file is independent; therefore, 1-year readmissions were assessed within the same calendar year. Study Population We included adult patients aged 18 years or older who were hospitalized with a primary diagnosis of HF, identified using ICD-10 codes I50.1 through I50.9. Index discharges resulting in in-hospital death, transfers to another acute facility, or records with missing demographic or readmission variables were excluded (Fig. 1 ). Readmissions flagged as ‘planned’ by HCUP were excluded from outcome ascertainment. Furthermore, each readmission was flagged with its corresponding “NRD_KEY” unique ID, thus each index admission represents a unique patient admission. The Nationwide Readmissions Database (NRD), 2016–2022, was used to identify adult (≥ 18 years) index hospitalizations with a principal diagnosis of heart failure. Records were excluded for in-hospital death during the index admission, transfer to another acute-care hospital, or missing outcome data. Unweighted counts reflect the NRD sample; weighted counts represent national estimates. Psychiatric comorbidity definitions To assess the effects of psychiatric comorbidities, we identified patients with one or more psychiatric diagnoses using the following ICD-10 codes: depression (F32.0-F32.9, F33.0-F33.9), anxiety (F40.0-F41.9), bipolar disorder (F31.0-F31.9), schizophrenia/psychotic disorders (F20.0-F20.9, F25.0-F25.9), PTSD (F43.10-F43.12), and SUDs (F10-F16, F18-F19, nicotine was excluded due to clinical differences from the other substances). Outcome Measures Outcomes included index admission LOS, inflation‑adjusted hospital costs, and mortality within 30 days and one year after the index discharge. LOS was calculated as the number of days between admission and discharge. Hospital charges were converted to costs using the HCUP cost‑to‑charge ratios and inflation-adjusted to 2022 US dollars with the CPI-U࿼. Mortality was assessed using the discharge disposition in the NRD and reflects only in-hospital deaths that occurred during a rehospitalization within 30 days or one year after discharge. Deaths during the index admission were excluded, and the NRD does not capture deaths occurring outside of the hospital. Covariates Covariates included patient demographics (age, sex, primary payer, and ZIP code-based income quartile), hospital-level characteristics (bed size, location, and teaching status), admission timing (weekend vs. weekday), discharge disposition, and comorbidity burden measured by the Charlson Comorbidity Index (CCI) [ 10 ]. Statistical Analysis Survey‑weighted estimates were derived using HCUP discharge weights. Baseline characteristics were summarized as weighted means with standard errors (SEs) for continuous variables and as weighted proportions with SEs for categorical variables. Differences between groups (i.e. with vs without SUD) were assessed using weighted univariate linear regression or chi‑square tests as appropriate. Multivariable linear regression models were constructed to estimate adjusted differences (β coefficients with 95% confidence intervals) in index LOS and inflation‑adjusted hospital costs, adjusting for demographic, socioeconomic, and hospital factors. For mortality outcomes, survey‑weighted logistic regression models estimated adjusted odds ratios (ORs) with 95% confidence intervals for in-hospital mortality at 30 days and 1 year during readmission among patients who were not transferred to another facility. Given the large sample size, a stringent significance threshold of p ≤ 0.001 was applied to limit type I error. Software and Ethics Compliance All analyses were performed using Stata SE 18 (StataCorp, 2023). The study adhered to the HCUP Data Use Agreement, and as a retrospective analysis of de-identified data, it was exempt from institutional review board approval. RESULTS Cohort characteristics The initial analysis included 31,886,859 weighted hospitalizations for HF from the NRD. The mean patient age was 71.3 years (SE, 0.004), and women comprised 48.9% of the cohort. Most patients were covered by Medicare (75.7%) and lived in urban settings (82.4%). The lowest income quartile included the most patients (32.8%). More than half of admissions occurred at large hospitals (53.8%), and the majority were treated at urban teaching institutions (69.8%). Nearly three-quarters of patients had a CCI score of 3 or higher. At discharge, 45.6% of patients returned home, 25.8% received home healthcare, and 25.7% were transferred to post-acute care facilities. Patients who were not transferred (n = 14,535,581) were statistically younger with a lower proportion of females. In addition, the percentage of patients within the lowest income quartile increased from 32.8% to 35.5%. The primary payer among non-transferred patients remained Medicare; however, the percentage of Medicare users decreased from 75.7% to 65.8%. Conversely, private insurance users increased from 10% to 14.0%. While statistically significant, the distribution of hospital characteristics remained similar between all heart disease patients and those who were not transferred. The prevalence of psychiatric comorbidities in the overall population included depression (14.6%), anxiety (0.29%), bipolar disorder (2.1%), schizophrenia or other psychotic disorders (1.5%), PTSD (0.8%), and SUD (19.2%). Among non-transferred patients, the percentage of these psychiatric disorders did not change clinically. A detailed summary of demographic and clinical characteristics is presented in Table 1. Table 1 Baseline demographic, clinical, and psychiatric characteristics of the study population Demographics (Weighted) Overall – Heart Disease Patients (n = 31,886,859) Non-Transferred Patients (n = 14,535,581) Age, years (mean, SE) 71.3 (0.004) 66.9 (0.005) Sex, Female (%, SE) 48.9 (0.013) 43.9 (0.019) Income Quartile (%, 95% CI) 1 2 3 4 32.8 (0.011) 27.7 (0.011) 22.9 (0.011) 16.7 (0.009) 35.5 (0.018) 27.9 (0.017) 22.2 (0.016) 14.6 (0.013) Primary Payer (%, SE) Medicare Medicaid Private Self-Pay Other 75.7 (0.011) 10.1 (0.007) 10.0 (0.008) 1.79 (0.003) 2.35 (0.004) 65.8 (0.018) 14.3 (0.013) 14.0 (0.013) 3.10 (0.007) 2.83 (0.006) Weekend Admission (%, SE) 24.4 (0.011) 24.0 (0.016) Patient Location (%, SE) Urban Rural 82.4 (0.008) 17.6 (0.008) 84.2 (0.011) 18.8 (0.012) Hospital Location (%, SE) Urban/Non-Teaching Urban/Teaching Rural 20.4 (0.002) 69.8 (0.003) 9.77 (0.002) 19.9 (0.003) 70.0 (0.004) 9.99 (0.003) Hospital Bed size (%, SE) Small Medium Large 18.5 (0.002) 27.7 (0.002) 53.8 (0.003) 18.1 (0.003) 26.9 (0.003) 55.0 (0.004) Charlson Comorbidity Index (%, SE) 0–1 2 > 3 7.30 (0.007) 17.8 (0.009) 74.9 (0.011) 9.47 (0.011) 20.9 (0.016) 69.6 (0.017) Disposition at Discharge (%, SE) Home Short Term Facility Designated Center Home Healthcare AMA Unknown 45.6 (0.012) 1.20 (0.003) 25.7 (0.011) 25.8 (0.011) 1.66 (0.003) 0.047 (6.5e-6) 100.0 Psychiatric Covariates Depression 14.6 (0.009) 13.2 (0.013) Anxiety 0.29 (0.001) 0.29 (0.002) Bipolar Disorder 2.06 (0.004) 2.16 (0.006) Schizophrenia/psychotic disorders 1.50 (0.003) 1.35 (0.004) PTSD 0.80 (0.002) 0.99 (0.004) Substance Use Disorder (excl. Nicotine) 4.69 (0.005) 5.59 (0.009) This table summarizes weighted baseline characteristics for 31,886,859 hospitalizations of adults with heart failure in the Nationwide Readmissions Database. Depression (14.6%) was the most prevalent psychiatric comorbidity. Index admission LOS and hospital cost During the index HF admission among non-transferred patients, the unadjusted mean LOS was approximately 4.6 days for patients without psychiatric comorbidities (Table 2). Patients with anxiety (5.77 days), schizophrenia/psychotic disorders (6.67 days), bipolar disorder (5.47 days), PTSD (5.51 days), and SUD (5.50 days) all exhibited longer stays than patients without these conditions. Multivariable linear regression adjusting for demographics, socioeconomic status, and hospital characteristics demonstrated diagnosis-specific effects on LOS (Table 2). Schizophrenia was associated with the greatest increase in LOS, with patients staying 1.67 days longer than those without schizophrenia (β = 1.67; 95% CI, 1.61 to 1.74; p < 0.001). Anxiety disorders were also linked to a substantial increase in LOS (β = 0.88; 95% CI, 0.78 to 0.96; p < 0.001), while substance use disorder was associated with a smaller but statistically significant increase (β = 0.60; 95% CI, 0.58 to 0.62; p < 0.001). In contrast, depression status (β = 0.30; 95% CI, 0.28 to 0.32; p < 0.001), bipolar disorder (β = 0.29; 95% CI, 0.26 to 0.34; p < 0.001), and PTSD (β = 0.25; 95% CI, 0.20 to 0.30; p < 0.001) were associated with smaller increases in LOS. Inflation-adjusted hospital costs showed a pattern distinct from LOS. Anxiety was associated with a substantial cost increase, with patients incurring an additional $2,778 (β = 2,778.9; 95% CI, 2,469.7 to 3,088.1; p < 0.001). Additionally, SUD status was associated with a $1,477.2 increase in cost compared to no SUD (β = 1,477.2; 95% CI, 1,396.8 to 1,557.5; p < 0.001). Depression, however, was paradoxically associated with lower costs, reducing expenditure by $227.3 (β = –227.3; 95% CI, –276.5 to –178.1; p < 0.001). Bipolar disorder conferred the largest cost reduction, with patients incurring $1,319.5 less than those without the condition (β = –1,319.5; 95% CI, –1424.6 to –1,114.4; p < 0.001). Schizophrenia/psychotic disorders and PTSD were not associated with cost. Table 2 Impact of psychiatric comorbidities on index hospital stay and costs Psychiatric Condition Length of Stay, days Mean (SE) Beta (95% CI) 1 p-value 2022 Inflation -Adjusted Total Cost, $ Mean (SE) Beta (95% CI) 1 p-value Depression No 4.62 (0.00) REF 15424.9 (8.37) REF Yes 5.03 (0.01) 0.30 (0.28, 0.32) < 0.001 15434.4 (23.0) -227.3 (-276.5, -178.1) <0.001 Anxiety No 4.67 (0.00) REF 15415.9 (7.86) REF Yes 5.77 (0.05) 0.88 (0.78, 0.96) < 0.001 18920.6 (157.7) 2778.9 (2469.7, 3088.1) <0.001 Bipolar No 4.65 (0.00) REF 15431.0 (7.95) REF Yes 5.47 (0.02) 0.29 (0.26, 0.34) < 0.001 15205.5 (50.9) -1319.5 (-1424.6, -1214.4) <0.001 Schizophrenia No 4.64 (0.00) REF 15417.5 (7.91) REF Yes 6.67 (0.03) 1.67 (1.61, 1.74) < 0.001 16057.7 (69.0) 106.5 (-33.3, 246.2) 0.14 PTSD No 4.66 (0.00) REF 15413.3 (7.88) REF Yes 5.51 (0.03) 0.25 (0.20, 0.30) < 0.001 16704.6 (87.3) 20.8 (-154.2, 195.7) 0.82 Substance Use Disorder No 4.62 (0.00) REF 15272.9 (7.99) REF Yes 5.50 (0.01) 0.60 (0.58, 0.62) < 0.001 18015.7 (39.5) 1477.2 (1396.8, 1557.5) <0.001 This table presents the unadjusted mean index LOS and inflation-adjusted hospital costs for heart failure admissions in patients with and without six psychiatric comorbidities (depression, anxiety, bipolar disorder, schizophrenia/psychotic disorders, PTSD, and SUD). Multivariable linear regression coefficients (β) indicate the adjusted differences in LOS (days) and costs (U.S. dollars), with 95% confidence intervals and p-values. Models were adjusted for demographics, socioeconomic status, and hospital characteristics. In-Hospital mortality within the 30-day and 1-year readmission Short-term (30-day readmitted) and long-term (1-year readmitted) mortality outcomes showed a paradoxical relationship with psychiatric comorbidity. Unadjusted mortality at the 30-day readmission ranged from 2.35% in patients with PTSD to 4.48% in those without Depression, while 1-year readmission mortality ranged from 2.00% (bipolar disorder) to 3.97% (no Depression) (Table 3). After multivariable adjustment, depression was associated with lower mortality at both time points: the OR for mortality within readmission at 30-day was 0.86 (95% CI, 0.84–0.88; p < 0.001), and the OR for mortality within 1-year readmission was 0.86 (95% CI, 0.85–0.88; p < 0.001). Bipolar disorder conferred the lowest odds of mortality, with a 30-day readmission death OR of 0.66 (95% CI, 0.62–0.71; p < 0.001) and a 1-year readmission death OR of 0.68 (95% CI, 0.65–0.73; p < 0.001), corresponding to ~33% lower odds of death compared with HF patients without bipolar disorder. Schizophrenia/psychotic disorders, PTSD, and SUD were also associated with significantly lower mortality. Schizophrenia/psychotic disorders observed 32% lower odds of 30-day readmission mortality compared to admissions with no schizophrenia/psychotic disorders (0.68 (95% CI, 0.62–0.74; p < 0.001). Furthermore, 1-year readmission mortality decreased by 28% among the same group (0.72 (95% CI, 0.67–0.76); p < 0.001). PTSD was associated with a 27% and 22% decrease in the odds of 30-day and 1-year readmission mortality, respectively (30-day: OR of 0.73 (95% CI, 0.65–0.80; p < 0.001 and 1-year: OR of 0.78 (95% CI, 0.72–0.84; p < 0.001). SUD showed a modest decrease in 30-day and 1-year readmission mortality of 13% and 8%, respectively (30-day: OR of 0.87 (95% CI, 0.84–0.90; p < 0.001 and 1-year: OR of 0.92 (95% CI, 0.89–0.95; p < 0.001). Only anxiety failed to demonstrate a mortality difference. Adjusted 30-day readmission mortality for patients with anxiety (4.23%) was similar to that of patients without anxiety (4.35%), with an OR of 1.11 (95% CI, 0.95–1.30; p = 0.19). Likewise, adjusted 1-year mortality did not differ significantly (OR = 1.02; 95% CI, 0.90–1.14; p = 0.77). Table 3 Association between psychiatric comorbidities and post-discharge mortality Psychiatric Condition 30-day Mortality N = 2,844, 722 Multivariable 1-year Mortality N = 6,304,799 Multivariable % (SE) OR (95 %CI) 2 p-value % (SE) OR (95 %CI) 2 p-value Depression No Yes 4.48 (0.019) 3.60 (0.044) REF 0.86 (0.84, 0.88) < 0.001 3.97 (0.012) 3.22 (0.028) REF 0.86 (0.85, 0.88) < 0.001 Anxiety No Yes 4.35 (0.018) 4.23 (0.32) REF 1.11 (0.95, 1.30) 0.19 3.86 (0.011) 3.48 (0.19) REF 1.02 (0.90, 1.14) 0.77 Bipolar Disorder No Yes 4.41 (0.018) 2.15 (0.073) REF 0.66 (0.62, 0.71) < 0.001 3.91 (0.011) 2.00 (0.05) REF 0.68 (0.65, 0.73) < 0.001 Schizophrenia/psychotic disorders No Yes 4.39 (0.018) 2.13 (0.088) REF 0.68 (0.62, 0.74) < 0.001 3.89 (0.011) 2.03 (0.062) REF 0.72 (0.67, 0.76) < 0.001 PTSD No Yes 4.37 (0.018) 2.35 (0.12) REF 0.73 (0.65, 0.80) < 0.001 3.88 (0.011) 2.25 (0.081) REF 0.78 (0.72, 0.84) < 0.001 Substance Use Disorder No Yes 4.44 (0.018) 3.03 (0.055) REF 0.87 (0.84, 0.90) < 0.001 3.93 (0.011) 2.86 (0.037) REF 0.92 (0.89, 0.95) < 0.001 This table reports 30-day and 1-year mortality after readmission among heart failure hospitalization in patients with and without six psychiatric comorbidities. Unadjusted mortality percentages are shown alongside multivariable logistic regression odds ratios (ORs) with 95% confidence intervals and p-values. ORs reflect the adjusted risk of death for patients with the comorbidity compared with those without, controlling for demographic, socioeconomic, hospital characteristics and discharge month. DISCUSSION LOS and Cost Outcomes Across a contemporary national cohort of patients hospitalized for HF, psychiatric comorbidities were common and showed heterogeneous associations with index admission resource use. Anxiety and SUD emerged as the only diagnoses linked to both longer LOS and higher costs, whereas depression and bipolar disorder were associated with lower index admission costs. Prior work in HF suggests psychiatric comorbidity is associated with greater downstream utilization, including higher readmission risk after HF hospitalization [ 11 ]. Our findings extend this literature by showing diagnosis-specific heterogeneity in index admission resource use: anxiety and SUD were associated with higher LOS and costs, whereas depression and bipolar disorder were associated with lower index admission costs. A recent retrospective, cross-sectional observational study found that patients with psychological distress after myocardial infarction incurred higher medical expenditures and health care utilization [ 12 ]. While that study reports higher expenditures overall, our results suggest the relationship is diagnosis-specific in HF, with higher costs for anxiety and SUD but lower index costs for depression and bipolar disorder. Furthermore, patients with psychiatric disorders are less likely to undergo percutaneous transluminal coronary angioplasty or coronary artery bypass graft upon index hospitalization [ 13 ], which may contribute to lower costs in select diagnostic groups. Importantly, the variation in findings across specific psychiatric diagnoses underscores the need for diagnosis-specific stratification. For example, anxiety was found to be associated with a longer LOS and higher cost, a potential product of the demonstrated association between anxiety and adverse cardiovascular outcomes [ 14 ]. The association of schizophrenia/psychotic disorders with increased LOS and no difference in cost may reflect complex discharge planning and unmeasured differences in inpatient management. Mortality Outcomes The paradoxical association between several psychiatric comorbidities and lower observed mortality warrants careful interpretation. With the exception of anxiety, psychiatric comorbidities were associated with lower in-hospital mortality during 30-day and 1-year readmissions. These findings may reflect diagnosis-specific differences in illness severity, inpatient monitoring, discharge dynamics, and incomplete outcome capture in administrative data, particularly because out-of-hospital deaths are not captured in the NRD. Taken together, these patterns underscore that psychiatric comorbidity in HF does not behave as a uniform risk factor and should be interpreted in the context of prior literature that has often reported adverse associations. It is important to acknowledge that psychiatric patients often experience poorer continuity of medical care [ 12 ], which may influence downstream outcomes. Taken together, these diagnosis-specific patterns highlight a complex and often paradoxical relationship between psychiatric illness and HF outcomes, underscoring the importance of placing our findings in the context of prior literature that has largely suggested the opposite. Comparison with Prior Evidence Most prior studies and reviews have found that psychiatric comorbidities, especially depression and anxiety, but also schizophrenia/psychotic disorders and SUD, are associated with worse outcomes in HF, including higher mortality and rehospitalization [ 6 – 8 , 13 – 17 ], with increased utilization also reported [ 6 , 11 , 18 , 19 ]. The data in this study challenge that narrative by demonstrating diagnosis-specific heterogeneity and lower observed mortality in several groups, underscoring the need to stratify risk by specific psychiatric diagnoses rather than treating psychiatric comorbidity as a single entity. Prior work frequently aggregated psychiatric conditions, which may obscure divergent associations; by disaggregating diagnoses and leveraging a large, nationally representative cohort, our analysis reveals patterns that smaller or pooled studies could miss. Recognizing these divergences from prior work, it is essential to consider the clinical and health-system implications of our findings, particularly how diagnosis-specific stratification may inform patient management and resource allocation. Clinical and Policy Implications Our findings demonstrate diagnosis-specific heterogeneity in how psychiatric comorbidities relate to HF outcomes, which argues against treating ‘any psychiatric comorbidity’ as a single risk factor. Clinical risk models for mortality and readmission may benefit from incorporating specific diagnoses rather than a binary indicator, which may improve risk stratification accuracy and resource planning. For C-L psychiatry, these results support routine, structured screening for depression, anxiety, PTSD, psychotic disorders, bipolar disorder, and substance use at the time of HF admission, coupled with embedded referral pathways that do not depend on ad hoc requests [ 6 , 12 ]. Because anxiety and SUD were associated with longer LOS and higher costs in our cohort, hospitals should consider early C-L psychiatry consultation for anxiety or SUD symptoms, standardized symptom management plans, and coordinated symptom management plans that may reduce potentially avoidable utilization [ 20 ]. For SUD, the combination of longer stays and increased costs alongside lower observed mortality suggests risk of incomplete outcome capture due to limitations within the NRD; care pathways should emphasize withdrawal management protocols, initiation of medications for opioid or alcohol use disorder when indicated, and warm handoffs to outpatient treatment, with targeted efforts to reduce discharge against medical advice and early readmissions [ 18 , 19 ]. For schizophrenia/psychotic disorders, prolonged stays without a corresponding increase in cost points to complex discharge planning; early coordination among C-L psychiatry, social work, and case management may prevent avoidable bed-days while maintaining post-discharge safety. For depression, PTSD, and bipolar disorder, pairing screening with timely treatment initiation and structured follow-up may support the survival advantages we observed while safeguarding against undertreatment of HF or mental health conditions [ 6 , 12 ]. At a policy level, health systems should align quality metrics and reimbursement with integrated cardiac-psychiatric care, including EHR-based consult triggers for positive screens and diagnosis-specific care bundles, and ensure coverage for outpatient follow-up that sustains these inpatient gains [ 12 ]. Study Limitations This study has several limitations. First, administrative ICD-10 coding may not reliably capture psychiatric comorbidities. These codes cannot distinguish symptom severity, chronicity, or timing of diagnosis, and underdiagnosis or undercoding could bias associations in either direction. Second, the NRD captures readmissions only within the same calendar year and participating states. Patients who die after discharge, relocate, or are rehospitalized across state lines may be missed, potentially underestimating longer-term outcomes. Because the NRD does not capture out-of-hospital deaths, mortality estimates reflect only in-hospital deaths during subsequent admissions and may underestimate total mortality. Third, the database lacks granular clinical information, including ejection fraction, ischemia burden, biomarker or angiographic findings, and medication adherence. Psychiatric treatment details, such as use of psychotropic medications, psychotherapy, or C-L psychiatry involvement, are also unavailable. As a result, we could not assess how illness severity or treatment might have influenced outcomes. Fourth, differential coding practices across hospitals and states may have introduced variability in the classification of both HF and psychiatric conditions, potentially biasing results. In addition, unmeasured social determinants of health (e.g., housing stability, caregiver support, and access to follow-up care) may contribute to differences in outcomes but were not available in this dataset. Finally, although the NRD provides a large and nationally representative sample of U.S. hospitalizations, findings may not be generalizable to non-participating states or to international healthcare systems. As with all retrospective observational studies, results reflect associations rather than causality, and residual confounding remains possible despite multivariable adjustment. CONCLUSION In this large, nationally representative cohort of patients hospitalized with HF, psychiatric comorbidities were common and showed heterogeneous effects on outcomes. Depression and bipolar disorder were each associated with lower cost, decreased mortality, and a slight increase in LOS, potentially reflecting diagnosis-specific differences in inpatient care patterns, discharge complexity, and incomplete outcome capture. These findings challenge prior literature, which has largely shown that depression and anxiety worsen outcomes in HF [ 6 , 8 , 11 ]. Anxiety and SUD were associated with longer LOS and higher costs without a mortality difference for anxiety. In contrast, depression, bipolar disorder, schizophrenia/psychotic disorders, PTSD, and SUD were associated with lower observed mortality; costs were lower for depression and bipolar disorder. These findings challenge the assumption that psychiatric illness uniformly worsens medical outcomes and underscore the need for diagnosis-specific risk stratification in HF. For C-L psychiatry, the results highlight both the importance of routine psychiatric screening and the opportunity to integrate mental health services directly into cardiovascular care pathways. Future work should explore mechanisms driving these heterogeneous associations and determine whether targeted psychiatric interventions can improve both survival and healthcare utilization in HF patients. Declarations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Competing Interests The authors declare that they have no competing interests. Ethics Approval This study used de-identified secondary data and was exempt from institutional review board oversight. Data Availability The data that support the findings of this study are available from the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) and are subject to HCUP data use agreements; therefore, they cannot be shared by the authors. Data are available to qualified researchers through HCUP following required training and purchase of the NRD dataset. Author Contribution A.C. and K.T. conceived the project. A.C. and K.T. wrote the initial manuscript draft. A.C., P.E., and K.T. contributed to manuscript revision and editing. P.K. performed the statistical analysis and contributed to interpretation of results. M.W. provided supervision and critical revision of the manuscript. All authors reviewed and approved the final manuscript. References Pratley R, Guan X, Moro RJ, do Lago R. Chapter 1: The Burden of Heart Failure. Am J Med. 2024;137(2S):S3–8. CDC. Heart Disease. 2025 [cited 2025 Sept 12]. About Heart Failure. Available from: https://www.cdc.gov/heart-disease/about/heart-failure.html Heidenreich PA, Albert NM, Allen LA, et al. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013;6(3):606–19. Zeng J, Qiu Y, Yang C, et al. Cardiovascular diseases and depression: A meta-analysis and Mendelian randomization analysis. Mol Psychiatry. 2025;30(9):4234–46. Freedland KE, Skala JA, Steinmeyer BC, Carney RM, Rich MW. Psychiatric multimorbidity in heart failure. J Psychosom Res. 2025;23:197:112368. Celano CM, Villegas AC, Albanese AM, Gaggin HK, Huffman JC. Depression and Anxiety in Heart Failure: A Review. Harv Rev Psychiatry. 2018;26(4):175–84. Rutledge T, Reis VA, Linke SE, Greenberg BH, Mills PJ. Depression in heart failure a meta-analytic review of prevalence, intervention effects, and associations with clinical outcomes. J Am Coll Cardiol. 2006;48(8):1527–37. Sokoreli I, de Vries JJG, Pauws SC, Steyerberg EW. Depression and anxiety as predictors of mortality among heart failure patients: systematic review and meta-analysis. Heart Fail Rev. 2016;21(1):49–63. NRD Overview [Internet]. [cited 2025 Sept 17]. Available from: https://hcup-us.ahrq.gov/nrdoverview.jsp Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. Sa Z, Badgery-Parker T, Long JC, et al. Impact of mental disorders on unplanned readmissions for congestive heart failure patients: a population-level study. ESC Heart Fail. 2024;11(2):962–73. Ward M. Increasing Psychiatrists’ Role in Addressing the Cardiovascular Health of Patients With Severe Mental Illness. Focus J Life Long Learn Psychiatry. 2021;19(1):24–30. Freedland KE, Carney RM, Rich MW. Effect of depression on prognosis in heart failure. Heart Fail Clin. 2011;7(1):11–21. Gathright EC, Goldstein CM, Josephson RA, Hughes JW. Depression Increases the Risk of Mortality in Patients with Heart Failure: A Meta-Analysis. J Psychosom Res. 2017;94:82–9. Lin T, Hsu B, Li Y, et al. Prognostic Value of Anxiety Between Heart Failure With Reduced Ejection Fraction and Heart Failure With Preserved Ejection Fraction. J Am Heart Assoc Cardiovasc Cerebrovasc Dis. 2019;8(12):e010739. Nishi M, Shikuma A, Seki T, Horiguchi G, Matoba S. In-hospital mortality and cardiovascular treatment during hospitalization for heart failure among patients with schizophrenia: a nationwide cohort study. Epidemiol Psychiatr Sci. 2023;32:e62. Adelborg K, Schmidt M, Sundbøll J, et al. Mortality Risk Among Heart Failure Patients With Depression: A Nationwide Population-Based Cohort Study. J Am Heart Assoc. 2016;5(9):e004137. Nishimura M, Bhatia H, Ma J, et al. The Impact of Substance Abuse on Heart Failure Hospitalizations. Am J Med. 2020;133(2):207–e2131. Thyagaturu HS, Bolton AR, Li S, et al. Effect of Cocaine, Amphetamine, and Cannabis Use Disorders on 30-day Readmissions of Patients with Heart Failure. Curr Probl Cardiol. 2023;48(8):101189. Celano CM, Daunis DJ, Lokko HN, Campbell KA, Huffman JC. Anxiety Disorders and Cardiovascular Disease. Curr Psychiatry Rep. 2016;18(11):101. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8745482","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587732951,"identity":"b0a27c20-1412-4ffd-827f-f342ae4b114b","order_by":0,"name":"Austin Charles","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYHACxsc/Kmzq+UHMBIYDEDEe/FqYjRnOpCVINkC0SBCjhU2aseVQggHEfCK08LcffiBd2HAgz/j42YcPHvy6U8cv3cD44G0bbi0SZ9IMjGfuuFNsdibd2CCx75mE5JwDzIZz8WhhOJDDkMB75hnjthtsbBKJPYclDG4ksEnz4tEif/4NwwHetsOMm2ewsf+AamH/jU+LwY0cxmaglsQNEmxsDAk/ILYw49NieOOZMeOMM2nGQE8xSyQ2HJacOSOxWXLOOdxa5M4nP//xocJGjr/9GOPHH38O8/NLJB/88KYMj/dRACPYPYwNxKoHgT+kKB4Fo2AUjIKRAgCVilutAhYs7wAAAABJRU5ErkJggg==","orcid":"","institution":"Creighton University","correspondingAuthor":true,"prefix":"","firstName":"Austin","middleName":"","lastName":"Charles","suffix":""},{"id":587732952,"identity":"5cc2afa7-abe0-4754-945f-4b99eacd9a76","order_by":1,"name":"Kyle Thurmann","email":"","orcid":"","institution":"Creighton University","correspondingAuthor":false,"prefix":"","firstName":"Kyle","middleName":"","lastName":"Thurmann","suffix":""},{"id":587732953,"identity":"14349d43-2c27-492a-9f44-9bc23d1a9450","order_by":2,"name":"Peter Ernst","email":"","orcid":"","institution":"Creighton University","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Ernst","suffix":""},{"id":587732954,"identity":"5b22c65c-a290-4e6c-bde6-9eaf4fc9e74a","order_by":3,"name":"Paul Kang","email":"","orcid":"","institution":"Creighton University","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Kang","suffix":""},{"id":587732955,"identity":"914485f3-b2c4-49a4-8a47-5e7b50b7e0a4","order_by":4,"name":"Michael White","email":"","orcid":"","institution":"Valleywise Health Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"White","suffix":""}],"badges":[],"createdAt":"2026-01-31 00:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8745482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8745482/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102491139,"identity":"6533b822-0b83-4cca-b350-0be8a5b72ae0","added_by":"auto","created_at":"2026-02-12 08:42:32","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCohort selection and sample size by year (unweighted and weighted)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8745482/v1/05e93fd2ab5b953ad167922b.jpeg"},{"id":107566261,"identity":"ad65de2d-ffbb-41b2-9c04-da22b426c703","added_by":"auto","created_at":"2026-04-22 16:55:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":640950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8745482/v1/001a6fd7-dcc9-4ac4-97d9-dd7610b76997.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnosis-Specific Psychiatric Comorbidity in Heart Failure: Associations With Length of Stay, Costs, and Mortality in a National Cohort","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHeart failure (HF) is a major cause of morbidity, mortality, and rising healthcare costs in the United States. As of 2024, HF was estimated to affect six million American adults aged 20 years or older, with that number expected to rise as the population ages [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2023, HF was cited on over 450,000 death certificates, accounting for nearly 15% of all U.S. deaths [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2012, the economic burden of HF was estimated at \u003cspan\u003e$\u003c/span\u003e30.7\u0026nbsp;billion, a figure projected to rise to \u003cspan\u003e$\u003c/span\u003e69.7\u0026nbsp;billion by 2030 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. HF therefore imposes a substantial and growing burden on the healthcare system and society in the United States.\u003c/p\u003e \u003cp\u003ePsychiatric comorbidities are common in HF and contribute to symptom burden, functional impairment, and reduced quality of life. The intersection of cardiovascular disease and psychiatric illness is central to consultation-liaison (C-L) psychiatry, where psychiatric symptoms, diagnoses, and treatment decisions can influence hospital course, discharge planning, and follow-up care in medically complex patients. Meta-analytic data suggest that approximately 25% of patients with HF also meet criteria for depression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the psychiatric burden in HF extends beyond depression, including substance use disorder (SUD), anxiety, post-traumatic stress disorder (PTSD), schizophrenia, and bipolar disorder [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These conditions may be under-recognized or undertreated in HF due to overlapping clinical presentations and competing medical priorities. When unaddressed, psychiatric comorbidities may contribute to worse HF-related outcomes through mechanisms such as reduced treatment adherence, functional decline, and increased healthcare utilization [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost prior studies have linked psychiatric comorbidities in HF to adverse outcomes, including higher hospitalization rates and mortality [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, existing evidence is limited by inconsistent measurement of psychiatric diagnoses, reliance on smaller or geographically restricted samples, and limited evaluation of diagnosis-specific effects. In addition, many studies evaluate psychiatric illness as a single exposure, which may obscure clinically important heterogeneity across diagnoses with distinct symptom profiles, treatment needs, and care pathways. Accordingly, we examined diagnosis-specific associations between psychiatric comorbidities and HF outcomes, including index hospital length of stay (LOS), inflation-adjusted hospital costs, and 30-day and 1-year mortality, using the large, nationally representative Nationwide Readmissions Database (NRD).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eWe performed a retrospective cohort study using the 2016\u0026ndash;2022 NRD, a component of the Healthcare Cost and Utilization Project (HCUP) that captures all-payer inpatient discharges from 30 states in the United States, representing approximately 60% of national hospitalizations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Each annual NRD file is independent; therefore, 1-year readmissions were assessed within the same calendar year.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eWe included adult patients aged 18 years or older who were hospitalized with a primary diagnosis of HF, identified using ICD-10 codes I50.1 through I50.9. Index discharges resulting in in-hospital death, transfers to another acute facility, or records with missing demographic or readmission variables were excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Readmissions flagged as \u0026lsquo;planned\u0026rsquo; by HCUP were excluded from outcome ascertainment. Furthermore, each readmission was flagged with its corresponding \u0026ldquo;NRD_KEY\u0026rdquo; unique ID, thus each index admission represents a unique patient admission.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Nationwide Readmissions Database (NRD), 2016\u0026ndash;2022, was used to identify adult (\u0026ge;\u0026thinsp;18 years) index hospitalizations with a principal diagnosis of heart failure. Records were excluded for in-hospital death during the index admission, transfer to another acute-care hospital, or missing outcome data. Unweighted counts reflect the NRD sample; weighted counts represent national estimates.\u003c/p\u003e\n\u003ch3\u003ePsychiatric comorbidity definitions\u003c/h3\u003e\n\u003cp\u003eTo assess the effects of psychiatric comorbidities, we identified patients with one or more psychiatric diagnoses using the following ICD-10 codes: depression (F32.0-F32.9, F33.0-F33.9), anxiety (F40.0-F41.9), bipolar disorder (F31.0-F31.9), schizophrenia/psychotic disorders (F20.0-F20.9, F25.0-F25.9), PTSD (F43.10-F43.12), and SUDs (F10-F16, F18-F19, nicotine was excluded due to clinical differences from the other substances).\u003c/p\u003e\n\u003ch3\u003eOutcome Measures\u003c/h3\u003e\n\u003cp\u003eOutcomes included index admission LOS, inflation‑adjusted hospital costs, and mortality within 30 days and one year after the index discharge. LOS was calculated as the number of days between admission and discharge. Hospital charges were converted to costs using the HCUP cost‑to‑charge ratios and inflation-adjusted to 2022 US dollars with the CPI-U࿼. Mortality was assessed using the discharge disposition in the NRD and reflects only in-hospital deaths that occurred during a rehospitalization within 30 days or one year after discharge. Deaths during the index admission were excluded, and the NRD does not capture deaths occurring outside of the hospital.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates included patient demographics (age, sex, primary payer, and ZIP code-based income quartile), hospital-level characteristics (bed size, location, and teaching status), admission timing (weekend vs. weekday), discharge disposition, and comorbidity burden measured by the Charlson Comorbidity Index (CCI) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSurvey‑weighted estimates were derived using HCUP discharge weights. Baseline characteristics were summarized as weighted means with standard errors (SEs) for continuous variables and as weighted proportions with SEs for categorical variables. Differences between groups (i.e. with vs without SUD) were assessed using weighted univariate linear regression or chi‑square tests as appropriate. Multivariable linear regression models were constructed to estimate adjusted differences (β coefficients with 95% confidence intervals) in index LOS and inflation‑adjusted hospital costs, adjusting for demographic, socioeconomic, and hospital factors. For mortality outcomes, survey‑weighted logistic regression models estimated adjusted odds ratios (ORs) with 95% confidence intervals for in-hospital mortality at 30 days and 1 year during readmission among patients who were not transferred to another facility. Given the large sample size, a stringent significance threshold of p\u0026thinsp;\u0026le;\u0026thinsp;0.001 was applied to limit type I error.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoftware and Ethics Compliance\u003c/h3\u003e\n\u003cp\u003eAll analyses were performed using Stata SE 18 (StataCorp, 2023). The study adhered to the HCUP Data Use Agreement, and as a retrospective analysis of de-identified data, it was exempt from institutional review board approval.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eCohort characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial analysis included 31,886,859 weighted hospitalizations for HF from the NRD. The mean patient age was 71.3 years (SE, 0.004), and women comprised 48.9% of the cohort. Most patients were covered by Medicare (75.7%) and lived in urban settings (82.4%). The lowest income quartile included the most patients (32.8%). More than half of admissions occurred at large hospitals (53.8%), and the majority were treated at urban teaching institutions (69.8%). Nearly three-quarters of patients had a CCI score of 3 or higher. At discharge, 45.6% of patients returned home, 25.8% received home healthcare, and 25.7% were transferred to post-acute care facilities.\u003c/p\u003e\n\u003cp\u003ePatients who were not transferred (n = 14,535,581) were statistically younger with a lower proportion of females. In addition, the percentage of patients within the lowest income quartile increased from 32.8% to 35.5%. \u0026nbsp;The primary payer among non-transferred patients remained Medicare; however, the percentage of Medicare users decreased from 75.7% to 65.8%. \u0026nbsp;Conversely, private insurance users increased from 10% to 14.0%. While statistically significant, the distribution of hospital characteristics remained similar between all heart disease patients and those who were not transferred. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe prevalence of psychiatric comorbidities in the overall population included depression (14.6%), anxiety (0.29%), bipolar disorder (2.1%), schizophrenia or other psychotic disorders (1.5%), PTSD (0.8%), and SUD (19.2%). \u0026nbsp;Among non-transferred patients, the percentage of these psychiatric disorders did not change clinically. A detailed summary of demographic and clinical characteristics is presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Baseline demographic, clinical, and psychiatric characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"480\" class=\"fr-table-selection-hover\" style=\"margin-right: calc(7%); width: 93%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eDemographics (Weighted)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003eOverall \u0026ndash; Heart Disease Patients\u003c/p\u003e\n \u003cp\u003e(n = 31,886,859)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003eNon-Transferred\u003c/p\u003e\n \u003cp\u003ePatients\u003c/p\u003e\n \u003cp\u003e(n = 14,535,581)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eAge, years (mean, SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e71.3 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e66.9 (0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eSex, Female (%, SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e48.9 (0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e43.9 (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eIncome Quartile (%, 95% CI)\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32.8 (0.011)\u003c/p\u003e\n \u003cp\u003e27.7 (0.011)\u003c/p\u003e\n \u003cp\u003e22.9 (0.011)\u003c/p\u003e\n \u003cp\u003e16.7 (0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35.5 (0.018)\u003c/p\u003e\n \u003cp\u003e27.9 (0.017)\u003c/p\u003e\n \u003cp\u003e22.2 (0.016)\u003c/p\u003e\n \u003cp\u003e14.6 (0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003ePrimary Payer (%, SE)\u003c/p\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003cp\u003eSelf-Pay\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75.7 (0.011)\u003c/p\u003e\n \u003cp\u003e10.1 (0.007)\u003c/p\u003e\n \u003cp\u003e10.0 (0.008)\u003c/p\u003e\n \u003cp\u003e1.79 (0.003)\u003c/p\u003e\n \u003cp\u003e2.35 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e65.8 (0.018)\u003c/p\u003e\n \u003cp\u003e14.3 (0.013)\u003c/p\u003e\n \u003cp\u003e14.0 (0.013)\u003c/p\u003e\n \u003cp\u003e3.10 (0.007)\u003c/p\u003e\n \u003cp\u003e2.83 (0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eWeekend Admission (%, SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e24.4 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e24.0 (0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003ePatient Location (%, SE)\u003c/p\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82.4 (0.008)\u003c/p\u003e\n \u003cp\u003e17.6 (0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e84.2 (0.011)\u003c/p\u003e\n \u003cp\u003e18.8 (0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eHospital Location (%, SE)\u003c/p\u003e\n \u003cp\u003eUrban/Non-Teaching\u003c/p\u003e\n \u003cp\u003eUrban/Teaching\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20.4 (0.002)\u003c/p\u003e\n \u003cp\u003e69.8 (0.003)\u003c/p\u003e\n \u003cp\u003e9.77 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19.9 (0.003)\u003c/p\u003e\n \u003cp\u003e70.0 (0.004)\u003c/p\u003e\n \u003cp\u003e9.99 (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eHospital Bed size (%, SE)\u003c/p\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.5 (0.002)\u003c/p\u003e\n \u003cp\u003e27.7 (0.002)\u003c/p\u003e\n \u003cp\u003e53.8 (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.1 (0.003)\u003c/p\u003e\n \u003cp\u003e26.9 (0.003)\u003c/p\u003e\n \u003cp\u003e55.0 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eCharlson Comorbidity Index (%, SE)\u003c/p\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e\u003cu\u003e\u0026gt;\u003c/u\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.30 (0.007)\u003c/p\u003e\n \u003cp\u003e17.8 (0.009)\u003c/p\u003e\n \u003cp\u003e74.9 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.47 (0.011)\u003c/p\u003e\n \u003cp\u003e20.9 (0.016)\u003c/p\u003e\n \u003cp\u003e69.6 (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eDisposition at Discharge (%, SE)\u003c/p\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003cp\u003eShort Term Facility\u003c/p\u003e\n \u003cp\u003eDesignated Center\u003c/p\u003e\n \u003cp\u003eHome Healthcare\u003c/p\u003e\n \u003cp\u003eAMA\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45.6 (0.012)\u003c/p\u003e\n \u003cp\u003e1.20 (0.003)\u003c/p\u003e\n \u003cp\u003e25.7 (0.011)\u003c/p\u003e\n \u003cp\u003e25.8 (0.011)\u003c/p\u003e\n \u003cp\u003e1.66 (0.003)\u003c/p\u003e\n \u003cp\u003e0.047 (6.5e-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003ePsychiatric Covariates\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e14.6 (0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e13.2 (0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e0.29 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e0.29 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eBipolar Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e2.06 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e2.16 (0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eSchizophrenia/psychotic disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e1.50 (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e1.35 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e0.80 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e0.99 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4373%;\"\u003e\n \u003cp\u003eSubstance Use Disorder (excl. Nicotine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2175%;\"\u003e\n \u003cp\u003e4.69 (0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2419%;\"\u003e\n \u003cp\u003e5.59 (0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThis table summarizes weighted baseline characteristics for 31,886,859 hospitalizations of adults with heart failure in the Nationwide Readmissions Database. Depression (14.6%) was the most prevalent psychiatric comorbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndex admission LOS and hospital cost\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the index HF admission among non-transferred patients, the unadjusted mean LOS was approximately 4.6 days for patients without psychiatric comorbidities (Table 2). Patients with anxiety (5.77 days), schizophrenia/psychotic disorders (6.67 days), bipolar disorder (5.47 days), PTSD (5.51 days), and SUD (5.50 days) all exhibited longer stays than patients without these conditions.\u003c/p\u003e\n\u003cp\u003eMultivariable linear regression adjusting for demographics, socioeconomic status, and hospital characteristics demonstrated diagnosis-specific effects on LOS (Table 2). Schizophrenia was associated with the greatest increase in LOS, with patients staying 1.67 days longer than those without schizophrenia (\u0026beta; = 1.67; 95% CI, 1.61 to 1.74; p \u0026lt; 0.001). Anxiety disorders were also linked to a substantial increase in LOS (\u0026beta; = 0.88; 95% CI, 0.78 to 0.96; p \u0026lt; 0.001), while substance use disorder was associated with a smaller but statistically significant increase (\u0026beta; = 0.60; 95% CI, 0.58 to 0.62; p \u0026lt; 0.001). In contrast, depression status (\u0026beta; = 0.30; 95% CI, 0.28 to 0.32; p \u0026lt; 0.001), bipolar disorder (\u0026beta; = 0.29; 95% CI, 0.26 to 0.34; p \u0026lt; 0.001), and PTSD (\u0026beta; = 0.25; 95% CI, 0.20 to 0.30; p \u0026lt; 0.001) were associated with smaller increases in LOS.\u003c/p\u003e\n\u003cp\u003eInflation-adjusted hospital costs showed a pattern distinct from LOS. Anxiety was associated with a substantial cost increase, with patients incurring an additional $2,778 (\u0026beta; = 2,778.9; 95% CI, 2,469.7 to 3,088.1; p \u0026lt; 0.001). Additionally, SUD status was associated with a $1,477.2 increase in cost compared to no SUD (\u0026beta; = 1,477.2; 95% CI, 1,396.8 to 1,557.5; p \u0026lt; 0.001). \u0026nbsp;Depression, however, was paradoxically associated with lower costs, reducing expenditure by $227.3 (\u0026beta; = \u0026ndash;227.3; 95% CI, \u0026ndash;276.5 to \u0026ndash;178.1; p \u0026lt; 0.001). Bipolar disorder conferred the largest cost reduction, with patients incurring $1,319.5 less than those without the condition (\u0026beta; = \u0026ndash;1,319.5; 95% CI, \u0026ndash;1424.6 to \u0026ndash;1,114.4; p \u0026lt; 0.001). Schizophrenia/psychotic disorders and PTSD were not associated with cost.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Impact of psychiatric comorbidities on index hospital stay and costs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"762\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003ePsychiatric Condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003eLength of Stay, days\u003cbr\u003e\u0026nbsp;Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBeta (95% CI)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e2022 Inflation -Adjusted Total Cost, $\u003cbr\u003e\u0026nbsp;Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBeta (95% CI)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e4.62 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e15424.9 (8.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e5.03 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e0.30 (0.28, 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e15434.4 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e-227.3 (-276.5, -178.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e4.67 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e15415.9 (7.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e5.77 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e0.88 (0.78, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e18920.6 (157.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e2778.9 (2469.7, 3088.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eBipolar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e4.65 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e15431.0 (7.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e5.47 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e0.29 (0.26, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e15205.5 (50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e-1319.5 (-1424.6, -1214.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e4.64 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e15417.5 (7.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e6.67 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e1.67 (1.61, 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e16057.7 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e106.5 (-33.3, 246.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e4.66 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e15413.3 (7.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e5.51 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e0.25 (0.20, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e16704.6 (87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e20.8 (-154.2, 195.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eSubstance Use Disorder\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e4.62 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e15272.9 (7.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2661%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1062%;\"\u003e\n \u003cp\u003e5.50 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3683%;\"\u003e\n \u003cp\u003e0.60 (0.58, 0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6933%;\"\u003e\n \u003cp\u003e18015.7 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8388%;\"\u003e\n \u003cp\u003e1477.2 (1396.8, 1557.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8637%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThis table presents the unadjusted mean index LOS and inflation-adjusted hospital costs for heart failure admissions in patients with and without six psychiatric comorbidities (depression, anxiety, bipolar disorder, schizophrenia/psychotic disorders, PTSD, and SUD). Multivariable linear regression coefficients (\u0026beta;) indicate the adjusted differences in LOS (days) and costs (U.S. dollars), with 95% confidence intervals and p-values. Models were adjusted for demographics, socioeconomic status, and hospital characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn-Hospital mortality within the 30-day and 1-year readmission\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShort-term (30-day readmitted) and long-term (1-year readmitted) mortality outcomes showed a paradoxical relationship with psychiatric comorbidity. Unadjusted mortality at the 30-day readmission ranged from 2.35% in patients with PTSD to 4.48% in those without Depression, while 1-year readmission mortality ranged from 2.00% (bipolar disorder) to 3.97% (no Depression) (Table 3).\u003c/p\u003e\n\u003cp\u003eAfter multivariable adjustment, depression was associated with lower mortality at both time points: the OR for mortality within readmission at 30-day was 0.86 (95% CI, 0.84\u0026ndash;0.88; p \u0026lt; 0.001), and the OR for mortality within 1-year readmission was 0.86 (95% CI, 0.85\u0026ndash;0.88; p \u0026lt; 0.001). Bipolar disorder conferred the lowest odds of mortality, with a 30-day readmission death OR of 0.66 (95% CI, 0.62\u0026ndash;0.71; p \u0026lt; 0.001) and a 1-year readmission death OR of 0.68 (95% CI, 0.65\u0026ndash;0.73; p \u0026lt; 0.001), corresponding to ~33% lower odds of death compared with HF patients without bipolar disorder.\u003c/p\u003e\n\u003cp\u003eSchizophrenia/psychotic disorders, PTSD, and SUD were also associated with significantly lower mortality. Schizophrenia/psychotic disorders observed 32% lower odds of 30-day readmission mortality compared to admissions with no schizophrenia/psychotic disorders (0.68 (95% CI, 0.62\u0026ndash;0.74; p \u0026lt; 0.001). Furthermore, 1-year readmission mortality decreased by 28% among the same group (0.72 (95% CI, 0.67\u0026ndash;0.76); p \u0026lt; 0.001). PTSD was associated with a 27% and 22% decrease in the odds of 30-day and 1-year readmission mortality, respectively (30-day: OR of 0.73 (95% CI, 0.65\u0026ndash;0.80; p \u0026lt; 0.001 and 1-year: OR of 0.78 (95% CI, 0.72\u0026ndash;0.84; p \u0026lt; 0.001). SUD showed a modest decrease in 30-day and 1-year readmission mortality of 13% and 8%, respectively (30-day: OR of 0.87 (95% CI, 0.84\u0026ndash;0.90; p \u0026lt; 0.001 and 1-year: OR of 0.92 (95% CI, 0.89\u0026ndash;0.95; p \u0026lt; 0.001). \u0026nbsp;Only anxiety failed to demonstrate a mortality difference. Adjusted 30-day readmission mortality for patients with anxiety (4.23%) was similar to that of patients without anxiety (4.35%), with an OR of 1.11 (95% CI, 0.95\u0026ndash;1.30; p = 0.19). Likewise, adjusted 1-year mortality did not differ significantly (OR = 1.02; 95% CI, 0.90\u0026ndash;1.14; p = 0.77).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Association between psychiatric comorbidities and post-discharge mortality\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"798\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5639%;\"\u003e\n \u003cp\u003ePsychiatric Condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.782%;\"\u003e\n \u003cp\u003e30-day Mortality\u003c/p\u003e\n \u003cp\u003eN = 2,844, 722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMultivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e1-year \u0026nbsp; \u0026nbsp; Mortality\u003c/p\u003e\n \u003cp\u003eN = 6,304,799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5489%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMultivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.782%;\"\u003e\n \u003cp\u003e% (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003eOR (95 %CI)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e% (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5489%;\"\u003e\n \u003cp\u003eOR (95 %CI)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5639%;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.782%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.48 (0.019)\u003c/p\u003e\n \u003cp\u003e3.60 (0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.86 (0.84, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.97 (0.012)\u003c/p\u003e\n \u003cp\u003e3.22 (0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5489%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.86 (0.85, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5639%;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.782%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.35 (0.018)\u003c/p\u003e\n \u003cp\u003e4.23 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e1.11 (0.95, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.86 (0.011)\u003c/p\u003e\n \u003cp\u003e3.48 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5489%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e1.02 (0.90, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5639%;\"\u003e\n \u003cp\u003eBipolar Disorder\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.782%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.41 (0.018)\u003c/p\u003e\n \u003cp\u003e2.15 (0.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.66 (0.62, 0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.91 (0.011)\u003c/p\u003e\n \u003cp\u003e2.00 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5489%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.68 (0.65, 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5639%;\"\u003e\n \u003cp\u003eSchizophrenia/psychotic disorders\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.782%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.39 (0.018)\u003c/p\u003e\n \u003cp\u003e2.13 (0.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.68 (0.62, 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.89 (0.011)\u003c/p\u003e\n \u003cp\u003e2.03 (0.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5489%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.72 (0.67, 0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5639%;\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.782%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.37 (0.018)\u003c/p\u003e\n \u003cp\u003e2.35 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.73 (0.65, 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.88 (0.011)\u003c/p\u003e\n \u003cp\u003e2.25 (0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5489%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.78 (0.72, 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5639%;\"\u003e\n \u003cp\u003eSubstance Use Disorder\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.782%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.44 (0.018)\u003c/p\u003e\n \u003cp\u003e3.03 (0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.87 (0.84, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.93 (0.011)\u003c/p\u003e\n \u003cp\u003e2.86 (0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5489%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e0.92 (0.89, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.76692%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThis table reports 30-day and 1-year mortality after readmission among heart failure hospitalization in patients with and without six psychiatric comorbidities. Unadjusted mortality percentages are shown alongside multivariable logistic regression odds ratios (ORs) with 95% confidence intervals and p-values. ORs reflect the adjusted risk of death for patients with the comorbidity compared with those without, controlling for demographic, socioeconomic, hospital characteristics and discharge month.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLOS and Cost Outcomes\u003c/h2\u003e \u003cp\u003eAcross a contemporary national cohort of patients hospitalized for HF, psychiatric comorbidities were common and showed heterogeneous associations with index admission resource use. Anxiety and SUD emerged as the only diagnoses linked to both longer LOS and higher costs, whereas depression and bipolar disorder were associated with lower index admission costs. Prior work in HF suggests psychiatric comorbidity is associated with greater downstream utilization, including higher readmission risk after HF hospitalization [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our findings extend this literature by showing diagnosis-specific heterogeneity in index admission resource use: anxiety and SUD were associated with higher LOS and costs, whereas depression and bipolar disorder were associated with lower index admission costs. A recent retrospective, cross-sectional observational study found that patients with psychological distress after myocardial infarction incurred higher medical expenditures and health care utilization [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While that study reports higher expenditures overall, our results suggest the relationship is diagnosis-specific in HF, with higher costs for anxiety and SUD but lower index costs for depression and bipolar disorder.\u003c/p\u003e \u003cp\u003eFurthermore, patients with psychiatric disorders are less likely to undergo percutaneous transluminal coronary angioplasty or coronary artery bypass graft upon index hospitalization [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which may contribute to lower costs in select diagnostic groups. Importantly, the variation in findings across specific psychiatric diagnoses underscores the need for diagnosis-specific stratification. For example, anxiety was found to be associated with a longer LOS and higher cost, a potential product of the demonstrated association between anxiety and adverse cardiovascular outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The association of schizophrenia/psychotic disorders with increased LOS and no difference in cost may reflect complex discharge planning and unmeasured differences in inpatient management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMortality Outcomes\u003c/h2\u003e \u003cp\u003eThe paradoxical association between several psychiatric comorbidities and lower observed mortality warrants careful interpretation. With the exception of anxiety, psychiatric comorbidities were associated with lower in-hospital mortality during 30-day and 1-year readmissions. These findings may reflect diagnosis-specific differences in illness severity, inpatient monitoring, discharge dynamics, and incomplete outcome capture in administrative data, particularly because out-of-hospital deaths are not captured in the NRD. Taken together, these patterns underscore that psychiatric comorbidity in HF does not behave as a uniform risk factor and should be interpreted in the context of prior literature that has often reported adverse associations. It is important to acknowledge that psychiatric patients often experience poorer continuity of medical care [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which may influence downstream outcomes. Taken together, these diagnosis-specific patterns highlight a complex and often paradoxical relationship between psychiatric illness and HF outcomes, underscoring the importance of placing our findings in the context of prior literature that has largely suggested the opposite.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Prior Evidence\u003c/h2\u003e \u003cp\u003eMost prior studies and reviews have found that psychiatric comorbidities, especially depression and anxiety, but also schizophrenia/psychotic disorders and SUD, are associated with worse outcomes in HF, including higher mortality and rehospitalization [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], with increased utilization also reported [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The data in this study challenge that narrative by demonstrating diagnosis-specific heterogeneity and lower observed mortality in several groups, underscoring the need to stratify risk by specific psychiatric diagnoses rather than treating psychiatric comorbidity as a single entity. Prior work frequently aggregated psychiatric conditions, which may obscure divergent associations; by disaggregating diagnoses and leveraging a large, nationally representative cohort, our analysis reveals patterns that smaller or pooled studies could miss. Recognizing these divergences from prior work, it is essential to consider the clinical and health-system implications of our findings, particularly how diagnosis-specific stratification may inform patient management and resource allocation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Policy Implications\u003c/h2\u003e \u003cp\u003eOur findings demonstrate diagnosis-specific heterogeneity in how psychiatric comorbidities relate to HF outcomes, which argues against treating \u0026lsquo;any psychiatric comorbidity\u0026rsquo; as a single risk factor. Clinical risk models for mortality and readmission may benefit from incorporating specific diagnoses rather than a binary indicator, which may improve risk stratification accuracy and resource planning. For C-L psychiatry, these results support routine, structured screening for depression, anxiety, PTSD, psychotic disorders, bipolar disorder, and substance use at the time of HF admission, coupled with embedded referral pathways that do not depend on ad hoc requests [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBecause anxiety and SUD were associated with longer LOS and higher costs in our cohort, hospitals should consider early C-L psychiatry consultation for anxiety or SUD symptoms, standardized symptom management plans, and coordinated symptom management plans that may reduce potentially avoidable utilization [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For SUD, the combination of longer stays and increased costs alongside lower observed mortality suggests risk of incomplete outcome capture due to limitations within the NRD; care pathways should emphasize withdrawal management protocols, initiation of medications for opioid or alcohol use disorder when indicated, and warm handoffs to outpatient treatment, with targeted efforts to reduce discharge against medical advice and early readmissions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For schizophrenia/psychotic disorders, prolonged stays without a corresponding increase in cost points to complex discharge planning; early coordination among C-L psychiatry, social work, and case management may prevent avoidable bed-days while maintaining post-discharge safety. For depression, PTSD, and bipolar disorder, pairing screening with timely treatment initiation and structured follow-up may support the survival advantages we observed while safeguarding against undertreatment of HF or mental health conditions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt a policy level, health systems should align quality metrics and reimbursement with integrated cardiac-psychiatric care, including EHR-based consult triggers for positive screens and diagnosis-specific care bundles, and ensure coverage for outpatient follow-up that sustains these inpatient gains [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, administrative ICD-10 coding may not reliably capture psychiatric comorbidities. These codes cannot distinguish symptom severity, chronicity, or timing of diagnosis, and underdiagnosis or undercoding could bias associations in either direction.\u003c/p\u003e \u003cp\u003eSecond, the NRD captures readmissions only within the same calendar year and participating states. Patients who die after discharge, relocate, or are rehospitalized across state lines may be missed, potentially underestimating longer-term outcomes. Because the NRD does not capture out-of-hospital deaths, mortality estimates reflect only in-hospital deaths during subsequent admissions and may underestimate total mortality.\u003c/p\u003e \u003cp\u003eThird, the database lacks granular clinical information, including ejection fraction, ischemia burden, biomarker or angiographic findings, and medication adherence. Psychiatric treatment details, such as use of psychotropic medications, psychotherapy, or C-L psychiatry involvement, are also unavailable. As a result, we could not assess how illness severity or treatment might have influenced outcomes.\u003c/p\u003e \u003cp\u003eFourth, differential coding practices across hospitals and states may have introduced variability in the classification of both HF and psychiatric conditions, potentially biasing results. In addition, unmeasured social determinants of health (e.g., housing stability, caregiver support, and access to follow-up care) may contribute to differences in outcomes but were not available in this dataset.\u003c/p\u003e \u003cp\u003eFinally, although the NRD provides a large and nationally representative sample of U.S. hospitalizations, findings may not be generalizable to non-participating states or to international healthcare systems. As with all retrospective observational studies, results reflect associations rather than causality, and residual confounding remains possible despite multivariable adjustment.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this large, nationally representative cohort of patients hospitalized with HF, psychiatric comorbidities were common and showed heterogeneous effects on outcomes. Depression and bipolar disorder were each associated with lower cost, decreased mortality, and a slight increase in LOS, potentially reflecting diagnosis-specific differences in inpatient care patterns, discharge complexity, and incomplete outcome capture. These findings challenge prior literature, which has largely shown that depression and anxiety worsen outcomes in HF [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Anxiety and SUD were associated with longer LOS and higher costs without a mortality difference for anxiety. In contrast, depression, bipolar disorder, schizophrenia/psychotic disorders, PTSD, and SUD were associated with lower observed mortality; costs were lower for depression and bipolar disorder. These findings challenge the assumption that psychiatric illness uniformly worsens medical outcomes and underscore the need for diagnosis-specific risk stratification in HF. For C-L psychiatry, the results highlight both the importance of routine psychiatric screening and the opportunity to integrate mental health services directly into cardiovascular care pathways. Future work should explore mechanisms driving these heterogeneous associations and determine whether targeted psychiatric interventions can improve both survival and healthcare utilization in HF patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used de-identified secondary data and was exempt from institutional review board oversight.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) and are subject to HCUP data use agreements; therefore, they cannot be shared by the authors. Data are available to qualified researchers through HCUP following required training and purchase of the NRD dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.C. and K.T. conceived the project. A.C. and K.T. wrote the initial manuscript draft. A.C., P.E., and K.T. contributed to manuscript revision and editing. P.K. performed the statistical analysis and contributed to interpretation of results. M.W. provided supervision and critical revision of the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePratley R, Guan X, Moro RJ, do Lago R. Chapter 1: The Burden of Heart Failure. Am J Med. 2024;137(2S):S3\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCDC. Heart Disease. 2025 [cited 2025 Sept 12]. About Heart Failure. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/heart-disease/about/heart-failure.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/heart-disease/about/heart-failure.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeidenreich PA, Albert NM, Allen LA, et al. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013;6(3):606\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng J, Qiu Y, Yang C, et al. Cardiovascular diseases and depression: A meta-analysis and Mendelian randomization analysis. Mol Psychiatry. 2025;30(9):4234\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreedland KE, Skala JA, Steinmeyer BC, Carney RM, Rich MW. Psychiatric multimorbidity in heart failure. J Psychosom Res. 2025;23:197:112368.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCelano CM, Villegas AC, Albanese AM, Gaggin HK, Huffman JC. Depression and Anxiety in Heart Failure: A Review. Harv Rev Psychiatry. 2018;26(4):175\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutledge T, Reis VA, Linke SE, Greenberg BH, Mills PJ. Depression in heart failure a meta-analytic review of prevalence, intervention effects, and associations with clinical outcomes. J Am Coll Cardiol. 2006;48(8):1527\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSokoreli I, de Vries JJG, Pauws SC, Steyerberg EW. Depression and anxiety as predictors of mortality among heart failure patients: systematic review and meta-analysis. Heart Fail Rev. 2016;21(1):49\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNRD Overview [Internet]. [cited 2025 Sept 17]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hcup-us.ahrq.gov/nrdoverview.jsp\u003c/span\u003e\u003cspan address=\"https://hcup-us.ahrq.gov/nrdoverview.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSa Z, Badgery-Parker T, Long JC, et al. Impact of mental disorders on unplanned readmissions for congestive heart failure patients: a population-level study. ESC Heart Fail. 2024;11(2):962\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWard M. Increasing Psychiatrists\u0026rsquo; Role in Addressing the Cardiovascular Health of Patients With Severe Mental Illness. Focus J Life Long Learn Psychiatry. 2021;19(1):24\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreedland KE, Carney RM, Rich MW. Effect of depression on prognosis in heart failure. Heart Fail Clin. 2011;7(1):11\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGathright EC, Goldstein CM, Josephson RA, Hughes JW. Depression Increases the Risk of Mortality in Patients with Heart Failure: A Meta-Analysis. J Psychosom Res. 2017;94:82\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin T, Hsu B, Li Y, et al. Prognostic Value of Anxiety Between Heart Failure With Reduced Ejection Fraction and Heart Failure With Preserved Ejection Fraction. J Am Heart Assoc Cardiovasc Cerebrovasc Dis. 2019;8(12):e010739.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishi M, Shikuma A, Seki T, Horiguchi G, Matoba S. In-hospital mortality and cardiovascular treatment during hospitalization for heart failure among patients with schizophrenia: a nationwide cohort study. Epidemiol Psychiatr Sci. 2023;32:e62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdelborg K, Schmidt M, Sundb\u0026oslash;ll J, et al. Mortality Risk Among Heart Failure Patients With Depression: A Nationwide Population-Based Cohort Study. J Am Heart Assoc. 2016;5(9):e004137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishimura M, Bhatia H, Ma J, et al. The Impact of Substance Abuse on Heart Failure Hospitalizations. Am J Med. 2020;133(2):207\u0026ndash;e2131.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThyagaturu HS, Bolton AR, Li S, et al. Effect of Cocaine, Amphetamine, and Cannabis Use Disorders on 30-day Readmissions of Patients with Heart Failure. Curr Probl Cardiol. 2023;48(8):101189.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCelano CM, Daunis DJ, Lokko HN, Campbell KA, Huffman JC. Anxiety Disorders and Cardiovascular Disease. Curr Psychiatry Rep. 2016;18(11):101.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"consultation-liaison psychiatry, heart failure, psychiatric comorbidity, anxiety, substance use disorder, health care utilization, mortality","lastPublishedDoi":"10.21203/rs.3.rs-8745482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8745482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePsychiatric comorbidities are common in patients hospitalized for heart failure (HF), but diagnosis-specific associations with hospital utilization and outcomes remain unclear, a core question in consultation-liaison (C-L) psychiatry.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo evaluate how specific psychiatric comorbidities relate to index length of stay (LOS), hospital costs, and mortality after HF hospitalization.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRetrospective cohort study using the 2016\u0026ndash;2022 Nationwide Readmissions Database. Adults with a principal HF diagnosis were included. Comorbidities were depression, anxiety, bipolar disorder, schizophrenia/psychotic disorders, post-traumatic stress disorder (PTSD), and substance use disorder (SUD). Outcomes were index LOS, inflation-adjusted costs, and 30-day and 1-year in-hospital mortality during readmissions. Survey-weighted multivariable models adjusted for demographics, socioeconomic factors, hospital characteristics, discharge disposition, and comorbidity burden; p\u0026thinsp;\u0026le;\u0026thinsp;0.001 was prespecified.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 31,886,859 weighted HF hospitalizations, psychiatric comorbidity was common. Anxiety was associated with longer LOS (β\u0026thinsp;=\u0026thinsp;0.88 days; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher costs (β=\u003cspan\u003e$\u003c/span\u003e2,779; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) without differences in 30-day or 1-year mortality. Several diagnoses were associated with lower mortality, including depression (30-day OR\u0026thinsp;=\u0026thinsp;0.86; 1-year OR\u0026thinsp;=\u0026thinsp;0.86), bipolar disorder (0.66; 0.68), schizophrenia/psychotic disorders (0.68; 0.72), PTSD (0.73; 0.78), and SUD (0.87; 0.92) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Bipolar disorder showed the largest cost reduction (β=\u0026minus;\u003cspan\u003e$\u003c/span\u003e1,320; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePsychiatric comorbidity in HF is heterogeneous. Anxiety is associated with increased hospital utilization without a mortality difference, whereas several other diagnoses are associated with lower observed mortality; costs were lower for depression and bipolar disorder. These diagnosis-specific patterns support targeted screening, early consultation, and integrated C-L care pathways during HF hospitalization.\u003c/p\u003e","manuscriptTitle":"Diagnosis-Specific Psychiatric Comorbidity in Heart Failure: Associations With Length of Stay, Costs, and Mortality in a National Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 08:40:13","doi":"10.21203/rs.3.rs-8745482/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8286c139-4d4e-4f88-b385-12d265cb09e4","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T16:54:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 08:40:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8745482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8745482","identity":"rs-8745482","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}