Abstract
Objective
We investigated the association of frailty and delirium, both independently and in combination, with in‐hospital mortality and nonroutine discharge among hospitalized older adults with rheumatic diseases.
Methods
We performed a retrospective study using data from the Nationwide Inpatient Sample, spanning 2016 to 2022. Adults aged ≥65 years with a diagnosis of select inflammatory rheumatic diseases were identified using International Code of Diseases, Tenth Revision (ICD‐10) codes. Frailty was ascertained using the Hospital Frailty Risk Score (categorized into three levels: low [<5], moderate [5–15], and high [>15] frailty risk), and delirium was determined by ICD‐10 codes. Multivariable logistic regressions adjusted for demographics and comorbidities were used to assess the association of delirium and frailty individually and combined with coprimary outcomes: in‐hospital mortality and nonroutine discharge. Comorbidities were identified using the Elixhauser Comorbidity Index. Sensitivity analysis evaluated associations among patients with rheumatoid arthritis only and with varying case definitions of rheumatic diseases.
Results
Among 938,595 weighted hospitalizations of older adults with rheumatic diseases (mean age 76.0 years, 74.6% female), 2.6% were classified as highly frail and 3.5% experienced delirium. Both delirium and high frailty independently increased the odds of in‐hospital mortality (odds ratio [OR] 4.99, 95% confidence interval [CI] 4.16–5.99 and OR 6.80, 95% CI 5.45–8.48, respectively) and nonroutine discharge (OR 3.14, 95% CI 2.95–3.35 and OR 5.69, 95% CI 5.26–6.16, respectively). The combined presence of delirium and high frailty conferred the highest risk of mortality (OR 16.2, 95% CI 11.5–22.9) and nonroutine discharge (OR 11.1, 95% CI 8.78–14.0) and associations remained consistent across sensitivity analyses.
Conclusion
Among hospitalized older adults with rheumatic diseases, both frailty and delirium were independently linked to poorer outcomes, including higher in‐hospital mortality and increased likelihood of discharge to a nonhome setting. Notably, patients with both conditions faced the greatest risk.
INTRODUCTION
Rheumatic diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are leading causes of chronic conditions associated with significant morbidity among older adults. 1 , 2 These diseases affect millions of Americans, and their prevalence notably increases with age. RA alone affects approximately 1.3 million adults in the United States, with nearly half of these cases occurring in individuals aged 65 years or older. 3 SLE in particular disproportionately contributes to cumulative organ damage and the burden of comorbidities in older populations, leading to hospitalizations that increase in both frequency and complexity during later adulthood. 4 , 5
Older adults hospitalized with rheumatic disease experience frequent complications due to chronic inflammation, immunosuppressive treatments, and functional decline. 6 , 7 , 8 These complications are further exacerbated by geriatric syndromes such as frailty and delirium, which are recognized as critical factors influencing hospitalization outcomes. 9 , 10 , 11 Frailty, which is characterized by diminished physiologic reserves, and delirium, which is marked by acute disturbances in attention and cognition, are common complications during hospitalization for older adults and significantly worsen patient outcomes. 12 , 13
Frailty is multidimensional and encompasses physical, cognitive, and functional domains, and it is conceptually distinct from disease‐specific activity. However, in patients with inflammatory rheumatic diseases, there can be a substantial overlap between features of frailty and manifestations of active disease, including fatigue, reduced mobility, and systemic inflammation, which may complicate its measurement and interpretation in this population. 7 , 14 Although frailty assessment tools vary in their composition and may capture overlapping aspects of disease burden, they have consistently demonstrated strong predictive value for adverse clinical outcomes. 15 , 16 For example, patients with RA are 20% to 30% more likely to develop frailty compared to those without rheumatic diseases, and frail patients with RA experience substantially higher hospital readmission rates compared to their nonfrail counterparts. 17 , 18 , 19
Despite their documented associations with prolonged hospital stays, functional deterioration, and increased mortality, 20 , 21 , 22 frailty and delirium are not routinely assessed or integrated into inpatient care strategies for older patients with rheumatic conditions, and literature specific to rheumatic disease remains limited. Furthermore, longitudinal data evaluating whether frailty‐directed interventions improve outcomes in this population are sparse. 23 These gaps highlight the need to better understand the clinical implications of frailty, particularly in the context of acute hospitalization. The combined effects of frailty and delirium on hospitalization outcomes have not specifically been examined in patients with rheumatic disease. Therefore, this study aimed to evaluate the independent and combined associations of frailty and delirium with in‐hospital mortality and nonroutine discharge in older adults hospitalized with rheumatic diseases.
MATERIALS AND METHODS
We conducted a retrospective analysis using data from the Nationwide Inpatient Sample (NIS), the largest publicly available all‐payer inpatient health care database in the United States, spanning the years 2016 to 2022. The NIS approximates a 20% stratified sample of discharges from US community hospitals and includes weighting variables that allow for national estimates. Our analysis was performed at the hospitalization level, as the NIS does not include patient‐level identifiers, and repeat admissions for the same individual may be present.
We included adults aged ≥65 years with a primary diagnosis of one of the following rheumatic diseases: RA, SLE, polymyalgia rheumatica (PMR), psoriatic arthritis (PsA), giant cell arteritis (GCA), or ankylosing spondylitis (AS), identified using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) diagnosis codes (Supplementary Table 1).
Exposure variables
Frailty was assessed using the Hospital Frailty Risk Score (HFRS), a validated claims‐based index derived from 109 weighted ICD‐10 codes. 24 HFRS scores were categorized into three predefined risk levels: low (<5), moderate (5–15), and high (>15) based on thresholds validated in previous literature. Conceptually, the HFRS reflects an accumulation‐of‐deficits model of frailty, capturing the cumulative burden of health deficits across multiple organ systems rather than a single functional domain. These deficits include a range of conditions commonly associated with frailty, such as mobility limitations, falls, incontinence, cognitive impairment, and chronic comorbidities (Supplementary Table 2). The HFRS was originally validated against clinical outcomes including 30‐day mortality, readmission, and length of stay in older hospitalized populations and has been shown to predict adverse outcomes across multiple settings. Its performance has also been compared with measured frailty using the Fried frailty phenotype, showing modest agreement (κ = 0.22, 95% confidence interval [CI] 0.15–0.30), highlighting that it is primarily a claims‐based risk stratification tool rather than a direct clinical assessment of physical frailty. 25 The HFRS has also been successfully applied in large administrative databases such as the NIS, demonstrating consistent prediction of worse outcomes. 26
Delirium was identified using ICD‐10‐CM codes encompassing behavioral syndromes associated with physiologic disturbances, such as acute confusional states, including both hyperactive and hypoactive presentations (Supplementary Table 1).
Outcomes
The two coprimary outcomes were (1) in‐hospital mortality and (2) nonroutine discharge (defined as death, left against medical advice, transfer to a skilled nursing facility, intermediate care facility, home with health care services, or any other facility). Discharge disposition was determined using standard NIS coding.
Covariates and statistical analysis
Models were adjusted for patient demographic characteristics (age, sex, and race and ethnicity), primary payer, hospital census division and region, and comorbidities. Comorbidities were identified using the Elixhauser Comorbidity Index, as recommended for use with administrative data. 27
Discharge‐level sampling weights, cluster variables, and strata provided by the NIS were applied using the survey package to account for the complex survey design and to produce nationally representative estimates. Descriptive statistics were used to summarize baseline characteristics across frailty and delirium categories.
Multivariable logistic regression models were constructed to evaluate the independent associations of frailty (categorized as low, moderate, and high) and delirium (present vs absent) with in‐hospital mortality and nonroutine discharge. We additionally created a combined exposure variable with six mutually exclusive categories: (1) no delirium with low frailty (reference group), (2) delirium with low frailty, (3) no delirium with moderate frailty, (4) delirium with moderate frailty, (5) no delirium with high frailty, and (6) delirium with high frailty. These categories were included in multivariable models to examine the gradient of risk and assess potential additive or synergistic effects of increasing frailty and the presence of delirium on outcomes.
Subgroup and sensitivity analyses
Prespecified subgroup analyses were conducted in patients with RA only. Additionally, we conducted sensitivity analyses using alternate case definitions of rheumatic diseases by considering patients where rheumatic disease diagnosis was only at top three and the top diagnostic positions and considering patients with just RA in the top five diagnostic positions.
All statistical analyses were conducted using RStudio v4.2.2 (R Foundation for Statistical Computing). All analyses incorporated two‐sided hypothesis testing with statistical significance defined at P < 0.05. This study was determined to be exempt by the institutional review board at the University of Washington.
RESULTS
Among 938,595 weighted hospitalizations of older adults with rheumatic diseases, 2.6% were classified as highly frail and 3.5% experienced delirium (Figure 1). The mean age was 76.0 years (SD 7.36 years), and 74.6% of hospitalizations were of female patients. White patients accounted for 80.1% of hospitalizations followed by Black (9.1%), Hispanic (6.6%), Asian or Pacific Islander (1.7%), and Native American (0.5%). Most hospitalizations were covered by Medicare (89.9%) with smaller proportions covered either by private insurance (7.6%), Medicaid (0.1%), or self‐pay (0.9%) (Table 1).
Figure 1.
Flowchart showing creation of the study cohort using NIS 2016 to 2022. ICD, Intrnational Code of Diseases, Tenth Revision; NIS, Nationwide Inpatient Sample; RD, rheumuatic disease.
Table 1.
Baseline characteristics of the study cohort, NIS, 2016–2022*
| Characteristic | Overall, N = 938,595 a | High, N = 24,785, n (%) a | Moderate, n = 338,100, n (%) a | Low, n = 575,710, n (%) a | No delirium, n = 905,865, n (%) a | Delirium, n = 32,730, n (%) a |
|---|---|---|---|---|---|---|
| Age group | ||||||
| 65–69 | 223,445 (23.8) | 3,735 (15.1) | 62,375 (18.4) | 157,335.0 (27.3) | 218,410 (24.1) | 5,035 (15.4) |
| 70–74 | 238,685 (25.4) | 4,790 (19.3) | 76,035 (22.5) | 157,859.9 (27.4) | 232,105 (25.6) | 6,580 (20.1) |
| 75–79 | 197,940 (21.1) | 5,040 (20.3) | 73,105 (21.6) | 119,794.9 (20.8) | 191,115 (21.1) | 6,825 (20.9) |
| 80–84 | 143,715 (15.3) | 4,975 (20.1) | 60,130 (17.8) | 78,609.9 (13.7) | 137,390 (15.2) | 6,325 (19.3) |
| 85–89 | 88,615 (9.4) | 3,870 (15.6) | 42,170 (12.5) | 42,575.0 (7.4) | 83,615 (9.2) | 5,000 (15.3) |
| 90 or older | 46,195 (4.9) | 2,375 (9.6) | 24,285 (7.2) | 19,535.0 (3.4) | 43,230 (4.8) | 2,965 (9.1) |
| Sex | ||||||
| Female | 700,195 (74.6) | 19,400 (78.3) | 262,265 (77.6) | 418,530 (72.7) | 675,155 (74.5) | 25,040 (76.5) |
| Male | 238,400 (25.4) | 5,385 (21.7) | 75,835 (22.4) | 157,180 (27.3) | 230,710 (25.5) | 7,690 (23.5) |
| Race | ||||||
| Asian or Pacific Islander | 15,930 (1.7) | 305. (1.2) | 5,550 (1.6) | 10,075 (1.8) | 15,515 (1.7) | 415 (1.3) |
| Black | 85,675 (9.1) | 2,925 (11.8) | 32,335 (9.6) | 50,415 (8.8) | 82,210 (9.1) | 3,465 (10.6) |
| Hispanic | 62,335 (6.6) | 1,365 (5.5) | 21,355 (6.3) | 39,615 (6.9) | 60,615 (6.7) | 1,720 (5.3) |
| Native American | 4,660 (0.5) | 140 (0.6) | 1,610 (0.5) | 2,910 (0.5) | 4,505 (0.5) | 155 (0.5) |
| Other | 18,190 (1.9) | 420 (1.7) | 6,250 (1.8) | 11,520 (2.0) | 17,625 (1.9) | 565 (1.7) |
| White | 751,805 (80.1) | 19,630 (79.2) | 271,000 (80.2) | 461,175 (80.1) | 725,395 (80.1) | 26,410 (80.7) |
| Census division | ||||||
| East North Central | 154,505 (16.5) | 6,840 (27.6) | 61,755 (18.3) | 85,910 (14.9) | 146,930 (16.2) | 7,575 (23.1) |
| East South Central | 55,730 (5.9) | 1,020 (4.1) | 19,455 (5.8) | 35,255 (6.1) | 53,745 (5.9) | 1,985 (6.1) |
| Middle Atlantic | 133,840 (14.3) | 2,655 (10.7) | 44,685 (13.2) | 86,500 (15.0) | 130,440 (14.4) | 3,400 (10.4) |
| Mountain | 57,280 (6.1) | 1,060 (4.3) | 19,815 (5.9) | 36,405 (6.3) | 55,610 (6.1) | 1,670 (5.1) |
| New England | 56,035 (6.0) | 1,070 (4.3) | 19,370 (5.7) | 35,595 (6.2) | 54,185 (6.0) | 1,850 (5.7) |
| Pacific | 113,875 (12.1) | 2,710 (10.9) | 39,915 (11.8) | 71,250 (12.4) | 110,390 (12.2) | 3,485 (10.6) |
| South Atlantic | 197,040 (21.0) | 5,240 (21.1) | 71,430 (21.1) | 120,370 (20.9) | 190,565 (21.0) | 6,475 (19.8) |
| West North Central | 66,355 (7.1) | 1,465 (5.9) | 24,050 (7.1) | 40,840 (7.1) | 64,120 (7.1) | 2,235 (6.8) |
| West South Central | 103,935 (11.1) | 2,725 (11.0) | 37,625 (11.1) | 63,585 (11.0) | 99,880 (11.0) | 4,055 (12.4) |
| Insurance | ||||||
| Medicaid | 9,280 (1.0) | 270 (1.1) | 3,455 (1.0) | 5,555 (1.0) | 9,010 (1.0) | 270 (0.8) |
| Medicare | 843,410 (89.9) | 22,730 (91.7) | 307,360 (90.9) | 513,319.7 (89.2) | 813,375 (89.8) | 30,035 (91.8) |
| No charge | 160 (0.0) | (0.0) b | (0.0) b | (0.0) b | (0.0) b | (0.0) b |
| Other | 10,730 (1.1) | 220 (0.9) | 3,555 (1.1) | 6,955 (1.2) | 10,355 (1.1) | 375 (1.1) |
| Private insurance | 71,415 (7.6) | 1,495 (6.0) | 22,420 (6.6) | 47,500 (8.3) | 69,515 (7.7) | 1,900 (5.8) |
| Self‐pay | 3,600 (0.4) | 65 (0.3) | 1,255 (0.4) | 2,280 (0.4) | 3,455 (0.4) | 145 (0.4) |
| Delirium | 32,730 (3.5) | 4,445 (17.9) | 20,850 (6.2) | 7,435 (1.3) | ‐ | ‐ |
| Frailty risk | ||||||
| High | 24,785 (2.6) | ‐ | ‐ | ‐ | 20,340 (2.2) | 4,445 (13.6) |
| Moderate | 338,100 (36.0) | ‐ | ‐ | ‐ | 317,250 (35.0) | 20,850 (63.7) |
| Low | 575,710 (61.3) | ‐ | ‐ | ‐ | 568,275 (62.7) | 7,435 (22.7) |
HCUP, Health care cost and utilization project; NIS, Nationwide Inpatient Sample.
Estimates derived from the NIS, taking into account sample weights provided by HCUP, and taking into account the NIS's complex stratified sampling design.
Cells with ≤10 unweighted discharges are suppressed in accordance with the HCUP Data Use Agreement to protect patient confidentiality.
The majority of hospitalizations were for RA (70%) followed by SLE (13.2%), PMR (10.8%), PsA (4.9%), AS (1.5%), and GCA (4.5%) (Supplementary Tables 3, 4–6). Hospitalizations with delirium tended to be older, female, and with a higher comorbidity burden compared to those without delirium. Diabetes, renal failure, and depression were more prevalent among hospitalizations with delirium.
Both delirium and high frailty were independently associated with increased odds of in‐hospital mortality in a dose–response manner. Compared with a reference group of hospitalizations without delirium, delirium was associated with an odds ratio (OR) of 4.99 (95% CI 4.16–5.99). As for frailty, compared to hospitalizations with low frailty, moderate frailty was associated with an OR of 2.34 (95% CI 2.04–2.67) and high frailty with an OR of 6.80 (95% CI 5.45–8.48).
The combined presence of delirium and high frailty conferred the highest risk of mortality (OR 16.2, 95% CI 11.5–22.9). Hospitalizations without delirium also showed increasing risk with higher frailty, moderate frailty having an OR of 2.08 (95% CI 1.81–2.40) and high frailty having an OR of 5.01 (95% CI 3.85–6.51). Similar patterns were seen for nonroutine discharge as well. Compared to hospitalizations without delirium and low frailty, delirium alone was associated with elevated odds of nonroutine discharge (OR 3.14, 95% CI 2.95–3.35). Increasing frailty also conferred higher odds of nonroutine discharge, with moderate frailty alone conferring an OR of 2.03 (95% CI 1.98–2.07) and high frailty with an OR of 5.14 (95% CI 4.73–5.59) (Table 2, 3 and Figure 2).
Table 2.
In‐hospital mortality and discharge disposition (primary outcome) stratified by frailty level and delirium status
| Frailty level | Delirium status | |||||
|---|---|---|---|---|---|---|
| Outcome | Overall, N = 938,595, n (%) a | High, N = 24,785, n (%) a | Moderate, N = 338,100, n (%) a | Low, N = 575,710, n (%) a | No delirium, N = 905,865, n (%) a | Delirium, N = 32,730, n (%) a |
| Mortality | ||||||
| Alive | 932,525 (99.4) | 24,130 (97.4) | 334,975 (99.1) | 573,420 (99.6) | 900,705 (99.4) | 31,820 (97.2) |
| Deceased | 6,070 (0.6) | 655 (2.6) | 3,125 (0.9) | 2,290 (0.4) | 5,160 (0.6) | 910 (2.8) |
| Discharge | ||||||
| Non‐routine* | 444,405 (47.3) | 19,570 (79.0) | 196,145 (58.0) | 228,690 (39.7) | 420,465 (46.4) | 23,940 (73.1) |
| Routine | 494,190 (52.7) | 5,215 (21.0) | 141,955 (42.0) | 347,020 (60.3) | 485,400 (53.6) | 8,790 (26.9) |
Abbreviation: NIS, Nationwide Inpatient Sample.
Estimates derived from the NIS. Non‐routine discharge included death, discharge against medical advice, transfer to post–acute care facilities, home with healthcare services, or other facility‐based discharges, based on standard NIS coding.
Table 3.
Multivariable‐adjusted odds of in‐hospital mortality and non‐routine discharge according to frailty level, delirium status, and their combined exposure categories*
| Variable | n | OR (95% CI)a |
|---|---|---|
| In‐hospital mortality | ||
| Delirium | 938,594 | |
| No delirium | — | |
| Delirium | 4.99 (4.16–5.99) | |
| Frailty risk | 938,594 | |
| Low | — | |
| Moderate | 2.34 (2.04–2.67) | |
| High | 6.80 (5.45–8.48) | |
| Combined exposure variable | 938,594 | |
| No delirium with low | — | |
| Delirium with low | 2.79 (1.63–4.75) | |
| No delirium with moderate | 2.08 (1.81–2.40) | |
| Delirium with moderate | 7.13 (5.69–8.95) | |
| No delirium with high | 5.01 (3.85–6.51) | |
| Delirium with high | 16.2 (11.5–22.9) | |
| Non‐routine discharge | ||
| Delirium | 938,594 | |
| No delirium | — | |
| Delirium | 3.14 (2.95–3.35) | |
| Frailty risk | 938,594 | |
| Low | — | |
| Moderate | 2.10 (2.05–2.14) | |
| High | 5.69 (5.26–6.16) | |
| Combined exposure variable | 938,594 | |
| No delirium with low | — | |
| Delirium with low | 2.24 (1.99–2.52) | |
| No delirium with moderate | 2.03 (1.98–2.07) | |
| Delirium with moderate | 4.58 (4.23–4.96) | |
| No delirium with high | 5.14 (4.73–5.59) | |
| Delirium with high | 11.1 (8.78–14.0) |
CI, confidence interval; OR, odds ratio.
Models were adjusted for patient demographic characteristics (age, sex, and race and ethnicity), primary payer, hospital census division and region, and comorbidities. Comorbidities were identified using the Elixhauser Comorbidity Index. The reference group was patients without delirium and with low frailty.
Figure 2.
Odds of in‐hospital death and nonroutine discharge across frailty risk categories, stratified by delirium status. Points represent adjusted odds ratios represented on the log scale; error bars indicate 95% confidence intervals. The dashed line denotes the null (odds ratio = 1) with reference: low frailty, no delirium.
Delirium alone was associated with elevated odds of nonroutine discharge (OR 3.14, 95% CI 2.95–3.35) as well as high frailty alone (OR 5.69, 95% CI 5.26–6.16), and both together (OR 11.1, 95% CI 8.78–14.0). These associations remained robust across multiple subgroup analyses (Supplementary Tables 4–6).
DISCUSSION
Using nationally representative hospitalization‐level data from the NIS, we found that both frailty and delirium were independently associated with worse patient outcomes, including higher in‐hospital mortality, increased likelihood of nonroutine discharge, longer hospital length of stay, and greater health care utilization. Notably, the co‐occurrence of frailty and delirium was associated with the highest risk across all outcomes, with a graded increase in adverse outcomes observed with increasing frailty severity in the presence of delirium.
Our findings align with prior work in other geriatric conditions, including acute decompensated heart failure and cardiovascular disease more broadly. For example, Irizarry‐Caro et al demonstrated that delirium and frailty exert independent and synergistic effects on in‐hospital mortality and nonroutine discharge, with a dose–response relationship across frailty levels. 28 Similarly, Damluji et al (2023) showed that the simultaneous development of physical frailty and cognitive impairment in older adults without previous coronary artery disease was associated with the highest risk of incident major adverse cardiovascular events, compared with the development of each syndrome alone. 29 Data examining the combined impact of frailty and delirium across other clinical settings remain limited, and findings have been heterogeneous. In critically ill intensive care unit populations, some studies have demonstrated clinically meaningful associations between these syndromes and adverse outcomes, 10 while others have reported weaker associations or clinically insignificant outcomes, 30 , 31 which likely reflects differences in patient populations, frailty definitions, and delirium ascertainment methods. Taken together, the graded associations observed in both studies, including those using the HFRS, reinforce the notion that the adverse prognostic impact of these geriatric syndromes transcends disease‐specific contexts and reflects a shared vulnerability across hospitalized older adults, emphasizing the importance of identifying and addressing frailty and cognitive or acute delirium syndromes in clinical care.
Frailty has been increasingly recognized as a clinically meaningful marker of vulnerability and a determinant of adverse outcomes across the lifespan. 14 , 20 Prior studies in RA and SLE have demonstrated strong associations between frailty and higher disease activity, increased hospitalizations and readmission rates, functional decline, and mortality, with emerging evidence suggesting that frailty occurs prematurely and may reflect accelerated biologic aging in immune‐mediated rheumatic diseases. 6 , 7 , 14 , 18 , 19 , 32 In a prospective cohort of women with SLE, baseline frailty was independently associated with worse patient‐reported outcomes and greater disability at one year, even after adjustment for cumulative organ damage, 32 while large administrative database studies in RA have linked frailty to increased inpatient mortality, longer hospital stays, and higher readmission rates. 19 Our findings extend this literature by demonstrating that frailty remains a strong predictor of adverse acute hospitalization outcomes across a broad spectrum of rheumatic diseases, particularly at higher frailty levels, underscoring its relevance beyond disease‐specific and longitudinal outcomes.
Importantly, distinguishing frailty from rheumatic disease activity remains a key challenge, as overlapping features such as fatigue, reduced physical function, and systemic inflammation may contribute to both constructs. As a result, measures of frailty may partially reflect disease burden rather than true physiologic frailty. However, prior studies have consistently demonstrated that frailty, regardless of measurement approach, remains a robust predictor of adverse clinical outcomes. Our findings build on this literature by demonstrating that frailty is strongly associated with adverse hospitalization outcomes across a spectrum of inflammatory rheumatic diseases, particularly at higher frailty levels, which further underscores its relevance. Taken together, our results further support the concept that frailty, whether reflecting underlying physiologic vulnerability, disease burden, or a combination of both, identifies a high‐risk population.
In contrast to frailty, delirium has been less extensively studied in hospitalized patients with rheumatic disease despite evidence that these patients are at increased risk for neuropsychiatric complications. 33 , 34 Previous population‐based studies suggest that patients with rheumatic diseases, particularly SLE, have an elevated risk of hospitalization for delirium and dementia compared to the general population. However, to our knowledge, few studies have specifically examined delirium as a predictor of in‐hospital outcomes for patients with rheumatic disease. In our analysis, delirium was independently associated with significantly higher odds of in‐hospital mortality and nonroutine discharge, as well as longer length of stay, underscoring its prognostic importance in hospitalized older adults with rheumatic disease. Patients with inflammatory rheumatic diseases may also be particularly vulnerable to cognitive impairment, which represents an important and underrecognized risk factor for both frailty and delirium. Chronic systemic inflammation, cumulative disease burden, and long‐term exposure to immunosuppressive therapies have all been implicated in cognitive dysfunction in this population. 35 , 36 Therefore, cognitive impairment may act as a shared pathway linking frailty and delirium, contributing to an increased vulnerability to adverse outcomes during hospitalization.
Although the precise contribution of individual mechanisms to adverse outcomes is difficult to disentangle, numerous biologic and clinical pathways link frailty and delirium to an increased morbidity and mortality in older adults. These include systemic inflammation, neuroendocrine dysregulation, oxidative stress, impaired immune function, reduced physiologic reserve, and heightened vulnerability to acute stressors. Both conditions are independently associated with worse outcomes, and their frequent co‐occurrence suggests overlapping rather than distinct pathophysiologic processes, with frailty acting as a key modifier that may amplify the downstream consequences of acute cognitive dysfunction during hospitalization. 12 , 37 , 38 , 39 The pronounced risks observed among patients with both frailty and delirium in our study support this conceptual framework and underscore the importance of identifying and addressing both syndromes in the inpatient care of older adults with rheumatic disease. Although delirium and frailty were strongly associated with adverse outcomes, further studies are needed to evaluate whether targeted in‐hospital interventions aimed at preventing or mitigating these syndromes can translate into improved clinical outcomes.
This study has several important strengths. First, we used data from the NIS, the largest publicly available all‐payer inpatient database in the United States, enabling nationally representative estimates across diverse hospital settings and geographic regions. The large sample size provided the statistical power to examine these clinically important exposures, as well as to evaluate gradients of risk across frailty categories and combined exposure groups. Second, frailty was operationalized using the HFRS, a validated claims‐based index, facilitating comparability with previous studies. The inclusion of a combined frailty–delirium exposure allowed for an assessment of additive risk and dose–response relationships.
Several limitations should be acknowledged. First, the retrospective observational design precludes causal inference, and residual confounding is possible despite adjustment. Our study also did not include a direct comparison group of hospitalized older adults without inflammatory rheumatic disease, limiting our ability to determine whether rheumatic disease confers additional risk beyond frailty and delirium alone. Nevertheless, the magnitude and graded nature of the associations observed in our cohort are broadly consistent with those reported in other patient populations, 28 , 40 , 41 , 42 suggesting that these geriatric syndromes confer substantial risk across diverse clinical settings.
Second, the NIS is an administrative claims database and relies on ICD‐10‐CM codes, which are subject to misclassification and undercoding. Delirium identified using diagnostic codes as opposed to standardized bedside assessments may have resulted in underestimation of prevalence and potential misclassification bias toward more severe cases. Supporting this, Sheehan et al (2024) demonstrated that ICD‐10 coding for delirium had high specificity but very low sensitivity (24.1%) compared with a validated chart review reference standard, meaning that many true delirium cases are missed when relying solely on administrative data, although identified cases are likely true positives. 43 Consequently, the observed prevalence in our study may underestimate the true burden, and misclassification may bias associations toward patients with more severe presentations.
Similarly, the HFRS, while validated, captures frailty indirectly through diagnostic codes and may not fully reflect functional, cognitive, or social dimensions of frailty. Some components of the HFRS such as mobility limitations, falls, and functional impairment may overlap with manifestations of inflammatory rheumatic disease itself. As a result, the HFRS may partially capture disease‐related disability rather than frailty alone, which could lead to overestimation of frailty prevalence or its association with adverse outcomes in this population. However, this overlap may still be clinically meaningful since it identifies a subgroup of patients with increased vulnerability and risk for poor outcomes.
Third, analyses were conducted at the hospitalization level, as patient‐level identifiers are not available; consequently, repeat admissions for the same individual could not be distinguished. Fourth, information on disease severity, duration of rheumatic disease, medication use (including immunosuppressive or glucocorticoid therapy), laboratory values, and in‐hospital clinical trajectories was unavailable, limiting mechanistic insight. Despite these limitations, the strengths of the large, nationally representative data set, validated exposure definitions, and consistent findings across multiple outcomes and sensitivity analyses support the robustness and clinical relevance of our results.
To conclude, our study demonstrates that both frailty and delirium are independently associated with significantly poorer outcomes among hospitalized older adults with rheumatic diseases, including higher in‐hospital mortality and increased nonroutine discharge. Notably, the concurrent presence of high frailty and delirium confers the highest risk for these adverse events. These findings underscore the critical need for routine assessment and integrated management of these geriatric syndromes to improve outcomes in this vulnerable population.
AUTHOR CONTRIBUTIONS
All authors contributed to at least one of the following manuscript preparation roles: conceptualization AND/OR methodology, software, investigation, formal analysis, data curation, visualization, and validation AND drafting or reviewing/editing the final draft. As corresponding author, Dr Singh confirms that all authors have provided the final approval of the version to be published and takes responsibility for the affirmations regarding article submission (eg, not under consideration by another journal), the integrity of the data presented, and the statements regarding compliance with institutional review board/Declaration of Helsinki requirements.
Supporting information
Disclosure Form:
Supplementary Table 1. Codes used for identifying study cohort and outcomes.
Supplementary Table 2. Distribution of RDs.
Supplementary Table 3. Sensitivity Analysis with rheumatic disease in the first three hospital diagnoses.
Supplementary Table 4. Sensitivity Analysis with rheumatic disease in the first hospital diagnosis.
Supplementary Table 5. Sensitivity Analysis with rheumatoid arthritis in the first five hospital diagnoses.
Supplementary Table 6. ICD‐10‐CM Codes and Weights Used for the Hospital Frailty Risk Score (HFRS).
Dr Damluji's work was supported by research funding from the Pepper Scholars Program of the Johns Hopkins University Claude D. Pepper Older Americans Independence Center funded by the National Institute on Aging, NIH (grant P30‐AG‐021334), and Dr Damluji's work was supported by the mentored patient‐oriented research career development award from the National Heart, Lung, and Blood Institute, NIH (grant K23‐HL‐15377101). Dr Singh's work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH (grant K23‐AR‐079588) and by the National Institute on Aging, NIH (grant R03‐AG‐082857).
1Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas; 2University of Washington, Seattle, Washington; 3Division of Geriatrics and Palliative Medicine, McGovern Medical School, University of Texas Health Science Center, Houston, Texas; 4Mass General Brigham Division of Gastroenterology, Hepatology and Endoscopy, Harvard Medical School, Boston, Massachusetts; 5Program for the Care and Study of the Aging Heart, Department of Medicine, Weill Cornell Medicine, New York, New York; 6Cardiovascular Center on Aging, Cleveland Clinic Foundation, Cleveland, Ohio; 7Department of Medicine, Division of Rheumatology, University of Washington, Seattle, Washington.
Additional supplementary information cited in this article can be found online in the Supporting Information section (https://acrjournals.onlinelibrary.wiley.com/doi/10.1002/art.90073).
Author disclosures are available at https://onlinelibrary.wiley.com/doi/10.1002/acr2.90073.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Disclosure Form:
Supplementary Table 1. Codes used for identifying study cohort and outcomes.
Supplementary Table 2. Distribution of RDs.
Supplementary Table 3. Sensitivity Analysis with rheumatic disease in the first three hospital diagnoses.
Supplementary Table 4. Sensitivity Analysis with rheumatic disease in the first hospital diagnosis.
Supplementary Table 5. Sensitivity Analysis with rheumatoid arthritis in the first five hospital diagnoses.
Supplementary Table 6. ICD‐10‐CM Codes and Weights Used for the Hospital Frailty Risk Score (HFRS).