Abstract
Background
The growing burden of frailty, multimorbidity, and polypharmacy among older adults presents major challenges to healthcare systems worldwide. These interrelated conditions are known to worsen clinical outcomes, yet data from the region are limited. This study aimed to examine the prevalence and clinical impact of frailty, complex multimorbidity, and polypharmacy among hospitalized older adults in Oman.
Methods
A prospective, multicenter cohort study was conducted in general internal medicine wards of two tertiary hospitals in Oman. Patients aged ≥ 65 years were consecutively enrolled and assessed for frailty using Clinical Frailty Scale (CFS), multimorbidity using the Charlson Comorbidity Index (CCI) and a definition of complex multimorbidity (≥ 3 chronic conditions), and polypharmacy (≥ 5 medications). Clinical outcomes included hospital length of stay (LOS), in-hospital mortality, HD/ICU admission, 30- and 90-day mortality, and 30- and 90-day readmission. Multivariable regression and Kaplan-Meier survival analyses were used.
Results
Among 369 patients, high frailty, complex multimorbidity, and polypharmacy were present in 139 (37.67%), 126 (34.15%), and 243 (65.85%) patients, respectively. Higher frailty was associated with longer LOS (6.49 vs. 4.88 days, p < 0.01) and increased in-hospital mortality (15.11% vs. 3.54%, p < 0.01). High frailty independently predicted 30-day mortality (adjusted odds ratio [aOR] 5.68, p < 0.01) and 90-day mortality (aOR 3.53, p < 0.01). Complex multimorbidity independently predicted 30-day readmission (aOR 1.92, p = 0.015), 30-day mortality (aOR 2.12, p = 0.026), and 90-day mortality (aOR 2.42, p < 0.01). Kaplan–Meier and adjusted Cox regression analyses showed lower 90-day survival and higher mortality risk among patients with high frailty (adjusted hazard ratio [aHR] 3.16, 95% CI 1.69–5.89, p < 0.001) and complex multimorbidity (aHR 1.85, 95% CI 1.15–2.98, p = 0.011).
Conclusion
Frailty, complex multimorbidity, and polypharmacy were common among hospitalized older adults in Oman. High frailty and complex multimorbidity were the most consistent predictors of adverse outcomes, supporting early recognition, structured geriatric assessment pathways, and future targeted patient-centered interventions.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-026-07889-7.
Keywords: Frailty, Multimorbidity, Polypharmacy, Geriatrics, Mortality, Readmission
Introduction
Frailty, multimorbidity, and polypharmacy are increasingly prevalent clinical challenges in the context of global population aging. The proportion of older adults is expected to rise from 8% in 2015 to 16% in 2050, highlighting the growing importance of addressing age-related health conditions [1, 2].
Frailty is a progressive decline in physiological function, increasing vulnerability to stressors and impairing homeostasis recovery [3]. It is a dynamic condition that can fluctuate over time, either improving or worsening [4]. Frail individuals face a heightened risk of significant physical and mental deterioration even with minor health events [4]. Independently, frailty is associated with adverse health outcomes and increased healthcare utilization [3, 5]. Early frailty assessment using tools like the Clinical Frailty Scale (CFS) enables risk stratification, facilitating a more targeted evaluation of high-risk patients and a holistic approach to care [5]. Identifying frail patients early also facilitates timely geriatric assessments and provides reliable prognostic data for clinical decision-making [5].
Multimorbidity, defined as the presence of two or more chronic conditions, is associated with reduced quality of life and increased mortality risk [6]. To further refine healthcare prioritization, the concept of complex multimorbidity has emerged, although no universally accepted definition exists [7]. Definitions vary across research groups and healthcare providers, incorporating factors such as the number of chronic conditions, the number of affected organ systems, and interactions between mental and physical health conditions [7]. Studies indicate that hospitalized older adults with three or more chronic conditions have a markedly higher risk of adverse outcomes, including prolonged hospital stays, readmissions, and mortality [1, 8]. The coexistence of frailty and multimorbidity exacerbates these risks [1].
Polypharmacy, commonly defined as the concurrent use of five or more medications, has a bidirectional relationship with frailty and is independently associated with increased mortality, disability, and hospitalization [9–12]. Frailty, multimorbidity, and polypharmacy form a complex interrelated syndrome, where each condition can either contribute to or result from the others [11, 13]. Older adults with multimorbidity are more likely to be frail, and their combined burden frequently leads to polypharmacy [13, 14]. Conversely, polypharmacy itself may contribute to frailty through adverse drug reactions and medication-related complications [11, 14]. This interplay exacerbates healthcare demands and worsens clinical outcomes, including increased hospitalizations and mortality [13].
The prevalence of multimorbidity and frailty varies widely due to differences in study populations and assessment methods. Multimorbidity prevalence ranges from 21.8% to 48%, rising above 60% in individuals with more than two chronic conditions [1, 5, 8, 15, 16]. Frailty prevalence varies between 3% and 53%, while the co-occurrence of both conditions has been reported in 6% to 33.6% of older adults [1, 15]. A recent systematic review and meta-analysis estimated the global prevalence of polypharmacy in older adults at 39%, with higher rates observed in individuals aged ≥ 70 years [17].
Despite the growing elderly population in Oman and the Gulf region, data on frailty, multimorbidity, and polypharmacy remain scarce. A cross-sectional study in an Omani primary care setting estimated the prevalence of frailty at 31.1% [18]. While no recent studies have specifically assessed the prevalence of multimorbidity in Oman, a 2018 study conducted at a primary health center highlighted a high burden of chronic diseases among older adults [19]. Similarly, a 2016 study from a tertiary hospital reported that 76% of elderly patients were discharged with polypharmacy [20]. Although international evidence links frailty, multimorbidity, and polypharmacy with adverse outcomes, including mortality, hospital readmission, and prolonged hospitalization, most available Omani data have focused on prevalence or medication burden rather than prospective clinical outcomes. Therefore, important gaps remain regarding the combined prognostic impact of these overlapping geriatric syndromes, particularly mortality and readmission, among hospitalized older adults in Oman.
This study aimed to assess the prevalence and severity of frailty among hospitalized older adults in Oman, its association with complex multimorbidity and polypharmacy, and their combined impact on clinical outcomes, including hospital length of stay (LOS), readmissions, HD/ICU admissions, and mortality. Understanding these overlapping geriatric syndromes is important for optimizing care pathways, guiding resource allocation, and informing health policy for Oman’s aging population. By evaluating frailty, complex multimorbidity, and polypharmacy together in a prospective hospitalized cohort, this study aimed to provide locally relevant evidence on their burden and association with short-term clinical outcomes.
Methods
Study design, setting, and population
A prospective multicenter cohort study was conducted at two major tertiary hospitals in Oman: Sultan Qaboos University Hospital (SQUH) and Royal Hospital (RH). All patients aged ≥ 65 years admitted to the acute medical unit were included. The study was conducted in two periods: from May to September 2023 at SQUH and from February to July 2024 at RH. Exclusion criteria included patients younger than 65 years and those who did not provide consent.
Sample size calculation
The sample size was calculated based on an expected frailty prevalence of approximately 30%, informed by the wide reported prevalence of frailty in older adults internationally and supported by a local Omani primary care/community-based study reporting a frailty prevalence of 31.1%. Using a 95% confidence interval and a 5% margin of error, the minimum required sample size was 323 patients. To account for potential missing data, the sample was further increased by 15% to 369 patients.
Data collection
The data sheet included patient-, admission-, and discharge-related information. Patient-related information involved demographics (age, date of birth, gender, height, weight, region, marital status, employment status, smoking/alcohol consumption history), code status, frailty index score (calculated using the Clinical Frailty Scale (CFS) [permission agreement (#20230515-03) from the Geriatric Medicine Research Unit]), medical comorbidities for Charlson Comorbidity Index (CCI) calculation (MI, CHF, peripheral vascular disease, CVA or TIA, dementia, COPD, connective tissue disease, peptic ulcer disease, liver disease, DM, hemiplegia, CKD, solid tumor, leukemia, lymphoma, AIDS), and polypharmacy status. CFS assessment was performed at admission by trained junior physicians under the supervision of specialist physicians. Data collectors received standardized training, and any uncertainty in scoring was clarified with senior clinicians. Scoring was based on available clinical information and patient or caregiver history, with emphasis on baseline functional status before the acute illness rather than the worst status during hospitalization. As outcomes were collected prospectively after the baseline assessment, assessors were not aware of subsequent outcomes at the time of CFS scoring.
Admission-related information included admission diagnosis (classified using the 10th edition of the International Classification of Diseases [ICD-10]), admission and discharge dates, in-hospital mortality, and high-dependency unit/intensive care unit (HD/ICU) admission. Discharge-related data were collected via phone calls and captured information related to readmission within 30 and 90 days of discharge, date of readmission, and mortality within 30 and 90 days of discharge. In-hospital mortality was defined as death during the index admission. Mortality at 30 and 90 days was reported cumulatively. Readmission outcomes were calculated among patients alive and eligible for readmission during the relevant follow-up period. To ensure data consistency and accuracy, all data collectors received standardized training on frailty scoring and data entry. Regular meetings were held to resolve inconsistencies and enhance data reliability.
Definitions and classifications
Frailty was assessed using the Clinical Frailty Scale (CFS), a validated 9-point tool originally developed and updated by Rockwood et al. [21, 22], in which higher scores indicate greater frailty: 1 = very fit, 2 = well, 3 = managing well, 4 = very mildly frail, 5 = mildly frail, 6 = moderately frail, 7 = severely frail, 8 = very severely frail, and 9 = terminally ill. For analysis, CFS scores were grouped into three categories: low frailty (CFS 1–4), intermediate frailty (CFS 5–6), and high frailty (CFS 7–8), consistent with previously published classifications. CFS 9, which represents terminal illness, was not included within the grouped frailty categories and was excluded from analyses comparing outcomes across CFS groups [23]. The CFS was selected because it is brief, feasible in acute inpatient settings, and clinically practical for risk stratification when comprehensive physical performance testing is not always possible. Compared with phenotype-based tools such as the Fried Frailty Phenotype, the CFS can be more readily implemented during routine hospital assessment; however, it is more dependent on assessor judgment and accurate estimation of baseline function. Multimorbidity was assessed using the Charlson Comorbidity Index (CCI), calculated with age-adjustment according to standard age categories, and classified as mild (CCI 1–2), moderate (CCI 3–4), and severe (CCI ≥ 5) [24]. Additionally, complex multimorbidity was defined as the presence of three or more chronic conditions based on predefined comorbidity variables recorded in the dataset, consisting mainly of CCI-related conditions with hypertension additionally included. Polypharmacy was defined as the concurrent use of five or more medications, a widely accepted threshold in geriatric populations [10].
Statistical analysis
Categorical variables were summarized as frequencies and percentages, while continuous variables were expressed as means ± standard deviation (SD) for normally distributed data or medians with interquartile ranges (IQR) for non-normally distributed data. Group comparisons were performed using Kruskal–Wallis or Wilcoxon rank-sum tests for continuous variables and chi-square or Fisher’s exact tests for categorical variables, as appropriate. Exploratory trend analyses across ordered CFS and CCI categories were performed using the Jonckheere–Terpstra test for LOS and logistic regression p-for-trend for binary outcomes. Multivariable logistic regression was used to assess associations between frailty, complex multimorbidity, polypharmacy, and clinical outcomes. Models were adjusted for clinically relevant covariates, including age, gender, frailty category, complex multimorbidity, and polypharmacy, as appropriate. Results are reported as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). For HD/ICU admission, code status was additionally included because treatment escalation status may influence this outcome. Median regression was used to assess factors associated with LOS. Kaplan–Meier curves were generated for cumulative 90-day survival using time-to-death data, and groups were compared using the log-rank test. Exploratory analyses were performed to assess the combined burden of geriatric syndromes. A cumulative geriatric burden score was created by assigning one point each for high frailty, complex multimorbidity, and polypharmacy. Exploratory model-comparison analyses were also performed to assess whether adding complex multimorbidity and polypharmacy to a frailty-based model improved model fit and discrimination for selected outcomes. Exploratory interaction analyses were performed to assess whether the association between polypharmacy and selected outcomes varied across frailty categories; these analyses were interpreted cautiously given the limited number of outcome events. Two-sided P-values < 0.05 were considered statistically significant. Statistical calculations were performed using the Stata v. 17 software package (StataCorp LLC, USA).
Ethical approval
Ethical approval was granted by the Medical and Research Ethics Committee at the College of Medicine and Health Sciences, Sultan Qaboos University (SQU) [MREC #3028; SQU-EC/099/2023], and the Health Studies and Research Approval Committee (HSRAC) at the Ministry of Health (MOH) [MoH/CSR/23/27310]. Written informed consent was obtained from patients or their legally authorized representatives in cases of impaired capacity.
Results
Baseline characteristics
As shown in Table 1, a total of 369 patients were included in the analysis. One participant had a CFS score of 9, representing terminal illness, and was excluded from grouped CFS analyses. Accordingly, frailty-group comparisons included 368 participants: 113 with low frailty (CFS 1–4), 116 with intermediate frailty (CFS 5–6), and 139 with high frailty (CFS 7–8). The median age was 76 years (IQR: 70–82), with significant differences across frailty groups, increasing from 74 years in the low-frailty group to 80 years in the high frailty group (p < 0.01). Female patients constituted 54.20% of the cohort, with the highest proportion observed in the intermediate-frailty group (62.07%; p = 0.036). The median BMI was 25.69 kg/m², with the lowest observed in the high-frailty group (24.54 kg/m², p < 0.01).
Table 1.
Characteristics and clinical features of all the included patients (n=369)
| Characteristic n (%) unless specified otherwise |
Total 369 (100%) |
Low Frailty (CFS 1-4) 113 (30.62%) |
Intermediate Frailty (CFS 5-6) 116 (31.44%) |
High Frailty (CFS 7-8) 139 (37.67%) |
p-value |
|---|---|---|---|---|---|
| Age, median (IQR), years | 76 (70 – 82) | 74 (69 – 79) | 73 (70 – 80) | 80 (72 – 85) | <0.01 |
| Female | 200 (54.20%) | 51 (45.13%) | 72 (62.07%) | 76 (54.68%) | 0.036 |
| BMI, median (IQR), kg/m² |
25.69 (22.83–29.47) |
26.01 (22.83–29.47) |
26.45 (23.61–31.13) |
24.54 (22.23–27.34) |
<0.01 |
| Marital status | 0.843 | ||||
| Single | 3 (0.81%) | 0 | 2 (1.72%) | 1 (0.72%) | |
| Married | 199 (53.92%) | 62 (54.87%) | 63 (54.31%) | 74 (53.24%) | |
| Widow | 151 (40.92%) | 45 (39.82%) | 46 (39.66%) | 59 (42.45%) | |
| Divorced | 16 (4.34%) | 6 (5.31%) | 5 (4.31%) | 5 (3.60%) | |
| Charlson comorbidity index (CCI) | <0.01 | ||||
| Mild (1-2) | 8 (2.17%) | 5 (4.42%) | 2 (1.72%) | 1 (0.72%) | |
| Moderate (3-4) | 81 (21.95%) | 35 (30.97%) | 31 (26.72%) | 15 (10.79%) | |
| Severe (≥5) | 280 (75.88%) | 73 (64.60%) | 83 (71.55%) | 123 (88.49%) | |
| Polypharmacy | 243 (65.85%) | 72 (63.72%) | 80 (68.97%) | 91 (65.47%) | 0.693 |
| Complex Multimorbidity (≥3) | 126 (34.15%) | 34 (30.09%) | 30 (25.86%) | 62 (44.60%) | <0.01 |
| Educational Level | 0.233 | ||||
| Illiterate | 226 (61.25%) | 64 (56.64%) | 73 (62.93%) | 88 (63.31%) | |
| Literate without formal schooling | 73 (19.78%) | 17 (15.04%) | 26 (22.41%) | 30 (21.58%) | |
| Below high school | 32 (8.67%) | 14 (12.39%) | 8 (6.90%) | 10 (7.19%) | |
| High school diploma | 19 (5.15%) | 8 (7.08%) | 5 (4.31%) | 6 (4.32%) | |
| Bachelor’s degree and above | 19 (5.15%) | 10 (8.85%) | 4 (3.45%) | 5 (3.60%) | |
| Employment Status | 0.014 | ||||
| Unemployed | 233 (63.14%) | 58 (51.33%) | 84 (72.41%) | 90 (64.75%) | |
| Employed | 4 (1.08%) | 2 (1.77%) | 1 (0.86%) | 1 (0.72%) | |
| Retired | 106 (28.73%) | 44 (38.94%) | 28 (24.14%) | 34 (24.46%) | |
| Others | 26 (7.05%) | 9 (7.96%) | 3 (2.59%) | 14 (10.07%) | |
| Smoking | 51 (13.82%) | 15 (13.27%) | 14 (12.07%) | 22 (15.83%) | 0.672 |
| Alcohol | 21 (5.69%) | 8 (7.08%) | 3 (2.59%) | 10 (7.19%) | 0.216 |
| Substance Use | 3 (0.81%) | 1 (0.88%) | 0 (0.00%) | 2 (1.44%) | 0.443 |
| Code Status: | <0.01 | ||||
| Full Code | 291 (78.86%) | 105 (92.92%) | 103 (88.79%) | 83 (59.71%) | |
| DNR | 78 (21.14%) | 8 (7.08%) | 13 (11.21%) | 56 (40.29%) | |
| Primary Diagnosis (ICD-10) | 0.415 | ||||
| Diseases of the respiratory system | 64 (17.39%) | 15 (13.39%) | 22 (18.97%) | 27 (19.42%) | |
| Diseases of the circulatory system | 51 (13.86%) | 21 (18.75%) | 18 (15.52%) | 12 (8.63%) | |
| Diseases of the nervous system | 40 (10.87%) | 11 (9.82%) | 13 (11.21%) | 16 (11.51%) | |
| Infectious diseases | 40 (10.87%) | 11 (9.82%) | 9 (7.76%) | 19 (13.67%) | |
| Symptoms, signs, and abnormal findings (R) | 46 (12.50%) | 15 (13.39%) | 13 (11.21%) | 18 (12.95%) | |
| Diseases of the genitourinary system | 37 (10.05%) | 13 (11.61%) | 10 (8.62%) | 14 (10.07%) | |
| Diseases of the digestive system | 30 (8.15%) | 8 (7.14%) | 11 (9.48%) | 11 (7.91%) | |
| Others | 61 (16.53%) | 19 (16.81%) | 20 (17.24%) | 22 (15.83%) | |
Values are presented as n (%) unless otherwise stated. Percentages are calculated within columns. Continuous variables are presented as median (interquartile range)
P-values were calculated using Kruskal–Wallis tests for continuous variables and chi-square or Fisher’s exact tests for categorical variables, as appropriate. One participant with CFS 9 (terminally ill) was excluded from grouped CFS analyses; therefore, CFS group columns sum to 368
The majority of patients had severe comorbidity burden (CCI ≥ 5, 75.88%), with the highest proportion observed in the high-frailty group (88.49%; p < 0.01). Polypharmacy was present in 243 patients (65.85%). Complex multimorbidity was present in 126 patients (34.15%) and was more frequent in the high-frailty group (44.60%, p < 0.01). Full code status was less frequent among patients with high frailty than among those with low or intermediate frailty (59.71% vs. 92.92% and 88.79%, respectively; p < 0.01).
Among the study population, 63.14% were unemployed, with significant differences across frailty groups (p = 0.014). There were no statistically significant differences across frailty groups regarding marital status, educational level, smoking, alcohol use, substance use, or primary diagnosis (Table 1).
Clinical outcomes by Clinical Frailty Scale (CFS)
Clinical outcomes by CFS are presented in Table 2. The median LOS increased with frailty severity, from 4.88 days in the low-frailty group to 6.49 days in the high frailty group (p < 0.01). In-hospital mortality was higher in the high-frailty group than in the low- and intermediate-frailty groups (15.11% vs. 3.54% and 4.31%, respectively, p < 0.01). Cumulative 30-day mortality also increased across frailty groups, reaching 27.34% in the high frailty group compared with 6.19% and 10.34% in the low and intermediate frailty groups, respectively (p < 0.01). A similar pattern was observed for cumulative 90-day mortality, which was 34.53% in the high frailty group compared with 12.39% and 12.07% in the low and intermediate groups, respectively (p < 0.01).
Table 2.
Clinical outcomes by clinical frailty scale, Charlson comorbidity index, complex multimorbidity, and polypharmacy of all the included patients (n=369)
| Clinical Outcomes by Clinical Frailty Scale (CFS) | |||||
|---|---|---|---|---|---|
| Clinical Outcome n (%) unless specified otherwise | Total 369 (100%) | Low Frailty(CFS 1-4)113 (30.62%) | Intermediate Frailty(CFS 5-6)116 (31.44%) | High Frailty(CFS 7-8)139 (37.67%) | p-value |
| Length of stay (LOS), median (IQR), days | 5.18 (3.46–9.06) | 4.88 (3.30–7.62) | 4.53 (3.02–8.07) | 6.49 (3.89–11.82) | <0.01 |
| HD/ICU admission | 57 (15.45%) | 15 (13.27%) | 16 (13.79%) | 26 (18.71%) | 0.411 |
| In-hospital mortality | 30 (8.13%) | 4 (3.54%) | 5 (4.31%) | 21 (15.11%) | <0.01 |
| 30-day readmission | 96 (28.32%) | 35 (32.11%) | 27 (24.32%) | 33 (27.97%) | 0.439 |
| Cumulative 30-day mortality | 58 (15.72%) | 7 (6.19%) | 12 (10.34%) | 38 (27.34%) | <0.01 |
| 90-day readmission | 125 (40.19 %) | 48 (45.28%) | 38 (36.54%) | 39 (38.61%) | 0.402 |
| Cumulative 90-day mortality | 77 (20.87%) | 14 (12.39%) | 14 (12.07%) | 48 (34.53%) | <0.01 |
| Clinical Outcomes by Charlson Comorbidity Index (CCI) | |||||
|---|---|---|---|---|---|
| Clinical Outcome n (%) unless specified otherwise | Total 369 (100%) | Mild-CCI8 (2.17%) | Moderate-CCI81 (21.95%) | Severe-CCI280 (75.88%) | p-value |
| Length of stay (LOS), median (IQR), days | 5.18 (3.46–9.06) | 4.75 (2.62–5.05) | 4.97 (3.18–7.62) | 5.43 (3.47–9.71) | 0.424 |
| HD/ICU admission | 57 (15.45%) | 1 (12.50%) | 15 (18.52%) | 41 (14.64%) | 0.764 |
| In-hospital mortality | 30 (8.13%) | 0 (0.00%) | 6 (7.41%) | 24 (8.57%) | 0.911 |
| 30-day readmission | 96 (28.32%) | 1 (12.50%) | 20 (26.67%) | 75 (29.30%) | 0.630 |
| Cumulative 30-day mortality | 58 (15.72%) | 0 (0.00%) | 9 (11.11%) | 49 (17.50%) | 0.177 |
| 90-day readmission | 125 (40.19 %) | 4 (50.00%) | 26 (36.11%) | 95 (41.13%) | 0.607 |
| Cumulative 90-day mortality | 77 (20.87%) | 0 (0.00%) | 10 (12.35%) | 67 (23.93%) | 0.027 |
| Clinical Outcomes by Complex Multimorbidity | |||||
|---|---|---|---|---|---|
| Clinical Outcome n (%) unless specified otherwise | Total 369 (100%) | With Complex Multimorbidity (≥ 3 conditions) 126 (34.15%) | Without Complex Multimorbidity 243 (65.85%) | p-value | |
| Length of stay (LOS), median (IQR), days | 5.18 (3.46–9.06) | 5.53 (3.55–11.03) | 5.04 (3.17–8.86) | 0.132 | |
| HD/ICU admission | 57 (15.45%) | 16 (12.70%) | 41 (16.87%) | 0.293 | |
| In-hospital mortality | 30 (8.13%) | 13 (10.32%) | 17 (7.00%) | 0.268 | |
| 30-day readmission | 96 (28.32%) | 42 (37.17%) | 54 (23.89%) | 0.011 | |
| Cumulative 30-day mortality | 58 (15.72%) | 27 (21.43%) | 31 (12.76%) | 0.030 | |
| 90-day readmission | 125 (40.19 %) | 48 (48.48%) | 77 (36.32%) | 0.042 | |
| Cumulative 90-day mortality | 77 (20.87%) | 38 (30.16%) | 39 (16.05%) | <0.01 |
| Clinical Outcomes by Polypharmacy | |||||
|---|---|---|---|---|---|
| Clinical Outcome n (%) unless specified otherwise | Total 369 (100%) | With Polypharmacy 243 (65.85%) | Without Polypharmacy 126 (34.15%) | p-value | |
| Length of stay (LOS), median (IQR), days | 5.18 (3.46–9.06) | 5.53 (3.47–9.96) | 4.72 (3.25–8.21) | 0.092 | |
| HD/ICU admission | 57 (15.45%) | 32 (13.17%) | 25 (19.84%) | 0.093 | |
| In-hospital mortality | 30 (8.13%) | 15 (6.17%) | 15 (11.90%) | 0.056 | |
| 30-day readmission | 96 (28.32%) | 66 (28.95%) | 30 (27.03%) | 0.713 | |
| Cumulative 30-day mortality | 58 (15.72%) | 30 (12.35%) | 28 (22.22%) | 0.013 | |
| 90-day readmission | 125 (40.19 %) | 91 (42.72%) | 34 (34.69%) | 0.180 | |
| Cumulative 90-day mortality | 77 (20.87%) | 45 (18.52%) | 32 (25.40%) | 0.123 |
Values are n (%) unless otherwise stated; percentages are column percentages. Length of stay is median (IQR). Cumulative mortality includes in-hospital and follow-up deaths. Readmission percentages include patients alive and eligible for readmission. P-values were calculated using appropriate non-parametric or categorical tests. One participant with CFS 9 (terminal illness) was excluded from grouped CFS analyses; therefore, CFS group columns sum to 368
HD/ICU admission was numerically higher in the high frailty group than in the low frailty group, although this was not statistically significant (18.71% vs. 13.27%, p = 0.411) (Table 2).
High frailty independently predicted in-hospital mortality (aOR 4.60, 95% CI 1.43–14.80, p = 0.010), cumulative 30-day mortality (aOR 5.68, 95% CI 2.32–13.89, p < 0.01), cumulative 90-day mortality (aOR 3.53, 95% CI 1.77–7.05, p < 0.01), HD/ICU admission (aOR 2.41, 95% CI 1.14–5.11, p = 0.021), and longer LOS (coefficient 2.06 days, 95% CI 0.76–3.36, p = 0.002) (Table 3). Kaplan–Meier analysis showed lower cumulative 90-day survival with increasing frailty severity (Fig. 1; log-rank p < 0.001).
Table 3.
Multivariable analysis of factors independently associated with clinical outcomes
| Dependent factors (clinical outcomes) | Independent predictors | Adjusted Odds Ratio (aOR)* | 95% CI | p-value |
|---|---|---|---|---|
| In-hospital death | High frailty (CFS 7–8) | 4.60 | 1.43–14.80 | 0.010 |
| Age, year | 1.05 | 1.00–1.11 | 0.042 | |
| Cumulative 30-day mortality | High frailty (CFS 7–8) | 5.68 | 2.32–13.89 | <0.01 |
| Complex multimorbidity (≥3 conditions) | 2.12 | 1.09–4.10 | 0.026 | |
| Cumulative 90-day mortality | High frailty (CFS 7–8) | 3.53 | 1.77–7.05 | <0.01 |
| Complex multimorbidity (≥3 conditions) | 2.42 | 1.34–4.35 | <0.01 | |
| 30-day readmission | Complex multimorbidity (≥3 conditions) | 1.92 | 1.14–3.24 | 0.015 |
| HD/ICU admission | High frailty (CFS 7–8) | 2.41 | 1.14–5.11 | 0.021 |
| Code status | 6.66 | 2.17–20.46 | <0.01 | |
| Length of stay (LOS)** | High frailty (CFS 7–8) | Coefficient: 2.06 | 0.76–3.36 | 0.002 |
*Multivariable logistic regression models included age, gender, frailty category, complex multimorbidity, and polypharmacy. The HD/ICU admission model additionally included code status. Only statistically significant predictors are shown; full model outputs are provided in Supplementary Table 1
**Median regression was used for length of stay
Fig. 1.
Kaplan–Meier curve for cumulative 90-day survival by Clinical Frailty Scale group. Survival decreased with increasing frailty severity (log-rank p < 0.001). In adjusted Cox regression, high frailty (CFS 7–8) was independently associated with higher cumulative 90-day mortality risk compared with low frailty (aHR 3.16, 95% CI 1.69–5.89, p < 0.001)
Exploratory trend analyses supported increasing LOS and mortality across ordered CFS categories, including LOS (p < 0.01), in-hospital mortality (p < 0.01), 30-day mortality (p < 0.01), and 90-day mortality (p < 0.01), while no significant ordered trend was observed for HD/ICU admission or readmission outcomes.
Clinical outcomes by Charlson Comorbidity Index (CCI)
Clinical outcomes by CCI are presented in Table 2. The median LOS increased slightly with comorbidity severity, from 4.75 days in the mild CCI group to 5.43 days in the severe CCI group, although this did not reach statistical significance (p = 0.424). HD/ICU admission rates were 12.50%, 18.52%, and 14.64% in the mild, moderate, and severe CCI groups, respectively (p = 0.764). In-hospital mortality was 0.00% in the mild group, 7.41% in the moderate group, and 8.57% in the severe group (p = 0.911).
30-day readmission was numerically higher in the severe CCI group than in the mild CCI group (29.30% vs. 12.50%), although the overall group comparison was not statistically significant (p = 0.630). Cumulative 30-day mortality also increased across CCI categories, from 0.00% in the mild group to 11.11% in the moderate group and 17.50% in the severe group, although this was not statistically significant (p = 0.177). Cumulative 90-day mortality increased significantly across CCI categories, from 0.00% in the mild group to 12.35% in the moderate group and 23.93% in the severe group (p = 0.027) (Table 2).
CCI was not identified as an independent predictor in the multivariable models summarized in Table 3. However, Kaplan–Meier analysis showed significantly different cumulative 90-day survival across CCI groups (Fig. 2; log-rank p = 0.035). Exploratory trend analysis supported an increasing pattern for 90-day mortality across ordered CCI categories (p < 0.01), while no significant ordered trend was observed for LOS, HD/ICU admission, in-hospital mortality, or readmission outcomes.
Fig. 2.
Kaplan–Meier curve for cumulative 90-day survival by complex multimorbidity status. Patients with complex multimorbidity had lower cumulative 90-day survival (log-rank p = 0.002). In adjusted Cox regression, complex multimorbidity was independently associated with higher cumulative 90-day mortality risk (aHR 1.85, 95% CI 1.15–2.98, p = 0.011)
Clinical outcomes by complex multimorbidity
Clinical outcomes by complex multimorbidity are presented in Table 2. Patients with complex multimorbidity had a higher 30-day readmission rate compared with those without complex multimorbidity (37.17% vs. 23.89%, p = 0.011). Cumulative 30-day mortality was also higher in patients with complex multimorbidity (21.43% vs. 12.76%, p = 0.030), as was cumulative 90-day mortality (30.16% vs. 16.05%, p < 0.01). The 90-day readmission rate was also higher in the complex multimorbidity group (48.48% vs. 36.32%, p = 0.042).
Several other outcomes were numerically higher among patients with complex multimorbidity, although these differences were not statistically significant. In-hospital mortality was higher among patients with complex multimorbidity than those without (10.32% vs. 7.00%, p = 0.268), and LOS was slightly longer (5.53 vs. 5.04 days, p = 0.132). HD/ICU admission was numerically lower among patients with complex multimorbidity (12.70% vs. 16.87%, p = 0.293) (Table 2).
In multivariable analysis, complex multimorbidity independently predicted cumulative 30-day mortality (aOR 2.12, 95% CI 1.09–4.10, p = 0.026) and cumulative 90-day mortality (aOR 2.42, 95% CI 1.34–4.35, p < 0.01). In the model restricted to patients alive and eligible for readmission, complex multimorbidity also independently predicted 30-day readmission (aOR 1.92, 95% CI 1.14–3.24, p = 0.015) (Table 3). Kaplan–Meier analysis showed lower cumulative 90-day survival among patients with complex multimorbidity (Fig. 3; log-rank p = 0.002).
Fig. 3.
Kaplan–Meier curve for cumulative 90-day survival by Charlson Comorbidity Index group. Survival differed significantly across CCI groups (log-rank p = 0.035)
Clinical outcomes by polypharmacy
Clinical outcomes by polypharmacy are presented in Table 2. Polypharmacy was present in 243 patients (65.85%). In univariate analysis, polypharmacy was not significantly associated with HD/ICU admission, in-hospital mortality, 30-day readmission, 90-day readmission, or cumulative 90-day mortality. The median LOS was longer among patients with polypharmacy compared with those without polypharmacy (5.53 vs. 4.72 days), although this did not reach statistical significance (p = 0.092). HD/ICU admission was numerically lower among patients with polypharmacy (13.17% vs. 19.84%, p = 0.093). Cumulative 30-day mortality was lower among patients with polypharmacy than those without polypharmacy (12.35% vs. 22.22%, p = 0.013), while cumulative 90-day mortality did not differ significantly (18.52% vs. 25.40%, p = 0.123). Readmission rates were not significantly different by polypharmacy status at either 30 or 90 days (Table 2). In the full multivariable models, polypharmacy showed inverse associations in some adjusted analyses, particularly for cumulative 30-day mortality (Supplementary Table 1). These findings were not interpreted as evidence of a protective effect and should be viewed cautiously in the context of residual confounding, treatment goals, and limitations of medication count as a measure of medication-related risk.
Sensitivity analysis by site/recruitment period
In a sensitivity analysis including a site/recruitment-period variable, the main findings for 30-day mortality, 90-day mortality, 30-day readmission, and LOS were materially unchanged. However, site/recruitment period was associated with HD/ICU admission, suggesting that this outcome may be influenced by hospital-level escalation-of-care practices.
Exploratory combined geriatric burden and model-comparison analyses
In exploratory analyses, a cumulative geriatric burden score was created by assigning one point each for high frailty, complex multimorbidity, and polypharmacy. Increasing geriatric burden was associated with higher 90-day mortality, rising from 8.45% among patients with none of these factors to 34.69% among those with all three factors (p = 0.005). After adjustment for age and gender, each additional burden factor was associated with higher 90-day mortality (OR 1.61, 95% CI 1.22–2.13, p = 0.001). A similar association was observed for 30-day mortality (OR 1.43, 95% CI 1.05–1.94, p = 0.023), while 30-day readmission was not significantly associated with the burden score. Exploratory model-comparison analyses for 90-day mortality showed that adding complex multimorbidity and polypharmacy to a frailty-based model improved model fit and modestly increased the AUC from 0.683 to 0.723; however, the formal AUC comparison was not statistically significant (p = 0.114).
Discussion
This study is among the first to comprehensively evaluate the prevalence and clinical impact of frailty, multimorbidity, and polypharmacy among hospitalized elderly patients in Oman. Our findings revealed a substantial burden of geriatric syndromes: 37.67% of patients had high frailty, 34.15% had complex multimorbidity, and 65.85% had polypharmacy. High frailty and complex multimorbidity were the most consistent predictors of adverse outcomes, particularly cumulative mortality, while polypharmacy was common and closely related to multimorbidity.
Oman has undergone a remarkable demographic and epidemiological transition over the past few decades. Life expectancy has increased dramatically—from 49.3 years in 1970 to 77.2 years in 2023 [25, 26]. This improvement reflects progress in healthcare delivery and socioeconomic development, but it also underscores a growing burden of age-related conditions such as frailty, chronic disease clusters, and polypharmacy. As a high-income country, Oman faces evolving healthcare demands that require systems to adapt to the complex needs of its aging population.
Our study focused on patients admitted to acute general internal medicine units, settings known to concentrate individuals with a high burden of frailty, functional impairment, and complex care needs. Frailty in these units is often underrecognized, despite its critical role in influencing clinical outcomes. A large meta-analysis of unplanned hospital admissions found that moderate to severe frailty in general medicine cohorts ranged from 14.3% to 79.6%, depending on the assessment method and population [27]. Regionally, a Middle East–based systematic review estimated frailty prevalence in community-dwelling older adults at 35%, with national data ranging from 21.4% in Saudi Arabia to 47% in the UAE [28–30]. As expected, hospitalized patients, particularly those admitted with acute medical issues, tend to exhibit higher levels of frailty compared to community-based cohorts, aligning with the burden observed in our study.
Frailty in our cohort was strongly associated with adverse outcomes, including longer hospital stay and increased in-hospital, cumulative 30-day, and cumulative 90-day mortality. High frailty remained independently associated with these mortality outcomes, and survival analysis showed a stepwise decline in cumulative 90-day survival as frailty severity increased.
Our findings align with multiple systematic reviews and meta-analyses across both hospital and community settings, which consistently demonstrate frailty as a strong predictor of mortality, prolonged hospitalization, functional decline, and other adverse outcomes—even in pre-frail individuals [27, 31–33]. These outcomes likely reflect the reduced physiological reserve and increased susceptibility to complications in frail individuals, reinforcing the need for routine frailty assessment and individualized care planning upon hospital admission.
Multimorbidity, broadly defined as the coexistence of multiple chronic conditions, remains inconsistently defined and measured in clinical research. CCI, though widely used, was originally developed to adjust for mortality risk in longitudinal studies and includes only conditions known to predict short-term mortality, excluding several prevalent and impactful age-related conditions such as hypertension and depression. Recent critiques highlight that using the CCI as a proxy for multimorbidity -particularly in mortality prediction-risks reinforcing circular associations rather than offering new insights [34, 35]. This limitation was evident in our cohort, where CCI severity was not retained as an independent predictor in the multivariable models. In contrast, complex multimorbidity, as defined in this study, provided a complementary and clinically interpretable measure of recorded chronic disease burden, independently predicting 30-day readmission and cumulative mortality. These findings support the value of simple disease-burden measures alongside mortality-weighted indices such as CCI when evaluating hospitalized older adults.
In our cohort, complex multimorbidity was present in 34.15% of hospitalized older adults. This is lower than some international estimates, which report prevalence rates ranging from 44% to 67% in older populations, likely reflecting differences in definitions and included chronic conditions [36, 37]. Complex multimorbidity was independently associated with 30-day readmission and cumulative mortality, consistent with prior evidence linking multimorbidity to hospitalization, readmission, functional decline, mortality, and adverse drug events. These findings highlight the clinical impact of multimorbidity and its broader implications for healthcare planning in aging populations [38].
Polypharmacy is increasingly prevalent among older adults due to the growing burden of chronic diseases and clinical guidelines focused on single conditions. In our cohort, polypharmacy was present in 65.85% of hospitalized older adults and was strongly associated with complex multimorbidity, reflecting how multiple chronic conditions often drive higher medication use. This prevalence is higher than global estimates reporting an overall prevalence of 32.1% among older adults, with rates rising with age and being higher in hospitalized than community-dwelling populations [39]. While polypharmacy is often viewed as a marker of care complexity, studies have shown that it independently contributes to adverse outcomes such as functional decline, readmission, and mortality, even after adjusting for comorbidities [39, 40]. In our cohort, polypharmacy was not consistently associated with adverse clinical outcomes. The inverse association observed in some mortality-related models should not be interpreted as a protective effect. This finding may reflect residual confounding, medication discontinuation among patients approaching end of life, differences in goals of care, or the limitation of medication count alone as a measure of medication-related risk. A simple threshold of five or more medications does not distinguish appropriate evidence-based prescribing from potentially inappropriate prescribing, anticholinergic burden, sedative exposure, drug–drug interactions, or deprescribing opportunities. Taken together, the findings on frailty, complex multimorbidity, and polypharmacy underscore the intricate interplay of clinical, functional, and pharmacologic factors affecting older hospitalized patients. Their observed associations with patient outcomes highlight the need for comprehensive risk assessment and individualized care approaches. These observations have several important implications for clinical practice and health system planning.
Our findings highlight the importance of integrating geriatric-focused assessments into routine inpatient care for older adults. The independent associations of frailty and complex multimorbidity with adverse outcomes such as prolonged hospital stay, readmission, and mortality support the routine use of simple, validated tools like the Clinical Frailty Scale and multimorbidity thresholds for early risk stratification. Similarly, recognizing polypharmacy as a marker of care complexity and a potentially modifiable contributor to medication-related risk emphasizes the need for careful medication reconciliation and deprescribing strategies when appropriate. Implementing multidisciplinary, individualized care approaches that address functional status, chronic disease burden, and pharmacologic complexity may support risk stratification, discharge planning, medication review, and more appropriate resource use in aging populations. These insights are particularly relevant for health systems experiencing demographic transitions, including Oman.
The exploratory combined-burden analysis further supports the value of assessing these geriatric syndromes together rather than in isolation. Increasing cumulative burden of high frailty, complex multimorbidity, and polypharmacy was associated with higher short-term mortality, particularly 90-day mortality. Model-comparison analyses also suggested that adding complex multimorbidity and polypharmacy to a frailty-based model modestly improved discrimination for 90-day mortality, although the formal AUC comparison was not statistically significant. These findings suggest that combined geriatric risk assessment may offer clinically useful information, but they should be interpreted as exploratory. Future studies with larger samples should evaluate interaction effects and externally validated predictive models incorporating functional, clinical, medication-related, and social determinants of risk.
This multicenter prospective study is the first in Oman and among the few in the region to jointly examine frailty, multimorbidity, and polypharmacy in hospitalized older adults using validated tools, standardized data collection, and comprehensive follow-up. The inclusion of both clinical and functional predictors enhances its relevance to real-world inpatient practice. However, several limitations should be acknowledged. First, post-discharge outcomes may be affected by recall or reporting bias. Second, as an observational study, causal inference cannot be established, and residual confounding remains possible. Unmeasured factors such as discharge destination, delirium, dementia severity, nutritional status, socioeconomic support, and medication appropriateness may have influenced the observed associations. Third, formal inter-rater reliability testing for CFS scoring was not performed, which may have introduced some risk of frailty misclassification, although data collectors received standardized training. Fourth, complex multimorbidity was defined using predefined chronic disease variables, mainly CCI-related conditions with hypertension additionally included, which may underestimate the broader multimorbidity burden. Fifth, detailed medication classes and potentially inappropriate medication measures were not systematically captured, limiting more granular interpretation of polypharmacy-related risk. Although frailty, complex multimorbidity, and polypharmacy are closely interrelated, the present study was not specifically powered to evaluate interaction effects among these constructs. Exploratory interaction analyses did not identify statistically significant interactions; however, larger studies are needed to better evaluate potential synergistic effects and develop externally validated prediction models. Finally, we did not perform formal competing-risk analysis for readmission outcomes, and differences between hospital sites and recruitment periods may also have affected outcome estimates, particularly outcomes influenced by local escalation-of-care practices such as HD/ICU admission.
Conclusion
Frailty, complex multimorbidity, and polypharmacy were highly prevalent and interrelated among hospitalized older adults in Oman. High frailty and complex multimorbidity were the most consistent predictors of adverse outcomes, particularly cumulative mortality. These findings support structured geriatric assessment pathways, careful medication review, and future interventional studies to determine whether targeted care strategies can improve outcomes in this population.
Supplementary Information
Authors’ contributions
Author Contributions: AJ: Data curation, Validation, Writing – original draft. ZF: Conceptualization, Supervision, Validation, Writing – review & editing. JM: Conceptualization, Formal analysis, Validation, Project administration, Writing – review & editing. KZ: Conceptualization, Funding acquisition, Project administration. ZA: Project administration, Writing – review & editing. AA: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision. SB: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing. All authors have read and approved the final version of the manuscript.
Funding
This study was supported by the Dean’s Grant, Sultan Qaboos University (Ref: RF/MED/MEDE/24/01).
Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Medical and Research Ethics Committee at the College of Medicine and Health Sciences, Sultan Qaboos University (SQU) [MREC #3028; SQU-EC/099/2023], and the Health Studies and Research Approval Committee (HSRAC) at the Ministry of Health, Oman [MoH/CSR/23/27310]. The study was conducted in accordance with the ethical standards of these committees and with the Declaration of Helsinki. Written informed consent was obtained from all patients or their legally authorized representatives.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.