Effect of Frailty on Hospital Length of Stay and Discharge Outcomes among Geriatric Trauma Patients: A Prospective Observational Study

Study design, setting and ethics

After obtaining approval from the Institutional Ethics Committee (Ref No 103/March 06, 2020), a 1-year (April 2022–March 2023) a prospective, observational, single-center study was conducted from April 2022 to March 2023 in geriatric trauma patients undergoing non-cardiac surgical procedures performed under general or regional anesthesia at a level I trauma center. This manuscript has been prepared in accordance with Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

Study population and sample size

Written informed consent was obtained from patients or their relatives. Patients aged ≥65 years who presented within 72 h of sustaining a traumatic injury were eligible for inclusion. Referred patients from other centers were eligible only if no intervention had been performed at the referring center. Patients with head injuries, those who refused to participate, those in whom frailty assessment could not be performed, and non-responsive patients were excluded. As no prior published literature was available demonstrating an association between CFS and LOS in geriatric trauma patients, a formal sample size calculation could not be performed. Therefore, all eligible patients admitted during the study period who provided informed consent were consecutively included to minimize selection bias, enhance the representativeness of the study population, and provide preliminary evidence regarding the association between frailty and outcomes.

Methodology

Patients were assessed and screened for frailty scores at the time of pre-anesthetic checkup before surgery. Frailty was assessed using the CFS and the 15-variable TFSI. The first and second investigators who were involved in providing anesthesia care independently assessed and determined the frailty scores. In case of disagreement, the score was reviewed and resolved by a third investigator. All other baseline and perioperative data were collected on the day of surgery.

Patients were followed until patient outcomes were categorized according to their discharge disposition as favorable or unfavorable. A favorable discharge outcome was defined as discharge to home, whereas an unfavorable discharge outcome was defined as in-hospital mortality.

Frailty assessment

Frailty was assessed using the CFS and TSFI in our study. The CFS is a validated 7-point judgment-based assessment tool that can rapidly stratify the degree of frailty in critically ill patients [Appendix I].³ Scores of 1–3 indicate patients who are not frail, scores of 4–5 indicate vulnerability to mild frailty, and scores of 6–7 indicate frailty.³ For analysis, CFS 4 (“apparently vulnerable”) and CFS 5 (“mildly frail”) were considered as separate categories to provide greater detail.³

The 15-variable TSFI, derived from the original 50-variable, was also used to assess frailty [Appendix II].² These 15 variables have been shown to predict adverse outcomes in geriatric patients. A TSFI score >0.25 was considered the cutoff for frailty and was used to identify patients at risk of an unfavorable discharge disposition.² The TSFI score was calculated by dividing the total number of positive deficits by the total number of variables assessed.

Data points

For each patient, demographic and clinical data were recorded, including age, sex, presenting complaints, comorbidities (diabetes mellitus, hypertension, coronary artery disease, stroke, dementia, bronchial asthma, thyroid disorders, and mental illness), mechanism of injury, discharge outcome, LOS, 28-day mortality, and in-hospital complications. The presence of polypharmacy was also recorded. The Glasgow Coma Scale (GCS) score, ISS, Abbreviated Injury Score, and type of anesthesia were also recorded. All in-hospital complications either in ward or ICU were documented. The presence of comorbidities and polypharmacy was also recorded.

Statistical analysis

Data were reported as mean ± standard deviation (SD) for continuous variables, median (interquartile range [IQR]) for ordinal variables, and proportions for categorical variables. The normality of continuous data was assessed using the Kolmogorov–Smirnov test before statistical analysis. The unpaired t-test was used to compare continuous variables between two independent groups, while one-way analysis of variance was used for comparisons among two or more groups. The Chi-square test was used to compare categorical variables. Correlations between clinical variables were assessed using Spearman’s rank correlation coefficient. A p value ≤0.05 was considered statistically significant. Data were coded and entered into a Microsoft Excel spreadsheet. Statistical analyses were performed using IBM Statistical Package for the Social Sciences Statistics for Windows, Version 25 (IBM Corp., Armonk, NY, USA).

Outcome measures

Patients were divided into two groups based on their CFS scores: Group 1 (Pre-frail), comprising patients with CFS scores of 1–3, and Group 2 (Frail), comprising patients with CFS scores of 4–7. The two groups were compared with respect to LOS, mortality, age, sex, ISS, type of anesthesia, comorbidities, in-hospital complications and TSFI scores.

LOS

The median hospital LOS was comparable between the pre-frail (CFS 1–3) and frail (CFS 4–7) groups: 7 days (IQR = 5–15) versus 8 days (IQR = 6–10), respectively (p = 0.427). This lack of a significant difference may partly be due to the relatively low overall injury burden in our cohort, as indicated by a similarly low median ISS of 9 (IQR = 4–25) in both groups. Although 8 deaths (5%) occurred in the frail group, mortality was not significantly different between the groups (p = 0.363). Thus, in this cohort, frailty was not associated with a significantly prolonged hospital LOS or increased mortality. These findings are consistent with those of Thompson et al.,¹³ who reported comparable median LOS between pre-frail and frail groups (11 vs. 12 days; p = 0.149). Despite a significantly higher median ISS in the pre-frail group (16; IQR = 9–12 vs. 10; IQR = 9–17; p = 0.002), this did not translate into a longer hospital stay, suggesting that CFS-defined frailty may have limited influence on hospital LOS. Plausible explanations include the subjective and judgment-based nature of CFS assessment and the predominance of lower-energy orthopaedic trauma in our cohort. Prompt surgical fixation and standardized care pathways may have further reduced frailty-related delays in recovery and discharge at our center. In contrast, Soh et al., in a non-trauma geriatric cohort, reported a median LOS of 7 days among patients with a median admission CFS of 6, which was similar to the LOS observed in our frail group.¹⁴ This suggests that the absence of an association between frailty and LOS may extend beyond trauma populations, particularly in settings where efficient and standardized management pathways are established, even among frail individuals.

A detailed review of the geriatric trauma literature has reported a consistent pattern of small and mostly non-significant differences in hospital LOS between frail and non-frail groups (5 vs. 4 days, p = 0.48; 5 vs. 3 days, p = 0.08; 13 vs. 12 days; 12 vs. 11 days, p = 0.64; 13 vs. 12 days).^15-19 Pecheva et al. reported slightly longer mean LOS among frail patients (18.8 vs. 18 days), although the difference was small and overlapping.²⁰ In contrast, Herklots et al.²¹ reported a significantly longer LOS among non-frail patients than frail patients (16.4 vs. 8.4 days, p = 0.006). The authors attributed this finding to survival bias, whereby frail patients may have died earlier rather than recovered more rapidly; thus, a shorter LOS may have reflected early mortality rather than a better clinical outcome. Overall, across different frailty assessment tools, patient cohorts, and healthcare settings, frailty has not been consistently associated with prolonged hospital LOS in geriatric trauma patients. Our findings are therefore consistent with the existing literature and further support the observation that frailty alone may have limited predictive value for hospital LOS in this population.

Discharge outcome

In the present study, there was no significant difference in 28-day mortality between the pre-frail and frail groups (p = 0.363), despite eight deaths occurring in the frail group. This finding is consistent with Arslan Erduhan et al., who reported no significant difference in 30-day mortality according to TSFI-based frailty status (p = 0.208).²² In contrast, Thompson et al.¹³ reported significantly higher mortality in frail patients than in pre-frail patients (12.8% vs. 5.3%, p = 0.001; odds ratio [OR] = 2.65, 95% confidence interval [CI] = 1.47–4.78). Their cohort also differed significantly between groups with respect to ISS, Charlson Comorbidity Index, and GCS, differences that were not observed in our study. This discrepancy may be explained by differences in patient characteristics and injury profiles across studies.

Several other studies have supported frailty as a predictor of adverse discharge outcomes. Joseph et al.¹⁵ reported that a TSFI >0.25 was associated with adverse disposition, including mortality or discharge to a nursing facility (OR = 1.6, 95% CI = 1.1–2.4), while a TSFI >0.27 was associated with higher in-hospital mortality (OR = 1.1, 95% CI = 1.04–4.7). Rickard et al.¹⁸ found that severe frailty (CFS 7–9) was associated with a higher risk of 30-day mortality (OR = 2.14, 95% CI = 0.81–5.68). Similarly, Guiab et al.¹⁹ identified severe frailty as an independent predictor of mortality (OR = 1.23, 95% CI = 1.15–1.32), while Cheung et al.⁹ reported that CFS 6–7 was associated with adverse discharge destinations, including mortality (OR = 5.1, 95% CI = 2.0–13.0, p < 0.001). Collectively, these findings underscore the prognostic value of frailty assessment in geriatric trauma populations.

In this context, the absence of a significant association between frailty and adverse outcomes in our cohort should be interpreted cautiously considering its relatively low-risk injury profile, early intervention, and standardized management pathways, rather than as evidence against a true relationship between frailty and adverse outcomes. These factors may have attenuated the impact of frailty on short-term outcomes in our population.

Association between CFS and TSFI

The TSFI score was significantly lower in the pre-frail group (median = 0.07, IQR = 0.05–0.13) than in the frail group (median = 0.20, IQR = 0.12–0.23; p < 0.001) and showed a moderate correlation with the CFS (r = 0.53), supporting reasonable convergent validity between the two frailty assessment tools. This finding supports the utility of the TSFI as a trauma-adapted tool for capturing frailty burden in trauma patients. The TSFI incorporates multiple domains, including comorbidities, activities of daily living, nutritional status (e.g., serum albumin), and functional health, which are not directly assessed by the more subjective, judgment-based CFS.

Despite the moderate correlation between the two tools, the TSFI did not predict hospital LOS, consistent with the findings for the CFS described above. TSFI scores also increased progressively with advancing age, consistent with the established age-related increase in frailty prevalence observed with the CFS, further supporting the validity of our findings. To our knowledge, no previous study has directly compared CFS and TSFI scores within the same geriatric trauma cohort, making the observed correlation between the two tools an important observation of our study.

A multivariable regression analysis would have determined whether CFS and TSFI independently predicted unfavorable discharge outcomes or prolonged hospital LOS after adjusting for potential confounders such as age, ISS, and comorbidity burden. However, given the low event rate in this cohort – only eight deaths (5%) and no significant difference in discharge disposition between the groups – a multivariable regression model would have been underpowered and potentially unreliable. Therefore, the analysis was limited to univariate comparisons between the frailty groups.

A few additional findings from our study warrant discussion to better characterize the observed frailty pattern. The majority of the cohort comprised women (52.9%), and spontaneous falls were the leading mechanism of injury, a pattern typical of geriatric trauma and consistent with the findings of Sut et al.⁶ Frailty was also more prevalent among women (59% vs. 41% in men), a disparity previously reported by Sut, Thompson et al., and Arslan Erduhan et al.^6,13,22 These studies similarly observed a higher burden of trauma among older women, predominantly resulting from spontaneous or low-impact falls. The predominance of women among both trauma and frailty populations is consistent with the broader geriatric trauma literature and may reflect sex-specific factors, including greater longevity, a higher prevalence of osteoporosis, reduced muscle mass, and differences in fall-risk profiles, such as gait instability and impaired balance. Low-energy falls from standing height remain a characteristic mechanism of injury among frail older adults and commonly result in hip fractures, pelvic injuries, and other orthopedic trauma. Collectively, these findings demonstrate the demographic and mechanistic similarity of our cohort to previously described geriatric trauma populations and provide important context for interpreting the observed relationship between frailty and outcomes.

Limitations

This study has several limitations. First, its single-center observational design limits the generalizability of the findings to other centers with different patient populations, healthcare systems, and trauma management protocols. Second, the sample size was relatively small, and follow-up was restricted to 28 days, precluding assessment of longer-term mortality and functional outcomes. Third, most patients had a relatively low injury burden (median = ISS 9), and the cohort predominantly comprised patients with orthopedic trauma following low-energy falls. This injury profile may have attenuated the measurable effect of frailty on the outcomes studied. A multivariable regression analysis could not be reliably performed to determine whether frailty independently predicted prolonged hospital LOS or unfavorable discharge outcomes after adjustment for potential confounders. Furthermore, LOS is a multifactorial outcome influenced not only by injury severity and frailty but also by institutional discharge policies, bed availability, access to rehabilitation facilities, socioeconomic support at home, surgical scheduling, and financial considerations related to discharge planning. These non-clinical determinants were not captured in our analysis and may have attenuated any true association between frailty and LOS. Frailty was assessed only at admission, despite its dynamic nature and was not reassessed at discharge; therefore, changes in frailty status during hospitalization could not be evaluated. Finally, the inherently subjective and judgement-based nature of CFS assessment may introduce inter-rater variability, particularly in the acute trauma setting, underscoring the need for further validation and standardized application of the CFS in geriatric trauma populations.