JCM, Vol. 11, Pages 7064: Impact of Frailty Risk on Adverse Outcomes after Traumatic Brain Injury: A Historical Cohort Study

1. IntroductionTraumatic brain injury (TBI) is one of the most severe types of injury in terms of both morbidity and long-term impact on survivors [1,2]. According to the Centers for Disease Control and Prevention, there were approximately 223,135 TBI-related hospitalizations in 2019 and 64,362 TBI-related deaths in 2020 [3]. TBI-related mortality rates increased over the ten years from 2008 to 2017 [3]. Patients with severe TBI could have long-term or life-long disabilities [3]. TBI results in long-term societal costs, and initiatives to improve prognosis and reduce societal costs after TBI have been reported [2,4].TBI is closely associated with frailty [5]. TBI can be caused by falls, traffic accidents, and other injuries, with falls causing approximately half of all TBIs [6]. The Centers for Disease Control and Prevention reported that TBI-related emergency hospitalizations in 2014 were the highest among seniors aged ≥ 75 years [6]. Frailty is a condition in which motor and cognitive functions decline with age, and falls related to frailty are particularly common in the older population [5,7]. Additionally, frailty has several characteristics that affect TBI management and outcomes, including multimorbidity and polypharmacy [8].Recently, the Hospital Frailty Risk Score (HFRS), based on the 10th revision of the International Classification of Diseases (ICD-10) diagnostic codes, has been developed and reported to be useful for identifying patients at high risk for adverse outcomes [9]. HFRS is calculated based on 109 ICD-10 codes, with specific weights applied to each code. HFRS could be implemented in most hospital information systems with less time and at a low cost [9,10]. HFRS has been reported to predict frailty and adverse outcomes in the general population [9,10].Assessment of the risk of frailty in patients with TBI could help predict and prevent adverse outcomes. Several studies have predicted prognosis using the frailty scale for TBI [11,12,13,14]. Some studies used frailty assessment scales based on medication and biochemical examination data [11,12,13], which are difficult to calculate automatically. One study [14] assessed frailty based on 11 comorbidities; however, this was reported to be less accurate at predicting adverse events than a frailty assessment scale based on a larger number of comorbidities [15]. Therefore, we focused on HFRS, which can be calculated automatically in the presence of several comorbidities. To the best of our knowledge, HFRS has not been investigated for its potential to predict adverse outcomes in TBI. We hypothesized that patients with TBI at a higher risk of frailty, as calculated by HFRS, would have longer hospital stays, more deaths, and lower activities of daily living (ADL) independence than patients with TBI at a lower risk of frailty. The purpose of this cohort study was to evaluate the utility of HFRS as a predictor of adverse events after TBI. 3. ResultsWe included 18,065 patients who were hospitalized due to TBI, who did not have missing data on JCS score at admission, BI at admission, and BI at discharge (Figure 1). Patients were classified into the low- (10,139 (56.1%)), intermediate- (7388 (40.9%)), and high- (538 (3.0%)) frailty risk groups based on HFRS.Table 1 shows the characteristics of the study participants. The high-frailty risk group had more women, a lower JCS score and BI on admission, and a higher proportion of patients aged > 75 years than the low- and intermediate-frailty risk groups.Table 2 shows the results of outcome comparisons among the groups. The frailty risk was significantly associated with LOS, the number of patients with BI score ≥ 95 on discharge, BI gain, and the number of in-hospital deaths.Table 3 shows the results of the multiple linear regression analyses on HFRS. The intermediate- and high-frailty risk groups were characterized by longer hospital stays than the low-frailty risk group (intermediate-frailty risk group: coefficient 1.952, 95% CI: 1.117–2.786; high-frailty risk group: coefficient 5.770, 95% CI: 3.160–8.379). The intermediate- and high-frailty risk groups were negatively associated with BI gain (intermediate-frailty risk group: coefficient −4.868, 95% CI: −5.599–−3.773; high-frailty risk group: coefficient −19.596, 95% CI: −22.242–−16.714).Table 4 shows the results of the logistic regression analysis on HFRS. The intermediate- and high-frailty risk groups were negatively associated with a BI score ≥ 95 on discharge (intermediate-frailty risk group: odds ratio 0.645; 95% CI: 0.595−0.699; high-frailty risk group: odds ratio 0.221; 95% CI: 0.157−0.311). The intermediate- and high-frailty risk groups were not significantly associated with in-hospital death (intermediate-frailty risk group: odds ratio 0.901; 95% CI: 0.766−1.061; high-frailty risk group: odds ratio 0.707; 95% CI: 0.459−1.091). 4. Discussion

This study investigated the association of HFRS assessed using ICD-10 codes with adverse events and functional outcomes in patients with TBI, using nationwide data from Japan. The results indicated that patients with a higher frailty risk had a longer hospital stay duration and lower ability to perform ADL during hospitalization.

High frailty risk was positively associated with the length of the hospital stay. HFRS assesses frailty risk based on a weighted score of comorbidities with a certain number of points assigned for each ICD-10 code [9]. Gilbert et al. [9] reported that, among patients aged > 75 years admitted to an acute care hospital, those with a high frailty risk had a nearly two-fold increase in 30-day mortality and a six-fold increase in the risk of long-term hospitalization compared to those with a low frailty risk. Subsequent studies have examined the association between HFRS and adverse events in patients admitted to medical facilities [23] with stroke/transient ischemic attack [24], hip fracture surgery [25], total hip and knee arthroplasty [26], osteoarthritis [27], chronic obstructive pulmonary disease [28], and heart failure [29]. These reports stated that HFRS could predict adverse events during hospitalization. Similar to previous studies, we found that HFRS could predict prolonged LOS in patients with TBI. Patients with more comorbidities had a higher risk for mortality and severe disease [30]. Frailty was also considered a condition with a high risk of adverse outcomes due to the loss of function in multiple organs, such as the brain, endocrine system, immune system, and skeletal muscles [7]. Therefore, HFRS using comorbidities could be a useful tool for predicting adverse events in patients with TBI.The high frailty risk was negatively associated with the number of patients with BI score ≥ 95 on discharge and BI gain. HFRS was suggested to predict functional outcomes, e.g., ADL, in TBI patients. High frailty risk was also not associated with in-hospital death. In HFRS, there is a greater weighting of ICD-10 codes for cerebrovascular, motor, gait, and cognitive disabilities [9]. HFRS includes diseases based on ICD-10 codes that affect functional outcomes [9,28]. Thus, patients at a higher risk of frailty are more likely to present with comorbidities that affect functional recovery more. HFRS has fewer comorbidities that are directly related to death than functional impairment and has less accuracy in predicting death [28,31]. Therefore, HFRS could be a useful predictive tool for functional prognoses, such as ADL, rather than death. In contrast, in a study that followed up patients with TBI for 1 year, death was found to be associated with frailty [32]. A long-term follow-up in the present study might have increased the number of deaths as well as the statistical power to detect significant differences.Patients at a intermediate-, and high risk of frailty accounted for about half of the patients with TBI (43.9%). The percentage of TBI patients with frailty was approximately 40% using the mFI-11 [14] and Groningen Frailty Indicator [13]. In contrast, patients with frailty alongside other conditions assessed using HFRS (total hip arthroplasty/total knee arthroplasty [21] and spinal surgery [27]) included fewer than 15% of patients at a moderate to severe frailty risk. Falls in the older population are closely related to frailty, an age-related decline in motor and cognitive function [5,6]. Factors contributing to falls include age, sex, gait disturbance, and neurological and cognitive impairments [5,7]. Thus, patients with TBI may have been affected by frailty even before the injury. TBI is also likely to result in life-long physical, cognitive, and psychosocial functional impairments [33,34,35]. HFRS has many items associated with TBI (cerebrovascular disease, motor impairment, gait disturbance, cognitive impairment, open or superficial head injury, delirium, emotional status, and so on), which may have contributed to the high proportion of patients at a high risk of frailty.This study has several strengths. Several comorbidity scores have been previously developed, including the Charlson Comorbidity Index and Elixhauser Comorbidity Index [36,37]. HFRS includes cognitive impairment, delirium, disorientation, falls, mobility impairments, dependence syndrome, urinary incontinence, and difficulties in managing life that have not received attention in the Charlson and Elixhauser Comorbidity Indexes [9]. HFRS includes more comorbidities related to injury in TBI than other comorbidity indices [9]. Therefore, compared to other comorbidity indices, HFRS could better detect functional adverse events at admission in TBI patients. Another strength is that HFRS can be automatically implemented in in-hospital information systems [9]. Because ICD-10 codes are routinely recorded electronically, HFRS data for TBI can be automatically embedded in in-hospital electronic medical records. HFRS could be made available automatically, potentially reducing patient burden by predicting adverse events.This study has several limitations that are common to database studies. The analysis is a historical cohort study, and data acquisition was limited to data available from the hospital information system. Therefore, we were unable to obtain long-term follow-up data or detailed data on parameters such as walking ability, muscle strength, or Glasgow Coma Scale score. However, a high correlation between the JCS and Glasgow Coma Scale scores has been reported [22], and we believe that we were able to adequately assess disease severity. In addition, the accuracy of this data could be somewhat problematic because HFRS used in this study was based on ICD-10 codes in the medical information system. In general, studies using reimbursement databases reported problems with data reliability owing to coding errors, inappropriate coding, and poor documentation [38,39]. However, the criterion-related validity of medical records based on ICD-10 codes has been validated in Japan [20]. Therefore, there may have been minimal bias due to coding inaccuracy.

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