Development and validation of a frailty index for use in the osteoarthritis initiative
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PROVIDER: S-EPMC11210396 | biostudies-literature | 2024 Jun
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe conventional count-based physical frailty phenotype (PFP) dichotomizes its criterion predictors-an approach that creates information loss and depends on the availability of population-derived cut-points. This study proposes an alternative approach to computing the PFP by developing and validating a model that uses PFP components to predict the frailty index (FI) in community-dwelling older adults, without the need for predictor dichotomization.MethodsA sample of 998 community-dwelling older adults (mean [SD], 68 [7] years) participated in this prospective cohort study. Participants completed a multi-domain geriatric screen and a physical fitness assessment from which the count-based PFP and the 36-item FI were computed. One-year prospective falls and hospitalization rates were also measured. Bayesian beta regression analysis, allowing for nonlinear effects of the non-dichotomized PFP criterion predictors, was used to develop a model for FI ("model-based PFP"). Approximate leave-one-out (LOO) cross-validation was used to examine model overfitting.ResultsThe model-based PFP showed good calibration with the FI, and it had better out-of-sample predictive performance than the count-based PFP (LOO-R2, 0.35 vs 0.22). In clinical terms, the improvement in prediction (i) translated to improved classification agreement with the FI (Cohen's kw, 0.47 vs 0.36) and (ii) resulted primarily in a 23% (95%CI, 18-28%) net increase in FI-defined "prefrail/frail" participants correctly classified. The model-based PFP showed stronger prognostic performance for predicting falls and hospitalization than did the count-based PFP.ConclusionThe developed model-based PFP predicted FI and clinical outcomes more strongly than did the count-based PFP in community-dwelling older adults. By not requiring predictor cut-points, the model-based PFP potentially facilitates usage and feasibility. Future validation studies should aim to obtain clear evidence on the benefits of this approach.
| S-EPMC10029224 | biostudies-literature
Project description:BackgroundFrailty is a key determinant of health status and outcomes of health care interventions in older adults that is not readily measured in Medicare data. This study aimed to develop and validate a claims-based frailty index (CFI).MethodsWe used data from Medicare Current Beneficiary Survey 2006 (development sample: n = 5,593) and 2011 (validation sample: n = 4,424). A CFI was developed using the 2006 claims data to approximate a survey-based frailty index (SFI) calculated from the 2006 survey data as a reference standard. We compared CFI to combined comorbidity index (CCI) in the ability to predict death, disability, recurrent falls, and health care utilization in 2007. As validation, we calculated a CFI using the 2011 claims data to predict these outcomes in 2012.ResultsThe CFI was correlated with SFI (correlation coefficient: 0.60). In the development sample, CFI was similar to CCI in predicting mortality (C statistic: 0.77 vs. 0.78), but better than CCI for disability, mobility impairment, and recurrent falls (C statistic: 0.62-0.66 vs. 0.56-0.60). Although both indices similarly explained the variation in hospital days, CFI outperformed CCI in explaining the variation in skilled nursing facility days. Adding CFI to age, sex, and CCI improved prediction. In the validation sample, CFI and CCI performed similarly for mortality (C statistic: 0.71 vs. 0.72). Other results were comparable to those from the development sample.ConclusionA novel frailty index can measure the risk for adverse health outcomes that is not otherwise quantified using demographic characteristics and traditional comorbidity measures in Medicare data.
| S-EPMC6001883 | biostudies-literature
Project description:BackgroundPhysical frailty phenotype is characterized by decreased physiologic reserve to stressors and associated with poor outcomes, such as delirium and mortality, that may result from post-kidney transplant (KT) inflammation. Despite a hypothesized underlying pro-inflammatory state, conventional measures of frailty typically do not incorporate inflammatory biomarkers directly. Among KT candidates and recipients, we evaluated the inclusion of inflammatory biomarkers with traditional physical frailty phenotype components.MethodsAmong 1154 KT candidates and recipients with measures of physical frailty phenotype and inflammation (interleukin 6 [IL6], tumor necrosis factor alpha [TNFα], C-reactive protein [CRP]) at 2 transplant centers (2009-2017), we evaluated construct validity of inflammatory-frailty using latent class analysis. Inflammatory-frailty measures combined 5 physical frailty phenotype components plus the addition of an individual inflammatory biomarkers, separately (highest tertiles) as a sixth component. We then used Kaplan-Meier methods and adjusted Cox proportional hazards to assess post-KT mortality risk by inflammatory-frailty (n = 378); Harrell's C-statistics assessed risk prediction (discrimination).ResultsBased on fit criteria, a 2-class solution (frail vs nonfrail) for inflammatory-frailty was the best-fitting model. Five-year survival (frail vs nonfrail) was: 81% versus 93% (IL6-frailty), 87% versus 89% (CRP-frailty), and 83% versus 91% (TNFα-frailty). Mortality was 2.07-fold higher for IL6-frail recipients (95% CI: 1.03-4.19, p = .04); there were no associations between the mortality and the other inflammatory-frailty indices (TNFα-frail: 1.88, 95% CI: 0.95-3.74, p = .07; CRP-frail: 1.02, 95% CI: 0.52-2.03, p = .95). However, none of the frailty-inflammatory indices (all C-statistics = 0.71) improved post-KT mortality risk prediction over the physical frailty phenotype (C-statistics = 0.70).ConclusionsMeasurement of IL6-frailty at transplantation can inform which patients should be targeted for pre-KT interventions. However, the traditional physical frailty phenotype is sufficient for post-KT mortality risk prediction.
| S-EPMC7907494 | biostudies-literature
Project description:BackgroundFrailty is a multidimensional geriatric syndrome recognized as a critical public health challenge in 771 million aging population worldwide. Although electronic frailty index (eFI) is successfully adopted for frailty screening in developed countries, such a tool is still absent in China. Furthermore, for facilitate early illness prevention, China offers annual physical examinations for the elderly which offers a potential opportunity for the early detection of frailty. This study aimed to develop a new eFI algorithm leveraging routinely collected healthcare data and validated it within both the development and an independent external cohort.MethodsIndividuals aged 65 or older from the development and external validation cohort were enrolled in this study. Data were extracted from the annual physical examinations and medical records. Based on the cumulative deficit model, a tailored eFI calculation algorithm was developed. The eFI's validity was assessed through correlation with the established FRAIL scale, and its predictive utility for hospitalization and mortality was prospectively evaluated.ResultsA set of 30 variables across 13 functional domains was selected to calculate the eFI. It demonstrated a strong correlation with the FRAIL scale (P < 0.001). In the development cohort, individuals categorized as prefrail and frail had higher (62% and 137% respectively) risk of hospitalization compared to the robust group. Regarding all-cause mortality, the risk was also higher (59% and 117% respectively) for prefrail and frail participants. Similar associations were observed in the external validation cohort.ConclusionUtilizing standardized healthcare records, this study successfully developed and validated an eFI algorithm that can offer a reliable and scalable tool for early frailty screening in China and populations with similar preventive physical examination data.
| S-EPMC11827203 | biostudies-literature
Project description:While social characteristics are well-known predictors of mortality, prediction models rely almost exclusively on demographics, medical comorbidities, and function. Lacking an efficient way to summarize the prognostic impact of social factor, many studies exclude social factors altogether. Our objective was to develop and validate a summary measure of social risk and determine its ability to risk-stratify beyond traditional risk models. We examined participants in the Health and Retirement Study, a longitudinal, survey of US older adults. We developed the model from a comprehensive inventory of 183 social characteristics using least absolute shrinkage and selection operator, a penalized regression approach. Then, we assessed the predictive capacity of the model and its ability to improve on traditional prediction models. We studied 8,250 adults aged ≥65 y. Within 4 y of the baseline interview, 22% had died. Drawn from 183 possible predictors, the Social Frailty Index included age, gender, and eight social predictors: neighborhood cleanliness, perceived control over financial situation, meeting with children less than yearly, not working for pay, active with children, volunteering, feeling isolated, and being treated with less courtesy or respect. In the validation cohort, predicted and observed mortality were strongly correlated. Additionally, the Social Frailty Index meaningfully risk-stratified participants beyond the Charlson score (medical comorbidity index) and the Lee Index (comorbidity and function model). The Social Frailty Index includes age, gender, and eight social characteristics and accurately risk-stratifies older adults. The model improves upon commonly used risk prediction tools and has application in clinical, population health, and research settings.
| S-EPMC9963593 | biostudies-literature
Project description:ObjectiveFrailty is a multisystem syndrome and its relationship with symptomatic osteoarthritis has been reported. We aimed to identify trajectories of knee pain in a large prospective cohort and to describe the effect of frailty status at baseline on the pain trajectories over 9 years.MethodsWe included 4419 participants (mean age 61.3 years, 58% female) from the Osteoarthritis Initiative cohort. Participants were classified as "no frailty," "pre-frailty," or "frailty" at baseline, based on 5 characteristics (ie, unintentional weight loss, exhaustion, weak energy, slow gait speed, and low physical activity). Knee pain was evaluated annually using the Western Ontario and McMaster Universities Osteoarthritis Index pain subscale (0-20) from baseline to 9 years.ResultsOf the participants included, 38.4%, 55.4%, and 6.3% were classified as "no frailty," "pre-frailty," and "frailty," respectively. Five pain trajectories were identified: "No pain" (n = 1010, 22.8%), "Mild pain" (n = 1656, 37.3%), "Moderate pain" (n = 1149, 26.0%), "Severe pain" (n = 477, 10.9%), and "Very Severe pain" (n = 127, 3.0%). Compared to participants with no frailty, those with pre-frailty and frailty were more likely to have more severe pain trajectories (pre-frailty: odds ratios [ORs] 1.5 to 2.1; frailty: ORs 1.5 to 5.0), after adjusting for potential confounders. Further analyses indicated that the associations between frailty and pain were mainly driven by exhaustion, slow gait speed, and weak energy.ConclusionsApproximately two-thirds of middle-aged and older adults were frail or pre-frail. The role of frailty in predicting pain trajectories suggests that frailty may be an important treatment target for knee pain.
| S-EPMC10690856 | biostudies-literature
Project description:BackgroundAlthough elderly population is generally frail, it is important to closely monitor their health deterioration to improve the care and support in residential aged care homes (RACs). Currently, the best identification approach is through time-consuming regular geriatric assessments. This study aimed to develop and validate a retrospective electronic frailty index (reFI) to track the health status of people staying at RACs using the daily routine operational data records.MethodsWe have access to patient records from the Royal Freemasons Benevolent Institution RACs (Australia) over the age of 65, spanning 2010 to 2021. The reFI was developed using the cumulative deficit frailty model whose value was calculated as the ratio of number of present frailty deficits to the total possible frailty indicators (32). Frailty categories were defined using population quartiles. 1, 3 and 5-year mortality were used for validation. Survival analysis was performed using Kaplan-Meier estimate. Hazard ratios (HRs) were estimated using Cox regression analyses and the association was assessed using receiver operating characteristic (ROC) curves.ResultsTwo thousand five hundred eighty-eight residents were assessed, with an average length of stay of 1.2 ± 2.2 years. The RAC cohort was generally frail with an average reFI of 0.21 ± 0.11. According to the Kaplan-Meier estimate, survival varied significantly across different frailty categories (p < 0.01). The estimated hazard ratios (HRs) were 1.12 (95% CI 1.09-1.15), 1.11 (95% CI 1.07-1.14), and 1.1 (95% CI 1.04-1.17) at 1, 3 and 5 years. The ROC analysis of the reFI for mortality outcome showed an area under the curve (AUC) of ≥0.60 for 1, 3 and 5-year mortality.ConclusionA novel reFI was developed using the routine data recorded at RACs. reFI can identify changes in the frailty index over time for elderly people, that could potentially help in creating personalised care plans for addressing their health deterioration.
| S-EPMC9714152 | biostudies-literature
Project description:ImportanceGrowing consensus suggests that frailty-associated risks should inform shared surgical decision making. However, it is not clear how best to screen for frailty in preoperative surgical populations.ObjectiveTo develop and validate the Risk Analysis Index (RAI), a 14-item instrument used to measure surgical frailty. It can be calculated prospectively (RAI-C), using a clinical questionnaire, or retrospectively (RAI-A), using variables from the surgical quality improvement databases (Veterans Affairs or American College of Surgeons National Surgical Quality Improvement Projects).Design, setting, and participantsSingle-site, prospective cohort from July 2011 to September 2015 at the Veterans Affairs Nebraska-Western Iowa Heath Care System, a Level 1b Veterans Affairs Medical Center. The study included all patients presenting to the medical center for elective surgery.ExposuresWe assessed the RAI-C for all patients scheduled for surgery, linking these scores to administrative and quality improvement data to calculate the RAI-A and the modified Frailty Index.Main outcomes and measuresReceiver operator characteristics and C statistics for each measure predicting postoperative mortality and morbidity.ResultsOf the participants, the mean (SD) age was 60.7 (13.9) years and 249 participants (3.6%) were women. We assessed the RAI-C 10 698 times, from which we linked 6856 unique patients to mortality data. The C statistic predicting 180-day mortality for the RAI-C was 0.772. Of these 6856 unique patients, we linked 2785 to local Veterans Affairs Surgeons National Surgical Quality Improvement Projects data and calculated the C statistic for both the RAI-A (0.823) and RAI-C (0.824), along with the correlation between the 2 scores (r = 0.478; P < .001). Of these 2785 patients, there were sufficient data to calculate the modified Frailty Index for 1021, in which the C statistics were 0.865 (RAI-A), 0.797 (RAI-C), and 0.811 (modified Frailty Index). The correlation between the RAI-A and RAI-C was 0.547, and the correlations of the modified Frailty Index to the RAI-A and RAI-C were 0.301 and 0.269, respectively (all P < .001). A cutoff of RAI-C of at least 21 classified 18.3% patients as "frail" with a sensitivity of 0.50 and specificity of 0.82, whereas the RAI-A was less sensitive (0.25) and more specific (0.97), classifying only 3.7% as "frail."Conclusions and relevanceThe RAI-C and RAI-A represent effective tools for measuring frailty in surgical populations with predictive ability on par with other frailty tools. Moderate correlation between the measures suggests convergent validity. The RAI-C offers the advantage of prospective, preoperative assessment that is proved feasible for large-scale screening in clinical practice. However, further efforts should be directed at determining the optimal components of preoperative frailty assessment.
| S-EPMC7140150 | biostudies-literature
Project description:BackgroundThere is no widely used instrument to detect frailty in people with intellectual disabilities (IDs). We aimed to develop and validate a shorter and more practical version of a published frailty index for people with IDs.MethodThis study was part of the longitudinal 'Healthy Ageing and Intellectual Disability' study. We included 982 people with IDs aged 50 years and over. The previously developed and validated ID-Frailty Index consisting of 51 deficits was used as the basis for the shortened version, the ID-FI Short Form. Content of the ID-FI Short Form was based on statistics and clinical and practical feasibility. We evaluated the precision and validity of the ID-FI Short Form using the internal consistency, the correlation between the ID-FI Short Form and the original ID-Frailty Index, the agreement in dividing participants in the categories non-frail, pre-frail and frail, and the association with survival.ResultsSeventeen deficits from the original ID-Frailty Index were selected for inclusion in the ID-FI Short Form. All deficits of the ID-FI Short Form are clinically and practically feasible to assess for caregivers and therapists supporting people with ID. We showed acceptable internal consistency with Cronbach's alpha of 0.75. The Pearson correlation between the ID-Frailty Index and the ID-FI Short Form was excellent (r = 0.94, P < 0.001). We observed a good agreement between the full and short forms in dividing the participants in the frailty categories, with a kappa statistic of 0.63. The ID-FI Short Form was associated with survival; with every 1/100 increase on the ID-FI Short Form, the mortality probability increased by 7% (hazard ratio 1.07, P < 0.001).ConclusionThe first validation of the ID-FI Short Form shows it to be a promising, practical tool to assess the frailty status of people with ID.
| S-EPMC9305773 | biostudies-literature
Project description:ObjectiveTo create an electronic frailty index (eFRAGICAP) using electronic health records (EHR) in Catalunya (Spain) and assess its predictive validity with a two-year follow-up of the outcomes: homecare need, institutionalization and mortality in the elderly. Additionally, to assess its concurrent validity compared to other standardized measures: the Clinical Frailty Scale (CFS) and the Risk Instrument for Screening in the Community (RISC).MethodsThe eFRAGICAP was based on the electronic frailty index (eFI) developed in United Kingdom, and includes 36 deficits identified through clinical diagnoses, prescriptions, physical examinations, and questionnaires registered in the EHR of primary health care centres (PHC). All subjects > 65 assigned to a PHC in Barcelona on 1st January, 2016 were included. Subjects were classified according to their eFRAGICAP index as: fit, mild, moderate or severe frailty. Predictive validity was assessed comparing results with the following outcomes: institutionalization, homecare need, and mortality at 24 months. Concurrent validation of the eFRAGICAP was performed with a sample of subjects (n = 333) drawn from the global cohort and the CFS and RISC. Discrimination and calibration measures for the outcomes of institutionalization, homecare need, and mortality and frailty scales were calculated.Results253,684 subjects had their eFRAGICAP index calculated. Mean age was 76.3 years (59.5% women). Of these, 41.1% were classified as fit, and 32.2% as presenting mild, 18.7% moderate, and 7.9% severe frailty. The mean age of the subjects included in the validation subsample (n = 333) was 79.9 years (57.7% women). Of these, 12.6% were classified as fit, and 31.5% presented mild, 39.6% moderate, and 16.2% severe frailty. Regarding the outcome analyses, the eFRAGICAP was good in the detection of subjects who were institutionalized, required homecare assistance, or died at 24 months (c-statistic of 0.841, 0.853, and 0.803, respectively). eFRAGICAP was also good in the detection of frail subjects compared to the CFS (AUC 0.821) and the RISC (AUC 0.848).ConclusionThe eFRAGICAP has a good discriminative capacity to identify frail subjects compared to other frailty scales and predictive outcomes.
| S-EPMC9080132 | biostudies-literature