Dataset Information

A Prospective Comparison of Frailty Scores and Fall Prediction in Acutely Injured Older Adults.

ABSTRACT:

Background

Elderly (65 and older) fall-related injuries are a significant cause of morbidity and mortality. Although frailty predicts poor outcomes in geriatric trauma, literature comparing frailty scoring systems remains limited. Herein, we evaluated which frailty scoring system best predicts falls over time in the elderly.

Materials and methods

Acute surgical patients 65 y and older were enrolled and prospectively observed. Demographics and frailty, assessed using the FRAIL Scale, Trauma Specific Frailty Index (TSFI), and Canadian Frailty Scale (CSHA-CFS), were collected at enrollment and 3 mo intervals following discharge for 1 y. Surveys queried the total number and timing of falls. Changes in frailty over time were assessed by logistic regression and area under the curve (AUC).

Results

Fifty-eight patients were enrolled. FRAIL Scale and CSHA-CFS scores did not change over time, but TSFI scores did (P ? 0.01). Worsening frailty was observed using TSFI at 6 (P ? 0.01) and 12 mo (P ? 0.01) relative to baseline. Mortality did not differ based on frailty using any frailty score. Increasing frailty scores and time postdischarge was associated with increased odds of a fall. AUC estimates with 95% CI were 0.72 [0.64, 0.80], 0.81 [0.74, 0.88], and 0.76 [0.68, 0.84] for the FRAIL Scale, TSFI, and CSHA-CFS, respectively.

Conclusions

The risk of falls postdischarge were associated with increased age, time postdischarge, and frailty in our population. No scale appeared to significantly outperform the other by AUC estimation. Further study on the longitudinal effects of frailty is warranted.

SUBMITTER: Tejiram S

PROVIDER: S-EPMC7736528 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Prospective Comparison of Frailty Scores and Fall Prediction in Acutely Injured Older Adults.

Tejiram Shawn S Cartwright Julia J Taylor Sandra L SL Hatcher Victor H VH Galet Colette C Skeete Dionne A DA Romanowski Kathleen S KS

The Journal of surgical research 20200902

<h4>Background</h4>Elderly (65 and older) fall-related injuries are a significant cause of morbidity and mortality. Although frailty predicts poor outcomes in geriatric trauma, literature comparing frailty scoring systems remains limited. Herein, we evaluated which frailty scoring system best predicts falls over time in the elderly.<h4>Materials and methods</h4>Acute surgical patients 65 y and older were enrolled and prospectively observed. Demographics and frailty, assessed using the FRAIL Scal ...[more]

PMID: 32889331

Similar Datasets

Project description:BackgroundAlthough there has been a range of studies that focused on physical frailty and associated fall outcomes within developed countries, similar studies from developing countries have been limited. This study aimed to examine the relationship between physical frailty and the prevalence of falls, multiple falls and fall-related injuries among the ageing population within the Indian context.MethodsIndividual-level data from the first wave of the Longitudinal Aging Study in India (LASI) with 28,285 older adults aged 60 years and above (male 48.9%) was used for this study. Physical frailty was assessed through the physical frailty phenotype adapted from Fried's criteria. Multivariable logistic regression was employed to examine the association of frailty status with falls, multiple falls, and fall-related injuries among Indian older adults.ResultsThe prevalence of frailty was found to be 29.94% within the sample and frail older adults had a higher prevalence of falls (15.43% vs 11.85%), multiple falls (7.73% vs 5.25%), and fall related injuries (6.68% vs 5.29%). The odds of falling among frail older adults were significantly higher in reference to the odds of falling among non-frail older adults [aOR: 1.24; CI: 1.09-1.41]. Similarly, the odds of multiple falls among frail older adults were significantly higher in reference to the odds of multiple falls among non-frail older adults [aOR: 1.24; CI: 1.05-1.48]. Moreover, the odds of fall-related injury among frail older adults were significantly higher in reference to the odds of fall-related injury among non-frail older adults [aOR: 1.21; CI: 1.01-1.45]. Falls, multiple falls and fall-related injuries were found to be significantly associated with employment and poor self-rated health, whereas, females and lone living older adults had a significantly higher likelihood of suffering from falls and multiple falls.ConclusionOlder individuals with physical frailty were found to be at increased risk of falls, multiple falls and fall-related injuries in India. The findings of our study also have important clinical implications in the measures undertaken to reduce falls and enable future healthcare practitioners and policymakers to factor in the key determinant of physical frailty.

Project description:As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the "Healthy Ageing Initiative" study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.

Project description:BackgroundOlder age and higher acuity are associated with prolonged hospital stays and hospital readmissions. Early discharge planning may reduce lengths of hospital stay and hospital readmissions; however, its effectiveness with acutely admitted older adults is unclear.MethodsIn this systematic review, we compared the effectiveness of early discharge planning to usual care in reducing index length of hospital stay, hospital readmissions, readmission length of hospital stay, and mortality; and increasing satisfaction with discharge planning and quality of life for older adults admitted to hospital with an acute illness or injury.We searched the Cochrane Library, DARE, HTA, NHSEED, ACP, MEDLINE, EMBASE, CINAHL, Proquest Dissertations and Theses, PubMed, Web of Science, SciSearch, PEDro, Sigma Theta Tau International's registry of nursing research, Joanna Briggs Institute, CRISP, OT Seeker, and several internet search engines. Hand-searching was conducted in four gerontological journals and references of all included studies and previous systematic reviews. Two reviewers independently extracted data and assessed risk of bias. Data were pooled using a random-effects meta-analysis. Where meta-analysis was not possible, narrative analysis was performed.ResultsNine trials with a total of 1736 participants were included. Compared to usual care, early discharge planning was associated with fewer hospital readmissions within one to twelve months of index hospital discharge [risk ratio (RR) = 0.78, 95% CI = 0.69 - 0.90]; and lower readmission lengths of hospital stay within three to twelve months of index hospital discharge [weighted mean difference (WMD) = -2.47, 95% confidence intervals (CI) = -4.13 - -0.81)]. No differences were found in index length of hospital stay, mortality or satisfaction with discharge planning. Narrative analysis of four studies indicated that early discharge planning was associated with greater overall quality of life and the general health domain of quality of life two weeks after index hospital discharge.ConclusionsEarly discharge planning with acutely admitted older adults improves system level outcomes after index hospital discharge. Service providers can use these findings to design and implement early discharge planning for older adults admitted to hospital with an acute illness or injury.

Dataset Information

A Prospective Comparison of Frailty Scores and Fall Prediction in Acutely Injured Older Adults.

Background

Materials and methods

Results

Conclusions

Publications

A Prospective Comparison of Frailty Scores and Fall Prediction in Acutely Injured Older Adults.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets