Dataset Information

Identifying Characteristics of Frailty in Female Mice Using a Phenotype Assessment Tool.

ABSTRACT: Preclinical studies are important in identifying the underlying mechanisms contributing to frailty. Frailty studies have mainly focused on male rodents with little directed at female rodents. Therefore, the purposes of this study were to identify the onset and prevalence of frailty across the life span in female mice, and to determine if frailty predicts mortality. Female C57BL/6 (n = 27) mice starting at 17 months of age were assessed across the life span using a frailty phenotype, which included body weight, walking speed, strength, endurance, and physical activity. The onset of frailty occurred at approximately 17 months (1/27 mice), with the prevalence of frailty increasing thereafter. At 17 months, 11.1% of the mice were pre-frail and by 26 months peaked at 36.9%. The percentage of frail mice progressively increased up to 66.7% at 32 months. Non-frail mice lived to 29 months whereas frail/pre-frail mice lived only to 26 months (p = .04). In closing, using a mouse frailty phenotype, we are able to identify that the prevalence of frailty in female mice increases across the life span and accurately predicts mortality. Together, this frailty phenotype has the potential to yield information about the underlying mechanisms contributing to frailty.

SUBMITTER: Kwak D

PROVIDER: S-EPMC7328207 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Identifying Characteristics of Frailty in Female Mice Using a Phenotype Assessment Tool.

Kwak Dongmin D Baumann Cory W CW Thompson LaDora V LV

The journals of gerontology. Series A, Biological sciences and medical sciences 20200301 4

Preclinical studies are important in identifying the underlying mechanisms contributing to frailty. Frailty studies have mainly focused on male rodents with little directed at female rodents. Therefore, the purposes of this study were to identify the onset and prevalence of frailty across the life span in female mice, and to determine if frailty predicts mortality. Female C57BL/6 (n = 27) mice starting at 17 months of age were assessed across the life span using a frailty phenotype, which includ ...[more]

PMID: 30958526

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Project description:OBJECTIVES:Frailty is a major clinical geriatric syndrome associated with serious adverse events including functional disability, falls, hospitalisation, increased morbidity and mortality. The aim of this study was to study associations between a frailty phenotype and frailty characteristics well known from the literature. DESIGN:Registry-based cross-sectional study. SETTING:The target population consists of inhabitants above the age of 50 living in the Danish municipalities of Lolland and Guldborgsund. Excluded are incapacitated people, inhabitants unable to understand Danish or English and inhabitants without a permanent residence. PARTICIPANTS:7327 individuals aged 50+ years were included. OUTCOME MEASURES:We examined associations between the frailty measurement and factors known to be associated with frailty: sex, age, income insufficiency, education, comorbidity, self-assessed health, morbidity and mortality. RESULTS:7327 individuals aged 50+ years were included. Of these, 6.5% had ≥3 frailty components (frail), 46.7% had 1-2 components (prefrail) and 46.9% had none (non-frail). Those who were frail were older and more likely female than those who were non-frail or prefrail. There was a stepwise decrease in educational level, and in self-assessed health with increasing frailty status, and a stepwise increase in difficulty in making ends meet, number of hospital contacts and mortality with increasing frailty status, p<0.0001 for each comparison. Compared with individuals who were non-frail, mortality was higher among those who were prefrail (HR: 2.90; 95% CI: 1.30 to 6.43) or frail (HR: 8.21; 95% CI: 3.37 to 20.0). CONCLUSIONS:Based on these findings, we consider the Lolland-Falster Health Study frailty assessment a valid instrument demonstrating the same characteristics as other validated frailty measures concerning associations with sex, age, income insufficiency, education, comorbidity, self-assessed health, morbidity and mortality. TRIAL REGISTRATION NUMBER:NCT02482896.

Project description:Background: Frailty is characterized by loss of biological reserves and is associated with an increased risk of adverse health outcomes. Frailty can be operationalized using a Frailty Index (FI) based on the accumulation of health deficits; items under health evaluation in the well-established Comprehensive Geriatric Assessment (CGA) have been used to generate an FI-CGA. Traditionally, constructing the FI-CGA has relied on paper-based recording and manual data processing. As this can be time-consuming and error-prone, it limits widespread uptake of this proven type of frailty assessment. Here, we report the development of an electronic tool, the eFI-CGA, for use on personal computers by frontline healthcare providers, to collect CGA data and automate FI-CFA calculation. The ultimate goal is to support early identification and management of frailty at points-of-care, and make uptake in Electronic Medical Records (EMR) feasible and transparent. Methods: An electronic CGA (eCGA) form was implemented to operate on Microsoft's WinForms platform and coded using C# programming language. Users complete the eCGA form, from which items under the CGA evaluation are automatically retrieved and processed to output an eFI-CGA score. A user-friendly interface and secured data saving methods were implemented. The software was debugged and tested using systematically designed simulation data, addressing different logic, syntax, and application errors, and then tested with clinical assessment. The user manual and manual scoring were used as ground truth to compare eFI-CGA input and automated eFI score calculations. Frontline health-provider user feedback was incorporated to improve the end-user experience. Results: The Standalone eFI-CGA software tool was developed and optimized for use on personal computers. The user interface adapted the design of paper-based CGA form to facilitate familiarity for clinical users. Compared to known scores, the software tool generated eFI-CGA scores with 100% accuracy to four decimal places. The eFI-CGA allowed secure data storage and retrieval of multiple types, including user input, completed eCGA form, coded items, and calculated eFI-CGA scores. It also permitted recording of actions requiring clinical follow-up, facilitating care planning. Application bugs were identified and resolved at various stages of the implementation, resulting in efficient system performance. Discussion: Accurate, robust, and reliable computerized frailty assessments are needed to promote effective frailty assessment and management, as a key tool in health care systems facing up to frailty. Our research has enabled the delivery of the standalone eFI-CGA software technology to empower effective frailty assessment and management by various healthcare providers at points-of-care, facilitating integrated care of older adults.

Project description:Demographic changes have led to an increase in the number of elderly frail persons and, consequently, systematic geriatric assessment is more important than ever. Frailty Indexes (FI) may be particularly useful to discriminate between various degrees of frailty but are not routinely assessed due, at least in part, to the large number of deficits assessed (from 30 to 70). Therefore, we have developed a new, more concise FI for rapid geriatric assessment (RGA)-the Frail-VIG index ("VIG" is the Spanish/Catalan abbreviation for Comprehensive Geriatric Assessment), which contains 22 simple questions that assess 25 different deficits. Here we describe this FI and report its ability to predict mortality at 24 months.Prospective, observational, longitudinal study of geriatric patients followed for 24 months or until death. The study participants were patients (n =?590) admitted to the Acute Geriatric Unit at the at the University Hospital of Vic (Barcelona) during the year 2014. Participants were classified into one of seven groups based on their Frail-VIG score (0-0.15; 0.16-0.25; 0.26-0.35; 0.36-0.45; 0.46-0.55; 0.56-0.65; and 0.66-1). Survival curves for these groups were compared using the log-rank test. ROC curves were used to assess the index's capacity to predict mortality at 24 months.Mean (standard deviation) patient age was 86.4 (5.6) years. The 24-month mortality rate was 57.3% for the whole sample. Significant between-group (deceased vs. living) differences (p <?0.05) were observed for most index variables. Survival curves for the seven Frail-VIG groups differed significantly (X2 =?433.4, p <?0.001), with an area under the ROC curve (confidence interval) of 0.90 (0.88-0.92) at 12 months and 0.85 (0.82-0.88) at 24 months. Administration time for the Frail-VIG index ranged from 5 to 10 min.The Frail-VIG index, which requires less time to administer than previously validated FIs, presents a good discriminative capacity for the degree of frailty and a high predictive capacity for mortality in the present cohort. Although more research is needed to confirm the validity of this instrument in other populations and settings, the Frail-VIG may provide clinicians with a RGA method and also a reliable tool to assess frailty in routine practice.

Project description:BACKGROUND:Polycystic ovary syndrome (PCOS) is an endocrine disrupting disorder affecting about 10% of reproductive-aged women. PCOS diagnosis may be delayed several years and may require multiple physicians, resulting in lost time for risk-reducing interventions. Menstrual tracking apps are a potential tool to alert women of their risk while also prompting evaluation from a medical professional. OBJECTIVE:The primary objective of this study was to develop and pilot test the irregular cycle feature, a predictive model that generated a PCOS risk score, in the menstrual tracking app, Clue. The secondary objectives were to run the model using virtual test subjects, create a quantitative risk score, compare the feature's risk score with that of a physician, and determine the sensitivity and specificity of the model before empirical testing on human subjects. METHODS:A literature review was conducted to generate a list of signs and symptoms of PCOS, termed variables. Variables were then assigned a probability and built into a Bayesian network. Questions were created based on these variables. A total of 9 virtual test subjects were identified using self-reported menstrual cycles and answers to the feature's questions. Upon completion of the questionnaire, a Result Screen and Doctor's Report summarizing the probability of having PCOS was displayed. This provided information about PCOS and data to facilitate diagnosis by a medical professional. To assess the accuracy of the feature, the same set of 9 virtual test subjects was assigned probabilities by the feature and the physician, who served as the gold standard. The feature recommended individuals with a score greater than or equal to 25% to follow-up with a physician. Differences between the feature and physician scores were evaluated using a t test and a Pearson correlation coefficient in 8 of the 9 virtual test subjects. A second iteration was conducted to assess the feature's probability capabilities. RESULTS:The irregular cycle feature's first iteration produced 1 false-positive compared with the physician score and had an absolute mean difference of 15.5% (SD 15.1%) among the virtual test subjects. The second iteration had 2 false positives compared with the physician score and had an absolute mean difference of 18.8% (SD 13.6%). The feature overpredicted the virtual test subjects' risk of PCOS compared with the physician. However, a significant positive correlation existed between the feature and physician score (Pearson correlation coefficient=0.82; P=.01). The second iteration performed worse, with a Pearson correlation coefficient of 0.73 (P=.03). CONCLUSIONS:The first iteration of the feature outperformed the second and better predicted the probability of PCOS. Although further research is needed with a more robust sample size, this pilot study indicates the potential value for developing a screening tool to prompt high-risk subjects to seek evaluation by a medical professional.

Dataset Information

Identifying Characteristics of Frailty in Female Mice Using a Phenotype Assessment Tool.

Publications

Identifying Characteristics of Frailty in Female Mice Using a Phenotype Assessment Tool.

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