scholarly journals Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study (Preprint)

2018 ◽  
Author(s):  
Anabela Correia Martins ◽  
Juliana Moreira ◽  
Catarina Silva ◽  
Joana Silva ◽  
Cláudia Tonelo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Prospective Study ◽  
Risk Prediction ◽  
Fall Risk ◽  
Screening Tool ◽  
Community Dwelling ◽  
Risk Prediction Model ◽  
Risk Of Falling ◽  
Home Hazards

BACKGROUND Falls are a major health problem among older adults. The risk of falling can be increased by polypharmacy, vision impairment, high blood pressure, environmental home hazards, fear of falling, and changes in the function of musculoskeletal and sensory systems that are associated with aging. Moreover, individuals who experienced previous falls are at higher risk. Nevertheless, falls can be prevented by screening for known risk factors. OBJECTIVE The objective of our study was to develop a multifactorial, instrumented, screening tool for fall risk, according to the key risk factors for falls, among Portuguese community-dwelling adults aged 50 years or over and to prospectively validate a risk prediction model for the risk of falling. METHODS This prospective study, following a convenience sample method, will recruit community-dwelling adults aged 50 years or over, who stand and walk independently with or without walking aids in parish councils, physical therapy clinics, senior’s universities, and other facilities in different regions of continental Portugal. The FallSensing screening tool is a technological solution for fall risk screening that includes software, a pressure platform, and 2 inertial sensors. The screening includes questions about demographic and anthropometric data, health and lifestyle behaviors, a detailed explanation about procedures to accomplish 6 functional tests (grip strength, Timed Up and Go, 30 seconds sit to stand, step test, 4-Stage Balance test “modified,” and 10-meter walking speed), 3 questionnaires concerning environmental home hazards, and an activity and participation profile related to mobility and self-efficacy for exercise. RESULTS The enrollment began in June 2016 and we anticipate study completion by the end of 2018. CONCLUSIONS The FallSensing screening tool is a multifactorial and evidence-based assessment which identifies factors that contribute to fall risk. Establishing a risk prediction model will allow preventive strategies to be implemented, potentially decreasing fall rate. REGISTERED REPORT IDENTIFIER RR1-10.2196/10304

A Screening Tool Using Five Risk Factors Was Developed for Fall-Risk Prediction in Chinese Community-Dwelling Elderly Individuals

2018 ◽  
Vol 21 (5) ◽  
pp. 416-422 ◽  
Author(s):  
Li Kang ◽  
Xiaoyu Chen ◽  
Peipei Han ◽  
Yixuan Ma ◽  
Liye Jia ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Fall Risk ◽  
Screening Tool ◽  
Community Dwelling ◽  
Chinese Community ◽  
Elderly Individuals

A screening tool with five risk factors was developed for fall-risk prediction in community-dwelling elderly

2011 ◽  
Vol 64 (10) ◽  
pp. 1152-1160 ◽  
Author(s):  
Bienvenu Bongue ◽  
Caroline Dupré ◽  
Olivier Beauchet ◽  
Arnaud Rossat ◽  
Bruno Fantino ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Fall Risk ◽  
Screening Tool ◽  
Community Dwelling

scholarly journals A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaona Jia ◽  
Mirza Mansoor Baig ◽  
Farhaan Mirza ◽  
Hamid GholamHosseini
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Model ◽  
Clinical Laboratory ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
Cvd Risk

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

scholarly journals Identification of risk factors for daptomycin-associated creatine phosphokinase elevation and development of a risk prediction model for incidence probability

2021 ◽  
Author(s):  
Masaru Samura ◽  
Naoki Hirose ◽  
Takenori Kurata ◽  
Keisuke Takada ◽  
Fumio Nagumo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Logistic Regression Analysis ◽  
Creatine Phosphokinase ◽  
Risk Prediction Model ◽  
Risk Scores

Abstract Background In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation. Methods Patients who received daptomycin at our hospital were classified into the normal or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis. Results The normal and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR] 4.45, 95% confidence interval [CI] 1.40–14.47, risk score 4), concomitant antihistamine use (OR 5.66, 95% CI 1.58–20.75, risk score 4), and trough concentration (Cmin) between 20 and <30 µg/mL (OR 14.48, 95% CI 2.90–87.13, risk score 5) and ≥30.0 µg/mL (OR 24.64, 95% CI 3.21–204.53, risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were <10% (low risk), 10%–<25% (moderate risk), and ≥25% (high risk) with the total risk scores of ≤4, 5–6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiving-operating characteristic curve 0.85, 95% CI 0.74–0.95). Conclusions These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.

Abstract 14508: Development of a Risk Prediction Model for 1-year Readmission Among Younger Adults Hospitalized for Acute Myocardial Infarction

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Rachel P Dreyer ◽  
Terrence E Murphy ◽  
Valeria Raparelli ◽  
Sui Tsang ◽  
Gail Onofrio ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Prediction Model ◽  
Risk Prediction ◽  
Bayesian Model Averaging ◽  
Risk Model ◽  
Young Patients ◽  
Risk Prediction Model ◽  
Younger Adults

Introduction: Although readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (18-55 yrs), there is no available risk prediction model for this age group. Existing risk models have been developed in older populations, have modest predictive ability, and exhibit methodological drawbacks. We developed a risk prediction model that considered a broad range of demographic, clinical, and psychosocial factors for readmission within 1-year of hospitalization for AMI among young adults. Methods: Young AMI adults (18-55 yrs) were enrolled from the prospective observational VIRGO study (2008-2012) of 3,572 patients. Data were obtained from medical record abstraction, interviews, and adjudicated hospitalization records. The outcome was all-cause readmission within 1-year. We used a two-stage selection process (LASSO followed by Bayesian Model Averaging) to develop a risk model. Results: The median age was 48 years (IQR: 44,52), 67.1% were women, and 20.1% were Non-white or Hispanic. Within 1-year, 906 patients (25.3%) were readmitted. Patients who were readmitted were more likely to be female, black, and had a clustering of adverse risk factors and co-morbidities. From 61 original variables considered, the final multivariable model of readmission within 1-year of discharge consisted of 14 predictors (Figure) . The model was well calibrated (Hosmer-Lemeshow P >0.05) with moderate discrimination (C statistic over 33 imputations: 0.69 development cohort). Conclusion: Adverse clinical risk factors such as diabetes, hypertension and prior AMI, but also female sex, access to specialist care, and major depression were associated with a higher risk of readmission at 1-year post AMI. This information is important to inform the development of interventions to reduce readmissions in young patients with AMI.

Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model

2007 ◽  
Vol 14 (2) ◽  
pp. 169-187 ◽  
Author(s):  
Richard J Santen ◽  
Norman F Boyd ◽  
Rowan T Chlebowski ◽  
Steven Cummings ◽  
Jack Cuzick ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Prediction Model ◽  
Cancer Prevention ◽  
Risk Prediction ◽  
Breast Cancer Prevention ◽  
Risk Prediction Model ◽  
Clinical Material ◽  
Collaborative Group ◽  
Density State

The majority of candidates for breast cancer prevention have not accepted tamoxifen because of the perception of an unfavorable risk/benefit ratio and the acceptance of raloxifene remains to be determined. One means of improving this ratio is to identify women at very high risk of breast cancer. Family history, age, atypia in a benign biopsy, and reproductive factors are the main parameters currently used to determine risk. The most powerful risk factor, mammographic density, is not presently employed routinely. Other potentially important factors are plasma estrogen and androgen levels, bone density, weight gain, age of menopause, and fracture history, which are also not currently used in a comprehensive risk prediction model because of lack of prospective validation. The Breast Cancer Prevention Collaborative Group (BCPCG) met to critically examine and prioritize risk factors that might be selected for further testing by multivariate analysis using existing clinical material. The BCPCG reached a consensus that quantitative breast density, state of the art plasma estrogen and androgen measurements, history of fracture and height loss, BMI, and waist–hip ratio had sufficient priority for further testing. As a practical approach, these parameters could be added to the existing Tyrer–Cuzick model which encompasses factors included in both the Claus and Gail models. The BCPCG analyzed potentially available clinical material from previous prospective studies and determined that a large case/control study to evaluate these new factors might be feasible at this time.

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