scholarly journals Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data

2016 ◽  
Vol 13 (2) ◽  
pp. 247-255 ◽  
Author(s):  
Shinichiroh Yokota ◽  
Kazuhiko Ohe
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Fall Risk ◽  
Risk Prediction Model ◽  
Nursing Data

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

Development of Neurobehavioral Deterioration Risk Prediction Model for Welder: A Proposed Study

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Nuur Azreen Paiman ◽  
◽  
Azian Hariri ◽  
Ibrahim Masood ◽  
Arma Noor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model

A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study

2020 ◽  
Author(s):  
Liwen Zhang ◽  
Di Dong ◽  
Wenjuan Zhang ◽  
Xiaohan Hao ◽  
Mengjie Fang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Prediction Model ◽  
Risk Prediction ◽  
Multicenter Study ◽  
Risk Prediction Model

Wound dehiscence after radical cystectomy: A novel risk-prediction model

2021 ◽  
Vol 79 ◽  
pp. S1112-S1113
Author(s):  
A.A. Nasrallah ◽  
M. Mansour ◽  
C.H. Ayoub ◽  
N. Abou Heidar ◽  
J.A. Najdi ◽  
...  
Keyword(s):  
Prediction Model ◽  
Radical Cystectomy ◽  
Risk Prediction ◽  
Wound Dehiscence ◽  
Risk Prediction Model

scholarly journals Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Jessica K. Sexton ◽  
Michael Coory ◽  
Sailesh Kumar ◽  
Gordon Smith ◽  
Adrienne Gordon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Risk Prediction ◽  
Late Pregnancy ◽  
Risk Prediction Model ◽  
Past Century ◽  
Reporting Standards ◽  
Robust Method ◽  
Predictive Values ◽  
Multivariable Logistic Regression

Abstract Background Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting. Methods This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005–2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current TransparentReporting of a multivariable prediction model forIndividualPrognosis orDiagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered. Discussion A robust method to predict a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards.

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