A risk prediction model for dysphagia in older patients: a single-center prospective study

2022 ◽  
Vol 44 ◽  
pp. 24-29
Author(s):  
Lili Yu ◽  
Yingqiang Li ◽  
Dongyun Zhang ◽  
Wanyun Huang ◽  
Runping Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prospective Study ◽  
Risk Prediction ◽  
Older Patients ◽  
Risk Prediction Model ◽  
Single Center

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 local risk prediction model for prolonged ventilation after adult heart valve surgery in a Chinese single center

Heart & Lung ◽  
2013 ◽  
Vol 42 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Chong Wang ◽  
Guan-xin Zhang ◽  
Fang-lin Lu ◽  
Bai-ling Li ◽  
Liang-jian Zou ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Heart Valve ◽  
Valve Surgery ◽  
Risk Prediction Model ◽  
Heart Valve Surgery ◽  
Single Center ◽  
Adult Heart ◽  
Prolonged Ventilation ◽  
Local Risk

scholarly journals A Nomogram To Predict The Cumulative Live Birth Rate for Patients with Low Prognosis According To The POSEIDON Criteria: A Retrospective Observational Cohort Study of 4,395 Patients in Chinese Population

2021 ◽  
Author(s):  
Lijuan Chen ◽  
Pengfei Qu ◽  
Jinfang Wu ◽  
Jinlin Xie ◽  
Hui Wang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Live Birth ◽  
Chinese Population ◽  
Birth Rate ◽  
Live Birth Rate ◽  
Risk Prediction Model ◽  
Single Center ◽  
Cumulative Live Birth Rate ◽  
Cumulative Live Birth

Abstract Purpose A small number of risk prediction model have been previously reported to predict the infertility treatment success. While the studies of the risk prediction model for the patients with low prognosis are limited. This study aimed to construct and validate a nomogram for the prediction of cumulative live birth rate (CLBR) in patients with low prognosis from a single center database in Chinese population. Methods Clinical data of 4,395 patients with low prognosis, who received in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) therapy between 2014 and 2018, were retrieved and randomly divided into training (70%) and the external validation (30%) sets. Multivariate analysis with logistic regression model was conducted. Results Multivariate analyses showed that maternal age, body mass index (BMI), basal serum follicle-stimulating hormone (FSH) level, type of infertility, male factors, uterine factors, and usable embryos number at day 3 were risk factors for CLBR in patients with low prognosis. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.769 (95% confident interval (CI): 0.751, 0.787) in training set. The validation set presented good performance with an AUC of 0.749 (95% CI: 0.720, 0.778). In addition, Hosmer-Lemeshow chi-square value was 10.194 (P = 0.252). Conclusion We constructed and validated a nomogram for the prediction of CLBR in low prognosis patients with a single center database in Chinese population. The validated nomogram for the prediction of CLBR could be potentially applied in clinic for IVF counselling in patients with low prognosis.

scholarly journals Development of a risk prediction model for lung cancer: The Japan Public Health Center-based Prospective Study

2018 ◽  
Vol 109 (3) ◽  
pp. 854-862 ◽  
Author(s):  
Hadrien Charvat ◽  
Shizuka Sasazuki ◽  
Taichi Shimazu ◽  
Sanjeev Budhathoki ◽  
Manami Inoue ◽  
...  
Keyword(s):  
Public Health ◽  
Lung Cancer ◽  
Prediction Model ◽  
Prospective Study ◽  
Health Center ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Public Health Center

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

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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