scholarly journals Development and internal validation of a predictive model for neonatal morbidity in pregnancies with diabetes

2022 ◽  
Vol 226 (1) ◽  
pp. S218-S219
Author(s):  
Megan G. Lord ◽  
Phinnara Has ◽  
Patricia Giglio Ayers ◽  
David A. Savitz ◽  
Matthew A. Esposito
Keyword(s):  
Predictive Model ◽  
Neonatal Morbidity ◽  
Internal Validation

scholarly journals Hematological Indexes Can Be Used to Predict the Incidence of Hypothyroidism in Nasopharyngeal Carcinoma Patients after Radiotherapy

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ling Zhou ◽  
Jia Chen ◽  
Chang-Juan Tao ◽  
Shuang Huang ◽  
Jiang Zhang ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Predictive Model ◽  
Immune Cells ◽  
Cross Validation ◽  
Complete Blood Count ◽  
Univariate Analysis ◽  
Optimal Method ◽  
Internal Validation ◽  
N Stage ◽  
Tenfold Cross Validation

Background. This study explored the relationship between thyroid-associated antibodies, immune cells, and hypothyroidism to establish a predictive model for the incidence of hypothyroidism in patients with nasopharyngeal carcinoma (NPC) after radiotherapy. Methods. A total of 170 patients with NPC treated at the Cancer Hospital of University of Chinese Academy of Sciences between January 2015 and August 2018 were included. The complete blood count, biochemical, coagulation function, immune cells, and thyroid-associated antibodies tested before radiotherapy were evaluated. A logistic regression model was performed to elucidate which hematological indexes were related to hypothyroidism development. A predictive model for the incidence of hypothyroidism was established. Internal verification of the multifactor model was performed using the tenfold cross-validation method. Results. The univariate analysis showed that immune cells had no statistically significant differences among the patients with and without hypothyroidism. Sex, N-stage, antithyroid peroxidase antibody (TPO-Ab), antithyroglobulin antibody (TG-Ab), thyroglobulin (TG), and fibrinogen (Fb) were associated with hypothyroidism. Males and early N-stage were protective factors of thyroid function, whereas increases in TPO-Ab, TG-Ab, TG, and Fb counts were associated with an increased rate of hypothyroidism incidence. The multivariate analysis showed that TPO-Ab, TG-Ab, TG, and Fb were independent predictors of hypothyroidism. The comprehensive effect of the significant model, including TPO-Ab, TG-Ab, TG, and Fb counts, represented the optimal method of predicting the incidence of radiation-induced hypothyroidism (AUC=0.796). Tenfold cross-validation methods were applied for internal validation. The AUCs of the training and testing sets were 0.792 and 0.798, respectively. Conclusion. A model combining TPO-Ab, TG-Ab, TG, and Fb can be used to screen populations at a high risk of developing hypothyroidism after radiotherapy.

scholarly journals Correction: Development and Internal Validation of a Predictive Model Including Pulse Oximetry for Hospitalization of Under-Five Children in Bangladesh

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0147560
Author(s):  
Shahreen Raihana ◽  
Dustin Dunsmuir ◽  
Tanvir Huda ◽  
Guohai Zhou ◽  
Qazi Sadeq-ur Rahman ◽  
...  
Keyword(s):  
Pulse Oximetry ◽  
Predictive Model ◽  
Internal Validation ◽  
Under Five ◽  
Under Five Children

scholarly journals A Systematic Review of the Literature Demonstrates Some Errors in the Use of Decision Curve Analysis but Generally Correct Interpretation of Findings

2019 ◽  
Vol 39 (5) ◽  
pp. 493-498 ◽  
Author(s):  
Paolo Capogrosso ◽  
Andrew J. Vickers
Keyword(s):  
Systematic Review ◽  
Literature Review ◽  
Predictive Model ◽  
Data Extraction ◽  
Clinical Decision ◽  
Curve Analysis ◽  
Similar Proportion ◽  
Internal Validation ◽  
Decision Curve Analysis ◽  
Almost All

Background. Decision curve analysis (DCA) is a widely used methodology in clinical research studies. Purpose. We performed a literature review to identify common errors in the application of DCA and provide practical suggestions for appropriate use of DCA. Data Sources. We first conducted an informal literature review and identified 6 errors found in some DCAs. We then used Google Scholar to conduct a systematic review of studies applying DCA to evaluate a predictive model, marker, or test. Data Extraction. We used a standard data collection form to collect data for each reviewed article. Data Synthesis. Each article was assessed according to the 6 predefined criteria for a proper analysis, reporting, and interpretation of DCA. Overall, 50 articles were included in the review: 54% did not select an appropriate range of probability thresholds for the x-axis of the DCA, with a similar proportion (50%) failing to present smoothed curves. Among studies with internal validation of a predictive model and correction for overfit, 61% did not clearly report whether the DCA had also been corrected. However, almost all studies correctly interpreted the DCA, used a correct outcome (92% for both), and clearly reported the clinical decision at issue (81%). Limitations. A comprehensive assessment of all DCAs was not performed. However, such a strategy would not influence the main findings. Conclusions. Despite some common errors in the application of DCA, our finding that almost all studies correctly interpreted the DCA results demonstrates that it is a clear and intuitive method to assess clinical utility.

scholarly journals Effectiveness of hysteroscopic resection of a uterine caesarean niche can be predicted: a prospective cohort study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qian Zhu ◽  
Xiaoqing He ◽  
Ling Jiang ◽  
Guiling Liang ◽  
Chenfeng Zhu ◽  
...  
Keyword(s):  
Predictive Model ◽  
Prospective Cohort ◽  
Multivariable Logistic Regression Analysis ◽  
Mri Findings ◽  
Internal Validation ◽  
Preoperative Prediction ◽  
Tenfold Cross Validation ◽  
Magnetic Resonance Imaging Mri ◽  
Individualized Prediction ◽  
Hysteroscopic Resection

Abstract This study aimed to develop and validate a model for the preoperative prediction of the effectiveness of hysteroscopic resection of a uterine cesarean niche in patients with postmenstrual spotting. The predictive model was developed in a primary prospective cohort consisting of 208 patients with niche treated by hysteroscopic resection. Multivariable logistic regression analysis was performed to develop the predictive model, which incorporated preoperative menstrual characteristics and magnetic resonance imaging (MRI) findings. Surgical efficacy was defined as a decrease in postmenstrual spotting duration of at least 3 days at the 3-month follow-up compared with baseline. The predictive model was presented with a nomogram, and the performance was assessed with respect to its calibration, discrimination, and clinical use. Internal validation was performed using tenfold cross-validation. The predictive factors in the final model were as follows: preoperative menstrual duration, thickness of the residual myometrium (TRM), length, TRM/thickness of the adjacent myometrium ratio, angle γ, area, and presence of a lateral branch of the niche. The model showed good performance in predicting the effectiveness of hysteroscopic niche resection. Incorporating the preoperative duration of the menstrual period and MRI findings of the niche into an easy-to-use nomogram facilitates the individualized prediction of the effectiveness of a hysteroscopic niche resection by 26 Fr resectoscope, but multicenter prospective studies are needed to validate it.

scholarly journals Prediction of psychosis: model development and internal validation of a personalized risk calculator

2020 ◽  
pp. 1-9
Author(s):  
Tae Young Lee ◽  
Wu Jeong Hwang ◽  
Nahrie S. Kim ◽  
Inkyung Park ◽  
Silvia Kyungjin Lho ◽  
...  
Keyword(s):  
High Risk ◽  
Predictive Model ◽  
Help Seeking ◽  
Test Score ◽  
Cox Regression ◽  
Verbal Learning ◽  
Model Development ◽  
Predictive Ability ◽  
Internal Validation ◽  
Early Intervention In Psychosis

Abstract Background Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years. Methods Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis. Results The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels. Conclusions Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.

Bootstrap internal validation command for predictive logistic regression models

2021 ◽  
Vol 21 (2) ◽  
pp. 498-509
Author(s):  
B. M. Fernandez-Felix ◽  
E. García-Esquinas ◽  
A. Muriel ◽  
A. Royuela ◽  
J. Zamora
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Predictive Model ◽  
Performance Measures ◽  
Predictive Models ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Model Performance ◽  
Internal Validation ◽  
Logistic Regression Models

Overfitting is a common problem in the development of predictive models. It leads to an optimistic estimation of apparent model performance. Internal validation using bootstrapping techniques allows one to quantify the optimism of a predictive model and provide a more realistic estimate of its performance measures. Our objective is to build an easy-to-use command, bsvalidation, aimed to perform a bootstrap internal validation of a logistic regression model.

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