scholarly journals Novel nomogram for predicting the 3-year incidence risk of osteoporosis in a Chinese male population

2021 ◽  
Author(s):  
Yaqian Mao ◽  
Lizhen Xu ◽  
Ting Xue ◽  
Jixing Liang ◽  
Wei Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Calibration Curve ◽  
Model Performance ◽  
Clinical Decision ◽  
Lasso Regression ◽  
Threshold Probability ◽  
Male Population ◽  
Incidence Risk ◽  
Chinese Male

Objective: To establish a rapid, cost-effective, accurate, and acceptable osteoporosis (OP) screening model for the Chinese male population (age ≥ 40years) based on data mining technology. Materials and methods: A total of 1,834 subjects who did not have OP at the baseline and completed a 3-year follow-up were included in this study. All subjects underwent quantitative ultrasound examinations for calcaneus at the baseline and follow-ups that lasted for 3 years. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select feature variables. The characteristic variables selected in the LASSO regression were analyzed by multivariable logistic regression (MLR) to construct the predictive model. This predictive model was displayed through a nomogram. We used the receiver operating characteristic (ROC) curve, C-index, calibration curve and clinical decision curve analysis (DCA) to evaluate model performance and the bootstrapping validation to internally validate the model. Results: The area under the ROC (AUC) curve of the risk nomogram was 0.882 (95%CI, 0.858-0.907), exhibiting good predictive ability and performance. The C-index for the risk nomogram was 0.882 in the prediction model, which presented good refinement. In addition, the nomogram calibration curve indicated that the prediction model was consistent. The DCA showed that when the threshold probability was between 1% and 100%, the nomogram had good clinical application value. More importantly, the internally verified C-index of the nomogram was still very high, at 0.870. Conclusions: This novel nomogram can effectively predict the 3-year incidence risk of OP in the male population.

scholarly journals Development and Validation of a Clinical Prediction Model for Sleep Disorders in the ICU: A Retrospective Cohort Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Yun Li ◽  
Lina Zhao ◽  
Chenyi Yang ◽  
Zhiqiang Yu ◽  
Jiannan Song ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Sleep Disorders ◽  
High Performance ◽  
Clinical Utility ◽  
Characteristic Curve ◽  
High Sensitivity ◽  
Clinical Decision ◽  
Net Benefit ◽  
Threshold Probability

BackgroundSleep disorders, the serious challenges faced by the intensive care unit (ICU) patients are important issues that need urgent attention. Despite some efforts to reduce sleep disorders with common risk-factor controlling, unidentified risk factors remain.ObjectivesThis study aimed to develop and validate a risk prediction model for sleep disorders in ICU adults.MethodsData were retrieved from the MIMIC-III database. Matching analysis was used to match the patients with and without sleep disorders. A nomogram was developed based on the logistic regression, which was used to identify risk factors for sleep disorders. The calibration and discrimination of the nomogram were evaluated with the 1000 bootstrap resampling and receiver operating characteristic curve (ROC). Besides, the decision curve analysis (DCA) was applied to evaluate the clinical utility of the prediction model.Results2,082 patients were included in the analysis, 80% of whom (n = 1,666) and the remaining 20% (n = 416) were divided into the training and validation sets. After the multivariate analysis, hemoglobin, diastolic blood pressure, respiratory rate, cardiovascular disease, and delirium were the independent risk predictors for sleep disorders. The nomogram showed high sensitivity and specificity of 75.6% and 72.9% in the ROC. The threshold probability of the net benefit was between 55% and 90% in the DCA.ConclusionThe model showed high performance in predicting sleep disorders in ICU adults, the good clinical utility of which may be a useful tool for providing clinical decision support to improve sleep quality in the ICU.

scholarly journals Evaluation and Validation of a Prediction Model for Extubation Success in Very Preterm Infants

2021 ◽  
Author(s):  
Rebecca Dryer ◽  
Anand Salem ◽  
Vivek Saroha ◽  
Rachel Greenberg ◽  
Matthew Rysavy ◽  
...  
Keyword(s):  
Prediction Model ◽  
Clinical Decision Making ◽  
Model Performance ◽  
Clinical Decision ◽  
Success Prediction ◽  
Receiver Operating Curve ◽  
Potential Utility ◽  
Single Center ◽  
Very Preterm Infants ◽  
Very Preterm

Abstract ObjectiveTo evaluate the performance of a publicly available model predicting extubation success in very preterm infants.Study DesignRetrospective study of infants born < 1250 g at a single center. Model performance evaluated using the area under the receiver operating curve (AUROC) and comparing observed and expected probabilities of extubation success, defined as survival ≥ 5 d without an endotracheal tube.ResultsOf 177 infants, 120 (68%) were extubated successfully. The median (IQR) gestational age was 27 weeks (25–28) and weight at extubation was 915 g (755–1050). The model had acceptable discrimination (AUROC 0.72 [95% CI 0.65–0.80]) and adequate calibration (calibration slope 0.96, intercept − 0.06, mean observed-to-expected difference in probability of extubation success − 0.08 [95% CI -0.01, -0.15]).ConclusionsThe extubation success prediction model has acceptable performance in an external cohort, supporting its potential utility in clinical decision-making. Additional studies are needed to determine if its use can improve outcomes.

scholarly journals Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

2021 ◽  
Vol 20 ◽  
pp. 153303382110246
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
Hyong Woo Moon ◽  
Jaewon Kim ◽  
Chanjung Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Random Forest ◽  
Radical Prostatectomy ◽  
Prediction Model ◽  
Biochemical Recurrence ◽  
Clinical Decision ◽  
Outcome Variable ◽  
Tertiary Hospitals ◽  
Input Variables

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

scholarly journals The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chao Guo ◽  
Ya-yue Gao ◽  
Qian-qian Ju ◽  
Chun-xia Zhang ◽  
Ming Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Hox Genes ◽  
Expression Profiles ◽  
Penalized Regression ◽  
Diagnostic Utility ◽  
Lasso Regression ◽  
Hub Genes ◽  
Npm1 Mutation ◽  
Genetic Abnormalities

Abstract Background The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA. Methods We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and ‘WGCNA’ package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by ‘glmnet’ package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. Results A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the ‘lightyellow’ module for NPM1 mutation, the ‘saddlebrown’ module for RUNX1 mutation, the ‘lightgreen’ module for TP53 mutation). ORA revealed that the ‘lightyellow’ module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The ‘saddlebrown’ module was enriched in immune response process. And the ‘lightgreen’ module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743–0.79). Conclusions We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

scholarly journals POS1439 A MULTI-PREDICTOR MODEL TO PREDICT RISK OF SCLERODERMA RENAL CRISIS IN SYSTEMIC SCLEROSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1004.1-1004
Author(s):  
D. Xu ◽  
R. Mu
Keyword(s):  
Systemic Sclerosis ◽  
Prediction Model ◽  
Renal Involvement ◽  
Area Under The Curve ◽  
Individual Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Scleroderma Renal Crisis ◽  
Lasso Regression ◽  
Patients At Risk ◽  
Renal Crisis

Background:Scleroderma renal crisis (SRC) is a life-threatening syndrome. The early identification of patients at risk is essential for timely treatment to improve the outcome[1].Objectives:We aimed to provide a personalized tool to predict risk of SRC in systemic sclerosis (SSc).Methods:We tried to set up a SRC prediction model based on the PKUPH-SSc cohort of 302 SSc patients. The least absolute shrinkage and selection operator (Lasso) regression was used to optimize disease features. Multivariable logistic regression analysis was applied to build a SRC prediction model incorporating the features of SSc selected in the Lasso regression. Then, a multi-predictor nomogram combining clinical characteristics was constructed and evaluated by discrimination and calibration.Results:A multi-predictor nomogram for evaluating the risk of SRC was successfully developed. In the nomogram, four easily available predictors were contained including disease duration <2 years, cardiac involvement, anemia and corticosteroid >15mg/d exposure. The nomogram displayed good discrimination with an area under the curve (AUC) of 0.843 (95% CI: 0.797-0.882) and good calibration.Conclusion:The multi-predictor nomogram for SRC could be reliably and conveniently used to predict the individual risk of SRC in SSc patients, and be a step towards more personalized medicine.References:[1]Woodworth TG, Suliman YA, Li W, Furst DE, Clements P (2016) Scleroderma renal crisis and renal involvement in systemic sclerosis. Nat Rev Nephrol 12 (11):678-91.Disclosure of Interests:None declared

Evaluation of predictive model performance of an existing model in the presence of missing data

2021 ◽  
Author(s):  
Pin Li ◽  
Jeremy M. G. Taylor ◽  
Daniel E. Spratt ◽  
R. Jeffery Karnes ◽  
Matthew J. Schipper
Keyword(s):  
Missing Data ◽  
Predictive Model ◽  
Model Performance

A Prediction Model for Positive Infant Meconium and Urine Drug Tests

2020 ◽  
Author(s):  
Elizabeth A. Simpson ◽  
David A. Skoglund ◽  
Sarah E. Stone ◽  
Ashley K. Sherman
Keyword(s):  
Prediction Model ◽  
Drug Testing ◽  
Clinical Decision Making ◽  
Drugs Of Abuse ◽  
Clinical Decision ◽  
P Value ◽  
Test Characteristics ◽  
Drug Test ◽  
Urine Drug ◽  
Urine Drug Test

Objective This study aimed to determine the factors associated with positive infant drug screen and create a shortened screen and a prediction model. Study Design This is a retrospective cohort study of all infants who were tested for drugs of abuse from May 2012 through May 2014. The primary outcome was positive infant urine or meconium drug test. Multivariable logistic regression was used to identify independent risk factors. A combined screen was created, and test characteristics were analyzed. Results Among the 3,861 live births, a total of 804 infants underwent drug tests. Variables associated with having a positive infant test were (1) positive maternal urine test, (2) substance use during pregnancy, (3) ≤ one prenatal visit, and (4) remote substance abuse; each p-value was less than 0.0001. A model with an indicator for having at least one of these four predictors had a sensitivity of 94% and a specificity of 69%. Application of this screen to our population would have decreased drug testing by 57%. No infants had a positive urine drug test when their mother's urine drug test was negative. Conclusion This simplified screen can guide clinical decision making for determining which infants should undergo drug testing. Infant urine drug tests may not be needed when a maternal drug test result is negative. Key Points

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