scholarly journals Development and validation of models to predict cesarean delivery among low-risk nulliparous women at term: a retrospective study in China

2020 ◽  
Author(s):  
Fangcan Sun ◽  
Bing Han ◽  
Fangfang Wu ◽  
Qianqian Shen ◽  
Minhong Shen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Delivery ◽  
Prediction Models ◽  
External Validation ◽  
Curve Analysis ◽  
Low Risk ◽  
Calibration Plot ◽  
Fetal Sex ◽  
Decision Curve Analysis ◽  
Nulliparous Women

Abstract Background A prediction algorithm to identify women with high risk of an emergency cesarean could help reduce morbidity and mortality associated with labor. The objective of the present study was to derive and validate a simple model to predict intrapartum cesarean delivery for low-risk nulliparous women in Chinese population.Methods We conducted a retrospective cohort study of low-risk nulliparous women with singleton, term, cephalic pregnancies. A predictive model for cesarean delivery was derived using univariate and multivariable logistic regression from the hospital of the First Affiliated Hospital of Soochow University. External validation of the prediction model was then performed using the data from Sihong county People’s Hospital. A new nomogram was established based on the development cohort to predict the cesarean. The ROC curve, calibration plot and decision curve analysis were used to assess the predictive performance.Results The intrapartum cesarean delivery rates in the development cohort and the external validation cohort were 8.79% (576/6,551) and 7.82% (599/7,657). Multivariable logistic regression analysis showed that maternal age, height, BMI, weight gained during pregnancy, gestational age, induction method, meconium-stained amniotic fluid and neonatal sex were independent factors affecting cesarean outcome. We had established two prediction models according to fetal sex was involved or not. The AUC was 0.782 and 0.774, respectively. The two prediction models were well-calibrated with Hosmer-Lemeshow test P=0.263 and P=0.817, respectively. Decision curve analysis demonstrated that two models had clinical application value, and they provided greatest net benefit between threshold probabilities of 4% to 60%. And internal validation using Bootstrap method demonstrated similar discriminatory ability. We external validated the model involving fetal sex, for which the AUC was 0.775, while the slope and intercept of the calibration plot were 0.979 and 0.004, respectively. On the external validation set, another model had an AUC of 0.775 and a calibration slope of 1.007. The online web server was constructed based on the nomogram for convenient clinical use.Conclusions Both two models established by these factors have good prediction efficiency and high accuracy, which can provide the reference for clinicians to guide pregnant women to choose an appropriate delivery mode.

Development And Validation Of Models To Predict Cesarean Delivery Among Low-Risk Nulliparous Women At Term: A Retrospective Study In China

2020 ◽  
Author(s):  
Fangcan Sun ◽  
Bing Han ◽  
Fangfang Wu ◽  
Qianqian Shen ◽  
Minhong Shen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Cesarean Delivery ◽  
Prediction Models ◽  
External Validation ◽  
Curve Analysis ◽  
Low Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Nulliparous Women

Abstract Background: Cesarean delivery after failure of trial of labor is associated with adverse maternal and perinatal outcomes. A prediction algorithm to identify women with high risk of an emergency cesarean could help reduce morbidity and mortality associated with labor. The objective of the present study was to derive and validate a simple model to predict cesarean delivery for low-risk nulliparous women in Chinese population.Methods: This retrospective study analyzed the low-risk nulliparous women with singleton cephalic full-term fetus delivered in two medical centers. After the clinical data of the women who delivered at the tertiary referral center (n=6 551) was collected and was used univariate and multivariable logistic regression analysis, the prediction model was fitted. We performed external validation using data from nulliparous who delivered from another hospital(secondary referral center, n=7 657). A new nomogram was established based on the development cohort to predict the cesarean. The ROC curve, calibration plot and decision curve analysis were used to assess the predictive performance. Results: The cesarean delivery rates in the development cohort and the external validation cohort were 8.79% (576/6 551) and 7.82% (599/7 657). Multivariable logistic regression analysis showed that maternal age, height, BMI, weight gained during pregnancy, gestational age, induction method, meconium-stained amniotic fluid and neonatal sex were independent factors affecting cesarean outcome. Because sex of the fetuses were unknown until they born(China's Fertility Policy), we established two prediction models according to fetal sex was involved or not. The AUC was 0.782 and 0.774, respectively. The Hosmer-Lemeshow goodness-of-fit test showed that these two models fitted well. Decision curve analysis demonstrated that the models were clinically useful. And internal validation using Bootstrap method showed that these prediction models perform well. On the external validation set, the AUC were 0.775 and 0.775, respectively. The calibration plots for the probability of cesarean showed a good correlation. The online web server was constructed based on the nomogram for convenient clinical use.Conclusions: Both two models established by these factors have good prediction efficiency and high accuracy, which can provide the reference for clinicians to guide pregnant women to choose an appropriate delivery mode.

scholarly journals Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

2020 ◽  
Author(s):  
Ruyi Zhang ◽  
Mei Xu ◽  
Xiangxiang Liu ◽  
Miao Wang ◽  
Qiang Jia ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
External Validation ◽  
Cancer Staging ◽  
Curve Analysis ◽  
Calibration Plot ◽  
Net Benefit ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Validation Set ◽  
Good Calibration

Abstract Objectives To develop a clinically predictive nomogram model which can maximize patients’ net benefit in terms of predicting the prognosis of patients with thyroid carcinoma based on the 8th edition of the AJCC Cancer Staging method. MethodsWe selected 134,962 thyroid carcinoma patients diagnosed between 2004 and 2015 from SEER database with details of the 8th edition of the AJCC Cancer Staging Manual and separated those patients into two datasets randomly. The first dataset, training set, was used to build the nomogram model accounting for 80% (94,474 cases) and the second dataset, validation set, was used for external validation accounting for 20% (40,488 cases). Then we evaluated its clinical availability by analyzing DCA (Decision Curve Analysis) performance and evaluated its accuracy by calculating AUC, C-index as well as calibration plot.ResultsDecision curve analysis showed the final prediction model could maximize patients’ net benefit. In training set and validation set, Harrell’s Concordance Indexes were 0.9450 and 0.9421 respectively. Both sensitivity and specificity of three predicted time points (12 Months,36 Months and 60 Months) of two datasets were all above 0.80 except sensitivity of 60-month time point of validation set was 0.7662. AUCs of three predicted timepoints were 0.9562, 0.9273 and 0.9009 respectively for training set. Similarly, those numbers were 0.9645, 0.9329, and 0.8894 respectively for validation set. Calibration plot also showed that the nomogram model had a good calibration.ConclusionThe final nomogram model provided with both excellent accuracy and clinical availability and should be able to predict patients’ survival probability visually and accurately.

Novel Model Predicts Diabetic Nephropathy in Type 2 Diabetes

2019 ◽  
Vol 51 (2) ◽  
pp. 130-138 ◽  
Author(s):  
Shimin Jiang ◽  
Jinying Fang ◽  
Tianyu Yu ◽  
Lin Liu ◽  
Guming Zou ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Nephropathy ◽  
Kidney Disease ◽  
External Validation ◽  
Curve Analysis ◽  
Calibration Plot ◽  
The Novel ◽  
Decision Curve Analysis ◽  
Novel Model

Background: Clinical indicators for accurately distinguishing diabetic nephropathy (DN) from non-diabetic renal disease in type 2 diabetes (T2D) are lacking. This study aimed to develop and validate a nomogram for predicting DN in T2D patients with kidney disease. Methods: A total of 302 consecutive patients with T2D who underwent renal biopsy at China-Japan Friendship Hospital between January 2014 and June 2019 were included in the study. The data were randomly split into a training set containing 70% of the patients (n = 214) and a validation set containing the remaining 30% of patients (n = 88). Multivariable logistic regression analyses were applied to develop a prediction nomogram incorporating the candidates selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using a concordance index (C-index), calibration plot, and decision curve analysis. Both internal and external validations were assessed. Results: A multivariable model that included gender, diabetes duration, diabetic retinopathy, hematuria, glycated hemoglobin A1c, anemia, blood pressure, urinary protein excretion, and estimated glomerular filtration rate was represented as the nomogram. The model demonstrated very good discrimination with a C-index of 0.934 (95% CI 0.904–0.964). The calibration plot diagram of predicted probabilities against observed DN rates indicated excellent concordance. The C-index value was 0.91 for internal validation and 0.875 for external validation. Decision curve analysis demonstrated that the novel nomogram was clinically useful. Conclusion: The novel model was very useful for predicting DN in patients with T2D and kidney disease, and thereby could be used by clinicians either in triage or as a replacement for biopsy.

scholarly journals External validation of nomogram for predicting malignant intraductal papillary mucinous neoplasm (IPMN): from the theory to the clinical practice using the decision curve analysis model

2021 ◽  
Author(s):  
Riccardo Casadei ◽  
Claudio Ricci ◽  
Carlo Ingaldi ◽  
Alessandro Cornacchia ◽  
Marina Migliori ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Pancreatic Resection ◽  
External Validation ◽  
Curve Analysis ◽  
Clinical Usefulness ◽  
Analysis Model ◽  
Net Benefit ◽  
Threshold Probability ◽  
Branch Duct ◽  
Decision Curve Analysis

AbstractThe management of IPMNs is a challenging and controversial issue because the risk of malignancy is difficult to predict. The present study aimed to assess the clinical usefulness of two preoperative nomograms for predicting malignancy of IPMNs allowing their proper management. Retrospective study of patients affected by IPMNs. Two nomograms, regarding main (MD) and branch duct (BD) IPMN, respectively, were evaluated. Only patients who underwent pancreatic resection were collected to test the nomograms because a pathological diagnosis was available. The analysis included: 1-logistic regression analysis to calibrate the nomograms; 2-decision curve analysis (DCA) to test the nomograms concerning their clinical usefulness. 98 patients underwent pancreatic resection. The logistic regression showed that, increasing the score of both the MD-IPMN and BD-IPMN nomograms, significantly increases the probability of IPMN high grade or invasive carcinoma (P = 0.029 and P = 0.033, respectively). DCA of MD-IPMN nomogram showed that there were no net benefits with respect to surgical resection in all cases. DCA of BD-IPMN nomogram, showed a net benefit only for threshold probability between 40 and 60%. For these values, useless pancreatic resection should be avoided in 14.8%. The two nomograms allowed a reliable assessment of the malignancy rate. Their clinical usefulness is limited to BD-IPMN with threshold probability of malignancy of 40–60%, in which the patients can be selected better than the “treat all” strategy.

scholarly journals Validation of a nomogram to predict survival in patients with ESCC based on examined lymph nodes

2020 ◽  
Author(s):  
Peng Liao ◽  
Li Cao ◽  
Hang Chen ◽  
Shui-Zi Pang
Keyword(s):  
Lymph Nodes ◽  
External Validation ◽  
Tnm Staging ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Calibration Plot ◽  
Decision Curve Analysis ◽  
Adaptive Partitioning ◽  
Good Agreement

Abstract Background: Current the number of examined LNs are controversial in predicting the survival of ESCC. We aimed to develop an alternative LN-classification-based nomogram to individualize ESCC prognosis.Methods: Using the data of patients diagnosed with ESCC from SEER database between 2004 and 2015, we determined the cut-off values for the number of LNs examined via the K-adaptive partitioning (KAPS) algorithm. A nomogram predicting the survival of ESCC was performed, internally and externally validated, and evaluated by calibration plot, C-index, and decision curve analysis, and compared to the 7th TNM stage.Results: Totally, we included 3629 patients with detailed information. The optimal cut-off for examined LN number was 8. The C-index for the nomogram was higher than the 7th TNM staging (internal: 0.708; 95%CI, 0.678-0.753 vs 0.601; 95%CI, 0.573-0.656, P<0.001; external: 0.687; 95%CI, 0.601-0.734 vs 0.605; 95%CI, 0.563-0.659, P<0.001). Additionally, the nomogram showed good agreement between internal and external validation. DCA analysis showed no matter in the internal cohort or external cohort, the nomogram showed a greater benefit across the period of follow-up compared to 7th TNM stage.Conclusion: We found examining LNs that was more than 8 benefited for prognosis of patients. Based on these, a nomogram with greater benefit for predicting survival of EC patients than TNM staging was constructed.

scholarly journals Development and Validation of the PREMM5 Model for Comprehensive Risk Assessment of Lynch Syndrome

2017 ◽  
Vol 35 (19) ◽  
pp. 2165-2172 ◽  
Author(s):  
Fay Kastrinos ◽  
Hajime Uno ◽  
Chinedu Ukaegbu ◽  
Carmelita Alvero ◽  
Ashley McFarland ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Genetic Testing ◽  
Lynch Syndrome ◽  
Prediction Models ◽  
Risk Estimation ◽  
Characteristic Curve ◽  
External Validation ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
And Performance

Purpose Current Lynch syndrome (LS) prediction models quantify the risk to an individual of carrying a pathogenic germline mutation in three mismatch repair (MMR) genes: MLH1, MSH2, and MSH6. We developed a new prediction model, PREMM5, that incorporates the genes PMS2 and EPCAM to provide comprehensive LS risk assessment. Patients and Methods PREMM5 was developed to predict the likelihood of a mutation in any of the LS genes by using polytomous logistic regression analysis of clinical and germline data from 18,734 individuals who were tested for all five genes. Predictors of mutation status included sex, age at genetic testing, and proband and family cancer histories. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUC), and clinical impact was determined by decision curve analysis; comparisons were made to the existing PREMM1,2,6 model. External validation of PREMM5 was performed in a clinic-based cohort of 1,058 patients with colorectal cancer. Results Pathogenic mutations were detected in 1,000 (5%) of 18,734 patients in the development cohort; mutations included MLH1 (n = 306), MSH2 (n = 354), MSH6 (n = 177), PMS2 (n = 141), and EPCAM (n = 22). PREMM5 distinguished carriers from noncarriers with an AUC of 0.81 (95% CI, 0.79 to 0.82), and performance was similar in the validation cohort (AUC, 0.83; 95% CI, 0.75 to 0.92). Prediction was more difficult for PMS2 mutations (AUC, 0.64; 95% CI, 0.60 to 0.68) than for other genes. Performance characteristics of PREMM5 exceeded those of PREMM1,2,6. Decision curve analysis supported germline LS testing for PREMM5 scores ≥ 2.5%. Conclusion PREMM5 provides comprehensive risk estimation of all five LS genes and supports LS genetic testing for individuals with scores ≥ 2.5%. At this threshold, PREMM5 provides performance that is superior to the existing PREMM1,2,6 model in the identification of carriers of LS, including those with weaker phenotypes and individuals unaffected by cancer.

scholarly journals Scoring system development and validation for prediction choledocholithiasis before open cholecystectomy

2015 ◽  
Vol 143 (11-12) ◽  
pp. 681-687 ◽  
Author(s):  
Tomislav Pejovic ◽  
Miroslav Stojadinovic
Keyword(s):  
Bile Duct ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Open Cholecystectomy ◽  
Univariate Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Scoring Model ◽  
Validation Set

Introduction. Accurate precholecystectomy detection of concurrent asymptomatic common bile duct stones (CBDS) is key in the clinical decision-making process. The standard preoperative methods used to diagnose these patients are often not accurate enough. Objective. The aim of the study was to develop a scoring model that would predict CBDS before open cholecystectomy. Methods. We retrospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography) data for 313 patients at the department of General Surgery at Gornji Milanovac from 2004 to 2007. The patients were divided into a derivation (213) and a validation set (100). Univariate and multivariate regression analysis was used to determine independent predictors of CBDS. These predictors were used to develop scoring model. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve (AUC), calibration and clinical utility using decision curve analysis. Results. In a univariate analysis, seven risk factors displayed significant correlation with CBDS. Total bilirubin, alkaline phosphatase and bile duct dilation were identified as independent predictors of choledocholithiasis. The resultant total possible score in the derivation set ranged from 7.6 to 27.9. Scoring model shows good discriminatory ability in the derivation and validation set (AUC 94.3 and 89.9%, respectively), excellent accuracy (95.5%), satisfactory calibration in the derivation set, similar Brier scores and clinical utility in decision curve analysis. Conclusion. Developed scoring model might successfully estimate the presence of choledocholithiasis in patients planned for elective open cholecystectomy.

scholarly journals Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Suyu Wang ◽  
Yue Yu ◽  
Wenting Xu ◽  
Xin Lv ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
External Validation ◽  
Predictive Performance ◽  
Information Criterion ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Concordance Index ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis

Abstract Background The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram. Methods 25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ2 test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis. Results NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ2 test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability. Conclusions We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

P9 Novel use of CHA2DS2VASC score to select patients to undergo repeat atrial fibrillation screening

2020 ◽  
Vol 41 (Supplement_1) ◽  
Author(s):  
W Sun ◽  
B P Y Yan
Keyword(s):  
Atrial Fibrillation ◽  
High Risk ◽  
Clinical Outcomes ◽  
Cross Validation ◽  
Risk Group ◽  
Curve Analysis ◽  
Low Risk ◽  
Decision Curve Analysis ◽  
Risk Patients

Abstract Background We have previously demonstrated unselected screening for atrial fibrillation (AF) in patients ≥65 years old in an out-patient setting yielded 1-2% new AF each time screen-negative patients underwent repeated screening at 12 to 18 month interval. Selection criteria to identify high-risk patients for repeated AF screening may be more efficient than repeat screening on all patients. Aims This study aimed to validate CHA2DS2VASC score as a predictive model to select target population for repeat AF screening. Methods 17,745 consecutive patients underwent 24,363 index AF screening (26.9% patients underwent repeated screening) using a handheld single-lead ECG (AliveCor) from Dec 2014 to Dec 2017 (NCT02409654). Adverse clinical outcomes to be predicted included (i) new AF detection by repeated screening; (ii) new AF clinically diagnosed during follow-up and (ii) ischemic stroke/transient ischemic attack (TIA) during follow-up. Performance evaluation and validation of CHA2DS2VASC score as a prediction model was based on 15,732 subjects, 35,643 person-years of follow-up and 765 outcomes. Internal validation was conducted by method of k-fold cross-validation (k = n = 15,732, i.e., Leave-One-Out cross-validation). Performance measures included c-index for discriminatory ability and decision curve analysis for clinical utility. Risk groups were defined as ≤1, 2-3, or ≥4 for CHA2DS2VASC scores. Calibration was assessed by comparing proportions of actual observed events. Results CHA2DS2VASC scores achieved acceptable discrimination with c-index of 0.762 (95%CI: 0.746-0.777) for derivation and 0.703 for cross-validation. Decision curve analysis showed the use of CHA2DS2VASC to select patients for rescreening was superior to rescreening all or no patients in terms of net benefit across all reasonable threshold probability (Figure 1, left). Predicted and observed probabilities of adverse clinical outcomes progressively increased with increasing CHA2DS2VASC score (Figure 1, right): 0.7% outcome events in low-risk group (CHA2DS2VASC ≤1, predicted prob. ≤0.86%), 3.5% intermediate-risk group (CHA2DS2VASC 2-3, predicted prob. 2.62%-4.43%) and 11.3% in high-risk group (CHA2DS2VASC ≥4, predicted prob. ≥8.50%). The odds ratio for outcome events were 4.88 (95%CI: 3.43-6.96) for intermediate-versus-low risk group, and 17.37 (95%CI: 12.36-24.42) for high-versus-low risk group.  Conclusion Repeat AF screening on high-risk population may be more efficient than rescreening all screen-negative individuals. CHA2DS2VASC scores may be used as a selection tool to identify high-risk patients to undergo repeat AF screening. Abstract P9 Figure 1

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