scholarly journals A nomogram for Antenatal Estimation of Cephalopelvic Disproportion in Primiparous Women based on MRI Measurements

2022 ◽  
Author(s):  
Cheng Chen ◽  
Mengmeng Yang ◽  
Weizeng Zheng ◽  
Yuan Chen ◽  
tian dong ◽  
...  
Keyword(s):  
Cesarean Delivery ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
University Teaching ◽  
Abdominal Circumference ◽  
Maternal Body Mass Index ◽  
Cephalopelvic Disproportion ◽  
Decision Curve Analysis ◽  
Mri Measurements ◽  
Primiparous Women

Objective: To develop and validate a predictive model assessing the risk of cesarean delivery in primiparous women based on the findings of magnetic resonance imaging (MRI) studies. Design: Observational study Setting: University teaching hospital. Population: 168 primiparous women with clinical findings suggestive of cephalopelvic disproportion. Methods: All women underwent MRI measurements prior to the onset of labor. A nomogram model to predict the risk of cesarean delivery was proposed based on the MRI data. The discrimination of the model was calculated by the area under the receiver operating characteristic curve (AUC) and calibration was assessed by calibration plots. The decision curve analysis was applied to evaluate the net clinical benefit. Main Outcome Measures: Cesarean delivery. Results: A total of 88 (58.7%) women achieved vaginal delivery, and 62 (41.3%) required cesarean section caused by obstructed labor. In multivariable modeling, the maternal body mass index before delivery, induction of labor, bilateral femoral head distance, obstetric conjugate, fetal head circumference and fetal abdominal circumference were significantly associated with the likelihood of cesarean delivery. The discrimination calculated as the AUC was 0.845 (95% CI: 0.783-0.908; P < 0.001). The sensitivity and specificity of the nomogram model were 0.918 and 0.629, respectively. The model demonstrated satisfactory calibration. Moreover, the decision curve analysis proved the superior net benefit of the model compared with each factor included. Conclusion: Our study provides a nomogram model that can accurately identify primiparous women at risk of cesarean delivery caused by cephalopelvic disproportion based on the MRI measurements.

scholarly journals Established the first clinical prediction model regarding the risk of hyperuricemia in IgA nephropathy

2021 ◽  
Author(s):  
Yin-Hong Geng ◽  
Zhe Zhang ◽  
Jun-Jun Zhang ◽  
Bo Huang ◽  
Zui-Shuang Guo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Iga Nephropathy ◽  
Interstitial Fibrosis ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Curve Analysis ◽  
University Hospital ◽  
Prediction Ability ◽  
Decision Curve Analysis ◽  
Incidence Risk

Abstract Objective. To construct a novel nomogram model that predicts the risk of hyperuricemia incidence in IgA nephropathy (IgAN) . Methods. Demographic and clinicopathological characteristics of 1184 IgAN patients in the First Affiliated Hospital of Zhengzhou University Hospital were collected. Univariate analysis and multivariate logistic regression were used to screen out hyperuricemia risk factors. The risk factors were used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Results. Independent predictors for hyperuricemia incidence risk included sex, hypoalbuminemia, hypertriglyceridemia, blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), 24-hour urinaryprotein (24h TP), Gross and tubular atrophy/interstitial fibrosis (T). The nomogram model exhibited moderate prediction ability with an AUC of 0.834 ((95% CI 0.804–0.864)). The AUC from validation reached 0.787 (95% CI 0.736-0.839). The decision curve analysis displayed that the hyperuricemia risk nomogram was clinically applicable.Conclusion. Our novel and simple nomogram containing 8 factors may be useful in predicting hyperuricemia incidence risk in IgAN.

scholarly journals Multiparameter MRI Radiomics Model Predicts Preoperative Peritoneal Carcinomatosis in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao Yu Yu ◽  
Jialiang Ren ◽  
Yushan Jia ◽  
Hui Wu ◽  
Guangming Niu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ovarian Cancer ◽  
Peritoneal Carcinomatosis ◽  
Calibration Curve ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Imaging Features ◽  
Decision Curve Analysis ◽  
Dce Mri

ObjectivesTo evaluate the predictive value of radiomics features based on multiparameter magnetic resonance imaging (MP-MRI) for peritoneal carcinomatosis (PC) in patients with ovarian cancer (OC).MethodsA total of 86 patients with epithelial OC were included in this retrospective study. All patients underwent FS-T2WI, DWI, and DCE-MRI scans, followed by total hysterectomy plus omentectomy. Quantitative imaging features were extracted from preoperative FS-T2WI, DWI, and DCE-MRI images, and feature screening was performed using a minimum redundancy maximum correlation (mRMR) and least absolute shrinkage selection operator (LASSO) methods. Four radiomics models were constructed based on three MRI sequences. Then, combined with radiomics characteristics and clinicopathological risk factors, a multi-factor Logistic regression method was used to construct a radiomics nomogram, and the performance of the radiomics nomogram was evaluated by receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis.ResultsThe radiomics model from the MP-MRI combined sequence showed a higher area under the curve (AUC) than the model from FS-T2WI, DWI, and DCE-MRI alone (0.846 vs. 0.762, 0.830, 0.807, respectively). The radiomics nomogram (AUC=0.902) constructed by combining radiomics characteristics and clinicopathological risk factors showed a better diagnostic effect than the clinical model (AUC=0.858) and the radiomics model (AUC=0.846). The decision curve analysis shows that the radiomics nomogram has good clinical application value, and the calibration curve also proves that it has good stability.ConclusionRadiomics nomogram based on MP-MRI combined sequence showed good predictive accuracy for PC in patients with OC. This tool can be used to identify peritoneal carcinomatosis in OC patients before surgery.

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 Pattern of primary caesarean deliveries in a Nigerian tertiary hospital

2021 ◽  
Author(s):  
Abimbola T. Ottun ◽  
Chinonye H. Okoye ◽  
Adeniyi A. Adewunmi ◽  
Faosat O. Jinadu ◽  
Ayokunle M. Olumodeji
Keyword(s):  
Caesarean Section ◽  
Tertiary Hospital ◽  
Cross Sectional Study ◽  
University Teaching ◽  
State University ◽  
Foetal Distress ◽  
Hypertensive Disease ◽  
Cross Sectional ◽  
Cephalopelvic Disproportion ◽  
Primiparous Women

Background: Primary caesarean section (CS) has become a major driver of the steadily rising total caesarean rate. This study determined the primary CS rate, pattern and associated factors.Methods: It was a retrospective, hospital-based cross-sectional study of 645 pregnant women who had primary caesarean section over a 3-year period in Lagos state university teaching hospital, Lagos, Nigeria. Data obtained were expressed in frequency and percentages.Results: Primary CS accounted for more than 50% of all the CS done during the study period with a primary CS rate of 16.7% and total CS rate was 30.6%. Primary CS was commonest among women of age group 30-39years (50.1%) and women with no prior parous experience (58.6%). The commonest indication for primary CS was poor progress in labour due to cephalopelvic disproportion, which occurred in 170 women (26.4%), followed by suspected foetal distress in 94 women (14.6%) and hypertensive disease in pregnancy in 91 women (14.1%). Post-operative wound infection and/or dehiscence was the most prevalent post-operative complication occurring in 12.1% of women who had primary CS.Conclusions: Primary CS rate is increasing and relatively more common among primiparous women. Cephalopelvic disproportion, suspected foetal distress and hypertensive disorders of pregnancy are the leading indications for primary CS. 

Machine Learning Models for Risk Prediction of Lymph Nodes Metastasis in Non-Small Cell Lung Cancer: Development and Validation Study

2020 ◽  
Author(s):  
Miaochun Cai ◽  
Dong Shen ◽  
Zhihao Li ◽  
Jianmeng Zhou ◽  
Yingjun Chen ◽  
...  
Keyword(s):  
Random Forest ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Model Performance ◽  
Small Cell ◽  
Curve Analysis ◽  
Selection Model ◽  
Full Model ◽  
Small Cell Lung ◽  
Decision Curve Analysis

Abstract Background: To develop and validate machine learning models for risk prediction of lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) using clinicopathologic parameters and immunohistochemical features. Methods: From January 2010 to December 2019, 639 patients' data were continuously collected in Nanfang Hospital. We exacted immunohistochemical features and clinicopathological features from the electronic medical records of patients. We established two models (a full model and a selection model) and implemented three algorithms (random forest, support vector machine and penalized logistic regression). The model performance was evaluated in terms of discrimination (receiver operating characteristic curve (AUC)), calibration, and decision curve analysis. Results: AUROC (area under receiver operating characteristic curve) analysis (also calibration curves) showed that the selection model (AUC values for training and testing, 0.843 and 0.840 respectively) and the full model constructed using random forest (AUC values for training and testing, 0.855 and 0.863 respectively) performed best among all models. Decision curve analysis depicted that the full model and the selection model using random forest was clinically useful. The model performance of the full model and the selection model were comparable. Conclusion: The random forest model using clinicopathologic- immunohistochemical features can predict the LNM of NSCLC patients.

scholarly journals C-reactive protein as a triage tool for adults with presumptive pulmonary tuberculosis in South Africa: a prospective cohort study

2021 ◽  
Author(s):  
Claire J Calderwood ◽  
Byron WP Reeve ◽  
Tiffeney Mann ◽  
Zaida Palmer ◽  
Georgina Nyawo ◽  
...  
Keyword(s):  
South Africa ◽  
Diagnostic Accuracy ◽  
Clinical Utility ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Hiv Status ◽  
C Reactive Protein ◽  
Decision Curve Analysis ◽  
Reactive Protein ◽  
Triage Test

Background: Identification of an accurate, low-cost triage test for pulmonary TB among people presenting to healthcare facilities is an urgent global research priority. We assessed the diagnostic accuracy and clinical utility of C-reactive protein (CRP) for TB triage among symptomatic adult outpatients, irrespective of HIV status. Methods: We prospectively enrolled adults reporting at least one (for people with HIV) or two (for people without HIV) symptoms of cough, fever, night sweats, or weight loss at two TB clinics in Cape Town, South Africa. Participants provided sputum for culture and Xpert MTB/RIF Ultra. We evaluated the diagnostic accuracy of CRP (measured using a laboratory-based assay) against a TB-culture reference standard as the area under the receiver operating characteristic curve (AUROC) and sensitivity and specificity at pre-specified thresholds. We assessed clinical utility using decision curve analysis, benchmarked against WHO recommendations. Results: Of 932 included individuals, 255 (27%) had culture-confirmed TB and 389 (42%) were living with HIV. CRP demonstrated an AUROC of 0.80 (95% confidence interval 0.77-0.83), with sensitivity 93% (89-95%) and specificity 54% (50-58%) using a primary cut-off of ≥10mg/L. Performance was similar among people with HIV to those without. In decision curve analysis, CRP-based triage offered greater clinical utility than confirmatory testing for all up to a number willing to test threshold of 20 confirmatory tests per true positive TB case diagnosed. Conclusions: CRP approached the WHO-defined minimum performance for a TB triage test and showed evidence of clinical utility among symptomatic outpatients, irrespective of HIV status.

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 Development of a hemoptysis risk prediction model for patients following CT-guided transthoracic lung biopsy

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Saibin Wang ◽  
Ke Dong ◽  
Wei Chen
Keyword(s):  
Predictive Model ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Tertiary Hospital ◽  
Curve Analysis ◽  
Lasso Regression ◽  
Transthoracic Needle Biopsy ◽  
Ct Guided ◽  
Decision Curve Analysis ◽  
Preoperative Prediction

Abstract Background Computed tomography-guided transthoracic needle biopsy (CT-TNB) is a widely used method for diagnosis of lung diseases; however, CT-TNB-induced bleeding is usually unexpected and this complication can be life-threatening. The aim of this study was to develop and validate a predictive model for hemoptysis following CT-TNB. Methods A total of 436 consecutive patients who underwent CT-TNB from June 2016 to December 2017 at a tertiary hospital in China were divided into derivation (n = 307) and validation (n = 129) cohorts. We used LASSO regression to reduce the data dimension, select variables and determine which predictors were entered into the model. Multivariate logistic regression was used to develop the predictive model. The discrimination capacity of the model was evaluated by the area under the receiver operating characteristic curve (AUROC), the calibration curve was used to test the goodness-of-fit of the model, and decision curve analysis was conducted to assess its clinical utility. Results Five predictive factors (diagnosis of the lesion, lesion characteristics, lesion diameter, procedure time, and puncture distance) selected by LASSO regression analysis were applied to construct the predictive model. The AUC was 0.850 (95% confidence interval [CI], 0.808–0.893) in the derivation, and 0.767 (95% CI, 0.684–0.851) in the validation. The model showed good calibration consistency (p > 0.05). Moreover, decision curve analysis indicated its clinical usefulness. Conclusion We established a predictive model that incorporates lesion features and puncture parameters, which may facilitate the individualized preoperative prediction of hemoptysis following CT-TNB.

scholarly journals Establishment and validation of prognosis model for patients with cerebral contusion

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yufeng Zhu ◽  
Xiaoqing Jin ◽  
Lulu Xu ◽  
Pei Han ◽  
Shengwu Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Third Ventricle ◽  
Curve Analysis ◽  
Imaging Features ◽  
Lasso Regression ◽  
Cerebral Contusion ◽  
Decision Curve Analysis ◽  
Training Cohort

Abstract Background And Objective Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). In this study, the baseline data, imaging features and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a prediction model of nomogram to evaluate the clinical outcomes of patients. Methods A total of 426 patients with cerebral contusion (CC) admitted to the People’s Hospital of Qinghai Province and Affiliated Hospital of Qingdao University from January 2018 to January 2021 were included in this study, We randomly divided the cohort into a training cohort (n = 284) and a validation cohort (n = 142) with a ratio of 2:1.At Least absolute shrinkage and selection operator (Lasso) regression were used for screening high-risk factors affecting patient prognosis and development of the predictive model. The identification ability and clinical application value of the prediction model were analyzed through the analysis of receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results Twelve independent prognostic factors, including age, Glasgow Coma Score (GCS), Basal cistern status, Midline shift (MLS), Third ventricle status, intracranial pressure (ICP) and CT grade of cerebral edema,etc., were selected by Lasso regression analysis and included in the nomogram. The model showed good predictive performance, with a C index of (0.87, 95% CI, 0.026–0.952) in the training cohort and (0.93, 95% CI, 0.032–0.965) in the validation cohort. Clinical decision curve analysis (DCA) also showed that the model brought high clinical benefits to patients. Conclusion This study established a high accuracy of nomogram model to predict the prognosis of patients with CC, its low cost, easy to promote, is especially applicable in the acute environment, at the same time, CSF-glucose/lactate ratio(C-G/L), volume of contusion, and mean CT values of edema zone, which were included for the first time in this study, were independent predictors of poor prognosis in patients with CC. However, this model still has some limitations and deficiencies, which require large sample and multi-center prospective studies to verify and improve our results.

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