scholarly journals Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

2020 ◽  
Author(s):  
Jessica 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 may inform decision-making around timing of birth to reduce the risk of stillbirth from 35 weeks 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 gestation including 5,188 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 Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (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 assessed 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 may inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for maternity 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.

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.

scholarly journals Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

2020 ◽  
Author(s):  
Jessica 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 timing of birth to reduce the risk of stillbirth from 35 weeks 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 gestation including 5,188 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 Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (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.

scholarly journals Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

2020 ◽  
Author(s):  
Jessica Sexton ◽  
Michael Coory ◽  
Sailesh Kumar ◽  
Gordon Smith ◽  
Adrienne Gordon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Risk Prediction ◽  
Late Pregnancy ◽  
Risk Prediction Model ◽  
Stepwise Logistic Regression ◽  
Past Century ◽  
Reporting Standards ◽  
Robust Method ◽  
Predictive Values

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 timing of birth to reduce the risk of stillbirth from 35 weeks gestation. Methods This is a protocol for a retrospective cohort study of all late-pregnancy births in Australia (1998-2015) from 35 weeks gestation including 7,200 stillbirths among 4.9 million births at an estimated rate of 1.47 stillbirths per 1000 live births. A multivariable logistic regression model will be developed in line with current T ransparent R eporting of a multivariable prediction model for I ndividual P rognosis or D iagnosis (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 assessed through univariate regression analysis. To generate a final model, elimination by backward stepwise logistic regression will be performed. The model will be internally validated using K-fold cross-validation and externally validated using a geographically unique dataset. Overall model performance will be assessed with R 2 in addition calibration and discrimination. Calibration will be visualized using a calibration plot. Discrimination will be measured by the C- statistic and visualized using area underneath the receiver-operator curves (AUROC). 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.

scholarly journals A Nomogram for Predicting In-Hospital Mortality in Patients with Takotsubo Syndrome

2021 ◽  
Author(s):  
Jun Chen ◽  
Yimin Wang ◽  
Xinyang Shou ◽  
Qiang Liu ◽  
Ziwei Mei
Keyword(s):  
Logistic Regression ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Risk Prediction ◽  
Clinical Utility ◽  
Risk Prediction Model ◽  
Multivariate Logistic Regression Model ◽  
Calibration Plot ◽  
Multivariable Logistic Regression Analysis ◽  
Takotsubo Syndrome

Abstract BACKGROUND Despite the large number of studies focus on the prognosis and in-hospital outcomes risk factors of patients with takotsubo syndrome, there was still lack of utility and visual risk prediction model for predicting the in-hospital mortality of patients with takotsubo syndrome. OBJECTIVES Our study aimed to establish a utility risk prediction model for the prognosis of in-hospital patients with takotsubo syndrome (TTS). METHODS The study is a retrospective cohort study. Model of in-hospital mortality of TTS patients was developed by multivariable logistic regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. The clinical utility of the model was evaluated by decision curve analysis (DCA). RESULTS Overall, 368 TTS patients (320 Survivals and 48 deaths) were included in our research from MIMIC-IV database. The incidence of in-hospital mortality with TTS is 13.04%. Lasso regression and multivariate logistic regression model verified that potassium, pt, age, myocardial infarction, WBC, hematocrit, anion gap and SOFA score were significantly associated with in-hospital mortality of TTS patients. The nomogram demonstrated a good discrimination with a AUC of ROC 0.811(95%Cl: 0.746-0.876) in training set and 0.793(95%Cl: 0.724-0.862) in test set. The calibration plot of risk prediction model showed predicted probabilities against observed death rates indicated excellent concordance. DCA showed that the nomogram has good clinical benefits. Conclusion We developed a nomogram that predict hospital mortality in patients with TTS according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.

scholarly journals Development and Validation of a Dynamic Nomogram to Predict the Risk of Neonatal White Matter Damage

2021 ◽  
Vol 14 ◽  
Author(s):  
Wenjun Cao ◽  
Chenghan Luo ◽  
Mengyuan Lei ◽  
Min Shen ◽  
Wenqian Ding ◽  
...  
Keyword(s):  
Logistic Regression ◽  
White Matter ◽  
Confidence Interval ◽  
Prediction Model ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Risk Prediction Model ◽  
Multivariate Logistic Regression ◽  
White Matter Damage ◽  
Web Based

PurposeWhite matter damage (WMD) was defined as the appearance of rough and uneven echo enhancement in the white matter around the ventricle. The aim of this study was to develop and validate a risk prediction model for neonatal WMD.Materials and MethodsWe collected data for 1,733 infants hospitalized at the Department of Neonatology at The First Affiliated Hospital of Zhengzhou University from 2017 to 2020. Infants were randomly assigned to training (n = 1,216) or validation (n = 517) cohorts at a ratio of 7:3. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model and web-based risk calculator based on the training cohort data. The predictive accuracy of the model was verified in the validation cohort.ResultsWe identified four variables as independent risk factors for brain WMD in neonates by multivariate logistic regression and LASSO analysis, including gestational age, fetal distress, prelabor rupture of membranes, and use of corticosteroids. These were used to establish a risk prediction nomogram and web-based calculator (https://caowenjun.shinyapps.io/dynnomapp/). The C-index of the training and validation sets was 0.898 (95% confidence interval: 0.8745–0.9215) and 0.887 (95% confidence interval: 0.8478–0.9262), respectively. Decision tree analysis showed that the model was highly effective in the threshold range of 1–61%. The sensitivity and specificity of the model were 82.5 and 81.7%, respectively, and the cutoff value was 0.099.ConclusionThis is the first study describing the use of a nomogram and web-based calculator to predict the risk of WMD in neonates. The web-based calculator increases the applicability of the predictive model and is a convenient tool for doctors at primary hospitals and outpatient clinics, family doctors, and even parents to identify high-risk births early on and implementing appropriate interventions while avoiding excessive treatment of low-risk patients.

scholarly journals Identification of risk factors for daptomycin-associated creatine phosphokinase elevation and development of a risk prediction model for incidence probability

2021 ◽  
Author(s):  
Masaru Samura ◽  
Naoki Hirose ◽  
Takenori Kurata ◽  
Keisuke Takada ◽  
Fumio Nagumo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Logistic Regression Analysis ◽  
Creatine Phosphokinase ◽  
Risk Prediction Model ◽  
Risk Scores

Abstract Background In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation. Methods Patients who received daptomycin at our hospital were classified into the normal or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis. Results The normal and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR] 4.45, 95% confidence interval [CI] 1.40–14.47, risk score 4), concomitant antihistamine use (OR 5.66, 95% CI 1.58–20.75, risk score 4), and trough concentration (Cmin) between 20 and <30 µg/mL (OR 14.48, 95% CI 2.90–87.13, risk score 5) and ≥30.0 µg/mL (OR 24.64, 95% CI 3.21–204.53, risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were <10% (low risk), 10%–<25% (moderate risk), and ≥25% (high risk) with the total risk scores of ≤4, 5–6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiving-operating characteristic curve 0.85, 95% CI 0.74–0.95). Conclusions These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.

scholarly journals Establishment of Risk Prediction Model of Postoperative Pancreatic Fistula after Pancreatoduodenectomy: 2016 Edition of Definition and Grading System of Pancreatic Fistula

2021 ◽  
Author(s):  
Jun Yu ◽  
Chao-yi Ren ◽  
Jun Wang ◽  
Wei Cui ◽  
Jin-juan Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Pancreatic Fistula ◽  
Pancreatic Duct ◽  
Risk Prediction ◽  
Main Pancreatic Duct ◽  
Postoperative Pancreatic Fistula ◽  
Risk Prediction Model ◽  
Validation Set

Abstract ObjectiveTo establish a risk prediction model for pancreatic fistula according to the pancreatic fistula standards of the 2016 edition.MethodsClinical data from 182 patients with PD admitted to Tianjin Third Central Hospital from January 2016 to February 2020 were retrospectively analyzed. Patients were divided into modeling (01/2016 to 12/2018) and validation (01/2019 to 02/2020) sets according to the time of admission. The risk factors for postoperative pancreatic fistula (POPF) were screened by univariate and multivariate logistic regression analyses, and a risk prediction model for POPF was established in the modeling set. This score was tested in the validation set.ResultsLogistic regression analysis showed that the main pancreatic duct index and CT value were independent risk factors according to the 2016 pancreatic fistula grading standard, based on which a risk prediction model for POPF was established. Receiver operating characteristic curve analysis showed that the area under the curve was 0.788 in the modeling set and 0.824 in the validation set.ConclusionThe main pancreatic duct index and CT value of the pancreas are closely related to the occurrence of pancreatic fistula after PD, and the established risk prediction model for pancreatic fistula has good prediction accuracy.

Interpretable Machine Learning–Based Prediction of Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Transsphenoidal Pituitary Surgery: A Pilot Study

2022 ◽  
Author(s):  
Pier Paolo Mattogno ◽  
Valerio M. Caccavella ◽  
Martina Giordano ◽  
Quintino G. D'Alessandris ◽  
Sabrina Chiloiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebrospinal Fluid ◽  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Cerebrospinal Fluid Leakage ◽  
Tumor Diameter ◽  
Predictive Values ◽  
Multivariable Logistic Regression ◽  
Csf Leakage

Abstract Purpose Transsphenoidal surgery (TSS) for pituitary adenomas can be complicated by the occurrence of intraoperative cerebrospinal fluid (CSF) leakage (IOL). IOL significantly affects the course of surgery predisposing to the development of postoperative CSF leakage, a major source of morbidity and mortality in the postoperative period. The authors trained and internally validated the Random Forest (RF) prediction model to preoperatively identify patients at high risk for IOL. A locally interpretable model-agnostic explanations (LIME) algorithm is employed to elucidate the main drivers behind each machine learning (ML) model prediction. Methods The data of 210 patients who underwent TSS were collected; first, risk factors for IOL were identified via conventional statistical methods (multivariable logistic regression). Then, the authors trained, optimized, and audited a RF prediction model. Results IOL reported in 45 patients (21.5%). The recursive feature selection algorithm identified the following variables as the most significant determinants of IOL: Knosp's grade, sellar Hardy's grade, suprasellar Hardy's grade, tumor diameter (on X, Y, and Z axes), intercarotid distance, and secreting status (nonfunctioning and growth hormone [GH] secreting). Leveraging the predictive values of these variables, the RF prediction model achieved an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78; 0.86), significantly outperforming the multivariable logistic regression model (AUC = 0.63). Conclusion A RF model that reliably identifies patients at risk for IOL was successfully trained and internally validated. ML-based prediction models can predict events that were previously judged nearly unpredictable; their deployment in clinical practice may result in improved patient care and reduced postoperative morbidity and healthcare costs.

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