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.

scholarly journals Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

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 Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning

2020 ◽  
Vol 10 (21) ◽  
pp. 7741
Author(s):  
Sang Yeob Kim ◽  
Gyeong Hee Nam ◽  
Byeong Mun Heo
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Strong Association ◽  
Forced Expiratory Volume ◽  
Binary Logistic Regression Analysis ◽  
Cross Sectional Survey ◽  
Cross Sectional

Metabolic syndrome (MS) is an aggregation of coexisting conditions that can indicate an individual’s high risk of major diseases, including cardiovascular disease, stroke, cancer, and type 2 diabetes. We conducted a cross-sectional survey to evaluate potential risk factor indicators by identifying relationships between MS and anthropometric and spirometric factors along with blood parameters among Korean adults. A total of 13,978 subjects were enrolled from the Korea National Health and Nutrition Examination Survey. Statistical analysis was performed using a complex sampling design to represent the entire Korean population. We conducted binary logistic regression analysis to evaluate and compare potential associations of all included factors. We constructed prediction models based on Naïve Bayes and logistic regression algorithms. The performance evaluation of the prediction model improved the accuracy with area under the curve (AUC) and calibration curve. Among all factors, triglyceride exhibited a strong association with MS in both men (odds ratio (OR) = 2.711, 95% confidence interval (CI) [2.328–3.158]) and women (OR = 3.515 [3.042–4.062]). Regarding anthropometric factors, the waist-to-height ratio demonstrated a strong association in men (OR = 1.511 [1.311–1.742]), whereas waist circumference was the strongest indicator in women (OR = 2.847 [2.447–3.313]). Forced expiratory volume in 6s and forced expiratory flow 25–75% strongly associated with MS in both men (OR = 0.822 [0.749–0.903]) and women (OR = 1.150 [1.060–1.246]). Wrapper-based logistic regression prediction model showed the highest predictive power in both men and women (AUC = 0.868 and 0.932, respectively). Our findings revealed that several factors were associated with MS and suggested the potential of employing machine learning models to support the diagnosis of MS.

scholarly journals A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liu ◽  
Jian Zhang ◽  
Haodong Huang ◽  
Yunting Wang ◽  
Zuyue Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Retrospective Study ◽  
Kawasaki Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Ivig Resistance ◽  
Independent Risk Factors

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.

Retrospective validation of a β-trace protein interpretation algorithm for the diagnosis of cerebrospinal fluid leakage

2017 ◽  
Vol 55 (4) ◽  
Author(s):  
Luca Bernasconi ◽  
Theresa Pötzl ◽  
Christian Steuer ◽  
Alexander Dellweg ◽  
Frank Metternich ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Rare Condition ◽  
Diagnostic Algorithm ◽  
Cerebrospinal Fluid Leakage ◽  
Predictive Values ◽  
Non Invasive ◽  
Csf Leakage ◽  
Using Data ◽  
Diagnostic Work ◽  
Interpretation Algorithm

Abstract Background: Cerebrospinal fluid (CSF) leakage is a rare condition that can potentially lead to the development of serious complications. In the last decade, β-trace protein (β-TP) has been shown to be a valuable immunological biomarker that allows prompt and non-invasive identification of CSF leakage. At our institution, the measurement of β-TP has been included in the diagnostic work-up of CSF leakage for more than 10 years. According to our diagnostic algorithm, the presence of CSF in secretion is excluded when β-TP values are <0.7 mg/L, whereas β-TP values ≥1.3 mg/L indicate the presence of CSF in secretion. β-TP values between 0.7 and 1.29 mg/L indicate the presence of CSF if the β-TP ratio (β-TP secretion/β-TP serum) is ≥2. This study aimed to validate this diagnostic algorithm using clinically defined nasal/ear secretions. Methods: We performed a retrospective statistical analysis of three β-TP interpretation strategies using data of 236 samples originating from 121 patients with suspect CSF leakage received at our laboratory between 2004 and 2012. Results: The highest odds ratio was obtained when the proposed algorithm has been used for the interpretation of β-TP results, showing a sensitivity of 98.3% and a specificity of 96%. Positive and negative predictive values were 89.2% and 99.4%, respectively. Conclusions: Our data suggest that the proposed β-TP interpretation algorithm is a valuable tool for the diagnosis of CSF leakage in the clinical practice.

scholarly journals Cerebrospinal fluid outflow resistance as a diagnostic marker of spontaneous cerebrospinal fluid leakage

2017 ◽  
Vol 27 (2) ◽  
pp. 227-234 ◽  
Author(s):  
Jürgen Beck ◽  
Christian Fung ◽  
Christian T. Ulrich ◽  
Michael Fiechter ◽  
Jens Fichtner ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Spontaneous Intracranial Hypotension ◽  
Volume Index ◽  
Csf Leak ◽  
Cerebrospinal Fluid Leakage ◽  
Outflow Resistance ◽  
Opening Pressure ◽  
Csf Dynamics ◽  
Predictive Values ◽  
Csf Leakage

OBJECTIVESpinal CSF leakage causes spontaneous intracranial hypotension (SIH). The aim of this study was to characterize CSF dynamics via lumbar infusion testing in patients with and without proven spinal CSF leakage in order to explore possible discriminators for the presence of an open CSF leak.METHODSThis analysis included all patients with suspected SIH who were treated at the authors’ institution between January 2012 and February 2015. The gold standard for “proven” CSF leakage is considered to be extrathecal contrast accumulation after intrathecal contrast injection. To characterize CSF dynamics, the authors performed computerized lumbar infusion testing to measure lumbar pressure at baseline (opening pressure) and at plateau, as well as pulse amplitude, CSF outflow resistance (RCSF), craniospinal elastance, and pressure-volume index.RESULTSThirty-one patients underwent clinical imaging and lumbar infusion testing and were included in the final analysis. A comparison of the 14 patients with proven CSF leakage with the 17 patients without leakage showed a statistically significantly lower lumbar opening pressure (p < 0.001), plateau pressure (p < 0.001), and RCSF (p < 0.001) in the group with leakage. Sensitivity, specificity, and positive and negative predictive values for an RCSF cutoff of ≤ 5 mm Hg/(ml/min) were 0.86, 1.0, 1.0, and 0.89 (area under the curve of 0.96), respectively. The median pressure-volume index was higher (p = 0.003), and baseline (p = 0.017) and plateau (p < 0.001) pulse amplitudes were lower in patients with a proven leak.CONCLUSIONSLumbar infusion testing captures a distinct pattern of CSF dynamics associated with spinal CSF leakage. RCSF assessed by computerized lumbar infusion testing has an excellent diagnostic accuracy and is more accurate than evaluating the lumbar opening pressure. The authors suggest inclusion of RCSF in the diagnostic criteria for SIH.

scholarly journals Comparison of Machine Learning With Logistic Regression for Prediction of Chronic Kidney Disease in the Thai Adult Population

2021 ◽  
Vol 44 (4) ◽  
pp. 1-12
Author(s):  
Ratchainant Thammasudjarit ◽  
Punnathorn Ingsathit ◽  
Sigit Ari Saputro ◽  
Atiporn Ingsathit ◽  
Ammarin Thakkinstian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Prediction Model ◽  
Prediction Models ◽  
The Neural Network

Background: Chronic kidney disease (CKD) takes huge amounts of resources for treatments. Early detection of patients by risk prediction model should be useful in identifying risk patients and providing early treatments. Objective: To compare the performance of traditional logistic regression with machine learning (ML) in predicting the risk of CKD in Thai population. Methods: This study used Thai Screening and Early Evaluation of Kidney Disease (SEEK) data. Seventeen features were firstly considered in constructing prediction models using logistic regression and 4 MLs (Random Forest, Naïve Bayes, Decision Tree, and Neural Network). Data were split into train and test data with a ratio of 70:30. Performances of the model were assessed by estimating recall, C statistics, accuracy, F1, and precision. Results: Seven out of 17 features were included in the prediction models. A logistic regression model could well discriminate CKD from non-CKD patients with the C statistics of 0.79 and 0.78 in the train and test data. The Neural Network performed best among ML followed by a Random Forest, Naïve Bayes, and a Decision Tree with the corresponding C statistics of 0.82, 0.80, 0.78, and 0.77 in training data set. Performance of these corresponding models in testing data decreased about 5%, 3%, 1%, and 2% relative to the logistic model by 2%. Conclusions: Risk prediction model of CKD constructed by the logit equation may yield better discrimination and lower tendency to get overfitting relative to ML models including the Neural Network and Random Forest.  

scholarly journals Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260517
Author(s):  
Jee Soo Park ◽  
Dong Wook Kim ◽  
Dongu Lee ◽  
Taeju Lee ◽  
Kyo Chul Koo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Treatment Guidelines ◽  
Ureteral Stone ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Ureteral Stones ◽  
Stone Passage

Objectives To develop a prediction model of spontaneous ureteral stone passage (SSP) using machine learning and logistic regression and compare the performance of the two models. Indications for management of ureteral stones are unclear, and the clinician determines whether to wait for SSP or perform active treatment, especially in well-controlled patients, to avoid unwanted complications. Therefore, suggesting the possibility of SSP would help make a clinical decision regarding ureteral stones. Methods Patients diagnosed with unilateral ureteral stones at our emergency department between August 2014 and September 2018 were included and underwent non-contrast-enhanced computed tomography 4 weeks from the first stone episode. Predictors of SSP were applied to build and validate the prediction model using multilayer perceptron (MLP) with the Keras framework. Results Of 833 patients, SSP was observed in 606 (72.7%). SSP rates were 68.2% and 75.6% for stone sizes 5–10 mm and <5 mm, respectively. Stone opacity, location, and whether it was the first ureteral stone episode were significant predictors of SSP. Areas under the curve (AUCs) for receiver operating characteristic (ROC) curves for MLP, and logistic regression were 0.859 and 0.847, respectively, for stones <5 mm, and 0.881 and 0.817, respectively, for 5–10 mm stones. Conclusion SSP prediction models were developed in patients with well-controlled unilateral ureteral stones; the performance of the models was good, especially in identifying SSP for 5–10-mm ureteral stones without definite treatment guidelines. To further improve the performance of these models, future studies should focus on using machine learning techniques in image analysis.

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 ◽  
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.

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