scholarly journals Newborn skin maturity models for gestational age prediction

2021 ◽  
Author(s):  
Zilma Silveira Nogueira Reis ◽  
Rodney Nascimento Guimarães ◽  
Roberta Maia de Castro Romanelli ◽  
Juliano de Souza Gaspar ◽  
Gabriela Silveira Neves ◽  
...  
Keyword(s):  
Gestational Age ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Antenatal Corticosteroid ◽  
Learning Models ◽  
Discriminate Analysis ◽  
Novel Device ◽  
Pregnancy Dating ◽  
Preterm Newborns ◽  
Age Prediction

Abstract A multicenter clinical trial evaluated the accuracy of a novel device to detect preterm newborns. A portable multiband reflectance photometric device assessed 781 newborns’ skin maturity and used machine learning models to predict reference gestational age, adjusting it to birth weight and antenatal corticosteroid therapy exposition. The day difference between the reference and the test had a median of -1.4 (IQR: -2.1). Using established methods such as comparator ultrasound and last menstrual period (LMP), the medians were 0 (IQR: 4) and 0.01 (IQR: 4), respectively. For prematurity discrimination, the area under the receiver operating characteristic curve (AUROC) was 0.986 (95% CI: 0.977 to 0.994). In newborns with absent or unreliable LMP, the intent-to-discriminate analysis showed that the test generated correct classifications 95.8% of the time. The assessment of the newborn's skin maturity adjusted by learning models promises accurate pregnancy dating at birth without the use of antenatal ultrasound reference.

scholarly journals Newborn skin maturity models for gestational age prediction

2022 ◽  
Author(s):  
Zilma Silveira Nogueira Reis ◽  
Rodney Nascimento Guimarães ◽  
Roberta Maia de Castro Romanelli ◽  
Juliano de Souza Gaspar ◽  
Gabriela Silveira Neves ◽  
...  
Keyword(s):  
Gestational Age ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Antenatal Corticosteroid ◽  
Learning Models ◽  
Discriminate Analysis ◽  
Novel Device ◽  
Pregnancy Dating ◽  
Preterm Newborns ◽  
Age Prediction

Abstract A multicenter clinical trial evaluated the accuracy of a novel device to detect preterm newborns. A portable multiband reflectance photometric device assessed 781 newborns’ skin maturity and used machine learning models to predict reference gestational age, adjusting it to birth weight and antenatal corticosteroid therapy exposure. The day difference between the reference and the test had a median of -1.4 (IQR: -2.1). Using established methods such as comparator ultrasound and last menstrual period (LMP), the medians were 0 (IQR: 4) and 0.01 (IQR: 4), respectively. For prematurity discrimination, the area under the receiver operating characteristic curve (AUROC) was 0.986 (95% CI: 0.977 to 0.994). In newborns with absent or unreliable LMP, the intent-to-discriminate analysis showed that the test generated correct classifications 95.8% of the time. The assessment of the newborn's skin maturity adjusted by learning models promises accurate pregnancy dating at birth without the use of antenatal ultrasound reference.

scholarly journals Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction

2020 ◽  
Vol 34 (7) ◽  
pp. 717-730 ◽  
Author(s):  
Matthew C. Robinson ◽  
Robert C. Glen ◽  
Alpha A. Lee
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Numerical Experiments ◽  
Large Scale ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Learning Models ◽  
Bioactivity Prediction ◽  
Operating Characteristic Curve ◽  
Machine Learning Models

Abstract Machine learning methods may have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and validated. We reanalyze the data generated by a recently published large-scale comparison of machine learning models for bioactivity prediction and arrive at a somewhat different conclusion. We show that the performance of support vector machines is competitive with that of deep learning methods. Additionally, using a series of numerical experiments, we question the relevance of area under the receiver operating characteristic curve as a metric in virtual screening. We further suggest that area under the precision–recall curve should be used in conjunction with the receiver operating characteristic curve. Our numerical experiments also highlight challenges in estimating the uncertainty in model performance via scaffold-split nested cross validation.

scholarly journals Assessing the risk of early unplanned rehospitalisation in preterm babies: EPIPAGE 2 study

2019 ◽  
Author(s):  
Robert A. Reed ◽  
Andrei S. Morgan ◽  
Jennifer Zeitlin ◽  
Pierre-Henri Jarreau ◽  
Héloïse Torchin ◽  
...  
Keyword(s):  
Gestational Age ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Predictive Modelling ◽  
Population Based ◽  
Very Preterm ◽  
Kaplan Meier ◽  
Preterm Babies ◽  
Predictor Model

Abstract Background Gaining a better understanding of the probability, timing and prediction of rehospitalisation amongst preterm babies could help improve outcomes. There is limited research addressing these topics amongst extremely and very preterm babies. In this context, unplanned rehospitalisations constitute an important, potentially modifiable adverse event. We aimed to establish the probability, time-distribution and predictability of unplanned rehospitalisation within 30 days of discharge in a population of French preterm babies.Methods This study used data from EPIPAGE 2, a population-based prospective study of French preterm babies. Only those babies discharged home alive and whose parents responded to the 1-year survey were eligible for inclusion in our study. For Kaplan-Meier analysis, the outcome was unplanned rehospitalisation censored at 30 days. For predictive modelling, the outcome was binary, recording unplanned rehospitalisation within 30 days of discharge. Predictors included routine clinical variables selected based on expert opinion.Results Of 3,841 eligible babies, 350 (9.1%, 95% CI 8.2-10.1) experienced an unplanned rehospitalisation within 30 days. The probability of rehospitalisation progressed at a consistent rate over the 30 days. There were significant differences in rehospitalisation probability by gestational age. The cross-validated performance of a ten predictor model demonstrated low discrimination and calibration. The area under the receiver operating characteristic curve was 0.62 (95% CI 0.59-0.65).Conclusions Unplanned rehospitalisation within 30 days of discharge was infrequent and the probability of rehospitalisation progressed at a consistent rate. Lower gestational age increased the probability of rehospitalisation. Predictive models comprised of clinically important variables had limited predictive ability.

scholarly journals Prediction Model for Bronchopulmonary Dysplasia in Preterm Newborns

Children ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 886
Author(s):  
Joanna Maria Jassem-Bobowicz ◽  
Dagmara Klasa-Mazurkiewicz ◽  
Anton Żawrocki ◽  
Katarzyna Stefańska ◽  
Iwona Domżalska-Popadiuk ◽  
...  
Keyword(s):  
Predictive Model ◽  
Bronchopulmonary Dysplasia ◽  
Gestational Age ◽  
Characteristic Curve ◽  
Ductus Arteriosus ◽  
Significant Risk ◽  
Area Under The Curve ◽  
Multifactorial Model ◽  
Model Based ◽  
Preterm Newborns

OBJECTIVE: To develop a multifactorial model that allows the prediction of bronchopulmonary dysplasia (BPD) in preterm newborns. MATERIALS AND METHODS: A single-center retrospective study of infants born below 32 + 0 weeks gestational age. We created a receiver operating characteristic curve to assess the multifactorial BPD risk and calculated the BPD risk accuracy using the area under the curve (AUC). The BPD risk was categorized using a multifactorial predictive model based on the weight of the evidence. RESULTS: Of the 278 analyzed preterm newborns, 127 (46%) developed BPD. The significant risk factors for BPD in the multivariate analysis were gestational age, number of red blood cell concentrate transfusions, number of surfactant administrations, and hemodynamically significant patent ductus arteriosus. The combination of these factors determined the risk of developing BPD, with an AUC value of 0.932. A multifactorial predictive model based on these factors, weighted by their odds ratios, identified four categories of newborns with mean BPD risks of 9%, 59%, 82%, and 100%. CONCLUSION: A multifactorial model based on easily available clinical factors can predict BPD risk in preterm newborns and inform potential preventive measures.

Sonographic prediction of small and large for gestational age in breech-presenting fetuses

2018 ◽  
Vol 46 (7) ◽  
pp. 744-749 ◽  
Author(s):  
Anat Shmueli ◽  
Amir Aviram ◽  
Ron Bardin ◽  
Kinneret Tenenbaum-Gavish ◽  
Arnon Wiznitzer ◽  
...  
Keyword(s):  
Systematic Error ◽  
Gestational Age ◽  
Random Error ◽  
Operating Characteristic ◽  
Medical Center ◽  
Large For Gestational Age ◽  
Control Group ◽  
Sensitivity Specificity ◽  
Prediction Parameters ◽  
Age Prediction

Abstract Introduction: To evaluate various sonographic estimated fetal weight (sEFW) formulas’ accuracy for small- and large-for-gestational age (SGA/LGA) prediction in breech-presenting fetuses. Materials and methods: A retrospective analysis of all ultrasound-based fetal biometrical measurements performed within 3 days of delivery in term pregnancies, in one medical center (2007–2014). Overall, 274 breech-presenting fetuses (study group) were compared to 274 vertex-presenting fetuses (control group) matched by gender, gestational age and birth weight. sEFW was calculated by six previously published formulas. Accuracy was compared utilizing systematic error and random error for every formula. Prediction precision of SGA and LGA was evaluated by calculating each formula’s sensitivity, specificity, +/− predictive value, and the area under the receiver-operating characteristic (ROC) curve (AUC). Results: Systematic error and random error varied greatly between formulas, ranging from −7.4% to 3.1%, 7.3% to 8.3% for the vertex-presenting fetuses and −8.9% to 1.9%, 7.9% to 8.6% for the breech-presenting fetuses, respectively. There was no statistical difference in small- or large-for-gestational age prediction parameters between the groups. The highest sensitivity and specificity for prediction was achieved by same formula regardless of presentation. Conclusion: In our cohort, overall accuracy was slightly superior among vertex-presenting fetuses without difference in prediction accuracy for small- and large-for-gestational age neonates.

scholarly journals Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Po-Chih Kuo ◽  
Cheng Che Tsai ◽  
Diego M. López ◽  
Alexandros Karargyris ◽  
Tom J. Pollard ◽  
...  
Keyword(s):  
Deep Learning ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Model Performance ◽  
X Rays ◽  
Abnormality Detection ◽  
Learning Models ◽  
Radiological Findings ◽  
Operating Characteristic Curve ◽  
Uncalibrated Model

AbstractImage-based teleconsultation using smartphones has become increasingly popular. In parallel, deep learning algorithms have been developed to detect radiological findings in chest X-rays (CXRs). However, the feasibility of using smartphones to automate this process has yet to be evaluated. This study developed a recalibration method to build deep learning models to detect radiological findings on CXR photographs. Two publicly available databases (MIMIC-CXR and CheXpert) were used to build the models, and four derivative datasets containing 6453 CXR photographs were collected to evaluate model performance. After recalibration, the model achieved areas under the receiver operating characteristic curve of 0.80 (95% confidence interval: 0.78–0.82), 0.88 (0.86–0.90), 0.81 (0.79–0.84), 0.79 (0.77–0.81), 0.84 (0.80–0.88), and 0.90 (0.88–0.92), respectively, for detecting cardiomegaly, edema, consolidation, atelectasis, pneumothorax, and pleural effusion. The recalibration strategy, respectively, recovered 84.9%, 83.5%, 53.2%, 57.8%, 69.9%, and 83.0% of performance losses of the uncalibrated model. We conclude that the recalibration method can transfer models from digital CXRs to CXR photographs, which is expected to help physicians’ clinical works.

Serum miR-21 and miR-26a Levels Negatively Correlate with Severity of Cirrhosis in Patients with Chronic Hepatitis B

MicroRNA ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Shili Jiang ◽  
Wei Jiang ◽  
Ying Xu ◽  
Xiaoning Wang ◽  
Yongping Mu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Pugh Class ◽  
Operating Characteristic Curve ◽  
Class C ◽  
Circulating Levels

Background and Objective: Accurately evaluating the severity of liver cirrhosis is essential for clinical decision making and disease management. This study aimed to evaluate the value of circulating levels of microRNA (miR)-26a and miR-21 as novel noninvasive biomarkers in detecting severity of cirrhosis in patients with chronic hepatitis B. </P><P> Methods: Thirty patients with clinically diagnosed chronic hepatitis B-related cirrhosis and 30 healthy individuals were selected. The serum levels of miR-26a and miR-21 were quantified by qRT-PCR. Receiver operating characteristic curve analysis was performed to evaluate the sensitivity and specificity of the miRNAs for detecting the severity of cirrhosis. Results: Serum miR-26a and miR-21 levels were found to be significantly downregulated in patients with severe cirrhosis scored at Child-Pugh class C in comparison to healthy controls (miR-26a p<0.01, and miR-21 p<0.001, respectively). The circulating miR-26a and miR-21 levels in patients were positively correlated with serum albumin concentration but negatively correlated with serum total bilirubin concentration and prothrombin time. Receiver operating characteristic curve analysis revealed that both serum miR-26a and miR-21 levels were associated with a high diagnostic accuracy for patients with cirrhosis scored at Child-Pugh class C (miR-26a Cut-off fold change at ≤0.4, Sensitivity: 84.62%, Specificity: 89.36%, P<0.0001; miR-21 Cut-off fold change at ≤0.6, Sensitivity: 84.62%, Specificity: 78.72%, P<0.0001). Our results indicate that the circulating levels of miR-26a and miR-21 are closely related to the extent of liver decompensation, and the decreased levels are capable of discriminating patients with cirrhosis at Child-Pugh class C from the whole cirrhosis cases.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

scholarly journals MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 949
Author(s):  
Cecil J. Weale ◽  
Don M. Matshazi ◽  
Saarah F. G. Davids ◽  
Shanel Raghubeer ◽  
Rajiv T. Erasmus ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Tolerance Test ◽  
Cross Sectional Study ◽  
Oral Glucose ◽  
Cross Sectional ◽  
Receiver Operating

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies.

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