scholarly journals The Urine Iodine to Creatinine as an Optimal Index of Iodine During Pregnancy in an Iodine Adequate Area in China

2016 ◽  
Vol 101 (3) ◽  
pp. 1290-1298 ◽  
Author(s):  
Chenyan Li ◽  
Shiqiao Peng ◽  
Xiaomei Zhang ◽  
Xiaochen Xie ◽  
Danyang Wang ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Pregnant Women ◽  
Receiver Operating Characteristic ◽  
Iodine Deficiency ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Iodine Intake ◽  
Excessive Iodine ◽  
Operating Characteristic Curve ◽  
Urine Iodine

Abstract Context: Iodine nutrition is a global event, especially for pregnant women. Objective: To develop applicable index of iodine intake for population during pregnancy. Design, Setting, and Participants: From 2012 to 2014, pregnant women at less than 8 weeks of gestation (n = 222) and reproductive-age women (n = 827) participated in this study. The pregnant women were evaluated at follow-up visits at 8, 12, 16, 20, 28, and 36 weeks of gestation and 3 and 6 months postpartum. Main Outcome Measures: Twenty-four-hour urine samples were collected at weeks 8 of gestation. Urine iodine (UI) and creatinine (Cr) and serum thyroglobulin were measured in all of the subjects. Circulatory iodine was measured using inductively coupled plasma-mass spectrometry at 8, 20, and 36 weeks of gestation and 6 months postpartum. Results: The median UI concentration decreased from 183.6 to 104.2 μg/L during pregnancy. The serum iodine (SI) changes were similar to the UI to creatinine ratio (UI/Cr). The SI level was lowest at the eighth week of gestation (60.5 μg/L), which rose significantly until 20 weeks (106.5 μg/L) and then began to decline (36 wk, 84.7 μg/L). The 24-hour UI excretion measurement was regarded as the gold standard. The area under the receiver-operating characteristic curve for UI/Cr was 0.92 for iodine deficiency diagnoses and 0.78 for SI. The area for SI was 0.82 for excessive iodine diagnoses and 0.75 for UI/Cr. The areas under these curves were significantly different (P < .001). The areas under the receiver-operating characteristic curve for UI were 0.61 (P = .11) and 0.65 (P = .08) for iodine deficiency and excessive iodine diagnoses, respectively. Additionally, for thyroglobulin, these values were 0.54 (P = .53) and 0.53 (P = .74), respectively. Conclusions: Iodine intake, as assessed by spot UI concentration in pregnant women, is inaccurate and increases the prevalence of iodine deficiency. The UI/Cr better reflects the 24-hour iodine excretion and circulating iodine levels during pregnancy and the postpartum period.

scholarly journals Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease

2020 ◽  
Vol 9 (14) ◽  
Author(s):  
Ran Chu ◽  
Wei Chen ◽  
Guangmin Song ◽  
Shu Yao ◽  
Lin Xie ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Receiver Operating Characteristic Curve ◽  
Pregnant Women ◽  
Receiver Operating Characteristic ◽  
Congenital Heart ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women with congenital heart disease. Methods and Results A total of 318 pregnant women with congenital heart disease were included; 213 women were divided into the development cohort, and 105 women were divided into the validation cohort. Least absolute shrinkage and selection operator was used for predictor selection. After validation, multivariate logistic regression analysis was used to develop the model. Machine learning algorithms (support vector machine, random forest, AdaBoost, decision tree, k‐nearest neighbor, naïve Bayes, and multilayer perceptron) were used to further verify the predictive ability of the model. Forty‐one (12.9%) women experienced adverse maternal events, and 93 (29.2%) neonates experienced adverse neonatal events. Seven high‐risk factors were discovered in the maternal model, including New York Heart Association class, Eisenmenger syndrome, pulmonary hypertension, left ventricular ejection fraction, sinus tachycardia, arterial blood oxygen saturation, and pregnancy duration. The machine learning–based algorithms showed that the maternal model had an accuracy of 0.76 to 0.86 (area under the receiver operating characteristic curve=0.74–0.87) in the development cohort, and 0.72 to 0.86 (area under the receiver operating characteristic curve=0.68–0.80) in the validation cohort. Three high‐risk factors were discovered in the neonatal model, including Eisenmenger syndrome, preeclampsia, and arterial blood oxygen saturation. The machine learning–based algorithms showed that the neonatal model had an accuracy of 0.75 to 0.80 (area under the receiver operating characteristic curve=0.71–0.77) in the development cohort, and 0.72 to 0.79 (area under the receiver operating characteristic curve=0.69–0.76) in the validation cohort. Conclusions Two prenatal risk assessment models for both adverse maternal and neonatal events were established, which might assist clinicians in tailoring precise management and therapy in pregnant women with congenital heart disease.

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.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring

2016 ◽  
Vol 25 (6) ◽  
pp. 2750-2766 ◽  
Author(s):  
Hélène Jacqmin-Gadda ◽  
Paul Blanche ◽  
Emilie Chary ◽  
Célia Touraine ◽  
Jean-François Dartigues
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Censored Data ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Disease Onset ◽  
Interval Censoring ◽  
Inverse Probability ◽  
Operating Characteristic Curve ◽  
Semicompeting Risks

Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account for these two issues. In recent years, methods for estimating the time-dependent receiver operating characteristic curve and the associated area under the ROC curve have been extended to account for right censored data and competing risks. In this paper, we show how an approximation allows to use the inverse probability of censoring weighting estimator for semicompeting events with interval censored data. Then, using an illness-death model, we propose two model-based estimators allowing to rigorously handle these issues. The first estimator is fully model based whereas the second one only uses the model to impute missing observations due to censoring. A simulation study shows that the bias for inverse probability of censoring weighting remains modest and may be less than the one of the two parametric estimators when the model is misspecified. We finally recommend the nonparametric inverse probability of censoring weighting estimator as main analysis and the imputation estimator based on the illness-death model as sensitivity analysis.

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