Background:Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States,there is a compelling need to investigate the intricate interplay among body mass index(BMI),pregesta-tio...Background:Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States,there is a compelling need to investigate the intricate interplay among body mass index(BMI),pregesta-tional,and gestational maternal diabetes,and their potential impact on the occurrence of congenital heart defects(CHD)during neonatal development.Methods:Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico,we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020.Our assessment encompassed a range of variables,including maternal age,gestational age,BMI,pregestational diabetes,gestational diabetes,hypertension,history of abortion,and presence of preeclampsia.Results:A cohort of 673 patients was included in our study.The average maternal age was 26 years,within a range of 22 to 32 years.The mean gestational age measured 39 weeks,with a median span of 38 to 39 weeks.Of the 673 patients,274(41%)mothers gave birth to neonates diagnosed with CHD.Within this group,22 cases were linked to pre-gestational diabetes,while 202 were not;20 instances were associated with gestational diabetes,compared to 200 without;and 148 cases exhibited an overweight or obese BMI,whereas 126 displayed a normal BMI.Conclusion:We identified a statistically significant correlation between pre-gestational diabetes mellitus and the occurrence of CHD.However,our analysis did not show a statistically significant association between maternal BMI and the likelihood of CHD.These results may aid in developing effective strategies to prevent and manage CHD in neonates.展开更多
Objective To evaluate the capability of wrist pulse analysis in distinguishing three physiolog-ical and pathological conditions:healthy individuals,coronary heart disease(CHD)patients without a history of ischemic str...Objective To evaluate the capability of wrist pulse analysis in distinguishing three physiolog-ical and pathological conditions:healthy individuals,coronary heart disease(CHD)patients without a history of ischemic stroke,and CHD patients with a history of ischemic stroke.Methods Study participants were recruited from Shuguang East Hospital,Yueyang Hospital of Integrated Traditional Chinese and Western Medicine,and Shanghai Municipal Hospital of Traditional Chinese Medicine,affiliated with Shanghai University of Traditional Chinese Medicine,from April 15 to September 15,2021.They were categorized into three groups:healthy controls(Group 1),CHD patients without a history of ischemic stroke(Group 2),and CHD patients with a history of ischemic stroke(Group 3).The wrist pulse signals of the study participants were non-invasively collected using a pulse diagnosis instrument.The linear time-domain features and nonlinear time-series multiscale entropy(MSE)features of the pulse signals were extracted using time-domain analysis and the MSE methods,which were subsequently compared between groups.Based on these extracted features,a recognition model was developed using a random forest(RF)algorithm.The classification performance of the models was evaluated using metrics,including accuracy,precision,recall,and F1-score derived from confusion matrix as well as the area under the receiver operating characteristics(ROC)curve(AUC).Results A total of 189 participants were enrolled,with 63 in Group 1,61 in Group 2,and 65 in Group 3.Compared with Group 1,Group 2 showed significant increases in pulse features H2/H1,H3/H1,W1,W2,and W2/T,and decreased in MSE_(1)-MSE7(P<0.05),while Group 3 showed significant increases in pulse features T5/T4,T,H1/T1,W1,W2,AS,and Ad,and de-creased in MSE_(1)-MSE_(20)(P<0.05).Compared with Group 2,Group 3 demonstrated notable increases in H1/T1 and As(P<0.05).The RF model achieved precision of 80.00%,61.54%,and 61.54%,recall of 74.29%,60.00%,and 68.97%,F1-scores of 70.04%,60.76%,and 65.04%,and AUC values of 0.92,0.74,and 0.81 for Groups 1,2,and 3,respectively.The overall accuracy was 67.69%,with micro-average AUC of 0.83 and macro-average AUC of 0.82.Conclusion Differences in pulse features reflect variations in arterial compliance,peripheral resistance,cardiac afterload,and pulse signal complexity among healthy individuals,CHD patients without a history of ischemic stroke,and those with such a history.The developed pulse-based recognition model holds the potential in distinguishing between these three groups,offering a novel diagnostic reference for clinical practice.展开更多
基金The San Juan Bautista School of Medicine’s Institutional Review Board approved the study(EMSJBIRB-7-2021).
文摘Background:Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States,there is a compelling need to investigate the intricate interplay among body mass index(BMI),pregesta-tional,and gestational maternal diabetes,and their potential impact on the occurrence of congenital heart defects(CHD)during neonatal development.Methods:Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico,we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020.Our assessment encompassed a range of variables,including maternal age,gestational age,BMI,pregestational diabetes,gestational diabetes,hypertension,history of abortion,and presence of preeclampsia.Results:A cohort of 673 patients was included in our study.The average maternal age was 26 years,within a range of 22 to 32 years.The mean gestational age measured 39 weeks,with a median span of 38 to 39 weeks.Of the 673 patients,274(41%)mothers gave birth to neonates diagnosed with CHD.Within this group,22 cases were linked to pre-gestational diabetes,while 202 were not;20 instances were associated with gestational diabetes,compared to 200 without;and 148 cases exhibited an overweight or obese BMI,whereas 126 displayed a normal BMI.Conclusion:We identified a statistically significant correlation between pre-gestational diabetes mellitus and the occurrence of CHD.However,our analysis did not show a statistically significant association between maternal BMI and the likelihood of CHD.These results may aid in developing effective strategies to prevent and manage CHD in neonates.
基金National Natural Science Foundation of China(82074332)Shanghai Key Laboratory of Health Identification and Assessment(21DZ2271000)the 14th Batch of Science and Innovation Program for Undergraduates(202110268031).
文摘Objective To evaluate the capability of wrist pulse analysis in distinguishing three physiolog-ical and pathological conditions:healthy individuals,coronary heart disease(CHD)patients without a history of ischemic stroke,and CHD patients with a history of ischemic stroke.Methods Study participants were recruited from Shuguang East Hospital,Yueyang Hospital of Integrated Traditional Chinese and Western Medicine,and Shanghai Municipal Hospital of Traditional Chinese Medicine,affiliated with Shanghai University of Traditional Chinese Medicine,from April 15 to September 15,2021.They were categorized into three groups:healthy controls(Group 1),CHD patients without a history of ischemic stroke(Group 2),and CHD patients with a history of ischemic stroke(Group 3).The wrist pulse signals of the study participants were non-invasively collected using a pulse diagnosis instrument.The linear time-domain features and nonlinear time-series multiscale entropy(MSE)features of the pulse signals were extracted using time-domain analysis and the MSE methods,which were subsequently compared between groups.Based on these extracted features,a recognition model was developed using a random forest(RF)algorithm.The classification performance of the models was evaluated using metrics,including accuracy,precision,recall,and F1-score derived from confusion matrix as well as the area under the receiver operating characteristics(ROC)curve(AUC).Results A total of 189 participants were enrolled,with 63 in Group 1,61 in Group 2,and 65 in Group 3.Compared with Group 1,Group 2 showed significant increases in pulse features H2/H1,H3/H1,W1,W2,and W2/T,and decreased in MSE_(1)-MSE7(P<0.05),while Group 3 showed significant increases in pulse features T5/T4,T,H1/T1,W1,W2,AS,and Ad,and de-creased in MSE_(1)-MSE_(20)(P<0.05).Compared with Group 2,Group 3 demonstrated notable increases in H1/T1 and As(P<0.05).The RF model achieved precision of 80.00%,61.54%,and 61.54%,recall of 74.29%,60.00%,and 68.97%,F1-scores of 70.04%,60.76%,and 65.04%,and AUC values of 0.92,0.74,and 0.81 for Groups 1,2,and 3,respectively.The overall accuracy was 67.69%,with micro-average AUC of 0.83 and macro-average AUC of 0.82.Conclusion Differences in pulse features reflect variations in arterial compliance,peripheral resistance,cardiac afterload,and pulse signal complexity among healthy individuals,CHD patients without a history of ischemic stroke,and those with such a history.The developed pulse-based recognition model holds the potential in distinguishing between these three groups,offering a novel diagnostic reference for clinical practice.
