Comparison of diagnostic validity of two autism rating scales for suspected autism in a large Chinese sample

BACKGROUND Autism is the most common clinical developmental disorder in children.The childhood autism rating scale(CARS)and autistic autism behavior checklist(ABC)are the most commonly used assessment scales for diagnosing autism.However,the diagnostic validations and the corresponding cutoffs for CARS and ABC in individuals with suspected autism spectrum disorder(ASD)remain unclear.Furthermore,for suspected ASD in China,it remains unclear whether CARS is a better diagnostic tool than ABC.Also unclear is whether the current cutoff points for ABC and CARS are suitable for the accurate diagnosis of ASD.AIM To investigate the diagnostic validity of CARS and ABC based on a large Chinese sample.METHODS A total of 591 outpatient children from the ASD Unit at Beijing Children’s Hospital between June and November 2019 were identified.First,the Clancy autism behavior scale(CABS)was used to screen out suspected autism from these children.Then,each suspected ASD was evaluated by CARS and ABC.Receiver operating characteristic(ROC)curve analysis was used to compare diagnostic validations.We also calculated the area under the curve(AUC)for both CARS and ABC.RESULTS We found that the Cronbach alpha coefficients of CARS and ABC were 0.772 and 0.426,respectively.Therefore,the reliability of the CARS was higher than that of the ABC.In addition,we found that the correlation between CARS and CABS was 0.732.Next,we performed ROC curve analysis for CARS and ABC,which yielded AUC values of 0.846 and 0.768,respectively.The cutoff value,which is associated with the maximum Youden index,is usually applied as a decision threshold.We found that the cutoff values of CARS and ABC were 34 and 67,respectively.CONCLUSION This result indicated that CARS is superior to ABC in the Chinese population with suspected ASD.

Application of intelligent algorithms in Down syndrome screening during second trimester pregnancy

BACKGROUND Down syndrome(DS)is one of the most common chromosomal aneuploidy diseases.Prenatal screening and diagnostic tests can aid the early diagnosis,appropriate management of these fetuses,and give parents an informed choice about whether or not to terminate a pregnancy.In recent years,investigations have been conducted to achieve a high detection rate(DR)and reduce the false positive rate(FPR).Hospitals have accumulated large numbers of screened cases.However,artificial intelligence methods are rarely used in the risk assessment of prenatal screening for DS.AIM To use a support vector machine algorithm,classification and regression tree algorithm,and AdaBoost algorithm in machine learning for modeling and analysis of prenatal DS screening.METHODS The dataset was from the Center for Prenatal Diagnosis at the First Hospital of Jilin University.We designed and developed intelligent algorithms based on the synthetic minority over-sampling technique(SMOTE)-Tomek and adaptive synthetic sampling over-sampling techniques to preprocess the dataset of prenatal screening information.The machine learning model was then established.Finally,the feasibility of artificial intelligence algorithms in DS screening evaluation is discussed.RESULTS The database contained 31 DS diagnosed cases,accounting for 0.03%of all patients.The dataset showed a large difference between the numbers of DS affected and non-affected cases.A combination of over-sampling and undersampling techniques can greatly increase the performance of the algorithm at processing non-balanced datasets.As the number of iterations increases,the combination of the classification and regression tree algorithm and the SMOTETomek over-sampling technique can obtain a high DR while keeping the FPR to a minimum.CONCLUSION The support vector machine algorithm and the classification and regression tree algorithm achieved good results on the DS screening dataset.When the T21 risk cutoff value was set to 270,machine learning methods had a higher DR and a lower FPR than statistical methods.

Impact of age and tumor size on the development of the Kasabach-Merritt phenomenon in patients with kaposiform hemangioendothelioma:a retrospective cohort study

Introduction The Kasabach–Merritt phenomenon(KMP)is a severe complication of kaposiform hemangioendothelioma(KHE).The risk factors for KMP need further investigation.Methods The medical records of patients with KHE were reviewed.Univariate and multivariate logistic regression models were used for the risk factors for KMP,and the area under the receiver operator characteristic(ROC)curve was used to assess the predictive power of risk factors.Results A total of 338 patients with KHE were enrolled.The incidence of KMP was 45.9%.Age of onset(P<0.001,odds ratio[OR]0.939;95%confidence interval[CI]0.914–0.966),lesion size(P<0.001,OR 1.944;95%CI 1.646–2.296),mixed type(P=0.030,OR 2.428;95%CI 1.092–5.397),deep type(P=0.010,OR 4.006;95%CI 1.389–11.556),and mediastinal or retroperitoneal lesion location(P=0.019,OR 11.864;95%CI 1.497–94.003)were correlated with KMP occurrence through multivariate logistic regression.ROC curve analysis revealed that the optimal cutoffs were 4.75 months for the age of onset(P<0.001,OR 7.206,95%CI 4.073–12.749)and a lesion diameter of 5.35 cm(P<0.001,OR 11.817,95%CI 7.084–19.714).Bounded by a lesion size of 5.35 cm,we found significant differences in tumor morphology,age of onset,treatments,and hematological parameters.Using an onset age of 4.75 months as a cutoff,we found significant differences in tumor morphology,lesion size,hematological parameters,and prognosis.Conclusion For KHE patients with an onset age<4.75 months and/or lesion diameter>5.35 cm,clinicians should be wary of the occurrence of KMP.Active management is recommended to improve the prognosis.