RESUMO
AIM: This study aims to analyse the developmental data from public health nurses (PHNs) to identify early indicators of neurodevelopmental disorders (NDDs) in young children using Bayesian network (BN) analysis to determine factor combinations that improve diagnosis accuracy. METHODS: The study cohort was 501 children who underwent health checkups at 18 and 36-month. Data included demographics, pregnancy, delivery, neonatal factors, maternal interviews, and physical and neurological findings. Diagnoses were made by paediatricians and child psychiatrists using standardised tools. Predictive accuracy was assessed by the receiver operating characteristic (ROC) curve analysis. RESULTS: We identified several infant/toddler factors significantly associated with NDD diagnoses. Predictive factors included meconium-stained amniotic fluid, 1 min Apgar score, and early developmental milestones. ROC curve analysis showed varying predictive accuracies based on evaluation timing. The 10-month checkup was valid for screening but less reliable for excluding low-risk cases. The 18-month evaluation accurately identified children at NDD risk. CONCLUSION: The study demonstrates the potential of using developmental records for early NDD detection, emphasising early monitoring and intervention for at-risk children. These findings could guide future infant mental health initiatives in the community.
RESUMO
Purpose: We investigated to what extent early motor development problems predict a future diagnosis of neurodevelopmental disorders (NDDs)/Early Symptomatic Syndromes Eliciting Neurodevelopmental Examinations (ESSENCE) by using a Bayesian network model (BN). Subjects and methods: Subjects were the children who had participated in the 18- and 36-month checkups in two cities in Japan between April 2014 and March 2015. Their motor development data at the 4-, 10- and 18-month-checkups were collected with ethical consideration. The diagnosis was confirmed at the age of six, after regular assessment in all developmental areas at a neurodevelopmental clinic. The accuracy of prediction of NDD based on posterior probabilities determined using the BN was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The posterior probability (the optimal cut-off value) yielding the maximum Youden Index (sensitivity + specificity - 1) is determined with the ROC curve, and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and utility index (UI) were computed. Results: BN models showed associations between early motor items and developmental coordination disorders, borderline intelligence/intellectual disability, and speech and language disorder. The ROC curve for any NDD had an AUC of 0.735. The posterior probability with the maximal Youden Index was 0.138; at the optimal cut-off value, the sensitivity, specificity, PPV, NPV, UI+, and UI- were 0.619, 0.761, 0.250, 0.940, 0.155 and 0.715, respectively. Conclusion: We utilized a novel approach in detailing the associations between certain early motor problems and specific NDDs. We showed that the presence of motor development problems early in development increases the probability of a future diagnosis of any NDD. Still, the sensitivity of early motor development problems as a screening tool was not high enough to be the sole instrument for detecting NDDs. The need for a broad, holistic ESSENCE perspective when looking at the course of motor development problems was stressed.
