Statistical and artificial intelligence techniques to identify risk factors for suicide in children and adolescents.

BACKGROUND: Suicidal Behaviors and Thoughts are a relevant public health issue that includes suicidal ideation, non-suicidal self-harm, attempted suicide, and failed suicides. Since there is a progression of suicidal behaviors, whereby suicide is more likely to be completed if there have already been previous behaviors or attempts to harm oneself, WHO has highlighted the need to detect early predictors of such suicidal behaviors, which can help identify individuals at risk, plan prevention strategies and implement specific therapeutic interventions, particularly in young people, thus reducing the number of deaths. This retrospective observational study aimed to identify early predictors of suicidal risk in 237 inpatients admitted for Suicidal Behaviors and Thoughts at Child and Adolescent Psychiatry Emergency Unit of the Meyer Children's Hospital, Florence, Italy. METHODS: The study was subdivided into three phases: data collection, statistical analysis, and neural network. For each patient, we collected epidemiological and psychopathological data. We stratified the inpatients into two groups: "suicidal volition patients" and "suicidal motivation patients." RESULTS: The hospitalization rate for suicidal behaviors and thoughts showed a growing trend from 2016 to 2020 (27.69 to 45.28%). Under 12 years of age, diagnosis of disruptive, impulse-control and conduct disorder, previous specialist care, history of attempted suicide, and intoxication as methods of suicide were statistically correlated to an increased risk of suicidal behaviors. Artificial intelligence, with an accuracy of 86.7%, confirmed these risk factors. LIMITATIONS: The most important limitation of the study is its retrospective nature. CONCLUSIONS: Our study identifies new early predictors of suicidal risk: age less than 12, diagnosis of disruptive, impulse-control and conduct disorder. In addition, suicidal volition behavior emerges as an important and underestimated risk factor. The use of artificial intelligence methods could be supporting the clinician in assessing suicidal risk.

Early infantile epileptic-dyskinetic encephalopathy due to biallelic PIGP mutations.

OBJECTIVE: To describe clinical, biochemical, and molecular genetic findings in a large inbred family in which 4 children with a severe early-onset epileptic-dyskinetic encephalopathy, with suppression burst EEG, harbored homozygous mutations of phosphatidylinositol glycan anchor biosynthesis, class P (PIGP), a member of the large glycosylphosphatidylinositol (GPI) anchor biosynthesis gene family. METHODS: We studied clinical features, EEG, brain MRI scans, whole-exome sequencing (WES), and measured the expression of a subset of GPI-anchored proteins (GPI-APs) in circulating granulocytes using flow cytometry. RESULTS: The 4 affected children exhibited a severe neurodevelopmental disorder featuring severe hypotonia with early dyskinesia progressing to quadriplegia, associated with infantile spasms, focal, tonic, and tonic-clonic seizures and a burst suppression EEG pattern. Two of the children died prematurely between age 2 and 12 years; the remaining 2 children are aged 2 years 7 months and 7 years 4 months. The homozygous c.384del variant of PIGP, present in the 4 patients, introduces a frame shift 6 codons before the expected stop signal and is predicted to result in the synthesis of a protein longer than the wild type, with impaired functionality. We demonstrated a reduced expression of the GPI-AP CD16 in the granulocytic membrane in affected individuals. CONCLUSIONS: PIGP mutations are consistently associated with an epileptic-dyskinetic encephalopathy with the features of early infantile epileptic encephalopathy with profound disability and premature death. CD16 is a valuable marker to support a genetic diagnosis of inherited GPI deficiencies.