Predicting suicide attempt in youth by integrating EHR, clinical, cognitive and imaging data

通过整合 EHR、临床、认知和影像数据来预测青少年自杀企图

基本信息

批准号：
10038009
负责人：
Ran Barzilay
金额：
$ 49.88万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10038009
关键词：
Adolescence Adolescent Adopted Adult Age Algorithms Anxiety Area Assessment tool Behavioral Cause of Death Child Childhood Classification Clinical Cognitive Complex Computer software Computerized Medical Record Coupled Data Data Analytics Data Set Depression screen Development Electronic Health Record Engineering Environment Evaluation Familiarity Feeling suicidal Frequencies Guidelines Gur Health Hippocampus (Brain)Hospitals Image Individual Insurance Intervention Lead Machine Learning Measures Medical Device Medical Records Medical center Medicine Memory Methods Mission Modeling Moods Neurocognitive Obsessive compulsive behavior Odds Ratio Outcome Participant Patients Pediatric Hospitals Phenotype Philadelphia Policies Population Positioning Attribute Primary Health Care Primary Prevention Proxy Psychiatrist Psychological Transfer Psychotic Disorders Race Recording of previous events Reporting Research Research Personnel Resources Rest Risk Risk Factors Sampling Source Subgroup Suicide Suicide attempt Symptoms System Teenagers Testing Thalamic structure Thinness Time Training Translating Universities Update Youth adolescent suicide aged base care providers clinical care clinical phenotype clinical practice cohort computerized tools data resource deep neural network depressive symptoms early adolescence executive function expectation experience falls improved learning progression machine learning algorithm machine learning method meetings modifiable risk multidisciplinary multiple data types neuroimaging novel novel strategies pediatric patients prediction algorithm predictive modeling prospective random forest reducing suicide routine screening screening screening guidelines social cognition suicidal adolescent suicidal morbidity suicidal risk suicide rate

项目摘要

Summary. Suicide in youths is a growing health concern, yet current clinical practice falls short of timely identifying youths at risk for suicide attempt (SA). The overarching aim of this research is to use data driven machine learning methods to facilitate primary prevention of youth SAs in primary care pediatric settings. Clinical guidelines recommend screening for depression, considered a proxy for suicide risk, from age 12 in pediatric setting. The proposed study aims at identification of variables (features) that can be collected by early adolescence, and contribute to prediction of SA in later adolescence. This study will leverage the effort that has been invested in previous projects: a study using electronic health records (EHR) to predict SAs and deaths in University of Pittsburgh Medical Center (UPMC) hospitals; and the Philadelphia Neurodevelopmental Cohort (PNC), that included comprehensive phenotyping of ~9,500 youths. These previous efforts will be integrated to develop and optimize SA prediction in youth from the Children’s Hospital of Philadelphia (CHOP) network, from which we have data on ~40,000 who were screened for a history of SA between the years 2014-2018 (n~1500). First, in the CHOP dataset, we will generate predictive models based on UPMC data, test their predictive validity in CHOP youth population, and then develop, optimize, and cross validate these predictive models using CHOP EHR data as a training set (Aim 1). Second, in the PNC dataset, we will use multiple data types (demographic, behavioral, cognitive, imaging) to classify youths with suicide ideation (SI, n~750) and identify features (potentially modifiable) that are indicative of SI and may also point to potential mechanisms underlying youth SI (Aim 2). Lastly, in a subset of 936 youths (49 with SAs) with both CHOP EHR data and research PNC evaluation that was conducted at mean age 11 (T1), ~5 years before SA screening (T2), we will test the validity of models from Aims 1&2, and aim to identify data features that were collected at T1 and can improve/optimize/outperform the prediction of SAs that relies solely on EHR data (Aim 3). The proposed study relies on the expertise of a highly capable multidisciplinary team comprised of Dr. Barzilay (PI), child- adolescent psychiatrist experienced in suicide research and analysis of suicide related phenotypes in PNC data; Dr. Tsui (PI), an expert in machine learning who has developed predictive algorithms of SA and deaths using UPMC data; and collaborators critical for meeting study aims, Dr. Raquel Gur as the lead researcher who established the PNC, Dr. Ruben Gur who developed the PNC neurocognitive assessment tools, and Dr. Oquendo who will provide expertise in suicide prediction research. The team’s access and familiarity with CHOP EHR and PNC data resources, coupled with its interdisciplinary expertise, creates a unique opportunity to identify childhood features that can optimize later adolescent SA prediction. Expected findings can ultimately translate to real world clinical practice, be integrated in EHR, and help flag youths at risk for a SA in a pediatric setting, allowing timely identification and intervention, contributing to the mission of reducing suicide in youth.

摘要：青少年自杀是一个日益严重的健康问题，但目前的临床实践还不够及时。识别有自杀企图风险的青少年 (SA) 这项研究的首要目标是使用数据驱动。机器学习方法促进初级保健儿科环境中青少年 SA 的初级预防。临床指南建议从 12 岁起对抑郁症进行筛查，抑郁症被认为是自杀风险的一个指标。拟议的研究旨在识别可以早期收集的变量（特征）。青春期，并有助于预测青春期后期的 SA。这项研究将利用已经取得的成果。已投资于之前的项目：一项使用电子健康记录 (EHR) 来预测 SA 和死亡的研究匹兹堡大学医学中心 (UPMC) 医院和费城神经发育队列； (PNC)，其中包括约 9,500 名年轻人的综合表型分析，这些先前的努力将被整合到其中。开发和优化来自费城儿童医院 (CHOP) 网络的青少年 SA 预测，我们拥有 2014 年至 2018 年间约 40,000 名接受过 SA 病史筛查的人的数据 (n~1500) 首先，在CHOP数据集中，我们将基于UPMC数据生成预测模型，测试它们。 CHOP 青年人群的预测有效性，然后开发、优化和交叉验证这些预测使用 CHOP EHR 数据作为训练集的模型（目标 1）其次，在 PNC 数据集中，我们将使用多个数据。类型（人口统计、行为、认知、影像）对有自杀意念的青少年进行分类 (SI, n~750) 和识别表明 SI 的特征（可能可修改），也可能指出潜在的机制最后，在 936 名青少年（49 名有 SA）的子集中，包含 CHOP EHR 数据和研究 PNC 评估在平均年龄 11 岁 (T1) 进行，即 SA 筛查 (T2) 前约 5 年，我们将测试目标 1 和 2 中模型的有效性，旨在识别在 T1 收集的数据特征，并且可以改进/优化/超越仅依赖 EHR 数据的 SA 预测（目标 3）。依靠由 Barzilay 博士（PI）、儿童专家组成的高素质多学科团队的专业知识青少年抑郁症患者自杀研究及 PNC 自杀相关表型分析 Tsui 博士（PI），机器学习专家，开发了 SA 和死亡的预测算法使用 UPMC 数据；以及对实现研究目标至关重要的合作者，首席研究员 Raquel Gur 博士 PNC 的创建者、PNC 神经认知评估工具的开发者 Ruben Gur 博士以及 PNC 神经认知评估工具的开发者 Ruben Gur 博士和 Oquendo 将提供自杀预测研究方面的专业知识。 CHOP EHR 和 PNC 数据资源及其跨学科专业知识创造了独特的机会确定可以优化以后青少年 SA 预测的童年特征。转化为现实世界的临床实践，融入 EHR，并帮助标记儿科中面临 SA 风险的青少年设定，以便及时识别和干预，有助于实现减少青少年自杀的使命。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Deconstructing the role of the exposome in youth suicidal ideation: Trauma, neighborhood environment, developmental and gender effects.

DOI：
10.1016/j.ynstr.2021.100314
发表时间：
2021-05
期刊：
Neurobiology of stress
影响因子：
5
作者：
Barzilay R;Moore TM;Calkins ME;Maliackel L;Jones JD;Boyd RC;Warrier V;Benton TD;Oquendo MA;Gur RC;Gur RE
通讯作者：
Gur RE

Connectome-wide Functional Connectivity Abnormalities in Youth With Obsessive-Compulsive Symptoms.

DOI：
10.1016/j.bpsc.2021.07.014
发表时间：
2022-11
期刊：
Biological psychiatry. Cognitive neuroscience and neuroimaging
影响因子：
0
作者：
Alexander-Bloch AF;Sood R;Shinohara RT;Moore TM;Calkins ME;Chertavian C;Wolf DH;Gur RC;Satterthwaite TD;Gur RE;Barzilay R
通讯作者：
Barzilay R

Identifying Youth at Risk for Suicidal Thoughts and Behaviors Using the "p" factor in Primary Care: An Exploratory Study.

使用初级保健中的“p”因素识别有自杀想法和行为风险的青少年：一项探索性研究。

DOI：
10.1080/13811118.2022.2106925
发表时间：
2023
期刊：
Archives of suicide research : official journal of the International Academy for Suicide Research
影响因子：
0
作者：
Ruan-Iu,Linda;Rivers,AlannahShelby;Barzilay,Ran;Moore,TylerM;Tien,Allen;Diamond,Guy
通讯作者：
Diamond,Guy

Association Between Discrimination Stress and Suicidality in Preadolescent Children.

DOI：
10.1016/j.jaac.2021.08.011
发表时间：
2022-05
期刊：
Journal of the American Academy of Child and Adolescent Psychiatry
影响因子：
13.3
作者：
Argabright ST;Visoki E;Moore TM;Ryan DT;DiDomenico GE;Njoroge WFM;Taylor JH;Guloksuz S;Gur RC;Gur RE;Benton TD;Barzilay R
通讯作者：
Barzilay R

Evaluation of Attention-Deficit/Hyperactivity Disorder Medications, Externalizing Symptoms, and Suicidality in Children.

DOI：
10.1001/jamanetworkopen.2021.11342
发表时间：
2021-06-01
期刊：
JAMA network open
影响因子：
13.8
作者：
Shoval G;Visoki E;Moore TM;DiDomenico GE;Argabright ST;Huffnagle NJ;Alexander-Bloch AF;Waller R;Keele L;Benton TD;Gur RE;Barzilay R
通讯作者：
Barzilay R