Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder

Objective: Cohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD. Methods: We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children’s Hospital (BCH) (N = 150) and Cincinnati Children’s Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups. Results: The rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children’s Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters. Conclusions: In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD

The Transition to Kindergarten: Predicting Socio-BehavioralOutcomes for Children With and Without Disabilities

The transition to kindergarten is regarded as acritical early childhood developmental milestone withimportant implications for later school outcomes. Littleprior research has focused on predictors of socio-behav?ioral kindergarten outcomes using longitudinal researchdesigns. Further, few studies have examined kindergartentransition using samples of children both with and withoutdisabilities. The goal of the current study was to explorepredictors of socio-behavioral kindergarten outcomes inchildren with and without developmental disabilities overtime. Data collection involved parent, preschool teacher,and kindergarten teacher reports of child behavior andinvolvement in kindergarten transition practices acrossthree time points during transition. Results of hierarchicallinear regression analyses demonstrated that preschoolchild behavioral variables (i.e., adaptive and problembehavior) were stronger predictors of kindergarten out?comes relative to caregiver concerns and involvement intransition preparation. Best practices in kindergarten tran?sition programming for children with and without disabil?ities are discussed

Baseline Results (ICD-9 codes) on Test Set.

<p>Baseline Results (ICD-9 codes) on Test Set.</p

Best Machine Learning Results on Test Set.

<p>Best Machine Learning Results on Test Set.</p

ASD Algorithm Project Overview.

<p>ASD–Autism Spectrum Disorder; ICD-9 –International Classification of Diseases 9^th edition; DSM IV–Diagnostic and Statistical Manual of Mental Diseases 4^th edition; ML—machine learning</p

Rule Based Results

<p>Rule Based Results</p

Distribution of patients

<p>Distribution of patients</p

ASD Rule-based Algorithm.

<p>ASD–Autism Spectrum Disorder; EHR–Electronic Health Records; ICD-9 –International Classification of Diseases 9^th edition; DSM IV–Diagnostic and Statistical Manual of Mental Diseases 4^th edition; PDD-NOS–pervasive developmental disorder not otherwise specified; sections 3a., 3b., 3c. refer to DSM IV ASD classification for Autism, Asperger’s and PDD-NOS, respectively</p

Dimensionality reduction using the t-SNE algorithm on PheWAS codes.

<p>Colors label clusters from the k-means algorithm. The clusters are labeled according to the comorbidity category with the highest relative prevalence for that cluster—duplicate labels appear when there is more than one cluster dominated by the same category. (t-distributed Stochastic Neighbor Embedding—t-SNE, Phenotype Wide Association Study–PheWAS, BCH–Boston Children’s Hospital; CCHMC–Cincinnati’s Children’s Hospital and Medical Center; VUMC–Vanderbilt University Medical Center, Deve.–Developmental Disorders, Seiz.–Seizure Disorders, Psych.—Psychological Disorders).</p

SVM-based Machine Learning Prediction System.

<p>SVM–Support Vector Machines.</p