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

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Best Machine Learning Results on Test Set.

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

Rule Based Results

<p>Rule Based Results</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

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

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

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

Distribution of patients

<p>Distribution of patients</p