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. 2014 Jan;133(1):e54-63.
doi: 10.1542/peds.2013-0819. Epub 2013 Dec 9.

Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis

Finale Doshi-Velez  1 , Yaorong Ge, Isaac Kohane
Affiliations

Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis

Finale Doshi-Velez et al. Pediatrics. 2014 Jan.

Abstract

Objective: The distinct trajectories of patients with autism spectrum disorders (ASDs) have not been extensively studied, particularly regarding clinical manifestations beyond the neurobehavioral criteria from the Diagnostic and Statistical Manual of Mental Disorders. The objective of this study was to investigate the patterns of co-occurrence of medical comorbidities in ASDs.

Methods: International Classification of Diseases, Ninth Revision codes from patients aged at least 15 years and a diagnosis of ASD were obtained from electronic medical records. These codes were aggregated by using phenotype-wide association studies categories and processed into 1350-dimensional vectors describing the counts of the most common categories in 6-month blocks between the ages of 0 to 15. Hierarchical clustering was used to identify subgroups with distinct courses.

Results: Four subgroups were identified. The first was characterized by seizures (n = 120, subgroup prevalence 77.5%). The second (n = 197) was characterized by multisystem disorders including gastrointestinal disorders (prevalence 24.3%) and auditory disorders and infections (prevalence 87.8%), and the third was characterized by psychiatric disorders (n = 212, prevalence 33.0%). The last group (n = 4316) could not be further resolved. The prevalence of psychiatric disorders was uncorrelated with seizure activity (P = .17), but a significant correlation existed between gastrointestinal disorders and seizures (P < .001). The correlation results were replicated by using a second sample of 496 individuals from a different geographic region.

Conclusions: Three distinct patterns of medical trajectories were identified by unsupervised clustering of electronic health record diagnoses. These may point to distinct etiologies with different genetic and environmental contributions. Additional clinical and molecular characterizations will be required to further delineate these subgroups.

Keywords: autism; clustering; comorbidity; psychiatric disorders; seizure.

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Figures

FIGURE 1
FIGURE 1
Prevalence of seizures and psychiatric disorders in each subgroup (with 95% confidence intervals). Subgroup 1 is characterized by seizures, whereas subgroup 3 is characterized by psychiatric disorders. Prevalence is with respect to the subgroup sizes recorded in Table 1. A, Prevalence of seizures. B, Prevalence of psychiatric disorders.
FIGURE 2
FIGURE 2
Prevalence of various specific delays in development (eg, reading and coordination delays) for each of the 4 subgroups.
FIGURE 3
FIGURE 3
Prevalence of ASD diagnoses over time. ASD diagnoses spike dramatically starting at age 10 for individuals for subgroup characterized by seizures (subgroup 1). In contrast, individuals in the subgroup characterized by psychiatric disorders (subgroup 3) have higher rates of ASD-related diagnoses starting at age 5.
FIGURE 4
FIGURE 4
Prevalence of the diagnoses characterizing subgroup 1.
FIGURE 5
FIGURE 5
Prevalence of the diagnoses characterizing subgroup 2.
FIGURE 6
FIGURE 6
Prevalence of the diagnoses characterizing subgroup 3 over the first 15 years of life._
See this image and copyright information in PMC

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