Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia

Hyojung Paik, Matthew J. Kan, Nadav Rappoport, Dexter Hadley, Marina Sirota, Bin Chen, Udi Manber, Seong Beom Cho, Atul J. Butte

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80–2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients.

Original languageEnglish
Article number201
JournalScientific data
Volume6
Issue number1
DOIs
StatePublished - 1 Dec 2019
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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