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Scientific DATA, 2019. 6(1), 201-, DOI: https://doi.org/10.1038/s41597-019-0220-5
Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia
Paik H, Matthew J. Kan; Nadav Rappoport; Dexter Hadley; Marina Sirota; Bin Chen; Udi Manber; Atul J. Butt; Cho SB
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.
- DOI: https://doi.org/10.1038/s41597-019-0220-5
- ISBN or ISSN: 2052-4463
- 본 연구는 질병관리본부 연구개발과제연구비를 지원받아 수행되었습니다.
- This research was supported by a fund by Research of Korea Centers for Disease Control and Prevention.