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Sci Data. 2019 Oct 15;6(1):201. doi: 10.1038/s41597-019-0220-5.

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

Paik H1,2,3,4, Kan MJ1,2, Rappoport N1,2, Hadley D1,2, Sirota M1,2, Chen B1,2, Manber U1,5, Cho SB6, Butte AJ7,8.

Author information

1
Bakar Computational Health Sciences Institute, University of California, San Francisco, 550 16th Street, San Francisco, CA, 9414, USA.
2
Department of Pediatrics, University of California, San Francisco, 550 16th Street, San Francisco, CA, 94143, USA.
3
Korea Institute of Science and Technology Information, Center for Supercomputing Application, Division of Supercomputing, Daejeon, 34141, South Korea.
4
National Institute of Health, Division of Bio-Medical Informatics, Center for Genome Science, OHTAC, 187 Osongsaengmyeong2(i)-ro, Gangoe-myeon, Cheongwon-gun, ChoongchungBuk-do, South Korea.
5
Department of Medicine, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA, 94143, USA.
6
National Institute of Health, Division of Bio-Medical Informatics, Center for Genome Science, OHTAC, 187 Osongsaengmyeong2(i)-ro, Gangoe-myeon, Cheongwon-gun, ChoongchungBuk-do, South Korea. sbcho@korea.kr.
7
Bakar Computational Health Sciences Institute, University of California, San Francisco, 550 16th Street, San Francisco, CA, 9414, USA. atul.butte@ucsf.edu.
8
Department of Pediatrics, University of California, San Francisco, 550 16th Street, San Francisco, CA, 94143, USA. atul.butte@ucsf.edu.

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.

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