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.
Highlights
One of the most basic aspects of clinical care in hospitals is understanding disease and mortality risk factors, for rare but preventable diseases or outcomes[1,2,3]
We showcase a novel temporal disease association that was discovered in the course of a large-scale analysis of California inpatient hospitalizations using data from the Healthcare Cost and Utilization Project (HCUP)[10]
We identified significant disease associations by establishing the relative association (RA) of diagnoses co-occurring within one year in a patient[4,6] using a binomial test, and compensating for multiple hypothesis testing using a false discovery rate (FDR) < 0.17
Summary
One of the most basic aspects of clinical care in hospitals is understanding disease and mortality risk factors, for rare but preventable diseases or outcomes[1,2,3]. Mapping disease relationships has a long history, the recent advent of digitalized health records and disease registries has led to an enhanced ability to organize and analyze healthcare data. The availability of these data allows the recapitulation of known temporal disease correlations using nonlongitudinal data[4], exploration of unordered disease pairs[5,6], and enables network analyses of disease relationships at a national scale[7]. We showcase a novel temporal disease association that was discovered in the course of a large-scale analysis of California inpatient hospitalizations using data from the Healthcare Cost and Utilization Project (HCUP)[10]. By using a combination of computational analysis and healthcare expert curation, an unexpected relationship between inpatient admissions for schizophrenia, a psychiatric disorder, and readmission for rhabdomyolysis was discovered
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