Abstract
Understanding mortality, derived from debilitations consisting of multiple diseases, is crucial for patient stratification. Here, in systematic fashion, we report comprehensive mortality data that map the temporal correlation of diseases that tend toward deaths in hospitals. We used a mortality trajectory model that represents the temporal ordering of disease appearance, with strong correlations, that terminated in fatal outcomes from one initial diagnosis in a set of patients throughout multiple admissions. Based on longitudinal healthcare records of 10.4 million patients from over 350 hospitals, we profiled 300 mortality trajectories, starting from 118 diseases, in 311,309 patients. Three-quarters (75%) of 59,794 end-stage patients and their deaths accrued throughout 160,360 multiple disease appearances in a short-term period (<4 years, 3.5 diseases per patient). This overlooked and substantial heterogeneity of disease patients and outcomes in the real world is unraveled in our trajectory map at the disease-wide level. For example, the converged dead-end in our trajectory map presents an extreme diversity of sepsis patients based on 43 prior diseases, including lymphoma and cardiac diseases. The trajectories involving the largest number of deaths for each age group highlight the essential predisposing diseases, such as acute myocardial infarction and liver cirrhosis, which lead to over 14,000 deaths. In conclusion, the deciphering of the debilitation processes of patients, consisting of the temporal correlations of diseases that tend towards hospital death at a population-wide level is feasible.
Highlights
Understanding which clinical risks lead to fatal consequences, such as prognosis variations among cardiac patients based on comorbidities, is a key component for the establishment of risk stratifications and health policy [1,2,3]
We used ICD-9-CM diagnosis codes to filter out records for non-disease conditions, such as diagnosis chapters referring to injuries, obstetrics, and healthcare-related contacts as defined in ICD-9-CM chapters (Fig 1A)
Condensed trajectory of the temporal correlation of diseases and mortality To model this process for near-term disease appearances, we considered only disease pairs that occurred in the same patient within one year
Summary
Understanding which clinical risks lead to fatal consequences, such as prognosis variations among cardiac patients based on comorbidities, is a key component for the establishment of risk stratifications and health policy [1,2,3]. Data-driven approaches using large-scale medical records for non-research purposes have demonstrated the validity of establishing correlated. Condensed trajectory of the temporal correlation of diseases and mortality. Ministry of Science and ICT (N-20-NM-CR12-S01, N-21-NM-CA08-S01). The computational analysis was supported by the National Supercomputing Center, including resources and technology
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