Division of National Supercomputing, Center for Supercomputing Applications, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea.
Department of Data and HPC Science, University of Science and Technology, Daejeon, Republic of Korea.
PLoS One. 2021 Oct 5;16(10):e0257894. doi: 10.1371/journal.pone.0257894. eCollection 2021.
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.
了解由多种疾病导致的衰弱状态导致的死亡率对于患者分层至关重要。在这里，我们以系统的方式报告了全面的死亡率数据，这些数据描绘了在医院趋向死亡的疾病的时间相关性。我们使用了一种死亡率轨迹模型，该模型代表了疾病出现的时间顺序，具有很强的相关性，这些疾病最终导致一组患者在多次住院期间从最初的一个诊断中出现致命结局。基于来自 350 多家医院的 1040 万患者的纵向医疗记录，我们在 311309 名患者中，从 118 种疾病中，分析了 300 种死亡率轨迹。在短时间内（<4 年，每位患者 3.5 种疾病），75%的 59794 名终末期患者及其死亡人数都与 160360 多种疾病的出现有关。在现实世界中，这种被忽视的、大量的疾病患者和结果的异质性在我们的轨迹图中得到了揭示。例如，我们的轨迹图中呈现的一个死亡终点汇聚，基于 43 种先前疾病，包括淋巴瘤和心脏病，呈现了一个极端多样化的败血症患者群体。对于每个年龄组来说，涉及死亡人数最多的轨迹突出了导致超过 14000 人死亡的主要潜在疾病，如急性心肌梗死和肝硬化。总之，从人群层面上对患者衰弱过程的解码，即疾病之间的时间相关性，导致医院死亡，是可行的。
