Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2–0.3%), medium (0.3–1%) and high in-hospital mortality (2–11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50–59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.

中文翻译：

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心血管死亡之路上的高风险多发病模式。

多病是一种常见的现象，即一名患者同时发生两种或多种疾病。了解不同疾病在患者一生中是如何相互影响的，可以显着促进个性化的预防工作。然而，我们目前关于患者健康（他们的疾病轨迹）长期发展的大部分知识要么局限于狭窄的时间跨度，要么局限于特定的（一组）疾病。在这里，我们旨在确定可能决定患者未来疾病进展的决定性事件。在对超过 17 年观察到的 9,000,000 名患者医院诊断历史的人群分析中，通过算法确定的多发病模式（包括或排除的疾病组）来描述患者的健康状况。随着时间的推移，患者可能会获得改变其健康状况的新诊断；他们描述了疾病轨迹。我们根据患者当前的健康状况衡量他们未来将患上某些疾病的年龄和性别特定风险。在本分析中，人口由一组 132 种不同的多发病模式描述。对于老年患者，我们发现 3 组与低（年住院死亡率为 0.2-0.3%）、中（0.3-1%）和高住院死亡率（2-11%）相关的多发病模式。我们确定了显着增加晚年达到高死亡率健康状态风险的疾病组合。例如，在 50-59 岁被诊断患有糖尿病和高血压的男性（女性）中，与所有同年龄和性别的患者相比，1年内进入高死亡率地区的风险分别增加了1.96±0.11（2.60±0.18）倍和2.09±0.12倍（3.04倍） ± 0.18) 如果另外诊断出代谢紊乱。我们的方法既可用于预测未来的疾病负担，也可用于识别患者职业生涯中强烈决定其疾病进展的关键事件，从而构成有效预防措施的目标。我们表明，当被诊断患有糖尿病、高血压和代谢紊乱时，女性患心血管疾病的风险显着高于男性。18) 如果另外被诊断患有代谢紊乱。我们的方法既可用于预测未来的疾病负担，也可用于识别患者职业生涯中强烈决定其疾病进展的关键事件，从而构成有效预防措施的目标。我们表明，当被诊断患有糖尿病、高血压和代谢紊乱时，女性患心血管疾病的风险显着高于男性。18) 如果另外被诊断患有代谢紊乱。我们的方法既可用于预测未来的疾病负担，也可用于识别患者职业生涯中强烈决定其疾病进展的关键事件，从而构成有效预防措施的目标。我们表明，当被诊断患有糖尿病、高血压和代谢紊乱时，女性患心血管疾病的风险显着高于男性。