We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients' hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyse how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p $<$ 0.001, relative risk $>$ 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient's career as points where initially overlapping trajectories start to diverge towards different states. We identified 1,260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2-6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.

翻译：我們旨在全面識別影響患者住院利用率和死亡率的典型終身軌跡與關鍵事件。本研究利用奧地利2003年至2014年近所有住院記錄的獨特數據集（含4400萬條記錄），探究疾病軌跡。我們開發了一種新型多層疾病網絡方法，定量分析兩種或以上診斷在患者生命歷程中如何形成及演化。節點代表十年為一年齡組的診斷；每個年齡組構成共病多層網絡中的一層。層間鏈接編碼不同診斷間的顯著相關性（p < 0.001，相對風險 > 1.5），而層內鏈接編碼跨年齡組的診斷相關性。我們採用無監督聚類算法，將多層共病網絡中的重疊聚類檢測為典型疾病軌跡。我們將患者歷程中的關鍵事件識別為初始重疊軌跡開始向不同狀態分化的節點。我們共識別出1,260條不同疾病軌跡（女性618條，男性642條），平均包含9種（四分位距2-6）不同診斷，涵蓋長達70年（平均23年）。我們發現70對分歧軌跡，它們在年輕時共享部分診斷，但進入老年後發展為顯著不同的診斷組合。該疾病軌跡框架可幫助我們將關鍵事件識別為特定風險因素組合，這些組合使患者在數十年後面臨不同診斷的高風險。我們的發現為將基於數據的個性化生命歷程視角整合到臨床決策中提供了支撐。