Alexander-Bloch Aaron F, Raznahan Armin, Shinohara Russell T, Mathias Samuel R, Bathulapalli Harini, Bhalla Ish P, Goulet Joseph L, Satterthwaite Theodore D, Bassett Danielle S, Glahn David C, Brandt Cynthia A
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Proc Math Phys Eng Sci. 2020 Jul;476(2239):20190790. doi: 10.1098/rspa.2019.0790. Epub 2020 Jul 1.
Co-morbidity between medical and psychiatric conditions is commonly considered between individual pairs of conditions. However, an important alternative is to consider all conditions as part of a co-morbidity network, which encompasses all interactions between patients and a healthcare system. Analysis of co-morbidity networks could detect and quantify general tendencies not observed by smaller-scale studies. Here, we investigate the co-morbidity network derived from longitudinal healthcare records from approximately 1 million United States military veterans, a population disproportionately impacted by psychiatric morbidity and psychological trauma. Network analyses revealed marked and heterogenous patterns of co-morbidity, including a multi-scale community structure composed of groups of commonly co-morbid conditions. Psychiatric conditions including posttraumatic stress disorder were strong predictors of future medical morbidity. Neurological conditions and conditions associated with chronic pain were particularly highly co-morbid with psychiatric conditions. Across conditions, the degree of co-morbidity was positively associated with mortality. Co-morbidity was modified by biological sex and could be used to predict future diagnostic status, with out-of-sample prediction accuracy of 90-92%. Understanding complex patterns of disease co-morbidity has the potential to lead to improved designs of systems of care and the development of targeted interventions that consider the broader context of mental and physical health.
医学疾病与精神疾病之间的共病通常是在个体的疾病对之间进行考量。然而，一个重要的替代方法是将所有疾病视为共病网络的一部分，该网络涵盖了患者与医疗系统之间的所有相互作用。对共病网络的分析能够检测并量化小规模研究中未观察到的总体趋势。在此，我们研究了源自约100万美国退伍军人纵向医疗记录的共病网络，这一群体受到精神疾病发病率和心理创伤的影响尤为严重。网络分析揭示了共病的显著且异质性模式，包括由常见共病疾病组构成的多尺度社区结构。包括创伤后应激障碍在内的精神疾病是未来医学发病的有力预测指标。神经系统疾病以及与慢性疼痛相关的疾病与精神疾病的共病尤为严重。在所有疾病中，共病程度与死亡率呈正相关。共病因生物性别而异，可用于预测未来的诊断状况，样本外预测准确率为90 - 92%。了解疾病共病的复杂模式有可能带来护理系统设计的改进以及考虑到更广泛身心健康背景的针对性干预措施的开发。
