Fallah Nader, Hong Heather A, Wang Di, Humphreys Suzanne, Parsons Jessica, Walden Kristen, Street John, Charest-Morin Raphaele, Cheng Christiana L, Cheung Candice J, Noonan Vanessa K
Praxis Spinal Cord Institute, Vancouver, BC, Canada.
Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
Front Neurol. 2024 Jan 5;14:1286143. doi: 10.3389/fneur.2023.1286143. eCollection 2023.
Multimorbidity, defined as the coexistence of two or more health conditions, is common in persons with spinal cord injury (SCI). Network analysis is a powerful tool to visualize and examine the relationship within complex systems. We utilized network analysis to explore the relationship between 30 secondary health conditions (SHCs) and health outcomes in persons with traumatic (TSCI) and non-traumatic SCI (NTSCI). The study objectives were to (1) apply network models to the 2011-2012 Canadian SCI Community Survey dataset to identify key variables linking the SHCs measured by the Multimorbidity Index-30 (MMI-30) to healthcare utilization (HCU), health status, and quality of life (QoL), (2) create a short form of the MMI-30 based on network analysis, and (3) compare the network-derived MMI to the MMI-30 in persons with TSCI and NTSCI.
Three network models (Gaussian Graphical, Ising, and Mixed Graphical) were created and analyzed using standard network measures (e.g., network centrality). Data analyzed included demographic and injury variables (e.g., age, sex, region of residence, date, injury severity), multimorbidity (using MMI-30), HCU (using the 7-item HCU questionnaire and classified as "felt needed care was not received" [HCU-FNCNR]), health status (using the 12-item Short Form survey [SF-12] Physical and Mental Component Summary [PCS-12 and MCS-12] score), and QoL (using the 11-item Life Satisfaction questionnaire [LiSAT-11] first question and a single item QoL measure).
Network analysis of 1,549 participants (TSCI: 1137 and NTSCI: 412) revealed strong connections between the independent nodes (30 SHCs) and the dependent nodes (HCU-FNCNR, PCS-12, MCS-12, LiSAT-11, and the QoL score). Additionally, network models identified that cancer, deep vein thrombosis/pulmonary embolism, diabetes, high blood pressure, and liver disease were isolated. Logistic regression analysis indicated the network-derived MMI-25 correlated with all health outcome measures ( <0.001) and was comparable to the MMI-30.
The network-derived MMI-25 was comparable to the MMI-30 and was associated with inadequate HCU, lower health status, and poor QoL. The MMI-25 shows promise as a follow-up screening tool to identify persons living with SCI at risk of having poor health outcomes.
多种疾病并存被定义为两种或更多健康状况同时存在，在脊髓损伤（SCI）患者中很常见。网络分析是一种强大的工具，可用于可视化和检查复杂系统中的关系。我们利用网络分析来探索30种继发性健康状况（SHC）与创伤性脊髓损伤（TSCI）和非创伤性脊髓损伤（NTSCI）患者的健康结局之间的关系。研究目标是：（1）将网络模型应用于2011 - 2012年加拿大SCI社区调查数据集，以识别通过多种疾病指数-30（MMI - 30）测量的SHC与医疗保健利用（HCU）、健康状况和生活质量（QoL）之间的关键变量；（2）基于网络分析创建MMI - 30的简表；（3）比较TSCI和NTSCI患者中网络衍生的MMI与MMI - 30。
创建并使用标准网络测量方法（如网络中心性）分析了三种网络模型（高斯图形模型、伊辛模型和混合图形模型）。分析的数据包括人口统计学和损伤变量（如年龄、性别、居住地区、日期、损伤严重程度）、多种疾病（使用MMI - 30）、HCU（使用7项HCU问卷并分类为“感觉需要的护理未得到”[HCU - FNCNR]）、健康状况（使用12项简短形式调查[SF - 12]身体和心理成分总结[PCS - 12和MCS - 12]得分）以及QoL（使用11项生活满意度问卷[LiSAT - 11]的第一个问题和单项QoL测量）。
对1549名参与者（TSCI：1137名和NTSCI：412名）的网络分析显示，独立节点（30种SHC）与依赖节点（HCU - FNCNR、PCS - 12、MCS - 12、LiSAT - 11和QoL得分）之间存在紧密联系。此外，网络模型确定癌症、深静脉血栓形成/肺栓塞、糖尿病、高血压和肝病是孤立的。逻辑回归分析表明，网络衍生的MMI - 25与所有健康结局指标相关（<0.001），并且与MMI - 30相当。
网络衍生的MMI - 25与MMI - 30相当，并且与HCU不足、健康状况较差和QoL较差相关。MMI - 25有望作为一种后续筛查工具，用于识别有不良健康结局风险的SCI患者。
