Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China.
Curr Top Med Chem. 2024;24(20):1799-1815. doi: 10.2174/0115680266295129240415120646.
Recently, we developed Research and Diagnostic Algorithm Rules (RADAR) to assess the clinical and pathway features of mood disorders. The aims of this paper are to review a) the methodology for developing continuous RADAR scores that describe the clinical and pathway features of schizophrenia, and b) a new method to visualize the clinical status of patients and the pathways implicated in RADAR graphs. We review how to interpret clinical RADAR scores, which serve as valuable tools for monitoring the staging of illness, lifetime suicidal behaviors, overall severity of illness, a general cognitive decline index, and a behavior-cognitive-psychosocial (BCPS) index that represents the "defect"; and b) pathway RADAR scores which reflect various protective (including the compensatory immune- inflammatory system) and adverse (including neuro-immune, neuro-oxidative, and neurotoxic biomarkers) outcome pathways. Using RADAR scores and machine learning, we created new, qualitatively different types of schizophrenia, such as major neurocognitive psychosis and simple psychosis. We also made RADAR graphs, which give us a quick way to compare the patient's clinical condition and pathways to those of healthy controls. We generated a personalized fingerprint for each patient, encompassing various clinical and pathway features of the disorder represented through RADAR graphs. The latter is utilized in clinical practice to assess the clinical condition of patients and identify treatment-required pathways to mitigate the risk of recurrent episodes, worsening BCPS, and increasing staging. The quantitative clinical RADAR scores should be used in schizophrenia research as dependent variables and regressed on the pathway RADAR scores.
最近，我们开发了研究和诊断算法规则（RADAR），以评估心境障碍的临床和途径特征。本文的目的是：a）审查用于开发连续 RADAR 评分的方法，该评分可描述精神分裂症的临床和途径特征；b）审查一种新方法，用于可视化患者的临床状况和 RADAR 图中涉及的途径。我们回顾了如何解释临床 RADAR 评分，这些评分可作为监测疾病分期、终生自杀行为、疾病总体严重程度、一般认知衰退指数以及代表“缺陷”的行为认知心理社会（BCPS）指数的有价值工具；以及 b）反映各种保护（包括代偿性免疫炎症系统）和不利（包括神经免疫、神经氧化和神经毒性生物标志物）结果途径的途径 RADAR 评分。使用 RADAR 评分和机器学习，我们创建了新的、定性不同类型的精神分裂症，例如主要神经认知精神病和单纯精神病。我们还制作了 RADAR 图，这使我们能够快速比较患者的临床状况和途径与健康对照组的临床状况和途径。我们为每位患者生成了一个个性化的指纹，其中包含通过 RADAR 图表示的各种临床和途径特征。后者在临床实践中用于评估患者的临床状况，并确定需要治疗的途径，以降低复发风险、BCPS 恶化和分期增加的风险。定量临床 RADAR 评分应作为依赖变量在精神分裂症研究中使用，并回归到途径 RADAR 评分。
