The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA.
Department of Psychology, University of New Mexico, 2001 Redondo S Dr., Albuquerque, NM 87106, USA.
Schizophr Res. 2019 Jun;208:344-352. doi: 10.1016/j.schres.2019.01.024. Epub 2019 Jan 31.
Patients with psychotic spectrum disorders share overlapping clinical/biological features, making it often difficult to separate them into a discrete nosology (i.e., Diagnostic and Statistical Manual of Mental Disorders [DSM]).
The current study investigated whether a continuum classification scheme based on symptom burden would improve conceptualizations for cognitive and real-world dysfunction relative to traditional DSM nosology. Two independent samples (New Mexico [NM] and Bipolar and Schizophrenia Network on Intermediate Phenotypes [B-SNIP]) of patients with schizophrenia (NM: N = 93; B-SNIP: N = 236), bipolar disorder Type I (NM: N = 42; B-SNIP: N = 195) or schizoaffective disorder (NM: N = 15; B-SNIP: N = 148) and matched healthy controls (NM: N = 64; B-SNIP: N = 717) were examined. Linear regressions examined how variance differed as a function of classification scheme (DSM diagnosis, negative and positive symptom burden, or a three-cluster solution based on symptom burden).
Symptom-based classification schemes (continuous and clustered) accounted for a significantly larger portion of captured variance of real-world functioning relative to DSM diagnoses across both samples. The symptom-based classification schemes accounted for large percentages of variance for general cognitive ability and cognitive domains in the NM sample. However, in the B-SNIP sample, symptom-based classification schemes accounted for roughly equivalent variance as DSM diagnoses. A potential mediating variable across samples was the strength of the relationship between negative symptoms and impaired cognition.
Current results support suggestions that a continuum perspective of psychopathology may be more powerful for explaining real-world functioning than the DSM diagnostic nosology, whereas results for cognitive dysfunction were sample dependent.
具有精神病谱系障碍的患者具有重叠的临床/生物学特征，这使得将它们准确地归入离散的分类学（即《精神障碍诊断与统计手册》[DSM]）变得非常困难。
本研究旨在探讨基于症状负担的连续分类方案是否可以改善认知和现实世界功能障碍的概念化，相对于传统的 DSM 分类学。两个独立的样本（新墨西哥州[NM]和双相及精神分裂症网络中间表型[B-SNIP]），包括精神分裂症患者（NM：N=93；B-SNIP：N=236）、单相 I 型双相障碍患者（NM：N=42；B-SNIP：N=195）或分裂情感障碍患者（NM：N=15；B-SNIP：N=148）以及匹配的健康对照组（NM：N=64；B-SNIP：N=717），进行了线性回归分析，以检验分类方案（DSM 诊断、阴性和阳性症状负担或基于症状负担的三聚类解决方案）如何作为功能差异的函数。
在两个样本中，基于症状的分类方案（连续和聚类）与 DSM 诊断相比，对现实世界功能的捕获方差具有更大的解释力。基于症状的分类方案在 NM 样本中对一般认知能力和认知领域的方差解释比例较大。然而，在 B-SNIP 样本中，基于症状的分类方案与 DSM 诊断的方差解释比例大致相当。在样本间可能存在一个中介变量，即阴性症状与认知障碍之间的关系强度。
目前的结果支持这样一种观点，即精神病病理学的连续观点可能比 DSM 诊断分类学更能解释现实世界的功能，而认知功能障碍的结果则依赖于样本。
