Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia.
Black Dog Institute, Sydney, NSW, Australia.
Aust N Z J Psychiatry. 2024 Jan;58(1):49-57. doi: 10.1177/00048674231201545. Epub 2023 Sep 28.
Differentiating schizophrenia from mania in acutely psychotic patients can be difficult, but is important in determining immediate and subsequent management. Such differentiation is generally addressed by clinical interviews, but an observational approach may assist. This paper therefore describes the development of a relevant observational measure.
We developed a provisional list of 49 items (weighting features with suggested specificity to schizophrenia and mania) for independent completion by two nurses and judged its ability to predict diagnosis provided by consultant psychiatrists.
Eighty-seven psychotic patients were recruited, and 173 completed data sets were analysed. We refined the item set to two sets of 10 items that best-differentiated schizophrenia from mania and vice versa. Optimal differentiation was achieved with a score of at least 7 on both the schizophrenia and mania item sets. Difference scores (i.e. schizophrenia items affirmed minus mania items affirmed) were also generated, with a difference score of +1 (i.e. one or more schizophrenia items being affirmed than mania items) showing optimal differentiation (sensitivity 0.67, specificity 0.82) between the two conditions. Evaluating all potential difference scores, we demonstrated that, as difference scores increased, diagnostic accuracy in identifying each condition was very high.
Analyses allow the properties of an observational measure (the 20-item Sydney Psychosis Observation Tool) to be described. While a single cut-off difference score was derived with acceptable discriminatory ability, we also established the capacity of varying difference scores to assign both schizophrenia and mania diagnoses with high accuracy.
在急性精神病患者中区分精神分裂症和躁狂症可能具有挑战性，但这对于确定当前和后续的治疗方案非常重要。这种区分通常通过临床访谈来进行，但观察方法可能会有所帮助。因此，本文描述了一种相关观察性测量工具的开发。
我们开发了一个包含 49 项的暂定清单（为精神分裂症和躁狂症分别加权具有特定意义的特征），由两名护士独立完成，并评估其预测顾问精神科医生提供的诊断的能力。
共招募了 87 名精神病患者，分析了 173 份完整的数据集。我们对项目集进行了精炼，得到了两组最佳区分精神分裂症和躁狂症的 10 项项目集。在两组项目集上的得分均至少为 7 时，可实现最佳区分。差异分数（即，肯定的精神分裂症项目数减去肯定的躁狂症项目数）也被生成，差异分数为+1（即，肯定的精神分裂症项目数比躁狂症项目数多一个或更多）在两种情况下显示出最佳区分（敏感性 0.67，特异性 0.82）。评估所有潜在的差异分数，我们表明，随着差异分数的增加，识别每种情况的诊断准确性非常高。
分析允许描述观察性测量工具（20 项悉尼精神病观察工具）的特性。虽然得出了具有可接受区分能力的单一截断差异分数，但我们还确定了不同差异分数以高精度分配精神分裂症和躁狂症诊断的能力。
