Luciano Battioni, Scatularo Cristhian E, Bellia Sebastián, Lescano Adrián, Pereiro Stella M, Giorgini Julio
Consejo de Insuficiencia Cardiaca e Hipertensión Pulmonar, Sociedad Argentina de Cardiología, Buenos Aires, Argentina.
Consejo de Aspectos Psicosociales. Sociedad Argentina de Cardiología, Buenos Aires, Argentina.
Arch Cardiol Mex. 2022 Oct 13;93(Supl 6):87-93. doi: 10.24875/ACM.22000101.
: SARS-COV2 pandemic has generated deleterious psychological and social effects as reported in our survey IMPPACTS-SAC.20.
: To determine which domains of the Patient Health Questionnaire (PHQ 9) have the biggest influence in the diagnosis of major depression, also, to identify subpopulations with high prevalence of this disease.
: IMPPACTS-SAC.20 survey analysis. Unsupervised machine learning techniques were used to perform a factorial analysis and to create groups of similar cases according to their performance in PHQ 9.
: 1221 participants that took the PHQ 9 questionnaire were included. Factorial analysis showed that two main dimensions (neurasthenia and negative self-perception) accounted for 67.2% of the questionnaire variance (KMO test 0.911; Bartlett p < 0.001). The combination of both dimensions in hierarchical analysis generated nine clusters. Groups 5, 4, 2 and 1 explained 93% of the major depression cases. Groups 5 and 4 presented high neurasthenia values, and groups 2 and 1 high negative self-perception. Groups 6, 7 and 8 combined, presented a prevalence of major depression of 0.6%.
: The implementation of machine learning techniques detected two dimensions within the PHQ 9 score, neurasthenia and negative self-perception. Subgroups with a high prevalence of major depression were found, whose main clinical characteristics were female sex, alcohol consumption, smoking and suicidal intention.
正如我们的IMPPACTS - SAC.20调查所报告的那样，SARS - COV2大流行已产生有害的心理和社会影响。
确定患者健康问卷（PHQ - 9）的哪些领域对重度抑郁症的诊断影响最大，同时，识别该疾病高患病率的亚人群。
IMPPACTS - SAC.20调查分析。使用无监督机器学习技术进行因子分析，并根据参与者在PHQ - 9中的表现创建相似病例组。
纳入了1221名进行PHQ - 9问卷调查的参与者。因子分析表明，两个主要维度（神经衰弱和消极自我认知）占问卷方差的67.2%（KMO检验0.911；Bartlett检验p < 0.001）。层次分析中这两个维度的组合产生了九个聚类。第5、4、2和1组解释了93%的重度抑郁症病例。第5和4组呈现出高神经衰弱值，第2和1组呈现出高消极自我认知。第6、7和8组合并呈现出0.6%的重度抑郁症患病率。
机器学习技术的应用在PHQ - 9评分中检测到两个维度，即神经衰弱和消极自我认知。发现了重度抑郁症高患病率的亚组，其主要临床特征为女性、饮酒、吸烟和自杀意图。
