Salem Haitham, Ruiz Ana, Hernandez Sarah, Wahid Kareem, Cao Fei, Karnes Brandi, Beasley Sarah, Sanches Marsal, Ashtari Elaheh, Pigott Teresa
J Psychiatr Pract. 2019 Jul;25(4):279-289. doi: 10.1097/PRA.0000000000000392.
BACKGROUND: Earlier research indicated that nearly 20% of patients diagnosed with either bipolar disorder (BD) or borderline personality disorder (BPD) also met criteria for the other diagnosis. Yet limited data are available concerning the potential impact of co-occurring BPD and/or BPD features on the course or outcome in patients with BD. Therefore, this study examined this comorbidity utilizing the standardized Borderline Personality Questionnaire (BPQ). METHODS: This study involved 714 adult patients with a primary diagnosis of BD per DSM-IV criteria who were admitted to the psychiatric unit at an academic hospital in Houston, TX between July 2013 and July 2018. All patients completed the BPQ within 72 hours of admission. Statistical analysis was used to detect correlations between severity of BD, length of stay (LOS), and scores on the BPQ. A machine learning model was constructed to predict the parameters affecting patients' readmission rates within 30 days. RESULTS: Analysis revealed that the severity of certain BPD traits at baseline was associated with mood state and outcome measured by LOS. Inpatients with BD who were admitted during acute depressive episodes had significantly higher mean scores on 7 of the 9 BPQ subscales (P<0.05) compared with those admitted during acute manic episodes. Inpatients with BD with greater BPQ scores on 4 of the 9 BPQ subscales had significantly shorter LOS than those with lower BPQ scores (P<0.05). The machine learning model identified 6 variables as predictors for likelihood of 30-day readmission with a high sensitivity (83%), specificity (77%), and area under the receiver operating characteristic curve of 86%. CONCLUSIONS: Although preliminary, these results suggest that inpatients with BD who have higher levels of BPD features were more likely to have depressive rather than manic symptoms, fewer psychotic symptoms, and a shorter LOS. Moreover, machine learning models may be particularly valuable in identifying patients with BD who are at the highest risk for adverse consequences including rapid readmission.
背景：早期研究表明，近20%被诊断为双相情感障碍（BD）或边缘型人格障碍（BPD）的患者也符合另一种诊断标准。然而，关于共病的BPD和/或BPD特征对BD患者病程或结局的潜在影响的数据有限。因此，本研究使用标准化的边缘型人格问卷（BPQ）对这种共病情况进行了研究。 方法：本研究纳入了714例根据《精神疾病诊断与统计手册》第四版（DSM-IV）标准初步诊断为BD的成年患者，这些患者于2013年7月至2018年7月期间入住德克萨斯州休斯顿一家学术医院的精神科病房。所有患者在入院72小时内完成了BPQ。采用统计分析来检测BD严重程度、住院时间（LOS）与BPQ得分之间的相关性。构建了一个机器学习模型来预测影响患者30天内再入院率的参数。 结果：分析显示，基线时某些BPD特质的严重程度与以LOS衡量的情绪状态和结局相关。与急性躁狂发作期间入院的BD患者相比，急性抑郁发作期间入院的BD患者在9个BPQ分量表中的7个上的平均得分显著更高（P<0.05）。在9个BPQ分量表中的4个上BPQ得分更高的BD住院患者的LOS明显短于得分较低的患者（P<0.05）。机器学习模型确定了6个变量作为30天再入院可能性的预测因素，其具有较高的敏感性（83%）、特异性（77%），以及受试者工作特征曲线下面积为86%。 结论：尽管这些结果是初步的，但表明具有较高BPD特征水平的BD住院患者更可能出现抑郁而非躁狂症状，精神病性症状较少，且LOS较短。此外，机器学习模型在识别BD患者中可能特别有价值，这些患者面临包括快速再入院在内的不良后果的风险最高。
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