METHODS: We analyzed a large sample of data from patients with MBS (n = 878) to identify potential predictors of TD risk. We modeled recurrent TD and failure to collect B1 labs using classical statistical and machine learning (ML) techniques.
RESULTS: We identified clusters of labs associated with increased risk of recurrent TD: micronutrient deficiencies, abnormal blood indices, malnutrition, and fluctuating electrolyte levels (aIRR range: 1.62-4.68). Additionally, demographic variables associated with lower socioeconomic status were predictive of recurrent TD. ML models predicting characteristics associated with failure to collect B1 labs achieved 75-81% accuracy, indicating that clinicians may fail to match symptoms with the underlying condition.
CONCLUSIONS: Our analysis suggests that both clinical and social factors can increase the risk of life-threatening TD episodes in some MBS patients. Identifying these indicators can help with diagnosis and treatment.
方法:我们分析了来自MBS患者(n=878)的大样本数据,以确定TD风险的潜在预测因子。我们使用经典的统计和机器学习(ML)技术对递归TD和未能收集B1实验室进行建模。
结果:我们确定了与复发性TD风险增加相关的实验室集群:微量营养素缺乏,血液指标异常,营养不良,和波动的电解质水平(aIRR范围:1.62-4.68)。此外,与较低社会经济地位相关的人口统计学变量是TD复发的预测因素.ML模型预测与未能收集B1实验室相关的特征,准确率达到75-81%,这表明临床医生可能无法将症状与潜在疾病相匹配。
结论:我们的分析表明,在某些MBS患者中,临床和社会因素都会增加危及生命的TD发作的风险。识别这些指标可以帮助诊断和治疗。