PDF(Pubmed)
Abstract：
BACKGROUND: Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is an adult-onset multisystemic disease, affecting the peripheral nerves, heart, gastrointestinal tract, eyes, and kidneys. Nowadays, several treatment options are available; thus, avoiding misdiagnosis is crucial to starting therapy in early disease stages. However, clinical diagnosis may be difficult, as the disease may present with unspecific symptoms and signs. We hypothesize that the diagnostic process may benefit from the use of machine learning (ML).
METHODS: 397 patients referring to neuromuscular clinics in 4 centers from the south of Italy with neuropathy and at least 1 more red flag, as well as undergoing genetic testing for ATTRv, were considered. Then, only probands were considered for analysis. Hence, a cohort of 184 patients, 93 with positive and 91 (age- and sex-matched) with negative genetics, was considered for the classification task. The XGBoost (XGB) algorithm was trained to classify positive and negative TTR mutation patients. The SHAP method was used as an explainable artificial intelligence algorithm to interpret the model findings.
RESULTS: diabetes, gender, unexplained weight loss, cardiomyopathy, bilateral carpal tunnel syndrome (CTS), ocular symptoms, autonomic symptoms, ataxia, renal dysfunction, lumbar canal stenosis, and history of autoimmunity were used for the model training. The XGB model showed an accuracy of 0.707 ± 0.101, a sensitivity of 0.712 ± 0.147, a specificity of 0.704 ± 0.150, and an AUC-ROC of 0.752 ± 0.107. Using the SHAP explanation, it was confirmed that unexplained weight loss, gastrointestinal symptoms, and cardiomyopathy showed a significant association with the genetic diagnosis of ATTRv, while bilateral CTS, diabetes, autoimmunity, and ocular and renal involvement were associated with a negative genetic test.
CONCLUSIONS: Our data show that ML might potentially be a useful instrument to identify patients with neuropathy that should undergo genetic testing for ATTRv. Unexplained weight loss and cardiomyopathy are relevant red flags in ATTRv in the south of Italy. Further studies are needed to confirm these findings.
METHODS: 397 patients referring to neuromuscular clinics in 4 centers from the south of Italy with neuropathy and at least 1 more red flag, as well as undergoing genetic testing for ATTRv, were considered. Then, only probands were considered for analysis. Hence, a cohort of 184 patients, 93 with positive and 91 (age- and sex-matched) with negative genetics, was considered for the classification task. The XGBoost (XGB) algorithm was trained to classify positive and negative TTR mutation patients. The SHAP method was used as an explainable artificial intelligence algorithm to interpret the model findings.
RESULTS: diabetes, gender, unexplained weight loss, cardiomyopathy, bilateral carpal tunnel syndrome (CTS), ocular symptoms, autonomic symptoms, ataxia, renal dysfunction, lumbar canal stenosis, and history of autoimmunity were used for the model training. The XGB model showed an accuracy of 0.707 ± 0.101, a sensitivity of 0.712 ± 0.147, a specificity of 0.704 ± 0.150, and an AUC-ROC of 0.752 ± 0.107. Using the SHAP explanation, it was confirmed that unexplained weight loss, gastrointestinal symptoms, and cardiomyopathy showed a significant association with the genetic diagnosis of ATTRv, while bilateral CTS, diabetes, autoimmunity, and ocular and renal involvement were associated with a negative genetic test.
CONCLUSIONS: Our data show that ML might potentially be a useful instrument to identify patients with neuropathy that should undergo genetic testing for ATTRv. Unexplained weight loss and cardiomyopathy are relevant red flags in ATTRv in the south of Italy. Further studies are needed to confirm these findings.
摘要：
背景：遗传性甲状腺素运载蛋白淀粉样变性多发性神经病（ATTRv）是一种成人发作的多系统疾病，影响周围神经，心,胃肠道,眼睛,还有肾脏.如今,有几种治疗选择；因此，避免误诊对于在疾病早期阶段开始治疗至关重要。然而,临床诊断可能很困难，因为该疾病可能存在非特异性症状和体征。我们假设诊断过程可能受益于机器学习（ML）的使用。
方法：397名来自意大利南部的4个中心的神经肌肉诊所患者，患有神经病和至少1个以上的危险信号，以及接受ATTRv的基因检测，被考虑。然后，只有先证者被考虑进行分析。因此，184名患者，93名阳性，91名(年龄和性别匹配)阴性遗传学，被考虑用于分类任务。训练XGBoost(XGB)算法以对阳性和阴性TTR突变患者进行分类。SHAP方法被用作可解释的人工智能算法来解释模型结果。
结果：糖尿病，性别,无法解释的体重减轻,心肌病,双侧腕管综合征(CTS),眼部症状,自主神经症状,共济失调,肾功能不全,腰椎管狭窄,和自身免疫病史用于模型训练。XGB模型显示出0.707±0.101的准确性，0.712±0.147的灵敏度，0.704±0.150的特异性和0.752±0.107的AUC-ROC。使用SHAP解释，证实了无法解释的体重减轻，胃肠道症状,心肌病与ATTRv的基因诊断有显著关联，而双侧CTS,糖尿病,自身免疫,眼部和肾脏受累与基因检测阴性相关。
结论：我们的数据表明，ML可能是鉴别应接受ATTRv基因检测的神经病变患者的有用工具。原因不明的体重减轻和心肌病是意大利南部ATTRv的相关危险信号。需要进一步的研究来证实这些发现。
方法：397名来自意大利南部的4个中心的神经肌肉诊所患者，患有神经病和至少1个以上的危险信号，以及接受ATTRv的基因检测，被考虑。然后，只有先证者被考虑进行分析。因此，184名患者，93名阳性，91名(年龄和性别匹配)阴性遗传学，被考虑用于分类任务。训练XGBoost(XGB)算法以对阳性和阴性TTR突变患者进行分类。SHAP方法被用作可解释的人工智能算法来解释模型结果。
结果：糖尿病，性别,无法解释的体重减轻,心肌病,双侧腕管综合征(CTS),眼部症状,自主神经症状,共济失调,肾功能不全,腰椎管狭窄,和自身免疫病史用于模型训练。XGB模型显示出0.707±0.101的准确性，0.712±0.147的灵敏度，0.704±0.150的特异性和0.752±0.107的AUC-ROC。使用SHAP解释，证实了无法解释的体重减轻，胃肠道症状,心肌病与ATTRv的基因诊断有显著关联，而双侧CTS,糖尿病,自身免疫,眼部和肾脏受累与基因检测阴性相关。
结论：我们的数据表明，ML可能是鉴别应接受ATTRv基因检测的神经病变患者的有用工具。原因不明的体重减轻和心肌病是意大利南部ATTRv的相关危险信号。需要进一步的研究来证实这些发现。
