Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus.
Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus.
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是不是狼疮？SLE风险概率指数（SLERPI）：一个简单的，临床友好的基于机器学习的模型，以帮助诊断系统性红斑狼疮。
DOI：
10.1136/annrheumdis-2020-219069
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发表时间：
2021-06
影响因子：
27.4
通讯作者：
Bertsias GK
中科院分区：
文献类型：
--
作者：
Adamichou C;Genitsaridi I;Nikolopoulos D;Nikoloudaki M;Repa A;Bortoluzzi A;Fanouriakis A;Sidiropoulos P;Boumpas DT;Bertsias GK
系统性红斑狼疮（SLE）的诊断推理是一个复杂的过程，反映了在给定时间点与竞争诊断相比的疾病概率。我们将机器学习应用于特征良好的患者数据集，以开发一种可以帮助SLE诊断的算法。从随机选择的802例SLE或对照风湿病成人的发现队列中，分析了临床选择的去卷积分类标准和非标准特征组。特征选择和模型构建使用随机森林和最小绝对收缩和选择算子-逻辑回归（LASSO-LR）进行。在验证队列（512例SLE，143例疾病对照）中测试了10倍交叉验证中的最佳模型。一种新的LASSO-LR模型具有最佳性能，包括14项加权特征，血小板减少/溶血性贫血、颧骨/斑丘疹、蛋白尿、低C3和C4、抗核抗体（ANA）和免疫学疾病是最强的SLE预测因子。我们的模型产生了与疾病严重程度和器官损伤正相关的SLE风险概率（取决于特征的组合），并允许基于SLE与其他诊断的可能性将验证队列无偏分类为诊断确定性水平（不太可能，确定性SLE）。将模型作为二元（狼疮/非狼疮）进行操作，我们注意到识别SLE的准确性极佳（94.8%），并且对早期疾病（93.8%）、肾炎（97.9%）、神经精神疾病（91.8%）和需要免疫抑制剂/生物制剂的严重狼疮（96.4%）具有高灵敏度。这被转换成评分系统，其中得分>7具有94.2%的准确性。我们基于经典疾病特征开发并验证了一种准确、临床医生友好的算法，用于早期SLE诊断和治疗，以改善患者预后。
Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis. From a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls). A novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy. We have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.
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