PDF(Pubmed)
Abstract：
UNASSIGNED: This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe.
UNASSIGNED: This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers.
UNASSIGNED: Involving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835-0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN.
UNASSIGNED: This study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.
UNASSIGNED: This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers.
UNASSIGNED: Involving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835-0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN.
UNASSIGNED: This study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.
摘要：
■本研究旨在开发和验证机器学习模型，以预测增殖性狼疮性肾炎（PLN）的发生，当肾活检不可行或不安全时，提供可靠的诊断选择。
■本研究回顾性分析了2011年至2021年在四川大学华西医院接受肾活检的SLE和肾脏受累患者的临床和实验室数据。我们将70％的患者随机分配到一个训练队列，其余30％随机分配到一个测试队列。在训练队列上构建了各种机器学习模型，包括广义线性模型(例如，逻辑回归，最小绝对收缩和选择运算符，岭回归，和弹性网)，支持向量机(线性和径向基核函数)，和决策树模型(例如，经典决策树,条件推理树，和随机森林)。使用ROC曲线评估诊断性能，校正曲线,和DCA为两个队列。此外，比较了不同的机器学习模型，以识别关键和共享特征，旨在筛选潜在的PLN诊断标志物。
■涉及1312名LN患者，对780例PLN/NPLN病例进行了分析。随机分为训练组(547例)和试验组(233例)。我们在训练组中开发了9种机器学习模型。七个模型在测试队列中表现出出色的辨别能力，随机森林模型的判别能力最高(AUC:0.880,95%置信区间(CI):0.835~0.926)。Logistic回归具有最佳的校准，而随机森林表现出最大的临床净效益。通过比较各种模型的特征，我们证实了传统指标如抗dsDNA抗体的功效，补码水平,血清肌酐,和尿红细胞和白细胞在预测和区分PLN中的作用。此外,我们发现了以前有争议或未充分利用的指标的潜在价值，如血清氯化物，中性粒细胞百分比，血清胱抑素C,血细胞比容,尿液pH值,血常规红细胞，和免疫球蛋白M在预测PLN中的作用。
■这项研究为纳入更广泛的生物标志物以诊断和预测PLN提供了全面的视角。此外,它为无法进行肾活检的SLE患者提供了理想的非侵入性诊断工具。
■本研究回顾性分析了2011年至2021年在四川大学华西医院接受肾活检的SLE和肾脏受累患者的临床和实验室数据。我们将70％的患者随机分配到一个训练队列，其余30％随机分配到一个测试队列。在训练队列上构建了各种机器学习模型，包括广义线性模型(例如，逻辑回归，最小绝对收缩和选择运算符，岭回归，和弹性网)，支持向量机(线性和径向基核函数)，和决策树模型(例如，经典决策树,条件推理树，和随机森林)。使用ROC曲线评估诊断性能，校正曲线,和DCA为两个队列。此外，比较了不同的机器学习模型，以识别关键和共享特征，旨在筛选潜在的PLN诊断标志物。
■涉及1312名LN患者，对780例PLN/NPLN病例进行了分析。随机分为训练组(547例)和试验组(233例)。我们在训练组中开发了9种机器学习模型。七个模型在测试队列中表现出出色的辨别能力，随机森林模型的判别能力最高(AUC:0.880,95%置信区间(CI):0.835~0.926)。Logistic回归具有最佳的校准，而随机森林表现出最大的临床净效益。通过比较各种模型的特征，我们证实了传统指标如抗dsDNA抗体的功效，补码水平,血清肌酐,和尿红细胞和白细胞在预测和区分PLN中的作用。此外,我们发现了以前有争议或未充分利用的指标的潜在价值，如血清氯化物，中性粒细胞百分比，血清胱抑素C,血细胞比容,尿液pH值,血常规红细胞，和免疫球蛋白M在预测PLN中的作用。
■这项研究为纳入更广泛的生物标志物以诊断和预测PLN提供了全面的视角。此外,它为无法进行肾活检的SLE患者提供了理想的非侵入性诊断工具。
