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<p>Renal impairment (RI) is a common complication of multiple myeloma (MM), which is associated with poor prognosis. Here, we revealed the association between regular examination data and RI&#xa0;incidence, RI response and survival in newly diagnosed multiple myeloma (NDMM) patients. A retrospective analysis was conducted on the initial clinical data of 647 NDMM patients, comprising 193 patients (29.83%, 193/647) with RI and 454 (70.17%, 454/647) without RI at diagnosis. Logistic regression analyses, both univariate and multivariate, were performed to identify the independent influencing factors of RI with bootstrap techniques and resampling. The model used to predict the RI response was established using the support vector machine-recursive feature elimination (SVM-RFE) machine learning algorithms. Six variables identified by multi-factorial logistic regression analysis were independently associated with the incidence of RI, including the secreted monoclonal immunoglobulin of IgG type (33.16% vs. 52.64%), deregulated serum free κ/λ light chain (58.12% vs. 33.93%), elevated serum calcium (&gt; 2.65 mmol/L, 31.61% vs. 11.01%), elevated urea (≥ 8.3 mmol/L, 92.23% vs. 20.26%), elevated uric acid (≥ 340 μmol/L, 74.61% vs. 35.46%), and ISS (International Staging System) stage of III (90.16% vs. 31.50%). The lactate dehydrogenase (≥ 250 U/L; HR = 1.786, <i>P</i> = 0.005) and CKD (chronic kidney disease) stage (G4-G5; HR = 5.830, <i>P</i> = 0.016) were the independent adverse factors of the overall survival of NDMM patients with RI. In addition, this study provided a model to predict the response of RI using 5 clinical features, including calcium, Durie-Salmon (DS) stage, creatinine level before treatment, age and gender. The sensitivity, specificity, area under the curve (AUC) and accuracy were 86.75%, 51.15%, 78.30% and 72.99% in the training group, while 79.31%, 52.94%, 72.40% and 69.57% in the validation group. In conclusion, this study clarified the relationship between clinicopathologic characteristics and the incidence of renal injury, response and survival of NDMM patients, supporting clinical decision-making, and offering significant clinical application value.</p>

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Clinicopathologic predictors of renal response and survival in newly diagnosed multiple myeloma with renal injury: a retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月4日

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  • Wei Wei,
  • Haotian Shi,
  • Haimin Chen,
  • Xiaoling Chen,
  • Rong Peng,
  • Wenjun Yu,
  • Lixia Wu,
  • Nian Zhou,
  • Wenhao Zhao,
  • Weiwei Xu,
  • Yan Zhou,
  • Jingjing Yu,
  • Daolin Wei,
  • Fan Zhou

摘要

Renal impairment (RI) is a common complication of multiple myeloma (MM), which is associated with poor prognosis. Here, we revealed the association between regular examination data and RI incidence, RI response and survival in newly diagnosed multiple myeloma (NDMM) patients. A retrospective analysis was conducted on the initial clinical data of 647 NDMM patients, comprising 193 patients (29.83%, 193/647) with RI and 454 (70.17%, 454/647) without RI at diagnosis. Logistic regression analyses, both univariate and multivariate, were performed to identify the independent influencing factors of RI with bootstrap techniques and resampling. The model used to predict the RI response was established using the support vector machine-recursive feature elimination (SVM-RFE) machine learning algorithms. Six variables identified by multi-factorial logistic regression analysis were independently associated with the incidence of RI, including the secreted monoclonal immunoglobulin of IgG type (33.16% vs. 52.64%), deregulated serum free κ/λ light chain (58.12% vs. 33.93%), elevated serum calcium (> 2.65 mmol/L, 31.61% vs. 11.01%), elevated urea (≥ 8.3 mmol/L, 92.23% vs. 20.26%), elevated uric acid (≥ 340 μmol/L, 74.61% vs. 35.46%), and ISS (International Staging System) stage of III (90.16% vs. 31.50%). The lactate dehydrogenase (≥ 250 U/L; HR = 1.786, P = 0.005) and CKD (chronic kidney disease) stage (G4-G5; HR = 5.830, P = 0.016) were the independent adverse factors of the overall survival of NDMM patients with RI. In addition, this study provided a model to predict the response of RI using 5 clinical features, including calcium, Durie-Salmon (DS) stage, creatinine level before treatment, age and gender. The sensitivity, specificity, area under the curve (AUC) and accuracy were 86.75%, 51.15%, 78.30% and 72.99% in the training group, while 79.31%, 52.94%, 72.40% and 69.57% in the validation group. In conclusion, this study clarified the relationship between clinicopathologic characteristics and the incidence of renal injury, response and survival of NDMM patients, supporting clinical decision-making, and offering significant clinical application value.