Sun Lei, Zhang Yue, Zuo Xinliang, Liu Yongmei
Chaohu Clinical Medical College of Anhui Medical University, Hefei, China.
Department of Nephrology, Chaohu Hospital of Anhui Medical University, Hefei, China.
Front Med (Lausanne). 2025 Jan 7;11:1508485. doi: 10.3389/fmed.2024.1508485. eCollection 2024.
The annual growth in the population of maintenance hemodialysis (MHD) patients is accompanied by a trend towards younger age groups among new cases. Despite the escalating mortality risk observed in MHD patients, there remains a dearth of research focused on young and middle-aged individuals in this cohort, leading to a deficiency in specialized predictive instruments for this demographic. This research seeks to explore the critical determinants impacting mortality risk in young and middle-aged MHD patients and to construct a prediction model accordingly.
This study involved 127 young and middle-aged patients undergoing MHD in the Blood Purification Center of Chaohu Hospital of Anhui Medical University from January 2019 to January 2022. The follow-up period for each patient ended either at the time of death or on January 31, 2024. Participants were monitored to determine their survival status and categorized into two groups: those who survived (98 patients) and those who deceased (29 patients). Clinical data were gathered for analysis. Logistic regression was utilized to pinpoint independent risk factors for mortality among these patients. Subsequently, a nomogram was established to predict mortality risk. The efficacy of this model was assessed through the area under the receiver operating characteristic curve (AUC-ROC), alongside a calibration curve and the Hosmer-Lemeshow test to examine its fit. Additionally, decision curve analysis (DCA) was conducted to ascertain the clinical relevance of the predictive model.
The study incorporated 127 young and middle-aged patients undergoing MHD, with a mortality rate recorded at 22.83% (29 cases). A logistic regression analysis revealed that age, hemoglobin (HB), serum magnesium (Mg), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-albumin ratio (PAR) were significant independent predictors of mortality among these patients. Utilizing these variables, a nomogram was developed to predict mortality risk, achieving an AUC of 0.899 (95% CI: 0.833-0.966). The model exhibited a specificity of 83.67% and a sensitivity of 82.76%, demonstrating substantial discriminative ability. The model's robustness was confirmed through internal validation with 1,000 bootstrap samples, yielding an AUC of 0.894 (95% CI: 0.806-0.949). The calibration curve closely aligned with the ideal curve, and the Hosmer-Lemeshow goodness-of-fit test yielded a value of 6.312 with a -value of 0.612, verifying the model's high calibration accuracy. Additionally, the DCA indicated that the model provides a net benefit across a wide range of decision thresholds from 0 to 0.99, underscoring its clinical utility.
The nomogram developed from variables including age, HB levels, serum Mg, NLR, and PAR exhibits high levels of discrimination and calibration. This model effectively predicts mortality risk among young and middle-aged patients undergoing MHD, proving its clinical relevance.
维持性血液透析（MHD）患者的人口数量逐年增长，新病例呈现出年轻化的趋势。尽管MHD患者的死亡风险不断上升，但针对该队列中青年和中年个体的研究仍然匮乏，导致针对这一人群的专门预测工具不足。本研究旨在探讨影响中青年MHD患者死亡风险的关键决定因素，并据此构建预测模型。
本研究纳入了2019年1月至2022年1月在安徽医科大学附属巢湖医院血液净化中心接受MHD治疗的127例中青年患者。每位患者的随访期至死亡时或2024年1月31日结束。对参与者进行监测以确定其生存状态，并分为两组：存活者（98例）和死亡者（29例）。收集临床数据进行分析。采用逻辑回归确定这些患者死亡的独立危险因素。随后，建立列线图以预测死亡风险。通过受试者工作特征曲线下面积（AUC-ROC）、校准曲线和Hosmer-Lemeshow检验评估该模型的效能，以检验其拟合度。此外，进行决策曲线分析（DCA）以确定预测模型的临床相关性。
本研究纳入了127例接受MHD治疗的中青年患者，死亡率为22.83%（29例）。逻辑回归分析显示，年龄、血红蛋白（HB）、血清镁（Mg）、中性粒细胞与淋巴细胞比值（NLR）和血小板与白蛋白比值（PAR）是这些患者死亡的重要独立预测因素。利用这些变量建立了列线图以预测死亡风险，AUC为0.899（95%CI：0.833-0.966）。该模型的特异性为83.67%，敏感性为82.76%，具有显著的判别能力。通过1000次自抽样的内部验证证实了该模型的稳健性，AUC为0.894（95%CI：0.806-0.949）。校准曲线与理想曲线密切吻合，Hosmer-Lemeshow拟合优度检验的卡方值为6.312，P值为0.612，验证了该模型的高校准准确性。此外，DCA表明该模型在0至0.99的广泛决策阈值范围内均提供净效益，突出了其临床实用性。
由年龄、HB水平、血清Mg、NLR和PAR等变量构建的列线图具有较高的判别力和校准度。该模型有效地预测了中青年MHD患者的死亡风险，证明了其临床相关性。
