从透析患者的血清白蛋白进行动态死亡预测,使用具有竞争风险的强健关节模型
Ivan Damgov1,2, Meinhard Kieser2, Peter Rutherford3
1Division of Pediatric Nephrology, Center for Pediatric and Adolescent Medicine, University of Heidelberg, Im Neuenheimer Feld 430, 69120, Heidelberg, Germany.
Scientific reports
|October 8, 2025
概括
先进的关节模型 (JM) 通过整合白蛋白水平,风险因素和竞争事件来改善透析患者的死亡率预测. 这些模型比传统方法提供了更高的准确性和效率.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 现有的透析患者死亡率预测模型缺乏临床准确性.
- 专蛋白轨迹是预测透析患者结果的关键因素,但未得到充分利用.
- 需要先进的统计模型来整合动态患者数据以提高预后准确度.
研究的目的:
- 开发和验证先进的联合模型 (JM) 以提高透析患者的死亡率预测.
- 评估白蛋白轨迹,异常值和竞争风险对死亡率预测准确性的影响.
- 优化JM软件以提高计算效率和性能.
主要方法:
- 开发了12个联合模型 (JM),包括基线风险因素和白蛋白轨迹.
- 包括处理个人和患者内轨迹异常值的模型.
- 纳入了竞争活动,如血液透析转移和脏移植.
- 将模型应用于腹膜透析 (n=314) 和血液透析 (n=315) 队列.
- 使用培训/验证数据集和模拟研究验证模型性能.
主要成果:
- 在所有JM中,一致的专蛋白死亡危险比率 (1.22-1.28) 在所有JM中.
- JM软件显示处理速度快3至6倍,效率高4.1至12.9倍.
- 与仅考虑异常值的模型相比,纳入竞争风险的模型表现优越.
- 所有JM的表现始终优于基准考克斯模型,在长达五年的时间里实现了明显更高的曲线下面积 (AUC) 成绩.
- 随着患者随访时间的延长,模型准确性得到改善.
结论:
- 先进的关节模型有效地整合了动态白蛋白配置文件,异常值和竞争风险,以便在透析患者中更好地预测死亡率.
- 开发的JM提供了一个更精确和临床相关的工具,用于病学的预后评估.
- 优化的JM软件为临床和研究应用提供了显著的计算优势.
相关概念视频
Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration
189
Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
189
Physiological Pharmacokinetic Models: Assumption with Protein Binding
211
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
211
Assumptions of Survival Analysis
392
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
392
Clearance Models: Noncompartmental Models
243
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
243
Mechanistic Models: Compartment Models in Individual and Population Analysis
245
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
245
Model Approaches for Pharmacokinetic Data: Physiological Models
247
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
247


