Meid Andreas D, Scherkl Camilo, Metzner Michael, Czock David, Seidling Hanna M
Internal Medicine IX: Department of Clinical Pharmacology and Pharmacoepidemiology, Medical Faculty Heidelberg/Heidelberg University Hospital, Heidelberg University, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany.
Internal Medicine IX: Department of Clinical Pharmacology and Pharmacoepidemiology-Cooperation Unit Clinical Pharmacy, Medical Faculty Heidelberg/Heidelberg University Hospital, Heidelberg University, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany.
Pharmaceuticals (Basel). 2024 Aug 7;17(8):1041. doi: 10.3390/ph17081041.
Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia.
定量系统药理学（QSP）模型很少前瞻性地应用于临床实践中的决策制定。因此，我们旨在建立一个钾稳态的QSP模型，以根据螺内酯的给药情况预测钾的变化轨迹。为此，我们提出了一个通用的工作流程，并将其应用于一家德国三级护理医院治疗的患者的电子健康记录（EHR）。工作流程步骤包括模型探索、局部和全局敏感性分析（SA）、模型参数的可识别性分析（IA）以及个体间变异性（IIV）的指定。患者协变量、选定参数和IIV随后为次日个体钾变化轨迹的贝叶斯后验预测定义了先验信息。按照这些步骤，通过一个Shiny应用程序对成功建立的QSP模型进行交互式探索。SA和IA产生了五个对贝叶斯预测有影响且可估计的参数（细胞外液量、醛固酮增多症、盐皮质激素受体丰度、钾摄入量、钠摄入量）。该实用化模型在九名试点患者中得到验证，并基于（绝对）平均倍数误差显示出令人满意的性能。这为医院系统中钾浓度的处方监测提供了原理证明，该监测可建议采取先发制人的临床措施，从而有可能避免危险的高钾血症或低钾血症。
