Dataset Information

Therapeutic Drug Monitoring, Electronic Health Records, and Pharmacokinetic Modeling to Evaluate Sirolimus Drug Exposure-Response Relationships in Renal Transplant Patients.

ABSTRACT: BACKGROUND:Sirolimus, an immunosuppressive agent used in renal transplantation, can prevent allograft rejection. Identification of the therapeutic index (the ratio of minimum toxic concentration to minimum therapeutic concentration) for immunosuppresants is necessary to optimize the care of patients and set standards for bioequivalence evaluation of sirolimus products. However, the therapeutic index for sirolimus has been inconsistently defined, potentially because of inconsistencies in sirolimus exposure-response relationships. METHODS:The authors used retrospective therapeutic drug monitoring data from the electronic health records of patients treated in a tertiary health care system from 2008 to 2014 to (1) develop a population pharmacokinetic (PK) model, (2) use the model to simulate sirolimus concentrations, and (3) characterize the exposure-response relationship. Using Wilcoxon rank-sum and Fisher exact tests, the authors determined relationships between sirolimus exposure and adverse events (AEs) (anemia, leukopenia, thrombocytopenia, hyperlipidemia, and decline in renal function) and the composite efficacy end point of graft loss or rejection. RESULTS:The developed 2-compartment population PK model showed appropriate goodness of fit. In a late-phase (>12 months), postrenal transplant population of 27 inpatients, the authors identified statistically significant relationships between 83 simulated peak and trough sirolimus concentrations and outcomes: graft loss or rejection (P = 0.018) and decline in renal function (P = 0.006), respectively. CONCLUSIONS:Use of therapeutic drug monitoring results and PK modeling permitted correlation of sirolimus concentrations with graft loss or rejection and decline in renal function. However, the method was limited in its assessment of other AEs. To better evaluate sirolimus exposure-response relationships, the method should be applied to a larger sample of newly transplanted patients with a higher propensity toward AEs or efficacy failure.

SUBMITTER: Zimmerman KO

PROVIDER: S-EPMC5025355 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Publications

Therapeutic Drug Monitoring, Electronic Health Records, and Pharmacokinetic Modeling to Evaluate Sirolimus Drug Exposure-Response Relationships in Renal Transplant Patients.

Zimmerman Kanecia O KO Wu Huali H Greenberg Rachel R Guptill Jeffrey T JT Hill Kevin K Patel Uptal D UD Ku Lawrence L Gonzalez Daniel D Hornik Christoph C Jiang Wenlei W Zheng Nan N Melloni Chiara C Cohen-Wolkowiez Michael M

Therapeutic drug monitoring 20161001 5

<h4>Background</h4>Sirolimus, an immunosuppressive agent used in renal transplantation, can prevent allograft rejection. Identification of the therapeutic index (the ratio of minimum toxic concentration to minimum therapeutic concentration) for immunosuppresants is necessary to optimize the care of patients and set standards for bioequivalence evaluation of sirolimus products. However, the therapeutic index for sirolimus has been inconsistently defined, potentially because of inconsistencies in ...[more]

PMID: 27259059

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Project description:ObjectiveTo optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.MethodsIn a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).ResultsThe EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).ConclusionIncorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

Dataset Information

Therapeutic Drug Monitoring, Electronic Health Records, and Pharmacokinetic Modeling to Evaluate Sirolimus Drug Exposure-Response Relationships in Renal Transplant Patients.

Publications

Therapeutic Drug Monitoring, Electronic Health Records, and Pharmacokinetic Modeling to Evaluate Sirolimus Drug Exposure-Response Relationships in Renal Transplant Patients.

Similar Datasets

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