Chang, Jack

Abstract Objective: Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network. Methods: We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit. Results: The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility. Conclusion: This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.

OBJECTIVES: Estimated glomerular filtration rate is more accurate with combined creatinine and cystatin C equations (eGFRcr-cys) than creatinine alone. This study created and evaluated a cefepime dosing nomogram based on eGFRcr-cys for initial dosing in the critically ill. DESIGN: Pharmacokinetic modeling and simulation study. SETTING: Academic medical center. PATIENTS: Critically ill adults treated with cefepime. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data from 120 patients with baseline cystatin C and follow-up cefepime levels were used to develop a nomogram based on eGFRcr-cys and weight for initial cefepime dosing. The predicted proportion of patients who achieved a free cefepime concentration above the minimum inhibitory concentration of the organism for 100% of the dosing interval in the first 24 hours (100% ƒT > MIC at 24 hr) was compared between administered doses and those predicted by the nomogram doses. Overall drug exposure was estimated with the free area under the concentration time curve from 0 to 24 hours (ƒAUC0–24) and compared between administered and nomogram doses. Achievement of 100% ƒT > MIC at 24 hours was predicted to be significantly better with the nomogram compared with the administered dose (76% vs. 38%; p < 0.001). The median ƒAUC0–24 as predicted by the nomogram (666 mg·hr/L) was slightly higher than the actual ƒAUC0–24 with administered doses (612 mg·hr/L; p = 0.01), but the nomogram led to fewer ƒAUC0–24 values which were either too high (> 900) or too low (< 300) (7% vs. 20%; p = 0.004). CONCLUSIONS: Use of a cystatin C-inclusive dosing nomogram for cefepime could improve target attainment without increasing the risk of potentially toxic levels in the critically ill.

IMPORTANCE: Meropenem dosing is typically guided by creatinine-based estimated glomerular filtration rate (eGFR), but creatinine is a suboptimal GFR marker in the critically ill. OBJECTIVES: This study aimed to develop and qualify a population pharmacokinetic model for meropenem in critically ill adults and to determine which eGFR equation based on creatinine, cystatin C, or both biomarkers best improves model performance. DESIGN, SETTING, AND PARTICIPANTS: This single-center study evaluated adults hospitalized in an ICU who received IV meropenem from 2018 to 2022. Patients were excluded if they had acute kidney injury, were on kidney replacement therapy, or were treated with extracorporeal membrane oxygenation. Two cohorts were used for population pharmacokinetic modeling: a richly sampled development cohort (n = 19) and an opportunistically sampled qualification cohort (n = 32). MAIN OUTCOMES AND MEASURES: A nonlinear mixed-effects model was developed using parametric methods to estimate meropenem serum concentrations. RESULTS: The best-fit structural model in the richly sampled development cohort was a two-compartment model with first-order elimination. The final model included time-dependent weight normalized to a 70-kg adult as a covariate for volume of distribution (Vd) and time-dependent eGFR for clearance. Among the eGFR equations evaluated, eGFR based on creatinine and cystatin C expressed in mL/min best-predicted meropenem clearance. The mean (se) Vd in the final model was 18.2 (3.5) liters and clearance was 11.5 (1.3) L/hr. Using the development cohort as the Bayesian prior, the opportunistically sampled cohort demonstrated good accuracy and low bias. CONCLUSIONS AND RELEVANCE: Contemporary eGFR equations that use both creatinine and cystatin C improved meropenem population pharmacokinetic model performance compared with creatinine-only or cystatin C-only eGFR equations in adult critically ill patients.

ABSTRACT Cefepime exhibits highly variable pharmacokinetics in critically ill patients. The purpose of this study was to develop and qualify a population pharmacokinetic model for use in the critically ill and investigate the impact of various estimated glomerular filtration rate (eGFR) equations using creatinine, cystatin C, or both on model parameters. This was a prospective study of critically ill adults hospitalized at an academic medical center treated with intravenous cefepime. Individuals with acute kidney injury or on kidney replacement therapy or extracorporeal membrane oxygenation were excluded. A nonlinear mixed-effects population pharmacokinetic model was developed using data collected from 2018 to 2022. The 120 included individuals contributed 379 serum samples for analysis. A two-compartment pharmacokinetic model with first-order elimination best described the data. The population mean parameters (standard error) in the final model were 7.84 (0.24) L/h for CL1 and 15.6 (1.45) L for V1. Q was fixed at 7.09 L/h and V2 was fixed at 10.6 L, due to low observed interindividual variation in these parameters. The final model included weight as a covariate for volume of distribution and the eGFR cr-cysC (mL/min) as a predictor of drug clearance. In summary, a population pharmacokinetic model for cefepime was created for critically ill adults. The study demonstrated the importance of cystatin C to prediction of cefepime clearance. Cefepime dosing models which use an eGFR equation inclusive of cystatin C are likely to exhibit improved accuracy and precision compared to dosing models which incorporate an eGFR equation with only creatinine.

Recent clinical studies have reported additive nephrotoxicity with the combination of vancomycin and piperacillin-tazobactam. However, preclinical models have failed to replicate this finding.

AbstractStudy ObjectiveIn critically ill patients, adequacy of early antibiotic exposure has been incompletely evaluated. This study characterized factors associated with inadequate cefepime exposure in the first 24 h of critical illness.DesignProspective cohort study.SettingAcademic Medical Center.PatientsCritically ill adults treated with cefepime. Patients with acute kidney injury or treated with kidney replacement therapy or extracorporeal membrane oxygenation were excluded.InterventionNone.MeasurementsA nonlinear mixed‐effects pharmacokinetic (PK) model was developed to estimate cefepime concentrations for each patient over time. The percentage of time the free drug concentration exceeded 8 mg/L during the first 24 h of therapy was calculated (%ƒT>8; appropriate for the susceptible breakpoint for Pseudomonas aeruginosa). Factors predictive of low %ƒT>8 were explored with multivariable regression.Main ResultsIn the 100 included patients, a one‐compartment PK model was developed with first‐order elimination with covariates for weight and estimated glomerular filtration rate based on creatinine and cystatin C (eGFRSCr‐CysC). The median (interquartile range) %ƒT>8 for cefepime in the first 24 h of therapy based on this model was 85% (66%, 100%). Less than 100% ƒT>8 during first 24 h of therapy occurred in 70 (70%) individuals. Lower Sequential Organ Failure Assessment score (p = 0.032) and higher eGFRSCr‐CysC (p < 0.001) predicted a lower %ƒT>8. Central nervous system infection source was protective (i.e., associated with a higher %ƒT>8; p = 0.008).ConclusionsDuring early critical illness, cefepime concentrations were inadequate in a significant proportion of patients. Antimicrobial optimization is needed to improve the precision of pharmacotherapy in the critically ill patients.

Clinical studies have reported additive nephrotoxicity associated with the combination of vancomycin (VAN) and piperacillin-tazobactam (TZP). This study assessed differences in glomerular filtration rate (GFR) and urinary biomarkers between rats receiving VAN and those receiving VAN + TZP.