ABC/2 formula for "bedside" postoperative pneumocephalus volume measurement.

BACKGROUND: Postoperative pneumocephalus is associated with a higher risk of recurrence of chronic subdural hematoma (cSDH). However, there is no verified simple way to measure the pneumocephalus volume at the bedside for daily clinical use. The ABC/2 method was shown to be a simple and reliable technique to estimate volumes of intracranial lesions, such as intracranial hematomas. This study aims to evaluate the accuracy of the ABC/2 formula in estimating volumes of pneumocephalus, as compared to the gold standard with computer-assisted volumetric analysis. METHODS: A total of 141 postoperative computed tomographic (CT) brain scans of cSDH patients with burr-hole drainage were analysed. Pneumocephalus volume was measured independently by both the ABC/2 formula and the computer-assisted volumetric measurement. For the computer-assisted measurement, the volume of the air was semiautomatically segmented and calculated by computer software. Linear regression was used to determine the correlation between the ABC/2 method and computer-assisted measurement. RESULTS: The postoperative pneumocephalus volume after bilateral burr-hole drainage was significantly larger than that of unilateral burr-hole drainage (29.34 ml versus 12.21 ml, p < 0.001). The estimated volumes by the formula ABC/2 significantly correlated to the volumes as measured by the computer-assisted volumetric technique, with r = 0.992 (p < 0.001). The Pearson correlation coefficient is very close to 1, which signifies a very strong positive correlation, and it is statistically significant. CONCLUSIONS: An excellent correlation is observed between the ABC/2 method and the computer-assisted measurement. This study verified that the ABC/2 method is an accurate and simple "bedside" technique to estimate pneumocephalus volume.

Medication Burden Among Pediatric Cancer Survivors: Analysis of a Population-Wide Electronic Database in Hong Kong.

BACKGROUND: Few studies have evaluated the medication burden borne by survivors of pediatric cancer. This study aimed to describe the drug utilization pattern of chronic medications in a cohort of young pediatric cancer survivors. METHODS: This was a population-based study of patients diagnosed with cancer at age 18 years or younger between 2000 and 2013 in Hong Kong and who had survived at least 5 years postdiagnosis. The primary outcome is the use of any chronic medication (medications that were prescribed for ≥30 consecutive days within a 6-month period). Multivariable log-binomial models were used to identify factors associated with chronic medication use. Kaplan-Meier analysis was used to present the cumulative proportion of survivors initiated on a chronic medication across time from cancer diagnosis. RESULTS: Of the 2444 survivors (median age = 22 years, interquartile range = 16-27 years), 669 (27.4%) required at least 1 chronic medication at least 5 years postdiagnosis. Survivors who developed a chronic health condition (CHC) had a 5.48 (95% confidence interval [CI] = 4.49 to 6.71) times higher risk of taking a chronic medication than those without CHC. At 10 years postdiagnosis, the cumulative proportion of survivors being initiated a chronic medication was 33.4% (95% CI = 31.1% to 35.6%) for the overall cohort. Higher cumulative proportions were observed in survivors with endocrine (74.6%, 95% CI = 68.4% to 79.6%), renal (68.8%, 95% CI = 54.2% to 78.7%), neurological (58.6%, 95% CI = 46.1% to 68.1%), and cardiovascular (54.7%, 95% CI = 44.0% to 63.4%) disorders. CONCLUSION: Survivors with certain CHCs had a higher risk of starting a prescription medication in the early phase of survivorship. Future studies include examining the impact of medication burden on survivors' functional status.

Multi-center prospective population pharmacokinetic study and the performance of web-based individual dose optimization application of intravenous vancomycin for adults in Hong Kong: A study protocol.

A recent consensus guideline recommends migrating the therapeutic drug monitoring practice for intravenous vancomycin for the treatment of methicillin-resistant Staphylococcus aureus infection from the traditional trough-based approach to the Bayesian approach based on area under curve to improve clinical outcomes. To support the implementation of the new strategy for hospitals under Hospital Authority, Hong Kong, this study is being proposed to (1) estimate and validate a population pharmacokinetic model of intravenous vancomycin for local adults, (2) develop a web-based individual dose optimization application for clinical use, and (3) evaluate the performance of the application by comparing the treatment outcomes and clinical satisfaction against the traditional approach. 300 adult subjects prescribed with intravenous vancomycin and not on renal replacement therapy will be recruited for population pharmacokinetic model development and validation. Sex, age, body weight, serum creatinine level, intravenous vancomycin dosing records, serum vancomycin concentrations etc. will be collected from several electronic health record systems maintained by Hospital Authority. Parameter estimation will be performed using non-linear mixed-effect modeling techniques. The web-based individual dose optimization application is based on a previously reported application and is built using R and the package shiny. Data from another 50 subjects will be collected during the last three months of the study period and treated as informed by the developed application and compared against historical control for clinical outcomes. Since the study will incur extra blood-taking procedures from patients, informed consent is required. Other than that, recruited subjects should receive medical treatments as usual. Identifiable patient data will be available only to site investigators and clinicians in each hospital. The study protocol and informed consent forms have been approved by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (reference number: NTEC-2021-0215) and registered at the Chinese Clinical Trial Registry (registration number: ChiCTR2100048714).

Using Electronic Health Records for Personalized Dosing of Intravenous Vancomycin in Critically Ill Neonates: Model and Web-Based Interface Development Study.

BACKGROUND: Intravenous (IV) vancomycin is used in the treatment of severe infection in neonates. However, its efficacy is compromised by elevated risks of acute kidney injury. The risk is even higher among neonates admitted to the neonatal intensive care unit (NICU), in whom the pharmacokinetics of vancomycin vary widely. Therapeutic drug monitoring is an integral part of vancomycin treatment to balance efficacy against toxicity. It involves individual dose adjustments based on the observed serum vancomycin concentration (VCs). However, the existing trough-based approach shows poor evidence for clinical benefits. The updated clinical practice guideline recommends population pharmacokinetic (popPK) model-based approaches, targeting area under curve, preferably through the Bayesian approach. Since Bayesian methods cannot be performed manually and require specialized computer programs, there is a need to provide clinicians with a user-friendly interface to facilitate accurate personalized dosing recommendations for vancomycin in critically ill neonates. OBJECTIVE: We used medical data from electronic health records (EHRs) to develop a popPK model and subsequently build a web-based interface to perform model-based individual dose optimization of IV vancomycin for NICU patients in local medical institutions. METHODS: Medical data of subjects prescribed IV vancomycin in the NICUs of Prince of Wales Hospital and Queen Elizabeth Hospital in Hong Kong were extracted from EHRs, namely the Clinical Information System, In-Patient Medication Order Entry, and electronic Patient Record. Patient demographics, such as body weight and postmenstrual age (PMA), serum creatinine (SCr), vancomycin administration records, and VCs were collected. The popPK model employed a 2-compartment infusion model. Various covariate models were tested against body weight, PMA, and SCr, and were evaluated for the best goodness of fit. A previously published web-based dosing interface was adapted to develop the interface in this study. RESULTS: The final data set included EHR data extracted from 207 subjects, with a total of 689 VCs measurements. The final model chosen explained 82% of the variability in vancomycin clearance. All parameter estimates were within the bootstrapping CIs. Predictive plots, residual plots, and visual predictive checks demonstrated good model predictability. Model approximations showed that the model-based Bayesian approach consistently promoted a probability of target attainment (PTA) above 75% for all subjects, while only half of the subjects could achieve a PTA over 50% with the trough-based approach. The dosing interface was developed with the capability to optimize individual doses with the model-based empirical or Bayesian approach. CONCLUSIONS: Using EHRs, a satisfactory popPK model was verified and adopted to develop a web-based individual dose optimization interface. The interface is expected to improve treatment outcomes of IV vancomycin for severe infections among critically ill neonates. This study provides the foundation for a cohort study to demonstrate the utility of the new approach compared with previous dosing methods.

Evaluation of the estimation and classification performance of NONMEM when applying mixture model for drug clearance.

Maximum likelihood estimation of parameters involving mixture model is known to have significant and specific patterns of errors. Population pharmacokinetic (PopPK) modeling using NONMEM is no exception. A few relevant studies on estimation and classification performance were done, but a comprehensive study was not yet available. The current study aims to evaluate performance and likelihood ratio test (LRT)-based true covariate detection rate when fitting a bimodal mixture of drug clearance (CL) in NONMEM. A large number of PopPK datasets with various settings were simulated and then estimated. The estimates were compared to the simulated values and summarized. The separation between the CL distributions of the two subpopulations is systematically overestimated. The major factor associated with the performance is the change in the minimum objective function value after removing the mixture component (dOFV). Other significant factors include estimated disparity index (DI), estimated mixing proportion, and number of subjects in the dataset. Small dOFV and large estimated DI are associated with the worst performance. Omitting a true mixture resulted in reduced true covariate detection rates. It is recommended that on top of routinely generated standard errors and model diagnostics, dOFV, and other factors when necessary, should be taken into account for the evaluation of performance when fitting mixture model using NONMEM. In addition, when fitting mixture model for CL is intended, the mixture component should be introduced prior to LRT-based covariate model development for CL.

Population Pharmacokinetic Study and Individual Dose Adjustments of High-Dose Methotrexate in Chinese Pediatric Patients With Acute Lymphoblastic Leukemia or Osteosarcoma.

High-dose methotrexate (>0.5 g/m2 ) is among the first-line chemotherapeutic agents used in treating acute lymphoblastic leukemia (ALL) and osteosarcoma in children. Despite rapid hydration, leucovorin rescue, and routine therapeutic drug monitoring, severe toxicity is not uncommon. This study aimed at developing population pharmacokinetic (popPK) models of high-dose methotrexate for ALL and osteosarcoma and demonstrating the possibility and convenience of popPK model-based individual dose optimization using R and shiny, which is more accessible, efficient, and clinician-friendly than NONMEM. The final data set consists of 36 ALL (354 observations) and 16 osteosarcoma (585 observations) patients. Covariate model building and parameter estimations were done using NONMEM and Perl-speaks-NONMEM. Diagnostic Plots and bootstrapping validated the models' performance and stability. The dose optimizer developed based on the validated models can obtain identical individual parameter estimates as NONMEM. Compared to calling a NONMEM execution and reading its output, estimating individual parameters within R reduces the execution time from 8.7-12.8 seconds to 0.4-1.0 second. For each subject, the dose optimizer can recommend (1) an individualized optimal dose and (2) an individualized range of doses. For osteosarcoma, recommended optimal doses by the optimizer resemble the final doses at which the subjects were eventually stabilized. The dose optimizers developed demonstrated the potential to inform dose adjustments using a model-based, convenient, and efficient tool for high-dose methotrexate. Although the dose optimizer is not meant to replace clinical judgment, it provides the clinician with the individual pharmacokinetics perspective by recommending the (range of) optimal dose.