Improvements and limitations in developing multivariate models of hemorrhage and transfusion risk for the obstetric population.

BACKGROUND: Maternal hemorrhage protocols involve risk screening. These protocols prepare clinicians for potential hemorrhage and transfusion in individual patients. Patient-specific estimation and stratification of risk may improve maternal outcomes. STUDY DESIGN AND METHODS: Prediction models for hemorrhage and transfusion were trained and tested in a data set of 74 variables from 63 973 deliveries (97.6% of the source population of 65 560 deliveries included in a perinatal database from an academic urban delivery center) with sufficient data at pertinent time points: antepartum, peripartum, and postpartum. Hemorrhage and transfusion were present in 6% and 1.6% of deliveries, respectively. Model performance was evaluated with the receiver operating characteristic (ROC), precision-recall curves, and the Hosmer-Lemeshow calibration statistic. RESULTS: For hemorrhage risk prediction, logistic regression model discrimination showed ROCs of 0.633, 0.643, and 0.661 for the antepartum, peripartum, and postpartum models, respectively. These improve upon the California Maternal Quality Care Collaborative (CMQCC) accuracy of 0.613 for hemorrhage. Predictions of transfusion resulted in ROCs of 0.806, 0.822, and 0.854 for the antepartum, peripartum, and postpartum models, respectively. Previously described and new risk factors were identified. Models were not well calibrated with Hosmer-Lemeshow statistic P values between .001 and .6. CONCLUSIONS: Our models improve on existing risk assessment; however, further enhancement might require the inclusion of more granular, dynamic data. With the goal of increasing translatability, this work was distilled to an online open-source repository, including a form allowing risk factor inputs and outputs of CMQCC risk, alongside our numerical risk estimation and stratification of hemorrhage and transfusion.

The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies.

The main objective of this tutorial is to provide the readers with a roadmap of how to establish increasingly complex target-mediated drug disposition (TMDD) models for monoclonal antibodies. To this end, we built mathematical models, each with a detailed visualization, starting from the basic TMDD model by Mager and Jusko to the well-established, physiologically based model by Li et al. in a step-wise fashion to highlight the relative importance of key physiological processes that impact mAb kinetics and system dynamics. As the models become more complex, the question of structural and parameter identifiability arises. To address this question, we work through a trastuzumab case example to guide the modeler's choice for model and parameter optimization in light of the context of use. We leave the readers of this tutorial with a brief summary of the advantages and limitations of each model expansion, as well as the model source codes for further self-guided exploration and hands-on analysis.

Informing a Comprehensive Risk Assessment of Infant Drug Exposure From Human Milk: Application of a Physiologically Based Pharmacokinetic Lactation Model for Sotalol.

Characterization of infant drug exposure through human milk is important and underexplored. Because infant plasma concentrations are not frequently collected in clinical lactation studies, modeling and simulation approaches can integrate physiology, available milk concentrations, and pediatric data to inform exposure in breastfeeding infants. A physiologically based pharmacokinetic model was built for sotalol, a renally eliminated drug, to simulate infant drug exposure from human milk. Intravenous and oral adult models were built, optimized, and scaled to an oral pediatric model for a breastfeeding-relevant age group (<2 years). Model simulations captured the data that were put aside for verification. The resulting pediatric model was applied to predict the impacts of sex, infant body size, breastfeeding frequency, age, and maternal dose (240 and 433 mg) on drug exposure during breastfeeding. Simulations suggest a minimal effect of sex or frequency on total sotalol exposure. Infants in the 90th percentile in height and weight have predicted exposures ≈20% higher than infants of the same age in the 10th percentile due to increased milk intake. The simulated infant exposures increase throughout the first 2 weeks of life and are maintained at the highest concentrations in weeks 2-4, with a consistent decrease observed as infants age. Simulations suggest that breastfeeding infants will have plasma concentrations in the lower range observed in infants administered sotalol. With further validation on additional drugs, physiologically based pharmacokinetic modeling approaches could use lactation data to a greater extent and provide comprehensive information to support decisions regarding medication use during breastfeeding.

Accelerating availability of clinically-relevant parameter estimates from thromboelastogram point-of-care device.

BACKGROUND: Modeling approaches offer a novel way to detect and predict coagulopathy in trauma patients. A dynamic model, built and tested on thromboelastogram (TEG) data, was used to generate a virtual library of over 160,000 simulated RapidTEGs. The patient-specific parameters are the initial platelet count, platelet activation rate, thrombus growth rate, and lysis rate (P(0), k1, k2, and k3, respectively). METHODS: Patient data from both STAAMP (n = 182 patients) and PAMPer (n = 111 patients) clinical trials were collected. A total of 873 RapidTEGs were analyzed. One hundred sixteen TEGs indicated maximum amplitude (MA) below normal and 466 TEGs indicated lysis percent above normal. Each patient's TEG response was compared against the virtual library of TEGs to determine library trajectories having the least sum-of-squared error versus the patient TEG up to each specified evaluation time ∈ (3, 4, 5, 7.5, 10, 15, 20 minutes). Using 10 nearest-neighbor trajectories, a logistic regression was performed to predict if the patient TEG indicated MA below normal (<50 mm), lysis percent 30 minutes after MA (LY30) greater than 3%, and/or blood transfusion need using the parameters from the dynamic model. RESULTS: The algorithm predicts abnormal MA values using the initial 3 minutes of RapidTEG data with a median area under the curve of 0.95, and improves with more data to 0.98 by 10 minutes. Prediction of future platelet and packed red blood cell transfusion based on parameters at 4 and 5 minutes, respectively, provides equivalent predictions to the traditional TEG parameters in significantly less time. Dynamic model parameters could not predict abnormal LY30 or future fresh-frozen plasma transfusion. CONCLUSION: This analysis could be incorporated into TEG software and workflow to quickly estimate if the MA would be below or above threshold value within the initial minutes following a TEG, along with an estimate of what blood products to have on hand. LEVEL OF EVIDENCE: Therapeutic/Care Management: Level IV.

Coupled Model of Blood Volume and Activated Clotting Factor Concentration during Childbirth.

Obstetric patients show an increased risk of developing venous thromboembolism (VTE). Modeling the changes that occur during and after delivery may help determine which patients will develop VTE and when they might be likely to develop this dangerous complication after delivery. Combining a model of blood volume changes with a model of activated clotting factor concentrations, which are both affected during delivery, can identify scenarios that may lead to an increased risk of developing clots in the venous vascular space. This Ordinary Differential Equation (ODE) model recapitulates known phenomena including an elevated coagulation response during delivery and hemorrhage leading to increased clotting factor concentration in the vascular space. The simulation from normal activation without hemorrhage results in a spike in clotting factors in the vascular space to reestablish hemostasis after delivery. With twice the activation rate, simulations show elevated and extended duration of activated clotting factor presence in the vascular space. With response to a hemorrhage with normal activation, the resulting elevation and duration is further increased. This model, when tailored to individual patients, could lead to the development of a VTE risk assessment tool for clinicians to help mitigate and reduce an individual's risk of developing this deadly complication.