BACKGROUND: The dose of protamine required following cardiopulmonary bypass (CPB) is often determined by the dose of heparin required pre-CPB, expressed as a fixed ratio. Dosing based on mathematical models of heparin clearance is postulated to improve protamine dosing precision and coagulation. We hypothesised that protamine dosing based on a 2-compartment model would improve thromboelastography (TEG) parameters and reduce the dose of protamine administered, relative to a fixed ratio. METHODS AND FINDINGS: We undertook a 2-stage, adaptive randomised controlled trial, allocating 228 participants to receive protamine dosed according to a mathematical model of heparin clearance or a fixed ratio of 1 mg of protamine for every 100 IU of heparin required to establish anticoagulation pre-CPB. A planned, blinded interim analysis was undertaken after the recruitment of 50% of the study cohort. Following this, the randomisation ratio was adapted from 1:1 to 1:1.33 to increase recruitment to the superior arm while maintaining study power. At the conclusion of trial recruitment, we had randomised 121 patients to the intervention arm and 107 patients to the control arm. The primary endpoint was kaolin TEG r-time measured 3 minutes after protamine administration at the end of CPB. Secondary endpoints included ratio of kaolin TEG r-time pre-CPB to the same metric following protamine administration, requirement for allogeneic red cell transfusion, intercostal catheter drainage at 4 hours postoperatively, and the requirement for reoperation due to bleeding. The trial was listed on a clinical trial registry (ClinicalTrials.gov Identifier: NCT03532594). Participants were recruited between April 2018 and August 2019. Those in the intervention/model group had a shorter mean kaolin r-time (6.58 [SD 2.50] vs. 8.08 [SD 3.98] minutes; p = 0.0016) post-CPB. The post-protamine thromboelastogram of the model group was closer to pre-CPB parameters (median pre-CPB to post-protamine kaolin r-time ratio 0.96 [IQR 0.78-1.14] vs. 0.75 [IQR 0.57-0.99]; p < 0.001). We found no evidence of a difference in median mediastinal/pleural drainage at 4 hours postoperatively (140 [IQR 75-245] vs. 135 [IQR 94-222] mL; p = 0.85) or requirement (as a binary outcome) for packed red blood cell transfusion at 24 hours postoperatively (19 [15.8%] vs. 14 [13.1%] p = 0.69). Those in the model group had a lower median protamine dose (180 [IQR 160-210] vs. 280 [IQR 250-300] mg; p 120 kg, and patients requiring therapeutic hypothermia to <28°C). CONCLUSIONS: Using a mathematical model to guide protamine dosing in patients following CPB improved TEG r-time and reduced the dose administered relative to a fixed ratio. No differences were detected in postoperative mediastinal/pleural drainage or red blood cell transfusion requirement in our cohort of low-risk patients. TRIAL REGISTRATION: ClinicalTrials.gov Unique identifier NCT03532594.

/pdf/optimal-protamine-dosing-after-cardiopulmonary-bypass-the-4qtm1d4lb0.pdf

Optimal protamine dosing after cardiopulmonary bypass: The PRODOSE adaptive randomised controlled trial

Background: Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations.
Objective: The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively.
Methods: Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy.
Results: Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively.
Conclusions: The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

/pdf/toward-optimal-heparin-dosing-by-comparing-multiple-machine-7iu8kuq9h3.pdf

Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study.

Purpose Results of a study to determine whether heparin dosing based on adjusted body weight (BWAdj) instead of actual body weight (ABW) can lead to faster achievement of therapeutic activated partial thromboplastin time (aPTT) values in obese patients are presented.

Methods A single-center retrospective cohort study was conducted to assess aPTT outcomes before and after implementation of a revised heparin protocol specifying BWAdj-based dosing for obese patients. The primary outcome was the percentage of first aPTT values within the target range after heparin initiation. Secondary outcomes included the median time to the first on-target aPTT and the rate of clinically significant bleeding.

Results After protocol implementation, there was no significant difference between obese and nonobese patients in the primary outcome (17% and 21%, respectively, had first aPTT values in the target range) or in the median time to achieve the first on-target aPTT value. Among obese patients, on-target aPTT values were achieved significantly faster with BWAdj-versus ABW-based dosing (14 hours versus 24 hours, p = 0.002). Prior to implementation of BWAdj-based heparin dosing, obese patients had a higher rate of clinically significant bleeding than nonobese patients (11% versus 1%, p = 0.01); postimplementation bleeding rates did not differ significantly.

Conclusion The percentages of first aPTT values in the targeted range did not differ significantly in obese and nonobese patients before and after protocol implementation. The use of BWAdj for dose calculation in obese patients was associated with faster achievement of an aPTT value in the target range.

/pdf/evaluation-of-heparin-dosing-based-on-adjusted-body-weight-4yrz92vki2.pdf

Evaluation of heparin dosing based on adjusted body weight in obese patients

One area of medicine that could benefit from personalized procedures is medication dosing. Mis-dosing medications may incur additional morbidity, or unnecessarily increase the length of patient stay. Here we illustrate a novel approach to personalized medication dosing that is robust to missing data, a common problem in the clinical care setting. We perform dose estimation using a novel take on multinomial logistic regression where model parameters are continuously estimated, for each patient, using a weighted combination of the data from a population of other patients, and a volatile data stream available from the individual under treatment. We evaluate our approach on 4,470 patients who received anticoagulation therapy during intensive care treatment. Our approach was 29% more accurate than intensive care staff, and better able to distinguish outcomes than a non-personalized baseline (0.11 improvement in model VUS, a multiclass version of AUC). The advantages of our approach are its ease of interpretation, robustness to missing features, and extensibility to other problems with similar structure.

Personalized Medication Dosing Using Volatile Data Streams.

BACKGROUND The heparin regimen providing anticoagulation during cardiopulmonary bypass (CPB) is usually adapted to total body weight (TBW), but may be inaccurate in obese patients in whom TBW exceeds their ideal body weight. OBJECTIVES The objective is to compare the effects of heparin injection based on TBW on haemostatic parameters between obese and nonobese patients during cardiac surgery and to calculate the optimal heparin regimen. DESIGN Prospective comparative study. SETTING University hospital. PATIENTS Two groups of 50 patients (BMI≥ or <30 kg m) were included in the study over a 9-month period in 2013. The study started on 27 February 2013. INTERVENTIONS An unfractionated heparin (UFH) bolus of 300 IU kg TBW was injected before initiation of CPB followed by additional doses (50 to 100 IU kg) to maintain a target activated coagulation time (ACT) of at least 400 s. MAIN OUTCOME MEASURES ACT and plasma heparin concentration were measured at different time points after initiation of, and weaning from CPB. RESULTS Obese patients received higher initial and total doses of heparin (P < 0.0001). Plasma heparin concentrations were significantly higher in obese patients at each time point (P < 0.001) and reached very high values after the initial bolus (5.90 vs. 4.48 IU ml, P < 0.0001). The relationship between plasma heparin concentration and ACT after the initial bolus was not linear and followed an asymptotic regression curve. Haemoglobin concentration decreased intraoperatively to a greater extent in the obese group (P < 0.001). No significant differences in postoperative bleeding or global transfusion requirements were observed. CONCLUSION The standard heparin regimen based on TBW in obese patients during CPB results in excessive plasma heparin concentrations and a significant intraoperative decrease in haemoglobin concentration. ACT monitoring was not accurate in identifying this excess dosage. An initial bolus of 340 IU kg ideal body weight would achieve a heparin concentration of 4.5 IU ml, similar to that observed in nonobese patients. Further investigations are warranted to confirm this heparin regimen.

Identifying optimal heparin management during cardiopulmonary bypass in obese patients: A prospective observational comparative study.

Unfractionated heparin dosing for therapeutic anticoagulation in critically ill obese adults

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Unfractionated heparin dosing for therapeutic anticoagulation in critically ill obese adults