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Individual variation in unfractionated heparin dosing after pediatric cardiac surgery.

ABSTRACT: We aimed to identify attributing factors to the interindividual variabilities of the infusion rates in unfractionated heparin therapy. We included patients who required unfractionated heparin therapy to achieve the target APTT after cardiac surgery between May 2014 and February 2018. Fifty-nine patients were included, of whom 8 underwent Blalock-Taussig shunt; 27, Glenn procedure; 19, Fontan procedure; 3, mechanical valve replacement; and 2, Rastelli procedure. Previously reported variables that influenced the response to unfractionated heparin treatment were initially compared, which included age; weight; sex; type of surgery; platelet count; fibrinogen, antithrombin III, total protein, albumin, alanine transaminase, and creatinine levels; and use of fresh frozen plasma. The type of surgical procedure was found to be significantly associated with the differences in heparin infusion rate (P?=?0.00073). Subsequently, the variance explained by these factors was estimated through a selection based on the minimum Akaike information criterion value; models constructed by various combinations of the surgery types were compared. The model including the Blalock-Taussig shunt, Glenn procedure, and mechanical valve replacement showed the highest summed variance explained (29.1%). More than 70% of the interindividual variability in initial heparin maintenance dosing was unexplained.

SUBMITTER: Hikino K

PROVIDER: S-EPMC7655810 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Individual variation in unfractionated heparin dosing after pediatric cardiac surgery.

Hikino Keiko K Koido Masaru M Ide Kentaro K Nishimura Nao N Terao Chikashi C Mushiroda Taisei T Nakagawa Satoshi S

Scientific reports 20201110 1

We aimed to identify attributing factors to the interindividual variabilities of the infusion rates in unfractionated heparin therapy. We included patients who required unfractionated heparin therapy to achieve the target APTT after cardiac surgery between May 2014 and February 2018. Fifty-nine patients were included, of whom 8 underwent Blalock-Taussig shunt; 27, Glenn procedure; 19, Fontan procedure; 3, mechanical valve replacement; and 2, Rastelli procedure. Previously reported variables that ...[more]

PMID: 33173059

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Project description:BackgroundUnfractionated heparin (UFH) is an anticoagulant drug that is considered a high-risk medication because an excessive dose can cause bleeding, whereas an insufficient dose can lead to a recurrent embolic event. Therapeutic response to the initiation of intravenous UFH is monitored using activated partial thromboplastin time (aPTT) as a measure of blood clotting time. Clinicians iteratively adjust the dose of UFH toward a target, indication-defined therapeutic aPTT range using nomograms, but this process can be imprecise and can take ≥36 hours to achieve the target range. Thus, a more efficient approach is required.ObjectiveIn this study, we aimed to develop and validate a machine learning (ML) algorithm to predict aPTT within 12 hours after a specified bolus and maintenance dose of UFH.MethodsThis was a retrospective cohort study of 3019 patient episodes of care from January 2017 to August 2020 using data collected from electronic health records of 5 hospitals in Queensland, Australia. Data from 4 hospitals were used to build and test ensemble models using cross-validation, whereas data from the fifth hospital were used for external validation. We built 2 ML models: a regression model to predict the aPTT value after a UFH bolus dose and a multiclass model to predict the aPTT, classified as subtherapeutic (aPTT <70 seconds), therapeutic (aPTT 70-100 seconds), or supratherapeutic (aPTT >100 seconds). Modeling was performed using Driverless AI (H2O), an automated ML tool, and 17 different experiments were iteratively conducted to optimize model accuracy.ResultsIn predicting aPTT, the best performing model was an ensemble with 4x LightGBM models with a root mean square error of 31.35 (SD 1.37). In predicting the aPTT class using a repurposed data set, the best performing ensemble model achieved an accuracy of 0.599 (SD 0.0289) and an area under the receiver operating characteristic curve of 0.735. External validation yielded similar results: root mean square error of 30.52 (SD 1.29) for the aPTT prediction model, and accuracy of 0.568 (SD 0.0315) and area under the receiver operating characteristic curve of 0.724 for the aPTT multiclassification model.ConclusionsTo the best of our knowledge, this is the first ML model applied to intravenous UFH dosing that has been developed and externally validated in a multisite adult general medical and surgical inpatient setting. We present the processes of data collection, preparation, and feature engineering for replication.

Project description:OBJECTIVE:To develop a postoperative mortality case-mix adjustment model to facilitate assessment of cardiac ICU quality of care, and to describe variation in adjusted cardiac ICU mortality across hospitals within the Pediatric Cardiac Critical Care Consortium. DESIGN:Observational analysis. SETTING:Multicenter Pediatric Cardiac Critical Care Consortium clinical registry. PARTICIPANTS:All surgical cardiac ICU admissions between August 2014 and May 2016. The analysis included 8,543 admissions from 23 dedicated cardiac ICUs. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We developed a novel case-mix adjustment model to measure postoperative cardiac ICU mortality after congenital heart surgery. Multivariable logistic regression was performed to assess preoperative, intraoperative, and immediate postoperative severity of illness variables as candidate predictors. We used generalized estimating equations to account for clustering of patients within hospital and obtain robust SEs. Bootstrap resampling (1,000 samples) was used to derive bias-corrected 95% CIs around each predictor and validate the model. The final model was used to calculate expected mortality at each hospital. We calculated a standardized mortality ratio (observed-to-expected mortality) for each hospital and derived 95% CIs around the standardized mortality ratio estimate. Hospital standardized mortality ratio was considered a statistically significant outlier if the 95% CI did not include 1. Significant preoperative predictors of mortality in the final model included age, chromosomal abnormality/syndrome, previous cardiac surgeries, preoperative mechanical ventilation, and surgical complexity. Significant early postoperative risk factors included open sternum, mechanical ventilation, maximum vasoactive inotropic score, and extracorporeal membrane oxygenation. The model demonstrated excellent discrimination (C statistic, 0.92) and adequate calibration. Comparison across Pediatric Cardiac Critical Care Consortium hospitals revealed five-fold difference in standardized mortality ratio (0.4-1.9). Two hospitals had significantly better-than-expected and two had significantly worse-than-expected mortality. CONCLUSIONS:For the first time, we have demonstrated that variation in mortality as a quality metric exists across dedicated cardiac ICUs. These findings can guide efforts to reduce mortality after cardiac surgery.

Project description:BackgroundThe current World Health Organization (WHO) pediatric tuberculosis dosing guidelines lead to suboptimal drug exposures. Identifying factors altering the exposure of these drugs in children is essential for dose optimization. Pediatric pharmacokinetic studies are usually small, leading to high variability and uncertainty in pharmacokinetic results between studies. We pooled data from large pharmacokinetic studies to identify key covariates influencing drug exposure to optimize tuberculosis dosing in children.Methods and findingsWe used nonlinear mixed-effects modeling to characterize the pharmacokinetics of rifampicin, isoniazid, and pyrazinamide, and investigated the association of human immunodeficiency virus (HIV), antiretroviral therapy (ART), drug formulation, age, and body size with their pharmacokinetics. Data from 387 children from South Africa, Zambia, Malawi, and India were available for analysis; 47% were female and 39% living with HIV (95% on ART). Median (range) age was 2.2 (0.2 to 15.0) years and weight 10.9 (3.2 to 59.3) kg. Body size (allometry) was used to scale clearance and volume of distribution of all 3 drugs. Age affected the bioavailability of rifampicin and isoniazid; at birth, children had 48.9% (95% confidence interval (CI) [36.0%, 61.8%]; p < 0.001) and 64.5% (95% CI [52.1%, 78.9%]; p < 0.001) of adult rifampicin and isoniazid bioavailability, respectively, and reached full adult bioavailability after 2 years of age for both drugs. Age also affected the clearance of all drugs (maturation), children reached 50% adult drug clearing capacity at around 3 months after birth and neared full maturation around 3 years of age. While HIV per se did not affect the pharmacokinetics of first-line tuberculosis drugs, rifampicin clearance was 22% lower (95% CI [13%, 28%]; p < 0.001) and pyrazinamide clearance was 49% higher (95% CI [39%, 57%]; p < 0.001) in children on lopinavir/ritonavir; isoniazid bioavailability was reduced by 39% (95% CI [32%, 45%]; p < 0.001) when simultaneously coadministered with lopinavir/ritonavir and was 37% lower (95% CI [22%, 52%]; p < 0.001) in children on efavirenz. Simulations of 2010 WHO-recommended pediatric tuberculosis doses revealed that, compared to adult values, rifampicin exposures are lower in most children, except those younger than 3 months, who experience relatively higher exposure for all drugs, due to immature clearance. Increasing the rifampicin doses in children older than 3 months by 75 mg for children weighing <25 kg and 150 mg for children weighing >25 kg could improve rifampicin exposures. Our analysis was limited by the differences in availability of covariates among the pooled studies.ConclusionsChildren older than 3 months have lower rifampicin exposures than adults and increasing their dose by 75 or 150 mg could improve therapy. Altered exposures in children with HIV is most likely caused by concomitant ART and not HIV per se. The importance of the drug-drug interactions with lopinavir/ritonavir and efavirenz should be evaluated further and considered in future dosing guidance.Trial registrationClinicalTrials.gov registration numbers; NCT02348177, NCT01637558, ISRCTN63579542.

Dataset Information

Individual variation in unfractionated heparin dosing after pediatric cardiac surgery.

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Individual variation in unfractionated heparin dosing after pediatric cardiac surgery.

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