A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression

Looy, Stijn Van; Verplancke, T; Benoît, Dominique; Hoste, Eric; Maele, Georges Van; Turck, Filip De; Decruyenaere, Johan

doi:10.1186/cc6081

Cited by 21 publications

(19 citation statements)

References 36 publications

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“…84 Verplancke et al 85 compared the use of support vector machines with logistic regression for the prediction of hospital mortalities among patients with haematological malignancies, but they did not find the discrimination of support vector machines to be statistically better than that of logistic regression. In contrast, Van Looy et al 86 compared the ability of support vector machines to predict tacrolimus blood concentrations with that of linear regression and found the support vector machines to be significantly better.…”

Section: Support Vector Machinesmentioning

confidence: 97%

Development and validation of a risk model for identification of non-neutropenic, critically ill adult patients at high risk of invasive Candida infection: the Fungal Infection Risk Evaluation (FIRE) Study

Harrison

Muskett

Harvey

et al. 2013

Health Technol Assess

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BackgroundThere is increasing evidence that invasive fungal disease (IFD) is more likely to occur in non-neutropenic patients in critical care units. A number of randomised controlled trials (RCTs) have evaluated antifungal prophylaxis in non-neutropenic, critically ill patients, demonstrating a reduction in the risk of proven IFD and suggesting a reduction in mortality. It is necessary to establish a method to identify and target antifungal prophylaxis at those patients at highest risk of IFD, who stand to benefit most from any antifungal prophylaxis strategy.ObjectivesTo develop and validate risk models to identify non-neutropenic, critically ill adult patients at high risk of invasiveCandidainfection, who would benefit from antifungal prophylaxis, and to assess the cost-effectiveness of targeting antifungal prophylaxis to high-risk patients based on these models.DesignSystematic review, prospective data collection, statistical modelling, economic decision modelling and value of information analysis.SettingNinety-six UK adult general critical care units.ParticipantsConsecutive admissions to participating critical care units.InterventionsNone.Main outcome measuresInvasive fungal disease, defined as a blood culture or sample from a normally sterile site showing yeast/mould cells in a microbiological or histopathological report. For statistical and economic modelling, the primary outcome was invasiveCandidainfection, defined as IFD-positive forCandidaspecies.ResultsSystematic review: Thirteen articles exploring risk factors, risk models or clinical decision rules for IFD in critically ill adult patients were identified. Risk factors reported to be significantly associated with IFD were included in the final data set for the prospective data collection.Data collection: Data were collected on 60,778 admissions between July 2009 and March 2011. Overall, 383 patients (0.6%) were admitted with or developed IFD. The majority of IFD patients (94%) were positive forCandidaspecies. The most common site of infection was blood (55%). The incidence of IFD identified in unit was 4.7 cases per 1000 admissions, and for unit-acquired IFD was 3.2 cases per 1000 admissions.Statistical modelling: Risk models were developed at admission to the critical care unit, 24 hours and the end of calendar day 3. The risk model at admission had fair discrimination (c-index 0.705). Discrimination improved at 24 hours (c-index 0.823) and this was maintained at the end of calendar day 3 (c-index 0.835). There was a drop in model performance in the validation sample.Economic decision model: Irrespective of risk threshold, incremental quality-adjusted life-years of prophylaxis strategies compared with current practice were positive but small. Incremental costs of the prophylaxis strategies compared with current practice were positive for most strategies, although a few strategies were cost saving. Incremental net benefits of each prophylaxis strategy compared with current practice were positive for most, but not all, of the strategies. Cost-effectiveness acceptability curves showed that risk assessment and prophylaxis at the end of calendar day 3 was the strategy most likely to be cost-effective when the risk threshold was 1% or 2%. At a lower risk threshold (0.5%) it was most cost-effective to assess risk at each time point; this led to a relatively high proportion of patients receiving antifungal prophylaxis (30%), which may lead to additional burden from increased resistance. The estimates of cost-effectiveness were highly uncertain and the value of further research for the whole population of interest is high relative to the research costs.ConclusionsThe results of the Fungal Infection Risk Evaluation (FIRE) Study, derived from a highly representative sample of adult general critical care units across the UK, indicated a low incidence of IFD among non-neutropenic, critically ill adult patients. IFD was associated with substantially higher mortality, more intensive organ support and longer length of stay. Risk modelling produced simple risk models that provided acceptable discrimination for identifying patients at ‘high risk’ of invasiveCandidainfection. Results of the economic model suggested that the current most cost-effective treatment strategy among non-neutropenic, critically ill adult patients admitted to NHS adult general critical care units is a strategy of risk assessment and antifungal prophylaxis at the end of calendar day 3, but this finding is highly uncertain and future studies should consider the potential impact of increased resistance.FundingFunding for this study was provided by the Health Technology Assessment programme of the National Institute for Health Research.A previous version of this report was published in February 2013. The report was subsequently modified to reflect a substantial reduction in the unit cost of fluconazole that took place between the original analysis being conducted and the publication of the report.

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Section: Support Vector Machinesmentioning

confidence: 97%

Development and validation of a risk model for identification of non-neutropenic, critically ill adult patients at high risk of invasive Candida infection: the Fungal Infection Risk Evaluation (FIRE) Study

Harrison

Muskett

Harvey

et al. 2013

Health Technol Assess

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“…The general term for a separating straight line in a high-dimensional space is a hyperplane. In clinical research, only a handful of articles have been published as a proof of concept of SVM [ 5 , 6 ], and none have been published till now for prediction of hospital mortality. This contrasts with the higher number of SVM publications in fundamental research such as bioinformatics [ 7 ] and genetics [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies

Verplancke

Looy

Benoît

et al. 2008

BMC Med Inform Decis Mak

101

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Background: Several models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II [1]. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiple logistic regression (MLR) and support vector machine (SVM) based models.

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“…Several previous studies adopted various machine learning algorithms, such as support vector machine [12,31], neural network [32], decision tree [12], and random forest [12], to assess the effect of genetic variations on tacrolimus pharmacokinetics. In those studies, subjects with renal transplantation [12,32] or liver transplant recipients [31] were investigated. The present study is different from those previous studies.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform

Gim

Kwon

Lee

et al. 2020

IJMS

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Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision tree, random forest, and least absolute shrinkage and selection operator (LASSO) regression. rs776746 (CYP3A5) and rs1137115 (CYP2A6) are single nucleotide polymorphisms (SNPs) that can affect exposure to tacrolimus. A decision tree, when coupled with random forest analysis, is an efficient tool for predicting the exposure to tacrolimus based on genotype. These tools are helpful to determine an individualized dose of tacrolimus.

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A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression

Cited by 21 publications

References 36 publications

Development and validation of a risk model for identification of non-neutropenic, critically ill adult patients at high risk of invasive Candida infection: the Fungal Infection Risk Evaluation (FIRE) Study

Development and validation of a risk model for identification of non-neutropenic, critically ill adult patients at high risk of invasive Candida infection: the Fungal Infection Risk Evaluation (FIRE) Study

Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies

A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform

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