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Predicting outcome on admission and post-admission for acetaminophen-induced acute liver failure using classification and regression tree models.

Speiser JL, Lee WM, Karvellas CJ, US Acute Liver Failure Study Gro - PLoS ONE (2015)

Bottom Line: Accuracy in prediction of outcome (AC), sensitivity (SN), specificity (SP), and area under receiver-operating curve (AUROC) were compared between 3 models: KCC (INR, creatinine, coma grade, pH), CART analysis using only KCC variables (KCC-CART) and a CART model using new variables (NEW-CART).CARTs offer simple prognostic models for APAP-ALF patients, which have higher AUROC and SN than KCC, with similar AC and negligibly worse SP.Admission and post-admission predictions were developed. • Prognostication in acetaminophen-induced acute liver failure (APAP-ALF) is challenging beyond admission • Little has been published regarding the use of King's College Criteria (KCC) beyond admission and KCC has shown limited sensitivity in subsequent studies • Classification and Regression Tree (CART) methodology allows the development of predictive models using binary splits and offers an intuitive method for predicting outcome, using processes familiar to clinicians • Data from the ALFSG registry suggested that CART prognosis models for the APAP population offer improved sensitivity and model performance over traditional regression-based KCC, while maintaining similar accuracy and negligibly worse specificity • KCC-CART models offered modest improvement over traditional KCC, with NEW-CART models performing better than KCC-CART particularly at late time points.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America.

ABSTRACT

Background/aim: Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients often presents significant challenges. King's College (KCC) has been validated on hospital admission, but little has been published on later phases of illness. We aimed to improve determinations of prognosis both at the time of and following admission for APAP-ALF using Classification and Regression Tree (CART) models.

Methods: CART models were applied to US ALFSG registry data to predict 21-day death or liver transplant early (on admission) and post-admission (days 3-7) for 803 APAP-ALF patients enrolled 01/1998-09/2013. Accuracy in prediction of outcome (AC), sensitivity (SN), specificity (SP), and area under receiver-operating curve (AUROC) were compared between 3 models: KCC (INR, creatinine, coma grade, pH), CART analysis using only KCC variables (KCC-CART) and a CART model using new variables (NEW-CART).

Results: Traditional KCC yielded 69% AC, 90% SP, 27% SN, and 0.58 AUROC on admission, with similar performance post-admission. KCC-CART at admission offered predictive 66% AC, 65% SP, 67% SN, and 0.74 AUROC. Post-admission, KCC-CART had predictive 82% AC, 86% SP, 46% SN and 0.81 AUROC. NEW-CART models using MELD (Model for end stage liver disease), lactate and mechanical ventilation on admission yielded predictive 72% AC, 71% SP, 77% SN and AUROC 0.79. For later stages, NEW-CART (MELD, lactate, coma grade) offered predictive AC 86%, SP 91%, SN 46%, AUROC 0.73.

Conclusion: CARTs offer simple prognostic models for APAP-ALF patients, which have higher AUROC and SN than KCC, with similar AC and negligibly worse SP. Admission and post-admission predictions were developed.

Key points: • Prognostication in acetaminophen-induced acute liver failure (APAP-ALF) is challenging beyond admission • Little has been published regarding the use of King's College Criteria (KCC) beyond admission and KCC has shown limited sensitivity in subsequent studies • Classification and Regression Tree (CART) methodology allows the development of predictive models using binary splits and offers an intuitive method for predicting outcome, using processes familiar to clinicians • Data from the ALFSG registry suggested that CART prognosis models for the APAP population offer improved sensitivity and model performance over traditional regression-based KCC, while maintaining similar accuracy and negligibly worse specificity • KCC-CART models offered modest improvement over traditional KCC, with NEW-CART models performing better than KCC-CART particularly at late time points.

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Post-Admission CART Models.The post-admission KCC-CART (left panel) has two decision rules and consists of four total nodes. Each node provides the total number of subjects within the node, as well as the number of survivors and dead/LT patients with the respective rates. Node 4 represents high risk of dead/LT outcome, and nodes 1 and 3 are low risk of dead/LT outcome. To calculate performance measures for the model, all subjects in node 4 were predicted as dead/LT outcomes, and all subjects in nodes 1 and 3 were predicted as spontaneous survivors. The post-admission NEW-CART (right panel) has three decision rules and consists of six total nodes. Node 6 patients were considered high risk for dead/LT outcome and were predicted as such, whereas nodes 1, 3 and 5 were predicted as survivors.
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pone.0122929.g003: Post-Admission CART Models.The post-admission KCC-CART (left panel) has two decision rules and consists of four total nodes. Each node provides the total number of subjects within the node, as well as the number of survivors and dead/LT patients with the respective rates. Node 4 represents high risk of dead/LT outcome, and nodes 1 and 3 are low risk of dead/LT outcome. To calculate performance measures for the model, all subjects in node 4 were predicted as dead/LT outcomes, and all subjects in nodes 1 and 3 were predicted as spontaneous survivors. The post-admission NEW-CART (right panel) has three decision rules and consists of six total nodes. Node 6 patients were considered high risk for dead/LT outcome and were predicted as such, whereas nodes 1, 3 and 5 were predicted as survivors.

Mentions: The KCC-CART post-admission model is illustrated in the left panel of Fig 3. Variables included within the KCC-CART day 3–7 model were creatinine and coma grade (high versus low), with only two splitting points. The NEW-CART model for post-admission, which considered as possible predictors the seventeen variables listed previously, is presented in the right panel of Fig 3. Variables selected for NEW-CART post-admission were coma grade (high versus low), lactate, and MELD, with one splitting point for each of these.


Predicting outcome on admission and post-admission for acetaminophen-induced acute liver failure using classification and regression tree models.

Speiser JL, Lee WM, Karvellas CJ, US Acute Liver Failure Study Gro - PLoS ONE (2015)

Post-Admission CART Models.The post-admission KCC-CART (left panel) has two decision rules and consists of four total nodes. Each node provides the total number of subjects within the node, as well as the number of survivors and dead/LT patients with the respective rates. Node 4 represents high risk of dead/LT outcome, and nodes 1 and 3 are low risk of dead/LT outcome. To calculate performance measures for the model, all subjects in node 4 were predicted as dead/LT outcomes, and all subjects in nodes 1 and 3 were predicted as spontaneous survivors. The post-admission NEW-CART (right panel) has three decision rules and consists of six total nodes. Node 6 patients were considered high risk for dead/LT outcome and were predicted as such, whereas nodes 1, 3 and 5 were predicted as survivors.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401567&req=5

pone.0122929.g003: Post-Admission CART Models.The post-admission KCC-CART (left panel) has two decision rules and consists of four total nodes. Each node provides the total number of subjects within the node, as well as the number of survivors and dead/LT patients with the respective rates. Node 4 represents high risk of dead/LT outcome, and nodes 1 and 3 are low risk of dead/LT outcome. To calculate performance measures for the model, all subjects in node 4 were predicted as dead/LT outcomes, and all subjects in nodes 1 and 3 were predicted as spontaneous survivors. The post-admission NEW-CART (right panel) has three decision rules and consists of six total nodes. Node 6 patients were considered high risk for dead/LT outcome and were predicted as such, whereas nodes 1, 3 and 5 were predicted as survivors.
Mentions: The KCC-CART post-admission model is illustrated in the left panel of Fig 3. Variables included within the KCC-CART day 3–7 model were creatinine and coma grade (high versus low), with only two splitting points. The NEW-CART model for post-admission, which considered as possible predictors the seventeen variables listed previously, is presented in the right panel of Fig 3. Variables selected for NEW-CART post-admission were coma grade (high versus low), lactate, and MELD, with one splitting point for each of these.

Bottom Line: Accuracy in prediction of outcome (AC), sensitivity (SN), specificity (SP), and area under receiver-operating curve (AUROC) were compared between 3 models: KCC (INR, creatinine, coma grade, pH), CART analysis using only KCC variables (KCC-CART) and a CART model using new variables (NEW-CART).CARTs offer simple prognostic models for APAP-ALF patients, which have higher AUROC and SN than KCC, with similar AC and negligibly worse SP.Admission and post-admission predictions were developed. • Prognostication in acetaminophen-induced acute liver failure (APAP-ALF) is challenging beyond admission • Little has been published regarding the use of King's College Criteria (KCC) beyond admission and KCC has shown limited sensitivity in subsequent studies • Classification and Regression Tree (CART) methodology allows the development of predictive models using binary splits and offers an intuitive method for predicting outcome, using processes familiar to clinicians • Data from the ALFSG registry suggested that CART prognosis models for the APAP population offer improved sensitivity and model performance over traditional regression-based KCC, while maintaining similar accuracy and negligibly worse specificity • KCC-CART models offered modest improvement over traditional KCC, with NEW-CART models performing better than KCC-CART particularly at late time points.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America.

ABSTRACT

Background/aim: Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients often presents significant challenges. King's College (KCC) has been validated on hospital admission, but little has been published on later phases of illness. We aimed to improve determinations of prognosis both at the time of and following admission for APAP-ALF using Classification and Regression Tree (CART) models.

Methods: CART models were applied to US ALFSG registry data to predict 21-day death or liver transplant early (on admission) and post-admission (days 3-7) for 803 APAP-ALF patients enrolled 01/1998-09/2013. Accuracy in prediction of outcome (AC), sensitivity (SN), specificity (SP), and area under receiver-operating curve (AUROC) were compared between 3 models: KCC (INR, creatinine, coma grade, pH), CART analysis using only KCC variables (KCC-CART) and a CART model using new variables (NEW-CART).

Results: Traditional KCC yielded 69% AC, 90% SP, 27% SN, and 0.58 AUROC on admission, with similar performance post-admission. KCC-CART at admission offered predictive 66% AC, 65% SP, 67% SN, and 0.74 AUROC. Post-admission, KCC-CART had predictive 82% AC, 86% SP, 46% SN and 0.81 AUROC. NEW-CART models using MELD (Model for end stage liver disease), lactate and mechanical ventilation on admission yielded predictive 72% AC, 71% SP, 77% SN and AUROC 0.79. For later stages, NEW-CART (MELD, lactate, coma grade) offered predictive AC 86%, SP 91%, SN 46%, AUROC 0.73.

Conclusion: CARTs offer simple prognostic models for APAP-ALF patients, which have higher AUROC and SN than KCC, with similar AC and negligibly worse SP. Admission and post-admission predictions were developed.

Key points: • Prognostication in acetaminophen-induced acute liver failure (APAP-ALF) is challenging beyond admission • Little has been published regarding the use of King's College Criteria (KCC) beyond admission and KCC has shown limited sensitivity in subsequent studies • Classification and Regression Tree (CART) methodology allows the development of predictive models using binary splits and offers an intuitive method for predicting outcome, using processes familiar to clinicians • Data from the ALFSG registry suggested that CART prognosis models for the APAP population offer improved sensitivity and model performance over traditional regression-based KCC, while maintaining similar accuracy and negligibly worse specificity • KCC-CART models offered modest improvement over traditional KCC, with NEW-CART models performing better than KCC-CART particularly at late time points.

Show MeSH
Related in: MedlinePlus