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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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Related in: MedlinePlus

ALFSG Subjects in this Study.There were 588 APAP-ALF subjects who spontaneously survived, and 215 subjects who had a LT or died by day 21.
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pone.0122929.g001: ALFSG Subjects in this Study.There were 588 APAP-ALF subjects who spontaneously survived, and 215 subjects who had a LT or died by day 21.

Mentions: Demographic and clinical characteristics of patients are displayed in Table 1 for the admission training and testing datasets. Of the 803 APAP-ALF patients (within both training and testing datasets), the median age was 37 (29–47) years and 76% were female. A total of 157 patients had missing outcome data. Fig 1 illustrates a flow chart of ALF-APAP patients included in this study. One hundred and eighty-eight (23%) patients were listed for transplant and 63 (8%) received a LT. Overall, 588 (73%) patients recovered and 152 (19%) died without a LT, while 55 (87%) of the LT patients were alive at the end of follow-up and had been transplanted by 21 days. Thus, the dead/LT outcome group contained 215 (27%) of all ALF-APAP patients. The median time from ALFSG enrollment to death was 4 days (Interquartile Range (IQR): 2–16) and the median time from ALFSG enrollment to transplant was 2 days (IQR: 1–3).


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)

ALFSG Subjects in this Study.There were 588 APAP-ALF subjects who spontaneously survived, and 215 subjects who had a LT or died by day 21.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0122929.g001: ALFSG Subjects in this Study.There were 588 APAP-ALF subjects who spontaneously survived, and 215 subjects who had a LT or died by day 21.
Mentions: Demographic and clinical characteristics of patients are displayed in Table 1 for the admission training and testing datasets. Of the 803 APAP-ALF patients (within both training and testing datasets), the median age was 37 (29–47) years and 76% were female. A total of 157 patients had missing outcome data. Fig 1 illustrates a flow chart of ALF-APAP patients included in this study. One hundred and eighty-eight (23%) patients were listed for transplant and 63 (8%) received a LT. Overall, 588 (73%) patients recovered and 152 (19%) died without a LT, while 55 (87%) of the LT patients were alive at the end of follow-up and had been transplanted by 21 days. Thus, the dead/LT outcome group contained 215 (27%) of all ALF-APAP patients. The median time from ALFSG enrollment to death was 4 days (Interquartile Range (IQR): 2–16) and the median time from ALFSG enrollment to transplant was 2 days (IQR: 1–3).

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