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Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein

Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein
Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein
Background: Acute pancreatitis (AP) has a variable course. Accurate early prediction of severity is essential to direct clinical care. Current assessment tools are inaccurate, and unable to adapt to new parameters. None of the current systems uses C-reactive protein (CRP). Modern machine-learning tools can address these issues.
Methods: 370 patients admitted with AP in a 5-year period were retrospectively assessed; after exclusions, 265 patients were studied. First recorded values for physical examination and blood tests, aetiology, severity and complications were recorded. A kernel logistic regression model was used to remove redundant features, and identify the relationships between relevant features and outcome. Bootstrapping was used to make the best use of data and obtain confidence estimates on the parameters of the model.
Results: A model containing 8 variables (age, CRP, respiratory rate, pO2 on air, arterial pH, serum creatinine, white cell count and GCS) predicted a severe attack with an area under the receiver-operating characteristic curve (AUC) of 0.82 (SD 0.01). The optimum cut-off value for predicting severity gave sensitivity and specificity of 0.87 and 0.71 respectively. The predictions were significantly better (p = 0.0036) than admission APACHE II scores in the same patients (AUC 0.74) and better than historical admission APACHE II data (AUC 0.68-0.75).
Conclusions: This system for the first time combines admission values of selected components of APACHE II and CRP for prediction of severe AP. The score is simple to use, and is more accurate than admission APACHE II alone. It is adaptable and would allow incorporation of new predictive factors.
acute pancreatitis, machine learning, severity, APACHE, prediction
1424-3903
123-131
Pearce, C.B.
82e5c6a5-21bd-45c8-a7cd-40971e185577
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Ahmed, A.
29a81242-835e-4661-bffd-3831f02ee2b6
Johnson, C.D.
ca994ad8-3406-4831-99b7-6915db56536c
Pearce, C.B.
82e5c6a5-21bd-45c8-a7cd-40971e185577
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Ahmed, A.
29a81242-835e-4661-bffd-3831f02ee2b6
Johnson, C.D.
ca994ad8-3406-4831-99b7-6915db56536c

Pearce, C.B., Gunn, S.R., Ahmed, A. and Johnson, C.D. (2006) Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein. Pancreatology, 6 (1-2), 123-131. (doi:10.1159/10.1159/000090032).

Record type: Article

Abstract

Background: Acute pancreatitis (AP) has a variable course. Accurate early prediction of severity is essential to direct clinical care. Current assessment tools are inaccurate, and unable to adapt to new parameters. None of the current systems uses C-reactive protein (CRP). Modern machine-learning tools can address these issues.
Methods: 370 patients admitted with AP in a 5-year period were retrospectively assessed; after exclusions, 265 patients were studied. First recorded values for physical examination and blood tests, aetiology, severity and complications were recorded. A kernel logistic regression model was used to remove redundant features, and identify the relationships between relevant features and outcome. Bootstrapping was used to make the best use of data and obtain confidence estimates on the parameters of the model.
Results: A model containing 8 variables (age, CRP, respiratory rate, pO2 on air, arterial pH, serum creatinine, white cell count and GCS) predicted a severe attack with an area under the receiver-operating characteristic curve (AUC) of 0.82 (SD 0.01). The optimum cut-off value for predicting severity gave sensitivity and specificity of 0.87 and 0.71 respectively. The predictions were significantly better (p = 0.0036) than admission APACHE II scores in the same patients (AUC 0.74) and better than historical admission APACHE II data (AUC 0.68-0.75).
Conclusions: This system for the first time combines admission values of selected components of APACHE II and CRP for prediction of severe AP. The score is simple to use, and is more accurate than admission APACHE II alone. It is adaptable and would allow incorporation of new predictive factors.

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Published date: 2006
Keywords: acute pancreatitis, machine learning, severity, APACHE, prediction
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 261932
URI: https://eprints.soton.ac.uk/id/eprint/261932
ISSN: 1424-3903
PURE UUID: 44170994-ed23-4b7b-81f4-8a02f4bda914

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Date deposited: 07 Feb 2006
Last modified: 13 Dec 2018 10:57

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Contributors

Author: C.B. Pearce
Author: S.R. Gunn
Author: A. Ahmed
Author: C.D. Johnson

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