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Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model.

ABSTRACT: Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n?=?99); and ii) patients experienced a significant reduction in size (Group 2, n?=?24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85-90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction?=?84-87%, n?=?39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria.

SUBMITTER: Yamamoto KN

PROVIDER: S-EPMC5046126 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model.

Yamamoto Kimiyo N KN Ishii Masatsugu M Inoue Yoshihiro Y Hirokawa Fumitoshi F MacArthur Ben D BD Nakamura Akira A Haeno Hiroshi H Uchiyama Kazuhisa K

Scientific reports 20161003

Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting t ...[more]

PMID: 27694914

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Project description:IntroductionOsteoarthritis (OA) is the most common bone and joint disease influenced by genetic and environmental factors. Recent association studies have uncovered the genetic factors behind OA, its susceptibility genes, which would enable us to predict disease occurrence based on genotype information. However, most previous studies have evaluated the effects of only a single susceptibility gene, and hence prediction based on such information is not as reliable. Here, we constructed OA-prediction models based on genotype information from a case-control association study and tested their predictability.MethodsWe genotyped risk alleles of the three susceptibility genes, asporin (ASPN), growth differentiation factor 5 (GDF5), and double von Willebrand factor A domains (DVWA) for a total of 2,158 Japanese subjects (933 OA and 1,225 controls) and statistically analyzed their effects. After that, we constructed prediction models by using the logistic regression analysis.ResultsWhen the effects of each allele were assumed to be the same and multiplicative, each additional risk allele increased the odds ratio (OR) by a factor of 1.23 (95% confidence interval (CI), 1.12 to 1.34). Individuals with five or six risk alleles showed significantly higher susceptibility when compared with those with zero or one, with an OR of 2.67 (95% CI, 1.46 to 4.87; P = 0.0020). Statistical evaluation of the prediction power of models showed that a model using only genotyping data had poor predictability. We obtained a model with good predictability by incorporating clinical data, which was further improved by rigorous age adjustment.ConclusionsOur results showed that consideration of adjusted clinical information, as well as increases in the number of risk alleles to be integrated, is critical for OA prediction by using data from case-control studies. To the authors' knowledge, this is the first report of the OA-prediction model combining both genetic and clinical information.

Dataset Information

Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model.

Publications

Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model.

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