Dataset Information

Prediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis.

ABSTRACT:

Objective

To develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis.

Methods

Clinical data of 1308 patients with acute pancreatitis (AP) were included in the retrospective study. A total of 603 patients who were admitted to the hospital within 36 hours of the onset of the disease were included at last according to the inclusion criteria. The clinical data were collected within 12 hours after admission. All the patients were classified as having mild acute pancreatitis (MAP), moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP) based on the revised Atlanta classification of acute pancreatitis. All the 603 patients were randomly divided into training group (402 cases) and test group (201 cases). Univariate and multiple regression analyses were used to identify the independent risk factors for the development of SAP in the training group. Then the prediction model was constructed using the decision tree method, and this model was applied to the test group to evaluate its validity.

Results

The decision tree model was developed using creatinine, lactate dehydrogenase, and oxygenation index to predict SAP. The diagnostic sensitivity and specificity of SAP in the training group were 80.9% and 90.0%, respectively, and the sensitivity and specificity in the test group were 88.6% and 90.4%, respectively.

Conclusions

The decision tree model based on creatinine, lactate dehydrogenase, and oxygenation index is more likely to predict the occurrence of SAP.

SUBMITTER: Yang Z

PROVIDER: S-EPMC4651493 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Prediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis.

Yang Zhiyong Z Dong Liming L Zhang Yushun Y Yang Chong C Gou Shanmiao S Li Yongfeng Y Xiong Jiongxin J Wu Heshui H Wang Chunyou C

PloS one 20151118 11

<h4>Objective</h4>To develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis.<h4>Methods</h4>Clinical data of 1308 patients with acute pancreatitis (AP) were included in the retrospective study. A total of 603 patients who were admitted to the hospital within 36 hours of the onset of the disease were included at last according to the inclusion criteria. The clinical data were collected within 12 hours after admission ...[more]

PMID: 26580397

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Project description:Severe acute pancreatitis (SAP) includes persistent systemic inflammation (SIRS) and multiorgan failure (MOF). The mechanism of transition from SIRS to MOF is unclear. We developed a fluid compartment model and used clinical data to test predictions. The model includes vascular, interstitial and "third-space" compartments with variable permeability of plasma proteins at the capillaries. Consented patients from University of Pittsburgh Medical Center Presbyterian Hospital were studied. Preadmission and daily hematocrit (HCT), blood urea nitrogen (BUN), creatine (Cr), albumin (Alb), and total protein (TP) were collected, and nonalbumin plasma protein (NAPP = TP minus the Alb) was calculated. Subjects served as their own controls for trajectory analysis. Of 57 SAP subjects, 18 developed MOF (5 died), and 39 were non-MOF (0 died). Compared with preadmission levels, admission HCT increased in MOF +5.00 [25%-75% interquartile range, IQR] versus non-MOF -0.10 [-1.55, 1.40] (P < 0.002) with HCT > +3 distinguishing MOF from non-MOF (odds ratio 17.7, P = 0.014). Preadmission Alb fell faster in MOF than non-MOF (P < 0.01). By day 2, TP and NAPP dropped in MOF but not non-MOF (P < 0.001). BUN and Cr levels increased in MOF (P = 0.001), but BUN-to-Cr ratios remained constant. Pancreatic necrosis was more common in MOF (56%) than non-MOF (23%). Changing capillary permeability to allow loss of NAPP in this model predicts loss of plasma oncotic pressure and reduced vascular volume, hypotension with prerenal azotemia and acute kidney dysfunction, pancreas necrosis, and pulmonary edema from capillary leak in the lung with acute respiratory distress syndrome. Sequential biomarker analysis in humans with or without MOF is consistent with this model. This study is registered on https://clinicaltrials.gov at NCT03075605.NEW & NOTEWORTHY Acute pancreatitis is a sudden inflammatory response to pancreatic injury that may spread to systemic inflammation, multiorgan failure, and death in some patients. With the use of the predictions of a new mechanistic model, we compared patients with severe acute pancreatitis with or without multiorgan failure. All biomarkers of capillary leak and clinical features of multiorgan failure were accurately predicted. This provides a new paradigm for understanding and developing new treatments for patients with severe acute pancreatitis.

Project description:BackgroundPatients with MMT often face difficulties such as sleep disturbance, headaches, and difficulty in complete abstinence from drugs. Research has shown that acupuncture can mitigate side effects while attenuating methadone dependence. It also has a synergistic and attenuated effect on methadone maintenance treatment (MMT). Exploring the predictors of the efficacy of acupuncture intervention in MMT might help clinicians and patients promote acupuncture-assisted participation in MMT, and improve clinical treatment strategies for MMT.ObjectiveTo describe the effect of potential predictors on MMT after acupuncture intervention by building a decision-tree model of data from A Clinical Study of Acupuncture-assisted MMT.Design setting and participantsIn this randomized controlled trial, 135 patients with MMT underwent acupuncture at the Substance Dependence Department of Guangzhou Huiai Hospital in Guangzhou, Guangdong Province, China.InterventionA total of 135 patients were 1:1 randomly assigned to either an acupuncture plus routine care group (acupuncture plus methadone) or a routine group (methadone only) for 6 weeks, and followed up for 10 weeks. Sex, age, education level, route of previous opioid use, years of opioid use, and MMT time were recorded before the trial.Outcome measurements and statistical analysisAll analyses were based on the intention-to-treat (ITT) population. The two decision tree models used the change of methadone dosage and the VAS score for opioid desire as response variables, respectively, and the evaluation criteria were positive effect (decreased by ≥20%) and no effect (decreased by <20%, or increased). We generated the respective feature weights for the decision tree and evaluated the model's accuracy and performance by Precision-Recall.ResultsThe overall accuracy of methadone reduction and psychological craving VAS scoring decision trees were 0.63 and 0.74, respectively. The Methadone Dosage Efficacy decision tree identified years of opioid use (weight = 0.348), acupuncture (weight = 0.346), and route of previous opioid use (weight = 0.162) as key features. For the VAS Score decision tree, acupuncture (weight = 0.618), MMT time (weight = 0.235), and age (weight = 0.043) were the important features.ConclusionExploratory decision tree analysis showed that acupuncture, years of opioid use, route of previous opioid use, MMT time, and age were key predictors of the MMT treatment. Thus, acupuncture-assisted MMT strategy should consider the relevant influencing factors mentioned above.Patient summaryUnderstanding patient characteristics and the impact of acupuncture regimens on methadone dosage reduction in MMT patients may help physicians determine the best treatment regimen for patients. An analysis of data from our clinical trial showed that acupuncture, years of opioid use, route of previous opioid use, age, and MMT time were key predictors of progressive recovery in patients with MMT. Eligible patients may benefit most from the MMT rehabilitation that reduces consumption and psychological cravings for methadone.Clinical trial registrationhttp://www.chictr.org.cn/index.aspx, identifier: ChiCTR1900026357.

Dataset Information

Prediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis.

Objective

Methods

Results

Conclusions

Publications

Prediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis.

Similar Datasets

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