Development and validation of a risk prediction score for severe acute pancreatitis.
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ABSTRACT:
SUBMITTER: Hong W
PROVIDER: S-EPMC6505180 | biostudies-other | 2019 May
REPOSITORIES: biostudies-other
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Project description:This study developed and validated the Early Death Risk Score Model for early identification of emergency patients with very severe aplastic anemia (VSAA). All 377 patients with VSAA receiving first-line immunosuppressive therapy (IST) were categorized into training (n=252) and validation (n=125) cohorts. In the training cohort, age >24 years, absolute neutrophil count ≤0.015×109/L, serum ferritin >900ng/mL and times of fever before IST >1 time were significantly associated with early death. Covariates were assigned scores and categorized as: low (score 0-4), medium (score 5-7) and high (score ≥8) risk. Early death rate was significantly different between risk groups and the validation cohort results were consistent with those of the training cohort. The area under the receiver operating characteristic curve for the model was 0.835 (0.734,0.936) in the training cohort and 0.862 (0.730,0.994) in the validation cohort. The calibration plots showed high agreement, and decision curve analysis showed good benefit in clinical applications. The VSAA Early Death Risk Score Model can help with early identification of emergency VSAA and optimize treatment strategies. Emergency VSAA with high risk is associated with high early death rate, and alternative donor hematopoietic stem cell transplantation could be a better treatment than IST even without HLA-matching.
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Project description:BackgroundMultiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed to develop and assess three machine-learning models to predict MOF.MethodsPatients with MSAP and SAP who were admitted from July 2014 to June 2017 were included. Firstly, parameters with significant differences between patients with MOF and without MOF were screened out by univariate analysis. Then, support vector machine (SVM), logistic regression analysis (LRA) and artificial neural networks (ANN) models were constructed based on these factors, and five-fold cross-validation was used to train each model.ResultsA total of 263 patients were enrolled. Univariate analysis screened out sixteen parameters referring to blood volume, inflammatory, coagulation and renal function to construct machine-learning models. The predictive efficiency of the optimal combinations of features by SVM, LRA, and ANN was almost equal (AUC = 0.840, 0.832, and 0.834, respectively), as well as the Acute Physiology and Chronic Health Evaluation II score (AUC = 0.814, P > 0.05). The common important predictive factors were HCT, K-time, IL-6 and creatinine in three models.ConclusionsThree machine-learning models can be efficient prognostic tools for predicting MOF in MSAP and SAP. ANN is recommended, which only needs four common parameters.
| S-EPMC6611034 | biostudies-literature
Project description:IntroductionSevere acute pancreatitis (AP) is associated with high morbidity and mortality. Early prediction of severe AP is needed to improve patient outcomes. The aim of the present study was to find novel cytokines or combinations of cytokines that can be used for the early identification of patients with AP at risk for severe disease.MethodsWe performed a prospective study of 163 nonconsecutive patients with AP, of whom 25 had severe AP according to the revised Atlanta criteria. Admission serum levels of 48 cytokines and growth factors were determined using Bio-Plex Pro Human Cytokine Assay 21-plex and 27-plex magnetic bead suspension panels. Admission plasma levels of C-reactive protein (CRP), creatinine and calcium were measured for comparison. In subgroup analyses, we assessed the cytokine profiles of patients with severe AP (n = 14) who did not have organ dysfunction (OD) upon admission (modified Marshall score <2).ResultsOf 14 cytokines elevated in the severe AP group, interleukin 6 (IL-6) and hepatocyte growth factor (HGF) levels were independent prognostic markers of severe AP. IL-6, HGF and a combination of them predicted severe AP with sensitivities of 56.0%, 60.0% and 72.0%, respectively, and specificities of 90.6%, 92.8% and 89.9%, respectively. The corresponding positive likelihood ratio (LR+) values were 5.9, 8.3 and 7.1, respectively. The predictive values of CRP, creatinine and calcium were comparable to those of the cytokines. In subgroup analyses of patients with severe AP and without OD upon admission, we found that IL-8, HGF and granulocyte colony-stimulating factor (G-CSF) levels predicted the development of severe AP, with G-CSF being the most accurate cytokine at a sensitivity of 35.7%, a specificity of 96.1% and a LR+ of 9.1.ConclusionsIL-6 and HGF levels upon admission have prognostic value for severe AP which is similar to levels of CRP, creatinine and calcium. Although IL-6 and HGF, as either single or combined markers, were not perfect in identifying patients at risk for severe AP, the possibility that combining them with novel prognostic markers other than cytokines might improve prognostic accuracy needs to be studied. The accuracy of IL-8, HGF and G-CSF levels in predicting severe AP in patients without clinical signs of OD upon admission warrants larger studies.
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Project description:To explore the risk factors of developing chronic pancreatitis (CP) in patients with acute pancreatitis (AP) and develop a prediction score for CP. Using the National Health Insurance Research Database in Taiwan, we obtained large, population-based data of 5971 eligible patients diagnosed with AP from 2000 to 2013. After excluding patients with obstructive pancreatitis and biliary pancreatitis and those with a follow-up period of less than 1 year, we conducted a multivariate analysis using the data of 3739 patients to identify the risk factors of CP and subsequently develop a scoring system that could predict the development of CP in patients with AP. In addition, we validated the scoring system using a validation cohort. Among the study subjects, 142 patients (12.98%) developed CP among patients with RAP. On the other hand, only 32 patients (1.21%) developed CP among patients with only one episode of AP. The multivariate analysis revealed that the presence of recurrent AP (RAP), alcoholism, smoking habit, and age of onset of < 55 years were the four important risk factors for CP. We developed a scoring system (risk score 1 and risk score 2) from the derivation cohort by classifying the patients into low-risk, moderate-risk, and high-risk categories based on similar magnitudes of hazard and validated the performance using another validation cohort. Using the prediction score model, the area under the curve (AUC) [95% confidence interval (CI)] in predicting the 5-year CP incidence in risk score 1 (without the number of AP episodes) was 0.83 (0.79, 0.87), whereas the AUC (95%CI) in risk score 2 (including the number of AP episodes) was 0.84 (0.80, 0.88). This result demonstrated that the risk score 2 has somewhat better prediction performance than risk score 1. However, both of them had similar performance between the derivation and validation cohorts. In the study,we identified the risk factors of CP and developed a prediction score model for CP.
| S-EPMC6262246 | biostudies-other
Project description:ObjectiveTo develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis.MethodsClinical 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.ResultsThe 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.ConclusionsThe decision tree model based on creatinine, lactate dehydrogenase, and oxygenation index is more likely to predict the occurrence of SAP.
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Project description:BackgroundCoronavirus disease 2019 (COVID-19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments.Research questionCan we develop and validate a web-based risk prediction model for identification of patients who may develop severe COVID-19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death?MethodsThis retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID-19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75%) was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%).ResultsThe median age was 66 years (Interquartile range [IQR] 54-77) and the majority were male (55%) and non-White (65.8%). The 14-day severe COVID-19 rate was 39.3%; 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID-19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile-friendly website.ConclusionsThis web-based risk prediction model can be used at the bedside for prediction of severe COVID-19 using data mostly available at the time of presentation.
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Project description:Purpose: The purpose of this study was to compare the hepatic transcriptome in the control group, acute pancreatitis, and severe acute pancreatitis, in order to identify metabolic gene changes during pancreatitis. Methods: 6-week Balb/c mice were subjected to AP (caerulein), SAP (caerulein and LPS) and control (saline), and mice liver tissues were harvested at 24 hours for RNA preparation (n=8 each group). Results: Among 11952 mapped genes, 8977 differentially expressed genes were identified by RNA-seq, including 351 genes induced in AP compare to Control and 5249 genes down regulated in SAP compare to AP. The genes up regulated were mainly related to fatty acid oxidation and ketone body synthesis especially Cpt2 and Acadvl, while genes that down regulated were associated with energy metabolism and inflammation. The overlap of two gene sets were 96 genes that involved in fatty acid metabolism. Conclusions: Our study revealed that in AP, genes associated with ketone body synthesis are upregulated in liver, in SAP, fatty acid oxidation related genes are downregulated in liver. Our data revealed the crosstalk between pancreas and liver, illustrated the difference of metabolism in AP and SAP.
2022-04-06 | GSE151927 | GEO
Project description:BackgroundSevere vitamin D deficiency (SVDD) dramatically increases the risks of mortality, infections, and many other diseases. Studies have reported higher prevalence of vitamin D deficiency in patients with critical illness than general population. This multicenter retrospective cohort study develops and validates a score-based model for predicting SVDD in patients with critical illness.MethodsA total of 662 patients with critical illness were enrolled between October 2017 and July 2020. SVDD was defined as a serum 25(OH)D level of < 12 ng/mL (or 30 nmol/L). The data were divided into a derivation cohort and a validation cohort on the basis of date of enrollment. Multivariable logistic regression (MLR) was performed on the derivation cohort to generate a predictive model for SVDD. Additionally, a score-based calculator (the SVDD score) was designed on the basis of the MLR model. The model's performance and calibration were tested using the validation cohort.ResultsThe prevalence of SVDD was 16.3% and 21.7% in the derivation and validation cohorts, respectively. The MLR model consisted of eight predictors that were then included in the SVDD score. The SVDD score had an area under the receiver operating characteristic curve of 0.848 [95% confidence interval (CI) 0.781-0.914] and an area under the precision recall curve of 0.619 (95% CI 0.577-0.669) in the validation cohort.ConclusionsThis study developed a simple score-based model for predicting SVDD in patients with critical illness.Trial registrationClinicalTrials.gov protocol registration ID: NCT03639584. Date of registration: May 12, 2022.
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Project description:BackgroundA few models have been proposed for the prediction of the risk of post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP), but many include factors that are not assessed routinely. Herein, we intend to develop and validate a predictive model for the occurrence of PEP.MethodsData of patients who underwent endoscopic retrograde cholangiopancreatography (ERCP) from January 01, 2016 to May 16, 2019 (training set and internal test set) and from May 17, 2019 to December 25, 2019 (external test set) were retrospectively collected. The performance of the model was validated in the two validation cohorts.ResultsA total of 342 patients were included for the external test set, and 47 (13.7%) developed PEP. The variables included in the scoring system were gastrectomy history, high direct bilirubin (DBIL), high albumin (ALB), villous type of papillary orifice, nodular type of papillary orifice, pancreatic guidewire passages, precut sphincterotomy, and high operator experience. A total score >5 indicated high risk. In the external test set, the area under the curve (AUC) was 0.718, the sensitivity was 0.723, and the specificity was 0.676. In the external test set, the probability of PEP was 6.1%, 17.0%, and 37.5% in patients with low (<0), moderate (0-5), and high (>5) risk scores, respectively.ConclusionsThis study established a scoring system for predicting the risk of PEP using routinely measured clinical variables. Its application in routine work warrants further investigation.
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Project description:Ferroptosis is a newly recognized form of regulatory cell death characterized by severe lipid peroxidation triggered by iron overload and the production of reactive oxygen species (ROS). However, the role of ferroptosis in severe acute pancreatitis(SAP) has not been fully elucidated. We established four severe acute pancreatitis models of rats including the sham control group, the SAP group, the Fer -1-treated SAP (SAP + Fer-1) group, the 3-MA-treated SAP (SAP + 3-MA) group. The SAP group was induced by retrograde injection of sodium taurocholate into the pancreatic duct. The other two groups were intraperitoneally injected with ferroptosis inhibitor (Fer-1) and autophagy inhibitor (3-MA), respectively. The model of severe acute pancreatitis with amylase crest-related inflammatory factors was successfully established. Then we detected ferroptosis (GPX4, SLC7A1 etc.) and autophagy-related factors (LC3II, p62 ect.) to further clarify the relationship between ferroptosis and autophagy. Our study found that ferroptosis occurs during the development of SAP, such as iron and lipid peroxidation in pancreatic tissues, decreased levels of reduced glutathione peroxidase 4 (GPX 4) and glutathione (GSH), and increased malondialdehyde(MDA) and significant mitochondrial damage. In addition, ferroptosis related proteins such as GPX4, solute carrier family 7 member 11(SLC7A11) and ferritin heavy chain 1(FTH1) were significantly decreased. Next, the pathogenesis of ferroptosis in SAP was studied. First, treatment with the ferroptosis inhibitor ferrostatin-1(Fer-1) significantly alleviated ferroptosis in SAP. Interestingly, autophagy occurs during the pathogenesis of SAP, and autophagy promotes the occurrence of ferroptosis in SAP. Moreover, 3-methyladenine (3-MA) inhibition of autophagy can significantly reduce iron overload and ferroptosis in SAP. Our results suggest that ferroptosis is a novel pathogenesis of SAP and is dependent on autophagy. This study provides a new theoretical basis for the study of SAP.
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