Dataset Information

Development and Validation of a Model for Postpancreatectomy Hemorrhage Risk.

ABSTRACT:

Importance

Postpancreatectomy hemorrhage (PPH) due to postoperative pancreatic fistula (POPF) is a life-threatening complication after pancreatoduodenectomy. However, there is no prediction tool for early identification of patients at high risk of late PPH.

Objective

To develop and validate a prediction model for PPH.

Design, setting, and participants

This retrospective prognostic study included consecutive patients with clinically relevant POPF who underwent pancreatoduodenectomy from January 1, 2009, to May 20, 2023, at the University Hospital Mannheim (derivation cohort), and from January 1, 2012, to May 31, 2022, at the University Hospital Dresden (validation cohort). Data analysis was performed from May 30 to July 29, 2023.

Exposure

Clinical and radiologic features of PPH.

Main outcomes and measures

Accuracy of a predictive risk score of PPH. A multivariate prediction model-the hemorrhage risk score (HRS)-was established in the derivation cohort (n = 139) and validated in the validation cohort (n = 154).

Results

A total of 293 patients (187 [64%] men; median age, 69 [IQR, 60-76] years) were included. The HRS comprised 4 variables with associations: sentinel bleeding (odds ratio [OR], 35.10; 95% CI, 5.58-221.00; P < .001), drain fluid culture positive for Candida species (OR, 14.40; 95% CI, 2.24-92.20; P < .001), and radiologic proof of rim enhancement of (OR, 12.00; 95% CI, 2.08-69.50; P = .006) or gas within (OR, 12.10; 95% CI, 2.22-65.50; P = .004) a peripancreatic fluid collection. Two risk categories were identified with patients at low risk (0-1 points) and high risk (≥2 points) to develop PPH. Patients with PPH were predicted accurately in the derivation cohort (C index, 0.97) and validation cohort (C index 0.83). The need for more invasive PPH management (74% vs 34%; P < .001) and severe complications (49% vs 23%; P < .001) were more frequent in high-risk patients compared with low-risk patients.

Conclusions and relevance

In this retrospective prognostic study, a robust prediction model for PPH was developed and validated. This tool may facilitate early identification of patients at high risk for PPH.

SUBMITTER: Birgin E

PROVIDER: S-EPMC10701614 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and Validation of a Model for Postpancreatectomy Hemorrhage Risk.

Birgin Emrullah E Hempel Sebastian S Reeg Alina A Oehme Florian F Schnizer Annika A Rink Johann S JS Froelich Matthias F MF Hetjens Svetlana S Plodeck Verena V Nebelung Heiner H Abdelhadi Schaima S Rahbari Mohammad M Téoule Patrick P Rasbach Erik E Reissfelder Christoph C Weitz Jürgen J Schoenberg Stefan O SO Distler Marius M Rahbari Nuh N NN

JAMA network open 20231201 12

<h4>Importance</h4>Postpancreatectomy hemorrhage (PPH) due to postoperative pancreatic fistula (POPF) is a life-threatening complication after pancreatoduodenectomy. However, there is no prediction tool for early identification of patients at high risk of late PPH.<h4>Objective</h4>To develop and validate a prediction model for PPH.<h4>Design, setting, and participants</h4>This retrospective prognostic study included consecutive patients with clinically relevant POPF who underwent pancreatoduode ...[more]

PMID: 38055279

Similar Datasets

Project description:ImportanceThe dilemma between natural rupture risk and adverse outcomes of intervention is of major concern for patients with unruptured arteriovenous malformations (AVMs). The existing risk score for AVM rupture includes factors that are controversial and lacks prospective validation.ObjectiveTo develop and robustly validate a reliable scoring system to predict the rupture risk of AVMs.Design, setting, and participantsThis prognostic study developed a prediction model derived from a single-center cohort (derivation cohort) and validated in a multicenter external cohort (multicenter external validation cohort) and a cohort of patients receiving conservative treatment management (conservative treatment validation cohort). Patients were recruited from a nationwide multicenter prospective collaboration registry in China. A total of 4135 patients were enrolled in the registry between August 1, 2011, and September 1, 2021. Of those, 3962 patients were included in the study (3585 in the derivation cohort and 377 in the multicenter external validation cohort); 1028 patients from the derivation cohort who had time-to-event data and prerupture imaging results were included in the conservative treatment validation cohort. Data were analyzed from March 10 to June 21, 2022.Main outcomes and measuresA scoring system was developed based on risk factors identified from a literature review and a robust selection process. Patients were stratified into different risk groups based on scores to calculate hemorrhage-free probability in future years, and Kaplan-Meier curves were plotted to visualize risk stratification. Receiver operating characteristic curves were used to assess the discrimination of models. Univariable analyses (logistic regression analysis for descriptive data and Cox regression analysis for survival data) were used to compare baseline information and assess bias.ResultsAmong 3962 patients (2311 men [58.3%]; median [IQR] age, 26.1 [14.6-35.5] years), 3585 patients (2100 men [58.6%]; median [IQR] age, 25.9 [14.6-35.0] years) were included in the derivation cohort, and 377 patients (211 men [56.0%]; median [IQR] age, 26.4 [14.5-39.2] years) were included in the multicenter external validation cohort. Thirty-six hemorrhages occurred over a median (IQR) follow-up of 4.2 (0.3-6.0) years among 1028 patients in the conservative treatment validation cohort. Four risk factors were used to develop the scoring system: ventricular system involvement, venous aneurysm, deep location, and exclusively deep drainage (VALE). The VALE scoring system performed well in all 3 cohorts, with areas under the receiver operating characteristic curve of 0.77 (95% CI, 0.75-0.78) in the derivation cohort, 0.85 (95% CI, 0.81-0.89) in the multicenter external validation cohort, and 0.73 (95% CI, 0.65-0.81) in the conservative treatment validation cohort. The 10-year hemorrhage-free rate was 95.5% (95% CI, 87.1%-100%) in the low-risk group, 92.8% (95% CI, 88.8%-97.0%) in the moderate-risk group, and 75.8% (95% CI, 65.1%-88.3%) in the high-risk group; the model discrimination was significant when comparing these rates between the high-risk group and the low- and moderate-risk groups (P < .001 for both comparisons).Conclusions and relevanceIn this prognostic study, the VALE scoring system was developed to distinguish rupture risk among patients with AVMs. The stratification of unruptured AVMs may enable patients with low risk of rupture to avoid unnecessary interventions. These findings suggest that the scoring system is a reliable and applicable tool that can be used to facilitate patient and physician decision-making and reduce unnecessary interventions or unexpected AVM ruptures.

Project description:Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self-reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67-0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57-0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model for lung cancer that is specific to African-Americans and thus more precise in predicting their risks. These findings highlight the importance of conducting further ethnic-specific analyses of disease risk.

Project description:BackgroundWe aimed to establish risk factors for stroke-associated pneumonia (SAP) following intracerebral hemorrhage (ICH) and develop an efficient and convenient model to predict SAP in patients with ICH.MethodsOur study involved 1333 patients consecutively diagnosed with ICH and admitted to the Neurology Department of the First Affiliated Hospital of Wenzhou Medical University. The 1333 patients were randomly divided (3:1) into the derivation cohort (n = 1000) and validation Cohort (n = 333). Variables were screened from demographics, lifestyle-related factors, comorbidities, clinical symptoms, neuroimaging features, and laboratory tests. In the derivation cohort, we developed a prediction model with multivariable logistic regression analysis. In the validation cohort, we assessed the model performance and compared it to previously reported models. The area under the receiver operating characteristic curve (AUROC), GiViTI calibration belt, net reclassification index (NRI), integrated discrimination index (IDI) and decision curve analysis (DCA) were used to assess the prediction ability and the clinical decision-making ability.ResultsThe incidence of SAP was 19.9% and 19.8% in the derivation (n = 1000) and validation (n = 333) cohorts, respectively. We developed a nomogram prediction model including age (Odds Ratio [OR] 1.037, 95% confidence interval [CI] 1.020-1.054), male sex (OR 1.824, 95% CI 1.206-2.757), multilobar involvement (OR 1.851, 95% CI 1.160-2.954), extension into ventricles (OR 2.164, 95% CI 1.456-3.215), dysphagia (OR 3.626, 95% CI 2.297-5.725), disturbance of consciousness (OR 2.113, 95% CI 1.327-3.362) and total muscle strength of the worse side (OR 0.93, 95% CI 0.876-0.987). Compared with previous models, our model was well calibrated and showed significantly higher AUROC, better reclassification ability (improved NRI and IDI) and a positive net benefit for predicted probability thresholds between 10% and 73% in DCA.ConclusionsWe developed a simple, valid, and clinically useful model to predict SAP following ICH, with better predictive performance than previous models. It might be a promising tool to assess the individual risk of developing SAP for patients with ICH and optimize decision-making.

Dataset Information

Development and Validation of a Model for Postpancreatectomy Hemorrhage Risk.

Importance

Objective

Design, setting, and participants

Exposure

Main outcomes and measures

Results

Conclusions and relevance

Publications

Development and Validation of a Model for Postpancreatectomy Hemorrhage Risk.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets