ArticleLangenbeck's archives of surgery2024
Risk factors and predictive model development for high blood loss in minimally invasive distal pancreatectomy: a retrospective cohort study.
Article in Langenbeck's archives of surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
Abstract
backgroundHigh blood loss is an adverse event related to increased morbidity and poorer outcomes in pancreatic surgery patients. The aim of this study was to identify risk factors and establish a predictive model for high perioperative blood loss (HPBL) in minimally invasive distal pancreatectomy (MIDP).
methodsWe collected data from 353 patients who underwent MIDP at a university affiliated tertiary hospital between January 2016 and October 2023. Perioperative blood loss was calculated based on pre- and postoperative hemoglobin concentrations according to a combination of the formulas provided by Nadler and Gross. Multivariate logistic regression analyses were performed for the training cohort to identify the clinical factors independently associated with perioperative blood loss (PBL). A predictive nomogram based on these factors was established and validated.
resultsWeight, imaging findings, serum albumin concentration, MIDP experience, spleen treatment, and operation time were independent predictors for HPBL. The established model for predicting HPBL showed that the area under the curve (AUC) was 0.799 (95% CI = 0.746-0.853) and 0.852 (95% CI = 0.760-0.943) for the training cohort and validation cohort, respectively. When utilized to predict blood transfusion, the AUC was 0.778 (95% CI = 0.691-0.865) in the training cohort and 0.818 (95% CI = 0.681-0.955) in the validation cohort. Patients with a high predicted risk had significantly higher incidences of postoperative pancreatic fistula, intra-abdominal infection, and longer hospital stays than patients with a low risk.
conclusionsWe established and validated a model for predicting HPBL in MIDP patients. This novel model may have future utility when generating surgical strategies.
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