| 1. |
- Albuquerque, João, et al.
(författare)
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Generation and validation of a classification model to diagnose familial hypercholesterolaemia in adults.
- 2023
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Ingår i: Atherosclerosis. - 1879-1484. ; 383
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Tidskriftsartikel (refereegranskat)abstract
- The early diagnosis of familial hypercholesterolaemia is associated with a significant reduction in cardiovascular disease (CVD) risk. While the recent use of statistical and machine learning algorithms has shown promising results in comparison with traditional clinical criteria, when applied to screening of potential FH cases in large cohorts, most studies in this field are developed using a single cohort of patients, which may hamper the application of such algorithms to other populations. In the current study, a logistic regression (LR) based algorithm was developed combining observations from three different national FH cohorts, from Portugal, Brazil and Sweden. Independent samples from these cohorts were then used to test the model, as well as an external dataset from Italy.The area under the receiver operating characteristics (AUROC) and precision-recall (AUPRC) curves was used to assess the discriminatory ability among the different samples. Comparisons between the LR model and Dutch Lipid Clinic Network (DLCN) clinical criteria were performed by means of McNemar tests, and by the calculation of several operating characteristics.AUROC and AUPRC values were generally higher for all testing sets when compared to the training set. Compared with DLCN criteria, a significantly higher number of correctly classified observations were identified for the Brazilian (p<0.01), Swedish (p<0.01), and Italian testing sets (p<0.01). Higher accuracy (Acc), G mean and F1 score values were also observed for all testing sets.Compared to DLCN criteria, the LR model revealed improved ability to correctly classify observations, and was able to retain a similar number of FH cases, with less false positive retention. Generalization of the LR model was very good across all testing samples, suggesting it can be an effective screening tool if applied to different populations.
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| 2. |
- DeAngelis, Nicola, et al.
(författare)
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2020 WSES guidelines for the detection and management of bile duct injury during cholecystectomy
- 2021
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Ingår i: World Journal of Emergency Surgery. - : BMC. - 1749-7922. ; 16:1
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Forskningsöversikt (refereegranskat)abstract
- Bile duct injury (BDI) is a dangerous complication of cholecystectomy, with significant postoperative sequelae for the patient in terms of morbidity, mortality, and long-term quality of life. BDIs have an estimated incidence of 0.4-1.5%, but considering the number of cholecystectomies performed worldwide, mostly by laparoscopy, surgeons must be prepared to manage this surgical challenge. Most BDIs are recognized either during the procedure or in the immediate postoperative period. However, some BDIs may be discovered later during the postoperative period, and this may translate to delayed or inappropriate treatments. Providing a specific diagnosis and a precise description of the BDI will expedite the decision-making process and increase the chance of treatment success. Subsequently, the choice and timing of the appropriate reconstructive strategy have a critical role in long-term prognosis. Currently, a wide spectrum of multidisciplinary interventions with different degrees of invasiveness is indicated for BDI management. These World Society of Emergency Surgery (WSES) guidelines have been produced following an exhaustive review of the current literature and an international expert panel discussion with the aim of providing evidence-based recommendations to facilitate and standardize the detection and management of BDIs during cholecystectomy. In particular, the 2020 WSES guidelines cover the following key aspects: (1) strategies to minimize the risk of BDI during cholecystectomy; (2) BDI rates in general surgery units and review of surgical practice; (3) how to classify, stage, and report BDI once detected; (4) how to manage an intraoperatively detected BDI; (5) indications for antibiotic treatment; (6) indications for clinical, biochemical, and imaging investigations for suspected BDI; and (7) how to manage a postoperatively detected BDI.
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