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- Jung, Christian, et al.
(författare)
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A comparison of very old patients admitted to intensive care unit after acute versus elective surgery or intervention
- 2019
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Ingår i: Journal of critical care. - : W B SAUNDERS CO-ELSEVIER INC. - 0883-9441 .- 1557-8615. ; 52, s. 141-148
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Tidskriftsartikel (refereegranskat)abstract
- Background: We aimed to evaluate differences in outcome between patients admitted to intensive care unit (ICU) after elective versus acute surgery in a multinational cohort of very old patients (80 years; VIP). Predictors of mortality, with special emphasis on frailty, were assessed.Methods: In total, 5063 VIPs were induded in this analysis, 922 were admitted after elective surgery or intervention, 4141 acutely, with 402 after acute surgery. Differences were calculated using Mann-Whitney-U test and Wilcoxon test. Univariate and multivariable logistic regression were used to assess associations with mortality.Results: Compared patients admitted after acute surgery, patients admitted after elective surgery suffered less often from frailty as defined as CFS (28% vs 46%; p < 0.001), evidenced lower SOFA scores (4 +/- 5 vs 7 +/- 7; p < 0.001). Presence of frailty (CFS >4) was associated with significantly increased mortality both in elective surgery patients (7% vs 12%; p = 0.01), in acute surgery (7% vs 12%; p = 0.02).Conclusions: VIPs admitted to ICU after elective surgery evidenced favorable outcome over patients after acute surgery even after correction for relevant confounders. Frailty might be used to guide clinicians in risk stratification in both patients admitted after elective and acute surgery.
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| 2. |
- Kosmidou, Ioanna, et al.
(författare)
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Incidence, Predictors, and Impact of Hospital Readmission After Revascularization for Left Main Coronary Disease.
- 2024
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Ingår i: Journal of the American College of Cardiology. - 1558-3597. ; 83:11, s. 1073-1081
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Tidskriftsartikel (refereegranskat)abstract
- The frequency of and relationship between hospital readmissions and outcomes after revascularization for left main coronary artery disease (LMCAD) are unknown.The purpose of this study was to study the incidence, predictors, and clinical impact of readmissions following percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) for LMCAD.In the EXCEL (XIENCE Versus Coronary Artery Bypass Surgery for Effectiveness of Left Main Revascularization) trial, 1,905 patients with LMCAD were randomized to PCI vs CABG. The cumulative incidence of readmissions was analyzed with multivariable Anderson-Gill and joint frailty models to account for recurrent events and the competing risk of death. The impact of readmission on subsequent mortality within 5-year follow-up was determined in a time-adjusted Cox proportional hazards model.Within 5 years, 1,868 readmissions occurred in 851 of 1,882 (45.2%) hospital survivors (2.2 ± 1.9 per patient with readmission[s], range 1-16), approximately one-half for cardiovascular causes and one-half for noncardiovascular causes (927 [49.6%] and 941 [50.4%], respectively). One or more readmissions occurred in 463 of 942 (48.6%) PCI patients vs 388 of 940 (41.8%) CABG patients (P = 0.003). After multivariable adjustment, PCI remained an independent predictor of readmission (adjusted HR: 1.22; 95% CI: 1.10-1.35; P < 0.0001), along with female sex, comorbidities, and the extent of CAD. Readmission was independently associated with subsequent all-cause death, with interaction testing indicating a higher risk after PCI than CABG (adjusted HR: 5.72; 95% CI: 3.42-9.55 vs adjusted HR: 2.72; 95% CI: 1.64-4.88, respectively; Pint = 0.03).In the EXCEL trial, readmissions during 5-year follow-up after revascularization for LMCAD were common and more frequent after PCI than CABG. Readmissions were associated with an increased risk of all-cause death, more so after PCI than with CABG.
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| 4. |
- Saux, Patrick, et al.
(författare)
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Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study.
- 2023
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Ingår i: The Lancet. Digital health. - 2589-7500. ; 5:10
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Tidskriftsartikel (refereegranskat)abstract
- Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery.In this multinational retrospective observational study we enrolled adult participants (aged ≥18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year follow-up after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI.10231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30602 patient-years. Among participants in all 12 cohorts, 7701 (75·3%) were female, 2530 (24·7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2·8 kg/m2 (95% CI 2·6-3·0) and mean RMSE BMI was 4·7 kg/m2 (4·4-5·0), and the mean difference between predicted and observed BMI was -0·3 kg/m2 (SD 4·7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery.We developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.SOPHIA Innovative Medicines Initiative 2 Joint Undertaking, supported by the EU's Horizon 2020 research and innovation programme, the European Federation of Pharmaceutical Industries and Associations, Type 1 Diabetes Exchange, and the Juvenile Diabetes Research Foundation and Obesity Action Coalition; Métropole Européenne de Lille; Agence Nationale de la Recherche; Institut national de recherche en sciences et technologies du numérique through the Artificial Intelligence chair Apprenf; Université de Lille Nord Europe's I-SITE EXPAND as part of the Bandits For Health project; Laboratoire d'excellence European Genomic Institute for Diabetes; Soutien aux Travaux Interdisciplinaires, Multi-établissements et Exploratoires programme by Conseil Régional Hauts-de-France (volet partenarial phase 2, project PERSO-SURG).
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