Background: Prediction of early mortality after hepatectomies for hepatocellular carcinoma is essential to identify high-risk patients and to decrease the operative mortality rate. Several post-operative clinical risk scores were developed recently to predict mortality post-hepatectomy; however, which one is the best remains undefined. Therefore, the aim of this study was to evaluate the performance of the different post-operative clinical risk scores in predicting early mortality after hepatectomies.
Methods: A total of 240 patients who underwent liver resection for hepatocellular carcinoma at our hospital between June 2011 and July 2016 were retrospectively reviewed. Post-operative clinical risk scores including 50-50 criteria, peak bilirubin >7 mg/dL, model for end-stage liver disease (MELD), risk assessment for early mortality and Hyder scores were evaluated for their performance in predicting early mortality after hepatic resection using the receiver operating characteristic (ROC) curve.
Results: The 90-day mortality rate after hepatic resection was around 2.5%. The 50-50 criteria and peak bilirubin >7 mg/dL were weak predictors of early mortality with low sensitivity (area under the ROC curve: 0.65, 0.66, respectively), whereas, Hyder, risk assessment for early mortality, and post-operative MELD were good predictors of early mortality (area under the ROC curve: 0.89, 0.91 and 0.88, respectively). Moreover, MELD score on post-operative day 3 was an independent risk factor for 90-day mortality with an odds ratio of 1.4 (95% confidence interval 1.06-1.81, P = 0.02).
Conclusions: Post-operative clinical risk scores, especially MELD, were capable of predicting early mortality after liver resection and should be used to identify high-risk patients and provide them with more intensive medical care.
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http://dx.doi.org/10.1111/ans.15368 | DOI Listing |
JACC Heart Fail
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Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Data from large-scale, randomized, controlled trials demonstrate that contemporary treatments for heart failure (HF) can substantially improve morbidity and mortality. Despite this, observed outcomes for patients living with HF are poor, and they have not improved over time. The are many potential reasons for this important problem, but inadequate use of optimal medical therapy for patients with HF, an important component of guideline-directed medical therapy, in routine practice is a principal and modifiable contributor.
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Department of Cardiothoracic Surgery, Lankenau Heart Institute, Wynnewood, PA, USA.
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J Clin Med
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Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy.
Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms.
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Department of Pediatric Rheumatology, Institut de Recerca Sant Joan de Déu, Hospital Sant Joan de Déu, Universitat de Barcelona, 08950 Barcelona, Spain.
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View Article and Find Full Text PDFJ Clin Med
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Cardiac Surgery Unit, Spedali Civili, University of Brescia, 25124 Brescia, Italy.
Heart failure (HF) remains a significant public health issue, with heart transplantation (HT) being the gold standard treatment for end-stage HF. The increasing use of mechanical circulatory support, particularly left ventricular assist devices (LVADs), as a bridge to transplant (BTT), presents new perspectives for increasingly complex clinical scenarios. This study aimed to compare long-term clinical outcomes in patients in heart failure with reduced ejection fraction (HFrEF) receiving an LVAD as BTT to those undergoing direct-to-transplant (DTT) without mechanical support, focusing on survival and post-transplant complications.
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