Acute kidney injury prediction in cardiac surgery patients by a urinary peptide pattern: a case-control validation study

Background Acute kidney injury (AKI) is a prominent problem in hospitalized patients and associated with increased morbidity and mortality. Clinical medicine is currently hampered by the lack of accurate and early biomarkers for diagnosis of AKI and the evaluation of the severity of the disease. In 2010, we established a multivariate peptide marker pattern consisting of 20 naturally occurring urinary peptides to screen patients for early signs of renal failure. The current study now aims to evaluate if, in a different study population and potentially various AKI causes, AKI can be detected early and accurately by proteome analysis. Methods Urine samples from 60 patients who developed AKI after cardiac surgery were analyzed by capillary electrophoresis-mass spectrometry (CE-MS). The obtained peptide profiles were screened by the AKI peptide marker panel for early signs of AKI. Accuracy of the proteomic model in this patient collective was compared to that based on urinary neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) ELISA levels. Sixty patients who did not develop AKI served as negative controls. Results From the 120 patients, 110 were successfully analyzed by CE-MS (59 with AKI, 51 controls). Application of the AKI panel demonstrated an AUC in receiver operating characteristics (ROC) analysis of 0.81 (95 % confidence interval: 0.72–0.88). Compared to the proteomic model, ROC analysis revealed poorer classification accuracy of NGAL and KIM-1 with the respective AUC values being outside the statistical significant range (0.63 for NGAL and 0.57 for KIM-1)

Specific issues concerning the management of patients on the waiting list and after liver transplantation

The present document is a second contribution collecting the recommendations of an expert panel of transplant hepatologists appointed by the Italian Association for the Study of the Liver (AISF) concerning the management of certain aspects of liver transplantation, including: the issue of prompt referral; the management of difficult candidates; malnutrition; living related liver transplants; hepatocellular carcinoma; and the role of direct acting antiviral agents before and after transplantation. The statements on each topic were approved by participants at the AISF Transplant Hepatology Expert Meeting organized by the Permanent Liver Transplant Commission in Mondello on 12-13 May 2017. They are graded according to the GRADE grading system

Predicting Bleeding and Thrombosis Complications in Patients with Continuous Flow Left Ventricular Assist Devices

Background: Left ventricular assist device (LVAD) therapy has been proven to relieve heart failure symptoms and improve survival, but is not devoid of bleeding and/or thrombotic complications. Risk stratification tools have been utilized in other cardiovascular disease populations to estimate the risk of bleeding and thrombosis with and without anticoagulation, including the HAS-BLED, HEMORR2HAGES, CHADS2 and CHA2DS2-VASc models. The study objective was to evaluate the predictive value of available risk models for bleeding and thrombotic complications in patients with an LVAD within one year of implantation. Methods: This was a retrospective, single-center analysis of patients implanted with the HeartMate II continuous-flow LVAD from July 2011 to June 2016. All patients who received an LVAD within the study period were eligible for inclusion. The primary endpoint was the first occurrence of bleeding or thrombosis within one year from implantation. Baseline risk model scores were calculated at the time of LVAD implantation. Chi-square and student’s t-test were used to measure baseline differences and compare mean risk model scores between patients who had an event. A receiver operator characteristic (ROC) curve analysis was performed to evaluate the accuracy of the risk models to predict an event. Results: A total of 129 patients underwent LVAD implantation within the study time period. Mean CHADS2, CHA2DS2-VASc, and HAS-BLED scores were not significantly different in patients with and without an event. The mean HEMORR2HAGES score was 3.09 and 2.51 in those with and without a bleeding event, respectively (p = 0.008). The ROC curve area for the HEMORR2HAGES model was the highest at 0.620. Conclusion: The HAS-BLED, HEMORR2HAGES, CHADS2and CHA2DS2-VASc risk stratification models did not accurately predict bleeding or thrombosis events in our population. The mean HEMORR2HAGES model score was higher in patients who experienced a bleeding event. However, this model did not have strong positive predictive value. Better risk models are needed to predict bleeding and thrombotic events in this patient population

The Multi-Biomarker Approach for Heart Failure in Patients with Hypertension

We assessed the predictive ability of selected biomarkers using N-terminal pro-brain natriuretic peptide (NT-proBNP) as the benchmark and tried to establish a multi-biomarker approach to heart failure (HF) in hypertensive patients. In 120 hypertensive patients with or without overt heart failure, the incremental predictive value of the following biomarkers was investigated: Collagen III N-terminal propeptide (PIIINP), cystatin C (CysC), lipocalin-2/NGAL, syndecan-4, tumor necrosis factor-α (TNF-α), interleukin 1 receptor type I (IL1R1), galectin-3, cardiotrophin-1 (CT-1), transforming growth factor β (TGF-β) and N-terminal pro-brain natriuretic peptide (NT-proBNP). The highest discriminative value for HF was observed for NT-proBNP (area under the receiver operating characteristic curve (AUC) = 0.873) and TGF-β (AUC = 0.878). On the basis of ROC curve analysis we found that CT-1 > 152 pg/mL, TGF-β 2.3 ng/mL, NT-proBNP > 332.5 pg/mL, CysC > 1 mg/L and NGAL > 39.9 ng/mL were significant predictors of overt HF. There was only a small improvement in predictive ability of the multi-biomarker panel including the four biomarkers with the best performance in the detection of HF—NT-proBNP, TGF-β, CT-1, CysC—compared to the panel with NT-proBNP, TGF-β and CT-1 only. Biomarkers with different pathophysiological backgrounds (NT-proBNP, TGF-β, CT-1, CysC) give additive prognostic value for incident HF in hypertensive patients compared to NT-proBNP alone.The study was financed by JUVENTUS PLUS grant 2012 (No. IP2011003271) of the Polish Ministry of Science and Higher Education (MNiSW) and research grant of Medical University in Lodz and MNiSW No. 502-03/5-139-02/502-54-008

Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

A new gender-specific model for skin autofluorescence risk stratification

Advanced glycation endproducts (AGEs) are believed to play a significant role in the pathophysiology of a variety of diseases including diabetes and cardiovascular diseases. Non-invasive skin autofluorescence (SAF) measurement serves as a proxy for tissue accumulation of AGEs. We assessed reference SAF and skin reflectance (SR) values in a Saudi population (n = 1,999) and evaluated the existing risk stratification scale. The mean SAF of the study cohort was 2.06 (SD = 0.57) arbitrary units (AU), which is considerably higher than the values reported for other populations. We show a previously unreported and significant difference in SAF values between men and women, with median (range) values of 1.77 AU (0.79–4.84 AU) and 2.20 AU (0.75–4.59 AU) respectively (p-value « 0.01). Age, presence of diabetes and BMI were the most influential variables in determining SAF values in men, whilst in female participants, SR was also highly correlated with SAF. Diabetes, hypertension and obesity all showed strong association with SAF, particularly when gender differences were taken into account. We propose an adjusted, gender-specific disease risk stratification scheme for Middle Eastern populations. SAF is a potentially valuable clinical screening tool for cardiovascular risk assessment but risk scores should take gender and ethnicity into consideration for accurate diagnosis

Benchmarking machine learning models on multi-centre eICU critical care dataset

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabled the possibility of establishing public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep learning models using eICU critical care dataset of around 73,000 patients. This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model. We also investigate the impact of numerical variables as well as handling of categorical variables on each of the defined tasks. The source code, detailing our methods and experiments is publicly available such that anyone can replicate our results and build upon our work.Comment: Source code to replicate the results https://github.com/mostafaalishahi/eICU_Benchmar

Cancer-Associated Thrombosis in Cirrhotic Patients with Hepatocellular Carcinoma.

It is common knowledge that cancer patients are more prone to develop venous thromboembolic complications (VTE). It is therefore not surprising that patients with hepatocellular carcinoma (HCC) present with a significant risk of VTE, with the portal vein being the most frequent site (PVT). However, patients with HCC are peculiar as both cancer and liver cirrhosis are conditions that can perturb the hemostatic balance towards a prothrombotic state. Because HCC-related hypercoagulability is not clarified at all, the aim of the present review is to summarize the currently available knowledge on epidemiology and pathogenesis of non-malignant thrombotic complications in patients with liver cirrhosis and HCC. They are at increased risk to develop both PVT and non-splanchnic VTE, indicating that both local and systemic factors can foster the development of site-specific thrombosis. Recent studies have suggested multiple and often interrelated mechanisms through which HCC can tip the hemostatic balance of liver cirrhosis towards hypercoagulability. Described mechanisms include increased fibrinogen concentration/polymerization, thrombocytosis, and release of tissue factor-expressing extracellular vesicles. Currently, there are no specific guidelines on the use of thromboprophylaxis in this unique population. There is the urgent need of prospective studies assessing which patients have the highest prothrombotic profile and would therefore benefit from early thromboprophylaxis