Citations

Objective Alcohol-related liver disease (ALD) ranges from never-decompensated ALD (ndALD) to the life-threatening decompensated phenotype, known as alcohol-related hepatitis (AH). A multidimensional study of the clinical, histological and molecular features of these subtypes is lacking. Design Two large cohorts of patients were recruited in an international, observational multicentre study: a retrospective cohort of patients with ndALD (n=110) and a prospective cohort of patients with AH (n=225). Clinical, analytical, immunohistochemistry and hepatic RNA microarray analysis of both disease phenotypes were performed. Results Age and mean alcohol intake were similar in both groups. AH patients had greater aspartate amino transferase/alanine amino transferase ratio and lower gamma-glutamyl transferase levels than in ndALD patients. Patients with AH demonstrated profound liver failure and increased mortality. One-year mortality was 10% in ndALD and 50% in AH. Histologically, steatosis grade, ballooning and pericellular fibrosis were similar in both groups, while advanced fibrosis, Mallory-Denk bodies, bilirubinostasis, severe neutrophil infiltration and ductular reaction were more frequent among AH patients. Transcriptome analysis revealed a profound gene dysregulation within both phenotypes when compare to controls. While ndALD was characterised by deregulated expression of genes involved in matrisome and immune response, the development of AH resulted in a marked deregulation of genes involved in hepatocyte reprogramming and bile acid metabolism. Conclusions Despite comparable alcohol intake, AH patients presented with worse liver function compared with ndALD patients. Bilirubinostasis, severe fibrosis and ductular reaction were prominent features of AH. AH patients exhibited a more profound deregulation of gene expression compared with ndALD patients.

Abstract Background There is an extensive literature highlighting the utility of blood-based liquid biopsies in several extracranial tumors for diagnosis and monitoring. Methods The RANO (Response Assessment in Neuro-Oncology) group developed a multidisciplinary international Task Force to review the English literature on liquid biopsy in gliomas focusing on the most frequently used techniques, that is circulating tumor DNA, circulating tumor cells, and extracellular vesicles in blood and CSF. Results ctDNA has a higher sensitivity and capacity to represent the spatial and temporal heterogeneity in comparison to circulating tumor cells. Exosomes have the advantages to cross an intact blood-brain barrier and carry also RNA, miRNA, and proteins. Several clinical applications of liquid biopsies are suggested: to establish a diagnosis when tissue is not available, monitor the residual disease after surgery, distinguish progression from pseudoprogression, and predict the outcome. Conclusions There is a need for standardization of biofluid collection, choice of an analyte, and detection strategies along with rigorous testing in future clinical trials to validate findings and enable entry into clinical practice.

Abstract: Globally, the burden of musculoskeletal conditions continues to rise, disproportionately affecting low and middle-income countries (LMICs). The ability to meet these orthopaedic surgical care demands remains a challenge. To help address these issues, many orthopaedic surgeons seek opportunities to provide humanitarian assistance to the populations in need. While many global orthopaedic initiatives are well-intentioned and can offer short-term benefits to the local communities, it is essential to emphasize training and the integration of local surgeon-leaders. The commitment to developing educational and investigative capacity, as well as fostering sustainable, mutually beneficial partnerships in low-resource settings, is critical. To this end, global health organizations, such as the Consortium of Orthopaedic Academic Traumatologists (COACT), work to promote and ensure the lasting sustainability of musculoskeletal trauma care worldwide. This article describes global orthopaedic efforts that can effectively address musculoskeletal care through an examination of 5 domains: clinical care, clinical research, surgical education, disaster response, and advocacy.

ABSTRACT Background: There are limited data on the temporal trends, incidence, and outcomes of ST-segment-elevation myocardial infarction-cardiogenic shock (STEMI-CS). Methods: Adult (>18 years) STEMI-CS admissions were identified using the National Inpatient Sample (2000–2017) and classified by tertiles of admission year (2000–2005, 2006–2011, 2012–2017). Outcomes of interest included temporal trends, acute organ failure, cardiac procedures, in-hospital mortality, hospitalization costs, and length of stay. Results: In ∼4.3 million STEMI admissions, CS was noted in 368,820 (8.5%). STEMI-CS incidence increased from 5.8% in 2000 to 13.0% in 2017 (patient and hospital characteristics adjusted odds ratio [aOR] 2.45 [95% confidence interval {CI} 2.40–2.49]; P < 0.001). Multiorgan failure increased from 55.5% (2000–2005) to 74.3% (2012–2017). Between 2000 and 2017, coronary angiography and percutaneous coronary intervention use increased from 58.8% to 80.1% and 38.6% to 70.6%, whereas coronary artery bypass grafting decreased from 14.9% to 10.4% (all P < 0.001). Over the study period, the use of intra-aortic balloon pump (40.6%–37.6%) decreased, and both percutaneous left ventricular assist devices (0%–12.9%) and extra-corporeal membrane oxygenation (0%–2.8%) increased (all P < 0.001). In hospital mortality decreased from 49.6% in 2000 to 32.7% in 2017 (aOR 0.29 [95% CI 0.28–0.31]; P < 0.001). During the 18-year period, hospital lengths of stay decreased, hospitalization costs increased and use of durable left ventricular assist device /cardiac transplantation remained stable (P > 0.05). Conclusions: In the United States, incidence of CS in STEMI has increased 2.5-fold between 2000 and 2017, while in-hospital mortality has decreased during the study period. Use of coronary angiography and PCI increased during the study period.

Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.

Background: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. Methods: We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. Results: We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Conclusions: Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.

Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Results: Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). Conclusion: The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.

Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster’s key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. Results: Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). Conclusion: Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.