Machine learning versus traditional risk stratification methods in acute coronary syndrome: a pooled randomized clinical trial analysis.

Traditional statistical models allow population based inferences and comparisons. Machine learning (ML) explores datasets to develop algorithms that do not assume linear relationships between variables and outcomes and that may account for higher order interactions to make individualized outcome predictions. To evaluate the performance of machine learning models compared to traditional risk stratification methods for the prediction of major adverse cardiovascular events (MACE) and bleeding in patients with acute coronary syndrome (ACS) that are treated with antithrombotic therapy. Data on 24,178 ACS patients were pooled from four randomized controlled trials. The super learner ensemble algorithm selected weights for 23 machine learning models and was compared to traditional models. The efficacy endpoint was a composite of cardiovascular death, myocardial infarction, or stroke. The safety endpoint was a composite of TIMI major and minor bleeding or bleeding requiring medical attention. For the MACE outcome, the super learner model produced a higher c-statistic (0.734) than logistic regression (0.714), the TIMI risk score (0.489), and a new cardiovascular risk score developed in the dataset (0.644). For the bleeding outcome, the super learner demonstrated a similar c-statistic as the logistic regression model (0.670 vs. 0.671). The machine learning risk estimates were highly calibrated with observed efficacy and bleeding outcomes (Hosmer-Lemeshow p value = 0.692 and 0.970, respectively). The super learner algorithm was highly calibrated on both efficacy and safety outcomes and produced the highest c-statistic for prediction of MACE compared to traditional risk stratification methods. This analysis demonstrates a contemporary application of machine learning to guide patient-level antithrombotic therapy treatment decisions.Clinical Trial Registration ATLAS ACS-2 TIMI 46: https://clinicaltrials.gov/ct2/show/NCT00402597. Unique Identifier: NCT00402597. ATLAS ACS-2 TIMI 51: https://clinicaltrials.gov/ct2/show/NCT00809965. Unique Identifier: NCT00809965. GEMINI ACS-1: https://clinicaltrials.gov/ct2/show/NCT02293395. Unique Identifier: NCT02293395. PIONEER-AF PCI: https://clinicaltrials.gov/ct2/show/NCT01830543. Unique Identifier: NCT01830543.

A Case of Salicylate Intoxication Complicated by Coagulopathy, Pulmonary Edema, and Pancreatitis.

The large availability of salicylic acid products makes them an often encountered source of poisoning in the emergency department. Even though in most cases the prognosis is good, with a low incidence of long-term morbidity and mortality, complications do occur, and some of those can be life threatening. We present an unusual case of salicylate intoxication in an adolescent in which several uncommon complications (severe coagulopathy, pulmonary edema, and pancreatitis) conjoined together. We review the literature and discuss the complications pathogenesis and differential diagnosis. We suggest that these potentially life-threatening complications be acknowledged, investigated, and rapidly treated.

Evaluation of growth production factors as predictors of the incidence and severity of white striping and woody breast in broiler chickens.

White striping (WS) and woody breast (WB) have been previously associated with older and heavier birds. However, there is limited information supporting the association between these 2 muscle conditions and growth parameters. The objectives of this study were 1) to investigate the relationship between WS and WB using different growth production factors and 2) to propose a predictive model that uses growth production factors to investigate the incidence and severity of WS and WB. A combined database of 4,332 broilers pooled from 7 research experiments conducted from 2016 to 2017 at Texas A&M University was used in this study. Parameters such as sex, age (4 wk, 6 wk, and 8 wk), strain (standard A vs. high-breast-yield [B and C]), live weight categories (500 g increments), and breast weight categories (250 g increments) were included in the model. Results showed that WS was 12% more likely to be present in non-WB fillets. The association between WS and WB suggests a moderate relationship between the ranks of both outcome variables (ρ = 0.57, P < 0.0001). Variables such as age, live weight, and sex were not as important as breast weight and strain in the severity prediction of WS and WB. Butterfly fillets above 750 g and with high-breast-yielding strains were more likely associated with higher severity of WS and WB scores. No post hoc variable selection was performed. Both models show good discrimination. The WS model produced an uncorrected area under the curve (AUC) of 0.739, with a bootstrap corrected estimate of 0.736. The WB model produced an uncorrected AUC of 0.753 and a bootstrap corrected estimate of 0.752. Therefore, the growth production factors analyzed in this study indicated that there is a moderate relationship between WS and WB myopathies and were jointly predictive of the severity of WS and WB. Potentially other factors not included in this study may play a major role in the relationship of these 2 myopathies. More research should be done to investigate this possibility.

Machine learning to predict venous thrombosis in acutely ill medical patients.

BACKGROUND: The identification of acutely ill patients at high risk for venous thromboembolism (VTE) may be determined clinically or by use of integer-based scoring systems. These scores demonstrated modest performance in external data sets. OBJECTIVES: To evaluate the performance of machine learning models compared to the IMPROVE score. METHODS: The APEX trial randomized 7513 acutely medically ill patients to extended duration betrixaban vs. enoxaparin. Including 68 variables, a super learner model (ML) was built to predict VTE by combining estimates from 5 families of candidate models. A "reduced" model (rML) was also developed using 16 variables that were thought, a priori, to be associated with VTE. The IMPROVE score was calculated for each patient. Model performance was assessed by discrimination and calibration to predict a composite VTE end point. The frequency of predicted risks of VTE were plotted and divided into tertiles. VTE risks were compared across tertiles. RESULTS: The ML and rML algorithms outperformed the IMPROVE score in predicting VTE (c-statistic: 0.69, 0.68 and 0.59, respectively). The Hosmer-Lemeshow goodness-of-fit P-value was 0.06 for ML, 0.44 for rML, and <0.001 for the IMPROVE score. The observed event rate in the lowest tertile was 2.5%, 4.8% in tertile 2, and 11.4% in the highest tertile. Patients in the highest tertile of VTE risk had a 5-fold increase in odds of VTE compared to the lowest tertile. CONCLUSION: The super learner algorithms improved discrimination and calibration compared to the IMPROVE score for predicting VTE in acute medically ill patients.

Betrixaban for first-line venous thromboembolism prevention in acute medically ill patients with risk factors for venous thromboembolism.

INTRODUCTION: Compared to other direct oral anticoagulants, betrixaban has a longer half-life, smaller peak-trough variance, minimal renal clearance, and minimal hepatic Cytochrome P (CYP) metabolism. The Acute Medically Ill VTE Prevention with Extended Duration Betrixaban (APEX) trial evaluated the efficacy and safety of extended duration betrixaban compared to standard duration enoxaparin in acutely ill hospitalized patients. Areas covered: This article describes the role of betrixaban in the prevention of venous thromboembolism (VTE) in acutely ill medical patients. This article provides a consolidated summary of the primary APEX study findings as well as prespecified and exploratory substudies. This article also provides a review of the results of studies in which other direct factor Xa inhibitors have been evaluated in an extended duration regimen in this patient population. Expert commentary: While previous agents have demonstrated that extended duration VTE prophylaxis can be efficacious, betrixaban is the first agent to demonstrate efficacy without an increase in major bleeding. The totality of the data from the APEX trial supports extended duration betrixaban for VTE prophylaxis in the acute medically ill patient population. As such, betrixaban has been approved in the USA for extended VTE prophylaxis in at-risk acute medically ill patients.