Diagnosis of acute myocardial infarction in patients with renal failure using high-sensitivity cardiac troponin T.

BACKGROUND: Diagnosing myocardial infarction (MI) in patients with chronic kidney disease (CKD) is difficult as they often have increased high-sensitivity cardiac troponin T (hs-cTnT) concentrations. METHODS: Observational U.S. cohort study of emergency department (ED) patients undergoing hs-cTnT measurement. Cases with >1 hs-cTnT increase >99th percentile were adjudicated following the Fourth Universal Definition of MI. Diagnostic performance of baseline and serial 2-hour hs-cTnT thresholds for ruling-in acute MI was compared between those without and with CKD (eGFR <60 ml/min/1.73m2). RESULTS: The study cohort included 1992 patients, amongst whom 501 (25%) had CKD. There were 75 (15%) and 350 (70%) patients with CKD and 80 (5%) and 351 (24%) without CKD who had acute MI and myocardial injury. In CKD patients with baseline hs-cTnT thresholds of >52, >100, >200 or >300 ng/L, PPVs for MI were 36% (95% CI 28-45), 53% (95% CI 39-67), 73% (95% CI 50-89) and 80% (95% CI 44-98), and in those without CKD, 61% (95% CI 47-73), 69% (95% CI 49-85), 59% (95% CI 33-82) and 54% (95% CI 25-81). In CKD patients with a 2-hour hs-cTnT delta of >10, >20 or >30 ng/L, PPVs were 66% (95% CI 51-79), 86% (95% CI 68-96) and 88% (95% CI 68-97), and in those without CKD, 64% (95% CI 50-76), 73% (95% CI 57-86) and 75% (95% CI 58-88). CONCLUSION: Diagnostic performance of standard baseline and serial 2-hour hs-cTnT thresholds to rule-in MI is suboptimal in CKD patients. It significantly improves when using higher baseline thresholds and delta values.

Protocol for Improving Care by FAster risk-STratification through use of high sensitivity point-of-care troponin in patients presenting with possible acute coronary syndrome in the EmeRgency department (ICare-FASTER): a stepped-wedge cluster randomised quality improvement initiative.

INTRODUCTION: Clinical assessment in emergency departments (EDs) for possible acute myocardial infarction (AMI) requires at least one cardiac troponin (cTn) blood test. The turn-around time from blood draw to posting results in the clinical portal for central laboratory analysers is ~1-2 hours. New generation, high-sensitivity, point-of-care cardiac troponin I (POC-cTnI) assays use whole blood on a bedside (or near bedside) analyser that provides a rapid (8 min) result. This may expedite clinical decision-making and reduce length of stay. Our purpose is to determine if utilisation of a POC-cTnI testing reduces ED length of stay. We also aim to establish an optimised implementation process for the amended clinical pathway. METHODS AND ANALYSIS: This quality improvement initiative has a pragmatic multihospital stepped-wedge cross-sectional cluster randomised design. Consecutive patients presenting to the ED with symptoms suggestive of possible AMI and having a cTn test will be included. Clusters (comprising one or two hospitals each) will change from their usual-care pathway to an amended pathway using POC-cTnI-the 'intervention'. The dates of change will be randomised. Changes occur at 1 month intervals, with a minimum 2 month 'run-in' period. The intervention pathway will use a POC-cTnI measurement as an alternate to the laboratory-based cTn measurement. Clinical decision-making steps and logic will otherwise remain unchanged. The POC-cTnI is the Siemens (Erlangen Germany) Atellica VTLi high-sensitivity cTnI assay. The primary outcome is ED length of stay. The safety outcome is cardiac death or AMI within 30 days for patients discharged directly from the ED. ETHICS AND DISSEMINATION: Ethics approval has been granted by the New Zealand Southern Health and Disability Ethics Committee, reference 21/STH/9. Results will be published in a peer-reviewed journal. Lay and academic presentations will be made. Maori-specific results will be disseminated to Maori stakeholders. TRIAL REGISTRATION NUMBER: ACTRN12619001189112.

Superstition in Surgery: A Population-Based Cohort Study to Assess the Association Between Surgery on Friday the 13th and Postoperative Outcomes.

Objective: We sought to examine whether the outcomes of patients who receive a surgical procedure on Friday the 13th differ from patients who receive surgery on flanking Fridays. Background: Numerous studies have demonstrated that increased anxiety from the provider or patient around the time of surgery can lead to worse outcomes. Superstitious patients often express significant concern and anxiety when undergoing a surgical procedure on Friday the 13th. Methods: A retrospective, population-based cohort study of 19,747 adults undergoing 1 of 25 common surgical procedures on Friday the 13th or flanking control Fridays (Friday the 6th and Friday the 20th) between January 1, 2007, and December 31, 2019, with 1 year of follow-up. The main outcomes included death, readmission, and complications at 30 days (short-term), 90 days (intermediate-term), and 1 year (long-term). Results: A total of 7,349 (37.2%) underwent surgery on Friday the 13th, and 12,398 (62.8%) underwent surgery on a flanking Friday during the study period. Patient characteristics were similar between the 2 groups. We found no evidence that patients receiving surgery on Friday the 13th group were more likely to experience the composite primary outcome at 30 days [adjusted odds ratio (aOR) = 1.02 (95% CI = 0.94-1.09)], 90 days [aOR = 0.97 (95% CI = 0.90-1.04)], and 1 year [aOR = 0.99 (95% CI = 0.94-1.04)] after surgery. Conclusion: Patients receiving surgery on Friday the 13th do not appear to fare worse than those treated on ordinary Fridays with respect to the composite outcome.

Description of lymphocyte and cytokine profiles in individuals with acute myeloid leukemia associated with FLT3-ITD and NPM1 mutation status.

The pathogenesis of acute myeloid leukemia (AML) involves mutations in genes such as FLT3 and NPM1, which are also associated with the prognosis of the disease. The immune system influences disease progression, but the mechanisms underlying the interaction between the immune system and AML are not clear. In this study, the profiles of lymphocytes and cytokines were described in individuals with AML stratified by molecular changes associated with prognosis. The participants included in this study were newly diagnosed AML patients (n = 43) who were about to undergo chemotherapy. Subtypes of lymphocytes in peripheral blood, including B cells, T cells, and natural killer cells, and serum concentrations of cytokines, including Th1, Th2, and Th17, were studied by flow cytometry assays (BD FACSCanto II). The correlations between lymphocyte subsets, cytokines, and genetic/prognostic risk stratification (based on the FLT3 and NPM1 genes) were analyzed. The differences in B lymphocytes (%), T lymphocytes (%), plasmablasts (%), leukocytes (cells/µl), and tumor necrosis factor (pg/ml) were determined between groups with FLT3-ITD+ and FLT3-ITD- mutations. The presence of mutations in NPM1 and FLT3-ITD and age suggested changes in the lymphocyte and cytokine profile in individuals with AML.

Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists.

Multiple Myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of plasma cells within the bone marrow. Diagnosing MM presents considerable challenges, involving the identification of plasma cells in cytology examinations on hematological slides. At present, this is still a time-consuming manual task and has high labor costs. These challenges have adverse implications, which rely heavily on medical professionals' expertise and experience. To tackle these challenges, we present an investigation using Artificial Intelligence, specifically a Machine Learning analysis of hematological slides with a Deep Neural Network (DNN), to support specialists during the process of diagnosing MM. In this sense, the contribution of this study is twofold: in addition to the trained model to diagnose MM, we also make available to the community a fully-curated hematological slide dataset with thousands of images of plasma cells. Taken together, the setup we established here is a framework that researchers and hospitals with limited resources can promptly use. Our contributions provide practical results that have been directly applied in the public health system in Brazil. Given the open-source nature of the project, we anticipate it will be used and extended to diagnose other malignancies.

An Evaluation of Fit Indices Used in Model Selection of Dichotomous Mixture IRT Models.

A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's information criterion (DIC), sample size adjusted BIC (SABIC), relative entropy, the integrated classification likelihood criterion (ICL-BIC), the adjusted Lo-Mendell-Rubin (LMR), and Vuong-Lo-Mendell-Rubin (VLMR). The accuracy of the fit indices was assessed for correct detection of the number of latent classes for different simulation conditions including sample size (2,500 and 5,000), test length (15, 30, and 45), mixture proportions (equal and unequal), number of latent classes (2, 3, and 4), and latent class separation (no-separation and small separation). Simulation study results indicated that as the number of examinees or number of items increased, correct identification rates also increased for most of the indices. Correct identification rates by the different fit indices, however, decreased as the number of estimated latent classes or parameters (i.e., model complexity) increased. Results were good for BIC, CAIC, DIC, SABIC, ICL-BIC, LMR, and VLMR, and the relative entropy index tended to select correct models most of the time. Consistent with previous studies, AIC and AICc showed poor performance. Most of these indices had limited utility for three-class and four-class mixture 3PL model conditions.

Association of cardiac biomarkers with long-term cardiovascular events in a community cohort.

MATERIALS AND METHODS: The study assessed major adverse cardiac events (MACE) (myocardial infarction, coronary artery bypass graft, percutaneous intervention, stroke, and death. Cox proportional hazards models assessed apolipoprotein AI (ApoA1), apolipoprotein B (ApoB), ceramide score, cystatin C, galectin-3 (Gal3), LDL-C, Non-HDL-C, total cholesterol (TC), N-terminal B-type natriuretic peptide (NT proBNP), high-sensitivity cardiac troponin (HscTnI) and soluble interleukin 1 receptor-like 1. In adjusted models, Ceramide score was defined by from N-palmitoyl-sphingosine [Cer(16:0)], N-stearoyl-sphingosine [Cer(18:0)], N-nervonoyl-sphingosine [Cer(24:1)] and N-lignoceroyl-sphingosine [Cer(24:0)]. Multi-biomarker models were compared with C-statistics and Integrated Discrimination Index (IDI). RESULTS: A total of 1131 patients were included. Adjusted NT proBNP per 1 SD resulted in a 31% increased risk of MACE/death (HR = 1.31) and a 31% increased risk for stroke/MI (HR = 1.31). Adjusted Ceramide per 1 SD showed a 13% increased risk of MACE/death (HR = 1.13) and a 29% increased risk for stroke/MI (HR = 1.29). These markers added to clinical factors for both MACE/death (p = 0.003) and stroke/MI (p = 0.034). HscTnI was not a predictor of outcomes when added to the models. DISCUSSION: Ceramide score and NT proBNP improve the prediction of MACE and stroke/MI in a community primary prevention cohort.

In a community cohort, where a wide range of biomarkers were evaluated, Ceramide score provided additive value over traditional cardiac risk factors alone for predicting stroke/MI. NT ProBNP provided additive value in prediction of MACE/death. Other biomarkers failed to improve the discrimination of these models.

Effects of soil on the development, survival, and oviposition of Culex quinquefasciatus (Diptera: Culicidae) mosquitoes.

BACKGROUND: Water quality is known to influence the development and survival of larval mosquitoes, which affects mosquito-borne pathogen transmission as a function of the number of mosquitoes that reach adulthood and blood feed. Although water properties are known to affect mosquito development, few studies have investigated the link among soil properties, water quality, and mosquito development. Given the large number of ground-breeding mosquito species, this linkage is a potentially important factor to consider in mosquito ecology. In this study, we explored the effects of different soils on multiple life history parameters of the ground-breeding mosquito species Culex quinquefasciatus (Diptera: Culicidae). METHODS: Cx. quinquefasciatus larvae were reared in water combined with different soil substrates (sandy, silt, or clay loam textures) at increasing soil to water volume ratios, with and without the addition of organic matter (fish food). Gravid mosquitoes were offered different soil-water extracts to investigate soil effects on oviposition preference. RESULTS: Without the addition of organic matter, larval survival and development differed significantly among waters with different soil textures and volumes of substrate. Mosquitoes in water with clay loam soil survived longer and developed further than mosquitoes in other soil waters. Larvae survived for longer periods of time with increased volumes of soil substrate. Adding organic matter reduced the differences in larval survival time, development, and pupation among soil-water extracts. Adult female mosquitoes oviposited more frequently in water with clay loam soil, but the addition of organic matter reduced the soil effects on oviposition preference. CONCLUSIONS: This study suggests soil composition affects larval mosquito survival and development, as well as the oviposition preference of gravid females. Future studies could differentiate abiotic and biotic soil features that affect mosquitoes and incorporate soil variation at the landscape scale into models to predict mosquito population dynamics and mosquito-borne pathogen transmission.

Attending physicians' annual service volume and use of virtual end-of-life care: A population-based cohort study in Ontario, Canada.

IMPORTANCE: Physicians and their practice behaviors influence access to healthcare and may represent potentially modifiable targets for practice-changing interventions. Use of virtual care at the end-of-life significantly increased during the COVID-19 pandemic, but its association with physician practice behaviors, (e.g., annual service volume) is unknown. OBJECTIVE: Measure the association of physicians' annual service volume with their use of virtual end-of-life care (EOLC) and the magnitude of physician-attributable variation in its use, before and during the pandemic. DESIGN, SETTING AND PARTICIPANTS: Population-based cohort study using administrative data of all physicians in Ontario, Canada who cared for adults in the last 90 days of life between 01/25/2018-12/31/2021. Multivariable modified Poisson regression models measured the association between attending physicians' use of virtual EOLC and their annual service volume. We calculated the variance partition coefficients for each regression and stratified by time period before and during the pandemic. EXPOSURE: Annual service volume of a person's attending physician in the preceding year. MAIN OUTCOMES AND MEASURES: Delivery of ≥1 virtual EOLC visit by a person's attending physician and the proportion of variation in its use attributable to physicians. RESULTS: Among the 35,825 unique attending physicians caring for 315,494 adults, use of virtual EOLC was associated with receiving care from a high compared to low service volume attending physician; the magnitude of this association diminished during the pandemic (adjusted RR 1.25 [95% CI 1.14, 1.37] pre-pandemic;1.10 (95% CI 1.08, 1.12) during the pandemic). Physicians accounted for 36% of the variation in virtual EOLC use pre-pandemic and 12% of this variation during the pandemic. CONCLUSIONS AND RELEVANCE: Physicians' annual service volume was associated with use of virtual EOLC and physicians accounted for a substantial proportion of the variation in its use. Physicians may be appropriate and potentially modifiable targets for interventions to modulate use of EOLC delivery.

High Baseline High-Sensitivity Cardiac Troponin T Concentrations and Risk of Index Acute Myocardial Infarction.

OBJECTIVE: To evaluate the diagnostic performance of the previously recommended baseline high-sensitivity cardiac troponin T (hs-cTnT) thresholds of 52 and 100 ng/L in identifying patients at high risk of acute myocardial infarction (AMI). PATIENTS AND METHODS: This study compared the positive predictive value (PPV) for index AMI of these high-risk hs-cTnT thresholds in adult patients in the emergency department undergoing hs-cTnT measurement. RESULTS: The adjudicated MAyo Southwest Wisconsin 5th Gen Troponin T ImplementatiON cohort included 2053 patients, with 157 (7.6%) who received a diagnosis of AMI. The hs-cTnT concentrations of greater than 52 and greater than 100 ng/L resulted in PPVs of 41% (95% CI, 35%-48%) and 57% (95% CI, 48%-66%). In patients with chest discomfort, hs-cTnT concentrations greater than 52 ng/L resulted in a PPV of 66% (95% CI, 56%-76%) and hs-cTnT concentrations greater than 100 ng/L resulted in a PPV of 77% (95% CI, 65%-87%). The CV Data Mart Biomarker cohort included 143,709 patients, and 3003 (2.1%) received a diagnosis of AMI. Baseline hs-cTnT concentrations greater than 52 and greater than 100 ng/L resulted in PPVs of 12% (95% CI, 11%-12%) and 17% (95% CI, 17%-19%), respectively. In patients with chest pain and hs-cTnT concentrations greater than 52 ng/L, the PPV for MI was 17% (95% CI, 15%-18%) and in those with concentrations greater than 100 ng/L, only 22% (95% CI, 19%-25%). CONCLUSION: In unselected patients undergoing hs-cTnT measurement, the hs-cTnT thresholds of greater than 52 and greater than 100 ng/L provide suboptimal performance for identifying high-risk patients. In patients with chest discomfort, an hs-cTnT concentration of greater than 100 ng/L, but not the European Society of Cardiology-recommended threshold of greater than 52 ng/L, provides an acceptable performance but should be used only with other clinical features.

Association of virtual end-of-life care with healthcare outcomes before and during the COVID-19 pandemic: A population-based study.

The use of virtual care for people at the end-of-life significantly increased during the COVID-19 pandemic, but its association with acute healthcare use and location of death is unknown. The objective of this study was to measure the association between the use of virtual end-of-life care with acute healthcare use and an out-of-hospital death before vs. after the introduction of specialized fee codes that enabled broader delivery of virtual care during the COVID-19 pandemic. This was a population-based cohort study of 323,995 adults in their last 90 days of life between January 25, 2018 and December 31, 2021 using health administrative data in Ontario, Canada. Primary outcomes were acute healthcare use (emergency department, hospitalization) and location of death (in or out-of-hospital). Prior to March 14, 2020, 13,974 (8%) people received at least 1 virtual end-of-life care visit, which was associated with a 16% higher rate of emergency department use (adjusted Rate Ratio [aRR] 1.16, 95%CI 1.12 to 1.20), a 17% higher rate of hospitalization (aRR 1.17, 95%CI 1.15 to 1.20), and a 34% higher risk of an out-of-hospital death (aRR 1.34, 95%CI 1.31 to 1.37) compared to people who did not receive virtual end-of-life care. After March 14, 2020, 104,165 (71%) people received at least 1 virtual end-of-life care visit, which was associated with a 58% higher rate of an emergency department visit (aRR 1.58, 95%CI 1.54 to 1.62), a 45% higher rate of hospitalization (aRR 1.45, 95%CI 1.42 to 1.47), and a 65% higher risk of an out-of-hospital death (aRR 1.65, 95%CI 1.61 to 1.69) compared to people who did not receive virtual end-of-life care. The use of virtual end-of-life care was associated with higher acute healthcare use in the last 90 days of life and a higher likelihood of dying out-of-hospital, and these rates increased during the pandemic.

Cardiac Troponin in Patients With Light Chain and Transthyretin Cardiac Amyloidosis: JACC: CardioOncology State-of-the-Art Review.

Cardiac amyloidosis (CA) is an infiltrative disease caused by amyloid fibril deposition in the myocardium; the 2 forms that most frequently involve the heart are amyloid light chain (AL) and amyloid transthyretin (ATTR) amyloidosis. Cardiac troponin (cTn) is the biomarker of choice for the detection of myocardial injury and is frequently found to be elevated in patients with CA, particularly with high-sensitivity assays. Multiple mechanisms of myocardial injury in CA have been proposed, including cytotoxic effect of amyloid precursors, interstitial amyloid fibril infiltration, coronary microvascular dysfunction, amyloid- and non-amyloid-related coronary artery disease, diastolic dysfunction, and heart failure. Regardless of the mechanisms, cTn values have relevant prognostic (and potentially diagnostic) implications in both AL and ATTR amyloidosis. In this review, the authors discuss the significant aspects of cTn biology and measurement methods, potential mechanisms of myocardial injury in CA, and the clinical application of cTn in the management of both AL and ATTR amyloidosis.

Raman spectroscopy and one-dimensional convolutional neural network modeling as a real-time monitoring tool for in vitro transaminase-catalyzed synthesis of a pharmaceutically relevant amine precursor.

Raman spectroscopy has been used to measure the concentration of a pharmaceutically relevant model amine intermediate for positive allosteric modulators of nicotinic acetylcholine receptor in a ω-transaminase-catalyzed conversion. A model based on a one-dimensional convolutional neural network was developed to translate raw data augmented Raman spectra directly into substrate concentrations, with which the conversion from ketone to amine by ω-transaminase could be determined over time. The model showed very good predictive capabilities, with R2 values higher than 0.99 for the spectra included in the modeling and 0.964 for an independent dataset. However, the model could not extrapolate outside the concentrations specified by the model. The presented work shows the potential of Raman spectroscopy as a real-time monitoring tool for biocatalytic reactions.