Yao, Xiaoxi

Abstract Rationale Many lung transplant centers prescribe antifungal medications after transplantation to prevent invasive fungal infections (IFIs); however, the effectiveness of antifungal prophylaxis at reducing the risk of all-cause mortality or IFI has not been established. Objectives We aimed to evaluate the effect of antifungal prophylaxis on all-cause mortality and IFI in lung transplant patients. Methods Using administrative claims data, we identified adult patients who underwent lung transplantation between January 1, 2005, and December 31, 2018. Propensity score analysis using inverse probability treatment–weighting approach was used to balance the differences in baseline characteristics between those receiving antifungal prophylaxis and those not receiving antifungal prophylaxis. Cox proportional hazards regression was used to compare rates of all-cause mortality and IFI in both groups. Results We identified 662 lung transplant recipients (LTRs) (387 received prophylaxis and 275 did not). All-cause mortality was significantly lower in those receiving antifungal prophylaxis compared with those not receiving antifungal prophylaxis (event rate per 100 person-years, 8.36 vs. 19.49; hazard ratio, 0.43; 95% confidence interval, 0.26–0.71; P = 0.003). Patients receiving antifungal prophylaxis had a lower rate of IFI compared with those not receiving prophylaxis (event rate per 100 person-years, 14.94 vs. 22.37; hazard ratio, 0.68; 95% confidence interval, 0.44–1.05; P = 0.079), but did not reach statistical significance. Conclusions In this real-world analysis, antifungal prophylaxis in LTRs was associated with reduced all-cause mortality compared with those not receiving antifungal prophylaxis. Rates of IFI were also lower in those receiving prophylaxis, but this was not statistically significant in our primary analysis.

SummaryBackgroundGastrointestinal bleeding (GIB) frequently occurs following percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) with the prescription of P2Y12 inhibiting antiplatelet agents. Compared with clopidogrel, the newer P2Y12 inhibitors lower major adverse cardiac events with similar or possibly higher major bleeding events. The comparative GIB rates of these medications remain poorly understood.AimTo compare GIB rates associated with clopidogrel, prasugrel and ticagrelor using national medical and pharmacy claims data from privately insured and Medicare Advantage enrollees .MethodsPropensity score and inverse probability treatment weighting were used to balance baseline characteristics among treatment groups. The 1‐year GIB risk was calculated using weighted Cox proportional hazard models and expressed as hazard ratios (HR) with 95% confidence intervals (CI) and number needed to harm (NNH).ResultsWe identified 37 019 patients with ACS (non‐ST elevation ACS [NSTE‐ACS] and ST‐elevation myocardial infarction [STEMI]) within 14 days of a PCI (mean age 63 years and 70% male). Clopidogrel prescription was most common (69%) with prasugrel (16%) and ticagrelor (14%) prescribed less frequently. When compared with clopidogrel, ticagrelor was associated with a 34% risk reduction (HR 0.66; 95% CI: 0.54–0.81) in GIB overall and with NSTE‐ACS, and a 37% GIB risk reduction (HR 0.63; 95% CI: 0.42–0.93) in STEMI patients. When compared with clopidogrel, prasugrel was associated with a 21% risk reduction (HR 0.79; 95% CI: 0.64–0.97) overall, a 36% GIB risk reduction (HR 0.64; 95% CI: 0.49–0.85) in STEMI patients but no reduction of GIB risk in NSTE‐ACS patients.ConclusionsIn the first year following PCI, ticagrelor or prasugrel are associated with fewer GIB events than clopidogrel.

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.

Background: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1) assess whether the performance of a deep learning algorithm designed to detect low left ventricular ejection fraction using the 12-lead ECG varies by race/ethnicity and to (2) determine whether its performance is determined by the derivation population or by racial variation in the ECG. Methods: We performed a retrospective cohort analysis that included 97 829 patients with paired ECGs and echocardiograms. We tested the model performance by race/ethnicity for convolutional neural network designed to identify patients with a left ventricular ejection fraction ≤35% from the 12-lead ECG. Results: The convolutional neural network that was previously derived in a homogeneous population (derivation cohort, n=44 959; 96.2% non-Hispanic white) demonstrated consistent performance to detect low left ventricular ejection fraction across a range of racial/ethnic subgroups in a separate testing cohort (n=52 870): non-Hispanic white (n=44 524; area under the curve [AUC], 0.931), Asian (n=557; AUC, 0.961), black/African American (n=651; AUC, 0.937), Hispanic/Latino (n=331; AUC, 0.937), and American Indian/Native Alaskan (n=223; AUC, 0.938). In secondary analyses, a separate neural network was able to discern racial subgroup category (black/African American [AUC, 0.84], and white, non-Hispanic [AUC, 0.76] in a 5-class classifier), and a network trained only in non-Hispanic whites from the original derivation cohort performed similarly well across a range of racial/ethnic subgroups in the testing cohort with an AUC of at least 0.930 in all racial/ethnic subgroups. Conclusions: Our study demonstrates that while ECG characteristics vary by race, this did not impact the ability of a convolutional neural network to predict low left ventricular ejection fraction from the ECG. We recommend reporting of performance among diverse ethnic, racial, age, and sex groups for all new artificial intelligence tools to ensure responsible use of artificial intelligence in medicine.