Faculty Bibliography

Background The duration of heightened stroke risk after acute myocardial infarction ( MI ) remains uncertain. Methods and Results We performed a retrospective cohort study using claims between 2008 and 2015 from a nationally representative 5% sample of Medicare beneficiaries aged ≥66 years. Both acute MI and ischemic stroke were ascertained using previously validated International Classification of Diseases, Ninth Revision, Clinical Modification ( ICD-9-CM), diagnosis codes. To exclude periprocedural strokes from percutaneous coronary intervention, we did not count strokes occurring during an acute MI hospitalization. Patients were censored at the time of ischemic stroke, death, end of Medicare coverage, or September 30, 2015. We fit Cox regression models separately for the groups with and without acute MI to examine its association with ischemic stroke after adjustment for demographics, stroke risk factors, and Charlson comorbidities. We used the corresponding survival probabilities to compute the hazard ratio in each 4-week interval after discharge. Confidence intervals were computed using the nonparametric bootstrap method. Among 1 746 476 eligible beneficiaries, 46 182 were hospitalized for acute MI and 80 466 for ischemic stroke. After adjustment for demographics, stroke risk factors, and Charlson comorbidities, the risk of ischemic stroke was highest in the first 4 weeks after discharge from the MI hospitalization (hazard ratio: 2.7; 95% confidence interval, 2.3-3.2), remained elevated during weeks 5 to 8 (hazard ratio: 2.0; 95% confidence interval, 1.6-2.4) and weeks 9 to 12 (hazard ratio: 1.6; 95% confidence interval, 1.3-2.0), and was no longer significantly elevated afterward. Conclusions Acute MI is associated with an elevated risk of ischemic stroke that appears to extend beyond the 1-month window that is currently considered the at-risk period.

BACKGROUND AND PURPOSE/OBJECTIVE:Risk factors for hemorrhagic transformation of ischemic stroke after mechanical thrombectomy (MT) are not well established. We conducted a study to determine if prominent angiographic cerebral vascularity following recanalization with thrombectomy (angiographic blush) is associated with hemorrhagic transformation. METHODS:Using the Cornell AcutE Stroke Academic Registry, we identified stroke patients who had thrombectomy and achieved recanalization of anterior circulation large-vessel occlusion between 2012 and 2015. The exposure variable was presence of angiographic blush after recanalization, defined as capillary blush with or without early venous drainage. The primary outcome was volume of hemorrhagic transformation on brain imaging after thrombectomy, as determined by semiautomated volumetric analysis on computed tomography or magnetic resonance imaging among those adjudicated to have hemorrhagic conversion by neuroradiology investigators blinded to angiography results. Using a doubly robust estimator with propensity scores and outcome regression adjusting for demographics and known risk factors for hemorrhagic transformation, we evaluated whether angiographic blush after recanalization is associated with an increased volume of hemorrhagic transformation. RESULTS:Among 48 eligible patients, 31 (64.6%) had angiographic blush and 26 (54.2%) had radiographic hemorrhagic transformation (mean volume, 7.6 ml). Patients with angiographic blush averaged lower thrombolysis in cerebral infarction scores and more often received intravenous thrombolysis. In adjusted analysis, angiographic blush was associated with an increased volume of hemorrhagic transformation: mean volume, 10.3ml (95% CI, 3.7-16.9 ml) with blush versus 1.8ml (95% Confidence Interval (CII = Confidence Interval), 0.1-3.4 ml) without (P = .01). CONCLUSIONS:Presence of angiographic blush after MT was independently associated with the volume of hemorrhagic transformation.

We consider estimation of an optimal individualized treatment rule when a high-dimensional vector of baseline variables is available. Our optimality criterion is with respect to delaying the expected time to occurrence of an event of interest. We use semiparametric efficiency theory to construct estimators with properties such as double robustness. We propose two estimators of the optimal rule, which arise from considering two loss functions aimed at directly estimating the conditional treatment effect and recasting the problem in terms of weighted classification using the 0-1 loss function. Our estimated rules are ensembles that minimize the crossvalidated risk of a linear combination in a user-supplied library of candidate estimators. We prove oracle inequalities bounding the finite-sample excess risk of the estimator. The bounds depend on the excess risk of the oracle selector and a doubly robust term related to estimation of the nuisance parameters. We discuss the convergence rates of our estimator to the oracle selector, and illustrate our methods by analysis of a phase III randomized study testing the efficacy of a new therapy for the treatment of breast cancer.

In practice, both testable and untestable assumptions are generally required to draw inference about the mean outcome measured at the final scheduled visit in a repeated measures study with drop-out. Scharfstein et al. (2014) proposed a sensitivity analysis methodology to determine the robustness of conclusions within a class of untestable assumptions. In their approach, the untestable and testable assumptions were guaranteed to be compatible; their testable assumptions were based on a fully parametric model for the distribution of the observable data. While convenient, these parametric assumptions have proven especially restrictive in empirical research. Here, we relax their distributional assumptions and provide a more flexible, semi-parametric approach. We illustrate our proposal in the context of a randomized trial for evaluating a treatment of schizoaffective disorder.

BACKGROUND AND PURPOSE:We sought to model the effects of interhospital transfer network design on endovascular therapy eligibility and clinical outcomes of stroke because of large-vessel occlusion for the residents of a large city. METHODS:We modeled 3 transfer network designs for New York City. In model A, patients were transferred from spoke hospitals to the closest hub hospitals with endovascular capabilities irrespective of hospital affiliation. In model B, which was considered the base case, patients were transferred to the closest affiliated hub hospitals. In model C, patients were transferred to the closest affiliated hospitals, and transfer times were adjusted to reflect full implementation of streamlined transfer protocols. Using Monte Carlo methods, we simulated the distributions of endovascular therapy eligibility and good functional outcomes (modified Rankin Scale score, 0-2) in these models. RESULTS:In our models, 200 patients (interquartile range [IQR], 168-227) with a stroke amenable to endovascular therapy present to New York City spoke hospitals each year. Transferring patients to the closest hub hospital irrespective of affiliation (model A) resulted in 4 (IQR, 1-9) additional patients being eligible for endovascular therapy and an additional 1 (IQR, 0-2) patient achieving functional independence. Transferring patients only to affiliated hospitals while simulating full implementation of streamlined transfer protocols (model C) resulted in 17 (IQR, 3-41) additional patients being eligible for endovascular therapy and 3 (IQR, 1-8) additional patients achieving functional independence. CONCLUSIONS:Optimizing acute stroke transfer networks resulted in clinically small changes in population-level stroke outcomes in a dense, urban area.

Two thirds of all persons with late-onset Alzheimer's disease (AD) are women. Identification of sex-based molecular mechanisms underpinning the female-based prevalence of AD would advance development of therapeutic targets during the prodromal AD phase when prevention or delay in progression is most likely to be effective. This 3-year brain imaging study examines the impact of the menopausal transition on Alzheimer's disease (AD) biomarker changes [brain β-amyloid load via 11C-PiB PET, and neurodegeneration via 18F-FDG PET and structural MRI] and cognitive performance in midlife. Fifty-nine 40-60 year-old cognitively normal participants with clinical, neuropsychological, and brain imaging exams at least 2 years apart were examined. These included 41 women [15 premenopausal controls (PRE), 14 perimenopausal (PERI), and 12 postmenopausal women (MENO)] and 18 men. We used targeted minimum loss-based estimation to evaluate AD biomarker and cognitive changes. Older age was associated with baseline Aβ and neurodegeneration markers, but not with rates of change in these biomarkers. APOE4 status influenced change in Aβ load, but not neurodegenerative changes. Longitudinally, MENO and PERI groups showed declines in estrogen-dependent memory tests as compared to men (p < .04). Adjusting for age, APOE4 status, and vascular risk confounds, the MENO and PERI groups exhibited higher rates of CMRglc decline as compared to males (p ≤ .015). The MENO group exhibited the highest rate of hippocampal volume loss (p's ≤ .001), and higher rates of Aβ deposition than males (p < .01). CMRglc decline exceeded Aβ and atrophy changes in all female groups vs. men. These findings indicate emergence and progression of a female-specific hypometabolic AD-endophenotype during the menopausal transition. These findings suggest that the optimal window of opportunity for therapeutic intervention to prevent or delay progression of AD endophenotype in women is early in the endocrine aging process.