Publications by Year: 2021

Cardiovascular implantable electronic device (CIED) infections are morbid, costly, and difficult to manage. This review explores the pathophysiology, diagnosis, and management of CIED infections. Diagnostic accuracy has been improved through increased awareness and improved imaging strategies. Pocket or bloodstream infection with virulent organisms often requires complete system extraction. Emerging prophylactic interventions and novel devices have expanded preventative strategies and options for re-implantation. A clear and nuanced understanding of CIED infection is important to the practicing electrophysiologist.

BACKGROUND: Frailty is associated with a higher risk for adverse outcomes after aortic valve replacement (AVR) for severe aortic valve stenosis, but whether or not frail patients derive differential benefit from transcatheter (TAVR) versus surgical (SAVR) AVR is uncertain.

METHODS: We linked adults ≥65 years old in the US CoreValve HiR trial (High-Risk) or SURTAVI trial (Surgical or Transcatheter Aortic-Valve Replacement in Intermediate-Risk Patients) to Medicare claims, February 2, 2011, to September 30, 2015. Two frailty measures, a deficit-based and phenotype-based frailty index (FI), were generated. The treatment effect of TAVR versus SAVR was evaluated within FI tertiles for the primary end point of death and nondeath secondary outcomes, using multivariable Cox regression.

RESULTS: Of 1442 (linkage rate =60.0%) individuals included, 741 (51.4%) individuals received TAVR and 701 (48.6%) received SAVR (mean age 81.8±6.1 years, 44.0% female). Although 1-year death rates in the highest FI tertiles (deficit-based FI 36.7% and phenotype-based FI 33.8%) were 2- to 3-fold higher than the lowest tertiles (deficit-based FI 13.4%; hazard ratio, 3.02 [95% CI, 2.26-4.02], P<0.001; phenotype-based FI 17.9%; hazard ratio, 2.05 [95% CI, 1.58-2.67], P<0.001), there were no significant differences in the relative or absolute treatment effect of SAVR versus TAVR across FI tertiles for all death, nondeath, and functional outcomes (all interaction P>0.05). Results remained consistent across individual trials, frailty definitions, and when considering the nonlinked trial data.

CONCLUSIONS: Two different frailty indices based on Fried and Rockwood definitions identified individuals at higher risk of death and functional impairment but no differential benefit from TAVR versus SAVR.

Background In aortic valve disease, the relationship between claims-based frailty indices (CFIs) and validated measures of frailty constructed from in-person assessments is unclear but may be relevant for retrospective ascertainment of frailty status when otherwise unmeasured. Methods and Results We linked adults aged ≥65 years in the US CoreValve Studies (linkage rate, 67%; mean age, 82.7±6.2 years, 43.1% women), to Medicare inpatient claims, 2011 to 2015. The Johns Hopkins CFI, validated on the basis of the Fried index, was generated for each study participant, and the association between CFI tertile and trial outcomes was evaluated as part of the EXTEND-FRAILTY substudy. Among 2357 participants (64.9% frail), higher CFI tertile was associated with greater impairments in nutrition, disability, cognition, and self-rated health. The primary outcome of all-cause mortality at 1 year occurred in 19.3%, 23.1%, and 31.3% of those in tertiles 1 to 3, respectively (tertile 2 versus 1: hazard ratio, 1.22; 95% CI, 0.98-1.51; P=0.07; tertile 3 versus 1: hazard ratio, 1.73; 95% CI, 1.41-2.12; P<0.001). Secondary outcomes (bleeding, major adverse cardiovascular and cerebrovascular events, and hospitalization) were more frequent with increasing CFI tertile and persisted despite adjustment for age, sex, New York Heart Association class, and Society of Thoracic Surgeons risk score. Conclusions In linked Medicare and CoreValve study data, a CFI based on the Fried index consistently identified individuals with worse impairments in frailty, disability, cognitive dysfunction, and nutrition and a higher risk of death, hospitalization, bleeding, and major adverse cardiovascular and cerebrovascular events, independent of age and risk category. While not a surrogate for validated metrics of frailty using in-person assessments, use of this CFI to ascertain frailty status among patients with aortic valve disease may be valid and prognostically relevant information when otherwise not measured.

With aging of the population, cardiovascular conditions (CC) are increasingly common in individuals undergoing PCI for stable angina pectoris (AP). It is unknown if the overall burden of CCs associates with diminished symptom improvement after PCI for stable AP. We prospectively administered validated surveys assessing AP, dyspnea, and depression to patients undergoing PCI for stable AP at our institution, 2016-2018. The association of CC burden and symptoms at 30-days post-PCI was assessed via linear mixed effects models. Included individuals (N = 121; mean age 68 ± 10 years; response rate = 42%) were similar to non-included individuals. At baseline, greater CC burden was associated with worse dyspnea, depression, and physical limitations due to AP, but not AP frequency or quality of life. PCI was associated with small improvements in AP and dyspnea (p ≤ 0.001 for both), but not depression (p = 0.15). After multivariable adjustment, including for baseline symptoms, CC burden was associated with a greater improvement in AP physical limitations (p = 0.01) and depression (p = 0.002), albeit small, but not other symptom domains (all p ≥ 0.05). In patients undergoing PCI for stable AP, increasing CC burden was associated with worse dyspnea, depression, and AP physical limitations at baseline. An increasing number of CCs was associated with greater improvements, though small, in AP physical limitations and depression. In conclusion, the overall number of cardiovascular conditions should not be used to exclude patients from PCI for stable AP on the basis of an expectation of less symptom improvement.

The Fish. The Pond. The Groundwater. Imagine that you have a personal pond filled with fish. When viewing your pond, you notice that one fish has died, floating belly-up. You decide that the fish must have been ill and think nothing more of it. The next day, you notice that half of the fish in your pond are now dead. You are alarmed and decide to contact the neighborhood management services to investigate your pond. Something must be wrong with the local system. The following day, however, you discover that all of your neighbors with ponds have noticed the same thing. In fact, half of the fish are dead throughout all waterways in the entire state. At this point, it is clear something deeper must be wrong. This is when you need to analyze the groundwater feeding these ponds. The fish are not at fault, and not even the local systems. Rather the underlying structures through which the fish seek life has failed. Imagine that instead of fish, we are discussing patients. —Paraphrase of Groundwater Approach Metaphor by Love and Hayes-Greene of The Racial Equity Institute.

BACKGROUND: Statistical testing in phase III clinical trials is subject to chance errors, which can lead to false conclusions with substantial clinical and economic consequences for patients and society.

METHODS: We collected summary data for the primary endpoints of overall survival (OS) and progression-related survival (PRS) (eg, time to other type of event) for industry-sponsored, randomized, phase III superiority oncology trials from 2008 through 2017. Using an empirical Bayes methodology, we estimated the number of false-positive and false-negative errors in these trials and the errors under alternative P value thresholds and/or sample sizes.

RESULTS: We analyzed 187 OS and 216 PRS endpoints from 362 trials. Among 56 OS endpoints that achieved statistical significance, the true efficacy of experimental therapies failed to reach the projected effect size in 33 cases (58.4% false-positives). Among 131 OS endpoints that did not achieve statistical significance, the true efficacy of experimental therapies reached the projected effect size in 1 case (0.9% false-negatives). For PRS endpoints, there were 34 (24.5%) false-positives and 3 (4.2%) false-negatives. Applying an alternative P value threshold and/or sample size could reduce false-positive errors and slightly increase false-negative errors.

CONCLUSIONS: Current statistical approaches detect almost all truly effective oncologic therapies studied in phase III trials, but they generate many false-positives. Adjusting testing procedures in phase III trials is numerically favorable but practically infeasible. The root of the problem is the large number of ineffective therapies being studied in phase III trials. Innovative strategies are needed to efficiently identify which new therapies merit phase III testing.