Baseline thrombocytopenia in acute coronary syndrome: The lower, the worse

BACKGROUND

Patients with baseline thrombocytopenia can have increased mortality and morbidity, but are typically excluded from randomized clinical trials studying acute coronary syndromes (ACS). We sought to better define the effect thrombocytopenia on clinical outcomes in ACS patients.

METHODS

Patients identified from the NCDR Chest Pain registry at Mayo Clinic Arizona from Oct 2015 to Sep 2018 were retrospectively classified into two groups: TP (platelet <150 × 10³ μL) and control (platelet ≥150 × 10³ μL). The groups were analyzed for the clinical outcome (all-cause mortality, major adverse cardiac events (MACE), and bleeding events). The TP group was divided into moderate-severe thrombocytopenia (TP_mod; platelet 50-100 × 10³ μL) and mild thrombocytopenia (TP_mild; platelet 100-150 × 10³ μL) for further analysis. P-value <0.05 is considered significant.

RESULTS

Five hundred and thirty-six patients were identified, and 72 patients (13%) had thrombocytopenia. The median follow-up time was 1.1 years. The TP group was older (TP vs. control: mean age 73 ± 13 years vs. 70 ± 13 years; P = 0.026). In patients discharged on dual-antiplatelet therapy, the TP group had higher all-cause mortality (23% vs. 7.3%; P = 0.007) but not major bleeding events (11% vs. 5.0%; P = 0.123). Only all-cause mortality increased with the severity of thrombocytopenia (TP_mod vs. TP_mild vs. control: 33% vs. 24% vs. 7.3%; P = 0.007).

CONCLUSIONS

In patients with ACS, baseline thrombocytopenia is associated with increased all-cause mortality and all bleeding events without net MACE benefit. Further study is needed to identify the optimal antiplatelet strategy in this higher risk population.

Machine Learning on High-Dimensional Data to Predict Bleeding Post Percutaneous Coronary Intervention

INTRODUCTION

The purpose of the current study is to determine the accuracy of machine learning in predicting bleeding outcomes post percutaneous coronary intervention (PCI) in comparison with the American College of Cardiology CathPCI bleeding risk (ACC-BR) model.

METHODS

Mayo Clinic CathPCI registry data were retrospectively analyzed from January, 2003 to June, 2018, including 15,603 patients who underwent PCI. The cohort was randomly divided into a training sample of 11,703 patients (75%) and a unique test sample of 3900 patients (25%) prior to model generation. The risk-prediction model was generated utilizing a boosted classification tree algorithm of 105 unique variables to predict the risk of major and minor bleeding complications within 72 hours after PCI or before hospital discharge. The receiver operating characteristic (ROC) curves and areas under the curve (AUC) for the boosted classification tree algorithm (AI-BR) model and ACC-BR model were compared for the test cohort.

RESULTS

The mean age of the patient cohort was 67 ± 12.7 years, and women constituted 30% of the cohort. The rate of major bleeding complications in the entire cohort was 1.8%. The sensitivity and specificity of the AIBR model were 77.3% and 80.9%, respectively. The ROC-AUC for the AI-BR model (0.873) was superior vs the ACC-BR model (0.764; P=.02) in predicting major bleeding for the test cohort.

CONCLUSION

The AI-BR model accurately predicts bleeding post PCI and outperforms the ACC-BR model in predicting the risk of bleeding in patients undergoing PCI.

Vasculitis on brain angiography is not always vasculitis: intravascular large B-cell lymphoma mimicking central nervous system vasculitis

A 68-year-old man, with a history of non-Hodgkin's lymphoma in remission, was admitted for homonymous hemianopsia, headaches and subacute progressive cognitive decline. Imaging revealed brain infarcts and angiography suggested vasculitis. A brain biopsy, however, revealed an intravascular large B-cell lymphoma (IVLBL). Central nervous system (CNS) vasculitis and IVLBL of the brain are extremely rare diseases that can have an almost identical clinical presentation. Angiographic findings are very similar but usually are reported as compatible with vasculitis. Brain biopsy or a random skin biopsy are crucial in diagnosing IVLBL as the accuracy of angiographic findings for CNS vasculitis is low.

Abstract B13: Growth kinetics in glioblastome multiforme: Response to radiation and the quantification of treatment response

High-grade gliomas, commonly known as glioblastoma multiforme (GBM), exhibit linear radial expansion. In untreated patients, these growth kinetics are believed to be constant. Not much is known about the changes in growth kinetics that different treatment regimes induce. In this study we sought to compare pre-treatment growth kinetics to post-treatment growth kinetics; investigate correlations between pre-, during, and post-treatment growth kinetics survival, and time-to-progression; and elucidate measures of response to treatment that define post-treatment growth characteristics.

Patient information and MRIs were obtained from 62 newly diagnosed patients with glioblastoma from Northwestern University, University of Washington, and University of California, Los Angeles who had at least 2 pre-treatment MRIs and 1 post-treatment MRI. The tumor growth kinetics were calculated by selecting 2 imaging events, separated by at least 5 days, and calculating the rate of growth according to:

Velocity=(T1Gdradius2-T1Gdradius1)/(t2-t1)

Velocities were compared with survival and time-to-progression using log-rank survival analysis in Prism 6.0 (Graphpad Software).

There was no significant correlation between pre-treatment velocity and overall survival or time-to-progression (n=50). The velocity during radiation treatment was correlated with overall survival. A growth rate of less than 9.125 mm/year during the course of radiation portended greater overall survival (p-value &lt; 0.001). To take into account the previous rate of growth, the pre-treatment velocity was compared to the growth rate during radiation. A relative change of greater than 110% (i.e. the tumor's growth was faster during the course of radiation) correlated with the time to recurrence (p-value= 0.016). The post-treatment velocity was correlated with overall survival. If the tumor grew at a rate greater than 2.87 mm/year after finishing radiation, the patient's survival was significantly less than those patients whose tumors grew at less than 2.87 mm/year.

Interestingly, the growth kinetics prior to treatment do not correlate with response metrics. Instead, the response to radiation stratifies patients into survival categories that allow providers to more accurately predict clinical progression. Rather than the current ‘wait-and-see’ scheme of clinical decision-making, metrics of GBM growth kinetics that are calculated off of standard-of-care MRIs could allow for implementation of salvage therapies and secondary options in patients with low treatment response.

Citation Format: Corbin Rayfield, Russ Rockne, Andrea Hawkins-Daruud, Joshua Jacobs, Kristin R. Swanson. Growth kinetics in glioblastome multiforme: Response to radiation and the quantification of treatment response. [abstract]. In: Proceedings of the AACR Special Conference: Advances in Brain Cancer Research; May 27-30, 2015; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2015;75(23 Suppl):Abstract nr B13.

Heterogeneity of HDAC3 expression and its modulation by valproic acid in glioblastoma cells

e13038

Background: Glioblastoma (gbm) is a devastating malignancy and a therapeutic challenge. Our group and others have observed long-term gbm survivors treated with valproic acid (VPA), a histone deacetylase (HDAC) inhibitor. While in vitro studies of HDAC inhibitors have been promising, clinical trials and retrospective analyses have been disappointing. We hypothesize that HDAC3 may be a more specific target or treatment marker for gbm. Since limited data is available pertaining to HDAC3 and its modulation in gbm, our goals were to: 1) develop methods for determining HDAC3 expression and localization in gbm cells, 2) study HDAC3 heterogeneity among different gbm cell lines, including one in low passage, and 3) test the effect of VPA treatment on HDAC3 expression of gbm cells in vitro. Methods: Three established gbm cell lines (U-87, U-251 and E297), were cultured using standard technique. HeLa cells were used as the positive HDAC3 control. Cells were stained using 2 different antibodies, and analyzed using immunofluoresence microscopy (IFM), and Western blotting (with β-actin standards). IgG was used for negative isotype controls. Cell counts, viability, and H&E staining for changes in morphology were also recorded. Lysates for Western blots were made from whole cells, nuclei, and cytoplasmic preparations. Band quantification was performed using the ImageJ software. Cells in exponential growth were treated for 48 hours with 10 mM VPA, and compared to untreated cells. Results: Untreated cells studied for HDAC3 using IFM showed nuclear and some cytoplasmic staining in all 4 lines, which varied according to cell type and morphology. Western blot analysis confirmed the variability of HDAC3 expression seen by IFM. The effect of VPA treatment on gbm HDAC3 expression (cytoplasmic and nuclear) was seen to be highly variable, ranging from marked down-modulation in some gbm cells, to minimal effect in others. Conclusions: These data provide the groundwork for studying HDAC3 levels in patients undergoing therapy with HDAC inhibitors. The effect of VPA on the HDAC3 expression of gbm cells is heterogeneous; individualized assessment of such effect will be necessary in delineating any subset of patients that may respond to VPA treatment.