Clinical and gated SPECT MPI parameters associated with super-response to cardiac resynchronization therapy

A predictive model of super response to cardiac resynchronization therapy in short-term period

BACKGROUND

The left bundle branch block, nonischemic heart failure (HF) and female gender are the most powerful predictors of a super response to cardiac resynchronization therapy (CRT). It is important to identify super responders who can derive most benefits from CRT. We aimed to establish a predicting model that could be used for prognosis of a super response to CRT in short-term period.

METHODS

Patients with QRS ≥ 130 ms, New York Heart Association (NYHA) II-III class of HF, left ventricle ejection fraction (LVEF) ≤ 35% and indications for CRT were included in the study. Before and 6 month after CRT the electrocardiography, echocardiography and cardiac scintigraphy were performed. The study's primary endpoint was the NYHA class improvement ≥ 1 and left ventricle end systolic volume decrease > 30% or LVEF improvement > 15% after 6 month CRT. Based on collected data, we developed a predictive model regarding a super response to CRT.

RESULTS

Of 49 (100.0%) patients, 32 (65.3%) had a super response to CRT. Patients with a super response were likelier to have a lower cardiac index (p = 0.007), higher rates of interventricular delay (IVD) (p = 0.003), phase standard deviation of left ventricle anterior wall (PSD LVAW) (p = 0.009) and ∆QRS (p = 0.02). Only IVD and PSD LVAW were independently associated with a super response to CRT in univariate and multivariate logistic regression. We created a logistic equation and calculated a cut-off value. The resulting ROC curve revealed a discriminative ability with AUC of 0.812 (sensitivity 90.62%; specificity 70.59%).

CONCLUSION

Our predictive model is able to distinguish patients with a super response to CRT.

A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support

PURPOSE

Cardiac resynchronization therapy (CRT) has been established as an important therapy for heart failure. Mechanical dyssynchrony has the potential to predict responders to CRT. The aim of this study was to report the development and the validation of machine learning models which integrate ECG, gated SPECT MPI (GMPS), and clinical variables to predict patients' response to CRT.

METHODS

This analysis included 153 patients who met criteria for CRT from a prospective cohort study. The variables were used to model predictive methods for CRT. Patients were classified as "responders" for an increase of LVEF ≥ 5% at follow-up. In a second analysis, patients were classified as "super-responders" for an increase of LVEF ≥ 15%. For ML, variable selection was applied, and Prediction Analysis of Microarrays (PAM) approach was used to model response while Naïve Bayes (NB) was used to model super-response. These ML models were compared to models obtained with guideline variables.

RESULTS

PAM had AUC of 0.80 against 0.72 of partial least squares-discriminant analysis with guideline variables (p = 0.52). The sensitivity (0.86) and specificity (0.75) were better than for guideline alone, sensitivity (0.75) and specificity (0.24). Neural network with guideline variables was better than NB (AUC = 0.93 vs. 0.87) however without statistical significance (p = 0.48). Its sensitivity and specificity (1.0 and 0.75, respectively) were better than guideline alone (0.78 and 0.25, respectively).

CONCLUSIONS

Compared to guideline criteria, ML methods trended toward improved CRT response and super-response prediction. GMPS was central in the acquisition of most parameters. Further studies are needed to validate the models.

Prognostic Role of Diastolic Left Ventricular Mechanical Dyssynchrony by Gated Single Photon Emission Computed Tomography Myocardial Perfusion Imaging in Post-Myocardial Infarction

Objective  This study is aimed to assess the prognostic value of diastolic left ventricular mechanical dyssynchrony (LVMD) measured by gated-single photon emission computed tomography (GSPECT) myocardial perfusion imaging (MPI) in post-myocardial infarction (MI). Subjects and Methods  The study was conducted on 106 post-MI from January 2015 to January 2019. First, the indices of diastolic LVMD phase standard deviation (PSD) and histogram bandwidth (HBW) of post-MI were measured using the Cardiac Emory Toolbox. Subsequently, the post-MI patients were followed up, and the primary endpoint was major adverse cardiac events (MACEs). Finally, the prognostic value of dyssynchrony parameters for MACE was analyzed by the receiver-operating characteristics curve and survival analyses. Results  With the cut-off values of 55.5 degrees of PSD, the sensitivity and specificity in prediction of MACE were 75% and 80.8%, with the cut-off values of 174.5 degrees of HBW, the sensitivity and specificity were 75% and 83.3% respectively. There was a significant difference of time to MACE between groups of PSD less than 55.5 degrees and more than 55.5 degrees. PSD, HBW, and left ventricle ejection fraction (LVEF) assessed on GSPECT were significant factors in the prediction of MACE. Conclusion  Diastolic LVMD parameters of PSD and HBW derived from GSPECT are significant prognostic factors in predicting MACE in post-MI patients.

Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2022: single photon emission computed tomography

In this review, we will summarize a selection of articles on single-photon emission computed tomography published in the Journal of Nuclear Cardiology in 2022. The aim of this review is to concisely recap major advancements in the field to provide the reader a glimpse of the research published in the journal over the last year. This review will place emphasis on myocardial perfusion imaging using single-photon emission computed tomography summarizing advances in the field including in prognosis, non-perfusion variables, attenuation compensation, machine learning and camera design. It will also review nuclear imaging advances in amyloidosis, left ventricular mechanical dyssynchrony, cardiac innervation, and lung perfusion. We encourage interested readers to go back to the original articles, and editorials, for a comprehensive read as necessary but hope that this yearly review will be helpful in reminding readers of articles they have seen and attracting their attentions to ones they have missed.

A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response

BACKGROUND

Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT myocardial perfusion imaging (MPI) have their own statistical limitations in predicting cardiac resynchronization therapy (CRT) response. The purpose of this study is to discover new predictors from the polarmaps of LVMD by deep learning to help select heart failure patients with a high likelihood of response to CRT.

METHODS

One hundred and fifty-seven patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at 6 [Formula: see text] 1 month follow up. The autoencoder (AE) technique, an unsupervised deep learning method, was applied to the polarmaps of LVMD to extract new predictors characterizing LVMD. Pearson correlation analysis was used to explain the relationships between new predictors and existing clinical parameters. Patients from the IAEA VISION-CRT trial were used for an external validation. Heatmaps were used to interpret the AE-extracted feature.

RESULTS

Complete data were obtained in 130 patients, and 68.5% of them were classified as CRT responders. After variable selection by feature importance ranking and correlation analysis, one AE-extracted LVMD predictor was included in the statistical analysis. This new AE-extracted LVMD predictor showed statistical significance in the univariate (OR 2.00, P = .026) and multivariate (OR 1.11, P = .021) analyses, respectively. Moreover, the new AE-extracted LVMD predictor not only had incremental value over PBW and significant clinical variables, including QRS duration and left ventricular end-systolic volume (AUC 0.74 vs 0.72, LH 7.33, P = .007), but also showed encouraging predictive value in the 165 patients from the IAEA VISION-CRT trial (P < .1). The heatmaps for calculation of the AE-extracted predictor showed higher weights on the anterior, lateral, and inferior myocardial walls, which are recommended as LV pacing sites in clinical practice.

CONCLUSIONS

AE techniques have significant value in the discovery of new clinical predictors. The new AE-extracted LVMD predictor extracted from the baseline gated SPECT MPI has the potential to improve the prediction of CRT response.

Super-Responders in Moderate–Severe Psoriasis under Guselkumab Treatment: Myths, Realities and Future Perspectives

A fast skin clearance is the main goal to achieve in psoriasis treatment. Patients that present a fast and exceptional improvement with treatment are called super-responders (SR). There is no consensus on the definition of SR with respect to psoriasis. Included herein is a retrospective analysis of a multicenter, observational study of real clinical practices including patients with moderate-to-severe plaque PSO undergoing treatment with Guselkumab (GUS). This cross-sectional analysis includes information on patients between February 2019 to February 2022. A SR is a patient that achieved a PASI = 0 at weeks 12 and 24. Analyses have been performed “as observed” using GraphPad Prism version 8.3.0 for Windows (GraphPad Software, San Diego, CA, USA, At baseline, the PASI is significantly correlated with VAS_pruritus, BSA, and DLQI, while DLQI is significantly correlated with VAS_pruritus. Significant correlations increase in number and magnitude over the follow-up time. In relation to the univariate logistic models carried out, only three variables showed a significant association with the super-responder variable: depression, VAS_pruritus, and DLQI.SR patients, who show a faster evolution in PASI and BSA improvement than non-SRs. Based on the results obtained, it would be possible to also include DLQI and VAS_pruritus in the broader concept of the SR.

Atopic Dermatitis: Background, Objectives and Future Perspectives (Superresponders)

In this Special Issue entitled Atopic Dermatitis: New Perspectives, we have tried to collect research of special interest related mainly to the incorporation of pathophysiological aspects and therapeutic novelties in this regard [...].

Incremental value of left ventricular shape parameters measured by gated SPECT MPI in predicting the super-response to CRT

BACKGROUND

The purpose of this study was to evaluate the predictive value of left ventricular (LV) shape parameters measured by gated SPECT myocardial perfusion imaging (MPI) in super-responders enrolled in the VISION-CRT trial.

METHODS

One hundred and ninety-nine patients who met standard criteria for CRT from multiple centers were enrolled in this study. End-systolic eccentricity (ESE) and end-diastolic eccentricity (EDE) were measures of LV shape. Super-responders were the patients who had a relative increase in left ventricular ejection fraction (LVEF) ≥ 15%.

RESULTS

Complete data were obtained in 165 patients, and 43.6% of them were classified as super-responders. ESE was an independent predictor of CRT super-responders in univariate (OR 12.59, 95% CI 1.56-101.35, P = .017) and multivariate analysis (OR 35.71, 95% CI 1.66-766.03, P = .006). ESE had an incremental value over significant clinical and SPECT imaging variables, including angiotensin-converting enzyme inhibitors or angiotensin II receptor blocker, coronary artery disease, myocardial infarction, LVEF, end-diastolic volume index, and scar burden (AUC 0.82 vs. 0.80, sensitivity 0.68 vs. 0.65, specificity 0.82 vs. 0.78).

CONCLUSIONS

LV shape parameters derived from gated SPECT MPI have the promise to improve the prediction of the super-response to CRT. Moreover, ESE provides incremental value over existing clinical and nuclear imaging variables.

Gated single-photon emission computed tomography myocardial perfusion imaging phase analysis as an imaging biomarker for mortality prediction in heart failure patients undergoing cardiac resynchronization therapy

OBJECTIVE

Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients. The purpose of this study was to assess the value of gated myocardial perfusion single-photon emission computed tomography (GMPS) phase analysis for predicting survival in heart failure patients undergoing CRT.

METHODS

This retrospective cohort study evaluated heart failure patients who underwent GMPS prior to CRT. Phase histogram bandwidth (PHB) and phase SD (PSD) were calculated using GMPS data. Cox proportional hazards model was used to identify independent predictors of overall survival (OS).

RESULTS

A total of 35 patients (age 65.1 ± 13.3, 27 men and 8 women), who were followed for mean of 4.1 ± 2.9 years, were enrolled in the study. PSD of greater than 45° was found to be an independent predictor of poor OS (hazard ratio = 12.63, P = 0.011) when compared with age (hazard ratio = 1.00, P = 0.922), gender (hazard ratio = 0.31, P = 0.155), NYHA class (hazard ratio = 0.45, P = 0.087), QRS duration greater than 150 ms (hazard ratio = 2.38, P = 0.401), pre-CRT left ventricular ejection fraction (LVEF) (hazard ratio = 0.95, P = 0.175) and etiology of heart failure (hazard ratio = 1.42, P = 0.641). Furthermore, PHB greater than 140° was also found to be an independent predictor of poor OS (hazard ratio = 5.63, P = 0.040) when compared with age, gender, NYHA class, QRS duration greater than 150 ms, pre-CRT LVEF and etiology of heart failure.

CONCLUSIONS

PSD and PHB, measured by GMPS, may serve as biomarkers for the prediction of survival in patients undergoing CRT.