Immune therapy using monoclonal antibodies against cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death 1 receptor (PD-1) for various cancers have been reported to cause ...thyroid dysfunction. Little is known, however, about the underlying pathogenic mechanisms and the course of hypothyroidism that subsequently develops. In this report, we use the change in thyroglobulin and thyroid antibody levels in patients on immune therapy who develop hypothyroidism to better understand its pathogenesis as well as examine the status of hypothyroidism in the long term.
We report a case series of 10 patients who developed hypothyroidism after initiation of immune therapy (either anti-PD-1 alone or in combination with anti-CTLA-4). Available thyroid antibodies including anti-thyroglobulin (anti-Tg), anti-thyroid peroxidase (anti-TPO), and thyroid stimulating immunoglobulin (TSI) were noted during the initial thyroiditis phase as well as the hypothyroid phase. Persistence or remission of hypothyroidism was noted at 6 months.
During the thyroiditis phase, 50% of the patients had elevated Tg titers, 40% had elevated anti-Tg, and 40% had elevated TSI. All of these titers decreased during the hypothyroid phase. Permanent hypothyroidism was noted in 80% of the cases.
Hypothyroidism following initiation of immune therapy has immunologic and non-immunologic mediated mechanisms and is likely to be persistent.
Background
An increasing number of methods are being used to map atrial fibrillation (AF), yet the sensitivity of identifying potential localized AF sources of these novel methods are unclear. Here, ...we report a comparison of two approaches to map AF based upon (1) electrographic flow mapping and (2) phase mapping in a multicenter registry of patients in whom ablation terminated persistent AF.
Methods
Fifty‐three consecutive patients with persistent AF in whom ablation terminated AF in an international multicenter registry were enrolled. Electrographic flow mapping (EGF) and phase mapping were applied to the multipolar simultaneous electrograms recorded from a 64‐pole basket catheter in the chamber (left vs right atrium) where AF termination occurred. We analyzed if the mapping methods were able to detect localized sources at the AF termination site. We also analyzed global results of mapping AF for each method, patterns of activation of localized sources.
Results
Patients were 64.3 ± 9.4 years old and 69.8% were male. EGF and phase mapping identified localized sources at AF termination sites in 81% and 83% of the patients, respectively. Methods were complementary and in only n = 2 (3.7%) neither method identified a source. Globally, EGF identified more localized sources than phase mapping (5.3 ± 2.8 vs 1.8 ± 0.5, P < 0.001), with a higher prevalence of focal (compared to rotational) activation pattern (49% vs 2%, P < 0.01).
Conclusions
EGF is a novel vectorial‐based AF mapping method, which can detect sites of AF termination, agreeing with, and complementary to, an alternative AF mapping method using phase analysis.
Assessing liver fibrosis is important for predicting the efficacy of direct-acting antivirals (DAAs) and patient prognosis. Non-invasive techniques to assess liver fibrosis are becoming important. ...Recently, serum Mac-2 binding protein glycosylation isomer (M2BPGi) was identified as a non-invasive marker of liver fibrosis.
To investigate the diagnostic accuracy of M2BPGi in assessing liver fibrosis in patients with chronic hepatitis C (CHC) treated with DAAs.
From December 2017 to August 2018, 80 treatment-naïve adult patients with CHC who were eligible for DAAs therapy were consecutively enrolled in this observational cohort study. For 12 weeks, 65 patients were treated with sofosbuvir/daclatasvir, and 15 patients were treated with sofosbuvir/daclatasvir and a weight-based dose of ribavirin at knowledge and technology association for hepatitis C management clinic, Cairo, Egypt. We measured serum M2BPGi levels, PAPAS index, fibrosis-4 (FIB-4) score and liver stiffness measurements (LSM) at baseline and 12 weeks after the end of treatment. Serum M2BPGi levels were measured using enzyme-linked immunosorbent assay.
All patients achieved sustained virologic response (SVR12) (100%). Serum M2BPGi levels, LSM, FIB-4 score and PAPAS index decreased significantly at SVR12 (
< 0.05). Serum M2BPGi levels correlated positively with LSM at baseline and SVR12 (
< 0.001). At baseline, compared with the FIB-4 score and PAPAS index, M2BPGi was the best marker to distinguish patients with grade F4 fibrosis (AUC = 0.801,
< 0.001), patients with grade F2 from grade F0-1 fibrosis (AUC = 0.713,
= 0.012), patients with grade F3-4 from grade F0-2 fibrosis (AUC = 0.730,
< 0.001), and patients with grade F2-4 from grade F0-1 fibrosis (AUC = 0.763,
< 0.001). At SVR12, M2BPGi had the greatest AUCs for differentiating patients with grade F4 fibrosis (AUC = 0.844,
< 0.001), patients with grade F3 from grade F0-2 fibrosis (AUC = 0.893,
= 0.002), patients with grade F3-4 from grade F0-2 fibrosis (AUC = 0.891,
< 0.001), and patients with grade F2-4 from grade F0-1 fibrosis (AUC = 0.750,
< 0.001).
M2BPGi is a reliable marker for the non-invasive assessment and prediction of liver fibrosis regression in patients with CHC who achieved an SVR with DAAs therapy.
Abstract
Funding Acknowledgements
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health (NIH)
Background
Low left ventricular ejection ...fraction (LVEF) is an imperfect predictor of sudden cardiac death (SCD) in patients with ischemic cardiomyopathy. Novel features from the ECG might provide a readily available tool to better predict risk.
Purpose
We hypothesized that machine learning (ML) of the ECG can be used to predict SCD, and the ML-learned ECG features could be referenced to interpretable intracardiac signals (monophasic action potentials: MAP) to provide mechanistic insights.
Methods
We studied 5603 ECG Lead V1 beats in 41 patients (64±10 Y) with coronary disease and LVEF≤40% in steady-state pacing. Patients were randomly allocated to independent training and test cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines were trained to predict mortality at 3Y from the top 20 features derived from these beats. Patient-level predictions were made by computing an ECG score that indicates the proportion of test set beats in that patient computed by the beat-level model to predict death. Explainability analysis was performed using the arithmetic mean of MAP and ECG beats that predicted SCD versus those that predicted survival.
Results
Fig 1A. shows ECG lead V1 and MAP in a 79 Y man with LVEF 29%. Fig 1B shows the dataflow in the study. Predictive accuracies of ML models were 78 and 70% and optimal with 20 features for both ECG and MAP models respectively (Fig. 1C). Beat-level predictions in the validation (n=1678 Lead I beats) cohorts yielded c-statistics of 0.78 with the ECG (95% CI, 0.62–0.91) and 0.75 with MAPs (95% CI, 0.75-0.76) (data not shown). In multivariable patient-level models, c-statistic was 0.87 with ECGs (95% CI, 0.76-0.98) (Fig 1D) and 0.82 with MAPs. On explainability analysis, ECG beats that predicted SCD (Fig 2; red) had lower amplitude and more notched T-waves in lead V1 than beats that predicted no SCD (Fig 2; blue). MAP that predicted SCD had higher repolarization current at the same time points. Both QT duration (ECG) and action potential duration (MAP) did not differ (Fig 2).
Conclusions
Machine learning of the ECG reveals novel predictors of SCD risk in patients with ischemic cardiomyopathy analogous to those identified in intracardiac signals. This approach can be used as a point-of-care ECG risk tool to improve risk stratification and allocation for ICD therapy beyond LVEF alone and may shed insights into the pathophysiology of ventricular arrhythmias.
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