Patient comfort with sexual orientation and gender identity questions in adult congenital cardiology clinics.

BACKGROUND: Lack of sexual orientation and gender identity (SOGI) data creates barriers for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people in health care. Barriers to SOGI data collection include physician misperception that patients do not want to answer these questions and discomfort asking SOGI questions. This study aimed to assess patient comfort towards SOGI questions across five quaternary care adult congenital heart disease (ACHD) centres. METHODS: A survey administered to ACHD patients (≥18 years) asked (1) two-step gender identity and birth sex, (2) acceptance of SOGI data, and (3) the importance for ACHD physicians to know SOGI data. Chi-square tests were used to analyse differences among demographic groups and logistic regression modelled agreement with statement of patient disclosure of SOGI improving patient-physician communication. RESULTS: Among 322 ACHD patients, 82% identified as heterosexual and 16% identified as LGBTQ+, across the age ranges 18-29 years (39.4%), 30-49 years (47.8%), 50-64 years (8.7%), and > 65 years (4.0%). Respondents (90.4%) felt comfortable answering SOGI questions. Respondents with bachelor's/higher education were more likely to "agree" that disclosure of SOGI improves patient-physician communication compared to those with less than bachelor's education (OR = 2.45; 95% CI 1.41, 4.25; p = .0015). CONCLUSION: These findings suggest that in this largely heterosexual population, SOGI data collection is unlikely to cause patient discomfort. Respondents with higher education were twice as likely to agree that SOGI disclosure improves patient-physician communication. The inclusion of SOGI data in future studies will provide larger samples of underrepresented minorities (e.g. LGBTQ+ population), thereby reducing healthcare disparities within the field of cardiovascular research.

Inadequate Rectal Pressure and Insufficient Relaxation and Abdominopelvic Coordination in Defecatory Disorders.

BACKGROUND & AIMS: Diagnostic tests for defecatory disorders (DDs) asynchronously measure anorectal pressures and evacuation and show limited agreement; thus, abdominopelvic-rectoanal coordination in normal defecation and DDs is poorly characterized. We aimed to investigate anorectal pressures, anorectal and abdominal motion, and evacuation simultaneously in healthy and constipated women. METHODS: Abdominal wall and anorectal motion, anorectal pressures, and rectal evacuation were measured simultaneously with supine magnetic resonance defecography and anorectal manometry. Evacuators were defined as those who attained at least 25% rectal evacuation. Supervised (logistic regression and random forest algorithm) and unsupervised (k-means cluster) analyses identified abdominal and anorectal variables that predicted evacuation. RESULTS: We evaluated 28 healthy and 26 constipated women (evacuators comprised 19 healthy participants and 8 patients). Defecation was initiated by abdominal wall expansion that was coordinated with anorectal descent, increased rectal and anal pressure, and then anal relaxation and rectal evacuation. Compared with evacuators, nonevacuators had lower anal diameters during simulated defecation, rectal pressure, anorectal junction descent, and abdominopelvic-rectoanal coordination (P < .05). Unsupervised cluster analysis identified 3 clusters that were associated with evacuator status (P < .01), that is, 10 evacuators (83%), 16 evacuators (73%), and 1 evacuator (5%) in clusters 1, 2, and 3, respectively. Each cluster had distinct characteristics (eg, maximum abdominosacral distance, rectal pressure, anorectal junction descent, anal diameter) and correlates that were more (clusters 1-2) or less (cluster 3) conducive to evacuation. Cluster 2 had 16 evacuators (73%) and intermediate characteristics (eg, lower anal resting pressure and relaxation during evacuation; P < .05). CONCLUSIONS: Women with DDs and a modest proportion of healthy women had specific patterns of anorectal dysfunction, including inadequate rectal pressurization, anal relaxation, and abdominopelvic-rectoanal coordination. These observations may guide individualized therapy for DDs in the future.

Quantifying a spectrum of clinical response in atrial tachyarrhythmias using spatiotemporal synchronization of electrograms.

AIMS: There is a clinical spectrum for atrial tachyarrhythmias wherein most patients with atrial tachycardia (AT) and some with atrial fibrillation (AF) respond to ablation, while others do not. It is undefined if this clinical spectrum has pathophysiological signatures. This study aims to test the hypothesis that the size of spatial regions showing repetitive synchronized electrogram (EGM) shapes over time reveals a spectrum from AT, to AF patients who respond acutely to ablation, to AF patients without acute response. METHODS AND RESULTS: We studied n = 160 patients (35% women, 65.0 ± 10.4 years) of whom (i) n = 75 had AF terminated by ablation propensity matched to (ii) n = 75 without AF termination and (iii) n = 10 with AT. All patients had mapping by 64-pole baskets to identify areas of repetitive activity (REACT) to correlate unipolar EGMs in shape over time. Synchronized regions (REACT) were largest in AT, smaller in AF termination, and smallest in non-termination cohorts (0.63 ± 0.15, 0.37 ± 0.22, and 0.22 ± 0.18, P < 0.001). Area under the curve for predicting AF termination in hold-out cohorts was 0.72 ± 0.03. Simulations showed that lower REACT represented greater variability in clinical EGM timing and shape. Unsupervised machine learning of REACT and extensive (50) clinical variables yielded four clusters of increasing risk for AF termination (P < 0.01, χ2), which were more predictive than clinical profiles alone (P < 0.001). CONCLUSION: The area of synchronized EGMs within the atrium reveals a spectrum of clinical response in atrial tachyarrhythmias. These fundamental EGM properties, which do not reflect any predetermined mechanism or mapping technology, predict outcome and offer a platform to compare mapping tools and mechanisms between AF patient groups.

Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit.

AIMS: Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalized predictions using complex, multimodal data. This study aimed to determine if risk stratification for implantable cardioverter-defibrillator (ICD) implantation can be improved by ML models that combine clinical variables with 12-lead electrocardiograms (ECG) time-series features. METHODS AND RESULTS: A multicentre study of 1010 patients (64.9 ± 10.8 years, 26.8% female) with ischaemic, dilated, or non-ischaemic cardiomyopathy, and LVEF ≤ 35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD in two academic hospitals was performed. For each patient, a raw 12-lead, 10-s ECG was obtained within 90 days before ICD implantation, and clinical details were collected. Supervised ML models were trained and validated on a development cohort (n = 550) from Hospital A to predict ICD non-arrhythmic mortality at three-year follow-up (i.e. mortality without prior appropriate ICD-therapy). Model performance was evaluated on an external patient cohort from Hospital B (n = 460). At three-year follow-up, 16.0% of patients had died, with 72.8% meeting criteria for non-arrhythmic mortality. Extreme gradient boosting models identified patients with non-arrhythmic mortality with an area under the receiver operating characteristic curve (AUROC) of 0.90 [95% confidence intervals (CI) 0.80-1.00] during internal validation. In the external cohort, the AUROC was 0.79 (95% CI 0.75-0.84). CONCLUSIONS: ML models combining ECG time-series features and clinical variables were able to predict non-arrhythmic mortality within three years after device implantation in a primary prevention population, with robust performance in an independent cohort.

Constipation and Fecal Incontinence in the Elderly.

PURPOSE OF REVIEW: To review the epidemiology, pathogenesis, clinical features, and management of primary constipation and fecal incontinence in the elderly. RECENT FINDINGS: Among elderly people, 6.5%, 1.7%, and 1.1% have functional constipation, constipation-predominant IBS, and opioid-induced constipation. In elderly people, the number of colonic enteric neurons and smooth muscle functions is preserved; decreased cholinergic function with unopposed nitrergic relaxation may explain colonic motor dysfunction. Less physical activity or dietary fiber intake and postmenopausal hormonal therapy are risk factors for fecal incontinence in elderly people. Two thirds of patients with fecal incontinence respond to biofeedback therapy. Used in combination, loperamide and biofeedback therapy are more effective than placebo, education, and biofeedback therapy. Vaginal or anal insert devices are another option. In the elderly, constipation and fecal incontinence are common and often distressing symptoms that can often be managed by addressing bowel disturbances. Selected diagnostic tests, prescription medications, and, infrequently, surgical options should be considered when necessary.

Predictive power of tumour budding for lymph node metastasis in colorectal carcinomas: A retrospective study.

Background & objectives: Tumour budding is a feature of epithelial-to-mesenchymal transformation that is characterized histologically within the tumour stroma by the presence of isolated cells or clusters of less than five cells which are different from the other malignant cells. This could be present around the invasive margin of the tumour, called peritumoural budding, or in the bulk of the tumour, called intratumoural budding. The aim of this study was to assess the predictive power of tumour budding for lymph node metastasis and its relationship with other features of tumour progression in colorectal carcinoma (CRC). Methods: Preoperative colonoscopic biopsies and consecutive resection specimens from 80 patients of colorectal cancer were taken. In the biopsy, intratumoural budding was looked for and graded. In the resection, peritumoural budding was seen and graded along with other features such as grade of the tumour, lymphovascular emboli and tumour border configuration. Results: Intratumoural budding was seen in 23 per cent (18/80) and peritumoural in 52 per cent (42/80) of cases. Intratumoural budding was associated with the presence of lymphovascular emboli (P=0.002) and irregular tumour border configuration (P=0.004). Peritumoural budding was also significantly associated with the presence of lymphovascular emboli and irregular margins (P < 0.001). Both intra- and peritumoural budding were not associated with the grade of the tumour. Both intra- and peritumoural budding had a significant association with lymph node metastasis (LNM) (P < 0.001). Interpretation & conclusions: Our findings indicate that tumour budding in preoperative biopsy and resection specimens may predict a possibility of finding LNM in patients with CRC.

Emphasizing Sexual Orientation and Gender Identity Data Capture for Improved Cardiovascular Care of the LGBTQ+ Population.

Importance: The rising self-identifying lesbian, gay, bisexual, transgender, and queer (LGBTQ+) population makes understanding the unique health care needs of sexual and gender minoritized patients an urgent one. The interaction between minority stress and cardiovascular disease has been well described among underrepresented minoritized populations. The underrepresentation of minoritized populations in clinical research is partly responsible for worse cardiovascular outcomes in these populations. The absence of sexual orientation and gender identity and expression (SOGIE) data makes it difficult to understand the cardiovascular health of LGBTQ+ adults, thereby widening health care disparities in this population. Advancing cardiovascular health equity for LGBTQ+ patients must begin with careful and accurate SOGIE data collection. Observations: Current SOGIE data capture remains inadequate despite federal mandates. Challenges in data collection include political and regulatory discrimination, patient/practitioner hesitancy, lack of supportive guidance on SOGIE data collection, improper terminology, regulatory inertia, and inadequate and often incorrect integration of SOGIE data into electronic health records (EHRs). Additional challenges include grouping participants as "others" for statistical significance. The inclusion of SOGIE data has demonstrated an impact in other fields like cancer survivorship and surgery. The same needs to be done for cardiology. Conclusions and Relevance: Potential solutions for improving much-needed SOGIE data collection include (1) implementing LGBTQ+ inclusive policies, (2) integrating SOGIE data into the EHR, (3) educating health care professionals on the relevance of SOGIE to patient-centered care, and (4) creating a diverse cardiovascular workforce. These steps can substantially enhance the ability to collect SOGIE data to address LGBTQ+ cardiovascular health care disparities.

Defecatory disorders are a common cause of chronic constipation in Parkinson disease.

BACKGROUND AND AIMS: Up to 50% of patients with Parkinson disease have constipation (PD-C), but the prevalence of defecatory disorders caused by rectoanal dyscoordination in PD-C is unknown. We aimed to compare anorectal function of patients with PD-C versus idiopathic chronic constipation (CC). METHODS: Anorectal pressures, rectal sensation, and rectal balloon expulsion time (BET) were measured with high-resolution anorectal manometry (HR-ARM) in patients with PD-C and control patients with CC, matched for age and sex. RESULTS: We identified 97 patients with PD-C and 173 control patients. Eighty-six patients with PD-C (89%) had early PD, and 39 (40%) had a defecatory disorder, manifest by a prolonged rectal balloon expulsion time (37 patients) or a lower rectoanal pressure difference during evacuation (2 patients). PD-C patients with a prolonged BET had a greater anal resting pressure (p = 0.02), a lower rectal pressure increment (p = 0.005), greater anal pressure (p = 0.047), and a lower rectoanal pressure difference during evacuation (p < 0.001). Rectal sensory thresholds were greater in patients with abnormal BET. In the multivariate model comparing CC and PD-C (AUROC = 0.76), PD-C was associated with a lower anal squeeze increment (odds ratio [OR] for PD-C, 0.93 [95% CI, 0.91-0.95]), longer squeeze duration (OR, 1.05 [95% CI, 1.03-1.08]), lower rectal pressure increment (OR per 10 mm Hg, 0.72 [95% CI, 0.66-0.79]), and negative rectoanal gradient during evacuation (OR per 10 mm Hg, 1.16 [95% CI, 1.08-1.26]). CONCLUSIONS: Compared with CC, PD-C was characterized by impaired squeeze pressure, longer squeeze duration, lower increase in rectal pressure, and a more negative rectoanal gradient during evacuation.

Most patients with disorders of gut-brain interaction receive pharmacotherapy with major or moderate drug-gene interactions.

BACKGROUND: How variations predicted by pharmacogenomic testing to alter drug metabolism and therapeutic response affect outcomes for patients with disorders of gut- brain interaction is unclear. AIMS: To assess the prevalence of pharmacogenomics-predicted drug-gene interactions and symptom outcomes for patients with disorders of gut-brain interaction. METHODS: Patients who were treated in our clinical practice for functional dyspepsia/bowel disorder underwent pharmacogenomic testing. The change in symptoms from baseline to 6 months was compared for patients with variations in CYP2D6 and CYP2C19, which metabolize neuromodulators, and SLC6A4, which encodes the sodium- dependent serotonin transporter. RESULTS: At baseline, 79 of 94 participants (84%) had at least one predicted major drug- gene interaction, and all 94 (100%) had at least one predicted moderate interaction. For the 44 participants who completed a survey of their symptoms at 6 months, the mean (SD) irritable bowel syndrome-symptom severity score decreased from 284 (71) at baseline to 231 (95) at 6 months (p < 0.001). Among patients taking selective serotonin reuptake inhibitors, the decrease in symptom severity (p = 0.03) and pain (p = 0.002) scores from baseline to 6 months was greater for patients with a homozygous SLC6A4 long/long genotype (n = 30) (ie, increased serotonin transporter activity) than for patients with homozygous short/short or heterozygous long/short genotypes (n = 64). Symptom outcomes were not affected by CYP2D6 or CYP2C19 variations. CONCLUSIONS: The homozygous SLC6A4 long/long genotype confers better symptom resolution for patients with disorders of gut-brain interaction who take selective serotonin reuptake inhibitors than do the homozygous short/short or heterozygous long/short genotypes.

Effects of ondansetron on symptoms during a gastric emptying study and enteral lipid challenge and on daily symptoms in diabetic gastroenteropathy.

BACKGROUND: This study compared the effects of ondansetron and placebo in patients with diabetes mellitus and symptoms of dyspepsia (diabetic gastroenteropathy [DGE]). METHODS: We performed a randomized, double-blinded, placebo-controlled study of ondansetron tablets (8 mg) three times daily for 4 weeks in DGE patients. Symptoms were assessed with the Gastroparesis Cardinal Symptom Index daily diaries. Gastric emptying (GE) of solids (scintigraphy) and duodenal lipid infusions (300 kcal over 2 h) were each assessed twice, with placebo and ondansetron. Drug effects on GE, symptoms during the GE study and during lipid infusion, and daily symptoms were analyzed. KEY RESULTS: Of 41 patients, 37 completed both GE studies and one completed 1; 31 completed both lipid infusions and four only placebo; and all 35 randomized patients completed 4 weeks of treatment. Compared to placebo, ondansetron reduced the severity of fullness (p = 0.02) and belching (p = 0.049) during lipid infusion but did not affect GE T1/2. Both ondansetron and placebo improved daily symptoms versus the baseline period (p < 0.05), but the differences were not significant. In the analysis of covariance of daily symptoms during the treatment period, the interaction term between treatment and the acute effect of ondansetron on symptoms during lipid challenge was significant (p = .024). CONCLUSIONS & INFERENCES: Ondansetron significantly reduced fullness during enteral lipid infusion in patients with DGE. Overall, ondansetron did not improve daily symptoms versus placebo. But patients in whom ondansetron improved symptoms during enteral lipid challenge were perhaps more likely to experience symptom relief during daily treatment.

Rationale and design of OPT-RATE AF: A randomized clinical trial of increased physiologic pacing rates in heart failure with preserved ejection fraction.

Cardiac physiologic pacing (CPP) after atrioventricular node (AVN) ablation for persistent atrial fibrillation (AF) has improved outcomes in patients with heart failure with reduced and preserved ejection fraction (HFpEF). Emerging evidence suggests patients with HFpEF benefit from higher heart rates, yet the optimal pacing rate after AVN ablation remains unknown. OPT-RATE AF is a prospective, randomized crossover study of patients with HFpEF following AVN ablation for persistent AF (NCT06445439). Approximately 60 patients with AF and AVN ablation, CPP, and HF with left ventricular ejection fraction ≥50% will be enrolled. Participants will be randomly assigned 1:1 to a pacing lower rate limit of 60 beats-per-minute (bpm) for 3 months and then switched to a rate of 80 bpm for 3 months, and vice versa. The primary endpoint is change in exercise capacity assessed by the 6-minute walk test. Notable secondary outcomes will include change in Kansas City Quality of Life Questionnaire (KCCQ-12), creatinine and natriuretic peptide, and clinical events. Patient mortality and HF hospitalizations will be recorded at each phase. EKG, echocardiogram, pacemaker interrogation, and primary and secondary outcomes will be recorded at baseline, 3 months, and 6 months. Study enrollment is ongoing and estimated to be completed by 2026. OPT-RATE AF is a randomized clinical trial that will determine the effect of a higher pacing rate in patients with persistent AF and HFpEF following AVN ablation and/or CPP. Study findings will provide insight on the role of chronotropy in improving QoL and other important cardiovascular outcomes.

Novel Domain Knowledge-Encoding Algorithm Enables Label-Efficient Deep Learning for Cardiac CT Segmentation to Guide Atrial Fibrillation Treatment in a Pilot Dataset.

Background: Segmenting computed tomography (CT) is crucial in various clinical applications, such as tailoring personalized cardiac ablation for managing cardiac arrhythmias. Automating segmentation through machine learning (ML) is hindered by the necessity for large, labeled training data, which can be challenging to obtain. This article proposes a novel approach for automated, robust labeling using domain knowledge to achieve high-performance segmentation by ML from a small training set. The approach, the domain knowledge-encoding (DOKEN) algorithm, reduces the reliance on large training datasets by encoding cardiac geometry while automatically labeling the training set. The method was validated in a hold-out dataset of CT results from an atrial fibrillation (AF) ablation study. Methods: The DOKEN algorithm parses left atrial (LA) structures, extracts "anatomical knowledge" by leveraging digital LA models (available publicly), and then applies this knowledge to achieve high ML segmentation performance with a small number of training samples. The DOKEN-labeled training set was used to train a nnU-Net deep neural network (DNN) model for segmenting cardiac CT in N = 20 patients. Subsequently, the method was tested in a hold-out set with N = 100 patients (five times larger than training set) who underwent AF ablation. Results: The DOKEN algorithm integrated with the nn-Unet model achieved high segmentation performance with few training samples, with a training to test ratio of 1:5. The Dice score of the DOKEN-enhanced model was 96.7% (IQR: 95.3% to 97.7%), with a median error in surface distance of boundaries of 1.51 mm (IQR: 0.72 to 3.12) and a mean centroid-boundary distance of 1.16 mm (95% CI: -4.57 to 6.89), similar to expert results (r = 0.99; p < 0.001). In digital hearts, the novel DOKEN approach segmented the LA structures with a mean difference for the centroid-boundary distances of -0.27 mm (95% CI: -3.87 to 3.33; r = 0.99; p < 0.0001). Conclusions: The proposed novel domain knowledge-encoding algorithm was able to perform the segmentation of six substructures of the LA, reducing the need for large training data sets. The combination of domain knowledge encoding and a machine learning approach could reduce the dependence of ML on large training datasets and could potentially be applied to AF ablation procedures and extended in the future to other imaging, 3D printing, and data science applications.

Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator.

BACKGROUND: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS: 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION: Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

The Use of Telemedicine to Improve Hypertension in an Urban Primary Care Clinic and Predictors of Improved Blood Pressure.

BACKGROUND AND OBJECTIVE: Meta-analysis of randomized controlled trials have demonstrated the efficacy of telemedicine in blood pressure (BP) management when compared to conventional care. We initiated a hypertension telehealth clinic in our urban primary care clinic and through this study aim to evaluate the strengths and limitations of telemedicine in hypertension (HTN) control. The primary outcome of the study is to identify the proportion of patients with improved HTN. Secondary outcomes included identifying: predictors for lower BP, predictors of missing telehealth appointments, and comorbid conditions that are more likely to necessitate use of more than 1 antihypertensive medication. METHODS AND ANALYSIS: Patients seen in the HTN telehealth clinic from May 1st, 2022 to October 31st, 2022 were identified. A retrospective chart review was done to compare the BP during in-person visit prior to first telehealth visit, telehealth visit home BP readings and last recorded in-office BP on chart at end of study period. Descriptive statistical analysis, Chi Square test, and multivariable logistic regression was used to analyze data. RESULTS: Of the 234 appointments, 83% were conducted and 154 patients were seen. A remarkable decrease in percentage of patients with BP >140/90 was seen when comparing in-office visit BP to first telehealth visit home BP, 72% versus 45% respectively. No remarkable difference was noted in percentage of patients with BP >140/90 when comparing first telehealth visit home BP to last in-office BP recorded on chart, 45% and 41% respectively. Patients with diabetes had lower odds of missing appointments, adjusted odds ratio (aOR): 0.34 ([0.12-0.91], P = .03). Patients with partners were more likely to have lower BP at the telehealth visit, aOR:3.2 ([1.15-9.86], P = .03) while patients with obstructive sleep apnea (OSA) (aOR 0.27 ([0.08-0.77], P = .02) and CAD, aOR 0.24 ([0.06-0.8], P = .03) were less likely to have lower BP. CONCLUSION: The study demonstrated telemedicine as a great tool to prevent overtreatment of hypertension as significant difference between in-office BP and home BP during telehealth visits was noted. We did not see a significant change in blood pressure when comparing home BP at first telehealth visit to the last in-person clinic BP at end of study period.

Characterization of Myocardial Injury With High-Sensitivity Troponin.

BACKGROUND: High-sensitivity troponin I, cardiac form (hs-cTnI) accelerates the assessment of acute coronary syndrome. Little has been documented about its performance, how it relates to different types of myocardial injury, and its impact on morbidity and mortality. This study sought to expand understanding of hs-cTnI by characterizing types of myocardial injury, the impact of comorbidities, and 30-day outcomes. METHODS: The study retrospectively evaluated 1,975 patients with hs-cTnI levels obtained in the emergency department or inpatient setting from June to September 2020. Troponin was considered elevated if it was higher than the 99th percentile for either sex. Charts were reviewed to determine the presence of myocardial injury. Troponin elevation was adjusted for demographics, comorbidities, and kidney dysfunction. Thirty-day mortality and readmission rates were calculated. RESULTS: Of 1,975 patients, 468 (24%) had elevated hs-cTnI, and 330 (17%) had at least 1 type of myocardial injury, type 2 myocardial infarction being the most frequent. Sensitivity and specificity using the 99th percentile as a cutoff were 99% and 92%, respectively. The average maximum hs-cTnI level was significantly higher for type 1 myocardial infarction (P < .001). Being male, Black, non-Hispanic, and a hospital inpatient were all associated with higher initial and peak hs-cTnI levels (P < .001). Elevated hs-cTnI level, age, heart disease, kidney dysfunction, and inpatient status were predictive of 30-day mortality on multivariate analysis. CONCLUSION: Elevated hs-cTnI levels in emergency department and inpatient settings occurs most commonly because of type 2 myocardial infarction. Maximum hs-cTnI level is associated with the patient's particular type of myocardial injury, certain demographics, and cardiovascular comorbidities, and it may be a predictor of 30-day outcomes.

Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding.

Background: Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation. Methods: We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N = 6 digital hearts. The model, termed "virtual dissection," was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study. Results: In independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%-97.7%) and 93.5% in external (IQR: 91.9%-94.7%) test data, with good agreement with experts (r = 0.99; p < 0.0001). In a prospective study of 42 patients at ablation, this approach reduced segmentation time by 85% (2.3 ± 0.8 vs. 15.0 ± 6.9 min, p < 0.0001), yet provided similar Dice scores to experts (93.9% (IQR: 93.0%-94.6%) vs. 94.4% (IQR: 92.8%-95.7%), p = NS). Conclusions: Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for large training sets while retaining accuracy in independent test data. Combining ML with domain knowledge may have broad applications.

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.

BACKGROUND: Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. METHODS: This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. FINDINGS: 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755-0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642-0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867-0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. INTERPRETATION: ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. FUNDING: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

Machine learning facilitates the prediction of long-term mortality in patients with tricuspid regurgitation.

OBJECTIVE: Tricuspid regurgitation (TR) is a prevalent valve disease associated with significant morbidity and mortality. We aimed to apply machine learning (ML) to assess risk stratification in patients with ≥moderate TR. METHODS: Patients with ≥moderate TR on echocardiogram between January 2005 and December 2016 were retrospectively included. We used 70% of data to train ML-based survival models including 27 clinical and echocardiographic features to predict mortality over a 3-year period on an independent test set (30%). To account for differences in baseline comorbidities, prediction was performed in groups stratified by increasing Charlson Comorbidity Index (CCI). Permutation feature importance was calculated using the best-performing model separately in these groups. RESULTS: Of 13 312 patients, mean age 72 ± 13 years and 7406 (55%) women, 7409 (56%) had moderate, 2646 (20%) had moderate-severe and 3257 (24%) had severe TR. The overall performance for 1-year mortality by 3 ML models was good, c-statistic 0.74-0.75. Interestingly, performance varied between CCI groups, (c-statistic = 0.774 in lowest CCI group and 0.661 in highest CCI group). The performance decreased over 3-year follow-up (average c-index 0.78). Furthermore, the top 10 features contributing to these predictions varied slightly with the CCI group, the top features included heart rate, right ventricular systolic pressure, blood pressure, diuretic use and age. CONCLUSIONS: Machine learning of common clinical and echocardiographic features can evaluate mortality risk in patients with TR. Further refinement of models and validation in prospective studies are needed before incorporation into the clinical practice.

Duodenal Mucosal Expression of COVID-19-Related Genes in Health, Diabetic Gastroenteropathy, and Functional Dyspepsia.

CONTEXT: SARS-CoV-2 infects the gastrointestinal tract and may be associated with symptoms that resemble diabetic gastroparesis. Why patients with diabetes who contract COVID-19 are more likely to have severe disease is unknown. OBJECTIVE: We aimed to compare the duodenal mucosal expression of SARS-CoV-2 and inflammation-related genes in diabetes gastroenteropathy (DGE), functional dyspepsia (FD), and healthy controls. METHODS: Gastrointestinal transit, and duodenal mucosal mRNA expression of selected genes were compared in 21 controls, 39 DGE patients, and 37 FD patients from a tertiary referral center. Pathway analyses were performed. RESULTS: Patients had normal, delayed (5 FD [13%] and 13 DGE patients [33%]; P = 0.03 vs controls), or rapid (5 FD [12%] and 5 DGE [12%]) gastric emptying (GE). Compared with control participants, 100 SARS-CoV-2-related genes were increased in DGE (FDR < 0.05) vs 13 genes in FD; 71 of these 100 genes were differentially expressed in DGE vs FD but only 3 between DGE patients with normal vs delayed GE. Upregulated genes in DGE include the SARS-CoV2 viral entry genes CTSL (|Fold change [FC]|=1.16; FDR < 0.05) and CTSB (|FC|=1.24; FDR < 0.05) and selected genes involved in viral replication (eg, EIF2 pathways) and inflammation (CCR2, CXCL2, and LCN2, but not other inflammation-related pathways eg, IL-2 and IL-6 signaling). CONCLUSION: Several SARS-CoV-2-related genes were differentially expressed between DGE vs healthy controls and vs FD but not between DGE patients with normal vs delayed GE, suggesting that the differential expression is related to diabetes per se. The upregulation of CTSL and CTSB and replication genes may predispose to SARS-CoV2 infection of the gastrointestinal tract in diabetes.

Optimizing techniques for measuring anal resting and squeeze pressures with high-resolution manometry.

BACKGROUND: The optimal methods for measuring and analyzing anal resting and squeeze pressure with high-resolution manometry (HRM) are unclear. METHODS: Anal resting and squeeze pressures were measured with HRM in 90 healthy women, 35 women with defecatory disorders (DD), and 85 with fecal incontinence (FI). Pressures were analyzed with Manoview™ software and a customized approach. Resting pressures measured for 20, 60, and 300 s were compared. During the squeeze period, (3 maneuvers, 20 s each), the squeeze increment, which was averaged over 5, 10, 15, and 20 s, and squeeze duration were evaluated. RESULTS: Compared to healthy women, the anal resting pressure, squeeze pressure increment, and squeeze duration were lower in FI (p ≤ 0.04) but not in DD. The 20, 60, and 300 s resting pressures were strongly correlated (concordance correlation coefficients = 0.96-0.99) in healthy and DD women. The 5 s squeeze increment was the greatest; 10, 15, and 20 s values were progressively lower (p < 0.001). The squeeze pressure increment and duration differed (p < 0.01) among the three maneuvers in healthy and DD women but not in FI women. The upper 95th percentile limit for squeeze duration was 19.5 s in controls, 19.9 s in DD, and 19.3 s in FI. Adjusted for age, resting pressure, and squeeze duration, a greater squeeze increment was associated with a lower risk of FI versus health (OR, 0.96; 95% CI, 0.94-0.97). CONCLUSIONS: These findings suggest that anal resting and squeeze pressures can be accurately measured over 20 s. In most patients, one squeeze maneuver is probably sufficient.