A novel digital rectoscope for the triage of lower gastrointestinal symptoms in primary care: a prospective multicentre feasibility study.

BACKGROUND: Access to community rectoscopy might help to ease the burden on hospital services and reduce costs for the NHS. To assess this, a prospective multicentre observational phase I feasibility study of a novel digital rectoscope and telestration software for the triage of lower gastrointestinal (GI) symptoms was undertaken. AIM: To determine if digital rectoscopy is feasible, acceptable, and clinically safe. DESIGN & SETTING: Evaluation of clinician case reports and patient questionnaires from patients recruited from five primary care centres. METHOD: Adults meeting 2-week wait (2WW) criteria for suspected lower GI cancer, suspected new diagnosis, or flare-up of inflammatory bowel disease (IBD) were enrolled. Examinations were performed by primary care practitioners using the LumenEye rectoscope. The CHiP platform allowed immediate remote review by secondary care. A prospective analysis was performed of patient and clinician experiences, diagnostic accuracy, and cost. RESULTS: A total of 114 patients were recruited and 110 underwent the procedure (46 [42%] females and 64 [58%] males). No serious adverse events were reported. Eighty-two (74.5%) patients reported that examination was more comfortable than expected, while 104 (94.5%) felt the intervention was most convenient if delivered in the community. Clinicians were confident of their assessment in 100 (87.7%) examinations. Forty-eight (42.1%) patients subsequently underwent colonoscopy, flexible sigmoidoscopy, or computed tomography virtual colonoscopy (CTVC). The overall sensitivity and specificity of LumenEye in identifying rectal pathology was 90.0% and 88.9%. It was 100% and 100% for cancer, and 83.3% and 97.8% for polyps. Following LumenEye examination, 19 (17.3%) patients were discharged, with projected savings of 11 305 GBP. CONCLUSION: Digital rectoscopy in primary care is safe, acceptable, and can reduce referrals. A phase III randomised controlled trial is indicated to define its utility in reducing the burden on hospital diagnostic services.

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. METHODS: Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. RESULTS: We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). CONCLUSION: A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.

Erectile Function Post Prostate Biopsy: A Systematic Review and Meta-analysis.

To determine the effect of prostate biopsy on erectile function we conducted a systematic review and meta-analysis up to August 2020. Mean difference of International Index of Erectile Function-5 compared to baseline scores were pooled at 1, 3 and 6-months using a random effect model. Of 47 full text articles identified, 7 were included in the meta-analysis. A significant reduction in mean IIEF5 of 4.61 (0.32-8.91, P= .04) was found at 1-month post biopsy. This resolved with non-significant differences at 3- and 6-months post procedure. Patients should be counselled regarding the transient effect on erectile function post biopsy.

In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation.

Catheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which use imaging data together with electroanatomic mapping data, depending on data availability. The aim of this study was to compare ablation techniques across a virtual cohort of AF patients. We constructed 20 paroxysmal and 30 persistent AF patient-specific left atrial (LA) bilayer models incorporating fibrotic remodeling from late-gadolinium enhancement (LGE) MRI scans. AF was simulated and post-processed using phase mapping to determine electrical driver locations over 15 s. Six different ablation approaches were tested: (i) PVI alone, modeled as wide-area encirclement of the pulmonary veins; PVI together with: (ii) roof and inferior lines to model posterior wall box isolation; (iii) isolating the largest fibrotic area (identified by LGE-MRI); (iv) isolating all fibrotic areas; (v) isolating the largest driver hotspot region [identified as high simulated phase singularity (PS) density]; and (vi) isolating all driver hotspot regions. Ablation efficacy was assessed to predict optimal ablation therapies for individual patients. We subsequently trained a random forest classifier to predict ablation response using (a) imaging metrics alone, (b) imaging and electrical metrics, or (c) imaging, electrical, and ablation lesion metrics. The optimal ablation approach resulting in termination, or if not possible atrial tachycardia (AT), varied among the virtual patient cohort: (i) 20% PVI alone, (ii) 6% box ablation, (iii) 2% largest fibrosis area, (iv) 4% all fibrosis areas, (v) 2% largest driver hotspot, and (vi) 46% all driver hotspots. Around 20% of cases remained in AF for all ablation strategies. The addition of patient-specific and ablation pattern specific lesion metrics to the trained random forest classifier improved predictive capability from an accuracy of 0.73 to 0.83. The trained classifier results demonstrate that the surface areas of pre-ablation driver regions and of fibrotic tissue not isolated by the proposed ablation strategy are both important for predicting ablation outcome. Overall, our study demonstrates the need to select the optimal ablation strategy for each patient. It suggests that both patient-specific fibrosis properties and driver locations are important for planning ablation approaches, and the distribution of lesions is important for predicting an acute response.