Citations

Introduction Mayo Clinic Connect is an online community of over 100,000 members who support each other through sharing lived experience when facing and managing new diagnoses. The community is moderated by Mayo Clinic staff and volunteer patient mentors. Methods Mayo Clinic breast clinic patients undergoing evaluation received a binder of support resources including a brochure about Mayo Clinic Connect at visits between January and May of 2019. Surveys were distributed at subsequent visits between May and December of 2019 to assess patient awareness about the online resource, participation frequency, purpose of use, and benefits for members, as well as reasons for not joining (non-members). The primary aim was to assess patient resilience, coping, and self-management after joining the online community. Results Nine hundred surveys were distributed, and 102 participants completed surveys between May and December 2019. Forty-five percent ( n = 46) had heard about Mayo Clinic Connect; 34% ( n = 15) through a brochure. The remainder heard about the community from a Mayo Clinic provider (43%; n = 19) or other resources (22%, n = 10; no response n = 2). Twenty percent ( n = 20) of survey participants registered as Breast Cancer group members, and most of this subgroup (55%; n = 11) reported understanding diagnosis, treatment plans, and finding peer support as reasons for joining. Seventy-five percent of Mayo Clinic Connect participants ( n = 15) reported the community met or exceeded expectations. Conclusion This pilot study reveals the potential positive impact of introducing an online peer support group into clinical care plans for patients coping with a new and anxiety-provoking cancer diagnosis.

Abstract Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.

A 104-year-old woman presented with dyspnoea. Workup revealed normal troponins, elevated D-dimer at 2285 with subsequent chest CT angiogram negative for pulmonary embolism. NT-pro BNP elevated to 5208 pg/mL. Chest X-ray revealed mild cardiac enlargement with bilateral pleural effusions. ECG revealed accelerated junctional rhythm. The initial plan was to obtain transthoracic echocardiogram and start diuresis for presumed heart failure. Given her specific cardiac history, pacemaker interrogation was ordered which revealed her pacemaker mode changed from DDDR to VVI. She underwent pacemaker battery exchange with complete resolution in her symptoms and ability to return to prior functional status. Overall, dyspnoea in centenarians could be multifactorial. Pacemaker elective replacement mode secondary to end of life battery can present as non-specific cardio respiratory symptoms. As the pacemaker population ages to the survival beyond the battery life, pacemaker aetiology behind heart failure symptoms needs to be highly considered by medical providers.

Purpose Gemcitabine as a single agent and the combination of gemcitabine and docetaxel have activity in patients with metastatic soft tissue sarcoma. To determine if the addition of docetaxel to gemcitabine improved clinical outcome of patients with metastatic soft tissue sarcomas, we compared a fixed dose rate infusion of gemcitabine versus a lower dose of gemcitabine with docetaxel. Patients and Methods In this open-label phase II clinical trial, the primary end point was tumor response, defined as complete or partial response or stable disease lasting at least 24 weeks. A Bayesian adaptive randomization procedure was used to produce an imbalance in the randomization in favor of the superior treatment, accounting for treatment-subgroup interactions. Results One hundred nineteen of 122 randomly assigned patients had assessable outcomes. The adaptive randomization assigned 73 patients (60%) to gemcitabine-docetaxel and 49 patients (40%) to gemcitabine alone, indicating gemcitabine-docetaxel was superior. The objective Response Evaluation Criteria in Solid Tumors response rates were 16% (gemcitabine-docetaxel) and 8% (gemcitabine). Given the data, the posterior probabilities that gemcitabine-docetaxel was superior for progression-free and overall survival were 0.98 and 0.97, respectively. Median progression-free survival was 6.2 months for gemcitabine-docetaxel and 3.0 months for gemcitabine alone; median overall survival was 17.9 months for gemcitabine-docetaxel and 11.5 months for gemcitabine. The posterior probability that patients receiving gemcitabine-docetaxel had a shorter time to discontinuation for toxicity compared with gemcitabine alone was .999. Conclusion Gemcitabine-docetaxel yielded superior progression-free and overall survival to gemcitabine alone, but with increased toxicity. Adaptive randomization is an effective method to reduce the number of patients receiving inferior therapy.