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Introduction: COVID-19 requires methods for screening patients that adhere to physical distancing and other Centers for Disease Control and Prevention guidelines. There is little data on the use of on-demand telehealth to meet this need. Methods: The functional performance of on-demand telehealth as a COVID-19 remote patient screening approach was conducted by analysing 9270 patient requests. Results: Most on-demand telehealth requests (5712 of 9270 total requests; 61.6%) had a visit reason that was likely COVID-19 related. Of these, 79.1% (4518 of 5712) resulted in a completed encounter and 20.9% (1194 of 5712) resulted in left without being seen. Of the 4518 completed encounters, 19.1% were referred to an urgent care centre, emergency department or COVID-19 testing centre. The average completed encounter wait time was 26.5 min and the mean visit length was 8.8 min. For patients that completed an encounter 42.8% (1935 of 4518) stated they would have sought in-person care and 9.1% stated they would have done nothing if on-demand telehealth was unavailable. Discussion: On-demand telehealth can serve as a low-barrier approach to screen patients for COVID-19. This approach can prevent patients from visiting healthcare facilities, which reduces physical contact and reduces healthcare worker use of personal protective equipment.

Aim: In osteoarthritis (OA) there is a need for automated screening systems for early detection of structural progressors. We built a comprehensive machine learning (ML) model that bridges major OA risk factors and serum levels of adipokines/related inflammatory factors at baseline for early prediction of at-risk knee OA patient structural progressors over time. Methods: The patient- and gender-based model development used baseline serum levels of six adipokines, three related inflammatory factors and their ratios (36), as well as major OA risk factors [age and bone mass index (BMI)]. Subjects (677) were selected from the Osteoarthritis Initiative (OAI) progression subcohort. The probability values of being structural progressors (PVBSP) were generated using our previously published prediction model, including five baseline structural features of the knee, i.e. two X-rays and three magnetic resonance imaging variables. To identify the most important variables amongst the 47 studied in relation to PVBSP, we employed the ML feature classification methodology. Among five supervised ML algorithms, the support vector machine (SVM) demonstrated the best accuracy and use for gender-based classifiers development. Performance and sensitivity of the models were assessed. A reproducibility analysis was performed with clinical trial OA patients. Results: Feature selections revealed that the combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP are the most important variables in predicting OA structural progressors in both genders. Classification accuracies for both genders in the testing stage (OAI) were >80%, with the highest sensitivity of CRP/MCP-1. Reproducibility analysis showed an accuracy ⩾92%; the ratio CRP/MCP-1 demonstrated the highest sensitivity in women and leptin/CRP in men. Conclusion: This is the first time that such a framework was built for predicting knee OA structural progressors. Using this automated ML patient- and gender-based model, early prediction of knee structural OA progression can be performed with high accuracy using only three baseline serum biomarkers and two risk factors. Plain language summary Machine learning model for early knee osteoarthritis structural progression Knee osteoarthritis is a well-known debilitating disease leading to reduced mobility and quality of life – the main causes of chronic invalidity. Disease evolution can be slow and span many years; however, for some individuals, the progression/evolution can be fast. Current treatments are only symptomatic and conventional diagnosis of osteoarthritis is not very effective in early identification of patients who will progress rapidly. To improve therapeutic approaches, we need a robust prediction model to stratify osteoarthritis patients at an early stage according to risk of joint structure disease progression. We hypothesize that a prediction model using a machine learning system would enable such an early identification of individuals for whom osteoarthritis knee structure will degrade rapidly. Data were from the Osteoarthritis Initiative, a National Institute of Health (United States) databank, and the robustness and generalizability of the developed model was further evaluated using osteoarthritis patients from an external cohort. Using the supervised machine learning system (support vector machine), we developed an automated patient- and gender-based model enabling an early clinical prognosis for individuals at high risk of structural progressive osteoarthritis. In brief, this model employed at baseline (when the subject sees a physician) easily obtained features consisting of the two main osteoarthritis risk factors, age and bone mass index (BMI), in addition to the serum levels of three molecules. Two of these molecules belong to a family of factors names adipokines and one to a related inflammatory factor. In brief, the model comprising a combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP were found very robust for both genders, and the high accuracy persists when tested with an external cohort conferring the gender-based model generalizability. This study offers a new automated system for identifying early knee osteoarthritis structural progressors, which will significantly improve clinical prognosis with real time patient monitoring.

Background/Aims: A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. Methods: We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)–(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. Results: We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. Conclusion: In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

Aldosterone antagonists represented by nonselective spironolactone and mineralocorticoid-selective eplerenone are approved for treatment of symptomatic heart failure with reduced systolic function. Their cardioprotective, antifibrotic, and antiarrhythmic effects have been proven in animal experiments, and their effects on morbidity and mortality have been demonstrated in randomized clinical trials. Yet, they remain the most underutilized of all classes of medications for heart failure, primarily because of fear of hyperkalemia. Thorough patient screening and selection is the key for minimizing risks and optimizing benefits from these drugs. Ongoing trials will demonstrate whether the indication for aldosterone antagonists can be expanded to less severe heart failure or patients with preserved systolic function.

The frequency of ESWL complications is low, but is increasing with its application to large stones; with careful patient screening, the same measure of precaution and an attentive follow up, it is possible to reduce the consequences of complications.

Continuous subcutaneous insulin infusion (CSII) devices are designed to provide physiologic insulinemia. Not only do they offer increased life-style flexi bility, they also make it possible to achieve near-normal levels of glycemia. These devices are programmed to deliver a continuous "basal" infusion. Preprandial bolus doses of insulin are given to prevent postprandial hyperglycemia. Careful patient screening and selection are necessary to ensure a successful outcome. Patients must have the maturity, technical ability, motivation, and financial resources needed to utilize CSII therapy. Initiation of CSII can be accomplished in an outpatient or inpatient setting, but both require a comprehensive educational curriculum.