Real-time Clinical Data Research Articles

Prevalence of GII.4 Sydney Norovirus Strains and Associated Factors of Acute Gastroenteritis in Children: 2019/2020 Season in Guangzhou, China

Norovirus, the leading cause of non-bacterial acute gastroenteritis (AGE) worldwide, is constantly mutating. Continuous monitoring of the evolution of epidemic genotypes and emergence of novel genotypes is, therefore, necessary. This study determined the prevalence and clinical characteristics of norovirus strains in AGE in Guangzhou, China in 2019/2020 season. This study included children aged 2–60 months diagnosed with AGE in Guangzhou Women and Children Hospital, from August 2019 to January 2020. Norovirus was detected by real-time polymerase chain reaction and clinical data were obtained. Genotyping and phylogenetic analyses were performed with partial gene sequence fragments located within the open reading frames 1 and 2. During the study period, 168 children (61.3% males) were confirmed as norovirus infectious AGE. The main symptoms were diarrhoea and vomiting and 38 patients (22.6%) had seizures. Norovirus was mainly prevalent in October and November, and GII.4 Sydney[P31] was the major genotype circulating in Guangzhou. The phylogenetic tree showed that the Guangzhou strains had high homology with the strains circulating in 2017–2019 worldwide. GII.4 Sydney was the main prevalent norovirus genotype in Guangzhou from August 2019 to January 2020, which had more severe diarrhoea than those of other genotypes. These findings provide a valuable reference for the prevention, control, and treatment of norovirus in the future.

Emerging technologies for the management of type 2 diabetes mellitus

Diabetes mellitus is a global health problem affecting 422 million people worldwide, of which 34.2 million live in the United States alone. Complications due to diabetes can lead to considerable morbidity and mortality related to both microvascular and macrovascular disease. While glycosylated hemoglobin testing is the standard test utilized to evaluate glycemic control, emerging targets like "time in range" and "glycemic variability" often provide more accurate assessments of glycemic fluctuations and have implications for diabetes complications and quality of life. Patients with diabetes face considerable burdens of self-care including frequent glucose monitoring, multiple insulin injections, dietary management, and the need to track daily activities, all of which lead to reduced adherence and psychological burnout. From the provider perspective, limited patient data and access to self-management tools lead to treatment inertia and a reduced ability to help patients achieve and maintain their glycemic goals. In the past few decades, there have been considerable advances in treatment-based technology and technological applications designed to help reduce patient burden and provide tools for better self-management. These advances make real-time clinical data available for clinicians to make necessary changes in treatment regimens. In this review, we discuss the latest emerging technologies available for the management of people with type 2 diabetes mellitus.

Engaging Patients in the Use of Real-Time Electronic Clinical Data to Improve the Safety and Reliability of Their Own Care.

There is considerable evidence that providing patients with access to their health information is beneficial, but there is limited evidence regarding the effect of providing real-time patient safety-related information on health outcomes. The aim of this study was to evaluate the association between use of an electronic patient safety dashboard (Safety Advisor) and health outcomes. The Safety Advisor was implemented in 6 adult medicine units at one hospital in the United States. Study participants were asked to use the Safety Advisor, which provides real-time patient safety-related information through a Web-based portal. The primary outcome was the association between the application usage and health outcomes (readmission rate and mortality rate) per 3 different usage groups, and the secondary outcome was the association of Patient Activation Measure (PAM) scores with use. One hundred eighty-one participants were included for the data analysis. Approximately 90% of users accessed the application during the first 4 days of enrollment: 51.6% of users only accessed it on 1 day, whereas 5.8% used it more than 3 days. Patients who used the application more had lower 30-day readmission rates (P = 0.01) compared with the lower-usage group. The PAM scores for users of Safety Advisor (71.8) were higher than the nonpatient portal users (60.8, P < 0.0001). We found an association between the use of Safety Advisor and health outcomes. Differences in PAM scores between groups were statistically significant. A larger-scale randomized control trial is warranted to evaluate the impact on patient outcomes among a high-risk patient population.

Detection of Viruses by Multiplex Real-Time Polymerase Chain Reaction in Bronchoalveolar Lavage Fluid of Patients with Nonresponding Community-Acquired Pneumonia.

Background Nonresponding pneumonia is responsible for the most mortality of community-acquired pneumonia (CAP). However, thus far, it is not clear whether viral infection plays an important role in the etiology of nonresponding CAP and whether there is a significant difference in the clinical characteristics between viral and nonviral nonresponding CAP. Methods From 2016 to 2019, nonresponding CAP patients were retrospectively enrolled in our study. All patients received bronchoalveolar lavage (BAL) and virus detection in BAL fluid by multiplex real-time polymerase chain reaction (PCR), and clinical, laboratory, and radiographic data were collected. Results A total of 43 patients were included. The median age was 62 years, and 65.1% of patients were male. Overall, 20 patients (46.5%) were identified with viral infection. Of these viruses, influenza virus (n = 8) and adenovirus (n = 7) were more frequently detected, and others included herpes simplex virus, human enterovirus, cytomegalovirus, human coronavirus 229E, rhinovirus, and parainfluenza virus. Compared with nonviral nonresponding CAP, only ground-glass opacity combined with consolidation was a more common imaging manifestation in viral nonresponding CAP. However, no obvious differences were found in clinical and laboratory findings between the presence and the absence of viral infections. Conclusions Viral infections were particularly frequent in adults with nonresponding CAP. The ground-glass opacity combined with consolidation was a specific imaging manifestation for viral nonresponding CAP, while the clinical and laboratory data showed no obvious differences between viral and nonviral nonresponding CAP.

Each patient is a research biorepository: informatics-enabled research on surplus clinical specimens via the living BioBank.

The ability to analyze human specimens is the pillar of modern-day translational research. To enhance the research availability of relevant clinical specimens, we developed the Living BioBank (LBB) solution, which allows for just-in-time capture and delivery of phenotyped surplus laboratory medicine specimens. The LBB is a system-of-systems integrating research feasibility databases in i2b2, a real-time clinical data warehouse, and an informatics system for institutional research services management (SPARC). LBB delivers deidentified clinical data and laboratory specimens. We further present an extension to our solution, the Living µBiome Bank, that allows the user to request and receive phenotyped specimen microbiome data. We discuss the details of the implementation of the LBB system and the necessary regulatory oversight for this solution. The conducted institutional focus group of translational investigators indicates an overall positive sentiment towards potential scientific results generated with the use of LBB. Reference implementation of LBB is available at https://LivingBioBank.musc.edu.

The National Endoscopy Database (NED) Automated Performance Reports to Improve Quality Outcomes Trial (APRIQOT) randomized controlled trial design.

Background and study aims Colonoscopists with low polyp detection have higher post colonoscopy colorectal cancer incidence and mortality rates. The United Kingdom’s National Endoscopy Database (NED) automatically captures patient level data in real time and provides endoscopy key performance indicators (KPI) at a national, endoscopy center, and individual level. Using an electronic behavior change intervention, the primary objective of this study is to assess if automated feedback of endoscopist and endoscopy center-level optimal procedure-adjusted detection KPI (opadKPI) improves polyp detection performance. Methods This multicenter, prospective, cluster-randomized controlled trial is randomizing NHS endoscopy centres to either intervention or control. The intervention is targeted at independent colonoscopists and each center’s endoscopy lead. The intervention reports are evidence-based from endoscopist qualitative interviews and informed by psychological theories of behavior. NED automatically creates monthly reports providing an opadKPI, using mean number of polyps, and an action plan. The primary outcome is opadKPI comparing endoscopists in intervention and control centers at 9 months. Secondary outcomes include other KPI and proximal detection measures at 9 and 12 months. A nested histological validation study will correlate opadKPI to adenoma detection rate at the center level. A cost-effectiveness and budget impact analysis will be undertaken. Conclusion If the intervention is efficacious and cost-effective, we will showcase the potential of this learning health system, which can be implemented at local and national levels to improve colonoscopy quality, and demonstrate that an automated system that collects, analyses, and disseminates real-time clinical data can deliver evidence- and theory-informed feedback.

The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases.

Clinical decision support systems (CDSS) aid health care professionals (HCP) in evaluating large sets of information and taking informed decisions during their clinical routine. CDSS are becoming particularly important in the perspective of precision medicine, when HCP need to consider growing amounts of data to create precise patient profiles for personalized diagnosis, treatment and outcome monitoring. In allergy care, several CDSS are being developed and investigated, mainly for respiratory allergic diseases. Although the proposed solutions address different stakeholders, the majority aims at facilitating evidence-based and shared decision-making, incorporating guidelines, and real-time clinical data. We offer here an overview on existing tools, new developments and novel concepts and discuss the potential of digital CDSS in improving prevention, diagnosis and monitoring of allergic diseases.

Quantifying misclassification and bias errors due to hierarchical sepsis scores in real-time sepsis diagnosis

Severe sepsis and septic shock are common causes of intensive care unit (ICU) admission, length of stay, mortality and cost. Sepsis is very difficult to diagnose in real-time and early treatment can show positive benefits. Current sepsis scores are hierarchically calculated, creating significant case-control and/or misclassification biases in developing real-time diagnostic classifiers based on real-time clinical data. This study examines the impact on kernel density classifier performance of a continuous sepsis score derived from a hierarchical score and based on current well-accepted metrics.Clinical data and model-based insulin sensitivity from 36 patients in the Christchurch Hospital ICU (6071 h) is used to segregate severe sepsis and septic shock from mild sepsis and SIRS using a modified continuous sepsis score using a kernel density estimator (KDE) method. Best case resubstitution, worst case bootstrap, and expected case .632 bootstrap estimates are calculated. Diagnostic quality is assessed using likelihood ratios (LHRs), ROC curves, diagnostic odds-ratios (DOR), sensitivity and specificity. Results were compared to those using the hierarchical sepsis score and the same KDE.The continuous sepsis score yielded smoother, more realistic time-varying trajectories for sepsis evolution. The classifier provided very good results, potentially able to change clinical decisions, but did not reach the best diagnostic accuracy for all performance metrics. Using several metrics avoided assessment bias, and likelihood ratios provided the best overall assessment relative to clinical decision making processes. Finally, quantifying infection presence is a source of case-control and misclassification bias in classifier development, showing clear need for improved sepsis scores.

Genomic Analysis of Mycobacterium abscessus Complex Isolates Collected in Ireland between 2006 and 2017.

Members of the Mycobacterium abscessus complex (MABC) are multidrug-resistant nontuberculous mycobacteria and cause opportunistic pulmonary infections in individuals with cystic fibrosis (CF). In this study, genomic analysis of MABC isolates was performed to gain greater insights into the epidemiology of circulating strains in Ireland. Whole-genome sequencing (WGS) was performed on 70 MABC isolates that had been referred to the Irish Mycobacteria Reference Laboratory between 2006 and 2017 across nine Irish health care centers. The MABC isolates studied comprised 52 isolates from 27 CF patients and 18 isolates from 10 non-CF patients. WGS identified 57 (81.4%) as M. abscessus subsp. abscessus, 10 (14.3%) as M. abscessus subsp. massiliense, and 3 (4.3%) as M. abscessus subsp. bolletii Forty-nine (94%) isolates from 25 CF patients were identified as M. abscessus subsp. abscessus, whereas 3 (6%) isolates from 2 CF patients were identified as M. abscessus subsp. massiliense Among the isolates from non-CF patients, 44% (8/18) were identified as M. abscessus subsp. abscessus, 39% (7/18) were identified as M. abscessus subsp. massiliense, and 17% (3/18) were identified as M. abscessus subsp. bolletii WGS detected two clusters of closely related M. abscessus subsp. abscessus isolates that included isolates from different CF centers. There was a greater genomic diversity of MABC isolates among the isolates from non-CF patients than among the isolates from CF patients. Although WGS failed to show direct evidence of patient-to-patient transmission among CF patients, there was a predominance of two different strains of M. abscessus subsp. abscessus Furthermore, some MABC isolates were closely related to global strains, suggesting their international spread. Future prospective real-time epidemiological and clinical data along with contemporary MABC sequence analysis may elucidate the sources and routes of transmission among patients infected with MABC.

Sputum neutrophil elastase associates with microbiota and Pseudomonas aeruginosa in bronchiectasis.

Neutrophilic inflammation is a major driver of bronchiectasis pathophysiology, and neutrophil elastase activity is the most promising biomarker evaluated in sputum to date. How active neutrophil elastase correlates with the lung microbiome in bronchiectasis is still unexplored. We aimed to understand whether active neutrophil elastase is associated with low microbial diversity and distinct microbiome characteristics. An observational, cross-sectional study was conducted at the bronchiectasis programme of the Policlinico Hospital in Milan, Italy, where adults with bronchiectasis were enrolled between March 2017 and March 2019. Active neutrophil elastase was measured on sputum collected during stable state, microbiota analysed through 16S rRNA gene sequencing, molecular assessment of respiratory pathogens carried out through real-time PCR and clinical data collected. Among 185 patients enrolled, decreasing α-diversity, evaluated through the Shannon entropy (ρ -0.37, p<0.00001) and Pielou's evenness (ρ -0.36, p<0.00001) and richness (ρ -0.33, p<0.00001), was significantly correlated with increasing elastase. A significant difference in median levels of Shannon entropy as detected between patients with neutrophil elastase ≥20 µg·mL-1 (median 3.82, interquartile range 2.20-4.96) versus neutrophil elastase <20 µg·mL-1 (4.88, 3.68-5.80; p<0.0001). A distinct microbiome was found in these two groups, mainly characterised by enrichment with Pseudomonas in the high-elastase group and with Streptococcus in the low-elastase group. Further confirmation of the association of Pseudomonas aeruginosa with elevated active neutrophil elastase was found based on standard culture and targeted real-time PCR. High levels of active neutrophil elastase are associated to low microbiome diversity and specifically to P. aeruginosa infection.

A review of ventilation in adult out-of-hospital cardiac arrest.

Out‐of‐hospital cardiac arrest continues to be a devastating condition despite advances in resuscitation care. Ensuring effective gas exchange must be weighed against the negative impact hyperventilation can have on cardiac physiology and survival. The goals of this narrative review are to evaluate the available evidence regarding the role of ventilation in out‐of‐hospital cardiac arrest resuscitation and to provide recommendations for future directions. Ensuring successful airway patency is fundamental for effective ventilation. The airway management approach should be based on professional skill level and the situation faced by rescuers. Evidence has explored the influence of different ventilation rates, tidal volumes, and strategies during out‐of‐hospital cardiac arrest; however, other modifiable factors affecting out‐of‐hospital cardiac arrest ventilation have limited supporting data. Researchers have begun to explore the impact of ventilation in adult out‐of‐hospital cardiac arrest outcomes, further stressing its importance in cardiac arrest resuscitation management. Capnography and thoracic impedance signals are used to measure ventilation rate, although these strategies have limitations. Existing technology fails to reliably measure real‐time clinical ventilation data, thereby limiting the ability to investigate optimal ventilation management. An essential step in advancing cardiac arrest care will be to develop techniques to accurately and reliably measure ventilation parameters. These devices should allow for immediate feedback for out‐of‐hospital practitioners, in a similar way to chest compression feedback. Once developed, new strategies can be established to guide out‐of‐hospital personnel on optimal ventilation practices.

Effects of Quality Control Targets (SpO2≠100%, PaCO2/10 cmH2O) on Outcomes in Patients in the ICU.

Objectives: A series of quality control (QC) targets (SpO2≠100%, PaCO2≮40 mmHg, Pmean≯10 cmH2O) was put forward and widely used in a single intensive care unit (ICU) setting. The aim of this study was to assess whether these QC targets could improve the outcomes of critically ill patients.Methods: The real-time clinical data of patients undergoing mechanical ventilation at ICU admission between May 2013 and May 2017 in the Department of Critical Care Medicine of Peking Union Medical College Hospital were collected and analyzed.Results: A total of 7,670 patients [mean age, 58 years; 3,943 (51.5%) male] were divided into the before QC (n = 3,936) and after QC (n = 3,734) groups. QC targets (SpO2, PaCO2, and Pmean) and respiratory parameters (FiO2%, PaO2, PEEP, tidal volume, and respiratory rate) within 72 h of ICU admission, primary outcomes (ICU mortality, 28-, 60-, and 90-day mortality) and secondary outcomes (discharge against medical advice, ICU admission days, mechanical ventilation times, and central venous pressure) were measured and compared between the before and after QC groups. The 72 h average of the Pmean, FiO2%, PaO2, and VT were significantly lower and PaCO2 was higher in the after QC than in the before QC group (P < 0.05). A lower 90-day mortality rate, less discharge against medical advice, fewer ICU admission days, and reduced mechanical ventilation times were found in the after QC group compared with the before QC group (P < 0.05). Interestingly, CVP was significantly lower in the after QC group than in the before QC group (P < 0.05).Conclusions: The QC targets (SpO2≠100%, PaCO2≮40 mmHg, Pmean≯10 cmH2O) contributed to avoiding high oxygen level hazards, protecting against lung injury, and improving circulatory function, which resulted in a better prognosis of critically ill patients.

Identification of Biomarkers for Osteosarcoma Based on Integration Strategy.

BackgroundOsteosarcoma (OS) is the most common primary malignant tumor of bone. The identification of novel biomarkers is necessary for the diagnosis and treatment of osteosarcoma.Material/MethodsWe obtained 11 paired fresh-frozen OS samples and normal controls from patients between September 2015 and February 2017. We used an integration strategy that analyzes next-generation sequencing data by bioinformatics methods based on the pathogenesis of osteosarcoma.ResultsOne susceptibility lncRNA and 7 susceptibility genes regulated by the lncRNA for osteosarcoma were effectively identified, and real-time PCR and clinical index ALP data were used to test their effectiveness.ConclusionsThe results showed that the expression levels of the 7 genes were highly consistent in the training and test sample sets, especially between the expression value of the gene ALPL and the plasma detection value of its encoded protein ALP. In particular, both the expression of gene ALPL and the plasma detection values of protein ALP encoded by gene ALPL showed a high degree of consistency among different data types. The identified lncRNA and genes effectively classified the samples proved so that they could be used as potential biomarkers of osteosarcoma. Our strategy may also be helpful for the identification of biomarkers for other diseases.

Bridging the Collaboration Gap: Real-time Identification of Clinical Specimens for Biomedical Research

Introduction: Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research. Methods: Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting. Results: Between June 2018 and December 2018, we identified 2992 unique specimens belonging to 2815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure E-mail notifications were sent to investigators to automatically notify of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 results per second. Conclusion: This work demonstrates that real-world clinical data can be analyzed in real time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification.

Simulation Of Pathological ECG Signal Using Transform Method

Abstract Electrocardiography (ECG) is the depiction of the hearts recorded electrical activity, obtained by attaching electrodes that are specifically designed to place at particular points of human body. The graphically represented signal has three main parts- P wave, QRS complex and thirdly T wave together giving normal ECG wave. Deviations in the normal graphical representation of ECG indicate types of cardiac disorders. An automatic system which involves MATLAB-based tool is being used to simulate synthetic ECG signals of cardiac pathology. The model of the pathological signals uses the real time clinical data of the pathologies and it is done with the help of Fourier series by mathematical modeling the ECG signal wave. This model is a useful tool that allows comparison with patient’s current ECG record collected by regular methods. Thus helps in the detection of pathologies that lead to serious cardiac arrhythmia.

Segmentation of Tumor in MRI Brain Images using Morphological Operators and Non-Local Means Filter

Brain Tumor is the abnormal development of tissues in the brain. According to survey report Times of India, 2019 around 5, 00,000 people are diagnosed with brain tumor in India. Among 5, 00,000 people 20 percent are children. Magnetic resonance image (MRI) used for clinical analysis of human body are sensitive to redundant Rician noise. Rician is the type of noise added during the acquisition of MRI. The removal of noise variance can be performed by constructing many filters. Among those filters, non-local means filter is used for denoising the Rician noise. In this project simulated MRI data and real time clinical data of T1, T2 and Proton Density weighted MRI images are de-noised and the performance metrics is analyzed using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Metric). The de-noised image is then subjected to thresholding and morphological operators and the tumor region is segmented.

Health Information Exchange: A Novel Re-linkage Intervention in an Urban Health System

BackgroundPublic health information exchanges (HIEs) link real-time surveillance and clinical data and can help to re-engage out-of-care people with HIV (PWH).MethodsWe conducted a retrospective cohort study of out-of-care PWH who generated an HIE alert in the Grady Health System (GHS) Emergency Department (ED) between January 2017 and February 2018. Alerts were generated for PWH who registered in the GHS ED without Georgia Department of Public Health (GDPH) CD4 or HIV-1 RNA in the prior 14 months. The alert triggered a social work (SW)–led re-linkage effort. Multivariate logistic regression analyses used HIE-informed SW re-linkage efforts as the independent variable, and linkage to care and 3- and 6-month viral suppression (HIV-1 RNA < 200 c/mL) as primary outcomes. Patients admitted to the hospital were excluded from primary analysis.ResultsOne hundred forty-seven out-of-care patients generated an alert. Ninety-eight were included in the primary analysis (mean age [SD], 41 ± 12 years; 70% male; 93% African American), and 20 received the HIE-informed SW intervention. Sixty percent of patients receiving the intervention linked to care in 6 months, compared with 35% who did not. Patients receiving the intervention were more likely to link to care (adjusted risk ratio [aRR], 1.63; 95% confidence interval [CI], 0.99–2.68) and no more likely to achieve viral suppression (aRR, 1.49; 95% CI, 0.50–4.46) than those who did not receive the intervention.ConclusionsAn HIE-informed, SW-led intervention systematically identified out-of-care PWH and may increase linkage to care for this important population. HIEs create an opportunity to intervene with linkage and retention strategies.

Predictive Data Analysis to Identify Heart Anomalies

Heart disease is a usually used word to describe diseases related to heart, when heart is not efficiently performing at is best, most of this disease is acquired because of unhealthy lifestyle and unhealthy food. Heart diseases need regular care to improve the patient’s quality of life. We can analyze cardiac disabilities of a individual by factors like historical health data and risk factors .The fusion of algorithms with clinical data can forecast the results of any disease so, incorporating these two things for Predicting and diagnosis of the heart functionality using the computational algorithms where the user interface in developed in R studio. Foremost objective of the system is for majorly predicting the heart anomalies collected using the real time clinical data .The proposed method uses the performance comparison of the algorithms and as well as the datasets like random forest and logistic regression to calculate which gives highest accuracy rate performance and this study also involves use of two different datasets, one which is available in the existing dataset for heart disease and another which was collected from the hospital in real time, so this can help in making an efficient system that can be utilized to predict the probability of heart diseases of any individual. Thus this can form a foundation for any therapy or treatment to be given this would increase the efficiency as well as help the medical staff and doctors to predict heart disease and more accurately. Computer diagnosis and prediction of a disease can solve many medical problems by predicting it beforehand.

39-LB: Individualised Screening for Diabetic Retinopathy: The ISDR Study—A Randomised Controlled Trial of Safety, Efficacy, and Cost-Effectiveness

Background: Varying the intervals in retinopathy screening informed by personal risk offers reallocation of resources to better target high risk groups and address the increasing prevalence of diabetes. However, safety data especially on extended intervals is minimal. Aim: To evaluate the safety, efficacy and cost effectiveness (CE) of individualized variable-interval risk-based screening in a population setting compared to usual care. Methods: Masked 2 arm, parallel assignment, equivalence RCT (largest to date in screening) with independent trials unit monitoring in people with diabetes aged ≥12 years attending screening in a single English program. Randomization was 1:1 to individualized screening (active group; 6, 12 or 24 months for high, medium and low risk) determined by a risk calculation engine using real-time local demographic, retinal and clinical data, compared with annual screening (control). CE analysis measuring NHS and societal costs took a 2-year time horizon. Findings: 4534 participants entered the study - after withdrawals/loss to follow-up: active 2097; control 2224. Attendance rates at first follow-up visit (primary outcome, safety) were equivalent (per protocol analysis, 5% margin): active 83.6% control 84.7% (difference 1.0, 90% CI -0.8, 2.9). STDR detection rates were non-inferior (1.5% margin): active 1.43% control 1.71% (difference -0.28, CI -0.93, 0.36). Quality of life (EQ5D5L, HUI3) was not significantly different between the groups. Incremental cost saving per person was £18.75 (NHS cost) rising to £49.96 with societal costs. A 39.3% reduction in number of appointments was seen. Conclusions: All parties involved in diabetes care can be reassured that extended and personalized screening intervals can safely be introduced in established screening program. Scale-up with further validation outside a research setting is recommended. Views expressed are solely those of the authors. Disclosure D.M. Broadbent: None. A. Wang: None. C.P. Cheyne: None. J.G. Lathe: None. I.M. Stratton: Consultant; Self; Novo Nordisk A/S. Research Support; Self; Bayer AG, Boehringer Ingelheim International GmbH. J. Vora: None. M. Garcia-Finana: None. M. James: None. S.P. Harding: None. Funding UK National Institute for Health Research

Minimising hospitalisation with telehealth technology

New technologies make it possible to remotely monitor patients, allowing them to stay in their home for longer and enabling clinicians to react to real-time clinical data. Christopher Turner outlines the possibilities of these technologies.