Current analysis of circulating tumor cells (CTCs) is hindered by sub-optimal sensitivity and specificity of devices or assays as well as lack of capability of characterization of CTCs with clinical ...biomarkers. Here, we validate a novel technology to enrich and characterize CTCs from blood samples of patients with metastatic breast, prostate and colorectal cancers using a microfluidic chip which is processed by using an automated staining and scanning system from sample preparation to image processing. The Celsee system allowed for the detection of CTCs with apparent high sensitivity and specificity (94% sensitivity and 100% specificity). Moreover, the system facilitated rapid capture of CTCs from blood samples and also allowed for downstream characterization of the captured cells by immunohistochemistry, DNA and mRNA fluorescence in-situ hybridization (FISH). In a subset of patients with prostate cancer we compared the technology with a FDA-approved CTC device, CellSearch and found a higher degree of sensitivity with the Celsee instrument. In conclusion, the integrated Celsee system represents a promising CTC technology for enumeration and molecular characterization.
Long-Term Results of the RAPCO Trials Buxton, Brian F; Hayward, Philip A; Raman, Jai ...
Circulation (New York, N.Y.),
2020-October-06, Letnik:
142, Številka:
14
Journal Article
Recenzirano
Odprti dostop
An internal thoracic artery graft to the left anterior descending artery is standard in coronary bypass surgery, but controversy exists on the best second conduit. The RAPCO trials (Radial Artery ...Patency and Clinical Outcomes) were designed to compare the long-term patency of the radial artery (RA) with that of the right internal thoracic artery (RITA) and the saphenous vein (SV).
In RAPCO-RITA (the RITA versus RA arm of the RAPCO trial), 394 patients <70 years of age (or <60 years of age if they had diabetes mellitus) were randomized to receive RA or free RITA graft on the second most important coronary target. In RAPCO-SV (the SV versus RA arm of the RAPCO trial), 225 patients ≥70 years of age (or ≥60 years of age if they had diabetes mellitus) were randomized to receive RA or SV graft. The primary outcome was 10-year graft failure. Long-term mortality was a nonpowered coprimary end point. The main analysis was by intention to treat.
In the RA versus RITA comparison, the estimated 10-year patency was 89% for RA versus 80% for free RITA (hazard ratio for graft failure, 0.45 95% CI, 0.23-0.88). Ten-year patient survival estimate was 90.9% in the RA arm versus 83.7% in the RITA arm (hazard ratio for mortality, 0.53 95% CI, 0.30-0.95). In the RA versus SV comparison, the estimated 10-year patency was 85% for the RA versus 71% for the SV (hazard ratio for graft failure, 0.40 95% CI, 0.15-1.00), and 10-year patient survival estimate was 72.6% for the RA group versus 65.2% for the SV group (hazard ratio for mortality, 0.76 95% CI, 0.47-1.22).
The 10-year patency rate of the RA is significantly higher than that of the free RITA and better than that of the SV. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00475488.
Inadequacy of spatial soil information is one of the limiting factors to making evidence-based decisions to improve food security and land management in the developing countries. Various digital soil ...mapping (DSM) techniques have been applied in many parts of the world to improve availability and usability of soil data, but less has been done in Africa, particularly in Tanzania and at the scale necessary to make farm management decisions. The Kilombero Valley has been identified for intensified rice production. However the valley lacks detailed and up-to-date soil information for decision-making. The overall objective of this study was to develop a predictive soil map of a portion of Kilombero Valley using DSM techniques. Two widely used decision tree algorithms and three sources of Digital Elevation Models (DEMs) were evaluated for their predictive ability. Firstly, a numerical classification was performed on the collected soil profile data to arrive at soil taxa. Secondly, the derived taxa were spatially predicted and mapped following SCORPAN framework using Random Forest (RF) and J48 machine learning algorithms. Datasets to train the model were derived from legacy soil map, RapidEye satellite image and three DEMs: 1 arc SRTM, 30m ASTER, and 12m WorldDEM. Separate predictive models were built using each DEM source. Mapping showed that RF was less sensitive to the training set sampling intensity. Results also showed that predictions of soil taxa using 1 arc SRTM and 12m WordDEM were identical. We suggest the use of RF algorithm and the freely available SRTM DEM combination for mapping the soils for the whole Kilombero Valley. This combination can be tested and applied in other areas which have relatively flat terrain like the Kilombero Valley.
•RF is less sensitive to training set sampling intensity than J48 algorithms.•Soil taxa predictions from 1 arc SRTM and 12m WordDEM are identical.•RF and SRTM combination is suggested to predict soil taxa in Kilombero valley.
Surveillance endoscopy is recommended after endoscopic eradication therapy (EET) for Barrett’s esophagus (BE) because of the risk of recurrence. Currently recommended biopsy protocols are based on ...expert opinion and consist of sampling visible lesions followed by random 4-quadrant biopsy sampling throughout the length of the original BE segment. Despite this protocol, some recurrences are not visibly identified. We aimed to identify the anatomic location and histology of recurrences after successful EET with the goal of developing a more efficient and evidence-based surveillance biopsy protocol.
We performed an analysis of a large multicenter database of 443 patients who underwent EET and achieved complete eradication of intestinal metaplasia (CE-IM) from 2005 to 2015. The endoscopic location of recurrence relative to the squamocolumnar junction (SCJ), visible recurrence identified during surveillance endoscopy, and time to recurrence after CE-IM were assessed.
Fifty patients with BE recurrence were studied in the final analysis. Seventeen patients (34%) had nonvisible recurrences. In this group, biopsy specimens demonstrating recurrence were taken from within 2 cm of the SCJ in 16 of these 17 patients (94%). Overall, 49 of 50 recurrences (98%) occurred either within 2 cm of the SCJ or at the site of a visible lesion. Late recurrences (>1 year) were more likely to be visible than early (<1 year) recurrences (P = .006).
Recurrence after EET detected by random biopsy sampling is identified predominately in the distal esophagus and occurs earlier than visible recurrences. As such, we suggest a modified biopsy protocol with targeted sampling of visible lesions followed by random biopsy sampling within 2 cm of the SCJ to optimize detection of recurrence after EET. (Clinical trial registration number: NCT02634645.)
Display omitted
Mobile Health (mHealth) has the potential to be transformative in the management of chronic conditions. Machine learning can leverage self-reported data collected with apps to predict periods of ...increased health risk, alert users, and signpost interventions. Despite this, mHealth must balance the treatment burden of frequent self-reporting and predictive performance and safety. Here we report how user engagement with a widely used and clinically validated mHealth app, myCOPD (designed for the self-management of Chronic Obstructive Pulmonary Disease), directly impacts the performance of a machine learning model predicting an acute worsening of condition (i.e., exacerbations). We classify how users typically engage with myCOPD, finding that 60.3% of users engage frequently, however, less frequent users can show transitional engagement (18.4%), becoming more engaged immediately ( < 21 days) before exacerbating. Machine learning performed better for users who engaged the most, however, this performance decrease can be mostly offset for less frequent users who engage more near exacerbation. We conduct interviews and focus groups with myCOPD users, highlighting digital diaries and disease acuity as key factors for engagement. Users of mHealth can feel overburdened when self-reporting data necessary for predictive modelling and confidence of recognising exacerbations is a significant barrier to accurate self-reported data. We demonstrate that users of mHealth should be encouraged to engage when they notice changes to their condition (rather than clinically defined symptoms) to achieve data that is still predictive for machine learning, while reducing the likelihood of disengagement through desensitisation.
Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging the data collected by these apps in prognostic models could ...provide increased personalization of care and reduce the burden of care for people who live with chronic conditions. This study evaluated the predictive ability of prognostic models for the prediction of acute exacerbation events in people with chronic obstructive pulmonary disease by using data self-reported to a digital health app.
The aim of this study was to evaluate if data self-reported to a digital health app can be used to predict acute exacerbation events in the near future.
This is a retrospective study evaluating the use of symptom and chronic obstructive pulmonary disease assessment test data self-reported to a digital health app (myCOPD) in predicting acute exacerbation events. We include data from 2374 patients who made 68,139 self-reports. We evaluated the degree to which the different variables self-reported to the app are predictive of exacerbation events and developed both heuristic and machine learning models to predict whether the patient will report an exacerbation event within 3 days of self-reporting to the app. The model's predictive ability was evaluated based on self-reports from an independent set of patients.
Users self-reported symptoms, and standard chronic obstructive pulmonary disease assessment tests displayed correlation with future exacerbation events. Both a baseline model (area under the receiver operating characteristic curve AUROC 0.655, 95% CI 0.689-0.676) and a machine learning model (AUROC 0.727, 95% CI 0.720-0.735) showed moderate ability in predicting exacerbation events, occurring within 3 days of a given self-report. Although the baseline model obtained a fixed sensitivity and specificity of 0.551 (95% CI 0.508-0.596) and 0.759 (95% CI 0.752-0.767) respectively, the sensitivity and specificity of the machine learning model can be tuned by dichotomizing the continuous predictions it provides with different thresholds.
Data self-reported to health care apps designed to remotely monitor patients with chronic obstructive pulmonary disease can be used to predict acute exacerbation events with moderate performance. This could increase personalization of care by allowing preemptive action to be taken to mitigate the risk of future exacerbation events.
Background Recognising the power of data analytics, researchers are anxious to gain access to personal data either directly from data subjects or via research data sets. This requires a secure ...environment, such as a trusted research environment (TRE). However, it is unclear how the data subjects themselves regard sharing their data with TREs, especially if research goals are difficult to specify upfront or data are used for secondary purposes, making informed consent difficult to manage. We review three empirical studies to throw some light on individual attitudes to sharing health data. Methods Three anonymous, online surveys were run. The first involving 800 UK residents aimed at understanding how participants view the health data security. The second involving 500 UK residents aimed at identifying private individual views on privacy. These two surveys used a crowdsourcing platform. The third involved 1086 students at a UK university reporting their engagement with a trial diagnostic method for SARS-CoV-2. Results The first survey demonstrated that private individuals could make security decisions though they usually assume the recipient of their personal data to be responsible for all aspects of keeping the data safe. The second highlighted that individuals were aware of privacy risks but are motivated to share their data based on different contextual assumptions. The third, involving the incidental sharing of sensitive data during the SARS-CoV-2 pilot highlighted that prosocial motivations override potential personal benefit of such testing. Conclusions The three, unconnected surveys make clear that there are tensions between private individual understanding of data security and privacy risk, on the one hand, and how they behave, on the other. Respondents rely on data stewards to keep their data safe, though are likely to share even sensitive data for prosocial benefit. These findings have implications for those offering TRE services for research.
IntroductionDiseases addressed by surgical, obstetric, trauma and anaesthesia (SOTA) care are rising globally due to an anticipated rise in the burden of non-communicable diseases and road traffic ...accidents. Low- and middle-income countries (LMICs) disproportionately bear the brunt. Evidence-based policies and political commitment are required to reverse this trend. The Lancet Commission of Global Surgery proposed National Surgical and Obstetric and Anaesthesia Plans (NSOAPs) to alleviate the respective SOTA burdens in LMICs. NSOAPs success leverages comprehensive stakeholder engagement and appropriate health policy analyses and recommendations. As Uganda embarks on its NSOAP development, policy prioritisation in Uganda remains unexplored. We, therefore, seek to determine the priority given to SOTA care in Uganda’s healthcare policy and systems-relevant documents.Methods and analysisWe will conduct a scoping review of SOTA health policy and system-relevant documents produced between 2000 and 2022 using the Arksey and O’Malley methodological framework and additional guidance from the Joanna Briggs Institute Reviewer’s manual. These documents will be sought from the websites of SOTA stakeholders by hand searching. We shall also search from Google Scholar and PubMed using well-defined search strategies. The Knowledge Management Portal for the Ugandan Ministry of Health, which was created to provide evidence-based decision-making data, is the primary source. The rest of the sources will include the following: other repositories like websites of relevant government institutions, international and national non-governmental organisations, professional associations and councils, and religious and medical bureaus. Data retrieved from the eligible policy and decision-making documents will include the year of publication, the global surgery specialty mentioned, the NSOAP surgical system domain, the national priority area involved and funding. The data will be collected in a preformed extraction sheet. Two independent reviewers will screen the collected data, and results will be presented as counts and their respective proportions. The findings will be reported narratively using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews.Ethics and disseminationThis study will generate evidence-based information on the state of SOTA care in Uganda’s health policy, which will inform NSOAP development in this nation. The review’s findings will be presented to the Ministry of Health planning task force. The study will also be disseminated through a peer-reviewed publication; oral and poster presentations at local, regional, national and international conferences and over social media.
Abstract
Turning the wealth of health and social data into insights to promote better public health, while enabling more effective personalized care, is critically important for society. In ...particular, social determinants of health have a significant impact on individual health, well-being, and inequalities in health. However, concerns around accessing and processing such sensitive data, and linking different datasets, involve significant challenges, not least to demonstrate trustworthiness to all stakeholders. Emerging
datatrust services
provide an opportunity to address key barriers to health and social care data linkage schemes, specifically a loss of control experienced by data providers, including the difficulty to maintain a remote reidentification risk over time, and the challenge of establishing and maintaining a social license.
Datatrust services
are a sociotechnical evolution that advances databases and data management systems, and brings together stakeholder-sensitive data governance mechanisms with data services to create a trusted research environment. In this article, we explore the requirements for
datatrust services
, a proposed implementation—the Social Data Foundation, and an illustrative test case. Moving forward, such an approach would help incentivize, accelerate, and join up the sharing of regulated data, and the use of generated outputs safely amongst stakeholders, including healthcare providers, social care providers, researchers, public health authorities, and citizens.
The health-related quality of life (HRQoL) is an important treatment goal that could serve as low-cost prognostication tool in resource poor settings. We sought to validate the Kansas City ...Cardiomyopathy Questionnaire (KCCQ) and evaluate its use as a predictor of 3 months all-cause mortality among heart failure participants in rural Uganda.
The Mbarara Heart Failure Registry Cohort study observes heart failure patients during hospital stay and in the community in rural Uganda. Participants completed health failure evaluations and HRQoL questionnaires at enrollment, 1 and 3 months of follow-up. We used Cronbach's alpha coefficients to define internal consistency, intraclass correlation coefficients as a reliability coefficient, and Cox proportional hazard models to predict the risk of 3 months all-cause mortality.
Among the 195 participants who completed HRQoL questionnaires, the mean age was 52 (standard deviation (SD) 21.4) years, 68% were women and 29% reported history of hypertension. The KCCQ had excellent internal consistency (87% Cronbach alpha) but poor reliability. Independent predictors of all-cause mortality within 3 months included: worse overall KCCQ score (Adjusted Hazard ratio (AHR) 2.9, 95% confidence interval (CI) 1.1, 8.1), highest asset ownership (AHR 3.6, 95% CI 1.2, 10.8), alcoholic drinks per sitting (AHR per 1 drink 1.4, 95% CI 1.0, 1.9), New York Heart Association (NYHA) functional class IV heart failure (AHR 2.6, 95% CI 1.3, 5.4), estimated glomerular filtration rate (eGFR) 30 to 59 ml/min/1.73 m
(AHR 3.4, 95% CI 1.1, 10.8), and eGFR less than 15 ml/min/1.73 m
(AHR 2.7, 95% CI 1.0, 7.1), each 1 pg/mL increase in Brain Natriuretic Peptide (BNP) (AHR, 1.0, 95% CI 1.0, 1.0), and each 1 ng/mL increase in Creatine-Kinase MB isomer (CKMB) (AHR 1.0, 95% CI 1.0, 1.1).
The KCCQ showed excellent internal consistency. Worse overall KCCQ score, highest asset ownership, increasing alcoholic drink per sitting, NYHA class IV, decreased estimated glomerular filtration rate, BNP, and CKMB predicted all-cause mortality at 3 months. The KCCQ could be an additional low-cost tool to aid in the prognostication of acute heart failure patients.
PDF
