Enhanced detection in CT colonography using adaptive diffusion filtering

Computer-aided detection (CAD) is a computerized procedure in medical science that supports the medical team’s interpretations and decisions. CAD uses information from a medical imaging modality such as Computed Tomography to detect suspicious lesions. Algorithms to detect these lesions are based on geometrical models which can describe the local structures and thus provide potential region candidates. Geometrical descriptive models are very dependent on the data quality which may affect the false positive rates in CAD. In this paper we propose an efficient adaptive diffusion technique that adaptively controls the diffusion flux of the local structures in the data using robust statistics. The proposed method acts isotropically in the homogeneous regions and anisotropically in the vicinity of jump discontinuities. This method structurally enhances the data and makes the geometrical descriptive models robust. For the iterative solver, we use an efficient gradient descent flows solver based on a PDE formulation of the problem. The whole proposed strategy, which makes use of adaptive diffusion filter coupled with gradient descent flows has been developed and evaluated on clinical data in the application to colonic polyp detection in Computed Tomography Colonoscopy

Evaluation of the reported data linkage process and associated quality issues for linked routinely collected healthcare data in multimorbidity research: a systematic methodology review

Objective The objective of this systematic review was to examine how the record linkage process is reported in multimorbidity research. Methods A systematic search was conducted in Medline, Web of Science and Embase using predefined search terms, and inclusion and exclusion criteria. Published studies from 2010 to 2020 using linked routinely collected data for multimorbidity research were included. Information was extracted on how the linkage process was reported, which conditions were studied together, which data sources were used, as well as challenges encountered during the linkage process or with the linked dataset. Results Twenty studies were included. Fourteen studies received the linked dataset from a trusted third party. Eight studies reported variables used for the data linkage, while only two studies reported conducting prelinkage checks. The quality of the linkage was only reported by three studies, where two reported linkage rate and one raw linkage figures. Only one study checked for bias by comparing patient characteristics of linked and non-linked records. Conclusions The linkage process was poorly reported in multimorbidity research, even though this might introduce bias and potentially lead to inaccurate inferences drawn from the results. There is therefore a need for increased awareness of linkage bias and transparency of the linkage processes, which could be achieved through better adherence to reporting guidelines. PROSPERO registration number CRD42021243188

Early diagnostic suggestions improve accuracy of GPs:a randomised controlled trial using computer-simulated patients

Background: Designers of computerised diagnostic support systems (CDSSs) expect physicians to notice when they need advice and enter into the CDSS all information that they have gathered about the patient. The poor use of CDSSs and the tendency not to follow advice once a leading diagnosis emerges would question this expectation.Aim: To determine whether providing GPs with diagnoses to consider before they start testing hypotheses improves accuracy.Design and setting: Mixed factorial design, where 297 GPs diagnosed nine patient cases, differing in difficulty, in one of three experimental conditions: control, early support, or late support.Method: Data were collected over the internet. After reading some initial information about the patient and the reason for encounter, GPs requested further information for diagnosis and management. Those receiving early support were shown a list of possible diagnoses before gathering further information. In late support, GPs first gave a diagnosis and were then shown which other diagnoses they could still not discount.Results: Early support significantly improved diagnostic accuracy over control (odds ratio [OR] 1.31; 95% confidence interval [95%CI] = 1.03 to 1.66, P = 0.027), while late support did not (OR 1.10; 95% CI = 0.88 to 1.37). An absolute improvement of 6% with early support was obtained. There was no significant interaction with case difficulty and no effect of GP experience on accuracy. No differences in information search were detected between experimental conditions.Conclusion: Reminding GPs of diagnoses to consider before they start testing hypotheses can improve diagnostic accuracy irrespective of case difficulty, without lengthening information search

Desensitisation to cigarette package graphic health warnings:a cohort comparison between London and Singapore

OBJECTIVES: We compared 2 sociocultural cohorts with different duration of exposure to graphic health warning labels (GHWL), to investigate a possible desensitisation to their use. We further studied how a differing awareness and emotional impact of smoking-associated risks could be used to prevent this. SETTING: Structured interviews of patients from the general respiratory department were undertaken between 2012 and 2013 in 2 tertiary hospitals in Singapore and London. PARTICIPANTS: 266 participants were studied, 163 Londoners (35% smokers, 54% male, age 52±18 years) and 103 Singaporeans (53% smokers, p=0.003; 78% male, p<0.001; age 58±15 years, p=0.012). MAIN OUTCOMES AND MEASURES: 50 items assessed demographics, smoking history, knowledge and the deterring impact of smoking-associated risks. After showing 10 GHWL, the impact on emotional response, cognitive processing and intended smoking behaviour was recorded. RESULTS: Singaporeans scored lower than the Londoners across all label processing constructs, and this was consistent for the smoking and non-smoking groups. Londoners experienced more ‘disgust’ and felt GHWL were more effective at preventing initiation of, or quitting, smoking. Singaporeans had a lower awareness of lung cancer (82% vs 96%, p<0.001), despite ranking it as the most deterring consequence of smoking. Overall, ‘blindness’ was the least known potential risk (28%), despite being ranked as more deterring than ‘stroke’ and ‘oral cancer’ in all participants. CONCLUSIONS: The length of exposure to GHWL impacts on the effectiveness. However, acknowledging the different levels of awareness and emotional impact of smoking-associated risks within different sociocultural cohorts could be used to maintain their impact

Exercise rehabilitation following intensive care unit discharge for recovery from critical illness:executive summary of a Cochrane Collaboration systematic review

Skeletal muscle wasting and weakness are major complications of critical illness and underlie the profound physical and func-\ud tional impairments experienced by survivors after discharge from the intensive care unit (ICU). Exercise-based rehabilitation\ud has been shown to be bene\ud fi\ud cial when delivered during ICU admission. This review aimed to determine the effectiveness of\ud exercise rehabilitation initiated after ICU discharge on primary outcomes of functional exercise capacity and health-related\ud quality of life. We sought randomized controlled trials, quasi-randomized controlled trials, and controlled clinical trials compar-\ud ing an exercise intervention commenced after ICU discharge vs. any other intervention or a control or\ud ‘\ud usual care\ud ’\ud programme\ud in adult survivors of critical illness. Cochrane Central Register of Controlled Trials, Medical Literature Analysis and Retrieval Sys-\ud tem Online (MEDLINE), Excerpta Medica Database, and Cumulative Index to Nursing and Allied Health Literature databases\ud were searched up to February 2015. Dual, independent screening of results, data extraction, and quality appraisal were per-\ud formed. We included six trials involving 483 patients. Overall quality of evidence for both outcomes was very low. All studies\ud evaluated functional exercise capacity, with three reporting positive effects in favour of the intervention. Only two studies\ud evaluated health-related quality of life and neither reported differences between intervention and control groups. Meta-\ud analyses of data were precluded due to variation in study design, types of interventions, and selection and reporting of out-\ud come measurements. We were unable to determine an overall effect on functional exercise capacity or health-related quality\ud of life of interventions initiated after ICU discharge for survivors of critical illness. Findings from ongoing studies are awaited.\ud Future studies need to address methodological aspects of study design and conduct to enhance rigour, quality, and synthesis

A systematic review of machine learning models for predicting outcomes of stroke with structured data

Background and purposeMachine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke.MethodsWe searched PubMed and Web of Science from 1990 to March 2019, using previously published search filters for stroke, ML, and prediction models. We focused on structured clinical data, excluding image and text analysis. This review was registered with PROSPERO (CRD42019127154).ResultsEighteen studies were eligible for inclusion. Most studies reported less than half of the terms in the reporting quality checklist. The most frequently predicted stroke outcomes were mortality (7 studies) and functional outcome (5 studies). The most commonly used ML methods were random forests (9 studies), support vector machines (8 studies), decision trees (6 studies), and neural networks (6 studies). The median sample size was 475 (range 70-3184), with a median of 22 predictors (range 4-152) considered. All studies evaluated discrimination with thirteen using area under the ROC curve whilst calibration was assessed in three. Two studies performed external validation. None described the final model sufficiently well to reproduce it.ConclusionsThe use of ML for predicting stroke outcomes is increasing. However, few met basic reporting standards for clinical prediction tools and none made their models available in a way which could be used or evaluated. Major improvements in ML study conduct and reporting are needed before it can meaningfully be considered for practice

Development and validation of a casemix classification to predict costs of specialist palliative care provision across inpatient hospice, hospital and community settings in the UK: a study protocol

Introduction Provision of palliative care is inequitable with wide variations across conditions and settings in the UK. Lack of a standard way to classify by case complexity is one of the principle obstacles to addressing this. We aim to develop and validate a casemix classification to support the prediction of costs of specialist palliative care provision.Methods and analysis Phase I: A cohort study to determine the variables and potential classes to be included in a casemix classification. Data are collected from clinicians in palliative care services across inpatient hospice, hospital and community settings on: patient demographics, potential complexity/casemix criteria and patient-level resource use. Cost predictors are derived using multivariate regression and then incorporated into a classification using classification and regression trees. Internal validation will be conducted by bootstrapping to quantify any optimism in the predictive performance (calibration and discrimination) of the developed classification. Phase II: A mixed-methods cohort study across settings for external validation of the classification developed in phase I. Patient and family caregiver data will be collected longitudinally on demographics, potential complexity/casemix criteria and patient-level resource use. This will be triangulated with data collected from clinicians on potential complexity/casemix criteria and patient-level resource use, and with qualitative interviews with patients and caregivers about care provision across difference settings. The classification will be refined on the basis of its performance in the validation data set.Ethics and dissemination The study has been approved by the National Health Service Health Research Authority Research Ethics Committee. The results are expected to be disseminated in 2018 through papers for publication in major palliative care journals; policy briefs for clinicians, commissioning leads and policy makers; and lay summaries for patients and public

The impact of outpatient <i>versus</i> inpatient management on health-related quality of life outcomes for patients with malignant pleural effusion: the OPTIMUM randomised clinical trial

Background: The principal aim of malignant pleural effusion (MPE) management is to improve health-related quality of life (HRQoL) and symptoms.Methods: In this open-label randomised controlled trial, patients with symptomatic MPE were randomly assigned to either indwelling pleural catheter (IPC) insertion with the option of talc pleurodesis or chest drain and talc pleurodesis. The primary end-point was global health status, measured with the 30-item European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC QLQ-C30) at 30 days post-intervention. 142 participants were enrolled from July 2015 to December 2019.Results: Of participants randomly assigned to the IPC (n=70) and chest drain (n=72) groups, primary outcome data were available in 58 and 56 patients, respectively. Global health status improved in both groups at day 30 compared with baseline: IPC (mean difference 13.11; p=0.001) and chest drain (mean difference 10.11; p=0.001). However, there was no significant between-group difference at day 30 (mean intergroup difference in baseline-adjusted global health status 2.06, 95% CI −5.86–9.99; p=0.61), day 60 or day 90. No significant differences were identified between groups in breathlessness and chest pain scores. All chest drain arm patients were admitted (median length of stay 4 days); seven patients in the IPC arm required intervention-related hospitalisation.Conclusions: While HRQoL significantly improved in both groups, there were no differences in patient-reported global health status at 30 days. The outpatient pathway using an IPC was not superior to inpatient treatment with a chest drain