Real-world effectiveness of airway clearance techniques in children with cystic fibrosis.

BACKGROUND: Cystic fibrosis (CF) is commonly characterised by thick respiratory mucus. From diagnosis, people with CF are prescribed daily physiotherapy, including airway clearance techniques (ACTs). ACTs consume a large proportion of treatment time, yet the efficacy and effectiveness of ACTs are poorly understood. This study aimed to evaluate associations between the quality and quantity of ACTs and lung function in children and young people with CF. METHODS: Project Fizzyo, a longitudinal observational cohort study in the UK, used remote monitoring with electronic pressure sensors attached to four different commercial ACT devices to record real-time, breath-by-breath pressure data during usual ACTs undertaken at home over 16 months in 145 children. ACTs were categorised either as conformant or not with current ACT recommendations based on breath pressure and length measurements, or as missed treatments if not recorded. Daily, weekly and monthly associations between ACT category and lung function were investigated using linear mixed effects regression models adjusting for clinical confounders. RESULTS: After exclusions, 45 224 ACT treatments (135 individuals) and 21 069 days without treatments (141 individuals) were analysed. The mean±sd age of participants was 10.2±2.9 years. Conformant ACTs (21%) had significantly higher forced expiratory volume in 1 s (FEV1) (mean effect size 0.23 (95% CI 0.19-0.27) FEV1 % pred per treatment) than non-conformant (79%) or missed treatments. There was no benefit from non-conformant or missed treatments and no significant difference in FEV1 between them (mean effect size 0.02 (95% CI -0.01-0.05) FEV1 % pred per treatment). CONCLUSIONS: ACTs are beneficial when performed as recommended, but most people use techniques that do not improve lung function. Work is needed to monitor and improve ACT quality and to increase the proportion of people doing effective airway clearance at home.

Exploring flexible polynomial regression as a method to align routine clinical outcomes with daily data capture through remote technologies.

BACKGROUND: Clinical outcomes are normally captured less frequently than data from remote technologies, leaving a disparity in volumes of data from these different sources. To align these data, flexible polynomial regression was investigated to estimate personalised trends for a continuous outcome over time. METHODS: Using electronic health records, flexible polynomial regression models inclusive of a 1st up to a 4th order were calculated to predict forced expiratory volume in 1 s (FEV1) over time in children with cystic fibrosis. The model with the lowest AIC for each individual was selected as the best fit. The optimal parameters for using flexible polynomials were investigated by comparing the measured FEV1 values to the values given by the individualised polynomial. RESULTS: There were 8,549 FEV1 measurements from 267 individuals. For individuals with > 15 measurements (n = 178), the polynomial predictions worked well; however, with < 15 measurements (n = 89), the polynomial models were conditional on the number of measurements and time between measurements. The method was validated using BMI in the same population of children. CONCLUSION: Flexible polynomials can be used to extrapolate clinical outcome measures at frequent time intervals to align with daily data captured through remote technologies.

Additive pre-diagnostic and diagnostic value of routine blood-based biomarkers in the detection of colorectal cancer in the UK Biobank cohort.

Survival rates from colorectal cancer (CRC) are drastically higher if the disease is detected and treated earlier. Current screening guidelines involve stool-based tests and colonoscopies, whose acceptability and uptake remains low. Routinely collected blood-based biomarkers may offer a low-cost alternative or aid for detecting CRC. Here we aimed to evaluate the pre-diagnostic and diagnostic value of a wide-range of multimodal biomarkers in the UK Biobank dataset, including sociodemographic, lifestyle, medical, physical, and blood and urine-based measures in detecting CRC. We performed a Cox proportional hazard and a tree-boosting model alongside feature selection methods to determine optimal combination of biomarkers. In addition to the modifiable lifestyle factors of obesity, alcohol consumption and cardiovascular health, we showed that blood-based biomarkers that capture the immune response, lipid profile, liver and kidney function are associated with CRC risk. Following feature selection, the final Cox and tree-boosting models achieved a C-index of 0.67 and an AUC of 0.76 respectively. We show that blood-based biomarkers collected in routine examinations are sensitive to preclinical and clinical CRC. They may provide an additive value and improve diagnostic accuracy of current screening tools at no additional cost and help reduce burden on the healthcare system.

Quantity and quality of airway clearance in children and young people with cystic fibrosis.

Children and young people with CF (CYPwCF) get advice about using positive expiratory pressure (PEP) or oscillating PEP (OPEP) devices to clear sticky mucus from their lungs. However, little is known about the quantity (number of treatments, breaths, or sets) or quality (breath pressures and lengths) of these daily airway clearance techniques (ACTs) undertaken at home. This study used electronic pressure sensors to record real time breath-by-breath data from 145 CYPwCF (6-16y) during routine ACTs over 2 months. ACT quantity and quality were benchmarked against individual prescriptions and accepted recommendations for device use. In total 742,084 breaths from 9,081 treatments were recorded. Individual CYPwCF maintained consistent patterns of ACT quantity and quality over time. Overall, 60% of CYPwCF did at least half their prescribed treatments, while 27% did fewer than a quarter. About 77% of pre-teens did the right number of daily treatments compared with only 56% of teenagers. CYPwCF usually did the right number of breaths. ACT quality (recommended breath length and pressure) varied between participants and depended on device. Breath pressures, lengths and pressure-length relationships were significantly different between ACT devices. PEP devices encouraged longer breaths with lower pressures, while OPEP devices encouraged shorter breaths with higher pressures. More breaths per treatment were within advised ranges for both pressure and length using PEP (30-31%) than OPEP devices (1-3%). Objective measures of quantity and quality may help to optimise ACT device selection and support CYPwCF to do regular effective ACTs.

Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders.

Oral cancer is the most common type of head and neck cancer worldwide, leading to approximately 177,757 deaths every year. When identified at early stages, oral cancers can achieve survival rates of up to 75-90%. However, the majority of the cases are diagnosed at an advanced stage mainly due to the lack of public awareness about oral cancer signs and the delays in referrals to oral cancer specialists. As early detection and treatment remain to be the most effective measures in improving oral cancer outcomes, the development of vision-based adjunctive technologies that can detect oral potentially malignant disorders (OPMDs), which carry a risk of cancer development, present significant opportunities for the oral cancer screening process. In this study, we explored the potential applications of computer vision techniques in the oral cancer domain within the scope of photographic images and investigated the prospects of an automated system for detecting OPMD. Exploiting the advancements in deep learning, a two-stage model was proposed to detect oral lesions with a detector network and classify the detected region into three categories (benign, OPMD, carcinoma) with a second-stage classifier network. Our preliminary results demonstrate the feasibility of deep learning-based approaches for the automated detection and classification of oral lesions in real time. The proposed model offers great potential as a low-cost and non-invasive tool that can support screening processes and improve detection of OPMD.

Regulation of aldosterone secretion by Cav1.3.

Aldosterone-producing adenomas (APAs) vary in phenotype and genotype. Zona glomerulosa (ZG)-like APAs frequently have mutations of an L-type calcium channel (LTCC) CaV1.3. Using a novel antagonist of CaV1.3, compound 8, we investigated the role of CaV1.3 on steroidogenesis in the human adrenocortical cell line, H295R, and in primary human adrenal cells. This investigational drug was compared with the common antihypertensive drug nifedipine, which has 4.5-fold selectivity for the vascular LTCC, CaV1.2, over CaV1.3. In H295R cells transfected with wild-type or mutant CaV1.3 channels, the latter produced more aldosterone than wild-type, which was ameliorated by 100 µM of compound 8. In primary adrenal and non-transfected H295R cells, compound 8 decreased aldosterone production similar to high concentration of nifedipine (100 µM). Selective CaV1.3 blockade may offer a novel way of treating primary hyperaldosteronism, which avoids the vascular side effects of CaV1.2-blockade, and provides targeted treatment for ZG-like APAs with mutations of CaV1.3.