Validity and interpretation of spirometric recordings to diagnose COPD in UK primary care.

BACKGROUND: The diagnosis of COPD is dependent upon clinical judgment and confirmation of the presence of airflow obstruction using spirometry. Spirometry is now routinely available; however, spirometry incorrectly performed or interpreted can lead to misdiagnosis. We aimed to determine whether spirometry undertaken in primary care for patients suspected to have COPD was of sufficient quality and whether their spirometry was correctly interpreted. METHODS: Two chest physicians re-read all spirometric readings for both quality of the procedure and interpretation, received as a part of COPD validation studies using data from the Clinical Practice Research Datalink (CPRD). We then used logistic regression to investigate predictors of correct interpretation. RESULTS: Spirometry traces were obtained for 306 patients, of which 221 (72.2%) were conducted in primary care. Of those conducted in primary care, 98.6% (n=218) of spirometry traces were of adequate quality. Of those traces that were of adequate quality and conducted in primary care, and in whom a general practitioner (GP) diagnosis of COPD had been made, 72.5% (n=218) were consistent with obstruction. Historical records for asthma diagnosis significantly decreased odds of correct interpretation. CONCLUSION: The quality of the spirometry procedure undertaken in primary care is high. However, this was not reflected in the quality of interpretation, suggesting an unmet training in primary care. The quality of the spirometry procedure as demonstrated by spirometric tracings provides a re-assurance for the use of spirometric values available in the electronic health care record databases for research purposes

Estimating Soil Hydraulic Properties from Infrared Measurements of Soil Surface Temperatures and TDR Data

The spatiotemporal development of soil surface temperatures (SST) depends on water availability in the near-surface soil layer. Because the soil loses latent heat during evaporation and water available for evaporation depends on soil hydraulic properties (SHP), the temporal variability of SST should contain information about the near-surface SHP. The objective of this study was to investigate the uncertainties of SHP derived from SST. The HYDRUS-1D code coupled with a global optimizer (DREAM) was used to inversely estimate van Genuchten-Mualem parameters from infrared-measured SST and time domain reflectometry (TDR)-measured water contents. This approach was tested using synthetic and real data, collected during September 2008 from a harrowed silty loam field plot in Selhausen, Germany. The synthetic data illustrated that SHP can be derived from SST and that additional soil water content measurements reduce the uncertainty of the estimated SHP. Unlike for the synthetic experiment with a vertically homogeneous soil profile, a layered soil profile had to be assumed to derive SHP from the real data. Therefore, the uncertainty of SHP derived from real data was considerably larger. Water retention curves of undisturbed soil cores were similar to those estimated from SST and TDR data for the deeper undisturbed soil. The retention curves derived from SST and TDR data for the harrowed topsoil layer were typical for a coarse-textured soil and deviated considerably from the retention curves of soil cores, which were typical for a fine-textured soil and similar to those from the subsoil

Monitoring nicotine intake in human volunteers transitioning from cigarette smoking to e-cigarette use

Introduction Use of electronic cigarettes (ECs) has increased rapidly since they were introduced in Europe in 2006 especially in the UK. Claims have been made for a role in tobacco harm reduction and smoking cessation. Key to these claims is the ability of EC to maintain nicotine levels to limit cravings, withdrawal and nicotine dependence. Therefore, the aim of the current study is to monitor nicotine intake in heavy smokers (>10 cig/day, n=13 completed) transitioning from cigarette smoking to EC-only use whilst collecting psychometric data on nicotine dependence (Fagerstrom), withdrawal (Minnesota), anxiety, depression and sleep quality. Material and Methods Urine and saliva samples as well as psychometric questionnaire data were collected at baseline (visits 1 and 2) and at days 3, 7, 21 and 28 following start of EC use. An UHPLC-HESI-HRMS method was developed, validated and applied to the determination of nicotine and its main metabolite cotinine in urine and saliva. Results The baseline median (interquartile range) urinary nicotine and cotinine levels were determined as 1534 ng/mg creatinine (317-2478) and 2082 ng/mg creatinine (1727-3632), whereas salivary nicotine and cotinine levels were 145 ng/ml (26-265) and 329 ng/ml (198-440), respectively. One-way repeated measures Anova demonstrated a significant decrease in urinary nicotine and cotinine concentration from baseline at 3 post-transition visits. A significant reduction was also observed in salivary nicotine levels on days 3 and 21 after EC transition and in salivary cotinine levels at each post-transition visit compared to baseline. Both urge and strength of urges to smoke were significantly decreased by 28days compared to baseline. Conclusions These findings offer support that EC can be effective in reducing nicotine consumption and cravings for cigarettes, suggesting the potential use of EC as a smoking cessation tool. However, the inability to achieve levels of nicotine comparable to smokers may contribute to relapse. Funding Statement Funded by European Commission Horizon 2020 programme

Monitoring the transition from cigarette smoking to vaping using exposure, biochemical, brain dynamics and psychometric markers: The SmokeFreeBrain Study

Introduction This project aims to monitor human volunteers as they switch from heavy (>10 cigarette/day) tobacco smoking to electronic cigarette (EC) use. Monitoring of markers will comprise expired carbon monoxide, DNA methylation, oxytocin, cortisol, nicotine cotinine and tobacco specific nitrosamine levels in urine and saliva together with psychometric data. In addition electroencephalography will be used to search for electrical changes in the brain correlated with changes in the above markers after transition to EC use. Methods Volunteers were recruited and consented at St George’s University of London. Volunteers were allowed to select their own EC and e-fluid including nicotine concentration. Blood, saliva, urine and buccal cell samples and questionnaire data (nicotine dependence, withdrawal, anxiety, depression, mood, quality of life and sleep quality) were collected at baseline (day-2 and day-1) and throughout the 28-day EC trial (days 3,21,28) and EEG data (eyes open-eyes fixed-eyes closed, each 3 x 30s) were collected at baseline (day-1) and on EC day 21. Nicotine, cotinine, NNK, NNAL and NNN were determined by UHPLC-HESI-HRMS, 5methyl cytosine by HPLC and oxytocin and cortisol by ELISA. Results Urinary and salivary nicotine, cotinine and tobacco specific nitrosamines and expired carbon monoxide decrease significantly following transition to EC. Behavioural data have revealed modest changes in nicotine craving, nicotine withdrawal symptoms, social anxiety and sleep quality following the transition to e-cigarettes. Preliminary comparison of regional spectral power extracted from EEG data collected pre- and post- transition to e-cigarettes use has identified significant changes in specific spectral bands across the brain. Conclusions Switching to EC quickly induces beneficial changes in exposure to toxicants and psychometrics. Changes in regional spectral power observed by EEG vary will be correlated to these other biomarkers. Funding This work is supported by H2020 funding from the European Union and by a PHE-funded PhD studentship