Diagnostic performance of automated red cell parameters in predicting bone marrow iron stores.

OBJECTIVES: The aim of the study was to determine the diagnostic performance of novel automated red cell parameters for estimating bone marrow iron stores. METHODS: The study was a retrospective single-centre study based on data from an automated haematology analyser and results of bone marrow iron staining. Red cell parameters were measured on a Sysmex XN-series haematology analyser. Bone marrow iron stores were assessed semiquantitatively by cytochemical reaction according to Perls. RESULTS: The analysis included 429 bone marrow aspirate smears from 393 patients. Median age of patients was 67 years, 52 % of them were female. The most common indication for bone marrow examination was a plasma cell dyscrasia (n=104; 24 %). Median values of percentage of hypochromic and hyperchromic red blood cells (%HYPO-He, %HYPER-He), reticulocyte haemoglobin equivalent (RET-He) and microcytic red blood cells (MicroR) were statistically significantly different between cases with iron deplete and iron replete bone marrow. In a logistic regression model, ferritin was the best predictor of bone marrow iron stores (AUC=0.891), outperforming RET-He and %HYPER-He (AUC=0.736 and AUC=0.722, respectively). In a combined model, ferritin/MicroR index achieved the highest diagnostic accuracy (AUC=0.915), outperforming sTfR/log ferritin index (AUC=0.855). CONCLUSIONS: While single automated red cell parameters did not show improved diagnostic accuracy when compared to traditional iron biomarkers, a novel index ferritin/MicroR has the potential to outperform ferritin and sTfR/log ferritin index for predicting bone marrow iron stores. Further research is needed for interpretation and implementation of novel parameters and indices, especially in the context of unexplained anaemia and myelodysplastic syndromes.

Oxygen resaturation rate is significantly associated with objectively assessed excessive daytime sleepiness in suspected obstructive sleep apnoea patients.

INTRODUCTION: Commonly utilised metrics such as the apnoea-hypopnoea index show limited correlation to excessive daytime sleepiness (EDS). Oxygen desaturation parameters show better predictive power, however oxygen resaturation parameters have not yet been investigated. Oxygen resaturation may represent increased cardiovascular fitness and thus we hypothesized that a higher resaturation rate would be protective against EDS. METHODS: Oxygen saturation parameters were computed via ABOSA software for adult patients referred for polysomnography and multiple sleep latency test in Israel Loewenstein hospital 2001-2011. EDS was defined as a mean sleep latency (MSL) below 8 min. RESULTS: 1629 patients (75% male, 53% obese, median age of 54 years) were included for analysis. The average desaturation event nadir was 90.4% and resaturation rate 0.59%/second. Median MSL was 9.6 min, and 606 patients met criteria for EDS. Patients who were younger, female, and with larger desaturations had significantly higher resaturation rates (p < 0.001). In multivariate models, adjusted for age, sex, body mass index, and average desaturation depth, resaturation rate showed a significant negative correlation with MSL (z-score standardised beta, -1 (95%CI -0.49, -1.52)), and significantly increased odds ratio (OR) of EDS (OR, 1.28 (95%CI 1.07, 1.53)). The beta associated with resaturation rate was larger, though non-significantly, than that of desaturation depth (difference 0.36 (95% CI -1.34, 0.62), p = 0.470). CONCLUSION: Oxygen resaturation parameters show significant associations with objectively assessed EDS independent of desaturation parameters. Thus, resaturation and desaturation parameters may reflect differing underlying mechanistic pathways and both be considered novel and appropriate markers for assessing sleep-disordered breathing and associated outcomes.

Non-linear models for black carbon exposure modelling using air pollution datasets.

Black carbon (BC) is a product of incomplete combustion, present in urban aerosols and sourcing mainly from road traffic. Epidemiological evidence reports positive associations between BC and cardiovascular and respiratory disease. Despite this, BC is currently not regulated by the EU Air Quality Directive, and as a result BC data are not available in urban areas from reference air quality monitoring networks in many countries. To fill this gap, a machine learning approach is proposed to develop a BC proxy using air pollution datasets as an input. The proposed BC proxy is based on two machine learning models, support vector regression (SVR) and random forest (RF), using observations of particle mass and number concentrations (N), gaseous pollutants and meteorological variables as the input. Experimental data were collected from a reference station in Barcelona (Spain) over a 2-year period (2018-2019). Two months of additional data were available from a second urban site in Barcelona, for model validation. BC concentrations estimated by SVR showed a high degree of correlation with the measured BC concentrations (R2 = 0.828) with a relatively low error (RMSE = 0.48 µg/m3). Model performance was dependent on seasonality and time of the day, due to the influence of new particle formation events. When validated at the second station, performance indicators decreased (R2 = 0.633; RMSE = 1.19 µg/m3) due to the lack of N data and PM2.5 and the smaller size of the dataset (2 months). New particle formation events critically impacted model performance, suggesting that its application would be optimal in environments where traffic is the main source of ultrafine particles. Due to its flexibility, it is concluded that the model can act as a BC proxy, even based on EU-regulatory air quality parameters only, to complement experimental measurements for exposure assessment in urban areas.

Amount of weight loss or gain influences the severity of respiratory events in sleep apnea.

Severity of obstructive sleep apnea (OSA) is estimated based on respiratory events per hour [i.e., apnea-hypopnea index (AHI)]. The aim of this study was to investigate effects of weight change on the severity of respiratory events. Respiratory event severity, including duration and morphology, was estimated by determining parameters quantifying obstruction and desaturation event lengths and areas, respectively. Respiratory events of 54 OSA patients treated with dietary intervention were evaluated at baseline and after 5-year follow-up in subgroups with different levels of weight change. AHI, oxygen desaturation index (ODI) and obstruction event severities decreased during weight loss. In lower level weight loss, the decrease was milder in obstruction severity than in AHI and ODI, indicating that the decrease in the number of events is more focused on less severe events. In weight gain groups, parameters incorporating obstruction event severity, AHI and ODI increased, although increase was greater in parameters incorporating obstruction event severity. The number and severity of respiratory events were modulated differently by the level of weight change. AHI misses this change in the severity of respiratory events. Therefore, parameters incorporating information on the respiratory event severities may bring additional information on the health effects obtained with dietary treatment of OSA.