Background Methods that predict prognosis and response to therapy in pulmonary hypertension (PH) are lacking. We tested whether the noninvasive estimation of hemodynamic parameters during 6MWT in PH patients provides information that can improve the value of the test. Methods We estimated hemodynamic parameters during the 6MWT using a portable, signal-morphology based, impedance cardiograph (PhysioFlow Enduro, Paris, France) with real time wireless monitoring via a bluetooth USB adapter. Results We recruited 48 subjects in the study (30 with PH and 18 healthy controls). PH patients had significantly lower maximum SV and CI and slower CO acceleration and decelerations slopes during the test when compared with healthy controls. In PH patients, CI change was associated with total distance walked (R=0.62, p<0.001) and percentage of predicted (R=0.4, p=0.03), HR recovery at 1 min (0.57, p<0.001), 2 min (0.65, p<0.001) and 3 min (0.66, p<0.001). Interestingly, in PH patients CO change during the test was predominantly related to an increase in SV instead of HR. Conclusions Estimation of hemodynamic parameters such as cardiac index during six-minute walk test is feasible and may provide useful information in patients with pulmonary hypertension.

In the present study, pulse oximetry commonly overestimated the SaO2. Increased carboxyhemoglobin levels are independently associated with the difference between SpO2 and SaO2, a finding particularly relevant in smokers.

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In pulmonary function testing by spirometry, bronchodilator responsiveness (BDR) evaluates the degree of volume and airflow improvement in response to an inhaled short-acting bronchodilator (BD). The traditional, binary categorization (present vs absent BDR) has multiple pitfalls and limitations. To overcome these limitations, a novel classification that defines five categories (negative, minimal, mild, moderate and marked BDR), and based on % and absolute changes in forced expiratory volume in 1 s (FEV1), has been recently developed and validated in patients with chronic obstructive pulmonary disease, and against multiple objective and subjective measurements. In this study, working on several large spirometry cohorts from two different institutions (n=31 598 tests), we redefined the novel BDR categories based on delta post-BD–pre-BD FEV1 % predicted values. Our newly proposed BDR partition is based on several distinct intervals for delta post-BD–pre-BD % predicted FEV1 using Global Lung Initiative predictive equations. In testing, training and validation cohorts, the model performed well in all BDR categories. In a validation set that included only normal baseline spirometries, the partition model had a higher rate of misclassification, possibly due to unrestricted BD use prior to baseline testing. A partition that uses delta % predicted FEV1 with the following intervals ≤0%, 0%–2%, 2%–4%, 4%–8% and >8% may be a valid and easy-to-use tool for assessing BDR in spirometry. We confirmed in our cohorts that these thresholds are characterized by low variance and that they are generally gender-independent and race-independent. Future validation in other cohorts and in other populations is needed.

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BACKGROUND We hypothesize that oxygen consumption (V̇O2) estimation in patients with respiratory symptoms is inaccurate and can be improved by considering arterial blood gases or spirometric variables. METHODS For this retrospective study, we included consecutive subjects who underwent cardiopulmonary exercise testing. Resting V̇O2 was determined using breath-by-breath testing methodology. Using a training cohort (n = 336), we developed 3 models to predict V̇O2. In a validation group (n = 114), we compared our models with 7 available formulae. RESULTS Our first model (V̇O2 = −184.99 + 189.64 × body surface area [BSA, m2] + 1.49 × heart rate [beats/min] + 51.51 × FIO2 [21% = 0; 30% = 1] + 30.62 × gender [male = 1; female = 0]) showed an R2 of 0.5. Our second model (V̇O2 = −208.06 + 188.67 × BSA + 1.38 × heart rate + 35.6 × gender + 2.06 × breathing frequency [breaths/min]) showed an R2 of 0.49. The best R2 (0.68) was obtained with our last model, which included minute ventilation (V̇O2 = −142.92 + 0.52 × heart rate + 126.84 × BSA + 14.68 × minute ventilation [L]). In the validation cohort, these 3 models performed better than other available equations, but had wide limits of agreement, particularly in older individuals with shorter stature, higher heart rate, and lower maximum voluntary ventilation. CONCLUSIONS We developed more accurate formulae to predict resting V̇O2 in subjects with respiratory symptoms; however, equations had wide limits of agreement, particularly in certain groups of subjects. Arterial blood gases and spirometric variables did not significantly improve the predictive equations.

BackgroundIn spirometry, the area under expiratory flow-volume curve (AEX-FV) was found to perform well in diagnosing and stratifying physiologic impairments, potentially lessening the need for complex lung volume testing. Expanding on prior work, this study assesses the accuracy and the utility of several models of estimating AEX-FV based on forced vital capacity (FVC) and several instantaneous flows. These models could be incorporated in regular spirometry reports, especially when actual AEX-FV measurements are not available.MethodsWe analysed 4845 normal spirometry tests, performed on 3634 non-smoking subjects without known respiratory disease or complaints. Estimated AEX-FV was computed based on FVC and several flows: peak expiratory flow, isovolumic forced expiratory flow at 25%, 50% and 75% of FVC (FEF25, FEF50 and FEF75, respectively). The estimations were based on simple regression with and without interactions, by optimised regression models and by a deep learning algorithm that predicted the response surface of AEX-FV without interference from any predictor collinearities or normality assumption violations.ResultsMedian/IQR of actual square root of AEX-FV was 3.8/3.1–4.5 L2/s. The per cent of variance (R2) explained by the models selected was very high (>0.990), the effect of collinearities was negligible and the use of deep learning algorithms likely unnecessary for regular or routine pulmonary function testing laboratory usage.ConclusionsIn the absence of actual AEX-FV, a simple regression model without interactions between predictors or use of optimisation techniques can provide a reasonable estimation for clinical practice, thus making AEX-FV an easily available additional tool for interpreting spirometry.