Dataset Information

Area under the expiratory flow-volume curve: predicted values by artificial neural networks

ABSTRACT: Area under expiratory flow-volume curve (AEX) has been proposed recently to be a useful spirometric tool for assessing ventilatory patterns and impairment severity. We derive here normative reference values for AEX, based on age, gender, race, height and weight, and by using artificial neural network (ANN) algorithms. We analyzed 3567 normal spirometry tests with available AEX values, performed on subjects from two countries (United States and Spain). Regular linear or optimized regression and ANN models were built using traditional predictors of lung function. The ANN-based models outperformed the de novo regression-based equations for AEXpredicted and AEX z scores using race, gender, age, height and weight as predictor factors. We compared these reference values with previously developed equations for AEX (by gender and race), and found that the ANN models led to the most accurate predictions. When we compared the performance of ANN-based models in derivation/training, internal validation/testing, and external validation random groups, we found that the models based on pooling samples from various geographic areas outperformed the other models (in both central tendency and dispersion of the residuals, ameliorating any cohort effects). In a geographically diverse cohort of subjects with normal spirometry, we computed by both regression and ANN models several predicted equations and z scores for AEX, an alternative measurement of respiratory function. We found that the dynamic nature of the ANN allows for continuous improvement of the predictive models’ performance, thus promising that the AEX could become an essential tool in assessing respiratory impairment.

SUBMITTER: Ioachimescu O

PROVIDER: S-EPMC7538954 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe expiratory time constant (RCEXP), which is defined as the product of airway resistance and lung compliance, enable us to assess the mechanical properties of the respiratory system in mechanically ventilated patients. Although RCEXP could also be applied to spontaneously breathing patients, little is known about RCEXP calculated from the maximal expiratory flow-volume (MEFV) curve. The aim of our study was to determine the reference value for RCEXP, as well as to investigate the association between RCEXP and other respiratory function parameters, including the forced expiratory volume in 1?s (FEV1)/ forced vital capacity (FVC) ratio, maximal mid-expiratory flow rate (MMF), maximal expiratory flow at 50 and 25% of FVC (MEF50 and MEF25, respectively), ratio of MEF50 to MEF25 (MEF50/MEF25).MethodsSpirometric parameters were extracted from the records of patients aged 15?years or older who underwent pulmonary function testing as a routine preoperative examination before non-cardiac surgery at the University of Tokyo Hospital. RCEXP was calculated in each patient from the slope of the descending limb of the MEFV curve using two points corresponding to MEF50 and MEF25. Airway obstruction was defined as an FEV1/FVC and FEV1 below the statistically lower limit of normal.ResultsWe retrospectively analyzed 777 spirometry records, and 62 patients were deemed to have airway obstruction according to Japanese spirometric reference values. The cut-off value for RCEXP was 0.601?s with an area under the receiver operating characteristic curve of 0.934 (95% confidence interval?=?0.898-0.970). RCEXP was strongly associated with FEV1/FVC, and was moderately associated with MMF and MEF50. However, RCEXP was less associated with MEF25 and MEF50/MEF25.ConclusionsOur findings suggest that an RCEXP of longer than approximately 0.6?s can be linked to the presence of airway obstruction. Application of the concept of RCEXP to spontaneously breathing subjects was feasible, using our simple calculation method.

Project description:BackgroundIncremental value (IncV) evaluates the performance change between an existing risk model and a new model. Different IncV metrics do not always agree with each other. For example, compared with a prescribed-dose model, an ovarian-dose model for predicting acute ovarian failure has a slightly lower area under the receiver operating characteristic curve (AUC) but increases the area under the precision-recall curve (AP) by 48%. This phenomenon of disagreement is not uncommon, and can create confusion when assessing whether the added information improves the model prediction accuracy.MethodsIn this article, we examine the analytical connections and differences between the AUC IncV (ΔAUC) and AP IncV (ΔAP). We also compare the true values of these two IncV metrics in a numerical study. Additionally, as both are semi-proper scoring rules, we compare them with a strictly proper scoring rule: the IncV of the scaled Brier score (ΔsBrS) in the numerical study.ResultsWe demonstrate that ΔAUC and ΔAP are both weighted averages of the changes (from the existing model to the new one) in separating the risk score distributions between events and non-events. However, ΔAP assigns heavier weights to the changes in higher-risk regions, whereas ΔAUC weights the changes equally. Due to this difference, the two IncV metrics can disagree, and the numerical study shows that their disagreement becomes more pronounced as the event rate decreases. In the numerical study, we also find that ΔAP has a wide range, from negative to positive, but the range of ΔAUC is much smaller. In addition, ΔAP and ΔsBrS are highly consistent, but ΔAUC is negatively correlated with ΔsBrS and ΔAP when the event rate is low.ConclusionsΔAUC treats the wins and losses of a new risk model equally across different risk regions. When neither the existing or new model is the true model, this equality could attenuate a superior performance of the new model for a sub-region. In contrast, ΔAP accentuates the change in the prediction accuracy for higher-risk regions.

Dataset Information

Area under the expiratory flow-volume curve: predicted values by artificial neural networks

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets