Assessment of concordance between diffusion of carbon monoxide through the lung using the 10 s breath-hold method, and the simultaneous NO/CO technique, in healthy participants

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs

OBJECTIVES

To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.

METHODS

To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM.

RESULTS

DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01).

CONCLUSIONS

A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC.

CLINICAL RELEVANCE STATEMENT

Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity.

KEY POINTS

• A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. • The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. • A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.

Prognostic value of deep learning-based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis

OBJECTIVE

To investigate the prognostic value of deep learning (DL)-driven CT fibrosis quantification in idiopathic pulmonary fibrosis (IPF).

METHODS

Patients diagnosed with IPF who underwent nonenhanced chest CT and spirometry between 2005 and 2009 were retrospectively collected. Proportions of normal (CT-Norm%) and fibrotic lung volume (CT-Fib%) were calculated on CT using the DL software. The correlations of CT-Norm% and CT-Fib% with forced vital capacity (FVC) and diffusion capacity of carbon monoxide (D_LCO) were evaluated. The multivariable-adjusted hazard ratios (HRs) of CT-Norm% and CT-Fib% for overall survival were calculated with clinical and physiologic variables as covariates using Cox regression. The feasibility of substituting CT-Norm% for D_LCO in the GAP index was investigated using time-dependent areas under the receiver operating characteristic curve (TD-AUCs) at 3 years.

RESULTS

In total, 161 patients (median age [IQR], 68 [62-73] years; 104 men) were evaluated. CT-Norm% and CT-Fib% showed significant correlations with FVC (Pearson's r, 0.40 for CT-Norm% and - 0.37 for CT-Fib%; both p < 0.001) and D_LCO (0.52 for CT-Norm% and - 0.46 for CT-Fib%; both p < 0.001). On multivariable Cox regression, both CT-Norm% and CT-Fib% were independent prognostic factors when adjusted to age, sex, smoking status, comorbid chronic diseases, FVC, and D_LCO (HRs, 0.98 [95% CI 0.97-0.99; p < 0.001] for CT-Norm% at 3 years and 1.03 [1.01-1.05; p = 0.01] for CT-Fib%). Substituting CT-Norm% for D_LCO showed comparable discrimination to the original GAP index (TD-AUC, 0.82 [0.78-0.85] vs. 0.82 [0.79-0.86]; p = 0.75).

CONCLUSION

CT-Norm% and CT-Fib% calculated using chest CT-based deep learning software were independent prognostic factors for overall survival in IPF.

KEY POINTS

• Normal and fibrotic lung volume proportions were automatically calculated using commercial deep learning software from chest CT taken from 161 patients diagnosed with idiopathic pulmonary fibrosis. • CT-quantified volumetric parameters from commercial deep learning software were correlated with forced vital capacity (Pearson's r, 0.40 for normal and - 0.37 for fibrotic lung volume proportions) and diffusion capacity of carbon monoxide (Pearson's r, 0.52 and - 0.46, respectively). • Normal and fibrotic lung volume proportions (hazard ratios, 0.98 and 1.04; both p < 0.001) independently predicted overall survival when adjusted for clinical and physiologic variables.

Pulmonary diffusing capacity to nitric oxide and carbon monoxide during exercise and in the supine position: a test-retest reliability study

NEW FINDINGS

What is the central question in this study? How reliable is the combined measurement of the pulmonary diffusing capacity to carbon monoxide and nitric oxide (DLCO/NO ) during exercise and in the resting supine position, respectively? What is the main finding and its importance? The DLCO/NO technique is reliable with a very low day-to-day variability both during exercise and in the resting supine position, and may thus provide a useful physiological outcome that reflects the alveolar-capillary reserve in humans.

ABSTRACT

DLCO/NO , the combined single-breath measurement of the diffusing capacity to carbon monoxide (DLCO ) and nitric oxide (DLNO ) measured either during exercise or in the resting supine position may be a useful physiological measure of alveolar-capillary reserve. In the present study, we investigated the between-day test-retest reliability of DLCO/NO -based metrics. Twenty healthy volunteers (10 males, 10 females; mean age 25 (SD 2) years) were randomized to repeated DLCO/NO measurements during upright rest followed by either exercise (n = 11) or resting in the supine position (n = 9). The measurements were repeated within 7 days. The smallest real difference (SRD), defined as the 95% confidence limit of the standard error of measurement (SEM), the coefficient of variance (CV), and intraclass correlation coefficients were used to assess test-retest reliability. SRD for DLNO was higher during upright rest (5.4 (95% CI: 4.1, 7.5) mmol/(min kPa)) than during exercise (2.7 (95% CI: 2.0, 3.9) mmol/(min kPa)) and in the supine position (3.0 (95% CI: 2.1, 4.8) mmol/(min kPa)). SRD for DLCOc was similar between conditions. CV values for DLNO were slightly lower than for DLCOc both during exercise (1.5 (95% CI: 1.2, 1.7) vs. 3.8 (95% CI: 3.2, 4.3)%) and in the supine position (2.2 (95% CI: 1.8, 2.5) vs. 4.8 (95% CI: 3.8, 5.4)%). DLNO increased by 12.3 (95% CI: 11.1, 13.4) and DLCOc by 3.3 (95% CI: 2.9, 3.7) mmol/(min kPa) from upright rest to exercise. The DLCO/NO technique provides reliable indices of alveolar-capillary reserve, both during exercise and in the supine position.

Test-retest reliability of lung diffusing capacity for nitric oxide during light to moderate intensity cycling exercise

This study examined test-retest reliability of single-breath lung diffusing capacity for nitric oxide (DLNO) and carbon monoxide (DLCO) during exercise. Sixteen healthy subjects (age 20-67 years) performed DLNO-DLCO tests during light and moderate intensity cycling exercise at 50% and 80% of individual anaerobic threshold (IAT). Primary endpoint was DLNO at 80% IAT. Precision of DLNO, DLCO, and alveolar volume was quantified by within-subject standard deviation (SD_ws, measurement error) and intraclass correlation coefficients (ICC). Reproducibility was determined by SD_ws* 2.77. Overall, reliability was excellent for all outcomes. SD_ws and reproducibility for DLNO at 80% IAT were 4.6 and 12.7 mL.min^-1.mmHg^-1, and the ICC was 0.99 (95% confidence interval 0.98-0.99). Median breathlessness at 80% IAT was 4 (interquartile range 3-6) on a 0-10 scale. Our data suggest excellent reliability of single-breath DLNO during moderate intensity exercise, but perceived levels of breathlessness may limit its usefulness, especially at exercise intensities beyond IAT.

Reference equations for pulmonary diffusing capacity using segmented regression show similar predictive accuracy as GAMLSS models

Purpose

To determine whether generalised additive models of location, scale and shape (GAMLSS) developed for pulmonary diffusing capacity are superior to segmented (piecewise) regression models, and to update reference equations for pulmonary diffusing capacity for carbon monoxide (DLCO) and nitric oxide (DLNO), which may be affected by the equipment used for its measurement.

Methods

Data were pooled from five studies that developed reference equations for DLCO and DLNO (n=530 F/546 M; 5–95 years old, body mass index 12.4–39.0 kg/m²). Reference equations were created for DLCO and DLNO using both GAMLSS and segmented linear regression. Cross-validation was applied to compare the prediction accuracy of the two models as follows: 80% of the pooled data were used to create the equations, and the remaining 20% was used to examine the fit. This was repeated 100 times. Then, the root-mean-square error was compared between both models.

Results

In males, GAMLSS models were 7% worse to 3% better compared to segmented regression for DLCO and DLNO. In females, GAMLSS models were 2% worse to 5% better compared to segmented linear regression for DLCO and DLNO. The Hyp'Air Compact measured DLNO and alveolar volume (VA) that was approximately 16–20 mL/min/mm Hg and 0.2–0.4 L higher, respectively, compared to the Jaeger MasterScreen Pro. The measured DLCO was similar between devices after controlling for altitude.

Conclusions

For the development of pulmonary function reference equations, we propose that segmented linear regression can be used instead of GAMLSS due to its simplicity, especially when the predictive accuracy is similar between the two models, overall.

Lung diffusing capacity for nitric oxide measured by two commercial devices: a randomised crossover comparison in healthy adults

In Europe, two commercial devices are available to measure combined single-breath diffusing capacity of the lung for nitric oxide (D_LNO) and carbon monoxide (D_LCO) in one manoeuvre. Reference values were derived by pooling datasets from both devices, but agreement between devices has not been established.

We conducted a randomised crossover trial in 35 healthy adults (age 40.0±15.5 years, 51% female) to compare D_LNO (primary end-point) between MasterScreen™ (Vyaire Medical, Mettawa, IL, USA) and HypAir (Medisoft, Dinant, Belgium) devices during a single visit under controlled conditions. Linear mixed models were used adjusting for device and period as fixed effects and random intercept for each participant.

Difference in D_LNO between HypAir and MasterScreen was 24.0 mL·min⁻¹·mmHg⁻¹ (95% CI 21.7–26.3). There was no difference in D_LCO (−0.03 mL·min⁻¹·mmHg⁻¹, 95% CI −0.57–0.12) between devices while alveolar volume (V_A) was higher on HypAir compared to MasterScreen™ (0.48 L, 95% CI 0.45–0.52). Disparity in the estimation of V_A and the rate of NO uptake (K_NO=D_LNO/V_A) could explain the discrepancy in D_LNO between devices. Disparity in the estimation of V_A and the rate of CO uptake (K_CO=D_LCO/V_A) per unit of V_A offset each other resulting in negligible discrepancy in D_LCO between devices. Differences in methods of expiratory gas sampling and sensor specifications between devices likely explain these observations.

These findings have important implications for derivation of D_LNO reference values and comparison of results across studies. Until this issue is resolved, reference values, established on the respective devices, should be used for test interpretation.

Large discrepancies between commercial devices to measure single-breath diffusing capacity of the lung for nitric oxide in healthy subjects caution against pooling or direct comparison of measurements obtained using different protocols and deviceshttps://bit.ly/3vKyF7U