Table 1. Spirometric indices of airflow impairment.
All suggested cutoffs are for airflow obstruction unless otherwise noted. FEV = forced expiratory volume, subscript denotes time in seconds; FVC = forced expiratory vital capacity; FIVC = forced inspiratory vital capacity; SVC = slow vital capacity; IC = inspiratory capacity; TLC = total lung capacity; FEF = forced expiratory flow, subscript denotes percentage of FVC; PEF = peak expiratory flow; LLN = lower limit of normal; -- = cutoff value is not well defined or not applicable.
| Category | Index | Suggested cutoff | Potential clinical applicability |
|---|---|---|---|
| Lung capacity indices | SVC – FVC | -- | Marker of air trapping; predicts exercise tolerance |
| FIVC – FVC | -- | Marker of air trapping | |
| FVC/SVC | -- | Indicator of small airway disease | |
| FEV1/SVC | < 0.7 or LLN | Obstruction in young individuals | |
| IC | -- | Indicates hyperinflation; predicts respiratory mortality | |
| Time-fractioned lung volume indices | FEV6 | LLN | More reproducible and less difficult to perform than FVC; predictor of lung function decline |
| FEV1/FEV6 | < 0.73 or LLN | In normal FEV1/FVC, associated with air-trapping, diffusion abnormalities, and respiratory exacerbations; identifies smokers | |
| FEV3/FEV6 and FEV3/FVC | LLN | In normal FEV1/FVC, associated with hyperinflation, air trapping, diffusion abnormalities; identifies smokers | |
| FEV0.5 or FEV0.75/FVC | LLN | Obstruction in infants and children | |
| Flow-based indices | FEF25-75 | < 65% predicted or LLN | Lower in some smokers normal FEV1/FVC; correlates with air trapping on CT |
| FEF75-85 | LLN | Distinguishes smokers from nonsmokers | |
| FEF50 (MEF50) or FEF75 | < 60% predicted | Reduced in GOLD zero patients | |
| FEF50/0.5FVC | -- | Correlates with FEV1/FVC | |
| FEF200-1200 | -- | Substitute for PEF | |
| PEF | Males < 350 L/min Females < 250 L/min |
Simple screening for undiagnosed COPD | |
| PIFR | < 60L/min | Predicts COPD-related hospital readmissions | |
| FEF50/FIF50 | -- | Evaluates upper airway obstruction; correlated with emphysema by CT | |
| Curvilinearity Measures | |||
| Classic geometric indices | Global concavity index | Males > 38.4 units Females > 26.3 units |
Based on FEF50, quantifies end-expiratory spirogram concavity |
| Peripheral concavity index | Males > 61.2 units Females > 63.1 units |
Based on FEF75, quantifies end-expiratory spirogram concavity | |
| Angle β | < 180° (concavity) | Lower in patients with dyspnea and wheezing than controls; improves in response to bronchodilators | |
| Slope ratio (SR) | > 1 (concavity) > 2.5 |
Indicates heterogenous lung emptying, obstruction | |
| Flow ratio at 75% FVC (FR75) | < 0 (concavity) | More negative in smokers than non-smokers | |
| Coefficient of maximal mid-expiratory flow (β-MMEF) | > 0.4 | Correlates with risk of hospitalization | |
| Curvature index (kmax) | -- | Exponentially associated with FEV1 | |
| Flow decay | Upper limit of normal (0.802 L−1) | Correlates with other measures of obstruction; not sensitive to artifactually low FVC | |
| Area under the curve in 3 seconds / Area of triangle 3 seconds (AUC3/AT3) | LLN | Surrogate for FEV1/FVC when 6 second expiratory effort not met (particularly young patients with obstruction) | |
| Area under the flow volume curve (AUFVC) | -- | Detects air trapping and hyperinflation; correlates with 6-minute walk | |
| Novel computational indices | Angle of collapse (AC) | < 131° ≤ 137° |
< 131° correlates significantly with emphysema extent; ≤137°asthma-COPD overlap syndrome |
| Volume dependence of slope ratio | SR decreases through exhalation in early COPD; SR increases through exhalation in elderly | Distinguish spirogram concavity caused by mild COPD from concavity due to physiologic changes with age | |
| Transfer function model of flow decline | -- | Correlates with traditional measures of obstruction well; offers additional inputs for machine learning algorithms | |
| Parameter D | -- | Identifies individuals with mild disease or unrecognized disease who have CT findings of structural lung disease | |
| Deep learning algorithms and other machine learning approaches | -- | May detect subtle patterns that distinguish disease from normal variation; may synthesize various indices to improve predictive power for relevant outcomes | |