Reduction in T_LCO and survival in a clinical population

Discussion

We have shown that the GLI prediction equations for T_LCO gave the best survival prediction model for our population of patients, which was better than the survival predictions from spirometry and is in keeping with what was found for predicting survival in the general population [1]. We initially found that the original GLI equations gave many more extremely low z-scores than did the Miller equations, but this was due to an error in the original GLI data. Using revised GLI equations we found that the 10.4% of our population who were of non-European ancestry had T_LCO z-scores that were distributed lower than anticipated. There was no a priori reason to expect that our patients of non-European ancestry would have a higher prevalence of moderate disease than those of European ancestry, so this suggested that accounting for ethnicity would be appropriate [17]. We chose to adopt the methodology used by GLI for ethnicity with respect to FVC and apply this to T_LCO, which assumes that the ethnic difference in T_LCO relates to size issues rather than any intrinsic difference in the ability to transfer carbon monoxide to the blood. Using these ethnicity-specific coefficients removed the uneven distribution of ethnicity across the T_LCO z-score values in our patients. GLI were originally unable to propose ethnicity-specific predictions for T_LCO due to a lack of available data. While our proposal is not as satisfactory as deriving ethnicity-appropriate reference values from suitable data from all ethnicities, it does offer a sensible approach until such data are available. This will mean that all patients can be assessed against the most appropriate standard available for them as an individual and so avoid unnecessary misclassification of lung function results. We have only been able to examine this using the GLI category “other” for ethnicity, and future work will need to see if the other GLI ethnicity FVC coefficients can improve T_LCO predictions for those ethnicities.

For spirometry, we previously found that in the extremely old, the GLI predictions were high [18], suggesting that rigorous entry criteria for data to be used in prediction equations might lead to using values from subjects not properly representative of usual people for that age. However, for GLI T_LCO equations the upper age limit was suggested to be 85 years [2], but coefficients are available up to the age of 90 years. Thus, our 42 patients who were older than this were being assessed against their 90-year-old predicted values. This means that the z-scores of these subjects may be inappropriately low. Despite this, there was no age effect on the severity grading categories for GLI z-scores (table 4), whereas there were possible age effects within the grading system using the Miller equations and with PP values.

We have proposed a new simplified four-tier system for grading lung function from z-scores based on survival analysis. The six-tier system of Quanjer et al. [9] proposed for assessing airways obstruction with FEV₁ did not show much separation in survival for the lower grades of severity (table 4). All gradings of severity are arbitrarily set on a continuous scale, and the six levels suggest a degree of separation between grades that does not appear to be justified. We think that using broader, but fewer, grades that reflect survival is a better and simpler approach. We have found marked differences in the severity groups identified by grading using z-scores versus PP for T_LCO. The analysis around the discordant subjects in quadrants A, B, C and D in figure 2 suggest that the z-score thresholds more correctly grade the severity with respect to all-cause mortality, since hazard for mortality better related to the z-score cut-offs than the PP cut-offs. These discordant groups showed significant skew in age, sex and height.

This difference in the way that z-scores and PP grade lung function deficit is related to the fact that PP takes no account of the scatter of the range of values in fit and healthy people and how this scatter relates to the magnitude of the index and the sex, ethnicity, age and height of the subject. PP assumes that the degree of lung function deficit is always simply proportional to the predicted. There is no evidence or law indicating that, for example, a 50% reduction in lung function for a 25-year-old is in some way equivalent to the same reduction in a 75-year-old. Consider T_LCO, an index that has a numerically relatively large predicted value, which, for a 75-year-old male of average height (175 cm) has a predicted value of 8.14 mmol·min^–1·kPa⁻¹ with a 5th centile value of 5.94 mmol·min^–1·kPa⁻¹, which is at 73.0% of the predicted value, whereas for a 25-year-old male of this height, the LLN is at 79.3% predicted. Now consider forced expiratory flow at 25–75% of FVC, an index that has a numerically smaller predicted value with a relatively wide range of values found in normal healthy people. For the same 75-year-old man, the predicted is 2.17 L·s^–1, 5th centile is 0.90 L·s^–1, which is at 41.4% predicted, whereas for a 25-year-old man of this height the LLN is at 63.6% predicted. So comparing degrees of lung function reduction by PP is going to be problematic, since 73% predicted and 79% predicted in one is equivalent in terms of grading lung function deficit to 41% and 64% predicted in the other. In most previous prediction equations, for example the European Community for Steel and Coal equations [5] and from the third National Health and Nutrition Examination Survey [19], the RSD from the regression equations was the same for all subjects. GLI have observed that the scatter of results in healthy subjects was not uniform for all subjects and this was accounted for in their predictions, so people of different age, height, sex and ethnicity may have different RSD values, as shown in supplementary figures S8 and S9. These differences in approach mean that z-scores will grade differently from PP and appear to relate better to outcome.

Although others have found that T-scores for FEV₁ were effective in determining abnormality in lung function [8], we have found that T-scores did not convincingly relate to survival in our patients. For FVC, T-scores were equivalently good at predicting survival to z-scores, but in all the other indices T-scores violated Cox regression assumptions and gave poorer predictions. The potential advantage of T-scores over z-scores is that in the older population z-scores cannot go so low as in younger subjects because the median value falls with age with the RSD not changing in proportion. Since T-scores are relating a subject's result to their predicted value at a much younger age, their values can go just as low in old age as in younger subjects. In our data this does not seem to be advantageous in predicting mortality since healthy older subjects have a much lower T-score than z-score due to age alone and this adversely distorts the survival analysis. Also the origins of T-scores with bone density [20] is predicated on that fact that values of bone density as a young adult can pertain into later life to define a normal range. This is not the case for lung function. The spread of lower values of T_LCO against age did not favour the easy adoption of a T_LCO quotient in the way that has proven effective for FEV₁ [7, 21–23]. This may just reflect the spread of our data and our referral pattern for younger people for lung transplantation, and so this should be further explored in other datasets.

The limitations to our study include that we could not separate patients into disease categories, since the referral information was not verifiable. Additionally, we did not know the cause of death, so we could not look for respiratory causes of mortality to enhance the analysis. Our results are only applicable for adults, and in paediatrics such survival analysis may not be appropriate. However, the z-score grading system should still be applicable and avoid sex and size effects. Our patients were classified as either “white” or “other” in terms of GLI with respect to ethnicity and it will be necessary to check from data in African American and Asian populations whether the application of the FVC methodology to T_LCO for these ethnic groups is appropriate.

We conclude that the revised GLI-2020 equations with the addition of ethnicity-specific coefficients give a better spread of z-score results compared to the Miller equations and this leads to better survival prediction. We have found that a four-tier grading system for grading T_LCO function (z-scores of −1.645, −3 and −5) was appropriately related to survival.