We read with interest the findings of Miele et al. on the relationship between environmental exposures and decline in lung function (1). The authors reported that living in urban settings and living at high altitude were associated with accelerated decline in pre-bronchodilator FEV1 and FVC. Investigating the effects at area level is important from a public health perspective and extra analysis on this valuable dataset as suggested below will help to untangle these links further.
Study participants were recruited from four settings in Peru: Lima, Tumbles, urban Puno and rural Puno (1). Urban living and high-altitude dwelling (as binary variables) were defined based on these four settings. The authors compared the effect of urban living (Lima and urban Puno) with rural living (Tumbes and rural Puno); and the effect of high-altitude dwelling (urban Puno and rural Puno) with low-altitude dwelling (Lima and Tumbes). It is possible that the observed independent effects found by the authors of urban living and high-altitude dwelling may be driven by the urban Puno group (high altitude and urban living). In other words, there may be an interaction between urban living and high-altitude dwelling and investigating this potential interaction would be informative.
As discussed by the authors, the adverse effect of high-altitude dwelling on lung function decline may partly be related to hypoxia and adverse effects from living in urban settings may be related to outdoor air...
We read with interest the findings of Miele et al. on the relationship between environmental exposures and decline in lung function (1). The authors reported that living in urban settings and living at high altitude were associated with accelerated decline in pre-bronchodilator FEV1 and FVC. Investigating the effects at area level is important from a public health perspective and extra analysis on this valuable dataset as suggested below will help to untangle these links further.
Study participants were recruited from four settings in Peru: Lima, Tumbles, urban Puno and rural Puno (1). Urban living and high-altitude dwelling (as binary variables) were defined based on these four settings. The authors compared the effect of urban living (Lima and urban Puno) with rural living (Tumbes and rural Puno); and the effect of high-altitude dwelling (urban Puno and rural Puno) with low-altitude dwelling (Lima and Tumbes). It is possible that the observed independent effects found by the authors of urban living and high-altitude dwelling may be driven by the urban Puno group (high altitude and urban living). In other words, there may be an interaction between urban living and high-altitude dwelling and investigating this potential interaction would be informative.
As discussed by the authors, the adverse effect of high-altitude dwelling on lung function decline may partly be related to hypoxia and adverse effects from living in urban settings may be related to outdoor air pollution and comorbid conditions (2, 3). Investigating interaction between high-altitude dwelling and urban living may help tease out the underlying drivers of accelerated lung function decline. It is also critical to understand what factors in these areas mediate the adverse impact on lung function decline and the data could be further analysed to investigate potential mediators. These analyses are the first step to translation of this evidence to public health measures.
We additionally note that the high-altitude dwelling group had higher prevalence of COPD at baseline (Table 1) and it appeared that COPD was not adjusted for in the final model (Table 3) (1). If this is the case, the inbalance of COPD may have confounded the association between high-altitude dwelling and lung function decline. We have shown that lung function trajectories leading to COPD often exceed the normal rate of decline (4). Therefore, it is also highly likely that, in this analysis, lung function decline may vary between those who have COPD and those who do not, which warrants an interaction analysis.
Understanding the adverse effect of residential place (i.e. urban and high-altitude dwelling) on lung function decline would have significant public health implications. Further research is needed to tease out the important drivers of these observed effects.
References
1. Miele CH, Grigsby MR, Siddharthan T, Gilman RH, Miranda JJ, Bernabe-Ortiz A, Wise RA, Checkley W. Environmental exposures and systemic hypertension are risk factors for decline in lung function. Thorax 2018.
2. Rice MB, Ljungman PL, Wilker EH, Dorans KS, Gold DR, Schwartz J, Koutrakis P, Washko GR, O'Connor GT, Mittleman MA. Long-term exposure to traffic emissions and fine particulate matter and lung function decline in the Framingham heart study. Am J Respir Crit Care Med 2015; 191: 656-664.
3. Schikowski T, Schaffner E, Meier F, Phuleria HC, Vierkotter A, Schindler C, Kriemler S, Zemp E, Kramer U, Bridevaux PO, Rochat T, Schwartz J, Kunzli N, Probst-Hensch N. Improved air quality and attenuated lung function decline: modification by obesity in the SAPALDIA cohort. Environmental health perspectives 2013; 121: 1034-1039.
4. Bui DS, Lodge CJ, Burgess JA, Lowe AJ, Perret J, Bui MQ, Bowatte G, Gurrin L, Johns DP, Thompson BR, Hamilton GS, Frith PA, James AL, Thomas PS, Jarvis D, Svanes C, Russell M, Morrison SC, Feather I, Allen KJ, Wood-Baker R, Hopper J, Giles GG, Abramson MJ, Walters EH, Matheson MC, Dharmage SC. Childhood predictors of lung function trajectories and future COPD risk: a prospective cohort study from the first to the sixth decade of life. Lancet Respir Med 2018.
We appreciate the points raised by the commentator about our study (Zeng et al.)[1] published in June 2018 issue of Thorax that (1) the prevalence of abnormal residual volume to total lung capacity ratio (RV:TLC) in our study of ever-smokers with preserved spirometry is substantially higher than that observed in the commentator’s past studies,[2-4] and (2) an assumption by the commentator that stringent exclusion of those with abnormally low TLC and those with diagnosis codes of interstitial lung diseases (ILD) in their electronic health records (EHR) may have resulted in overestimation of the prevalence of abnormally high RV:TLC among smokers with preserved spirometry.
We would like to draw the attention of commentator and readers to the following points:
1- The studies referenced by the commentator used pulmonary function tests (PFT) data collected from 708 patients in 2013 across 5 clinical sites associated with University of Minnesota Medical Center with inclusion criteria of patients 18 years of age or older with or without history of smoking.[2] They included about 50% women and 3 African Americans. Our study was performed on PFT data obtained from 1985 through 2017 through the United States Veterans Affairs (VA) nationwide EHR from 7,479 patients across 37 VA medical centers in the United States with inclusion criteria of patients 40 years of age or older with an EHR diagnosis code of smoking, which likely suggests heavy smoking for VA patients. Our st...
We appreciate the points raised by the commentator about our study (Zeng et al.)[1] published in June 2018 issue of Thorax that (1) the prevalence of abnormal residual volume to total lung capacity ratio (RV:TLC) in our study of ever-smokers with preserved spirometry is substantially higher than that observed in the commentator’s past studies,[2-4] and (2) an assumption by the commentator that stringent exclusion of those with abnormally low TLC and those with diagnosis codes of interstitial lung diseases (ILD) in their electronic health records (EHR) may have resulted in overestimation of the prevalence of abnormally high RV:TLC among smokers with preserved spirometry.
We would like to draw the attention of commentator and readers to the following points:
1- The studies referenced by the commentator used pulmonary function tests (PFT) data collected from 708 patients in 2013 across 5 clinical sites associated with University of Minnesota Medical Center with inclusion criteria of patients 18 years of age or older with or without history of smoking.[2] They included about 50% women and 3 African Americans. Our study was performed on PFT data obtained from 1985 through 2017 through the United States Veterans Affairs (VA) nationwide EHR from 7,479 patients across 37 VA medical centers in the United States with inclusion criteria of patients 40 years of age or older with an EHR diagnosis code of smoking, which likely suggests heavy smoking for VA patients. Our study included only 7.5% women but 8.8% African Americans. The populations in our study and that of the commentator’s study are very different. Given the heavier smoking burden at the VA,[5] it is not unexpected to identify higher smoking-related pathology including higher prevalence of air trapping among the VA patients. Of note, through their deployments, veterans are also exposed to high levels of air pollutants,[6] which have been shown to contribute considerably to development of chronic obstructive pulmonary disease (COPD).[7]
2- As mentioned in our article, a limitation of our study was that spirometries, and specifically full PFT, were likely done due to clinical concerns for respiratory diseases, and given the inclusion of plethysmography, the likely existence of concerns for ILD. This selection bias may then contribute to a higher prevalence of receiving an ILD diagnosis in those patients’ records in our study.
3- Abnormally low TLC could occur for reasons other than ILD including obesity, which is a common health problem and thus may have resulted in exclusions of many patients from our study.
4- It is important to note that the assumption of stringent exclusion of those with ILD and abnormally low TLC should result in the remaining included patients to have higher than expected TLC, and should not result in the remaining patients to have high RV; that is, the remaining subjects should have the same level RV but higher TLC, and hence lower RV:TLC ratio. Thus, if the commentator’s assumption that “stringent exclusion criteria resulted in a very small denominator” were correct, then the algorithm of our study indeed would have been biased towards generating lower than expected prevalence of abnormally high RV:TLC, leading to an underestimation, not overestimation, of the prevalence of abnormally high RV:TLC.
5- Most remarkably, a finding that we would like to emphasize and should not be overlooked is that regardless of whether the RV:TLC ratio was normal or abnormal, it was linearly and directly associated with respiratory morbidity and progression to spirometric COPD. No threshold level was observed.
6- It is now estimated that approximately 20% of COPD is caused by exposure to air pollutants (including pollutants other than biomass smoke such as the daily air pollution experienced by all across the globe).[8] Given above, the use of “normal” or “abnormal” values and reference equations should be approached cautiously as it is potentially extremely difficult if not impossible to develop a “normal” cohort when all earth inhabitants are being exposed to air pollutants. This is consistent with our related findings in a previous article showing that in never-smokers with history of exposure to air pollutants, all with preserved spirometry AND normal range of RV:TLC, higher RV:TLC ratio was associated with worse physiologic performance.[9]
7- Finally, the commentator’s statement about lesser reliability and lack of rationale for use of lung volumes in smokers at risk for COPD is fascinating. Spirometry is an extremely important tool in assessment of respiratory diseases including COPD. It is easier to use than plethysmography and more widely available. However, COPD is a complex disease and reducing it to one or two dimensions is unhelpful.[10] As demonstrated in our article, lung volumes, regardless of whether in the normal or abnormal range, provide an additional dimension to help prognosticate in early obstructive lung disease in those at risk for COPD. In their study of 708 patients with normal spirometry,[2] the commentator and colleagues did conclude that their “findings point out the importance of measuring lung volumes in symptomatic patients who have normal spirometry.” We fully agree with them.
References:
1. Zeng, S., et al., Lung volume indices predict morbidity in smokers with preserved spirometry. Thorax, 2018.
2. Fortis, S., E.O. Corazalla, and H.J. Kim, Does normal spirometry rule out an obstructive or restrictive ventilatory defect? Respir Investig, 2017. 55(1): p. 55-57.
3. Fortis, S., et al., The difference between slow and forced vital capacity increases with increasing body mass index: a paradoxical difference in low and normal body mass indices. Respir Care, 2015. 60(1): p. 113-8.
4. Fortis, S., et al., Persistent Empiric COPD Diagnosis and Treatment After Pulmonary Function Test Showed No Obstruction. Respir Care, 2016. 61(9): p. 1192-200.
5. Odani, S., et al., Tobacco Product Use Among Military Veterans - United States, 2010-2015. MMWR Morb Mortal Wkly Rep, 2018. 67(1): p. 7-12.
6. Falvo, M.J., et al., Airborne hazards exposure and respiratory health of Iraq and Afghanistan veterans. Epidemiol Rev, 2015. 37: p. 116-30.
7. van Koeverden, I., et al., Secondhand Tobacco Smoke and COPD Risk in Smokers: A COPDGene Study Cohort Subgroup Analysis. COPD, 2015. 12(2): p. 182-9.
8. Eisner, M.D., et al., An official American Thoracic Society public policy statement: Novel risk factors and the global burden of chronic obstructive pulmonary disease. Am J Respir Crit Care Med, 2010. 182(5): p. 693-718.
9. Arjomandi, M., et al., Lung volumes identify an at-risk group in persons with prolonged secondhand tobacco smoke exposure but without overt airflow obstruction. BMJ Open Respir Res, 2018. 5(1): p. e000284.
10. Agusti, A., B. Celli, and R. Faner, What does endotyping mean for treatment in chronic obstructive pulmonary disease? Lancet, 2017. 390(10098): p. 980-987.
Should lung volumes measurement accompany every spirometry?
Spyridon Fortis MD1
1Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Hospital and Clinics, Iowa City, IA, USA
Corresponding Author:
Spyridon Fortis, MD
UIHC – Internal Medicine
200 Hawkins Drive – C33 GH
Iowa City, IA 52242
Email: spyridon-fortis@uiowa.edu
Word Count:
Author Disclosures: Authors declare that there is no conflict of interest regarding the publication of this paper.
Running Head: Lung volumes with every spirometry
Key Words: COPD, diagnosis, lung volumes, RV/TLC, preserved lung function.
In their study published in the June 2018 issue of Thorax, Zeng et al showed that RV/TLC ratio in smokers with preserved lung function is associated with clinical diagnosis of COPD, higher rates of respiratory medications prescriptions, emergency room visits, hospitalizations, and all cause-mortality[1]. The findings strongly support that patients with respiratory symptoms and normal spirometry who have air trapping in lung volume measurements have worse outcomes than those with no air trapping. Those patients at risk for COPD may suffer early obstructive lung disease which has not yet met the spirometric criteria for COPD diagnosis.
I congratulate the authors for their study as they address a very clinically relevant topic. Further studies are neede...
Should lung volumes measurement accompany every spirometry?
Spyridon Fortis MD1
1Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Hospital and Clinics, Iowa City, IA, USA
Corresponding Author:
Spyridon Fortis, MD
UIHC – Internal Medicine
200 Hawkins Drive – C33 GH
Iowa City, IA 52242
Email: spyridon-fortis@uiowa.edu
Word Count:
Author Disclosures: Authors declare that there is no conflict of interest regarding the publication of this paper.
Running Head: Lung volumes with every spirometry
Key Words: COPD, diagnosis, lung volumes, RV/TLC, preserved lung function.
In their study published in the June 2018 issue of Thorax, Zeng et al showed that RV/TLC ratio in smokers with preserved lung function is associated with clinical diagnosis of COPD, higher rates of respiratory medications prescriptions, emergency room visits, hospitalizations, and all cause-mortality[1]. The findings strongly support that patients with respiratory symptoms and normal spirometry who have air trapping in lung volume measurements have worse outcomes than those with no air trapping. Those patients at risk for COPD may suffer early obstructive lung disease which has not yet met the spirometric criteria for COPD diagnosis.
I congratulate the authors for their study as they address a very clinically relevant topic. Further studies are needed to examine whether treatment of those patients improve their outcomes. The prevalence of this specific pulmonary function test pattern, though, may be much less than 30%. Using more stringent exclusion criteria, authors excluded 15,236 patients for low TLC. They also excluded 16,904 patients with interstitial lung disease to ensure that the high-risk for COPD sample is not contaminated by other lung diseases like interstitial lung disease. These numbers appear extremely high given that the prevalence of ILD is much less than 1%. The stringent exclusion criteria (120,461 of 127,940) have resulted in a very small denominator which led to overestimation of the prevalence of this PFT pattern. In previous studies, including only PFTs that met the American Thoracic Society standards from one single health system, we have shown that only 4.9% of all PFTs had TLC, RV and/or RV/TLC above the upper limit of normal[2 , 3]. Of those PFTs with normal spirometry, 12.4% of them had TLC, RV and/or RV/TLC above the upper limit of normal and 10.4% of them had RV and/or RV/TLC above the upper limit of normal[3].
Using lung volumes like RV/TLC in every day practice should be exercised with caution, in particular, in the presence of normal spirometry. The reference values determine whether a spirometric or lung volume value of an individual is normal or abnormal. Spirometric reference values are derived from large cohorts[4] while lung volume measurement values are derived from small cohort and often from samples that are not representative of our population[5]. Thus, lung volume measurements are less reliable than spirometry.
Although, lung volume measurement may offer additional prognostic information, it is still unclear whether they should be routinely performed in every patient with respiratory symptoms.
References
1. Zeng S, Tham A, Bos B, Jin J, Giang B, Arjomandi M. Lung volume indices predict morbidity in smokers with preserved spirometry. Thorax 2018 doi: 10.1136/thoraxjnl-2018-211881[published Online First: Epub Date]|.
2. Fortis S, Corazalla EO, Jacobs DR, Jr., Kim HJ. Persistent Empiric COPD Diagnosis and Treatment After Pulmonary Function Test Showed No Obstruction. Respiratory care 2016;61(9):1192-200 doi: 10.4187/respcare.04647[published Online First: Epub Date]|.
3. Fortis S, Corazalla EO, Kim HJ. Does normal spirometry rule out an obstructive or restrictive ventilatory defect? Respiratory investigation 2017;55(1):55-57 doi: 10.1016/j.resinv.2016.07.005[published Online First: Epub Date]|.
4. Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general U.S. population. American journal of respiratory and critical care medicine 1999;159(1):179-87 doi: 10.1164/ajrccm.159.1.9712108[published Online First: Epub Date]|.
5. Quanjer PH, Tammeling GJ, Cotes JE, Pedersen OF, Peslin R, Yernault JC. Lung volumes and forced ventilatory flows. Report Working Party Standardization of Lung Function Tests, European Community for Steel and Coal. Official Statement of the European Respiratory Society. The European respiratory journal. Supplement 1993;16:5-40
We thank the authors of the letter in response to our paper for their interest and positive appraisal of our model. Likewise, we appreciate the design of the Multicenter Italian Lung Detection (MILD) trial which, despite its small sample size, demonstrates that annual intervals are unnecessary for the majority of screenees. Once more European data is available to perform cost-effectiveness analyses, we hypothesize that personalised screening intervals will prove to be the preferred design. Furthermore, it is estimated that most inclusion criteria used to select high-risk participants encompass only 70% of all lung cancer cases in the population; reassessing risk and tailoring interval groups after the baseline scan may enable the inclusion of persons of lower risk. As Silva et al mentioned, there is no reason to set the upper limit of follow-up intervals at 2-years. We also agree that volumetric nodule measurements are better suited for determining follow-up procedures than (perpendicular) diameter, and hope to be able to implement this into a future model. Moreover, risk scores may be calculated autonomously by computers in the future, with only a select few dubious cases requiring radiologist attention.
The EPICC trial addresses the rarely investigated topic of rehabilitation in the critical care setting [1]. We note with interest that no improvement was found in outcomes in the rehabilitation group compared to the standard treatment group. Some of the reasons are clearly highlighted by Schaller et al. in their response to the paper including the time to starting intervention, therapy times and also sample size. Only 41% of the participants in the intervention group and 35% of the standard treatment group contributed data throughout the study period. In addition to this, only 8% of the intervention group managed over half the target therapy time and the EPICC trial showed that ‘an extra 10 minutes of physical therapy per day does not make a difference [2]’
This study triggered an audit within our own 16 bedded mixed surgical and medical intensive care department assessing the number of sessions carried out over a 2 week period compared to those attempted. We investigated the actual duration of sessions achieved as compared to a target of 45 minutes rehabilitation each day during the working week (Monday-Friday). On average, 23.3 (standard deviation 20.19 minutes) minutes of rehabilitation per day was achieved and only 35% of attempted physical therapy sessions were completed. These figures are similar to those cited within the EPICC trial and highlight some of the difficulties of achieving longer therapy times within a busy intensive care department. Some of the fac...
The EPICC trial addresses the rarely investigated topic of rehabilitation in the critical care setting [1]. We note with interest that no improvement was found in outcomes in the rehabilitation group compared to the standard treatment group. Some of the reasons are clearly highlighted by Schaller et al. in their response to the paper including the time to starting intervention, therapy times and also sample size. Only 41% of the participants in the intervention group and 35% of the standard treatment group contributed data throughout the study period. In addition to this, only 8% of the intervention group managed over half the target therapy time and the EPICC trial showed that ‘an extra 10 minutes of physical therapy per day does not make a difference [2]’
This study triggered an audit within our own 16 bedded mixed surgical and medical intensive care department assessing the number of sessions carried out over a 2 week period compared to those attempted. We investigated the actual duration of sessions achieved as compared to a target of 45 minutes rehabilitation each day during the working week (Monday-Friday). On average, 23.3 (standard deviation 20.19 minutes) minutes of rehabilitation per day was achieved and only 35% of attempted physical therapy sessions were completed. These figures are similar to those cited within the EPICC trial and highlight some of the difficulties of achieving longer therapy times within a busy intensive care department. Some of the factors cited by our physiotherapists include patient fatigue, limitations on staff availability and also clinical appropriateness of the patients for rehabilitation. Of interest, Schaller et al. have also suggested some solutions to the problems encountered in our own audit and also within the EPICC trial. We agree that the combined effort of the multi-disciplinary team is extremely important in order to maximise opportunities for rehabilitation sessions but also the ability to then consider multiple short sessions each day.
In summary, we applaud the attempts of Wright and colleagues for addressing this under-investigated area of rehabilitation. We have reservations about some of the conclusions of the paper due to limited differences between intervention and treatment and underpowering of the study. Our own unpublished data supports the finding that to consistently achieve 90 minutes of therapy per patient as stipulated for the intervention group in this study would be very difficult in the current framework but this is an area to improve with some solutions already suggested.
References:
1. Wright SE, Thomas K, Watson G, et al. Intensive versus standard physical rehabilitation therapy in the critically ill (EPICC): a multicentre, parallel-group, randomised controlled trial. Thorax 2017:thoraxjnl-2016-209858
2. Schaller SJ, Nydahl P, Blobner M, et al. What does the EPICC trial really tell us? Thorax 2018
In 2011, the National Lung Cancer Screening Trial (NLST) showed that annual low-dose computed tomography (LDCT) improved overall survival (1). More recently, longer interval between LDCT rounds was advocated to improve screening efficiency after baseline (2).
Schreuder et al reported a comprehensive model for optimization of LDCT by biennial rounds for subjects at lower 2-year risk of lung cancer (3). They built a promising polynomial model including both patient characteristics and nodule descriptors. The retrospective simulation on NLST data provided enough power to test Schreuder’s model (3) as well as other models for selection of subjects to be forwarded to biennial screening (2, 4). We appreciate this approach to parsimonious LDCT administration as we are strongly convinced that annual screening should be tailored to subjects with remarkably high risk of lung cancer. The authors refer that prospective randomized controlled trial with tailored screening intervals would be hardly feasible, however we would like to remind that some experience was already reported in the literature.
Since 2005, the Multicenter Italian Lung Detection (MILD) trial conducted a prospective comparison between annual (LDCT1 = 1,152 screenees) and biennial LDCT (LDCT2 = 1,151 screenees) (5). The LDCT2 screenees were shifted to annual screening in case of solid nodule > 60 mm^3 and/or subsolid nodules. In other words, the MILD trial prospectively tested a risk model for tailored s...
In 2011, the National Lung Cancer Screening Trial (NLST) showed that annual low-dose computed tomography (LDCT) improved overall survival (1). More recently, longer interval between LDCT rounds was advocated to improve screening efficiency after baseline (2).
Schreuder et al reported a comprehensive model for optimization of LDCT by biennial rounds for subjects at lower 2-year risk of lung cancer (3). They built a promising polynomial model including both patient characteristics and nodule descriptors. The retrospective simulation on NLST data provided enough power to test Schreuder’s model (3) as well as other models for selection of subjects to be forwarded to biennial screening (2, 4). We appreciate this approach to parsimonious LDCT administration as we are strongly convinced that annual screening should be tailored to subjects with remarkably high risk of lung cancer. The authors refer that prospective randomized controlled trial with tailored screening intervals would be hardly feasible, however we would like to remind that some experience was already reported in the literature.
Since 2005, the Multicenter Italian Lung Detection (MILD) trial conducted a prospective comparison between annual (LDCT1 = 1,152 screenees) and biennial LDCT (LDCT2 = 1,151 screenees) (5). The LDCT2 screenees were shifted to annual screening in case of solid nodule > 60 mm^3 and/or subsolid nodules. In other words, the MILD trial prospectively tested a risk model for tailored screening intervals that was based on baseline LDCT findings. Given the small sample of this trial, we underline that the absence of outcome difference between LDCT1 and LDCT2 should be carefully interpreted (5).
In summary, baseline LDCT2 prospectively selected 147/1,151 (12.8%) screenees for annual screening, whilst 1,004/1,151 (87.2%) were forwarded to biennial round (6). The LDCT2 risk model allowed a sharp reduction by almost 90% LDCTs at the first incidence round. This is a considerable proportion compared to the retrospective analysis presented by Schreuder et al (simulations led to potential LDCT reduction ranging from 10.4% to 81.6%) (3). A similar figure was seen every other year throughout 7.3 years, with overall cuts of 32% LDCTs in LDCT2 compared with LDCT1 (6).
We would like to emphasize that the volumetric risk model allowed higher detection rate in LDCT2 screenees selected for annual screening (1.36% namely 2 lung cancers in 147 screenees) compared to LDCT1 screenees (0.45% namely 5 lung cancers in 1,111 screenees), at the first annual LDCT after baseline. Under this prospective condition, the number of scans needed to diagnose one lung cancer case (NND) was 74 in LDCT2, which is quite close to the 60 NND proposed by Schreuder by accepting a delay of diagnosis in 25% of screenees. On the other hand, NND was 222.2 in LDCT1, namely within the NND range reported in the literature (from 125 to 385) at the first annual LDCT by fixed annual screening algorithms (1, 7, 8). Hence, the LDCT2 algorithm prompted the lowest prospective NND reported in the literature. Through the median 7.3 years of LDCT screening in MILD, the cumulative NND during annual screenings was 63 for selected LDCT2 screenees and 250 for LDCT1 (p=0.003). Selective 3-year screening interval is now being tested in the bioMILD trial with higher volumetric threshold for solid nodules and including circulating biomarkers (ClinicalTrials.gov: NCT02247453; >4,000 baseline LDCTs acquired, results expected by 2020).
We hypothesize that the prospective application of Schreuder’s model might even improve the efficiency of MILD LDCT2. Furthermore, enrichment by clinical variables is encouraged, including circulating biomarkers. More models like Schreuder’s are fostered for optimization of radiologists’ workload for the seemingly approaching practice of population-based lung cancer screening by LDCT.
References
1. National Lung Screening Trial Research T, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. The New England journal of medicine. 2011;365(5):395-409.
2. Patz EF, Jr., Greco E, Gatsonis C, Pinsky P, Kramer BS, Aberle DR. Lung cancer incidence and mortality in National Lung Screening Trial participants who underwent low-dose CT prevalence screening: a retrospective cohort analysis of a randomised, multicentre, diagnostic screening trial. The Lancet Oncology. 2016;17(5):590-9.
3. Schreuder A, Schaefer-Prokop CM, Scholten ET, Jacobs C, Prokop M, van Ginneken B. Lung cancer risk to personalise annual and biennial follow-up computed tomography screening. Thorax. 2018.
4. White CS, Dharaiya E, Campbell E, Boroczky L. The Vancouver Lung Cancer Risk Prediction Model: Assessment by Using a Subset of the National Lung Screening Trial Cohort. Radiology. 2017;283(1):264-72.
5. Pastorino U, Rossi M, Rosato V, Marchiano A, Sverzellati N, Morosi C, et al. Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the MILD trial. European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation. 2012;21(3):308-15.
6. Sverzellati N, Silva M, Calareso G, Galeone C, Marchiano A, Sestini S, et al. Low-dose computed tomography for lung cancer screening: comparison of performance between annual and biennial screen. European radiology. 2016;26(11):3821-9.
7. Yousaf-Khan U, van der Aalst C, de Jong PA, Heuvelmans M, Scholten E, Walter J, et al. Risk stratification based on screening history: the NELSON lung cancer screening study. Thorax. 2017.
8. Tammemagi MC, Schmidt H, Martel S, McWilliams A, Goffin JR, Johnston MR, et al. Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study. The Lancet Oncology. 2017.
We are grateful to Dr. Duerden and Dr. Levy for their comments on our paper which highlight the difficulty of comparing doses of ICS steroids when there is no gold standard comparator. Our aim in compiling Table 1 was to point out that the NICE table does not allow for the greater potency of HFA FP compared to HFA BDP. We were concerned that this was a significant safety issue especially in children [1]. In our efforts to simplify this message, we had not fully explained or allowed for some of the other variables.
1. Dr. Levy is correct to point out that the original GINA table (used by NICE) of “Low, medium and high daily doses of inhaled corticosteroid for children 6-11 years” has a statement below indicating that the table is not a table of “dose equivalency”, the term we used in Table 1, but of “estimated clinical comparability”.
2. The GINA table (but not NICE) also has a footnote explaining the inclusion of beclometasone dipropionate CFC (BDP CFC) as a comparison with older literature. CFCs (chlorofluorocarbons), as propellants in metered dose inhalers, were phased out under the Montreal Protocol and were replaced by HFAs (hydrofluorolakanes). However, CFC BDP is still often used as the reference standard when comparing ICS in terms of their potency.
3. Most newer HFA ICSs have been formulated to be equipotent with the CFC ICS they were replacing. As one example, the BTS/SIGN table includes the proprietary HFA BPD, Clenil modulite, commonly us...
We are grateful to Dr. Duerden and Dr. Levy for their comments on our paper which highlight the difficulty of comparing doses of ICS steroids when there is no gold standard comparator. Our aim in compiling Table 1 was to point out that the NICE table does not allow for the greater potency of HFA FP compared to HFA BDP. We were concerned that this was a significant safety issue especially in children [1]. In our efforts to simplify this message, we had not fully explained or allowed for some of the other variables.
1. Dr. Levy is correct to point out that the original GINA table (used by NICE) of “Low, medium and high daily doses of inhaled corticosteroid for children 6-11 years” has a statement below indicating that the table is not a table of “dose equivalency”, the term we used in Table 1, but of “estimated clinical comparability”.
2. The GINA table (but not NICE) also has a footnote explaining the inclusion of beclometasone dipropionate CFC (BDP CFC) as a comparison with older literature. CFCs (chlorofluorocarbons), as propellants in metered dose inhalers, were phased out under the Montreal Protocol and were replaced by HFAs (hydrofluorolakanes). However, CFC BDP is still often used as the reference standard when comparing ICS in terms of their potency.
3. Most newer HFA ICSs have been formulated to be equipotent with the CFC ICS they were replacing. As one example, the BTS/SIGN table includes the proprietary HFA BPD, Clenil modulite, commonly used in children. Clenil is clinically comparable to the legacy BDP CFC with a recommended starting dose of Clenil 50 2 puffs BD (100ug bd) in children (2). The BTS/SIGN table also includes the HFA BDP, Qvar, which is formulated as an extra-fine particle preparation and is usually considered as effective at half the dose. GINA and NICE give the doses for HFA BDP in children as half those of the BDP CFC but do not specify which preparations of HFA BDP they are referring to in their table.
4. HFA Fluticasone propionate (HFA FP) is as effective as other inhaled steroids at approximately half the microgram daily dose (3). So, for clinical comparability HFA FP should be used at half the dose of HFA BDP, as indicated in the BTS/SIGN table.
5. A further complication is that GINA provides doses as daily doses: BTS/SIGN provides puffs and frequency; NICE only specifies dose (though assumed to be daily dose as the table was taken from GINA). In preparing the table we should have made clear that the doses from the BTS/SIGN table were to be given twice a day.
The challenge was how to explain this simply in limited text and a small table and it was complicated by the other points discussed above. To clarify these issues, we suggest the table should be modified as follows:
More fundamentally, this discussion highlights the potential complexity for prescribers from the increasing number of corticosteroid molecules available in an ever-increasing number of devices. If a national guideline body and ‘experts’ who have spent some hours poring over these data struggle to achieve clarity what hope is there for the busy prescriber in clinical practice? Perhaps, this discussion should stimulate regulatory authorities to consider whether manufacturers of inhaled corticosteroids could be required to label their devices as providing doses equivalent to an agreed standard.
Finally, prescribers should always remember that the numbers in all these tables are only a guide. The correct dose of an inhaled steroid is the lowest dose that keeps the patient free of symptoms and that this dose should be adjusted dynamically in response to changes in status in accordance with an agreed action plan.
References
1. Paton J, Jardine E, McNeill E, et al. Adrenal responses to low dose synthetic ACTH (Synacthen) in children receiving high dose inhaled fluticasone. Arch Dis Child. 2006 Oct;91(10):808-13.
2. The electronic Medicines Compendium. https://www. medicines. org. uk/ emc/ medicine/30651 (accessed 14 Jan 2018).
3. The electronic Medicines Compendium. https://www. medicines. org. uk/ emc/ medicine/2913 (accessed 14 Jan 2018).
We welcome the letter by Anna Humphreys and colleagues highlighting the secondary benefits of screening contacts of extra pulmonary tuberculosis for LTBI in areas where active cases are predominantly amongst the non-UK born (1).
We share the view that novel approaches are needed to identify and offer testing to those at risk of LTBI, and that contact tracing provides a unique opportunity to reach those who may be eligible.
Early results from the London Borough of Newham, the pilot site for the national latent TB screening programme highlight that uptake of LTBI screening amongst recent migrants is only 40 percent (2). Efforts are being made to improve awareness including animated health promotion tools (https://youtu.be/tKwAHJ7JeV0) and TB Alert’s Latent TB Handbook (https://www.tbalert.org/health-professionals/ltbi-toolkit/) and novel interventions to improve LTBI screening and treatment uptake are being implemented across the country. We are currently investigating the efficacy of managing LTBI entirely within primary care (https://clinicaltrials.gov/ct2/show/NCT03069807). Recent work has also identified that opportunistic LTBI screening in non-health settings is acceptable to recent migrants (3).
In areas where the majority of active cases are amongst those...
We welcome the letter by Anna Humphreys and colleagues highlighting the secondary benefits of screening contacts of extra pulmonary tuberculosis for LTBI in areas where active cases are predominantly amongst the non-UK born (1).
We share the view that novel approaches are needed to identify and offer testing to those at risk of LTBI, and that contact tracing provides a unique opportunity to reach those who may be eligible.
Early results from the London Borough of Newham, the pilot site for the national latent TB screening programme highlight that uptake of LTBI screening amongst recent migrants is only 40 percent (2). Efforts are being made to improve awareness including animated health promotion tools (https://youtu.be/tKwAHJ7JeV0) and TB Alert’s Latent TB Handbook (https://www.tbalert.org/health-professionals/ltbi-toolkit/) and novel interventions to improve LTBI screening and treatment uptake are being implemented across the country. We are currently investigating the efficacy of managing LTBI entirely within primary care (https://clinicaltrials.gov/ct2/show/NCT03069807). Recent work has also identified that opportunistic LTBI screening in non-health settings is acceptable to recent migrants (3).
In areas where the majority of active cases are amongst those born in high TB-burden countries, a reinterpretation of contact screening that encompasses the aims of identifying active TB cases, as well as recently and remotely acquired LTBI would allow TB programmes to reach many more people at risk.
The National guidelines were updated based on evidence that non-pulmonary TB is a lower risk for transmission (4). Screening contacts is justified given the public health risk of active disease in those recently exposed and a case can be made for screening contacts of extra pulmonary cases as they may have been infected by a previously unknown pulmonary index case.
However, should we decide to change our approach to screening contacts with the explicit aim of identifying LTBI due to remote infection then we must at least acknowledge this when we talk to patients about their recent contacts and ask them to share personal information. And, as a TB community, we must agree that this approach is ethical.
We would support this broader eligibility for screening contacts and believe it would strengthen an already vital tool in the fight to eliminate Tuberculosis in the UK.
1. Humphreys A, Abbara A, Williams S, John L, Corrah T, McGregor A, et al. Screening contacts of patients with extrapulmonary TB for latent TB infection. Thorax. 2018;73(3):277-8.
2. Loutet M. National Roll-out of Latent Tuberculosis Testing and Treatment for New Migrants in England: a retrospective evaluation in a high-incidence area. European Respiratory Journal. 2017:1-25.
3. Walker CL, Duffield K, Kaur H, Dedicoat M, Gajraj R. Acceptability of latent tuberculosis testing of migrants in a college environment in England. Public Health. 2018;158:55-60.
4. Hoppe LE, Kettle R, Eisenhut M, Abubakar I. Tuberculosis—diagnosis, management, prevention, and control: summary of updated NICE guidance. BMJ. 2016:h6747-9.
We have read with great interest the multi-centred EPICC trial that randomized over 300 patients [1]. While the delivery of a complex physical rehabilitation intervention in clinical trials is difficult, we believe that several aspects of the trial may have resulted in the inability to detect a difference between the control and intervention groups. These factors include the delayed time to start the intervention, inadequate delivery of the intervention and the large loss to follow-up for the primary outcome measure. In our opinion, these three factors limit the interpretation of the results of the study. While the authors have mentioned some of these concerns in their discussion, and Connolly et al. raised some of these points already [2], we hope to learn some important lessons from the authors to better understand these limitations and how they can be minimized in future studies.
The number of randomized controlled trials evaluating early physical rehabilitation in ICUs is increasing [3]. Positive effects on primary outcomes were only found in studies in which physical rehabilitation was started within 72 hours of ICU admission [4-6]. Studies, which did not meet this criterion of early onset of physical rehabilitation, did not demonstrate benefit of the intervention [7]. Therefore, this time frame has been defined in rehabilitation guidelines [8]. Based on this evidence, we are not surprised that the authors of the EPICC trial were unable to demonstrate beneficial...
We have read with great interest the multi-centred EPICC trial that randomized over 300 patients [1]. While the delivery of a complex physical rehabilitation intervention in clinical trials is difficult, we believe that several aspects of the trial may have resulted in the inability to detect a difference between the control and intervention groups. These factors include the delayed time to start the intervention, inadequate delivery of the intervention and the large loss to follow-up for the primary outcome measure. In our opinion, these three factors limit the interpretation of the results of the study. While the authors have mentioned some of these concerns in their discussion, and Connolly et al. raised some of these points already [2], we hope to learn some important lessons from the authors to better understand these limitations and how they can be minimized in future studies.
The number of randomized controlled trials evaluating early physical rehabilitation in ICUs is increasing [3]. Positive effects on primary outcomes were only found in studies in which physical rehabilitation was started within 72 hours of ICU admission [4-6]. Studies, which did not meet this criterion of early onset of physical rehabilitation, did not demonstrate benefit of the intervention [7]. Therefore, this time frame has been defined in rehabilitation guidelines [8]. Based on this evidence, we are not surprised that the authors of the EPICC trial were unable to demonstrate beneficial effects of their intervention, since it approximately started on day 8 (the median (IQR) duration of ventilation at randomisation was 4 (3–7) days with a further 3 (1–6) days until the first physical rehabilitation was received), which may be too late to confer benefit.
The EPICC protocol aimed to compare 30 min vs. 90 min of physical rehabilitation, which was not achieved in either group. The authors report that the daily physical therapy was applied for 23 min in the intervention group (26% of the planned intervention) and for 13 min in standard of care group (43% of the planned standard care), respectively. Not only this amount of physical rehabilitation is hardly conceivable as an effective therapeutic strategy, but in terms of testing the hypothesis, it did not achieve the planned separation between the groups. In other words, the EPICC trial teaches us that an additional 10 minutes of physical rehabilitation per day does not make a difference.
The authors describe two main reasons for not achieving their planned intervention: (a) sedation and (b) patient fatigue. We have difficulties understanding why these two factors could not be controlled. The authors do not report the sedation regime during the study. If there was no standardized sedation protocol with daily awakening trials, this is a further shortcoming of the protocol as well as another reason for inability to improve the patients’ outcome in the intervention group. Deep sedation not only inhibits effective physical rehabilitation but is associated with increased mortality and length of stay [8]. To better appreciate the study results, it would be important that the authors report the sedation protocols/regimes of the participating centres.
Furthermore, how was the treatment plan of the intervention in “difficult” patients with low physical reserve or pre-existing frailty implemented? From our own experience patients with impaired functional reserves or frailty might only be trained for a short period of time. However, to achieve an intervention time of 90 minutes per day, such patients must be trained several times a day (e.g. for 15 minutes every hour). If 23 minutes are achieved in the intervention group, there might have been an additional “resource” limitations which is not presented, e.g. that a physical therapist would not be able to do more than a session every 4 hours or than four times a day. Would it be possible to elaborate on the available staff resources to deliver the intervention? If we assume that the lack of staff resources were an additional limitation not mentioned by the authors, it supports our belief that a successful early rehabilitation program has to be interprofessional, including both physical therapists and nursing staff to maximize physical rehabilitation opportunities throughout the day [5, 10].
Finally, the authors chose a primary outcome (Physical Component Summary Score of the SF-36 version 2) that does take into account mortality. Approximately one third of patients enrolled died before the functional outcome could be assessed. This functional outcome “truncated due to death” creates challenges in the definition and statistical evaluation of the treatment effect, and deserves careful planning of statistical analyses [11].
An additional 25% of enrolled patients were lost to follow-up. Therefore 57% of the enrolled patients did not contribute to the primary outcome measure, making the overall results of the study difficult to interpret.
In summary, we think that the conclusion and key message do not entirely reflect the study results. Physical therapy interventions of 90 min vs. 30 minutes were not achieved in the study. Additionally, less than half of enrolled patients contributed data to the primary outcome measure and excluding patients who did not survive limits the interpretation of this complex intervention.
References
1. Wright, S.E., et al., Intensive versus standard physical rehabilitation therapy in the critically ill (EPICC): a multicentre, parallel-group, randomised controlled trial. Thorax, 2017.
2. Connolly B and Denehy L Hindsight and moving the needle forwards on rehabilitation trial design. Thorax. 2018 Mar;73(3):203-205. doi: 10.1136/thoraxjnl-2017-210588. Epub 2017 Nov 14. http://thorax.bmj.com/content/73/3/203
3. Fuest, K. and S.J. Schaller, Recent evidence on early mobilization in critical-Ill patients. Curr Opin Anaesthesiol, 2018.
4. Schweickert, W.D., et al., Early physical and occupational therapy in mechanically ventilated, critically ill patients: a randomised controlled trial. Lancet, 2009. 373(9678): p. 1874-82.
5. Schaller, S.J., et al., Early, goal-directed mobilisation in the surgical intensive care unit: a randomised controlled trial. Lancet, 2016. 388(10052): p. 1377-1388.
6. Investigators, T.S., et al., Early mobilization and recovery in mechanically ventilated patients in the ICU: a bi-national, multi-centre, prospective cohort study. Crit Care, 2015. 19: p. 81.
7. Moss, M., et al., A Randomized Trial of an Intensive Physical Therapy Program for Patients with Acute Respiratory Failure. Am J Respir Crit Care Med, 2016. 193(10): p. 1101-10.
8. Bein, T., et al., S2e guideline: positioning and early mobilisation in prophylaxis or therapy of pulmonary disorders : Revision 2015: S2e guideline of the German Society of Anaesthesiology and Intensive Care Medicine (DGAI). Anaesthesist, 2015. 64 Suppl 1: p. 1-26.
9. Stephens, R.J., et al., Practice Patterns and Outcomes Associated With Early Sedation Depth in Mechanically Ventilated Patients: A Systematic Review and Meta-Analysis. Crit Care Med, 2018. 46(3): p. 471-479.
10. McWilliams, D., et al., Earlier and enhanced rehabilitation of mechanically ventilated patients in critical care: A feasibility randomised controlled trial. J Crit Care, 2018. 44: p. 407-412.
11. Colantuoni, E., et al., Statistical methods to compare functional outcomes in randomized controlled trials with high mortality. BMJ, 2018. 360: p. j5748.
The article of Bhatt et al addresses an important topic (1). The authors assessed the relative contribution of intensity and duration of tobacco smoke exposure to the development of chronic obstructive pulmonary disease (COPD). They concluded that smoking duration alone provides stronger risk than the composite index of pack years. In other words, the effect of long and low intensity exposure has a stronger association with COPD than short exposures of high intensities. The article of Marks consents this finding, concluding that pack years are a suboptimal index of exposure (2).
A major limitation of the study of Bhatt, which surprisingly is not stated as such, is the use of a cross-sectional design that does not allow drawing causal conclusions. The conclusions drawn therefore might be flawed.
Selection bias due the healthy ‘survivor’ effect might have occurred. The duration of smoking could have been influenced by the deleterious effects a person experiences from the exposure to smoke. Those with a long smoke duration are more likely not to experience (or experience less) health issues due to smoking, and might therefore have less severe (or no) COPD than those with a short smoke duration. In line with this, selective ‘drop-out’ of the more diseased persons may have biased the results.
Furthermore, the authors use retrospective data, while this often leads to recall bias. Participants often do not precisely remember the numbers of cigarettes smoked...
The article of Bhatt et al addresses an important topic (1). The authors assessed the relative contribution of intensity and duration of tobacco smoke exposure to the development of chronic obstructive pulmonary disease (COPD). They concluded that smoking duration alone provides stronger risk than the composite index of pack years. In other words, the effect of long and low intensity exposure has a stronger association with COPD than short exposures of high intensities. The article of Marks consents this finding, concluding that pack years are a suboptimal index of exposure (2).
A major limitation of the study of Bhatt, which surprisingly is not stated as such, is the use of a cross-sectional design that does not allow drawing causal conclusions. The conclusions drawn therefore might be flawed.
Selection bias due the healthy ‘survivor’ effect might have occurred. The duration of smoking could have been influenced by the deleterious effects a person experiences from the exposure to smoke. Those with a long smoke duration are more likely not to experience (or experience less) health issues due to smoking, and might therefore have less severe (or no) COPD than those with a short smoke duration. In line with this, selective ‘drop-out’ of the more diseased persons may have biased the results.
Furthermore, the authors use retrospective data, while this often leads to recall bias. Participants often do not precisely remember the numbers of cigarettes smoked per day in a specific year or time period. Assuming that participants underestimate smoke intensity, it is likely that the association between smoke intensity and COPD is less strong than the association between smoke duration and COPD. This weaker association is then not due to the relative contribution of smoke intensity compared to that of duration, but to recall bias.
To conclude, the statements made in the paper seem to be very convincing, but are based on research with methodological limitations. Prospective studies assessing the relative contribution of smoke intensity and smoke duration are thus urgently needed.
1. Bhatt SP, Kim YI, Harrington KF, Hokanson JE, Lutz SM, Cho MH, et al. Smoking duration alone provides stronger risk estimates of chronic obstructive pulmonary disease than pack-years. Thorax. 2018.
2. Marks GB. Guiding policy to reduce the burden of COPD: the role of epidemiological research. Thorax. 2018.
We read with interest the findings of Miele et al. on the relationship between environmental exposures and decline in lung function (1). The authors reported that living in urban settings and living at high altitude were associated with accelerated decline in pre-bronchodilator FEV1 and FVC. Investigating the effects at area level is important from a public health perspective and extra analysis on this valuable dataset as suggested below will help to untangle these links further.
Show MoreStudy participants were recruited from four settings in Peru: Lima, Tumbles, urban Puno and rural Puno (1). Urban living and high-altitude dwelling (as binary variables) were defined based on these four settings. The authors compared the effect of urban living (Lima and urban Puno) with rural living (Tumbes and rural Puno); and the effect of high-altitude dwelling (urban Puno and rural Puno) with low-altitude dwelling (Lima and Tumbes). It is possible that the observed independent effects found by the authors of urban living and high-altitude dwelling may be driven by the urban Puno group (high altitude and urban living). In other words, there may be an interaction between urban living and high-altitude dwelling and investigating this potential interaction would be informative.
As discussed by the authors, the adverse effect of high-altitude dwelling on lung function decline may partly be related to hypoxia and adverse effects from living in urban settings may be related to outdoor air...
We appreciate the points raised by the commentator about our study (Zeng et al.)[1] published in June 2018 issue of Thorax that (1) the prevalence of abnormal residual volume to total lung capacity ratio (RV:TLC) in our study of ever-smokers with preserved spirometry is substantially higher than that observed in the commentator’s past studies,[2-4] and (2) an assumption by the commentator that stringent exclusion of those with abnormally low TLC and those with diagnosis codes of interstitial lung diseases (ILD) in their electronic health records (EHR) may have resulted in overestimation of the prevalence of abnormally high RV:TLC among smokers with preserved spirometry.
We would like to draw the attention of commentator and readers to the following points:
1- The studies referenced by the commentator used pulmonary function tests (PFT) data collected from 708 patients in 2013 across 5 clinical sites associated with University of Minnesota Medical Center with inclusion criteria of patients 18 years of age or older with or without history of smoking.[2] They included about 50% women and 3 African Americans. Our study was performed on PFT data obtained from 1985 through 2017 through the United States Veterans Affairs (VA) nationwide EHR from 7,479 patients across 37 VA medical centers in the United States with inclusion criteria of patients 40 years of age or older with an EHR diagnosis code of smoking, which likely suggests heavy smoking for VA patients. Our st...
Show MoreShould lung volumes measurement accompany every spirometry?
Spyridon Fortis MD1
1Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Hospital and Clinics, Iowa City, IA, USA
Corresponding Author:
Spyridon Fortis, MD
UIHC – Internal Medicine
200 Hawkins Drive – C33 GH
Iowa City, IA 52242
Email: spyridon-fortis@uiowa.edu
Word Count:
Author Disclosures: Authors declare that there is no conflict of interest regarding the publication of this paper.
Running Head: Lung volumes with every spirometry
Key Words: COPD, diagnosis, lung volumes, RV/TLC, preserved lung function.
In their study published in the June 2018 issue of Thorax, Zeng et al showed that RV/TLC ratio in smokers with preserved lung function is associated with clinical diagnosis of COPD, higher rates of respiratory medications prescriptions, emergency room visits, hospitalizations, and all cause-mortality[1]. The findings strongly support that patients with respiratory symptoms and normal spirometry who have air trapping in lung volume measurements have worse outcomes than those with no air trapping. Those patients at risk for COPD may suffer early obstructive lung disease which has not yet met the spirometric criteria for COPD diagnosis.
Show MoreI congratulate the authors for their study as they address a very clinically relevant topic. Further studies are neede...
We thank the authors of the letter in response to our paper for their interest and positive appraisal of our model. Likewise, we appreciate the design of the Multicenter Italian Lung Detection (MILD) trial which, despite its small sample size, demonstrates that annual intervals are unnecessary for the majority of screenees. Once more European data is available to perform cost-effectiveness analyses, we hypothesize that personalised screening intervals will prove to be the preferred design. Furthermore, it is estimated that most inclusion criteria used to select high-risk participants encompass only 70% of all lung cancer cases in the population; reassessing risk and tailoring interval groups after the baseline scan may enable the inclusion of persons of lower risk. As Silva et al mentioned, there is no reason to set the upper limit of follow-up intervals at 2-years. We also agree that volumetric nodule measurements are better suited for determining follow-up procedures than (perpendicular) diameter, and hope to be able to implement this into a future model. Moreover, risk scores may be calculated autonomously by computers in the future, with only a select few dubious cases requiring radiologist attention.
The EPICC trial addresses the rarely investigated topic of rehabilitation in the critical care setting [1]. We note with interest that no improvement was found in outcomes in the rehabilitation group compared to the standard treatment group. Some of the reasons are clearly highlighted by Schaller et al. in their response to the paper including the time to starting intervention, therapy times and also sample size. Only 41% of the participants in the intervention group and 35% of the standard treatment group contributed data throughout the study period. In addition to this, only 8% of the intervention group managed over half the target therapy time and the EPICC trial showed that ‘an extra 10 minutes of physical therapy per day does not make a difference [2]’
Show MoreThis study triggered an audit within our own 16 bedded mixed surgical and medical intensive care department assessing the number of sessions carried out over a 2 week period compared to those attempted. We investigated the actual duration of sessions achieved as compared to a target of 45 minutes rehabilitation each day during the working week (Monday-Friday). On average, 23.3 (standard deviation 20.19 minutes) minutes of rehabilitation per day was achieved and only 35% of attempted physical therapy sessions were completed. These figures are similar to those cited within the EPICC trial and highlight some of the difficulties of achieving longer therapy times within a busy intensive care department. Some of the fac...
In 2011, the National Lung Cancer Screening Trial (NLST) showed that annual low-dose computed tomography (LDCT) improved overall survival (1). More recently, longer interval between LDCT rounds was advocated to improve screening efficiency after baseline (2).
Show MoreSchreuder et al reported a comprehensive model for optimization of LDCT by biennial rounds for subjects at lower 2-year risk of lung cancer (3). They built a promising polynomial model including both patient characteristics and nodule descriptors. The retrospective simulation on NLST data provided enough power to test Schreuder’s model (3) as well as other models for selection of subjects to be forwarded to biennial screening (2, 4). We appreciate this approach to parsimonious LDCT administration as we are strongly convinced that annual screening should be tailored to subjects with remarkably high risk of lung cancer. The authors refer that prospective randomized controlled trial with tailored screening intervals would be hardly feasible, however we would like to remind that some experience was already reported in the literature.
Since 2005, the Multicenter Italian Lung Detection (MILD) trial conducted a prospective comparison between annual (LDCT1 = 1,152 screenees) and biennial LDCT (LDCT2 = 1,151 screenees) (5). The LDCT2 screenees were shifted to annual screening in case of solid nodule > 60 mm^3 and/or subsolid nodules. In other words, the MILD trial prospectively tested a risk model for tailored s...
We are grateful to Dr. Duerden and Dr. Levy for their comments on our paper which highlight the difficulty of comparing doses of ICS steroids when there is no gold standard comparator. Our aim in compiling Table 1 was to point out that the NICE table does not allow for the greater potency of HFA FP compared to HFA BDP. We were concerned that this was a significant safety issue especially in children [1]. In our efforts to simplify this message, we had not fully explained or allowed for some of the other variables.
1. Dr. Levy is correct to point out that the original GINA table (used by NICE) of “Low, medium and high daily doses of inhaled corticosteroid for children 6-11 years” has a statement below indicating that the table is not a table of “dose equivalency”, the term we used in Table 1, but of “estimated clinical comparability”.
2. The GINA table (but not NICE) also has a footnote explaining the inclusion of beclometasone dipropionate CFC (BDP CFC) as a comparison with older literature. CFCs (chlorofluorocarbons), as propellants in metered dose inhalers, were phased out under the Montreal Protocol and were replaced by HFAs (hydrofluorolakanes). However, CFC BDP is still often used as the reference standard when comparing ICS in terms of their potency.
3. Most newer HFA ICSs have been formulated to be equipotent with the CFC ICS they were replacing. As one example, the BTS/SIGN table includes the proprietary HFA BPD, Clenil modulite, commonly us...
Show MoreWe welcome the letter by Anna Humphreys and colleagues highlighting the secondary benefits of screening contacts of extra pulmonary tuberculosis for LTBI in areas where active cases are predominantly amongst the non-UK born (1).
We share the view that novel approaches are needed to identify and offer testing to those at risk of LTBI, and that contact tracing provides a unique opportunity to reach those who may be eligible.
Early results from the London Borough of Newham, the pilot site for the national latent TB screening programme highlight that uptake of LTBI screening amongst recent migrants is only 40 percent (2). Efforts are being made to improve awareness including animated health promotion tools (https://youtu.be/tKwAHJ7JeV0) and TB Alert’s Latent TB Handbook (https://www.tbalert.org/health-professionals/ltbi-toolkit/) and novel interventions to improve LTBI screening and treatment uptake are being implemented across the country. We are currently investigating the efficacy of managing LTBI entirely within primary care (https://clinicaltrials.gov/ct2/show/NCT03069807). Recent work has also identified that opportunistic LTBI screening in non-health settings is acceptable to recent migrants (3).
Show MoreIn areas where the majority of active cases are amongst those...
We have read with great interest the multi-centred EPICC trial that randomized over 300 patients [1]. While the delivery of a complex physical rehabilitation intervention in clinical trials is difficult, we believe that several aspects of the trial may have resulted in the inability to detect a difference between the control and intervention groups. These factors include the delayed time to start the intervention, inadequate delivery of the intervention and the large loss to follow-up for the primary outcome measure. In our opinion, these three factors limit the interpretation of the results of the study. While the authors have mentioned some of these concerns in their discussion, and Connolly et al. raised some of these points already [2], we hope to learn some important lessons from the authors to better understand these limitations and how they can be minimized in future studies.
Show MoreThe number of randomized controlled trials evaluating early physical rehabilitation in ICUs is increasing [3]. Positive effects on primary outcomes were only found in studies in which physical rehabilitation was started within 72 hours of ICU admission [4-6]. Studies, which did not meet this criterion of early onset of physical rehabilitation, did not demonstrate benefit of the intervention [7]. Therefore, this time frame has been defined in rehabilitation guidelines [8]. Based on this evidence, we are not surprised that the authors of the EPICC trial were unable to demonstrate beneficial...
The article of Bhatt et al addresses an important topic (1). The authors assessed the relative contribution of intensity and duration of tobacco smoke exposure to the development of chronic obstructive pulmonary disease (COPD). They concluded that smoking duration alone provides stronger risk than the composite index of pack years. In other words, the effect of long and low intensity exposure has a stronger association with COPD than short exposures of high intensities. The article of Marks consents this finding, concluding that pack years are a suboptimal index of exposure (2).
A major limitation of the study of Bhatt, which surprisingly is not stated as such, is the use of a cross-sectional design that does not allow drawing causal conclusions. The conclusions drawn therefore might be flawed.
Selection bias due the healthy ‘survivor’ effect might have occurred. The duration of smoking could have been influenced by the deleterious effects a person experiences from the exposure to smoke. Those with a long smoke duration are more likely not to experience (or experience less) health issues due to smoking, and might therefore have less severe (or no) COPD than those with a short smoke duration. In line with this, selective ‘drop-out’ of the more diseased persons may have biased the results.
Furthermore, the authors use retrospective data, while this often leads to recall bias. Participants often do not precisely remember the numbers of cigarettes smoked...
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