We thank Associate Professor Magnus Ekström et al for their research letter regarding our Cochrane Review: Opioids for the palliation of refractory breathlessness in adults with advanced disease and terminal illness (1,2). We also acknowledge that following the publication of their letter in Thorax, feedback was provided through the appropriate mechanism to the Cochrane Review Group (2). We have published a detailed response to their comments in the feedback section of our review, however, given the seriousness of the criticisms published in Thorax, we think it is important that our response also sit alongside their Thorax letter.
We acknowledge the statistical difficulties in the interpretation and summation of the complex data on opioids for breathlessness. One such issue is the inclusion of crossover studies in a meta-analysis, however, a crossover design is an appropriate way to assess short term interventions, particularly when patient recruitment may be challenging. The Cochrane Handbook outlines several methods to incorporate crossover data into meta-analyses (3). In using the data as if it was a parallel study, the limitations should be acknowledged, in that it can give rise to a unit of analysis error whereby confidence intervals may be wide, and the overall effect is under-estimated. An alternative method is to calculate correlation co-efficients (which describe the ratio of between-patient standard deviation with the within patient variation) to impute...
We acknowledge the statistical difficulties in the interpretation and summation of the complex data on opioids for breathlessness. One such issue is the inclusion of crossover studies in a meta-analysis, however, a crossover design is an appropriate way to assess short term interventions, particularly when patient recruitment may be challenging. The Cochrane Handbook outlines several methods to incorporate crossover data into meta-analyses (3). In using the data as if it was a parallel study, the limitations should be acknowledged, in that it can give rise to a unit of analysis error whereby confidence intervals may be wide, and the overall effect is under-estimated. An alternative method is to calculate correlation co-efficients (which describe the ratio of between-patient standard deviation with the within patient variation) to impute a corrected standard error. Some included studies provided appropriate data to calculate this (standard error of the differences), or a corrected standard error can be imputed using “borrowed” correlation co-efficients from other studies.
In our Cochrane Review we used the former method (2). In response to the feedback provided by Ekström et al, we conducted a sensitivity analysis with an alternative meta-analysis (accounting for use of cross over data) using correlation co-efficients and corrected standard errors. The data are presented using standardised mean differences. The results demonstrate a change from baseline SMD -0.14 (95% CI -0.40 to 0.13) and a post treatment score SMD -0.55 (95% CI -0.76 to -0.35) (Figure 1). This is similar to our original published results which found a change from baseline score SMD -0.09 (95% CI -0.36 to 0.19) and a post treatment score SMD -0.28 (95% CI -0.5 to -0.05). Both analyses draw the same conclusion that there is a significant but small effect size for the use of opioids for breathlessness.
Ekström et al raise concerns regarding the use of a fixed effects versus a random effects model (1). Based on the assumption that studies would have a small sample size we chose a priori to use a fixed effects model. As Higgins and Green describe: a random effects model will award relatively more weight to smaller studies because smaller studies are more informative for learning about the distribution of the effects across studies than for learning about an assumed common intervention effect (3). Therefore, if a random effects model is inappropriately applied, in particular, if the results of small studies are systematically different to the results of larger ones, the random effects model can inappropriately exacerbate the effects of any bias (4).
The choice and rationale for a fixed effects model was outlined in advance in our protocol. This protocol was peer reviewed prior to publication (2). Consistent with Higgins and Green, we presented both a fixed effects and random effects model in the sensitivity analysis, and found no differences in effect (3). Following additional sensitivity analysis as described above, there remains very little difference between the fixed effects model in the change from baseline scores (SMD -0.14 (95% CI -0.40 to 0.13)) and the random effects model (SMD -0.21 (95% CI -0.55 to 0.12)), and in the post treatment score fixed effects model (SMD -0.55 (95% CI -0.76 to -0.35)) and random effects model (SMD -0.69 (95% CI -1.08 to -0.29)).
A second limitation from the opioids for breathlessness data is the use of different scales to measure the same outcome (e.g. visual analogue scale (VAS) or Borg scale), with scales measured on different lengths, with different extremes, and different gradations of intensity. In order to combine data on different scales, standardised mean differences are required, which are calculated by dividing the mean difference by a pooled estimate of the between-patient standard deviation. However, combining this between-patient standard deviation with the within patient variation imputed from the corrected standard error described as above to incorporate crossover trials is not always possible from the available data. It is difficult to interpret the resulting standardised mean differences from cross-over trials.
Transforming the data as described above works if the data are reported as either change from baseline or post treatment scores, however it is unclear if it is also appropriate to combine them in a single meta-analysis, and to combine them in a single meta-analysis using standardised mean difference (SMD). Higgins and Green state that post treatment scores can be combined with change from baseline scores when using an unstandardised mean difference, however, they should not be combined as a standardised mean difference using the standard deviation of the change scores (as these are not the same units as the standard deviation of the final scores) (3). Therefore, it makes it difficult to combine data from different scales as outlined above, as well as combining post treatment and change from baseline scores in one single meta-analysis. Originally, we separated post treatment and change from baseline scores. In a further subsequent sensitivity analyses performed in response to the feedback, we combined these but separated by scale, see Figure 2.
Ekström et al discussed at length the primary outcome of breathlessness, but they did not take into account adverse events or multidimensional assessment of the use of opioids (1). We noted increased adverse events including drowsiness, nausea, and constipation, as well as a significant difference in the mastery domain scores in one included trial, suggesting that participants may feel less in control when using morphine. We believe it is important to consider the evidence in its entirety, rather than focusing on only one effect size score.
Ekström et al have suggested that we downgraded the quality of evidence based on concerns about study size alone (1). We used GRADE methodology to rate the quality of the evidence and our decision to downgrade the quality of the evidence was based on the fact that more than 50% of included trials did not report on allocation concealment, blinding of participants or personnel, or blinding of outcome assessment. This is potentially a serious limitation when the primary outcome (i.e. change in breathlessness) is entirely subjective. We acknowledge that study size per se does not influence the internal validity of trial results and that some of the trials included in the review were designed with sufficient statistical power.
The ‘size bias’ criterion was suggested by the Cochrane editorial team during the review process of our manuscript, as there is empiric evidence that study size may be a surrogate marker of trial quality when the reporting on aspects of trial quality is poor (4). In other fields, small study effects have been shown to distort the results of meta-analyses (5). Many of the papers included in the review did not provide sufficient information to adequately assess trial quality, and because all the studies included were small in relative terms (with less than 50 participants per trial) we believe that it is important to highlight that the quantitative data synthesis was based on the pooling of relatively small studies.
We included the study by Woodcock 1982, but this is more correctly referenced in our review as Bar-Or 1982 (6). We included the study by Johnson et al (2002) in the review, but excluded it from the meta-analyses as the data was not normally distributed and medians and interquartile ranges cannot be imputed into a meta-analysis, consistent with the Cochrane Handbook (3, 7). Although Ekström et al commented that study selection should align to predefined eligibility criteria with reasons for exclusion stated to minimise selection bias, our studies were selected according to a published protocol with study types, inclusion and exclusion criteria specified (1).
While we value the opinion provided by Ekström et al, the additional sensitivity analyses reported here do not change our review conclusions (1,2). There is some small, low quality evidence that shows benefit for the use of parental or oral opioids to palliate breathlessness in the short term. The magnitude of this benefit is at best modest and given the potential adverse events and the lack of any evidence suggesting an improvement in overall quality of life, longer-term studies with multi-dimensional scales are required to ascertain whether any benefits outweigh the potential long-term risks, particularly where opioids are being used in those with chronic stable disease in the outpatient setting (8).
We thank Christopher Cates for his extensive input on this sensitivity analysis and comments on this letter, Kerry Dwan, Toby Lasserson and the Statistical Methods Group, and Julian Higgins for his report on the interpretation of this data.
1. Ekström M, Bajwah S, Bland JM, Currow D, Hussain J, Johnson M. One evidence base; three stories: do opioids relieve chronic breathlessness? Thorax 2017
2. Barnes H, McDonald J, Smallwood N, Manser R. Opioids for the palliation of refractory breathlessness in adults with advanced disease and terminal illness. Cochrane Database of Systematic Reviews 2016, Issue 3. Art. No.: CD011008. DOI: 10.1002/14651858.CD011008.pub2.
3. Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.cochrane-handbook.org.
4. Kjaergard LL, Villumsen J, Gluud C. Reported methodologic quality and discrepancies between large and small randomized trials in meta-analyses. Annals of Internal Medicine 2001;135(11):982-9.
5. Nüesch E, Trelle S, Reichenbach S, Rutjes AW, Tschannen B, Altman DG, et al. Small study effects in meta-analyses of osteoarthritis trials: meta-epidemiological study. BMJ 2010;341:c3515.
6. Bar-Or D, Marx JA, Good J. Breathlessness, alcohol and opiates. The New England Journal of Medicine 1982;306(22):1363–4.
7. Johnson MJ, McDonagh TA, Harkness A, McKayd SE, Dargie HJ. Morphine for the relief of breathlessness in patients with chronic heart failure--a pilot study. European Journal of Heart Failure 2002;4(6):753–6.
Figure 1: Meta-analysis – Opioids for palliation of breathlessness conducted using alternative methods for use of crossover data with results separated according to post treatment scores and change from baseline scores.
Figure 2: Meta-analysis – Opioids for the palliation of breathlessness using alternative method for use of crossover data, combining post treatment and change from baseline scores but separating by scale used to measure breathlessness.
We read with interest the timely editorial by Gerrard Phillips (1), concerning the importance of providing physiological training to trainee respiratory physicians. This reviewed a French study indicating that trainees who had received an internship in a respiratory lab were substantially better at diagnosing respiratory abnormalities compared with trainees without such training. (2) Dr Phillips made a persuasive, “essential” case for an integrated understanding of respiratory physiology/pathophysiology, lung function testing and interpretation in clinical trainees.
We strongly agree and also argue that the problem is the recognition of the importance of physiology in general, across the specialist service. We are involved in work that aims to build physiologist numbers, leadership and lab capacity, and feel this could lead to improved training for trainee doctors, as has been shown in the audit of French trainees (2). This would very much benefit from further support from colleagues and hope that the following information helps to make this case.
In December 2015 the first NHS physiology scientist students of the new national NHS Masters in Respiratory medicine graduated from Newcastle University. This course is part of the national Modernising Scientific Careers (MSC) program in the NHS. The respiratory teaching faculty is consultant led, with delivery in hospital clinical teaching facilities.
Modernising scientific careers (MSC) is a UK wide initia...
Modernising scientific careers (MSC) is a UK wide initiative, led by the Chief Scientific Officer of the Department of Health to address training and education in healthcare science. This drew on stakeholder consultation in 2008 with policy proposals published in 2010. A central component of this initiative is a three year, part time Master of Science degree aimed at equipping future scientist leaders. The students involved include NHS employed Medical Physicists, Cardiologists, Vascular, Gastrointestinal and Respiratory Physiologists. These capacities are all relevant to Respiratory medicine and an integrated future training of Specialist trainee Physicians with scientists.
It is noteworthy, and a concern, that Cardiology trainee scientists have consistently outnumbered Respiratory trainees in the NHS scientist masters course (c.3:1). There are a number of potential explanations for this but we feel it is possible that some respiratory centres do not know of the program and the potential for fully funded trainee places. Funding is available covering both the MSc course and the salary of the scientists. This is made available following the provision of a business case made by individual departments and funds currently derive from demarcated regional education budgets and not the host Department or hospital. We feel this represents an important opportunity for capacity building for respiratory medicine, and the discussion raised in Dr Phillips editorial, indicating that internship in a respiratory lab led to better respiratory training for clinicians.
Precision, personalised medicine is exemplified by recent breakthroughs in Respiratory Medicine. These include the use of small molecule modulators of the Cystic Fibrosis Transmembrane Regulator (CFTR) channel in patients with Cystic Fibrosis lung disease. Future research of such approaches and measuring the efficacy of potentially transformational treatments in Respiratory Medicine is dependent on the capacity for U.K respiratory physiology and training for both clinicians and scientists. We feel that the current 4–5 year window to address respiratory physiology training for specialist training of Physicians highlighted by Dr Phillips is an opportunity for further coordination involving both Physicians and physiological scientists. The faculty developed for the training of NHS respiratory scientists in Newcastle would be very keen to pursue this with all respiratory colleagues.
Chris Ward, Ian Forrest, Graham Burns
1. How do we improve training in pulmonary physiology and the interpretation of lung function tests? Phillips G. Thorax. 2018 Jan;73(1):2-3. doi: 10.1136/thoraxjnl-2017-210140. Epub 2017 Oct 17.
2. Does training respiratory physicians in clinical respiratory physiology and interpretation of pulmonary function tests improve core knowledge? M Patout, L Sesé, T Gille, B Coiffard, S Korzeniewski, E Lhuillier, A Pradel, C Tardif, A Chambellan, C Straus, S Matecki, T Perez, L Thiberville, A Didier. Thorax. 2018 Jan;73(1):78-81. doi: 10.1136/thoraxjnl-2016-209136. Epub 2017 Mar 3.
A quit rate of 21% in controls and 24% in screened persons show that CT screening is a poor motivation to quit. The authors emphasize that the quit rate was 30% in patients with a positive result on CT who needed additional clinical investigation, however, the quit rate was only 15% in persons receiving a negative CT result. This shows that CT screening lowers the motivation to quit if a negative result (expected for the majority) nourishes misperceptions. Zeliadt et al. ( JAMA Intern Med 2015; 175:1530-7) found that in 49% these beliefs were reinforced and potentially exacerbated by screening and lowered the motivation to participate in smoking cessation programs. Therefore CT screening for lung cancer without accompanying smoking cessation program could be harmful.
I read with great interest the article “The use of pretest probability increases the value of high-resolution CT in diagnosing usual interstitial pneumonia”, by Brownell and colleagues . The study has great methodological strength, and I applaud the authors for such an elegant work. But what really caught my attention was the clear use of pre-test probability and likelihood ratio to establish the diagnosis of usual interstitial pneumonia (UIP) in patients with suspected UIP. I believe this article should change the way we care for those patients.
The study included patients with “possible UIP" and “inconsistent with UIP” patterns on high-resolution computed tomography (HRCT) of the chest. Those patients represent a diagnostic dilemma we commonly face in interstitial lung diseases clinical practice. Three different radiologists (two in the derivation and one in the validation cohort) reviewed the HRCT scans, and most importantly: they were blinded to clinical information and pathology results. All patients had the reference standard surgical lung biopsy, which were prospectively evaluated by expert pathologists.
The likelihood ratio for male patients, with ≥ 60 years-old, and possible UIP with traction bronchiectasis score ≥ 4 was as high as 47 in the derivation cohort. Since likelihood ratios are a ratio of two likelihoods (the likelihood of a test results in disease / the likelihood of the same test result in no disease ), the further away from on...
The likelihood ratio for male patients, with ≥ 60 years-old, and possible UIP with traction bronchiectasis score ≥ 4 was as high as 47 in the derivation cohort. Since likelihood ratios are a ratio of two likelihoods (the likelihood of a test results in disease / the likelihood of the same test result in no disease ), the further away from one the better. So, what a likelihood of 47 means is that male patients with ≥ 60 years-old and possible UIP with traction bronchiectasis score ≥ 4 are 47 times more likely to have UIP versus not have UIP. This would move us from a pretest probability of IPF of 60% to a post-test probability of IPF of 98% in the derivation cohort, for example.
How to translate that into clinical practice? We have to start with an estimation of the pre-test probability of UIP in our patients. For that, it would have been helpful if the authors had used the variables gender and age to stratify the pre-test probability, similarly to the pre-test probability stratification we do in the evaluation for pulmonary embolism (with the Well’s criteria ) and lung cancer (with the Mayo Clinic prediction rule ). Then, we could apply the likelihood ratios of the HRCT findings to reach a post-test probability. It would be useful to know the likelihood ratio of a possible UIP with traction bronchiectasis score ≥ 4 encountered in the present study.
In conclusion, we will probably continue to struggle to make an accurate diagnosis of UIP in our patients with suspected UIP. But at least, this study allows us to struggle with some good data in our hands.
 Brownell R, Moua T, Henry TS, et al. The use of pretest probability increases the value of high-resolution CT in diagnosing usual interstitial pneumonia. Thorax. 2017;72:424–429.
 Richardson WS, Wilson MC, Keitz SA, et al. Tips for Teachers of Evidence-based Medicine : Making Sense of Diagnostic Test Results Using Likelihood Ratios When to Use This Tip When to Use This Tip. J Gen Intern Med. 2007;23:87–92.
 Chunilal SD, Simel DL. Does This Patient Have Pulmonary Embolism ? JAMA J. Am. Med. Assoc. 2014;290:2849–2858.
 Swensen SJ, Silverstein MD, Edell ES, et al. Solitary pulmonary nodules : Clinical prediction model versus physicians. Mayo Cllinic Proc. 1999;74:319–329.
Magnetic resonance imaging (MRI) of the lung is an exciting field that is currently undergoing a period of rapid advancement. With its ability to measure lung function as well as structure, MRI stands to greatly improve our understanding of cystic fibrosis (CF) pathophysiology in children. However, there are still a number of significant hurdles to overcome if MRI is to become a tool for routine monitoring of paediatric CF lung disease.
Compared to other commonly used modalities such as computed tomography (CT), spirometry, and multiple breath washout (MBW), MRI is considerably more expensive and, due to high demand, generally has long wait times for access. In addition, the cost of Helium for inhalation as a contrast agent is substantial, and due to diminishing reserves, access is likely to be more problematic in the future. The use of hyperpolarised gas requires expensive equipment that is not available in all centres, such as specially tuned radiofrequency coils and a gas hyperpolariser, as well as the expertise to run them . The significant cost to set up and maintain such a system presents a huge barrier to entry for many CF centres, compared to the nearly universal presence of CT and lung function testing facilities.
Standardisation of MRI between centres is challenging. Many sequences are protected under intellectual property law resulting in vendor-specific protocols, hampering comparisons between platforms . Magnetic field inhomogeneity can lea...
Standardisation of MRI between centres is challenging. Many sequences are protected under intellectual property law resulting in vendor-specific protocols, hampering comparisons between platforms . Magnetic field inhomogeneity can lead to variability between individual scanners, even of the same model/manufacturer . In comparison, the SCI-FI project has demonstrated that image quality standardisation of CT is feasible, facilitating collaborative studies and longitudinal lung disease monitoring .
Finally, due to long imaging times, the need for the patient to remain still, and loud noises generated by the scanner, lung MRI is challenging to perform in children under six years unless sedation is used. As a result, the sensitivity of MRI to detect early CF-related lung disease in these young children has not yet been established. In contrast, with the advent of high-pitch, rapid acquisition techniques, CT can be performed in infants and young children without the need for sedation and with minimal motion artefact , with a high sensitivity [6, 7].
Radiation-free lung imaging is an attractive prospect for monitoring respiratory disease in children with CF. However, it is important to recognise that the risks from CT, if any, are extremely small [8, 9], especially in the era of ultra-low dose CT imaging . The long-term risks of, for example, sedation or Xenon inhalation, are not well characterised and have not undergone such scrutiny as medical radiation.
In summary, lung MRI is a promising research tool that has an important role to play in understanding and treating CF lung disease. However, there are still many challenges that need to be overcome before MRI becomes a routine clinical tool for monitoring lung disease in children with CF.
1. Kauczor H-U, Surkau R, Roberts T. MRI using hyperpolarized noble gases. Eur Radiol 1998: 8(5): 820-827.
2. Biederer J, Beer M, Hirsch W, Wild J, Fabel M, Puderbach M, Van Beek EJ. MRI of the lung (2/3). Why ... when ... how? Insights Imaging 2012: 3(4): 355-371.
3. Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE transactions on medical imaging 2007: 26(3): 405-421.
4. Kuo W, Kemner-van de Corput MPC, Perez-Rovira A, de Bruijne M, Fajac I, Tiddens HAWM, van Straten M. Multicentre chest computed tomography standardisation in children and adolescents with cystic fibrosis: the way forward. European Respiratory Journal 2016: 47(6): 1706-1717.
5. Lell MM, May M, Deak P, Alibek S, Kuefner M, Kuettner A, Kohler H, Achenbach S, Uder M, Radkow T. High-pitch spiral computed tomography: effect on image quality and radiation dose in pediatric chest computed tomography. Investigative radiology 2011: 46(2): 116-123.
6. Ramsey KA, Rosenow T, Turkovic L, Skoric B, Banton G, Adams AM, Simpson SJ, Murray C, Ranganathan SC, Stick SM, Hall GL, Cf A. Lung Clearance Index and Structural Lung Disease on Computed Tomography in Early Cystic Fibrosis. American journal of respiratory and critical care medicine 2016: 193(1): 60-67.
7. Rosenow T, Oudraad MCJ, Murray CP, Turkovic L, Kuo W, de Bruijne M, Ranganathan SC, Tiddens HAWM, Stick SM, Fibrosis ARESTfC. PRAGMA-CF A Quantitative Structural Lung Disease Computed Tomography Outcome in Young Children with Cystic Fibrosis. American journal of respiratory and critical care medicine 2015: 191(10): 1158-1165.
8. Kuo W, Ciet P, Tiddens HA, Zhang W, Guillerman RP, van Straten M. Monitoring cystic fibrosis lung disease by computed tomography. Radiation risk in perspective. American journal of respiratory and critical care medicine 2014: 189(11): 1328-1336.
9. Rosenow T, Oudraad MCJ, Murray CP, Turkovic L, Kuo W, de Bruijne M, Ranganathan SC, Tiddens HAWM, Stick SM. Reply: Excess Risk of Cancer from Computed Tomography Scan Is Small but Not So Low as to Be Incalculable. American journal of respiratory and critical care medicine 2015: 192(11): 1397-1399.
The paper by Yoon et al  addressees an important subject - diabetes mellitus (DM) probably increases the risk of TB by a factor of three . The authors present data showing an association of poorer diabetes control status with both the characteristics of pulmonary TB at presentation, and the response to treatment. Compared to patients with no or controlled DM, those with uncontrolled DM reported worse symptoms at presentation, were more likely to be sputum smear positive, and had more substantial radiographic changes. Patients with uncontrolled DM were also more likely to remain sputum culture positive at two months, and either fail treatment or die.
Although these observations are entirely consistent with a biologically plausible explanation that hyperglycaemia itself influences the development of TB and its response to treatment, there is an important confounding factor which may not have been fully accounted for: treatment adherence, and the wider general use of health care.
Patients with uncontrolled diabetes, by definition, are less well treated than those with controlled diabetes. Part of the reason for this will be treatment adherence. Such patients may also be less well engaged with health services. Hence a reason for more advanced TB disease at diagnosis in those with uncontrolled DM compared to controlled or no DM might be due to later presentation to health services. Indeed, a recent study in China reported that patients with hyperglycaemia a...
Patients with uncontrolled diabetes, by definition, are less well treated than those with controlled diabetes. Part of the reason for this will be treatment adherence. Such patients may also be less well engaged with health services. Hence a reason for more advanced TB disease at diagnosis in those with uncontrolled DM compared to controlled or no DM might be due to later presentation to health services. Indeed, a recent study in China reported that patients with hyperglycaemia are more likely to delay presenting to health services with symptoms of pulmonary TB .
And at least part of the reason for delayed sputum culture conversion in uncontrolled DM in the current study could be poorer adherence to TB treatment. Although this was not entirely borne out by the data, there was a suggestion of poorer treatment compliance in patients who remained culture positive at two months compared to those who did not (Table 3; p=0.22). It is not clear from the paper how TB treatment compliance was associated with DM control status. Neither is it clear precisely what methods were employed by the TB nurse in the study to assess treatment adherence.
So although the findings of Yoon et al support a direct effect of diabetes control status on TB presentation and treatment response, further work is required to exclude the potential confounding effects of delayed presentation of TB and adherence to treatment. Health records could be used to assess level of engagement with health services prior to the TB diagnosis, and directly-observed TB treatment in all study subjects could address potential compliance issues.
1. Yoon YS, Jung JW, Jeon EJ et al. The effect of diabetes control status on treatment response in pulmonary tuberculosis: A prospective study. Thorax. 2017; 72: 263–70.
2. Jeon CY, Murray MB. Diabetes mellitus increases the risk of active tuberculosis: A systematic review of 13 observational studies. PLoS Med. 2008; 5: e152.
3. Wang Q, Ma A, Han X et al. Hyperglycemia is associated with increased risk of patient delay in pulmonary tuberculosis in rural areas. J Diabetes. 2017; 9: 648–655.
I have read the paper by McDowell et al with great interest. While the trial showed no significant improvement in the main outcome measure it is crucial to understand why. The intervention group had 30 patients who were recruited from 6 hospitals over a period of 3 years or in other words hospitals recruited 1-2 patients per year who had personalised (lonely) exercise sessions. Outcomes from rehabilitation of COPD are thought to be driven by a multi-disciplinary approach  and peer-support from fellow patients . The latter is likely to improve resilience  and impact on overall self-reported quality of life.
 Griffiths TL, Burr ML, Campbell IA, Lewis-Jenkins V, Mullins J, Shiels K, Turner-Lawlor PJ, Payne N, Newcombe RG, Ionescu AA, Thomas J, Tunbridge J. Results at 1 year of outpatient multidisciplinary pulmonary rehabilitation: a randomised controlled trial. Lancet. 2000 Jan 29;355(9201):362-8.
 Poureslami I, Camp P, Shum J, Afshar R, Tang T, FitzGerald JM. Using Exploratory Focus Groups to Inform the Development of a Peer-Supported Pulmonary Rehabilitation Program: DIRECTIONS FOR FURTHER RESEARCH. J Cardiopulm Rehabil Prev. 2017 Jan;37(1):57-64.
 Bradley-Roberts EM, Subbe CP. Role of Psychological Resilience on Health-Outcomes in Hospitalized Patients with Acute Illness: A Scoping Review. Acute Med. 2017;16(1):10-15.
We are grateful to the authors for their comments on the PEARL paper, especially those supporting our decision to assess outcome over 90 days. In regard to CODEX, most, but not all, patients had been hospitalised and, more importantly, death or readmission was not the primary outcome.1 Developed tools tend to be optimal for their primary outcome; a tool specifically designed to predict readmission/ death without readmission is likely to be a better predictor of this outcome than one that was not developed primarily for this purpose. This may, at least in part, explain the observed difference in performance. Prognostic tools should also undergo external validation. However, we acknowledge that the brevity of the abstract makes this unclear. At the editor’s discretion, we suggest the abstract could be amended to state: “no tool has been developed and externally validated…”
We agree that data about mortality alone is relevant, and highlight that this is included in table E3 in the online supplement. The optimal predictors of death and readmission are not identical, although there is overlap. The reasons for including readmission or death without readmission as a combined outcome are: 1) they are competing risks, and assessing readmission alone would mean that death without readmission would be categorised as a favourable outcome; 2) a patient who would otherwise have died at home may be readmitted if they are identified as high risk and appropriate services are put in...
We agree that data about mortality alone is relevant, and highlight that this is included in table E3 in the online supplement. The optimal predictors of death and readmission are not identical, although there is overlap. The reasons for including readmission or death without readmission as a combined outcome are: 1) they are competing risks, and assessing readmission alone would mean that death without readmission would be categorised as a favourable outcome; 2) a patient who would otherwise have died at home may be readmitted if they are identified as high risk and appropriate services are put in place; and 3) hospital admission may prevent death. One way to analyse readmissions alone without including death as a favourable outcome would be to exclude those that died. However, this would bias the population by excluding those at higher risk of readmission. We plan to separately publish data on long-term predictors of death.
We acknowledge the difficulties diagnosing heart failure with preserved ejection fraction, previously termed diastolic heart failure, and its prevalence in this population. However, this does not carry the same mortality risk as left ventricular dysfunction. We separately assessed heart failure as a clinical diagnosis alone (without the need for evidence of reduced left ventricular function on echocardiography); this did not have the same predictive power as left ventricular failure. Consequently, left ventricular failure based on echocardiogram results was appropriately selected. We also highlight the European Society of Cardiology position: “Echocardiography is the most useful, widely available test in patients with suspected HF to establish the diagnosis.”2
The Charlson Index comprises of 19 indices and is a component of CODEX and LACE. The PEARL index includes only two measures of co-morbidity (individually shown to be strong predictors of outcome), and was superior to LACE in all three cohorts, and to CODEX in two of three cohorts. It is clear that the PEARL score is more parsimonious than scores containing the Charlson index, and therefore it is easier to score. Furthermore, it can be recalled and calculated at the bedside. Whilst we agree that ideally a full medical history should be performed in all patients, the more indices that appear in a score, the more likely that there will be missing data leading to biased estimates.
1. Almagro P, Soriano JB, Cabrera FJ, et al. Short- and medium-term prognosis in patients hospitalized for COPD exacerbation: the CODEX index. Chest 2014; 145(5): 972-80.
2. Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail 2016; 18(8): 891-975.
We read with great interest the recent study by Negatu et al. which illustrated significantly increased risks for respiratory troubles such as chronic cough and breath shortness and decreased lung functions in farm workers exposed to pesticide as compared to unexposed workers 1. However, the authors have not controlled for farming practices of both exposed and unexposed groups; did they use diesel-powered or gasoline-powered vehicles to plow their fields? Diesel exhaust may exacerbate, in particular, allergic airway inflammation 2 and thus could account for increased risk of adverse respiratory health. Also, pesticide could contribute to asthma exacerbation 3. Therefore, there might existed synergistic effects of pesticide and diesel exhaust particles on impaired respiratory health in exposed subjects as compared to unexposed ones (in particular, office workers) in their studies, which raise the possibility to exaggerate the results.
We thank Dr Zhang and colleagues for their comments on our paper1. We certainly agree that in this emerging field of extracellular vesicle (EV) research, it is vital that identification and characterisation of different EV populations are as robust as possible. To this end, we very much welcome detailed discussions on methodologies used for each study, to enhance and improve the quality of EV-related work within the lung research community.
In our paper, we specifically chose to examine the role of microvesicles (MVs) in acute lung injury (ALI), and the roles of apoptotic bodies and exosomes are beyond the scope of the study. We do not exclude the presence of apoptotic bodies or surfactant micelles in our in vivo samples, or indeed single or clustered MVs larger than 1µm, however our surface marker analysis of MV subpopulations by flow cytometry was deliberately conservative and limited to only events below the conventional size cut off of 1µm. Hence figure 3 of our paper shows effectively only one EV population, i.e. MVs. For our isolation of MVs for functional studies, we used differential centrifugation to enrich MVs but these technical matters were discussed in some detail in the published manuscript.
Dr Zhang and colleagues have concerns about the dose of LPS (20µg) used in our in vivo ALI model. However, intratracheal (i.t.) instillation of high dose LPS (20µg or more per mouse) is a clinically-relevant, well established model of AL...
Dr Zhang and colleagues have concerns about the dose of LPS (20µg) used in our in vivo ALI model. However, intratracheal (i.t.) instillation of high dose LPS (20µg or more per mouse) is a clinically-relevant, well established model of ALI, used very widely by investigators in ALI research including ourselves2-5. Dr Zhang stated that large doses of LPS often result in release of apoptotic bodies but few MVs from alveolar macrophages, but we wonder if this statement is based on in vitro experiments using non-primary cells, rather than in vivo ALI models? Dr Zhang’s group recently showed6 the production of apoptotic bodies with 1µg LPS treatment, but their results were obtained using an immortalised cell line (MH-S alveolar macrophages) in vitro, rather than primary alveolar macrophages in vivo. Interestingly, they observed that apoptotic body production peaked later (at 6 hours) when primary cells (bone marrow derived macrophages) were treated with LPS in vitro, highlighting a clear difference between primary cells and immortalised cell lines (such as RAW cells, THP-1 and MH-S cells)6. While we cannot entirely exclude the possibility that some apoptotic bodies were produced within our model, it has been shown that i.t. LPS in vivo does not initiate apoptosis of alveolar macrophages until much later time points7,8. Taken together, we believe that concerns regarding apoptotic bodies influencing our conclusions are unsubstantiated for the acute responses investigated in our model. This is of course not to say that the release of apoptotic bodies or other EVs does not play an important role during subsequent phases of ALI pathophysiology.
Sanooj Soni, Michael R Wilson, Kieran P O’Dea, Masao Takata
Section of Anaesthetics, Pain Medicine & Intensive Care, Imperial College London, UK
1. Soni S, Wilson MR, O'dea KP, et al. Alveolar macrophage-derived microvesicles mediate acute lung injury. Thorax 2016;71(11):1020-29.
2. Woods SJ, Waite AA, O'Dea KP, et al. Kinetic profiling of in vivo lung cellular inflammatory responses to mechanical ventilation. American Journal of Physiology-Lung Cellular and Molecular Physiology 2015;308(9):L912-L21.
3. Gong J, Wu Zy, Qi H, et al. Maresin 1 mitigates LPS‐induced acute lung injury in mice. British journal of pharmacology 2014;171(14):3539-50.
4. Islam MN, Das SR, Emin MT, et al. Mitochondrial transfer from bone-marrow-derived stromal cells to pulmonary alveoli protects against acute lung injury. Nature medicine 2012;18(5):759-65.
5. Dorr AD, Wilson MR, Wakabayashi K, et al. Sources of alveolar soluble TNF receptors during acute lung injury of different etiologies. Journal of Applied Physiology 2011;111(1):177-84.
6. Zhu Z, Zhang D, Lee H, et al. Macrophage-derived apoptotic bodies promote the proliferation of the recipient cells via shuttling microRNA-221/222. Journal of Leukocyte Biology 2017:jlb. 3A1116-483R.
7. Vernooy JH, Dentener MA, Van Suylen RJ, et al. Intratracheal instillation of lipopolysaccharide in mice induces apoptosis in bronchial epithelial cells: no role for tumor necrosis factor-α and infiltrating neutrophils. American journal of respiratory cell and molecular biology 2001;24(5):569-76.
8. Kearns MT, Barthel L, Bednarek JM, et al. Fas ligand-expressing lymphocytes enhance alveolar macrophage apoptosis in the resolution of acute pulmonary inflammation. American Journal of Physiology-Lung Cellular and Molecular Physiology 2014;307(1):L62-L70.