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Recent eLetters

Displaying 1-10 letters out of 527 published

  1. Physical fitness predicts long-term survival after a cardiovascular event: implications for United Kingdom (UK) Cardiac Rehabilitation

    To the Editor

    The prognostic importance of cardiorespiratory fitness (CRF) in men and women with coronary heart disease (CHD) has been reported within epidemiological studies (1-3). However, this association has received limited attention within the context of UK Cardiac Rehabilitation (CR) services. We therefore read with interest the recent study published in BMJ Open by Barons and co-investigators (4). The authors examined predictors of long-term survival, including physical fitness, in a cohort study of predominantly post-myocardial infarction patients (mean age 61 years; 86% male) participating in an NHS outpatient CR programme in Basingstoke and Alton, Hampshire, UK. Low baseline CRF (< 13 mL.kg- 1.min-1 for women and < 15 mL.kg-1.min-1 for men) in ~1500 patients estimated by maximal exercise testing was a strong predictor of all-cause mortality (HR 2.47; 95% CI 1.78 to 3.42), relative to a moderate or high CRF level (? 22 mL.kg-1.min-1 for men and ? 19 mL.kg-1.min-1 for women) over 10.7 years follow-up. These findings are timely given the paucity of epidemiological evidence showing long-term outcomes from the UK CR setting. The data from Barons et al. (2015) are consistent with findings from our own long-term, community-based (non-NHS delivery) CR cohort in Leeds, West Yorkshire, UK, recently published in BMJ Open (5). We examined more than 650 participants undertaking extended exercise-based rehabilitation. Routine submaximal, incremental exercise testing found similarly favourable long-term mortality outcomes associated with baseline fitness status. Indeed, estimated submaximal cardiorespiratory fitness (sCRF) level at CR programme entry was the strongest modifiable predictor of long -term survival, surpassed only by older age and other co-existing CV disease. Relative to the lowest fit (bottom 20%) in our cohort; moderate and higher sCRF levels were associated with 40-60% lower risk of death over a median follow-up period of 14 years. Barons and co-workers do not explicitly examine the associations between CRF change during CR and mortality, though report a mean 1.08 MET gain following 40 minutes of once or twice weekly supervised circuit training exercise. This is approximately double the submaximal fitness improvement estimated by Sandercock et al. (6) within 950 patients across four UK CR centres (following twice weekly CR exercise training, over 8 weeks). The estimated short-term sCRF improvement in our cohort was approximately 0.8 MET (24 minutes of supervised training, median 2 sessions per week, over 14 weeks). Thus, collectively these data tend to support the contention that short-term CRF improvements from standard UK CR centres are more modest than that those reported internationally (7). A novel aspect of our study was to estimate risk associated with CR-derived fitness change and the evaluation of this relationship across the sCRF distribution. Importantly, compatible with some prior studies (8, 9) we found the largest sCRF improvements occurred among those with the lowest fitness and a quantifiable reduction in all-cause mortality risk per MET increase achieved during CR. Until now, there has been limited published UK-derived evidence supporting the efficacy of CR in improving mortality outcomes (10-13) and the UK multi-centre clinical trial (14) reported no survival benefit from CR. Importantly however, this trial did not consider CRF changes. Our observation contrasts with recently updated review and meta-analysis (15), which incorporated point estimates from previous UK trials (11-13) and the RAMIT trial of West et al. (14), showing modest, non-significant benefits for all-cause mortality (47 trials: RR: 0.96, 95% CI: 0.88 to 1.04). Similar non-significant findings were also reported in analysis restricted to studies with >3 years follow-up (11 trials: RR: 0.91; 95% CI: 0.75 to 1.10). The inclusion of more recent RCT's, conducted in an era of optimal medical therapy for CHD and within a more varied mix of CHD patients are potential explanations for these non-significant findings. However, as acknowledged by the Cochrane investigators, RCT evidence examining the efficacy of CR is restricted to short-term follow-up of patients (median 12 months) and thus, has inherent limitations for assessing all-cause mortality outcomes. Indeed, in our CR cohort only 13% of participants had died at 10 years, compared to one-third at 14 years. By utilising indirect estimates of aerobic capacity and submaximal fitness data (as opposed to direct respiratory gas analysis) we recognise that both recent UK epidemiological studies (4, 5) cannot precisely quantify patients' individualised CRF or exercise-based improvement. However, our findings are likely to be clinically important, in view of the higher baseline mortality risk of the lowest fit compared to their higher fit counterparts shown in both cohorts. Given that patients in the Basingstoke and Alton cohort were maximally tested, it would have been valuable to see estimated VO2 peak and MET changes from CR associated with long-term outcomes in their cohort. This area remains poorly characterised and data from well-controlled studies of supervised exercise training are required to quantify dose-related improvements in clinical outcomes. Future studies should evaluate an overall measure of exercise dose, or corresponding cardiorespiratory fitness level, which could serve as the basis of a minimal dose recommendation for clinical benefit to better understand the effect of exercise and CRF change on longer-term recurrent CVD and all- cause mortality risk. Together, the study by Barons and co-workers and data from our own extended, community-based CR cohort carry an important public health message about the importance of CRF for long-term (> 10 years) mortality risk in adults with CVD. Both studies demonstrate the more-than- two-fold adverse prognostic risk associated with low CRF fitness levels at entry to CR, and thus corroborate observational data from larger CR cohorts internationally (2, 3, 16). Moreover, our data highlight the substantive long-term survival benefits associated with improving CRF levels through exercise-based CR. Together these data have implications for the delivery of CR and exercise training services. Our findings, in particular, emphasise the importance of promoting CRF improvement through supervised and structured exercise within extended community-based programmes to prolong survival following cardiac events.

    References 1. Vanhees L, Fagard R, Thijs L, Staessen J, Amery A. Prognostic significance of peak exercise capacity in patients with coronary artery disease. JACC. 1994 Feb;23(2):358-63. PubMed PMID: 8294687. 2. Kavanagh T, Mertens DJ, Hamm LF, Beyene J, Kennedy J, Corey P, et al. Prediction of Long-Term Prognosis in 12 169 Men Referred for Cardiac Rehabilitation. Circulation. 2002;106(6):666-71. 3. Kavanagh T, Mertens J, Hamm LF, Beyene J, Kennedy J, Corey P, et al. Peak Oxygen Intake and Cardiac Mortality in Women Referred for Cardiac Rehabilitation. JACC. 2003;42(12):2139-43. 4. Barons MJ, Turner S, Parsons N, Griffiths F, Bethell H, Weich S, et al. Fitness predicts long-term survival after a cardiovascular event: a prospective cohort study. BMJ Open. 2015;5(10). 5. Taylor C, Tsakirides C, Moxon J, Moxon JW, Dudfield M, Witte KK, et al. Submaximal fitness and mortality risk reduction in coronary heart disease: a retrospective cohort study of community-based exercise rehabilitation. BMJ Open. 2016;6(e011125). 6. Sandercock GR, Cardoso F, Almodhy M, Pepera G. Cardiorespiratory fitness changes in patients receiving comprehensive outpatient cardiac rehabilitation in the UK: a multicentre study. Heart. 2013 Jun;99(11):785- 90. PubMed PMID: 23178183. 7. Sandercock G, Hurtado V, Cardoso F. Changes in cardiorespiratory fitness in cardiac rehabilitation patients: A meta-analysis. International journal of cardiology. 2011 Dec 27. PubMed PMID: 22206636. 8. Martin B-J, Arena R, Haykowsky M, Hauer T, Austford LD, Knudtson M, et al. Cardiovascular Fitness and Mortality After Contemporary Cardiac Rehabilitation. Mayo Clinic proceedings Mayo Clinic. 2013;88(5):455-63. 9. Mandic S, Myers J, Oliveira RB, Abella J, Froelicher VF. Characterizing differences in mortality at the low end of the fitness spectrum in individuals with cardiovascular disease. European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology. 2010 Jun;17(3):289-95. 10. Bertie J, King A, Reed N, Marshall AJ, Ricketts C. Benefits and weaknesses of a cardiac rehabilitation programme. Journal of the Royal College of Physicians of London. 1992;26(2):147-51. 11. Bethell HJN, Mullee MA. A controlled trial of community based coronary rehabilitation. British heart journal. 1990;64 (6):370-5. 12. Carson P, Phillips R, Lloyd M, Tucker H, Neophytou M, Buch NJ, et al. Exercise after myocardial infarction: a controlled trial. Journal of the Royal College of Physicians of London. 1982;16(3):147-51. 13. Bell JM. A comparison of a multi-disciplinary home based cardiac rehabilitation programme with comprehensive conventional rehabilitation in post-myocardial infarction patients [PhD]: University of London; 1998. 14. West RR, Jones DA, Henderson AH. Rehabilitation after myocardial infarction trial (RAMIT): multi-centre randomised controlled trial of comprehensive cardiac rehabilitation in patients following acute myocardial infarction. Heart. 2012 Apr;98(8):637-44. PubMed PMID: 22194152. 15. Anderson L, Oldridge N, Thompson DR, Zwisler AD, Rees K, Martin N, et al. Exercise-Based Cardiac Rehabilitation for Coronary Heart Disease: Cochrane Systematic Review and Meta-Analysis. J Am Coll Cardiol. 2016 Jan 5;67(1):1-12. PubMed PMID: 26764059. Epub 2016/01/15. eng. 16. Martin BJ, Hauer T, Arena R, Austford LD, Galbraith PD, Lewin AM, et al. Cardiac rehabilitation attendance and outcomes in coronary artery disease patients. Circulation. 2012 Aug 7;126(6):677-87. PubMed PMID: 22777176.

    Conflict of Interest:

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  2. Sleep duration, physical activity and television viewing in patients with cardiovascular disease and type 2 diabetes mellitus

    I read the article by Cassidy et al. with interest [1]. The authors examined the associations among sleep duration, physical activity and television viewing in adults with special reference to combination of cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM). Adjusted odds ratios (95% confidence intervals) of patients with CVD for low physical activity, high TV viewing and poor sleep duration were 1.23 (1.20 to 1.25), 1.42 (1.39 to 1.45) and 1.37 (1.34 to 1.39), respectively. In addition, adjusted odds ratios (95% confidence intervals) of patients with CVD and T2DM for low physical activity, high TV viewing and poor sleep duration were 1.71 (1.64 to 1.78), 1.92 (1.85 to 1.99) and 1.52 (1.46 to 1.58), respectively. Low physical activity, high TV and poor sleep duration were more prominent in patients with CVD and T2DM by adjusting several confounders. I have some concerns on their study.

    First, Patterson et al. examined the associations among sleep duration, physical activity and sedentary behavior in adults [2]. They reported that short and long sleep duration were associated with cardiovascular risk behaviors such as physical inactivity and sedentary behavior. Cassidy et al. categorized short and short sleep duration as "poor sleep duration", and additional lifestyle effects in patients with CVD and/or T2DM existed. I agree with their study result that short and long sleep duration were both risks for CVD and T2DM, although causal association cannot be confirmed by a cross-sectional study.

    Second, there is a sex difference on the relationship between Sleep duration and obesity [3]. Li et al. also reported the effect of sleep duration on the incidence of metabolic syndrome (MetS), presenting short and long sleep duration as a risk for MetS in men [4]. In contrast, there was no significant association in females. Cassidy et al. handled a large sample and stratified analysis by sex is recommended for their analyses.

    Finally, working status is closely related to sleep duration and lifestyle factors [5,6]. In addition, there is a bi-directional association between short sleep duration and mental health [7]. In any case, further studies are needed to confirm the association between sleep duration, physical activity and television viewing and CVD and/or T2DM by considering several additional variables.

    References

    1. Cassidy S, Chau JY, Catt M, et al. Cross-sectional study of diet, physical activity, television viewing and sleep duration in 233,110 adults from the UK Biobank; the behavioural phenotype of cardiovascular disease and type 2 diabetes. BMJ Open 2016;6:e010038.

    2. Patterson F, Malone SK, Lozano A, et al. Smoking, Screen-Based Sedentary Behavior, and Diet Associated with Habitual Sleep Duration and Chronotype: Data from the UK Biobank. Ann Behav Med 2016 doi: 10.1007/s12160-016-9797-5

    3. Sun W, Huang Y, Wang Z, et al. Sleep duration associated with body mass index among Chinese adults. Sleep Med 2015;16:612-6.

    4. Li X, Lin L, Lv L, et al. U-shaped relationships between sleep duration and metabolic syndrome and metabolic syndrome components in males: a prospective cohort study. Sleep Med 2015;16:949-54.

    5. Grano N, Vahtera J, Virtanen M, et al. Association of hostility with sleep duration and sleep disturbances in an employee population. Int J Behav Med 2008;15:73-80.

    6. Yu E, Rimm E, Qi L, et al. Diet, Lifestyle, Biomarkers, Genetic Factors, and Risk of Cardiovascular Disease in the Nurses' Health Studies. Am J Public Health 2016;106:1616-23.

    7. Yoo H, Franke WD. Sleep habits, mental health, and the metabolic syndrome in law enforcement officers. J Occup Environ Med 2013;55:99-103.

    Conflict of Interest:

    None declared

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  3. Re: Impact of holding the baby following stillbirth on maternal mental health and well-being: findings from a national survey. BMJ Open 2016;6(8):e010996.

    Dear Editor: We were interested in the recent article by Redshaw et al. which reported higher rates of mental health and relationship difficulties among women who held their stillborn baby.1 We agree this is an important topic, but after reviewing the article in depth, we would like to raise several concerns.

    (1) We note that this was a retrospective survey with a 30.2% response rate in which just 3% of women did not see and 16% did not hold their baby; these limitations were acknowledged but we believe they also restrict the ability to draw broad conclusions. (2) There was little exploration into the reasons why women did not hold their babies and if they had any regrets about their decisions. While four out of five women reported they did not hold because they could not or did not want to, the study did not account for the fact that women who declined may be fundamentally different at baseline, so that mental health outcomes may be due to underlying differences in mothers rather than their choices or experiences at birth. (3) While the authors emphasize that holding was associated with a trend toward worse mental health outcomes, their actual multivariable analyses show that at 9 months, the only statistically significant difference was higher odds of anxiety. Pre-existing anxiety could contribute to a woman's hesitance to hold the baby after delivery and separately serves as a predictor of postpartum mental health. (4) Even though there are many validated, widely-tested measures to assess postpartum depression,2-5 anxiety,6 and PTSD,7, 8 in both live birth and bereaved mothers, this study used non-validated self-report measures which leads to the need for very cautious interpretation of the results. (5) The factors which have been demonstrated to be strong predictors of postpartum depression and PTSD include prior mental health conditions, interpersonal violence, and lack of social support.9-12 This study did not measure or control for any of these factors. (6) Another issue not addressed in this article is the well-acknowledged preference by parents to be given the option to see or hold their baby and strong evidence that the majority of women are satisfied with their decision.10, 13 Events surrounding the birth of a stillborn baby can have lasting impact on how a mother experiences, remembers, and copes with this event.14 The decision to see or hold a stillborn baby warrants additional investigation, but research must adjust for the known confounders which have been shown to predict development of mental health problems. Moreover, there should be recognition that the experience of a mother at the time of delivery is complex, and multiple pre-existing and intrapartum factors may affect subsequent outcomes and grief.

    In summary, we believe it is not possible to reach a conclusion from this study about whether the decision to see or hold a stillborn baby is detrimental or helpful to bereaved parents and urge research to gain a more nuanced understanding of the factors which contribute to parental experiences at the time of delivery and which may influence long-term mental health outcomes. We strongly urge health care providers to continue to offer women the option to hold their stillborn baby, and to make this offer in a respectful, supportive, and normative manner.

    No author has any conflicts of interest to declare.

    ? References 1. Redshaw M, Henderson J. Fathers' engagement in pregnancy and childbirth: evidence from a national survey. BMC Pregnancy Childbirth. 2013;13:70.

    2. Myers ER, Aubuchon-Endsley N, Bastian LA, et al. Efficacy and Safety of Screening for Postpartum Depression. Comparative Effectiveness Review 106. Agency for Healthcare Research and Quality Publication No. 13- EHC064-EF. 2013.

    3. Boyle FM, Vance JC, Najman JM, Thearle MJ. The mental health impact of stillbirth, neonatal death or SIDS: prevalence and patterns of distress among mothers. Soc Sci Med. Oct 1996;43(8):1273-1282.

    4. Ji S, Long Q, Newport DJ, et al. Validity of depression rating scales during pregnancy and the postpartum period: impact of trimester and parity. J Psychiatr Res. Feb 2011;45(2):213-219.

    5. Dennis CL. Can we identify mothers at risk for postpartum depression in the immediate postpartum period using the Edinburgh Postnatal Depression Scale? Journal of Affective Disorders. Feb 2004;78(2):163-169.

    6. Ross LE, McLean LM. Anxiety disorders during pregnancy and the postpartum period: A systematic review. J Clin Psychiatry. Aug 2006;67(8):1285-1298.

    7. Youngblut JM, Brooten D, Cantwell GP, Del Moral T, Totapally B. Parent Health and Functioning 13 Months After Infant or Child NICU/PICU Death. Pediatrics. Oct 7 2013.

    8. Murphy S, Shevlin M, Elklit A. Psychological Consequences of Pregnancy Loss and Infant Death in a Sample of Bereaved Parents. Journal of Loss & Trauma. Jan 1 2014;19(1):56-69.

    9. Cerulli C, Talbot NL, Tang W, Chaudron LH. Co-occurring intimate partner violence and mental health diagnoses in perinatal women. J Womens Health (Larchmt). Dec 2011;20(12):1797-1803.

    10. Gold KJ, Leon I, Boggs ME, Sen A. Depression and Posttraumatic Stress Symptoms After Perinatal Loss in a Population-Based Sample. J Womens Health (Larchmt). Mar 2016;25(3):263-269.

    11. Gold KJ, Boggs ME, Muzik M, Sen A. Anxiety disorders and obsessive compulsive disorder 9 months after perinatal loss. General Hospital Psychiatry. Nov-Dec 2014;36(6):650-654.

    12. Surkan PJ, Radestad I, Cnattingius S, Steineck G, Dickman PW. Social support after stillbirth for prevention of maternal depression. Acta Obstet Gynecol Scand. 2009;88(12):1358-1364.

    13. Radestad I, Surkan PJ, Steineck G, Cnattingius S, Onelov E, Dickman PW. Long-term outcomes for mothers who have or have not held their stillborn baby. Midwifery. Aug 2009;25(4):422-429.

    14. Lisy K, Peters MD, Riitano D, Jordan Z, Aromataris E. Provision of Meaningful Care at Diagnosis, Birth, and after Stillbirth: A Qualitative Synthesis of Parents' Experiences. Birth. Mar 2016;43(1):6-19.

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  4. Differences by areas and other tests

    I would like to know whether the authors ran statistical tests to check whether there have been any differences per geographical area and by deprivation level. If not, it would be worth exploring this further, particularly if there have been changes in prescribing in particular areas. Also, I would recommend running additional statistical tests apart from the Kolmogorov-Smirnov -e.g. the Cramer-von Mises test and the Anderson- Darling test, which under certain conditions have been reported to perform better than the KS.

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  5. Re: Effect of warning symbols in combination with education on the frequency of erroneously crushing medication in nursing homes: an uncontrolled before and after study. BMJ Open 2016;6(8):e012286.

    Dear Editor,

    I read with interest the recently published study by van Welie et al(1) on the effect of combining warning symbols and education on the frequency of erroneously crushing medications in 3 nursing homes in the North of the Netherlands. With an ageing population who are intrinsically predisposed to dysphagia this study is timely.(2) Inappropriate crushing of medicines can pose a clinical risk to patients, so how do nursing staff determine whether a medicine can be crushed? Should we in the first instance conduct an error analysis to establish the cause of erroneous crushing before recommendations for solutions are made? It is known that both systems and individuals contribute to the problem.(3) Could errors be due to:

    * Medication orders not being clear that a medicine is modified release, or could pose safety concerns,

    * Annotations on medication chart with regards to safe administration not completed by pharmacist,

    * References on the crushability of medicines not being readily available to nursing staff,

    * Interruption during the medication round leading to a slip,

    * Lack of knowledge on how to proceed when for example an order for extended release medicine is prescribed?

    In his research van Welie(1) addressed some of these causes by implementing education, placement of posters and addition of warning symbols to medication sachets. This intervention demonstrated a reduction in rate of wrongly crushed medicines being administered from 3.1% to 0.5%.(1)

    Could this intervention translate to Australian Residential Aged Care Facilities (RACFs)? Supply of medications to RACFs varies between dispensing of original containers to individually prepared Dose Administration Aids (DDAs), of which there are a number of types utilizing a variety of dispensing systems. Software vendors would need to be engaged to include warning symbols on their labels, and as in the study, widespread education would need to occur.

    The symbol provides a warning, notifying nursing staff of non- crushable medicines in a pack. However if only some are non-crushable this requires their identification and removal. Identification of the individual medicine may not be easy despite there being a requirement of colour, shape, size and manufacturer marks on DAA labels in Australia.(4)

    In Australian RACFs medications are usually prescribed manually on the National Residential Medication Chart as electronic medication charts are still not prevalent. In Nursing Homes in the Netherlands daily computerized monitoring of all new prescriptions is conducted by pharmacists. This provides increased patient safety and must be developed for Australian patients in the future. In addition there should also be a patient information link alerting to swallowing difficulties.

    Multidisciplinary medication review of all patients occurred in this study, which is paramount in ensuring medication safety and is a component of patient care in RACFs.(5) The multidisciplinary team must include a pharmacist, who as the medication expert, will be pivotal in making recommendations when changes to dosage forms are needed especially with patients experiencing swallowing difficulty. In Australia it is best practice and an important safety process for pharmacists to endorse special requirements such as "swallow whole", "do not crush", "Cytotoxic - Use contact precautions" on medication charts to assist with safe administration of medicines, especially as reading of the chart is the last step before medication selection and administration occurs. Until and even when these initiatives are available pharmacists play an important role in keeping the patient safe.

    References:

    1. van Welie S, Wijma L, Beerden T, et al. Effect of warning symbols in combination with education on the frequency of erroneously crushing medication in nursing homes: an uncontrolled before and after study. BMJ Open [Internet] 2016[cited 2016 Sep 2];6(8):e012286.Available from: http://bmjopen.bmj.com/content/6/8/e012286.full doi: 10.1136/bmjopen-2016 -012286

    2. Aslam M, Vaezi MF. Dysphagia in the Elderly. Gastroenterol Hepatol. [Internet] 2013 Dec[cited 2016 Sep 8];9(12):784-95 Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3999993/

    3. Leape LL Error in medicine. JAMA [Internet] 1994 Dec[cited 2016 Sep 8];272(23):1851-7 Available from: http://jama.jamanetwork.com.acs.hcn.com.au.monash.idm.oclc.org/data/Journals/JAMA/9292/jama_272_23_039.pdf 4. Australian Pharmaceutical Advisory Council. Guiding principles for medication management in the community. Canberra: Commonwealth of Australia; 2006.[cited 2016 Sep 4] Available from: https://www.health.gov.au/internet/main/publishing.nsf/Content/0A434BB6C6456749CA257BF0001A9578/$File/booklet.pdf

    5. Wilson NM, March LM, Sambrook PN, et al. Medication safety in residential aged-care facilities: a perspective. Ther Adv Drug Saf [Internet] 2010 Oct[cited2016 Sep 4];1(1):11-20 Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4110799/ doi: 10.1177/2042098610381418

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  6. RE: Initial experience using a femtosecond laser cataract surgery system at a UK National Health Service cataract surgery day care centre (BMJ Open 2016;6:e012078)

    We read with interest the recently published study by Day et al[1], describing the initial outcomes of femtosecond laser-assisted cataract surgery (FLACS) after its introduction at Moorfield's at St Anne's Hospital. The authors described the rates of intraoperative complications as well as any issues relating to laser docking and delivery for 158 cataract operations over a 2 month period. A total of 32 surgeons, both consultants and non-consultants took part in the "learning curve". However, two surgeons were already experienced at FLACS, having performed over 200 cases each. In our opinion, theses surgeons and their results should have been excluded as they were clearly not in a learning curve phase.. The authors report that 2.7% of operations were complicated by vitreous loss. It would be of interest to see this rate in non-experienced surgeons in their true learning curve. Laser docking was abandoned in 6% of cases, but no specific reasons for docking failure were given and would be of interest. Other parameters which the authors could have collected retrospectively and are useful in assessing the learning curve of FLACS would include total surgical time and the effective phacoemulsification time (EPT). The authors conclude that their transition to FLACS was safe, based on comparing their complication rates and average post-operative visual acuity to national averages[2]. We feel that assessing the learning curve of FLACS with an average of 3 cases per surgeon (range 1-20) does not adequately provide useful information about the safety during the introduction of FLACS. In the protocol for their randomized controlled trial, the same authors stipulate that each surgeon complete at least 10 operations before being allowed to participate in the clinical trial[3]. Furthermore, the existing literature suggests the learning curve may include approximately the first 100 cases with the femtosecond laser (FL) [4-6]. In our own clinical practice, using the LenSx FL (Alcon Inc, Fort Worth, Tx, USA), we have found that the learning curve can be considered to have 2 parts. The first part includes adapting to the challenges introduced by the laser: managing any capsulotomy tags[7], post-laser miosis[8], and the increased difficulty of aspiration of cortical lens matter[9]. Subsequently, the surgeon learns how to modify their technique to take advantage of the benefits of FL: reducing both their surgical time[6] and EPT[10].

    1 Day AC, Dhallu SK, Maurino V, et al. Initial experience using a femtosecond laser cataract surgery system at a UK National Health Service cataract surgery day care centre. BMJ Open 2016;6:e012078. doi:10.1136/bmjopen-2016-012078 2 Day AC, Donachie PHJ, Sparrow JM, et al. The Royal College of Ophthalmologists' National Ophthalmology Database study of cataract surgery: report 1, visual outcomes and complications. Eye 2015;29:552-60. doi:10.1038/eye.2015.3 3 Day AC, Burr JM, Bunce C, et al. Randomised, single-masked non- inferiority trial of femtosecond laser-assisted versus manual phacoemulsification cataract surgery for adults with visually significant cataract: the FACT trial protocol. BMJ Open 2015;5:e010381. doi:10.1136/bmjopen-2015-010381 4 Bali SJ, Hodge C, Lawless M, et al. Early experience with the femtosecond laser for cataract surgery. Ophthalmology 2012;119:891-9. doi:10.1016/j.ophtha.2011.12.025 5 DSc ZZNM, MD AIT, MD TF, et al. Complications of femtosecond laser- assisted cataract surgery. J Cataract Refract Surg 2014;40:20-8. doi:10.1016/j.jcrs.2013.08.046 6 Grewal DS, Dalal RR, Jun S, et al. Impact of the Learning Curve on Intraoperative Surgical Time in Femtosecond Laser-Assisted Cataract Surgery. J Refract Surg 2016;32:311-7. doi:10.3928/1081597X-20160217-02 7 Arbisser LB, Schultz T, Dick HB. Central dimple-down maneuver for consistent continuous femtosecond laser capsulotomy. J Cataract Refract Surg 2013;39:1796-7. doi:10.1016/j.jcrs.2013.09.009 8 Yeoh R. Intraoperative miosis in femtosecond laser-assisted cataract surgery. J Cataract Refract Surg 2014;40:852-3. doi:10.1016/j.jcrs.2014.02.026 9 Roberts TV, Lawless M, Bali SJ, et al. Surgical outcomes and safety of femtosecond laser cataract surgery: a prospective study of 1500 consecutive cases. Ophthalmology 2013;120:227-33. doi:10.1016/j.ophtha.2012.10.026 10 Abell RG, Kerr NM, Vote BJ. Toward zero effective phacoemulsification time using femtosecond laser pretreatment. Ophthalmology 2013;120:942-8. doi:10.1016/j.ophtha.2012.11.045

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  7. Please don't forget the role of Vitamin K2 in CAD

    Dear Editor,

    I read the elaborate study design of this interesting and expensive study. The authors deserve congratulations for addressing the common issue amongst elderly people.

    The data is emerging about high intake of Vitamin K2 (menaquinone)linking with reduced coronary calcification.1

    In Rotterdam study dietary intake of Vitamin K2 is associated with reduced risk of coronary artery diseaes (CAD).2 It is timely, the vitamin K2 is also included in the assessment in this elaborate study.

    Many elderly patients opt for non-invasive management of CAD, therefore such patients should not be excluded from the study design, particularly while focusing on real life design of the study.

    References:

    1.Beulens JW1, Bots ML, Atsma F, et al. High dietary menaquinone intake is associated with reduced coronary calcification.Atherosclerosis. 2009 Apr;203(2):489-93.

    2. Geleijnse JM, Veermeer C, Diederick EG, et al. Dietary Intake of Menaquinone Is Associated with a Reduced Risk of Coronary Heart Disease:The Rotterdam Study. J.Nutr. November 1, 2004 vol. 134 no. 11 3100 -3105

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  8. Faulty reporting about mortality regarding women, the elder, and others.

    The Johns Hopkins authors, Eatz et al, critique Ravnskov et al for poor science and methods and then they state this: " Given that statins are known to reduce all-cause mortality .." This ignores the fact that there has NEVER been a placebo controlled cholesterol-lowering intervention that ended with a mortality benefit in women, and this includes statins.

    For example, the land mark 4S study ended with 3 more women heart- patient deaths on statin, while 2 other large supposedly "successful" trials [HPS and ASCOT] have refused to report on women's deaths, a dozen years after completion -but we know at least for HPS that it was not significant.

    SPARCL in (mainly) ischemic stroke patients ended with numerically more death on the statin.

    Moreover, the one study in over 70 year olds, the placebo controlled PROSPER study [part primary / part secondary prevention], ended without a mortality benefit but with a p-value for increased "newly diagnosed cancer" [the frightening serious type] of p=0.02. Who would not prefer the placebo?

    There are now 4 large rosuvastatin [Crestor] vs placebo studies, 2 ending without a cardiovascular mortality benefit [both p=~0.35; JUPITER, HOPE-3] and 2 without benefit in any vascular department [CORONA, AURORA] .

    For any of those patients, the quote in the first paragraph is wrong, misleading and potentially harmful.

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  9. Re:Effectiveness of the Assessment of Burden of COPD (ABC) tool on health-related quality of life in patients with COPD: Twitter Discussions from the University of Toronto Respirology and Sleep Journal Club (@respandsleepjc)

    In their correspondence on our cluster randomised controlled trial evaluating the Assessment of Burden of COPD (ABC) tool, Abhyankar and colleagues raise some issues regarding the reported results and its implications. First, we would like to clarify that the PACIC is not a questionnaire measuring quality of life, but quality of care as experienced by patients. With respect to randomisation, sample size and baseline imbalance, the discussants express concerns that are understandable but unfounded, as we will now explain. Page 3 of our paper clearly states the randomisation procedure used, but of course chance imbalance in spite of randomisation can occur (we get back to this point below). Page 4 and reference 17 give all details of the sample size calculation, showing this study to be sufficiently powered. In fact, with a total of about 60 clusters, this trial is certainly not small. For instance, a review by Adams and colleagues of 19 cluster randomised trials found a median number of clusters of 41, with an interquartile range of 24 to 64.1 Our answer to the worry about baseline imbalance and regression to the mean is necessarily a bit longer and more technical, so please bear with us. We focus on the total SGRQ score, but the reasoning applies equally to the other outcome measures reported.

    For the total SGRQ score, the group difference at baseline as evaluated by our mixed regression model (for details, see page 4) had a p- value of 0.195, far from significant. Moreover, the reported effect analyses do adjust for any baseline imbalance as well as for regression to the mean. The initial mixed regression model included treatment arm as predictor on top of time and treatment by time effects, thus allowing for a group difference at baseline (since baseline was the reference category for time). The treatment by time interaction effect in that model is equivalent to the difference between treatment arms with respect to change from baseline (for a proof and demo, see Van Breukelen2,3). Now, as stated on page 4 bottom - page 5 top, if the group difference at baseline was not significant then it was removed from the model. The treatment by time effect of interest then became equivalent to the treatment effect at follow-up adjusted for the baseline as a covariate.4 This equivalence cannot be seen intuitively, but formal proofs and empirical demonstrations are given in Liu et al.5 and Van Breukelen.2,3 Since the baseline group difference on SGRQ total was far from significant, it was removed from the model, and Table 2 in our paper reports the treatment group differences at each follow-up based on the reduced mixed model (which comes down to adjusting for baseline as a covariate). The effect size and significance before model reduction was very similar to the one reported after reduction (beta = 3.36 instead of 3.08, and p = 0.005 instead of 0.008 at 18 months follow-up). Both the model reduction and the similarity between effects before and after reduction are stated in a footnote to Table 2. Relatedly, the chance imbalance in FEV1 and FEV1/FVC ratio that the discussants worry about, was adjusted for by including both measures as covariates in a secondary analysis, and this gave results very similar to those of the primary analysis (see page 6 bottom - page 7 top). '

    To appreciate these results, please note that in an RCT both methods of analysis, change from baseline (our mixed model with baseline group effect) and adjustment for baseline as a covariate (mixed model without baseline group effect), are unbiased. This means that, in the long run of an infinite number of replications, both methods give the true treatment effect on the average. Further, the covariate adjustment method is even unbiased given baseline imbalance in a single RCT.6-8 This is because the covariate adjustment method takes regression to the mean into account. In our case, this regression to the mean effect was very small because the correlation between SGRQ at baseline and at 18 months follow-up was as high as 0.80. This also explains why the two methods of analysis, change from baseline (mixed model with baseline group difference) and covariate adjustment (mixed model without baseline difference) gave quite similar effects at follow-up. This answer and our paper would have been more simple if we had analysed our data with the classical methods. One of the reasons for using mixed models was to include all randomised clusters and patients in spite of dropout (intention-to-treat analysis), which is possible with mixed regression but not with the classical methods. As Figure 2 in our paper shows, there was sufficient dropout to justify our approach. In fact, there was some very small dropout even before baseline, which can hardly be prevented in a cluster randomised trial because clusters are randomised at the start of the trial, whereas patients are measured as they come in. It was because of this small dropout before baseline that we applied and compared both methods, the mixed model with and without baseline group difference. As said before, we did not find a significant baseline difference and we reported the treatment effects at follow-up based on the reduced mixed model.

    There is much more to be said about regression to the mean and about the best method of analysis than can be done in this reply, see e.g. Senn8 and Van Breukelen2,3. The point here is that (a) there is no convincing evidence of a true baseline imbalance in our trial, and (b) the methods of analysis used take any imbalance into account in the best possible way, and (c) the treatment effect reported is not an artefact of regression to the mean.

    References

    1. Adams G, Gulliford MC, Ukoumunne OC, Eldridge S, Chinn S, Campbell MJ (2004). Patterns of intracluster correlation from primary care research to inform study design and analysis. Journal of Clinical Epidemiology , 57 , 785-794. 2. Van Breukelen GJP (2006). ANCOVA versus change from baseline: more power in randomized studies, more bias in nonrandomized studies. Journal of Clinical Epidemiology, 59, 920-925. 3. Van Breukelen GJP (2013). ANCOVA versus change from baseline in nonrandomized studies: the difference. Multivariate Behavioral Research, 48 (6), 1-28. 4. Laird NM, Wang F (1990). Estimating rates of change in randomized clinical trials. Controlled Clinical Trials, 11, 405-419. 5. Liu GF, Lu K, Mogg R, Mallick M, Mehrotra DV (2009). Should baseline be a covariate or dependent variable in analyses of change from baseline in clinical trials ? Statistics in Medicine, 28, 2509-2530. 6. Senn SJ (1989). Covariate imbalance and random allocation in clinical trials. Statistics in Medicine, 8, 467-475. 7. Senn SJ (1994a). Testing for baseline balance in clinical trials. Statistics in Medicine, 13, 1715-1726. 8. Senn SJ (1994b). Repeated measures in clinical trials: analysis using mean summary statistics and its implications for design (Letter to the editor). Statistics in Medicine, 13, 197-198.

    Conflict of Interest:

    None declared

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  10. Request for correction

    Dear Editor,

    We would like to make corrections on the above paper. We assure you the corrections are minor, and do not affect the analyses and the conclusions we made in the paper.

    1) Page 2, 3rd line in the last paragraph

    (current) "quintiles of dietary Na-K ratio (mmol/mmol)." (correct) "quintiles of dietary Na-K ratio (mg/mg)."

    2) Table 1. We have noticed that the unit for sodium-to-potassium ratio we provided was wrong. It should be "mg/mg," not "mol/mol", and this applies to "Men", "Women" and "Men and Women combined" in Table 1.

    We would like to provide values in "mmol/mmol" unit as well corresponding to each value (mg/mg) in Table 1 as follows.

    Sodium-to-potassium ration in mmol/mmol, mean (SD) for Q1, Q2, Q3, Q4, Q5 in Men were 2.20 (0.30), 2.78 (0.13), 3.22 (0.13), 3.71 (0.16), 4.75 (0.82); the corresponding range (min) (max) for men were (0.94) (2.57), (2.56) (3.00), (3.00) (3.45), (3.45) (4.02), (4.01) (10.36).

    Sodium-to-potassium ration in mmol/mmol, mean (SD) for Q1, Q2, Q3, Q4, Q5 in Women were 2.07 (0.26), 2.63 (0.12), 3.05 (0.12), 3.52 (0.15), 4.47 (0.74); the corresponding range (min) (max) for women were (1.00) (2.41), (2.41) (2.83), (2.83) (3.26), (3.26) (3.81), (3.81) (9.84).

    Sodium-to-potassium ration in mmol/mmol, mean (SD) for Q1, Q2, Q3, Q4, Q5 in Men and Women combined were 2.13 (0.29), 2.70 (0.15), 3.13 (0.15), 3.61 (0.18), 4.60 (0.79); the corresponding range (min) (max) for men were (0.94) (2.57), (2.41) (3.00), (2.83) (3.45), (3.26) (4.02), (3.80) (10.36).

    Again, this change (i.e. correction) does not influence our results and conclusions because the quintiles of the participants remain the same regardless of the unit used. For further conversation on this issue, please email to Akira Fujiyoshi (afujiy@belle.shiga-med.ac.jp).

    Thank you in advance for your understanding.

    August 13, 2016 Akira Fujiyoshi (an author), MD, PhD. Akira Okayama (corresponding author), MD, PhD

    Conflict of Interest:

    None declared

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