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• The potential effect of dichotomising age (a continuous variable) in multivariate model analysis
  Abebaw M Yohannes
  Published on: 29 April 2016
• BTS recommendations for assessing risk and for managing Mycobacterium tuberculosis infection and dis
  Timothy P Cotter
  Published on: 29 April 2016
• Comments and queries
  Rajendra K Sinha
  Published on: 29 April 2016

• Published on: 29 April 2016

  The potential effect of dichotomising age (a continuous variable) in multivariate model analysis

  • Abebaw M Yohannes, Senior Lecturer
  • Other Contributors:
    • Connolly MJ, Senior Lecturer, University of Manchester

  Dear Editor

  We have read with great interest, Soler-Catauna and colleagues [1] article that examined, in an impressive prospective study with five years follow-up, factors predicting poor prognosis and mortality in patients with severe acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Their findings are complimentary with the current available literature in identifying that older age, arteri...

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  Dear Editor

  We have read with great interest, Soler-Catauna and colleagues [1] article that examined, in an impressive prospective study with five years follow-up, factors predicting poor prognosis and mortality in patients with severe acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Their findings are complimentary with the current available literature in identifying that older age, arterial carbon dioxide tension and acute exacerbations were independent predictors of mortality in their cohort group.

  We have concerns however regarding both their analyses and conclusions. Firstly several studies [2,3,4] have given advice on the limitations of dichotomizing continuous predictors as they come at a cost, “as explanatory variables could be seriously misleading, both in respect of which variables are significant in the model, and perhaps to also with respect to the overall predictive ability” [2]. Soler-Cataluna and colleagues state in their “multivariate model the frequency of acute exacerbations, age and Charlson index were analysed as categorical variables” [1].

  Secondly, and perhaps more importantly, the authors have reported older age (clearly a non-modifiable factor) as a predictor of death. They do not state whether they believe this to be old age per se or an age- related potentially modifiable variable. Have the authors collected data on social support, physical disability, depression, quality of life and any palliative care their patients may have received during the follow-up period? These variables may have some implication on mortality in this exclusively male COPD patient cohort. Our own group has recently published data on one-year mortality following hospitalisation for AECOPD in a slightly older subject group (mean age 73 years vs. 71 years in the Soler- Cataluna study) and with worse baseline spirometry (mean percentage predicted FEV1 of 39%). In our study age was not a mortality predictor on either univariate or multivariate analysis. Quality of life, level of disability, severity of depression, readmission, use of LTOT and duration of original admission (all of which are arguably related to age), were all univariate predictors of 12-month mortality, with only quality of life score remaining a significant predictor on multivariate analysis.

  We wonder whether the inclusion of age-related variables in Soler- Cataluna’s analysis together with the use of age as a continuous variable might have resulted in qualitatively or quantitatively different conclusions regarding the effect of age on prognosis. However the inclusion of duration of original admission and of frequency of readmission in our own list of predictors [5] would support Soler Cataluna’s suggestion that severe AECOPD could have adverse impact on longer term mortality.

  References

  1.Soler-Cataluna JJ, Martinez-Garcia MA, Roman Sanchez P, Salcedo E, Navarro M. Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease. Thorax 2005;60:925-931.

  2.Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression a bad idea. Statist Med (in press) – published on line.

  3.Cohen J. The cost of dichotomization. Applied psychological Measurement 1983;7:249-253.

  4.Irwin JR, McClelland GH. Negative consequences of dichotomizing continuous predictor variables. Journal of Marketing Research 2003;40:366- 371.

  5.Yohannes AM, Baldwin RC, Connolly MJ. Predictors of 1-year mortality in patients discharged from hospital following acute exacerbation of chronic obstructive pulmonary disease. Age Ageing 2005;34:491-496.

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  Conflict of Interest:
  None declared.

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• Published on: 29 April 2016

  BTS recommendations for assessing risk and for managing Mycobacterium tuberculosis infection and dis

  • Timothy P Cotter, consultant respiratory physician

  Dear Editor,

  The above guidelines will prove useful to rheumatologists or gastroenterologists who will be prescribing these revolutionary drugs for their patients. I, however, have difficulty with algorithm of figure where tuberculin testing comes before risk stratification. For patients with normal chest x-rays and a low risk stratification it will not really matter if their tuberculin test is positive or negativ...

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  Dear Editor,

  The above guidelines will prove useful to rheumatologists or gastroenterologists who will be prescribing these revolutionary drugs for their patients. I, however, have difficulty with algorithm of figure where tuberculin testing comes before risk stratification. For patients with normal chest x-rays and a low risk stratification it will not really matter if their tuberculin test is positive or negative. In neither scenario will they receive chemoprophyllaxis. Section 4.4 should be updated in the light of Chiron-Evans defection from the tuberculin reagent market and I would suggest that tuberculin doses should abandon the dilutional expressions of 1:10,000 etc. for clarity.

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  Conflict of Interest:
  None declared.

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• Published on: 29 April 2016

  Comments and queries

  • Rajendra K Sinha, Respiratory Physician

  Dear Editor,

  Thanks to all concerned for putting together a very useful and long awaited guideline. I have the following questions and comments.

  1. What does "All Patient Rate" in table 3B mean and when should one use it, if at all for UK born patients (say white) as opposed to table 3A?

  Does a UK born white of 75 have a risk of 11 (Table 3A) or 4 (Table 3B - All patient rate column)?

  2. I...

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  Dear Editor,

  Thanks to all concerned for putting together a very useful and long awaited guideline. I have the following questions and comments.

  1. What does "All Patient Rate" in table 3B mean and when should one use it, if at all for UK born patients (say white) as opposed to table 3A?

  Does a UK born white of 75 have a risk of 11 (Table 3A) or 4 (Table 3B - All patient rate column)?

  2. I think that an arrow is missing between "Tuberculin test unreliable" in "On Immunosuppressant" group AND "Stratify for TB Risk" box in Figure 1.

  3. I assume that patients who develop TB whilst on Anti TNF Alfa treatment would qualify for 4 drug treatment simply by virtue of being on immunosuppressants.

  4. What is the guidance about patients who have developed TB whilst on treatment with Anti TNF Alfa drugs and have not been able to tolerate full treatment with 4 drugs, with regards to concomitant treatment with Anti TNF Alfa drugs in "Normal CXR group" or time lag before treatment can be started in "abnormal/suspicious group"? I have a couple of such patients.

  Yours faithfully,
  R.K.Sinha, FRCP
  Consultant Respiratory Physician
  Yeovil District Hospital
  Somerset

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  Conflict of Interest:
  None declared.

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