Concomitant Gastroparesis has a negative affect on children with Biliary Dyskinesia

Reference: Chumpitazi, B.P., et al., Concomitant gastroparesis negatively affects children with functional gallbladder disease. J Pediatr Gastroenterol Nutr, 2012. 54(6): p. 776-9.

A study out from Baylor on how concomitant gastroparesis (CG) can negatively affect biliary dyskinesia (BD). 

Brief Summary: Gastroparesis is a medical condition of the stomach where the food remains in the stomach for too long. It is thought that this condition can negatively effect patients with biliary dyskinesia (a disturbance of the biliary ducts).  The main objective of this retrospective study was to determine the prognostic effect that concomitant gastroparesis (CG) has on biliary dyskinesia (BD).  A total of 35 children were accrued into the investigation who had biliary dyskinesia (BD).  Of these 35 patients, 20 had CG, and the other 15 did not have CG.  The time of the last follow-up was then checked, and assigned to 4 clinical outcome groups based on the symptoms: poor, fair, good, excellent.  The children with CG were then compared to the children without CG. 

Results: The children who had CG were more likely to have un-favorable clinical outcome (defined as poor or fair) than children who did not have CG.   Furthermore, in children undergoing a cholecystectomy, the clinical outcomes were worse for children with CG.   

Implications for Practice:  Children who have both CG and BD need to be monitored closely as their clinical outcome may be worse than children who only have BD.  Particularly, the Gastroenterologist needs to be wary of mis-diagnosing CG patients as BD.   

Discussion:  Interesting study here on the interaction between CG and BD on clinical outcome.  I liked how the authors gave an explanation for the reason why children with both CG and BD have a less favorable outcome.  The authors noted that this may be due to miss-directed therapies.  Therapies that are directed solely against BD may not address the symptoms against CG.    

It is interesting to see that the author’s used schools absences as an indicator for limitations in activity.   I have never seen this variable used before to determine the level of severity, but I suppose that it would be a good indicator of severity.  As good as anything else I suppose for a more objective indicator.  

Commentary on Statistics and Study Design:  I guess I have two major suggestions for the authors: one related to the statistical technique used and one with the presentation of the results. 

First, let’s start with the statistical technique. The author’s took what was originally a 4 category response variable for the clinical outcome (poor, fair, good, excellent) and converted it into a 2 category (binary) response (favorable and un-favorable) and then performed your typical 2 category logistic regression analysis.  However, the author’s just have used a multi-variate outcome logistic regression technique for an ordinal response.   This is called polytomous logistic regression.  Polytomous logistic regression is incredibly easy to implement in SPSS.   In fact, the interpretation is just about exactly the same as your standard binary response logistic regression.    But back to the point: running this polytomous logistic regression would have prevented the loss of information from going down to 2 categories from 4 with the response variable.   The authors then could have reported the individual p-values for each level of the response variable.  Simple and easy to do really. 

As for the presentation, the author’s performed a multi-variate regression technique, but it would have been helpful to see the results in more of a table-like format.  I wish I would have my primer up already on how to visually report statistical results up correctly in a publication, but alas, in my busyness I don’t. The typical procedure here is to report both the uni-variate and final multi-variate (adjusted) results.   The reason you want to also report the multi-variate results is that it gives the reader a good idea of what final predictors are most indicative of outcome.   Come back when I have this final primer up – it could have helped this paper tremendously. 

Also, it may have been helpful to obtain more samples.  Given the small sample size - 35 children - it may have been difficult to make confident conclusions. This is one limitation that the author's should have probably noted at the end.  

A big  thanks to our Baylor pals for  doing this. Love RG3 – go Skins!