Great time to be a Gastroenterologist!

Well, as you all know, the Supreme Court came down very favorably yesterday for people with pre-existing conditions, the type of patients that Gastroenterologists typically care for.

Right now, I am just soaking this whole decision in right now, and I will have a very lengthy write-up later.  This has been a day-in-the-coming for many years now.  Just a great day for Gastroenterology. There should be a big write-up by tomorrow night.

In related, news, I look forward to updating this blog again more on a regular basis.  Thanks to all of you who read my blog, it's been fun!

Gastroenterologists need to be cautious of prescribing metoclopramide for non-diabetic patients

Reference: Parkman, H.P., et al., Clinical response and side effects of metoclopramide: associations with clinical, demographic, and pharmacogenetic parameters. J Clin Gastroenterol, 2012. 46(6): p. 494-503

A study out from Temple on the side effects of metoclopramide. 

Brief Summary: Gastroperesis is a disorder indicative of slow stomach emptying during digestion and is often associated with diabates.  A common drug prescribed for this condition is metoclopramide – an antiemetic drug effective against nausea and vommitting.  The main aim of this study was to determine the factors which are associated with response vs. no-response and side effects vs. no side effects with the use of metoclopramide.   100 patients from a hospital near Philadephia were accrued into the study, and clinical features were recorded including age, dosage, etiology (diabetes or no-diabetes) and genotype testing for a number of various genes though to implicate in the main find. 

Results: The patients who responsed were older and had a heavier body mass index.  Genetic polymorphisms in KCNH2 and DRA1D genes were associated with clinical response.   The patients who hads side effects to metoclopramide tended to be nondiabetic with normal gastric emptying.  Genetic polymorphsisms occurred in CYP2D6, KCNH2, and 5-HT4 receptor HTR4 genes.  Some of the side affects were very severe.      

Implications for Practice: Since some of the side effects were severe, gastroenterologists should be very wary of prescribing metoclopramide to nondiabetic patients.  For these non-diabetic patients, if genetic testing can be under-done, it may be very helpful in determing the effectiveness of metoclopramide.

Discussion: Really good paper here, and I had a really fun time reading it.  These types of studies are important in order to determine the effectiveness of various metabolic drugs.  I am glad to see that the authors included the genotype variables.   Furthermore, I really liked how the authors went in depth in the discussion section on the various genes which were under investigation and the potential biological mechanism at play.

Also, at the beginning of the discussion section, it was good to see that the authors included a brief section on the implications for practice in Gastroenterology. This is always important and something that many authors do not do.

Overall, just a very well investigated  and written paper with very interesting finds which every Gastroenterologist who prescribes metoclopramide should know really.

Commentary on Statistics and Study Design: My biggest suggestion has to with the absense of any multi-variate analysis, which should always be done in any type of risk factor study.  The authors only included a uni-variate (un-corrected) analysis, but it would also would have been helpful to conduct a multi-variate (corrected) analysis with logistic regression using either a forward or backward stepwise regression approach.  Doing this, the authors could have presented a set of clinical factors which were most associated with the outcome variable: having a positive response.  For instance, it may be that one of the clinical factors (body mass index) is not associated with the outcome variable (response vs. no-response) while controlling for one of the genes.  Due to the very large number of genes, it probably would not have been possible to include all of the genetic factors at once, but you could test just a sub-sample at a time.  You always want to conduct this type of multi-variate analysis, and report both the uni-variate and multi-variate results. 

Also, in the statistical analysis section, it seems as if three separate statistical techniques were used (Fisher’s Exact Test, ANOVA, and uni-varite logistic regression); however, it seems as if the entire paper could have been performed with just the logistic regression.  I don’t even see any of the results for the ANOVA analysis.  I’m not too sure here. It would have been helpful if the name of the statistical test used was under the tables.  This could be very helpful.   

Overall, a good investigation. Thanks to our buddies at Temple for doing this!        

Health Care Decision To Come Tommorrow

So, the big health care decision will be coming down tomorrow from the Supreme Court.   Time will be inching along until then. As expected, I will be following this decision very closely on this blog, and I look forward to the decision.  Expect a good and lengthy post on here about it some time tomorrow.  

Hopefully, the SCOTUS decides not to over-turn the part of the bill which outlaws insurance companies from denying people based on pre-existing conditions.  That is the important part which is most relevant to the Gastroenterology field.  Here is hoping!  

The Effectiveness of Fecal Immunochemical Tests May Decrease After Repeated Diagnostic Rounds For Colon Cancer

Another great experimental study out of the Netherlands on the effects of the Fecal Immunochemical Test (FIT) after repeated rounds of testing.

Summary: The main objective of the study was to determine the effects of repeated testing using the Fecal Immunochemical Tests (FIT).  Tests such as these can often have low evaluative test measures (high false negatives and positives) so it is important to do repeated testing - especially after an initial positive finding.  The study was conducted in two concurrent stages in order to determine the effectiveness .  Somewhat alarmingly, the Positive Predictive Value (PPV) was found to decrease from the first round to the second round of testing for the FIT test for those patients which had a negative result in the first round.  The PPV is an evaluative measure for a diagnostic test which divides the number of people who actually have the condition by all of those who tested positive for the condition: True Positives/(True Positives+False Positives)).  

Implications for Practice: The fact that the PPV decreased for the Fecal Immunochemical Test should come at somewhat of an alarm for Gastroenterologists. This result just helpsenforce the importance of getting a Colononscopy for patients if there is suspect of any bowel instabilities, as the the other diagnostic tests really can not measure up to a Colonoscopy.  Interestingly, the authors also found that there was no significant difference between a fecal immunochemical test (FIT) and a fecal occult blood test (FOBT: another diagnostic measure).  Thus, there is no reason to switch from a FIT based program to a FOBT based program.

Interestingly, the authors showed that there were several different sub-types of colon cancers which were diagnosed, and this suggests the potential that some sub-types may be more easily detected than others.  Another study did show that the sensitivity of the Fecal Immunochemical Test (FIT) is higher for cancers which are detected distal rather than proximal, and this may have something to do with it.  

Affordability Care Act Ruling Soon

Hey guys, sorry for not posting in some time. I have just been so busy here working on my dissertation and what-not.  But for sure, this blog is a high priority on my list, and I want to start updating it daily. I love this blog, and really hope to continue with it.

Right now, the focus is on the Patient and Affordable Care Act.  I wrote a post a few days ago on my personal opinions of the case and it's implications on Gastroenterology.  The USA Today recently had a really good article on the possible outcomes that can come of the Supreme Court's decision. Essentially, the outcome can range anywhere from the law being upheld to total defeat of the bill.  What may be likely is that the individual mandate that everyone have health insurance gets killed but the rest of the law remains including the part about illegal discrimination against those with pre-existing conditions.

I don't know what will happen, but one thing is for sure: for the good of Gastroenterology patients and doctors everywhere, it is important that the provision making it illegal for insurance companies to discriminate against pre-existing conditions to stay in place.  Here is thinking and hoping that it will stay in place.  As I said previously, it's very likely that your typical Gastroenterology patient would be prone to be discriminated against because they have a pre-existing condition. This is not good for the field of Gastroenterology, and this practice of discriminating against people because they have a pre-existing condition like Chron's Disease - through no fault of their own - needs to end.  Let's all pray that the results come out good on this.  A ruling should come out any second here.

The risk factors for an inadequate bowel preparation

Reference: Hassan, C., et al., A predictive model identifies patients most likely to have inadequate bowel preparation for colonoscopy. Clin Gastroenterol Hepatol, 2012.10(5): p. 501-6.

A study from Italy on the the factors which are most likely to lead to inadaquate bowel preperation for Colonoscopy. 

Brief Summary:   An inadaquate bowel preperation can severly affect the safety and efficacy of Colonoscopy – particularly for the detection of polyps and cancer lesions.  The main objective of this investigation was to identify the risk factors for an inadaquate level of bowel preperation.  Both patient (gender, age, etc.) and non-patient (type of regimine, time of preperation, etc) risk factors were considered.  A  total of 2811 patients were recruited for the study from a number of clinics throughout Italy.  The patients were accessed to have either an adaquate or inadaquate bowel preperation by an endoscopist at time of colonoscopy. 

Results: The patient factors deemed to be predictive of an inadaquate bowel preperation were being over weight, male sex, a high body mass index, older age, previous coloretal surgery, cirrhosis, having Parkison’s disease, having diabaetes and a negative result in a fecal occult test.   Among the non-patient risk factors, some of the factors which were deemed to be predictive were the type of bowel preperation regimine , the difference in time between the preperation and the actual colonoscopy (less time is better), and information on the preperation (oral+written info is better than just oral).   The authors found a moderate predictive accuracy on a validaiton test set (AUC=0.63).   The clinical detection rate of polyps and cancer lesions along with the cecal intubation rate was significantly higher in patients with an adaquate bowel prep vs. an inadaquate bowel prep.      

Implications for Practice: It is important that patients undergo an adaquate bowel preperation, and the probability of an adaquate bowel prep can be enhanced if the patient is given both a written and oral information for the bowel prep. 

Discussion: Really interesting finds here and very comprehensive to say the least.  It is alarming the difference in clinical detection rate for polpys and cancer lesions between adaquate vs. inadaquate bowel preperations.  For adaquate bowel preperations, the rate was 41% and for inadaquate bowel preps, the rate was 35% which is a rather significant difference. 

Some of the risk factors that came up positive were rather surprising.  It is interesting to see that males and those with Parkinson’s and diabates had a higher chance of an inadaquate test.  It would be neat to do a follow up explanation on many of the risk factors that came up.  It seems like the best bowel prep regimine was the Sennosides.  This seemed to be better than the use of any regimines which used the polyethylene glycol (PEG) solution or sodium phosphate. 

Commentary on Statistics and Study Design:  The study design and presentation was very good. This is an exploratory based perspective study.  In any type of exploratory based study, there are often many variables at play in the model building step, and the investigators handled them well here.  I particularly like how the authors included the results of both the uni-variate and multi-variate analysis for the logistic regression. It is always helpful to see the results of both, because it gives the reader a sense of what variables are independently and also dependently associated with the outcome variable.  The multi-variate results also tell you which variables (or risk factors) would be most likely to be associated with the outcome variable.  The only suggestion I have for the authors here would be to also include the odds ratios for the uni-variate analysis.  This could help the reader and statisticians like me.

            Going on, I also liked how the authors left out data for a validation sample.  In many exploratory based investigations, this is not done. Fortunately, the authors were able to collect a large among of data points from a quite a few clinics in order to do this, and this is why collecting a sufficient data sample size can always be a big plus.  In short, the investigator should always collect as much data points as possible for the investigation - regardless of what the power sample sizes might tell you.  More is always better if possible.      

            Overall, great layout and study design for this investigation.  I really don’t have too much to suggest.  I always try to assemble in my mind the best study designs and layouts of various papers in order to bring everything together, and this is definitely a paper I hope to reference in the future. 

Patients over age 60 are considerably more likely to have a 30-day mortality

Reference: Bae, S., et al., Incidence and 30-day mortality of peptic ulcer bleeding in Korea. Eur J Gastroenterol Hepatol, 2012. 24(6): p. 675-682.

A study out from Korea on the incidence and 30-day mortality rate of peptic ulcer bleeding in Korea.

Brief Summary:  A peptic ulcer is an ulcer which occurs somewhere in the gastrointestinal tract (large or small intestine) and can provide complications in the form of peptic ulcer bleeding (PUB).  The main objective of this study was to estimate the rate of peptic ulcer bleed bleeding and the risk factors for 30 day mortality from a PUB event.  An improved PUB diagnostic algorithm which relied on a prescription of histamine type-2 receptor antagonists (H2RA) or proton pump inhibitors (PPI) was used to diagnose the PUB patients.

Results:  On the basis of the aforementioned diagnostic algorithm, the incidence rate for PUB was 22.1 per 100,000 individuals.  The 30-day mortality rate for patients with presenting PUB over 80 was 7.65%, between the ages of 60 and 79, 2.87%, and for those less than 60, 0.83%. The overall 30 day mortality rate for all patients was 2.15 percent.  Overall, in a uni-variate analysis, the risk factors which predicted a greater likelihood of mortality for PUB were over age 60, female sex, ulcer-related drug use (aspirin, oral glucocorticoids, vitamin K antagonists,etc.), and antiulcer drug use (proton pump inhibitors and H2 receptor antagonists).   When adjusting for all the factors, only age was found to be a significant 30-day risk factor.  

Implications for Practice: Doctors need to be more wary of patients who are older than 60 and present with a peptic ulcer bleeding, because these patients are considerably more likely to have a 30-day mortality event. 

Discussion: Really interesting study here.   The incidence of gastrointestinal conditions can really vary from one geographical region to another, because the environmental and dietary variables can really vary from one region to another, and thus studies like this is important.  Whenever this geographical dependency occurs, it is important to limit the analysis to a certain sub-population in the world as this investigation did.   

It was surprising to see that the PPV jumped so significantly with the use of a more restrictive diagnostic algorithm (prescription of the PPI or H2RA).  The PPV went from 53 to 88 percent – astounding increase! I am interested to see whether other epidemiological based studies could take advantage of this prescription fix. 

Also, I liked the discussion section that the author’s wrote-up.  In short, they gave a detailed but succinct description of the various risk factors, and their influence on the eventual disease.   The authors noted that patients of age greater than 75 are more likely to have a peptic ulcer than those between the ages of 25 and 44 due to consumption of NSAID/aspirin or the high prevalence of H. pylori among the elderly.  This would seem to be the main causative factor here.      

Also, the author’s noted that the result for gender was different for this study compared to past studies.  Past studies showed that males have a higher incidence rate due to higher rates of alcohol consumption and smoking among men, which are known to be risk factors of PUB. It seems as if a significantly more work needs to be done to elucidate the exact effect of gender here – especially given the confliction results from this investigation to past.  It no doubt has something to do with the inherent differences in the sub-populations under study.    

Commentary on Statistics and Study Design: Overall, the investigators did a really good job with the statistical analysis.  I like how the author’s reported both the un-adjusted (uni-variate) and un-adjusted (multi-variate) results.  This is really important in a study like this, because it’s important to give the reader a final set of risk factors (variables) which are most associated with the response (morbidity).   For instance, it’s highly suspicious that the use of an ulcer-related drug – which was significant in the uni-variate analysis – would also be significant in the multi-variate analysis when also adjusting for age. 

Also, I really like how the authors used the Charlson co-morbidity index and used this as a controlling factor multi-variate analysis. This is obviously crucial to do, since the presence of another disease could seriously confound the experiment.  Typically, many investigators would just totally exclude any patients from the study all-together with the presence of another disease, but in doing this, the analyst is essentially losing data samples. It is better just to control for the confounding factor if possible. 

I guess my only suggestion for the authors would be to include a more explicit description of the odds ratios which were presented in Table 3 which detailed the individual risk factors for the un-adjusted (uni-variate) model.  For instance, say something like “the odds of a 30 day mortality increase by 350% for patients who are between the ages of 60 to 79 compared to those patients who are less than 60.”  This type of succinct yet explicit detail can be really helpful.  Other than that, everything looks good!  I really liked the tables and figures – not too much to say there.

As the authors alluded to in the discussion section, there are several lifestyle variables (smoking, alcohol consumption, etc.) which could have been included in the study.  In fact, the introduction of these lifestyle variables would probably change the relationship with 30-day co-morbidity significantly.  For instance, I bet you that sex will not even be significant if you introduce these lifestyle variables, but this is another good study for the future.

A big thanks to our Korea pals for doing this!        

    

  

How will the new Patient Protection and Affordable Act Affect Gastroenterologists?

If you have been following this blog, you know that this blog tends to be more of a professional related blog.  Most of the posts are summary reviews of research articles, and this is how this blog will be continued into the future.  However, I do like to mix it up a little bit, and this post will serve as a good example.

Most notably, the entire country right now - especially the entire health care community - is on edge about the Supreme Court's ruling on the Patient Protection and Affordable Care Act (PPACA) which should be released some time later this month most likely.  It is no exaggeration to say that this decision is the most influential one to come down from the Supreme Court since perhaps Bush. vs. Gore back in 2000. And before that decision, you have to go way back to 1972 for the Roe vs. Wae decision to find any decision that has real influence on your typical American. In short, there are few rulings from the Supreme Court of the United States (SCOTUS) that have any impact on your average American - once every 20 years maybe - but this one definitely does have a major impact on everybody.

I have been following this case extremely closely, and I have absolutely no idea how the court will eventually rule.  The general consensus is that the Obama administration lawyers did not rack it up well in the 3 days of arguments at the end of March against the opposing lawyers.  Particularly, the questions from the 5 conservative judges were rather harsh.  However, given the nature of the case, one would expect tough questions regardless for the PPACA, so you can't put a whole lot of weight on the questions necessarily. The way it breaks down is this: you have 4 justice which will definitely vote to uphold the law, 1 justice who will definitely vote against it, and 4 other conservative leaning justices who could go either way.  The key here is that the Obama administration only needs 1 of those 4 justices to vote for it,  and I have a feeling this will occur - although I am not as confident as I was two months ago before the hearings began.  If I had to guess, I would say there is a 75% chance that the SCOTUS will up-hold the PPACA when the ruling comes out later this month.

But anyway, enough legal rambling, this post will assume that the PPACA will in fact be supported by the SCOTUS.  So, a good question is this: how will this ruling affect your average Joe Gastroenterologist?  A-lot has been written about how it will affect the patient, and this is a good article from the CCFA on the ramifications from a patient's perspective.  

My answer to this question is this: anything that is good for the Gastroenterologist patient is also good for the Gastroenterologist.  Simple and straight forward as this really.  It is no secret that your typical Gastroenterologist patient would probably be prone to be lumped in that group of people who have a "pre-existing condition" and get discriminated against by insurance companies and subsequently be denied coverage.   I have no statistics to back this up, but I would guess there is a rather large pool of patients out there with Gastroenterology problems who limit (if not completely) their access to a Gastroenterologist in order to reduce their own personal cost. This is bad for the patient, bad for the Gastroenterology community, and also bad for Gastroenterologists in the end.  These are patients which could be brought into greater Gastroenterology care and help support your typical Gastroenterology practice.

So, in short, all Gastroenterology related people should be hoping that the PPACA gets passed. It would be good for the patients and also good for your typical Gastroenterologist.  Feel free to comment on this post with any thoughts or opinions. Here is hoping 9 justices don't think otherwise.

The Clinical Factors associated with upper gastrointestinal hemorrhage

Reference: Freedman, S.B., et al., Predictors of clinically significant upper gastrointestinal hemorrhage among children with hematemesis. J Pediatr Gastroenterol Nutr, 2012. 54(6): p. 737-43.

A study out from Toronto on the clinical risk factors which predict a significant upper gastrointestinal hemorrhage. 

Brief Summary: Hematemesis is the vomiting of blood and there is currently no study which details the clinical findings which predict upper gastrointestinal hemorrhage (UGIH) – defined by bleeding in the upper gastrointestinal tract and which often requires surgery.  The main objective of this retrospective study was to determine the percentage of children with hematemesis who have UGIH and to identify the clinical features which predict UGIH.  A total of 613 eligible children with determined hematemesis from a tertiary care center in Toronto were accrued into the investigation. 

Results:  A total of 27 of the 613 hematemesis patients (4%) had upper gastrointestinal hemorrhage (UGIH).  The clinical features which were deemed to be predictive were: older age (9.7 vs. 2.9 years), vomiting moderate to large amounts of fresh blood, melena, significant medical history, unwell appearance and tachycardia.  Furthermore, children with a medical history of esophageal/gastric varices and a low hemoglobin and platelet count in the blood are also highly indicative of UGIH. 

Implications for Practice:  A patient with hemorrhage and the following clinical findings (older age (9.7 vs. 2.9 years), vomiting moderate to large amounts of fresh blood, melena, significant medical history, unwell appearance and tachycardia) should be wary of UGIH and perform the appropriate diagnostic tests.  If a child presents with high risk features and there is suspicion of UGIH, then a complete blood count is the only test likely to yield clinically helpful information.

Discussion: Good study here. It is very important that Gastroenterologists know these types of clinical risk factor statistics like a baseball manager knows his own players. 

It was good to see that the author’s included the laboratory investigative tests such as hemoglobin and blood count.   You don’t see this in many clinical risk factor studies. 

Commentary on Statistics and Study Design:  The author’s did several things in this study that I really like. I really liked the sample size calculations – good to see that. Also, the analyst checked for normality and used a different test based on whether the data was normal or not.  Also, it was good to see a correction for multiple comparison testing – you rarely (if ever) see this done, even though it should be in most risk factor studies.

                I guess my first major suggestion comes in the form of a presentation standpoint.  The authors essentially broke up the clinical factor results into historical and clinical features (Table 2), medical history (Table 4), and laboratory investigations (Table 5). Personally, I would have combined all these results into one table.  In the table, one could then just have demarcated the different types of clinical features.  It is just a personal preference of mine – it’s fine either way really.

                My other suggestions have more to do with the actual statistical analysis.  The authors did not perform a logistic regression due to the perceived class imbalance of 4% (positive for UGIH) vs. 96% (negative for UGIH) for the response variable. However, I don’t think this should have prevented the use of the logistic regression model. Generally, as long as the class distribution is the same as the total population (which it’s assumed that it would be?), then only having 4% as the positive class would be OK.  You want to read the paper titled “Sampling Bias and Class Imbalance in Maximum-likelihood Logistic Regression” by Freedman et. al. which explains all this well.  Conversely, what the author’s could done is compared the results from the logistic regression to the 2 sample statistical methods that were used (Mann Whitney and 2-sample t-test).  The results should have been roughly the same.  If they were, then it would be OK to just go ahead and use the logistic regression.  In fact, the 2 sample statistical methods would be just as susceptible to a class im-balance as the logistic regression technique if I’m correct – there is really no way around this.

                Also, I see that the author’s excluded blood pressure and oxygen saturation due to missing data with these variables.  This was probably the best approach here, assuming a large percentage of the data was indeed missing. Generally speaking, if you have missing data for greater than 5% of the data for a given variable, then it is best to just remove the variable if possible.

                As the author’s noted, there is a-lot of potential selection and measurement based limitations with this investigation, and I won’t go into all of them – the author’s would certainly be much better at identifying the appropriate variables than I would.  It’s always important that you be as précises as possible with the measurements on both the predictor and response variable.

                A big thanks to the guys up in Toronto for doing this!        

Quality of life factors are reduced in patients with Hepatitis B

Reference: Wang, L., et al., Quality of life and the relevant factors in patients with chronic hepatitis B. Hepatogastroenterology, 2012. 59(116): p. 1036-42.

A little bit different of an article that I am used to publishing on this blog, but I like difference.  Here is a study quantifying the quality of life factors for patients with Hepatitis B. I can’t tell you how much fun I had reading this article!    

Brief Summary: Hepatitis B is an infectious inflammatory disease of the liver caused by the Hepatitis B virus.  Currently, little is known regarding the health-related quality of life (HRQL) factors which are affected by Hepatitis B.  The main objective of this study was to access the HRQL factors for patients with Hepatitis B.  The HRQL factors were physical functioning (PF), role physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH) from the Chinese version of the medical outcomes study 36-Item Short-Form health survey.  A total of 407 patients were accrued into the study, and they were compared to a literature based control group.  Secondary objectives were to ascertain the effect of genotype differences in the ACE and DRD4 genes on the HRQL factors, and also to study the effect of anti-viral therapies on some of the HRQL factors.  These factors included physiology function (PHD) and psychology function (PSD)

Results: The HRQL factors were significantly lower for the patients with Hepatitis B compared to the literature-based control group, and they were lower for all 8 previously mentioned factors.  Furthermore, the genotype of the ACE and DRD4 genes were found to be associated with HRQL, and the anti-viral therapies were found to improve several HRQL factors which included physiology function and society function.   Other therapies (hepatoprotective, jaundice eliminating and supportive treatment ) were not shown to add an improvement.   

Implications for Practice: Patients with Hepatitis B have significantly lower quality of life factor scores.   If the Hepatitis B patient has a highly negative physiology function psychology function, then anti-viral therapies can help improve these quality of life factors.     

   

Discussion: I found it really interesting how genotype can have such a drastic effect on the quality of life factors.  I imagine this relationship would hold for other types of Gastroenterology based diseases as well. Just very fascinating.   

In the discussion, the authors stated that clinical therapy has a minimal effect on the emotional state of a patient Hepatitis B.  Rather, family and community care are more effective than any therapy at increasing the quality of life. All of this is probably true, and I’ve seen some past quality of life studies say the same thing.

 

Commentary on Statistics  and Study Design:  Overall, the statistical analysis was very good. I like how the author’s performed both a uni-variate and multi-variate analysis between the factors and the main response variable which was the HRQL scores.  Since the version I read was not in current print, I could not see the figures, but I assume that both the uni- and multi- variate analysis was shown.  Also, it was good that the author’s pointed out the limitation with using a literature-based control group.  This is a definite limitation. I didn’t see a sample size for the control group, but I’m assuming it was rather large, so this technique should have been fine. 

My one major suggestion would be in the interpretation of the coefficients. I am assuming that the multi-variate table (which again, I couldn’t see) included the coefficients from the multi-variate analysis. If it did not, then it needs to be there, and there should be an explicit and detailed explanation of the coefficients. For instance, if the coefficient is -20.0 for one of the factors (let’s say physical functioning), then an explicit interpretive statement needs to be stated such as “this coefficient means that the patients in the Hepatitis B group had a physical functioning quality of life score that was 20 less those patients in the control group, and this coefficient is significantly different than 0.0.”  This type of explicit and descriptive statement can really help out your non-statistician tremendously, and remember, your average Joe doctor is not a statistician.

Other than that, everything looks good. I assume the sample size was good enough. 

It was good to read a paper way over in China. Thanks!