Reviewer's report
Title:Postoperative Complications Following Colectomy for Ulcerative Colitis: A
Validation Study
Version: 1Date:10 October 2011
Reviewer: Edward Loftus
Reviewer's report:
Administrative datasets are inherently “messy” because in many (if not most)
cases the case-finding algorithm for diseases or events hasn’t been validated by
medical record review. Ma and colleagues have utilized an administrative
database in the Calgary Health Zone of Alberta Health Services to identify
hospitalized ulcerative colitis patients, many of whom underwent colectomy. The
authors reviewed the medical records of over 1,400 such patients in order to
validate that the patients had been admitted for ulcerative colitis and to validate
colectomy, age, comorbidities and complications. Logistic regression analysis
was performed to identify which factors were associated with postoperative
complications, using both medical record data and administrative data. In
general, the same factors were found to be associated with complications,
although the administrative data tended to magnify the effect somewhat. This
was explained by the fact that administrative data tended to miss comorbidities
that weren’t active during the hospitalizations, and the administrative data tended
to miss less severe complications.
Excellent job. I have only a few comments.
1. Methods, Variables: please be more specific about the comorbidities. How far
back in the medical record were these searched? Similarly, it isn’t clear if you
only examined the administrative database for comorbidities during the
hospitalization itself, or if you search backwards. If the latter, how far back did
you search? As you know, many administrative dataset studies will employ a
run-in period (e.g., 6 months before the index event) in order to ensure the event
is an incident OR to establish the baseline comorbidities. It would be fascinating
for you to explore via a sensitivity analysis how the PPV, sensitivity and
specificity changed by varying the preadmission run-in period for comorbidities
(e.g., 3 months, 6 months, 12 months, 24 months).
2. Methods: tell us more about the data abstraction. How many chart reviewers?
Who were they—physicians, nurses, or non-MD/RN study coordinators? You
allude to trying to limit interobserver variability in the last paragraph of the
Discussion, but you don’t really tell us anything about the chart abstraction
process. Did you have a “gold-standard” abstracter check the work of the other
abstracters?
3. Results, Outcome Validation: In the penultimate paragraph of the Discussion
you allude to misclassification in this area. For UC and for colectomy you showed
us reasons why these were misclassified. Could you show us reasons why/how
complications were misclassified?
4. Methods, Study Population: “ICD-10-CA”, should this be “ICD-10-CM” or does
this mean something else, like the Canadian version of it?
5. Results, Study Population, 1st paragraph, 6th sentence: Spell out “33%” since
it is starting the sentence, can’t start a sentence with a number.
6. Supplementary Table 1, consider adding a reference to the Clavien et al
paper.
Level of interest: An article of outstanding merit and interest in its field
Quality of written English: Acceptable
Statistical review: No, the manuscript does not need to be seen by a
statistician.
Declaration of competing interests:
Research support from Abbott, UCB, Centocor, Bristol Myers Squibb,
GlaxoSmithKline, Millenium-Takeda, Amgen, Pfizer, Shire.
Consultant for Abbott, UCB, Bristol Myers Squibb, Pfizer.
Reviewer's report
Title:Postoperative Complications Following Colectomy for Ulcerative Colitis: A
Validation Study
Version: 1Date:10 October 2011
Reviewer: Edward Loftus
Reviewer's report:
Administrative datasets are inherently “messy” because in many (if not most)
cases the case-finding algorithm for diseases or events hasn’t been validated by
medical record review. Ma and colleagues have utilized an administrative
database in the Calgary Health Zone of Alberta Health Services to identify
hospitalized ulcerative colitis patients, many of whom underwent colectomy. The
authors reviewed the medical records of over 1,400 such patients in order to
validate that the patients had been admitted for ulcerative colitis and to validate
colectomy, age, comorbidities and complications. Logistic regression analysis
was performed to identify which factors were associated with postoperative
complications, using both medical record data and administrative data. In
general, the same factors were found to be associated with complications,
although the administrative data tended to magnify the effect somewhat. This
was explained by the fact that administrative data tended to miss comorbidities
that weren’t active during the hospitalizations, and the administrative data tended
to miss less severe complications.
Excellent job. I have only a few comments.
1. Methods, Variables: please be more specific about the comorbidities. How far
back in the medical record were these searched? Similarly, it isn’t clear if you
only examined the administrative database for comorbidities during the
hospitalization itself, or if you search backwards. If the latter, how far back did
you search? As you know, many administrative dataset studies will employ a
run-in period (e.g., 6 months before the index event) in order to ensure the event
is an incident OR to establish the baseline comorbidities. It would be fascinating
for you to explore via a sensitivity analysis how the PPV, sensitivity and
specificity changed by varying the preadmission run-in period for comorbidities
(e.g., 3 months, 6 months, 12 months, 24 months).
2. Methods: tell us more about the data abstraction. How many chart reviewers?
Who were they—physicians, nurses, or non-MD/RN study coordinators? You
allude to trying to limit interobserver variability in the last paragraph of the
Discussion, but you don’t really tell us anything about the chart abstraction
process. Did you have a “gold-standard” abstracter check the work of the other
abstracters?
3. Results, Outcome Validation: In the penultimate paragraph of the Discussion
you allude to misclassification in this area. For UC and for colectomy you showed
us reasons why these were misclassified. Could you show us reasons why/how
complications were misclassified?
4. Methods, Study Population: “ICD-10-CA”, should this be “ICD-10-CM” or does
this mean something else, like the Canadian version of it?
5. Results, Study Population, 1st paragraph, 6th sentence: Spell out “33%” since
it is starting the sentence, can’t start a sentence with a number.
6. Supplementary Table 1, consider adding a reference to the Clavien et al
paper.
Level of interest: An article of outstanding merit and interest in its field
Quality of written English: Acceptable
Statistical review: No, the manuscript does not need to be seen by a
statistician.
Declaration of competing interests:
Research support from Abbott, UCB, Centocor, Bristol Myers Squibb,
GlaxoSmithKline, Millenium-Takeda, Amgen, Pfizer, Shire.
Consultant for Abbott, UCB, Bristol Myers Squibb, Pfizer.
Please review this paper.
user
Reviewer's report
Title:Postoperative Complications Following Colectomy for Ulcerative Colitis: A
Validation Study
Version: 1Date:10 October 2011
Reviewer: Edward Loftus
Reviewer's report:
Administrative datasets are inherently “messy” because in many (if not most)
cases the case-finding algorithm for diseases or events hasn’t been validated by
medical record review. Ma and colleagues have utilized an administrative
database in the Calgary Health Zone of Alberta Health Services to identify
hospitalized ulcerative colitis patients, many of whom underwent colectomy. The
authors reviewed the medical records of over 1,400 such patients in order to
validate that the patients had been admitted for ulcerative colitis and to validate
colectomy, age, comorbidities and complications. Logistic regression analysis
was performed to identify which factors were associated with postoperative
complications, using both medical record data and administrative data. In
general, the same factors were found to be associated with complications,
although the administrative data tended to magnify the effect somewhat. This
was explained by the fact that administrative data tended to miss comorbidities
that weren’t active during the hospitalizations, and the administrative data tended
to miss less severe complications.
Excellent job. I have only a few comments.
1. Methods, Variables: please be more specific about the comorbidities. How far
back in the medical record were these searched? Similarly, it isn’t clear if you
only examined the administrative database for comorbidities during the
hospitalization itself, or if you search backwards. If the latter, how far back did
you search? As you know, many administrative dataset studies will employ a
run-in period (e.g., 6 months before the index event) in order to ensure the event
is an incident OR to establish the baseline comorbidities. It would be fascinating
for you to explore via a sensitivity analysis how the PPV, sensitivity and
specificity changed by varying the preadmission run-in period for comorbidities
(e.g., 3 months, 6 months, 12 months, 24 months).
2. Methods: tell us more about the data abstraction. How many chart reviewers?
Who were they—physicians, nurses, or non-MD/RN study coordinators? You
allude to trying to limit interobserver variability in the last paragraph of the
Discussion, but you don’t really tell us anything about the chart abstraction
process. Did you have a “gold-standard” abstracter check the work of the other
abstracters?
3. Results, Outcome Validation: In the penultimate paragraph of the Discussion
you allude to misclassification in this area. For UC and for colectomy you showed
us reasons why these were misclassified. Could you show us reasons why/how
complications were misclassified?
4. Methods, Study Population: “ICD-10-CA”, should this be “ICD-10-CM” or does
this mean something else, like the Canadian version of it?
5. Results, Study Population, 1st paragraph, 6th sentence: Spell out “33%” since
it is starting the sentence, can’t start a sentence with a number.
6. Supplementary Table 1, consider adding a reference to the Clavien et al
paper.
Level of interest: An article of outstanding merit and interest in its field
Quality of written English: Acceptable
Statistical review: No, the manuscript does not need to be seen by a
statistician.
Declaration of competing interests:
Research support from Abbott, UCB, Centocor, Bristol Myers Squibb,
GlaxoSmithKline, Millenium-Takeda, Amgen, Pfizer, Shire.
Consultant for Abbott, UCB, Bristol Myers Squibb, Pfizer.
assistant