Extraction form for project: SGS 2022 Obesity associations

Design Details

1. Include or Reject
Single Choice
INCLUDE
Reject
2. Rejection reason
Confirm none of these is a reason for rejection. If you select a rejection reason, stop and move on to next article
Single Choice
P/I: Not urogyn surgery of interest
Pr: Not obesity (categorical)
O: Not outcome of interest
D: No multivariate regression
D: Obesity not analyzed (for inclusion in MV model)
D: Estimate (OR, beta) for obesity not reported [confer with Nancy if NR because the estimate was NS]
D: N<30
Not journal publication (eg, conf abstract)
Non-English
Duplicate
Other...
3. Obesity threshold
In the multivariable analysis, what was the threshold for obesity? If the analysis included multiple thresholds, you can check multiple options. If "Other", enter the threshold (a single number) in the follow-up question.
Multiple Choice
≥25
≥30
≥40
Other
4. Mesh
Did the study involve use of mesh?
Single Choice
Yes
No
NR/Uncear
5. Study design
The underlying study design
Single Choice
RCT
Nonrandomized comparison of interventions
Single group (no comparison of interventions analyzed)
Case-control
Other/unclear...
6. Do the data come from a registry or other large/major database? If so, which?
Dependency
  • Question Position: 5
    • - Option: Nonrandomized comparison of interventions
    • - Option: Single group (no comparison of interventions analyzed)
    • - Option: Case-control
    • - Option: Other/unclear...
    Single Choice
    Yes
    No/unclear
    7. Directionality
    Prospective = data collected based on a study protocol. (Nested case-control are generally prospective. Registries are generally retrospective, even if data entered into registry "prospectively".) If unclear, explain why.
    Dependency
  • Question Position: 5
    • - Option: Nonrandomized comparison of interventions
    • - Option: Single group (no comparison of interventions analyzed)
    • - Option: Case-control
    • - Option: Other/unclear...
    Single Choice
    Prospective
    Retrospective
    Unclear
    8. Timepoints
    Longitudinal = outcomes followed after surgery/hospitalization. Cross-sectional = one time point (which may be the perioperative period).
    Single Choice
    Longitudinal
    Cross-sectional
    9. Population/Condition
    If >10% of women had condition other than prolapse or incontinence, confer with Nancy before continuing.
    Single Choice
    Prolapse (only)
    Incontinence (only)
    Prolapse and incontinence (analyzed separately in separate models)
    Prolapse or incontinence (analyzed together, included in regression model)
    Prolapse or incontinence (analyzed together, NOT included in regression model)
    10. Prolapse surgery category
    Dependency
  • Question Position: 9
    • - Option: Prolapse (only)
    • - Option: Prolapse and incontinence (analyzed separately in separate models)
    • - Option: Prolapse or incontinence (analyzed together, included in regression model)
    • - Option: Prolapse or incontinence (analyzed together, NOT included in regression model)
    Multiple Choice
    Vaginal prolapse repair
    Laparoscopic prolapse repair
    Robotic prolapse repair
    Open/abdominal prolapse repair
    Other...
    11. Incontinence surgery category
    Dependency
  • Question Position: 9
    • - Option: Incontinence (only)
    • - Option: Prolapse and incontinence (analyzed separately in separate models)
    • - Option: Prolapse or incontinence (analyzed together, included in regression model)
    • - Option: Prolapse or incontinence (analyzed together, NOT included in regression model)
    Multiple Choice
    Sling
    Bulking
    Burch
    Other...
    12. Study dates
    Calendar years of surgery/procedure/inclusion in study. Just the years. If unclear, you can enter "<" the publication year (eg, <2105 for study published in 2015); in this case, leave "Earliest" blank.
    Earliest
    Latest
    13. Study location
    Country or region or city. Select one or more options and enter data. Especially where multicenter, no need to include every detail (eg, every city). Can include a summary description (e.g., "Across the East Coast"). Use your judgment what will be sufficiently informative.
    Multiple Choice
    Multiple countries (nation-wide)
    Country (nation-wide)
    Region (of a country)
    Multiple cities/locations
    Single city/location
    Unclear
    14. Setting
    Excuse any redundancy with Q12.
    Multiple Choice
    National
    Urban
    Suburban/metropolitan
    Rural
    Other...
    Unclear
    15. Centers
    Single Choice
    Multicenter
    Single center
    Unclear
    16. Hospital type
    Multiple Choice
    Academic/teaching
    Non-academic/non-teaching
    Unclear
    17. Eligibility: BMI
    Describe restrictions on eligibility based on BMI. If they EXCLUDED "underweight" (eg, BMI <18), in "Lowest weight included" select 18; leave "Highest weight included" blank. If they INCLUDED BMI 25-40, in "Lowest weight included" row select 25; in "Highest weight included" row select 40. Don't worry about < vs. <= (or > vs. >=).
    No exclusions based on BMI (explicit)
    Multiple Choice
    No BMI exclusions (explicit)
    No exclusions based on BMI (implicit, NR)
    Multiple Choice
    No BMI exclusions (implicit, NR)
    Lowest BMI included
    Highest BMI included
    18. Eligibility criteria
    Other than BMI [You captured this in the prior question]. Be concise; use common abbreviations. Omit nonclinical/demographic factors (like consent). Do not be redundant between inclusion and exclusion. (It's okay to convert their exclusions into inclusions; for example, instead of "we excluded children <18, it will make for better consistency across studies to extract as an inclusion that they included adults >=18). It's okay to leave exclusions blank.) If you copy and paste, please clean up the resulting typos and line breaks.
    Inclusion
    Exclusion
    19. Lack of generalizability (atypical population)?
    Is this a "special" or "focused" or "atypical" population? If yes, succinctly describe (eg, inner-city Blacks, ...). Use your judgment as to what would be an interesting "special" or highly restricted population that Nancy should be aware of.
    Single Choice
    Yes
    No
    20. Sample size
    Total eligible = sample size after eligibility exclusions (not including dropout due to missing data). Model N = number included in final model. Enter NR if either is not reported.
    Total eligible
    Model N
    21. BMI
    Note that full range might be derived from eligibility criteria. Enter data for the whole sample only. For ranges, put commas between numbers. FOR THIS AND ALL BASELINE CHARACTERISTICS, if the article reports only by intervention (or by group), please calculated the weighted average ((N1*mean1)+(N2*mean2))/(N1+N2). Enter NR for SD.)
    Full range
    Mean (SD)
    Median (IQR)
    Not reported
    Single Choice
    NR
    22. Age
    Note that full range might be derived from eligibility criteria. Enter data for the whole sample only. For ranges, put commas between numbers.
    Full range
    Mean (SD)
    Median (IQR)
    Not reported
    Single Choice
    NR
    23. Parity
    Note that full range might be derived from eligibility criteria. Enter data for the whole sample only. For ranges, put commas between numbers.
    Full range
    Mean (SD)
    Median (IQR)
    Not reported
    Single Choice
    NR
    24. Race/Ethnicity
    Enter % (0%-100%); NOT proportion (0-1). For "Asian (specific)" or "Other" enter race/nationality and %. If there are multiple, enter all together, with commas between. If study is from Asia, you may be able to assume they are 100% Asian (eg, Japanese). Leave rows with NR blank, but if overall NR then select that option at the bottom of the table.
    White (any)
    White, non-Hispanic
    Black (any)
    Black, non-Hispanic
    Non-White
    Non-Black
    Hispanic (any)
    Hispanic, White
    Hispanic, Black
    Asian (any)
    Asian (specific)
    Native/Indigenous
    Other
    NR
    Single Choice
    NR
    25. Comorbidities etc.
    Enter % (0-100; not proportion) with each comorbidity. If NR, leave blank (except if overall NR, then check this in last row). Enter Other (comorbidity and %) only if a particularly pertinent factor was omitted from our list. Let Nancy know (now) for possible explicit inclusion in the extraction form.
    Diabetes
    CHF
    HTN
    Smoking
    Post-menopausal
    Diuretic use
    Other
    NR
    Single Choice
    NR
    26. Regression method
    If linear regression, confer with Nancy and Ethan before continuing. If Other/unclear, explain and confer with Nancy and Ethan before continuing.
    Single Choice
    Logistic regression
    Cox proportional hazards
    Log binomial
    Linear regression [!]
    Other/unclear...
    27. Outcome analyzed
    If outcome category is Other, check with Nancy, before proceeding. If specific outcome is Composite complications, specify whether UTI is included.
    Outcome category
    Single Choice
    Complication/adverse event
    Objective (clinical) outcome
    Subjective outcome (PROM/PREM)
    Other
    Specific outcome
    Follow-up duration
    Single Choice
    Absolute
    Mean/median
    Cross-sectional (perioperative)
    Comment/Note
    28. Was complete list of association estimates reported for all analyzed covariates (both significant and nonsignificant)?
    In other words, is there an OR or aOR (or RR or HR or beta) for all analyzed covariates, not only the factors in the final model? FOR THIS, AND ALL RELEVANT SUBSEQUENT QUESTIONS, EXTRACT THE FULLEST MODEL (unless they include inappropriate covariates and a less-full model excludes these). Feel free to check with Ethan if you can't decide. Add comment only if necessary.
    Y/N
    Single Choice
    Yes
    No
    Unclear
    Comment/Note
    29. Was final model reported in full?
    This question does not refer to variables omitted from the model. If they say something like "adjusted for x, y, z" without reporting numbers for x, y, z, then No. If they say only that the adjusted OR for obesity is X (without listing all the other variables in the model and their estimates), then No. If the answer to Q25 is Yes, then the answer to this question is Yes. Add comment only if necessary.
    Dependency
  • Question Position: 28
    • - Cell: (Y/N x )
    • - Option: No
    • - Cell: (Y/N x )
    • - Option: Unclear
    Single Choice
    Yes
    No
    Comment
    30. Inappropriate covariates
    Did model include covariates measured after/during surgery (like EBL, LOS, complications, post-op incontinence)? Or any other questionable covariates (feel free to check with Ethan)? If yes, list these (AND STOP HERE).
    Single Choice
    Yes
    No
    31. Number of covariates
    How many covariates are included in the final model? Include interaction terms and listed covariates that are not reported explicitly). Do not include the intercept. If univariate, enter 1.
    Number
    Cannot determine
    Single Choice
    Unknown
    32. Estimate 1
    Each comparison of BMI categories entered separately. If they divided into 3 groups (eg, <25, 25-30, >30) and used <25 as the reference group, they should have two rows of estimates/data. One for 25-30 vs. <25 and one for >30 vs. <25. The 25-30 vs. <25 will go here as "Estimate 1". The >30 vs. <25 will go in the next question (Estimate 2), etc. Enter the reference group (<25) for each Estimate. Succinctly describe the BMI categories being analyzed (eg, <25, 25-30). Under n/N, check radio button then enter data in text box that appears. Calculate the % (not the proportion) as needed; the denominator is the number in the model. For n/N and %, enter "NR" if the case. Please enter the estimate (eg, OR) in the form E.EE (L.LL, U.UU) with spaces between items and a comma not a dash. REMEMBER, we are including univariate estimates when BMI categories were tested and excluded from final model for being NS. If they report only NS (without an estimate), enter NS under P value and check NR in the second column of the Estimate row.
    Definitionn/N%
    BMI category (evaluated)
    BMI category (reference)
    Measure
    Single Choice
    OR
    HR
    RR
    Beta
    Single Choice

    Single Choice

    Estimate (95% CI): E.EE (L.LL, U.UU)
    Single Choice
    NR
    Single Choice

    P value
    Single Choice

    Single Choice

    Univariate?
    Single Choice
    Univariate (excluded from final model)
    Single Choice

    Single Choice

    33. Estimate 2
    Each comparison of BMI categories entered separately. If they divided into 3 groups (eg, <25, 25-30, >30) and used <25 as the reference group, they should have two rows of estimates/data. One for 25-30 vs. <25 and one for >30 vs. <25. The 25-30 vs. <25 will go here as "Estimate 1". The >30 vs. <25 will go in the next question (Estimate 2), etc. Enter the reference group (<25) for each Estimate. Succinctly describe the BMI categories being analyzed (eg, <25, 25-30). Under n/N, check radio button then enter data in text box that appears. Calculate the % (not the proportion) as needed; the denominator is the number in the model. For n/N and %, enter "NR" if the case. Please enter the estimate (eg, OR) in the form E.EE (L.LL, U.UU) with spaces between items and a comma not a dash. REMEMBER, we are including univariate estimates when BMI categories were tested and excluded from final model for being NS. If they report only NS (without an estimate), enter NS under P value and check NR in the second column of the Estimate row.
    Definitionn/N%
    BMI category (evaluated)
    BMI category (reference)
    Measure
    Single Choice
    OR
    HR
    RR
    Beta
    Single Choice

    Single Choice

    Estimate (95% CI): E.EE (L.LL, U.UU)
    Single Choice
    NR
    Single Choice

    P value
    Single Choice

    Single Choice

    Univariate?
    Single Choice
    Univariate (excluded from final model)
    Single Choice

    Single Choice

    34. Estimate 3
    Each comparison of BMI categories entered separately. If they divided into 3 groups (eg, <25, 25-30, >30) and used <25 as the reference group, they should have two rows of estimates/data. One for 25-30 vs. <25 and one for >30 vs. <25. The 25-30 vs. <25 will go here as "Estimate 1". The >30 vs. <25 will go in the next question (Estimate 2), etc. Enter the reference group (<25) for each Estimate. Succinctly describe the BMI categories being analyzed (eg, <25, 25-30). Under n/N, check radio button then enter data in text box that appears. Calculate the % (not the proportion) as needed; the denominator is the number in the model. For n/N and %, enter "NR" if the case. Please enter the estimate (eg, OR) in the form E.EE (L.LL, U.UU) with spaces between items and a comma not a dash. REMEMBER, we are including univariate estimates when BMI categories were tested and excluded from final model for being NS. If they report only NS (without an estimate), enter NS under P value and check NR in the second column of the Estimate row.
    Definitionn/N%
    BMI category (evaluated)
    BMI category (reference)
    Measure
    Single Choice
    OR
    HR
    RR
    Beta
    Single Choice

    Single Choice

    Estimate (95% CI): E.EE (L.LL, U.UU)
    Single Choice
    NR
    Single Choice

    P value
    Single Choice

    Single Choice

    Univariate?
    Single Choice
    Univariate (excluded from final model)
    Single Choice

    Single Choice

    35. List of covariates in model
    List the covariates in the model (other than obesity). Please simplify down to single words as possible (eg, "age", not a list of each of the 3 age categories they included). PUT A SEMICOLON (;) BETWEEN EACH COVARIATE.
    36. 2nd Review
    For 2nd reviewer: List corrections (from your perspective) pertinent to the extracted data. You don't need to list trivial corrections (like typos; except maybe number typos).
    Reviewer name
    Non-trivial corrections made
    Confirmed with extractor and/or Nancy
    Single Choice
    Confirmed/finalized/no edits made
    Pending
    37. Was the comparison of interventions/procedures included as a variable in the regression model?
    If, for example, they compared 2 surgical approaches, the regression model should have a covariate for surgical approach (whether significant or not).
    Dependency
  • Question Position: 5
    • - Option: RCT
    • - Option: Nonrandomized comparison of interventions
    Single Choice
    Yes
    No
    38. Comments/Notes

    Risk of Bias Assessment

    1. Were eligibility criteria clear?
    Comment if No
    Rating
    Single Choice
    Yes
    No
    Notes/Comments:
    2. Were interventions adequately described?
    Comment if No
    Rating
    Single Choice
    Yes
    No
    Notes/Comments:
    3. Were the outcomes fully defined?
    Comment if No
    Rating
    Single Choice
    Yes
    No
    Notes/Comments:
    4. Clear reporting with no discrepancies
    Comment if No
    Rating
    Single Choice
    Yes
    No
    Notes/Comments:
    5. Statistical methods adequately conducted and described
    Comment if No
    Rating
    Single Choice
    Yes
    No
    Notes/Comments:
    6. Analyzed rate >80 percent
    Is No. included in model divided by No. in database> 80%? What % if No
    Rating
    Single Choice
    Yes
    No
    Notes/Comments:
    7. No. analyzed ÷ No. variables >10
    Rating
    Single Choice
    Yes
    No
    Ratio
    8. Full accounting of all analyzed variables
    Did the article list/describe the full list of variables that were assessed for possible inclusion in the model. Or did they fully list all variables in the model AND all nonsignificant variables that were excluded from the model? Comment if No
    Rating
    Single Choice
    Yes
    No
    Notes/Comments:
    9. 2nd Review
    Changes made
    Confirmed with extractor and/or Nancy
    Single Choice
    Confirmed/finalized/no edits made
    Pending