MAKING THE NSQIP PARTICIPANT USE DATA FILE (PUF) WORK FOR YOU

Transcription

1 MAKING THE NSQIP PARTICIPANT USE DATA FILE (PUF) WORK FOR YOU Hani Tamim, PhD Clinical Research Institute Department of Internal Medicine American University of Beirut Medical Center Beirut - Lebanon

2 Participant Use Data File The Participant Use Data File (PUF) is a Health Insurance Portability and Accountability Act (HIPAA) compliant data file containing cases submitted to the ACS NSQIP. ACS NSQIP provides a prospective and validated database of pre-operative to 30-day post-operative surgical outcomes. The intended purpose of this file is to provide researchers at participating sites with a data resource they can use to investigate and advance the quality of care delivered to the surgical patient through the analysis of cases captured by ACS NSQIP.

3 Available information in the database The ACS NSQIP collects data around 240 variables for patients undergoing major surgical procedures in both the inpatient and outpatient setting. The variables include: Demographic (age, gender, race.) Surgical profile (principal procedure, anesthesia technique ) Preoperative risk assessment (pulmonary, renal ) Laboratory data (INR, Creatinine, WBC, Hct ) Operative information (other procedures, current procedures ) Post operative occurrences (outcomes) within 30 days (wound, respiratory, urinary ) Hospital discharge/readmission/mortality/reoperation

4 Outline Format of the PUF Merge the files Clean the data Identify a research question Specify inclusion / exclusion criteria Manage the data Structure tables format Analyze the data Prepare tables for publication Write up the paper

5 Format of PUF The data files are made available in a delimited text, SAS, and SPSS file type. Data for each year is provided in a separate file.

6 Format of PUF

7 Format of PUF Proc contents

8 Format of PUF Proc contents

9 Merge files

10 Different PUF files Following is the number of patients in each of the different PUF files: 2008: 2009: 2010: 2011: 2012: 271,368 patients 336,190 patients 363,431 patients 442,149 patients 543,885 patients Merged file: 1,957,023 patients

11 Clean data It is a process used in databases, where we identify incomplete, incorrect, inaccurate, or irrelevant data entry and replacing, modifying, or deleting them. For the ACS NSQIP data, the most important step is to identify the extreme values for each of the variables. Done by constructing frequency distributions for the available variables.

12 Clean data

13 Clean data

14 Clean data

15 Clean data What needs to be done when identifying an extreme value: Delete the entry (while keeping the patient) Delete the patient Depends on the topic and the other information of the patient

16 Identify a research question Specifying the primary and secondary objectives of the study to be carried out Exposure Outcome Other factors It is directly related to the steps to be carried out from the statistical point of view

17 Examples of research questions Evaluates the effect of preoperative anemia on 30-day postoperative morbidity and mortality in patients undergoing major non cardiac surgery. Determine the relationship between preoperative anemia and postoperative cardiac events (myocardial infarction, cardiac arrest or angina) in patients undergoing unilateral primary total knee arthroplasty. Assess whether there is an association between INR and postoperative bleeding and mortality in patients undergoing surgery.

18 Inclusion/exclusion criteria Based on the clinical question to be answered, specific restriction should be applied to the data to keep patients who meet these criteria. The inclusion/exclusion criteria could be: - Demographic (such as age and gender) - Surgical procedure (Cardiac, non cardiac, specific CPT code, etc) - Emergency (Cardiac arrest, Myocardial infarction.) - Lab results (such as INR) - Any other piece of information that characterizes the population to be studied

19 Inclusion/exclusion criteria

20 Inclusion/exclusion criteria Comparing patients receiving gastric bypass (CPT: and 43645) with or without band removal (CPT: and 43774)

21 Data management Data management is a step where data is changed to a format that will allow the investigator answer the question under investigation. Three types of data management apply: Recoding Categorization Calculation Other

22 Recoding Recoding is a step carried out to change the codes for an already existing variable

23 Recoding

24 Recoding

25 Recoding

26 Recoding

27 Recoding

28 Categorization Categorization is a step carried out to change a continuous variable into a categorical one, based on clinical or statistical basis For example, age and creatinine are both continuous variables, and are to be categorized according to the following criteria.

29 Categorization

30 Categorization

31 Calculation Calculation step is a process of creating new variable based on a certain mathematical equation. For example, using weight and height, the body mass index (BMI) could be calculated.

32 Calculation

33 Calculation

34 Other In some instances, we need to compile information from different variables into one variable For example, we might be interested in assessing whether the patient had any cardiac event. The cardiac events we are interested in are arrest and MI.

35 Other

36 Other

37 Structure tables format The researchers should finalize the statistical analysis plan according to the research question. To do so, a template for tables to be included in the manuscript is provided to direct the statistical analyses To be decided upon based on: Literature review Objective of the study Statistical analysis plan Some feedback from statistician They need to include: Results for the univariate, bivariate and multivariate analysis

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41 Statistical analyses Univariate analysis: Frequency distribution for the categorical variables, descriptive analysis including mean, standard deviation, median, range for the continuous variables. Bivariate analysis: between the independent variables and the outcomes, such as Chi square, Independent sample t test, paired sample t test, ANOVA, Pearson correlation, etc. Multivariate analysis: to identify the predictors of the outcome, such as linear regression, binary logistic regression, hierarchical linear regression, stepwise regression, etc. Other specific analyses, such as stratified analyses, graphical representation (such as histograms, boxplots, error bars, etc.). Programs prepared through SAS and results generated.

42 Statistical analyses

43 Prepare tables for publication

44 Write up the paper A manuscript to be prepared with the contribution of all authors It includes: - A summary of the study; background, methods, findings, interpretation - An introduction - The Methods - The sample size - The statistical analysis - The findings - The discussion of the findings