What would you like to see implemented in nsqipr
? Open an
issue! Are you
interested in using or helping develop nsqipr
? Send me an
email!
See the companion book for a more detailed guide!
Welcome to nsqipr
! If you’re reading this, you are likely already
familiar with the American College of Surgeons National Surgical Quality
Improvement Program (ACS NSQIP©). If not, you can read
about it here. The
ACS NSQIP © is a nationally validated, risk-adjusted,
outcomes-based program to measure and improve the quality of surgical
care.
As of 13 May, 2022, there are currently 699 hospitals that participate in and contribute to the program. The entire database contains more than 8 million cases for data analysis.
ACS NSQIP© captures and reports 30-day morbidity and mortality outcomes for all major inpatient and outpatient surgical procedures as determined by Current Procedural Terminology (CPT©) code. This list is updated annually as new codes become available. Excluded cases are:
The data from ACS NSQIP© is used to produce an exponentially increasing number of publications per year. As of 13 May, 2022, there are currently 3311 PubMed search results for the search term “NSQIP”.
These papers are often published in high quality journals. The following graph shows the top 10 most common journals in which the above search results were published.
nsqipr
ACS NSQIP© requires that members request specific datasets
for use in research. The files are then delivered as .exe
executable
files available for download for a limited duration of time. The
archived files can be unzipped and contain a .txt
tab-delimited file.
Some will also contain a PDF version of the Participant Use File
(PUF); these define the variables in the dataset. The .txt
tab-delimited file must be read into R as a data frame and meticulously
cleaned prior to being used for data analysis. Researchers often want to
combine data across multiple years. This complicates data preparation as
variables are removed or added every year and sometimes the same
variable may have differently worded outcomes between years.
The purpose of nsqipr
is to streamline this process. This package is
geared towards those surgical interns, residents, and attendings who
have limited experience with R, SQL, or “big data” analysis. It is also
designed to be a useful tool for that experienced researcher or computer
scientist making frequent use of ACS NSQIP© PUFs.
For a detailed dive into nsqipr
, please refer to the companion
book or the documentation:
help("nsqipr")
You can install or upgrade nsqipr
with:
devtools::install_github("dylanrussellmd/nsqipr")
We are not (yet) available on CRAN.
.exe
executable files from ACS NSQIP© in a
single directory (dir
) (do not change the default file names).nsqip(dir)
.nsqipr
will take care of the rest. You’re now ready to use the ACS
NSQIP© data for data analysis!
Track progress on how the various data sets are being incorporated into
nsqipr
here.
Check back often for updates!
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.