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Review
. 2019 Jan;32(1):41-53.
doi: 10.1055/s-0038-1673353. Epub 2019 Jan 8.

Using the National Surgical Quality Improvement Project (NSQIP) to Perform Clinical Research in Colon and Rectal Surgery

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Review

Using the National Surgical Quality Improvement Project (NSQIP) to Perform Clinical Research in Colon and Rectal Surgery

Samuel Eisenstein et al. Clin Colon Rectal Surg. 2019 Jan.

Abstract

The American College of Surgeons' National Surgical Quality Improvement Project (ACS-NSQIP) is probably the most well-known surgical database in North American and worldwide. This clinical database was first proposed by Dr. Clifford Ko, a colorectal surgeon, to the ACS, and NSQIP first started collecting data ca. 2005 with the intent of comparing hospitals (benchmarking) and for hospital-level quality improvement projects. Since then, its popularity has grown from just a few participating hospitals in the United States to more than 708 participating hospitals worldwide, and collaboration allows regional or disease-specific data sharing. Importantly, from a methodological perspective, as the number of hospitals has grown so has the hospital heterogeneity and thus generalizability of the results and conclusions of the individual studies. In this article, we will first briefly present the structure of the database (aka the Participant User File) and other important methodological considerations specific to performing clinical research. We will then briefly review and summarize the approximately 60 published colectomy articles and 30 published articles on proctectomy. We will conclude with future directions relevant to colorectal clinical research.

Keywords: American College of Surgeons; National Quality Improvement Program; biostatistics; clinical research; colectomy; data analysis; outcomes; proctectomy.

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Figures

Fig. 1
Fig. 1
Postoperative complications according to type of bowel preparation. *Statistical significance, p  < 0.0001. (Reproduced with permission from Kiran et al, by Wolters Kluwer Health, Inc.)
Fig. 2
Fig. 2
Presurgical hospitalization length of stay on the risk of VTE using a multivariable logistic regression model. (Reproduced with permission from Greaves and Holubar, by Wolters Kluwer Health, Inc.)
Fig. 3
Fig. 3
Schematic representation of propensity score stratification used in this study.

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