Gabriela Batista Rodríguez1,2, Andrea Balla3,4, Santiago Corradetti3, Carmen Martinez3, Pilar Hernández3, Jesús Bollo3, Eduard M Targarona3. 1. General and Digestive Surgery Unit, Hospital de la Santa Creu i Sant Pau, UAB Universidad Autónoma de Barcelona, Sant Antoni Maria Claret, 167, 08025, Barcelona, Spain. batistagaby@gmail.com. 2. Surgical Oncology Unit, Department of Hemato-Oncology, Hospital Dr. Rafael A. Calderón Guardia, Caja Costarricense del Seguro Social, San José, Costa Rica. batistagaby@gmail.com. 3. General and Digestive Surgery Unit, Hospital de la Santa Creu i Sant Pau, UAB Universidad Autónoma de Barcelona, Sant Antoni Maria Claret, 167, 08025, Barcelona, Spain. 4. Department of General Surgery and Surgical Specialties "Paride Stefanini", Sapienza, University of Rome, Viale del Policlinico 155, 00161, Rome, Italy.
Abstract
BACKGROUND: "Big data" refers to large amount of dataset. Those large databases are useful in many areas, including healthcare. The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and the National Inpatient Sample (NIS) are big databases that were developed in the USA in order to record surgical outcomes. The aim of the present systematic review is to evaluate the type and clinical impact of the information retrieved through NISQP and NIS big database articles focused on laparoscopic colorectal surgery. METHODS: A systematic review was conducted using The Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. The research was carried out on PubMed database and revealed 350 published papers. Outcomes of articles in which laparoscopic colorectal surgery was the primary aim were analyzed. RESULTS: Fifty-five studies, published between 2007 and February 2017, were included. Articles included were categorized in groups according to the main topic as: outcomes related to surgical technique comparisons, morbidity and perioperatory results, specific disease-related outcomes, sociodemographic disparities, and academic training impact. CONCLUSIONS: NSQIP and NIS databases are just the tip of the iceberg for the potential application of Big Data technology and analysis in MIS. Information obtained through big data is useful and could be considered as external validation in those situations where a significant evidence-based medicine exists; also, those databases establish benchmarks to measure the quality of patient care. Data retrieved helps to inform decision-making and improve healthcare delivery.
BACKGROUND: "Big data" refers to large amount of dataset. Those large databases are useful in many areas, including healthcare. The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and the National Inpatient Sample (NIS) are big databases that were developed in the USA in order to record surgical outcomes. The aim of the present systematic review is to evaluate the type and clinical impact of the information retrieved through NISQP and NIS big database articles focused on laparoscopic colorectal surgery. METHODS: A systematic review was conducted using The Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. The research was carried out on PubMed database and revealed 350 published papers. Outcomes of articles in which laparoscopic colorectal surgery was the primary aim were analyzed. RESULTS: Fifty-five studies, published between 2007 and February 2017, were included. Articles included were categorized in groups according to the main topic as: outcomes related to surgical technique comparisons, morbidity and perioperatory results, specific disease-related outcomes, sociodemographic disparities, and academic training impact. CONCLUSIONS: NSQIP and NIS databases are just the tip of the iceberg for the potential application of Big Data technology and analysis in MIS. Information obtained through big data is useful and could be considered as external validation in those situations where a significant evidence-based medicine exists; also, those databases establish benchmarks to measure the quality of patient care. Data retrieved helps to inform decision-making and improve healthcare delivery.
Entities:
Keywords:
Big data; Laparoscopic colorectal surgery; NIS; NSQIP; Systematic literature review
Authors: David A Etzioni; Nabil Wasif; Amylou C Dueck; Robert R Cima; Samuel F Hohmann; James M Naessens; Amit K Mathur; Elizabeth B Habermann Journal: JAMA Date: 2015-02-03 Impact factor: 56.272
Authors: Deepak K Ozhathil; YouFu Li; Jillian K Smith; Elan Witkowski; Elizaveta Ragulin Coyne; Karim Alavi; Jennifer F Tseng; Shimul A Shah Journal: J Surg Res Date: 2011-07-23 Impact factor: 2.192
Authors: Rachel M Owen; Sebastian D Perez; Nathan Lytle; Ankit Patel; S S Davis; Edward Lin; John F Sweeney Journal: Surg Endosc Date: 2013-04-13 Impact factor: 4.584
Authors: Andrew T Schlussel; Michael B Lustik; Eric K Johnson; Justin A Maykel; Brad J Champagne; Joel E Goldberg; Scott R Steele Journal: Dis Colon Rectum Date: 2015-04 Impact factor: 4.585
Authors: David Yu Greenblatt; Victoria Rajamanickam; Andrew J Pugely; Charles P Heise; Eugene F Foley; Gregory D Kennedy Journal: J Am Coll Surg Date: 2011-03-16 Impact factor: 6.113
Authors: Tafari Mbadiwe; Augustine C Obirieze; Edward E Cornwell; Patricia Turner; Terrence M Fullum Journal: J Am Coll Surg Date: 2013-04 Impact factor: 6.113
Authors: Paul J Speicher; Brian R Englum; Betty Jiang; Ricardo Pietrobon; Christopher R Mantyh; John Migaly Journal: J Gastrointest Surg Date: 2013-07-30 Impact factor: 3.452
Authors: Reilly P Musselman; Tara Gomes; Deanna M Rothwell; Rebecca C Auer; Husein Moloo; Robin P Boushey; Carl van Walraven Journal: J Gastrointest Surg Date: 2018-12-03 Impact factor: 3.452