title: A prediction model for Clostridium difficile recurrence.
abstract: BACKGROUND
  Clostridium difficile infection (CDI) is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR); however, there is little consensus on the impact of most of the identified risk factors.
  
  
  METHODS
  Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR) from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF) to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods.
  
  
  RESULTS
  We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results.
  
  
  CONCLUSIONS
  We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.
authors: Department of Internal Medicine, PinnacleHealth Hospital, Harrisburg, PA, USA; flabarbera@pinnaclehealth.org.
authors: Francis D
authors: FD
authors: LaBarbera
authors: Department of Internal Medicine, PinnacleHealth Hospital, Harrisburg, PA, USA.
authors: Ivan
authors: I
authors: Nikiforov
authors: Department of Internal Medicine, PinnacleHealth Hospital, Harrisburg, PA, USA.
authors: Arvin
authors: A
authors: Parvathenani
authors: Department of Internal Medicine, PinnacleHealth Hospital, Harrisburg, PA, USA.
authors: Varsha
authors: V
authors: Pramil
authors: Department of Internal Medicine, PinnacleHealth Hospital, Harrisburg, PA, USA.
authors: Subhash
authors: S
authors: Gorrepati
keywords: Random Forest; hospital infection; machine learning algorithm
references: Gastroenterology. 2009 Apr;136(4):1206-14
references: 19162027
references: Clin Infect Dis. 2012 Aug;55 Suppl 2:S77-87
references: 22752869
references: Am J Gastroenterol. 2013 Nov;108(11):1794-801
references: 24060760
references: BMC Infect Dis. 2014 Jun 04;14:306
references: 24898123
references: Clin Infect Dis. 2006 Mar 15;42(6):758-64
references: 16477549
references: Clin Microbiol Rev. 2010 Jul;23(3):529-49
references: 20610822
references: Am J Gastroenterol. 2002 Jul;97(7):1769-75
references: 12135033
references: Hepatogastroenterology. 2013 Sep;60(126):1351-4
references: 23262313
references: Clin Microbiol Infect. 2012 Dec;18 Suppl 6:21-7
references: 23121551
references: Therap Adv Gastroenterol. 2012 Nov;5(6):403-20
references: 23152734
references: Clin Infect Dis. 2000 Oct;31(4):1012-7
references: 11049785
references: J Hosp Infect. 2010 Apr;74(4):309-18
references: 20153547
references: Clin Infect Dis. 1997 Mar;24(3):324-33
references: 9114180
references: J Med Microbiol. 2005 Feb;54(Pt 2):101-11
references: 15673502
references: J Community Hosp Intern Med Perspect. 2012 Apr 30;2(1):null
references: 23882354
references: Artif Intell Med. 2008 Mar;42(3):247-59
references: 18063351
references: Infect Control Hosp Epidemiol. 2010 May;31(5):431-55
references: 20307191