Hospital-onset clostridium difficile infection rates in persons with cancer or Hematopoietic stem cell transplant: A C3IC network report

A multicenter survey of 11 cancer centers was performed to determine the rate of hospital-onset Clostridium difficile infection (HO-CDI) and surveillance practices. Pooled rates of HO-CDI in patients with cancer were twice the rates reported for all US patients (15.8 vs 7.4 per 10,000 patient-days). Rates were elevated regardless of diagnostic test used

Jean du Tillet et les illustrations du grand «Recueil des roys»

Brown Elizabeth A.R., Bouniort Jeanne, Dickman Orth Myra. Jean du Tillet et les illustrations du grand «Recueil des roys». In: Revue de l'Art, 1997, n°115. pp. 7-24

Administrative Coding Data, Compared With CDC/NHSN Criteria, Are Poor Indicators of Health Care-Associated Infections

Background: ICD-9-CM coding alone has been proposed as a method of surveillance for health care-associated infections (HAIs). The accuracy of this method, however, relative to accepted infection control criteria is not known. Methods: Retrospective analysis of patients at an academic medical center in 2005 who underwent surgical procedures or who were at risk for catheter-associated bloodstream infections or ventilator-associated pneumonia was performed. Patients previously identified with HAIs by Centers for Disease Control and Prevention\u27s National Healthcare Safety Network surveillance methods were compared with those of the same risk group identified by secondary infection ICD-9-CM codes. Discordant cases identified by only coding were all rereviewed and adjusted prior to final analysis. When coding and surveillance were both negative, a sample of patients was used to estimate the proportion of false negatives in this group. Results: The positive predictive values (PPVs) ranged from 0.14 to 0.51 with an aggregate of 0.23, even after adjustment for additional cases detected on subsequent medical record review. The negative predictive values (NPVs) ranged from 0.91 to 1.00, with an aggregate of 0.96. The estimates of the true variance of PPVs and NPVs across surgical procedures were small (0.0129, standard error, 0.009; 0.000145, standard error, 0.00019, respectively) and could be mostly explained by variation in prevalence of surgical site infections. Conclusion: Administrative coding alone appears to be a poor tool to be used as an infection control surveillance method. Its proposed use for routine HAI surveillance, public reporting of HAIs, interfacility comparisons, and nonpayment for performance should be seriously questioned

Artificial Differences in Clostridium difficile Infection Rates Associated with Disparity in Testing

In 2015, Clostridium difficile testing rates among 30 US community, multispecialty, and cancer hospitals were 14.0, 16.3, and 33.9/1,000 patient-days, respectively. Pooled hospital onset rates were 0.56, 0.84, and 1.57/1,000 patient-days, respectively. Higher testing rates may artificially inflate reported rates of C. difficile infection. C. difficile surveillance should consider testing frequency

Artificial differences in Clostridium difficile infection rates associated with disparity in testing

In 2015, Clostridium difficile testing rates among 30 US community, multispecialty, and cancer hospitals were 14.0, 16.3, and 33.9/1,000 patient-days, respectively. Pooled hospital onset rates were 0.56, 0.84, and 1.57/1,000 patient-days, respectively. Higher testing rates may artificially inflate reported rates of C. difficile infection. C. difficile surveillance should consider testing frequency

Artificial Differences in Clostridium difficile Infection Rates Associated with Disparity in Testing.

In 2015, Clostridium difficile testing rates among 30 US community, multispecialty, and cancer hospitals were 14.0, 16.3, and 33.9/1,000 patient-days, respectively. Pooled hospital onset rates were 0.56, 0.84, and 1.57/1,000 patient-days, respectively. Higher testing rates may artificially inflate reported rates of C. difficile infection. C. difficile surveillance should consider testing frequency