Toxin assay is more reliable than ICD‐9 data and less time‐consuming than chart review for public reporting of Clostridium difficile hospital case rates

Abstract

OBJECTIVE: Clostridium difficile–associated disease (CDAD) is common and has a 6.1% mortality. Governmental agencies have recommended surveillance, but reporting increases health care costs. We sought to identify a reliable method of reporting CDAD that will not significantly increase health care costs. METHODS: Patients were identified via database query for International Statistical Classification of Diseases and Related Health Problems, 9th Edition (ICD‐9) codes and C. difficile toxin positivity. All identified patients underwent a chart review, which was used to determine the accuracy of the database query methods. Methods of determining whether CDAD was acquired at the reporting institution were studied, and time required to perform each method was measured. RESULTS: The toxin assay reported 96.1% (369/384) of cases and had a positive predictive value of 100%. No difference was found in comparison of the toxin assay case rate of 15.7 per 1000 discharged patients to the rate of 16.3 identified by chart review (P = 0.440; 95% confidence interval [CI], 14.1–17.4), whereas the ICD‐9 method was found to be significantly different by reporting 116.1% (446/384) of cases for a case rate of 19.0 per 1000 discharges (P = 0.001; 95% CI, 17.3–20.8). The time for data extraction via the toxin assay method required only 842 minutes, while the chart review method consumed 21,899 minutes. CONCLUSION: A positive C. difficile toxin assay accurately reports the institutional incidence of disease and is more reliable than ICD‐9 query. This process can be instituted at a fraction of the cost of the standard chart review, and enables governmental agencies to inexpensively add CDAD to their list of reportable diseases. Journal of Hospital Medicine 2011; © 2011 Society of Hospital Medicine.

The burden of clostridium difficile infection: estimates of the incidence of CDI from U.S. Administrative databases

“…In analysis of complete Medicare claims, 33 % of the CDI events were categorized as hospital-onset, whereas in the analyses using only inpatient Medicare facility data, approximately 60 % of the CDI events were categorized as hospital-onset, suggesting that hospital-onset cases will be over-estimated by almost two-fold when only inpatient claims or billing data are used. These results are consistent with previous reports of the over-attribution of hospital-onset CDI [ 20 , 21 ] and the over-estimation of CDI cases identified by the ICD-9-CM diagnosis code compared to positive C. difficile toxin assay from facility billing data [ 20 , 22 – 25 ]. More hospital-onset cases were identified in the HCUP and Premier data, likely due to misclassification of CDI with onset in the community.…”

Hospital discharge abstracts have limited accuracy in identifying occurrence of Clostridium difficile infections among hospitalized individuals with inflammatory bowel disease: A population-based study

“…However, in one large study of general population, using the ICD-9-CM code resulted in higher estimates of CDI occurrence and time trends of a greater increase in rates than that reported using the laboratory results; the use of the ICD-9-CM code overestimated the number of CDI cases relative to the use of the toxin assay[ 23 ] In that study, a total of 10,832 cases of CDI were identified of which 27% had ICD-9-CM code identification alone, 15.2% a positive toxin result only and 57.8% both. An overestimation of CDI rates using the ICD-9-CM code has been reported in other studies[ 24 ].…”

Validity and Reliability of Administrative Coded Data for the Identification of Hospital‐Acquired Infections: An Updated Systematic Review with Meta‐Analysis and Meta‐Regression Analysis

“…As the number of HAI types assessed in each individual document ranged from one to five, the 38 papers accounted for a total of 53 "HAI-related included studies." Meta-analyzable results were provided by 33 of the 38 selected documents, overall providing data from 45 source evaluations across different HAIs that were then included in quantitative summaries (Table 1; Landis and Koch 1977;Hirschhorn, Currier, and Platt 1993;Baker et al 1995;Hebden 2000;Cadwallader et al 2001;Romano, Schembri, and Rainwater 2002;Curtis et al 2004;Dubberke et al 2006;Scheurer et al 2007;Azaouagh and Stausberg 2008;Stausberg and Azaouagh 2008;Stevenson et al 2008;Chang et al 2008;Bolon et al 2009;Schmiedeskamp et al 2009;Zhan et al 2009;Olsen and Fraser 2010;Schaefer et al 2010;Verelst et al 2010;Gerbier et al 2011;Hollenbeak et al 2011;Inacio et al 2011;Schweizer et al 2011;Shaklee et al 2011;Chan et al 2011;Gerbier-Colomban et al 2012;Jones et al 2012;Welker and Bertumen 2012;Yokoe et al 2012;Calderwood et al 2012;Knepper et al 2013;Patrick et al 2013;Van Mourik et al 2013;Cass et al 2013;Gardner et al 2014;Grammatico-Guillon et al 2014;…”