Identification of Recurrent Clostridium difficile Infection Using Administrative Codes: Accuracy and Implications for Surveillance.
OBJECTIVE
To develop an algorithm using administrative codes, laboratory data, and medication data to identify recurrent Clostridium difficile infection (CDI) and to examine the sensitivity, specificity, positive and negative predictive values, and performance of this algorithm.


METHODS
We identified all patients with 2 or more International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) codes for CDI (008.45) from January 1 through December 31, 2013. Information on number of diagnosis codes, stool toxin assays (enzyme immunoassay or polymerase chain reaction), and unique prescriptions for metronidazole and vancomycin was identified. Logistic regression was used to identify independent predictors of recurrent CDI and a predictive model was developed.


RESULTS
A total of 591 patients with at least 2 ICD-9 codes for CDI were included (median age, 66 years). The derivation cohort consisted of 157 patients among whom 43 (27%) had recurrent CDI. Presence of 3 or more ICD-9 codes for CDI (odds ratio, 2.49), 2 or more stool tests (odds ratio, 2.88), and 2 or more prescriptions for vancomycin (odds ratio, 5.87) were independently associated with confirmed recurrent CDI. A classifier incorporating 2 or more prescriptions for vancomycin and either 2 or more stool tests or 3 or more ICD-9-CM codes had a positive predictive value of 41% and negative predictive value of 90%. The area under the receiver operating characteristic curve for this combined classifier was modest (0.69).


CONCLUSIONS
Identification of recurrent episodes of CDI in administrative data poses challenges. Accurate assessment of burden requires individual case review to confirm diagnosis.
1Department of Biostatistics,Harvard School of Public Health,Boston,Massachusetts.
Jing
J
Wen
2Harvard Medical School,Boston,Massachusetts.
Grant E
GE
Barber
2Harvard Medical School,Boston,Massachusetts.
Ashwin N
AN
Ananthakrishnan
D000067575:Administrative Claims, Healthcare; D000368:Aged; D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000627:therapeutic use; D019540:Area Under Curve; D001431:Bacteriological Techniques / Q000706:statistics & numerical data*; D016360:Clostridioides difficile; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D004761:Enterocolitis, Pseudomembranous / Q000175:diagnosis* / Q000188:drug therapy / Q000382:microbiology; D005243:Feces / Q000382:microbiology; D005260:Female; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance / Q000379:methods*; D011237:Predictive Value of Tests; D012372:ROC Curve; D012008:Recurrence; D014640:Vancomycin / Q000627:therapeutic use
D000900:Anti-Bacterial Agents; D014640:Vancomycin