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

Global burden of Clostridium difficile infections: a systematic review and meta-analysis

Background

Clostridium difficile is a leading cause of morbidity and mortality in several countries. However, there are limited evidence characterizing its role as a global public health problem. We conducted a systematic review to provide a comprehensive overview of C. difficile infections (CDI) rates.

Methods

Seven databases were searched (January 2016) to identify studies and surveillance reports published between 2005 and 2015 reporting CDI incidence rates. CDI incidence rates for health care facility-associated (HCF), hospital onset-health care facility-associated, medical or general intensive care unit (ICU), internal medicine (IM), long-term care facility (LTCF), and community-associated (CA) were extracted and standardized. Meta-analysis was conducted using a random effects model.

Results

229 publications, with data from 41 countries, were included. The overall rate of HCF-CDI was 2.24 (95% confidence interval CI = 1.66-3.03) per 1000 admissions/y and 3.54 (95%CI = 3.19-3.92) per 10 000 patient-days/y. Estimated rates for CDI with onset in ICU or IM wards were 11.08 (95%CI = 7.19-17.08) and 10.80 (95%CI = 3.15-37.06) per 1000 admission/y, respectively. Rates for CA-CDI were lower: 0.55 (95%CI = 0.13-2.37) per 1000 admissions/y. CDI rates were generally higher in North America and among the elderly but similar rates were identified in other regions and age groups.

Conclusions

Our review highlights the widespread burden of disease of C. difficile, evidence gaps, and the need for sustainable surveillance of CDI in the health care setting and the community.

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

OBJECTIVE

To conduct an updated assessment of the validity and reliability of administrative coded data (ACD) in identifying hospital-acquired infections (HAIs).

METHODS

We systematically searched three libraries for studies on ACD detecting HAIs compared to manual chart review. Meta-analyses were conducted for prosthetic and nonprosthetic surgical site infections (SSIs), Clostridium difficile infections (CDIs), ventilator-associated pneumonias/events (VAPs/VAEs) and non-VAPs/VAEs, catheter-associated urinary tract infections (CAUTIs), and central venous catheter-related bloodstream infections (CLABSIs). A random-effects meta-regression model was constructed.

RESULTS

Of 1,906 references found, we retrieved 38 documents, of which 33 provided meta-analyzable data (N = 567,826 patients). ACD identified HAI incidence with high specificity (≥93 percent), prosthetic SSIs with high sensitivity (95 percent), and both CDIs and nonprosthetic SSIs with moderate sensitivity (65 percent). ACD exhibited substantial agreement with traditional surveillance methods for CDI (κ = 0.70) and provided strong diagnostic odds ratios (DORs) for the identification of CDIs (DOR = 772.07) and SSIs (DOR = 78.20). ACD performance in identifying nosocomial pneumonia depended on the ICD coding system (DORICD-10/ICD-9-CM  = 0.05; p = .036). Algorithmic coding improved ACD's sensitivity for SSIs up to 22 percent. Overall, high heterogeneity was observed, without significant publication bias.

CONCLUSIONS

Administrative coded data may not be sufficiently accurate or reliable for the majority of HAIs. Still, subgrouping and algorithmic coding as tools for improving ACD validity deserve further investigation, specifically for prosthetic SSIs. Analyzing a potential lower discriminative ability of ICD-10 coding system is also a pending issue.

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

BACKGROUND

Hospital discharge databases are used to study the epidemiology of Clostridium difficile infections (CDI) among hospitalized patients with inflammatory bowel disease (IBD). CDI in IBD is increasingly important and accurately estimating its occurrence is critical in understanding its comorbidity. There are limited data on the reliability of the International Classification of Diseases 10th revision (ICD-10) (now widely used in North America) CDI code in determining occurrence of CDI among hospitalized patients. We compared the performance of ICD-10 CDI coding to laboratory confirmed CDI diagnoses.

METHODS

The University of Manitoba IBD Epidemiology Database was used to identify individuals with and without IBD discharged with CDI diagnoses between 07/01/2005 and 3/31/2014. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of ICD-10 CDI code was compared to laboratory CDI diagnoses recorded in a province wide CDI dataset. Multivariable logistic regression models were performed to test the predictors of diagnostic inaccuracy of ICD-10 CDI code.

RESULTS

There were 273 episodes of laboratory confirmed CDI (hospitalized and non-hospitalized) among 7396 individuals with IBD and 536 among 66,297 matched controls. The sensitivity, specificity, PPV and NPV of ICD-10 CDI code in discharge abstracts was 72.8%, 99.6%, 64.1% and 99.7% among those with IBD and 70.8%, 99.9%, 79.0% and 99.9% among those without IBD. Predictors of diagnostic inaccuracy included IBD, older age, increased co-morbidity and earlier years of hospitalization.

CONCLUSIONS

Identification of CDI using ICD-10 CDI code in hospital discharge abstracts may not identify up to 30% of CDI cases, with worse performance among those with IBD.

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

Background

Many administrative data sources are available to study the epidemiology of infectious diseases, including Clostridium difficile infection (CDI), but few publications have compared CDI event rates across databases using similar methodology. We used comparable methods with multiple administrative databases to compare the incidence of CDI in older and younger persons in the United States.

Methods

We performed a retrospective study using three longitudinal data sources (Medicare, OptumInsight LabRx, and Healthcare Cost and Utilization Project State Inpatient Database (SID)), and two hospital encounter-level data sources (Nationwide Inpatient Sample (NIS) and Premier Perspective database) to identify CDI in adults aged 18 and older with calculation of CDI incidence rates/100,000 person-years of observation (pyo) and CDI categorization (onset and association).

Results

The incidence of CDI ranged from 66/100,000 in persons under 65 years (LabRx), 383/100,000 in elderly persons (SID), and 677/100,000 in elderly persons (Medicare). Ninety percent of CDI episodes in the LabRx population were characterized as community-onset compared to 41 % in the Medicare population. The majority of CDI episodes in the Medicare and LabRx databases were identified based on only a CDI diagnosis, whereas almost ¾ of encounters coded for CDI in the Premier hospital data were confirmed with a positive test result plus treatment with metronidazole or oral vancomycin. Using only the Medicare inpatient data to calculate encounter-level CDI events resulted in 553 CDI events/100,000 persons, virtually the same as the encounter proportion calculated using the NIS (544/100,000 persons).

Conclusions

We found that the incidence of CDI was 35 % higher in the Medicare data and fewer episodes were attributed to hospital acquisition when all medical claims were used to identify CDI, compared to only inpatient data lacking information on diagnosis and treatment in the outpatient setting. The incidence of CDI was 10-fold lower and the proportion of community-onset CDI was much higher in the privately insured younger LabRx population compared to the elderly Medicare population. The methods we developed to identify incident CDI can be used by other investigators to study the incidence of other infectious diseases and adverse events using large generalizable administrative datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1501-7) contains supplementary material, which is available to authorized users.

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).

CONCLUSION

Identification of recurrent episodes of CDI in administrative data poses challenges. Accurate assessment of burden requires individual case review to confirm diagnosis.

Infect Control Hosp Epidemiol 2015;36(8):893–898