Accuracy of Administrative Billing Codes to Detect Urinary Tract Infection Hospitalizations

Department of Pediatrics, University of Washington School of Medicine, Seattle, WA 98105-0371, USA.
PEDIATRICS (Impact Factor: 5.47). 08/2011; 128(2):323-30. DOI: 10.1542/peds.2010-2064
Source: PubMed


Hospital billing data are frequently used for quality measures and research, but the accuracy of the use of discharge codes to identify urinary tract infections (UTIs) is unknown.
To determine the accuracy of International Classification of Diseases, 9th revision (ICD-9) discharge codes to identify children hospitalized with UTIs.
This multicenter study conducted in 5 children's hospitals included children aged 3 days to 18 years who had been admitted to the hospital, undergone a urinalysis or urine culture, and discharged from the hospital. Data were obtained from the pediatric health information system database and medical record review. With the use of 2 gold-standard methods, the positive predictive value (PPV) was calculated for individual and combined UTI codes and for common UTI identification strategies. PPV was measured for all groupings for which the UTI code was the principal discharge diagnosis.
There were 833 patients in the study. The PPV was 50.3% with the use of the gold standard of laboratory-confirmed UTIs but increased to 85% with provider confirmation. Restriction of the study cohort to patients with a principle diagnosis of UTI improved the PPV for laboratory-confirmed UTI (61.2%) and provider-confirmed UTI (93.2%), as well as the ability to benchmark performance. Other common identification strategies did not markedly affect the PPV.
ICD-9 codes can be used to identify patients with UTIs but are most accurate when UTI is the principal discharge diagnosis. The identification strategies reported in this study can be used to improve the accuracy and applicability of benchmarking measures.

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Available from: Karen E Jerardi, Feb 05, 2014
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    • "Only five conditions were investigated in more than one study: diabetes (10.6%) [13-16], inflammatory bowel disease (5.4%) [17,18], asthma (5.4%) [19,20], rotavirus infection (5.4%) [21,22], and tuberculosis (5.4%) [23,24]. However, a diverse range of conditions were investigated in single studies, including obesity [25], vaccine-related illness [26], injuries [27], autism [28], febrile neutropenia in oncology patients [29], high risk conditions [30], dermatologic conditions [31-33], congenital anomalies [34], cardiac defects [35], respiratory illnesses excluding asthma [36-38], neurologic conditions [39], other gastrointestinal conditions [40-43], genitourinary conditions [44,45], serum sickness [46], thrombosis [47], maternal/perinatal conditions [48], and drug-related anaphylaxis [49]. "
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    ABSTRACT: Background The purpose of this research was to perform a scoping review of published literature on the validity of administrative health data for ascertaining health conditions in the pediatric population (≤20 years). Methods A comprehensive search of OVID Medline (1946 - present), CINAHL (1937 - present) and EMBASE (1947 - present) was conducted. Characteristics of validation studies that were abstracted included the study population, health condition, topic of the validation (e.g., single diagnosis code versus case-finding algorithm), administrative and validation data sources. Inter-rater agreement was measured using Cohen’s κ. Extracted data were analyzed using descriptive statistics. Results A total of 37 articles met the study inclusion criteria. Cohen’s κ for study inclusion/exclusion and data abstraction was 0.88 and 0.97, respectively. Most studies validated administrative data from the USA (43.2%) and Canada (24.3%), and focused on inpatient records (67.6%). Case-finding algorithms (56.7%) were more frequently validated than diagnoses codes alone (37.8%). Five conditions were validated in more than one study: diabetes mellitus, inflammatory bowel disease, asthma, rotavirus infection, and tuberculosis. Conclusions This scoping review identified a number of gaps in the validation of administrative health data for pediatric populations, including limited investigation of outpatient populations and older pediatric age groups.
    BMC Health Services Research 05/2014; 14(1):236. DOI:10.1186/1472-6963-14-236 · 1.71 Impact Factor
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    • "The EHR is a rich source of data, including free text entries as well as coded terms, such as the diagnosis coding system ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification). ICD-9 codes are entered into the record for administrative purposes and may not capture all cases, but they have been shown to have a strong predictive value in a wide range of diseases, including skin infection [12], urinary tract infection [13], acute myocardial infarction [14], and chronic obstructive pulmonary disease [15]. As ICD-9 is an international standard, it also suggests that a uniform methodology could be applied across EHR data from multiple systems. "
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    ABSTRACT: Patterns of disease incidence can identify new risk factors for the disease or provide insight into the etiology. For example, allergies and infectious diseases have been shown to follow periodic temporal patterns due to seasonal changes in environmental or infectious agents. Previous work searching for seasonal or other temporal patterns in disease diagnosis rates has been limited both in the scope of the diseases examined and in the ability to distinguish unexpected seasonal patterns. Electronic Health Records (EHR) compile extensive longitudinal clinical information, constituting a unique source for discovery of trends in occurrence of disease. However, the data suffer from inherent biases that preclude an identification of temporal trends. Motivated by observation of the biases in this data source, we developed a method (Lomb-Scargle periodograms in detrended data, LSP-detrend) to find periodic patterns by adjusting the temporal information for broad trends in incidence, as well as seasonal changes in total hospitalizations. LSP-detrend can sensitively uncover periodic temporal patterns in the corrected data and identify the significance of the trend. We apply LSP-detrend to a compilation of records from 1.5 million patients encoded by ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), including 2,805 disorders with more than 500 occurrences across a 12 year period, recorded from 1.5 million patients. Results and conclusions Although EHR data, and ICD-9 coded records in particular, were not created with the intention of aggregated use for research, these data can in fact be mined for periodic patterns in incidence of disease, if confounders are properly removed. Of all diagnoses, around 10% are identified as seasonal by LSP-detrend, including many known phenomena. We robustly reproduce previous findings, even for relatively rare diseases. For instance, Kawasaki disease, a rare childhood disease that has been associated with weather patterns, is detected as strongly linked with winter months. Among the novel results, we find a bi-annual increase in exacerbations of myasthenia gravis, a potentially life threatening complication of an autoimmune disease. We dissect the causes of this seasonal incidence and propose that factors predisposing patients to this event vary through the year.
    BMC Bioinformatics 05/2014; 15(Suppl 6):S3. DOI:10.1186/1471-2105-15-S6-S3 · 2.58 Impact Factor
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    • "The inherently subjective nature of various medical diagnoses combined with extrinsic factors such as human error or delays in data entry may affect the validity of these codes. The variability in ICD-9 code validity for identifying specific diagnoses depends upon the disease studied and clinical setting, as well as the specific algorithm used for case identification (i.e., which specific ICD-9 codes were used and if other clinical data were included).[7-10] Further, in scenarios where there is no definitive gold standard, the definition of ‘true’ disease may impact positive predictive value (PPV).[10] "
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    ABSTRACT: Background Epidemiologic studies of skin and soft tissue infections (SSTIs) depend upon accurate case identification. Our objective was to evaluate the positive predictive value (PPV) of electronic medical record data for identification of SSTIs in a primary care setting. Methods A validation study was conducted among primary care outpatients in an academic healthcare system. Encounters during four non-consecutive months in 2010 were included if any of the following were present in the electronic health record: International Classification of Diseases, Ninth Revision (ICD-9) code for an SSTI, Current Procedural Terminology (CPT) code for incision and drainage, or a positive wound culture. Detailed chart review was performed to establish presence and type of SSTI. PPVs and 95% confidence intervals (CI) were calculated among all encounters, initial encounters, and cellulitis/abscess cases. Results Of the 731 encounters included, 514 (70.3%) were initial encounters and 448 (61.3%) were cellulitis/abscess cases. When the presence of an ICD-9 code, CPT code, or positive culture was used to identify SSTIs, 617 encounters were true positives, yielding a PPV of 84.4% [95% CI: 81.8–87.0%]. The PPV for using ICD-9 codes alone to identify SSTIs was 90.7% [95 % CI: 88.5–92.9%]. For encounters with cellulitis/abscess codes, the PPV was 91.5% [95% CI: 88.9–94.1%]. Conclusions ICD-9 codes may be used to retrospectively identify SSTIs with a high PPV. Broadening SSTI case identification with microbiology data and CPT codes attenuates the PPV. Further work is needed to estimate the sensitivity of this method.
    BMC Infectious Diseases 04/2013; 13(1):171. DOI:10.1186/1471-2334-13-171 · 2.61 Impact Factor
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