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Validating billing/encounter codes as indicators of lung, colorectal, breast, and prostate cancer recurrence using 2 large contemporary cohorts.

ABSTRACT: A substantial proportion of cancer-related mortality is attributable to recurrent, not de novo metastatic disease, yet we know relatively little about these patients. To fill this gap, investigators often use administrative codes for secondary malignant neoplasm or chemotherapy to identify recurrent cases in population-based datasets. However, these algorithms have not been validated in large, contemporary, routine care cohorts.To evaluate the validity of secondary malignant neoplasm and chemotherapy codes as indicators of recurrence after definitive local therapy for stage I-III lung, colorectal, breast, and prostate cancer.We assessed the sensitivity, specificity, and positive predictive value (PPV) of these codes 14 and 60 months after diagnosis using 2 administrative datasets linked with gold-standard recurrence status information: CanCORS/Medicare (diagnoses 2003-2005) and HMO/Cancer Research Network (diagnoses 2000-2005).We identified 929 CanCORS/Medicare patients and 5298 HMO/CRN patients. Sensitivity, specificity, and PPV ranged widely depending on which codes were included and the type of cancer. For patients with lung, colorectal, and breast cancer, the combination of secondary malignant neoplasm and chemotherapy codes was the most sensitive (75%-85%); no code-set was highly sensitive and highly specific. For prostate cancer, no code-set offered even moderate sensitivity (? 19%).Secondary malignant neoplasm and chemotherapy codes could not identify recurrent cancer without some risk of misclassification. Findings based on existing algorithms should be interpreted with caution. More work is needed to develop a valid algorithm that can be used to characterize outcomes and define patient cohorts for comparative effectiveness research studies.

SUBMITTER: Hassett MJ

PROVIDER: S-EPMC3600389 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Validating billing/encounter codes as indicators of lung, colorectal, breast, and prostate cancer recurrence using 2 large contemporary cohorts.

Hassett Michael J MJ Ritzwoller Debra P DP Taback Nathan N Carroll Nikki N Cronin Angel M AM Ting Gladys V GV Schrag Deb D Warren Joan L JL Hornbrook Mark C MC Weeks Jane C JC

Medical care 20141001 10

<h4>Background</h4>A substantial proportion of cancer-related mortality is attributable to recurrent, not de novo metastatic disease, yet we know relatively little about these patients. To fill this gap, investigators often use administrative codes for secondary malignant neoplasm or chemotherapy to identify recurrent cases in population-based datasets. However, these algorithms have not been validated in large, contemporary, routine care cohorts.<h4>Objective</h4>To evaluate the validity of sec ...[more]

PMID: 23222531

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Project description:BackgroundDischarge diagnoses are used to track national trends and patterns of maternal morbidity. There are few data regarding the validity of the International Classification of Diseases (ICD) codes used for this purpose. The goal of our study was to try to better understand the validity of administrative data being used to monitor and assess trends in morbidity.MethodsHospital stay billing records were queried to identify all delivery admissions at the Massachusetts General Hospital for the time period 2001 to 2011 and the University of Michigan Health System for the time period 2005 to 2011. From this, we identified patients with ICD-9-Clinical Modification (CM) diagnosis and procedure codes indicative of severe maternal morbidity. Each patient was classified with 1 of 18 different medical/obstetric categories (conditions or procedures) based on the ICD-9-CM code that was recorded. Within each category, 20 patients from each institution were selected at random, and the corresponding medical charts were reviewed to determine whether the ICD-9-CM code was assigned correctly. The percentage of correct codes for each of 18 preselected clinical categories was calculated yielding a positive predictive value (PPV) and 99% confidence interval (CI).ResultsThe overall number of correctly assigned ICD-9-CM codes, or PPV, was 218 of 255 (86%; CI, 79%-90%) and 154 of 188 (82%; CI, 74%-88%) at Massachusetts General Hospital and University of Michigan Health System, respectively (combined PPV, 372/443 [84%; CI, 79-88%]). Codes within 4 categories (Hysterectomy, Pulmonary edema, Disorders of fluid, electrolyte and acid-base balance, and Sepsis) had a 99% lower confidence limit ≥75%. Codes within 8 additional categories demonstrated a 99% lower confidence limit between 74% and 50% (Acute respiratory distress, Ventilation, Other complications of obstetric surgery, Disorders of coagulation, Cardiomonitoring, Acute renal failure, Thromboembolism, and Shock). Codes within 6 clinical categories demonstrated a 99% lower confidence limit <50% (Puerperal cerebrovascular disorders, Conversion of cardiac rhythm, Acute heart failure [includes arrest and fibrillation], Eclampsia, Neurotrauma, and Severe anesthesia complications).ConclusionsICD-9-CM codes capturing severe maternal morbidity during delivery hospitalization demonstrate a range of PPVs. The PPV was high when objective supportive evidence, such as laboratory values or procedure documentation supported the ICD-9-CM code. The PPV was low when greater judgment, interpretation, and synthesis of the clinical data (signs and symptoms) was required to support a code, such as with the category Severe anesthesia complications. As a result, these codes should be used for administrative research with more caution compared with codes primarily defined by objective data.

Project description:Community-acquired pneumonia (CAP) remains one of the most common indications for pediatric hospitalization in the United States, and it is frequently the focus of research and quality studies. Use of administrative data is increasingly common for these purposes, although proper validation is required to ensure valid study conclusions.To validate administrative billing data for hospitalizations owing to childhood CAP.Case-control study of 4 tertiary care, freestanding children’s hospitals in the United States.A total of 998 medical records of a 25% random sample of 3646 children discharged in 2010 with at least 1 International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code representing possible pneumonia were reviewed. Discharges (matched on date of admission) without a pneumonia-related discharge code were also examined to identify potential missed pneumonia cases. Two reference standards, based on provider diagnosis alone (provider confirmed) or in combination with consistent clinical and radiographic evidence of pneumonia (definite), were used to identify CAP.Twelve ICD-9-CM–based coding strategies, each using a combination of primary or secondary codes representing pneumonia or pneumonia-related complications. Six algorithms excluded children with complex chronic conditions.Sensitivity, specificity, and negative and positive predictive values (NPV and PPV, respectively) of the 12 identification strategies.For provider-confirmed CAP (n?=?680), sensitivity ranged from 60.7% to 99.7%; specificity, 75.7% to 96.4%; PPV, 67.9% to 89.6%; and NPV, 82.6% to 99.8%. For definite CAP (n?=?547), sensitivity ranged from 65.6% to 99.6%; specificity, 68.7% to 93.0%; PPV, 54.6% to 77.9%; and NPV, 87.8% to 99.8%. Unrestricted use of the pneumonia-related codes was inaccurate, although several strategies improved specificity to more than 90% with a variable effect on sensitivity. Excluding children with complex chronic conditions demonstrated the most favorable performance characteristics. Performance of the algorithms was similar across institutions.Administrative data are valuable for studying pediatric CAP hospitalizations. The strategies presented here will aid in the accurate identification of relevant and comparable patient populations for research and performance improvement studies.

Project description:Importance:Insurance billing claim databases represent a growing field of scientific inquiry within ophthalmology. Validating the accuracy of billing claim codes used during the care of diabetic retinopathy is a necessary precursor to fully understanding the underlying data and subsequent results of these types of studies. Objective:To determine the accuracy of diagnostic, procedural, and therapeutic billing codes used in the treatment of diabetic retinopathy. Design, Setting, and Participants:This retrospective medical record review was conducted at 3 clinical practices (1 academic and 2 private). Insured patients with diabetic retinopathy were seen by the practices between 2011 and 2013. Each patient then had every visit for 2 years reviewed twice, once for billing data and the second for data from the medical record. Data were collected and analyzed from October 2015 to July 2016. Main Outcomes and Measures:The positive predictive value (PPV) and negative predictive value (NPV) for each code of interest. Sensitivity and specificity were secondary outcomes. Results:A total of 146 patients (mean [SD] age, 60.3 [12.5] years) from 11 physicians had 1072 encounters reviewed over 2 calendar years. Among the included patients, 49.3% were female (n?=?72), 48.6% were white (n?=?71), 37.0% were black (n?=?54), and 18.5% had type 1 diabetes and a mean (SD) hemoglobin A1C level of 7.7% (1.8) (n?=?27). Nearly all codes of interest that were used frequently also had a high PPV (range, 89.5%-100%) and NPV (88.6%-100%) including billing codes for intravitreal injection, focal laser, panretinal photocoagulation, laterality of procedure, ranibizumab, bevacizumab, fundus photographs, fluorescein angiography, and optical coherence tomography. Codes that were used infrequently (<20 instances) but still had a high PPV (all 100%) and NPV (99.7%-100%) were codes for aflibercept, triamcinolone, and the dexamethasone implant. Only the codes for infrequently used B-scan ultrasonography (PPV, 69.6%) and subtenon injection (PPV, 100%; NPV, 99.7%, but sensitivity of only 40%) were found to be of questionable accuracy. Other than subtenon injection (40%), all codes were also found to have a high sensitivity (range, 87.6%-100%) and a high specificity (range, 97.2%-100%). Conclusions and Relevance:These data suggest diagnostic, procedure, and therapeutic codes derived from insurance billing claims accurately reflect the medical record for patients with diabetic retinopathy.

Project description:Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)).The study sample comprised 4,330 syndrome-positive visits from the claims of 1,098 randomly-selected physicians working in Quebec, Canada in 2005-2007. For each visit, physician-facilitated chart review was used to assess whether the same syndrome was present in the medical chart (gold standard). We used multivariate logistic regression analyses to estimate the association between claim-chart agreement about the presence of a syndrome and physician, patient, encounter, and billing characteristics.The likelihood of the medical chart agreeing with the physician claim about the presence of a syndrome was higher when the treating physician had billed many visits for the same syndrome recently (ORper 10 visit, 1.05; 95% CI, 1.01-1.08), had a lower workload (ORper 10 claims, 0.93; 95% CI, 0.90-0.97), and when the patient was younger (ORper 5 years of age, 0.96; 95% CI, 0.94-0.97), and less socially deprived (ORmost versus least deprived, 0.76; 95% CI, 0.60-0.95).Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values.

Project description:ImportancePatients can develop multiple skin cancers, and their medical data can be spread over multiple health care systems. This fragmented care, combined with the lack of skin cancer registries, has limited our ability both to provide accurate estimates of incidence and to study the pathogenesis of multiple skin cancers.ObjectiveTo assess whether standard diagnostic and procedural codes present in the electronic health records at a single health care system are a valid proxy for estimating the number of overall skin cancers.Design, setting, and participantsRetrospective cohort study of patients seen at a single-center tertiary care hospital (ie, Vanderbilt University Medical Center) between July 1, 2008, and June 30, 2018. All patients with at least 1 electronic health record-based diagnostic or procedural code for any skin cancer and at least 1 pathology report of a skin cancer.ExposureThe number of International Classification of Disease (ICD) or Current Procedural Terminology (CPT) codes relating to skin cancer.Main outcomes and measuresPearson correlation coefficient and R2 were calculated for the total number of ICD or CPT codes for skin cancer and histologically verified skin cancers.ResultsIn this cohort study of 35 901 patients, the mean (SD) age was 70.4 (14.4) years, 20 404 (56.8%) were men, and 31 623 (88.1%) were White individuals. Of these patients, 6307 had at least 1 ICD or CPT code or pathology report for a skin cancer, of whom 5688 patients had both a CPT code related to skin malignancy and a histologically verified skin cancer. There was a strong linear correlation between the number of CPT codes and pathology records (r = 0.87). There was a poor correlation between the number of ICD codes and pathology records (r = 0.22).Conclusions and relevanceThis cohort study found that the use of ICD codes was a poor proxy measure for the number of skin cancers per patient. In contrast, CPT codes accounted for more than 75% of the variability in the number of skin cancers (R2 = 0.76) and were a better proxy measure for the total number of skin cancers per patient.

Project description:ObjectiveTo evaluate the phenotyping performance of three major electronic health record (EHR) components: International Classification of Disease (ICD) diagnosis codes, primary notes, and specific medications.Materials and methodsWe conducted the evaluation using de-identified Vanderbilt EHR data. We preselected ten diseases: atrial fibrillation, Alzheimer's disease, breast cancer, gout, human immunodeficiency virus infection, multiple sclerosis, Parkinson's disease, rheumatoid arthritis, and types 1 and 2 diabetes mellitus. For each disease, patients were classified into seven categories based on the presence of evidence in diagnosis codes, primary notes, and specific medications. Twenty-five patients per disease category (a total number of 175 patients for each disease, 1750 patients for all ten diseases) were randomly selected for manual chart review. Review results were used to estimate the positive predictive value (PPV), sensitivity, andF-score for each EHR component alone and in combination.ResultsThe PPVs of single components were inconsistent and inadequate for accurately phenotyping (0.06-0.71). Using two or more ICD codes improved the average PPV to 0.84. We observed a more stable and higher accuracy when using at least two components (mean ± standard deviation: 0.91 ± 0.08). Primary notes offered the best sensitivity (0.77). The sensitivity of ICD codes was 0.67. Again, two or more components provided a reasonably high and stable sensitivity (0.59 ± 0.16). Overall, the best performance (Fscore: 0.70 ± 0.12) was achieved by using two or more components. Although the overall performance of using ICD codes (0.67 ± 0.14) was only slightly lower than using two or more components, its PPV (0.71 ± 0.13) is substantially worse (0.91 ± 0.08).ConclusionMultiple EHR components provide a more consistent and higher performance than a single one for the selected phenotypes. We suggest considering multiple EHR components for future phenotyping design in order to obtain an ideal result.

Dataset Information

Validating billing/encounter codes as indicators of lung, colorectal, breast, and prostate cancer recurrence using 2 large contemporary cohorts.

Publications

Validating billing/encounter codes as indicators of lung, colorectal, breast, and prostate cancer recurrence using 2 large contemporary cohorts.

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