Bayesian estimation of the accuracy of ICD-9-CM- and CPT-4-based algorithms to identify cholecystectomy procedures in administrative data without a reference standard

Demographics of Children with Feeding Difficulties from a large Electronic Health Record Database

BACKGROUND

Feeding difficulties are amongst the most common concerns expressed by parents in younger children. However, few studies have reported on the characteristics of patients with clinically significant feeding diagnoses. The aim of the current study is to describe the characteristics of patients diagnosed with feeding difficulties including concurrent conditions, age and gender, sampled nationwide utilizing the Cerner Health Facts® Database (CHFD).

METHODS

We identified patients with a diagnosis of feeding difficulties (ICD9 783.3 or ICD10 R63.3), age 7 months to 17 years, with an outpatient visit between 2010 and 2017. The demographics and complex clinical conditions of this population were categorized. The cohort was then collapsed into a matrix defining recognized phenotype codes for ICD9 and ICD10 diagnoses to identify associated conditions.

RESULTS

We identified 39,674 patients (0.95%) representing 101,684 encounters from 68 health systems across the United States; 43% of patients were female. Gastrointestinal conditions were the most common, followed by malnutrition, developmental and behavioral diagnoses, and neurologic conditions.

CONCLUSIONS

This study is one of the most robust studies defining the prevalence, demographic characteristics and phenotypic profiling of patients with feeding difficulties. In the United States. Our observations have implications on screening and resource allocation to recognize and manage this poorly understood population. This article is protected by copyright. All rights reserved.

Validity of ICD-based algorithms to estimate the prevalence of injection drug use among infective endocarditis hospitalizations in the absence of a reference standard

BACKGROUND

International Classification of Diseases (ICD) code algorithms are routinely used to estimate the frequency of illicit injection drug use (IDU)-associated hospitalizations in administrative health datasets despite a lack of evidence regarding their validity. We aimed to measure the sensitivity and specificity of ICD code algorithms used to estimate the prevalence of current/recent IDU among infective endocarditis (IE) hospitalizations without a reference standard.

METHODS

We reviewed medical records of 321 patients aged 18-64 years old from an urban academic hospital with an IE diagnosis between 2007 and 2017. Diagnostic tests for IDU included self-reported IDU in medical records; a drug use, abuse and dependence (UAD) ICD algorithm; a Hepatitis C Virus (HCV) ICD algorithm; and a combination drug UAD/HCV ICD algorithm. Sensitivity, specificity and the misclassification error (ME)-adjusted IDU prevalence were estimated using Bayesian latent class models.

RESULTS

The combination algorithm had the highest sensitivity and lowest specificity. Sensitivity increased for the drug UAD algorithm in the ICD-10 period compared to the ICD-9 period. The ME-adjusted current/recent IDU prevalence estimated using the drug UAD and HCV algorithms was 23 % (95 % Bayesian credible interval: 16 %, 31 %). The unadjusted prevalence estimate from the drug UAD algorithm underestimated the ME-adjusted prevalence, while the combination algorithm overestimated it.

CONCLUSION

The validity of ICD code algorithms for IDU among IE hospitalizations is imperfect and differs between ICD-9 and ICD-10. Commonly used ICD-based algorithms could lead to substantially biased prevalence estimates in IDU-associated hospitalizations when using administrative health data.

Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard - An update

OBJECTIVE

To systematically review methods developed and employed to evaluate the diagnostic accuracy of medical test when there is a missing or no gold standard.

STUDY DESIGN AND SETTINGS

Articles that proposed or applied any methods to evaluate the diagnostic accuracy of medical test(s) in the absence of gold standard were reviewed. The protocol for this review was registered in PROSPERO (CRD42018089349).

RESULTS

Identified methods were classified into four main groups: methods employed when there is a missing gold standard; correction methods (which make adjustment for an imperfect reference standard with known diagnostic accuracy measures); methods employed to evaluate a medical test using multiple imperfect reference standards; and other methods, like agreement studies, and a mixed group of alternative study designs. Fifty-one statistical methods were identified from the review that were developed to evaluate medical test(s) when the true disease status of some participants is unverified with the gold standard. Seven correction methods were identified and four methods were identified to evaluate medical test(s) using multiple imperfect reference standards. Flow-diagrams were developed to guide the selection of appropriate methods.

CONCLUSION

Various methods have been proposed to evaluate medical test(s) in the absence of a gold standard for some or all participants in a diagnostic accuracy study. These methods depend on the availability of the gold standard, its' application to the participants in the study and the availability of alternative reference standard(s). The clinical application of some of these methods, especially methods developed when there is missing gold standard is however limited. This may be due to the complexity of these methods and/or a disconnection between the fields of expertise of those who develop (e.g. mathematicians) and those who employ the methods (e.g. clinical researchers). This review aims to help close this gap with our classification and guidance tools.

Updated Estimates Suggest a Much Higher Prevalence of Arthritis in United States Adults Than Previous Ones

OBJECTIVE

National estimates of arthritis prevalence rely on a single survey question about doctor-diagnosed arthritis without using survey information on joint symptoms, even though some subjects with only the latter have been shown to have arthritis. The sensitivity of the current surveillance definition is only 53% and 69% in subjects ages 45-64 years and ages ≥65 years, respectively, resulting in misclassification of nearly one-half and one-third of subjects in those age groups. This study was undertaken to estimate arthritis prevalence based on an expansive surveillance definition that is adjusted for the measurement errors in the current definition.

METHODS

Using the 2015 National Health Interview Survey, we developed a Bayesian multinomial latent class model for arthritis surveillance based on doctor-diagnosed arthritis, joint symptoms, and whether symptom duration exceeded 3 months.

RESULTS

Of 33,672 participants, 19.3% of men and 16.7% of women ages 18-64 years and 15.7% of men and 13.5% of women ages ≥65 years affirmed joint symptoms without doctor-diagnosed arthritis. The measurement error-adjusted prevalence of arthritis was 29.9% (95% Bayesian probability interval [95% PI] 23.4-42.3) in men ages 18-64 years, 31.2% (95% PI 25.8-44.1) in women ages 18-64 years, 55.8% (95% PI 49.9-70.4) in men ages ≥65 years, and 68.7% (95% PI 62.1-79.9) in women ages ≥65 years. Arthritis affected 91.2 million adults (of 247.7 million; 36.8%) in the US in 2015, which included 61.1 million persons between 18 and 64 years of age (of 199.9 million; 30.6%). Our arthritis prevalence estimate was 68% higher than the previously reported national estimate.

CONCLUSION

Arthritis prevalence in the US population has been substantially underestimated, especially among adults younger than 65 years of age.

Sepsis surveillance from administrative data in the absence of a perfect verification

PURPOSE

Past studies of sepsis epidemiology did not address misclassification bias due to imperfect verification of sepsis detection methods to estimate the true prevalence.

METHODS

We examined 273,126 hospitalizations from 2008 to 2012 at a tertiary-care center to develop surveillance-aimed sepsis detection criteria, based on the presence of the sepsis-explicit International Classification of Diseases, Ninth Revision, Clinical Modification codes (995.92 or 785.52), blood culture orders, and antibiotics administration. We used Bayesian multinomial latent class models to estimate the true prevalence of sepsis, while adjusting for the imperfect sensitivity and specificity and the conditional dependence among the individual criteria.

RESULTS

The apparent annual prevalence of sepsis hospitalizations based on explicit International Classification of Diseases, Ninth Revision, Clinical Modification codes were 1.5%, 1.4%, 1.6%, 2.2%, and 2.5% for the years 2008 to 2012. Bayesian posterior estimates for the true prevalence of sepsis suggested that it remained stable from 2008, 19.2% (95% credible interval [CI]: 17.9%, 22.9%), to 2012, 17.8% (95% CI: 16.8%, 20.2%). The sensitivity of sepsis-explicit codes, however, increased from 7.6% (95% CI: 6.4%, 8.4%) in 2008 to 13.8% (95% CI: 12.2%, 14.9%) in 2012.

CONCLUSIONS

The true prevalence of sepsis remained high, but stable despite an increase in the sensitivity of sepsis-explicit codes in administrative data.