Common outpatient diagnoses and associated treatments logged by osteopathic medical students within a geriatric population.

CONTEXT: Clinical clerkships provide osteopathic medical students the opportunity to participate in the diagnosis and treatment of commonly encountered medical conditions. Appropriate management of these conditions may include pharmacotherapy and/or nonpharmacologic interventions, such as osteopathic manipulative treatment (OMT). Opportunities may exist to expand the utilization of OMT in the management of common conditions, particularly for geriatric patients, who are at increased risk for adverse outcomes from pharmacologic treatments. OBJECTIVES: This study aimed to assess the most common diagnoses and corresponding treatments logged by osteopathic medical students within an ambulatory geriatric population. METHODS: Patient encounters logged electronically by osteopathic medical students were retrospectively reviewed to determine the most commonly reported diagnostic codes and their treatments. Logged interventions were filtered to include patients over the age of 65 years who were seen on family medicine rotations within an ambulatory setting. The top 10 diagnoses were sorted and assessed to determine the associated treatments, including medications, procedures, and OMT. RESULTS: Between January 2018 and June 2020, a total of 11,185 primary diagnoses were logged pertaining to the defined patient population. The most frequently documented diagnoses were essential hypertension (n=1,420; 12.7 %), encounter for well examination (n=1,144; 10.2 %), type 2 diabetes mellitus (n=837; 7.5 %), hyperlipidemia (n=346; 3.1 %), chronic obstructive pulmonary disease (COPD; n=278; 2.5 %), osteoarthritis (OA; n=221; 2.0 %), low back pain (LBP; n=202; 1.8 %), pain in joint (n=187; 1.7 %), hypothyroidism (n=164; 1.5 %), and urinary tract infections (n=160; 1.4 %). Three of the top 10 logged diagnoses were musculoskeletal in nature (OA, LBP, and pain in joint). Pharmacotherapy was reported as the predominant treatment for musculoskeletal conditions, with OMT being logged as a treatment for 10.9 % (n=50) of those cases. The most commonly logged medication class in the management of patients with those musculoskeletal conditions was nonsteroidal anti-inflammatory drugs (NSAIDs; n=128; 27.9 %), while opioids were the second most frequently documented class of medications (n=65; 14.2 %). CONCLUSIONS: Musculoskeletal complaints were commonly logged by osteopathic medical students within the studied population. Opioids were documented as a treatment for musculoskeletal conditions more frequently than OMT. As such, opportunities exist to expand the utilization of OMT during clinical clerkships and to decrease the frequency of prescribed medications for pain management.

Accuracy of routinely collected hospital administrative discharge data and death certificate ICD-10 diagnostic coding in progressive supranuclear palsy and corticobasal syndrome: a systematic review and validation study.

BACKGROUND: We conducted a systematic review to identify existing ICD-10 coding validation studies in progressive supranuclear palsy and corticobasal syndrome [PSP/CBS]) and, in a new study, evaluated the accuracy of ICD-10 diagnostic codes for PSP/CBS in Scottish hospital inpatient and death certificate data. METHODS: Original studies that assessed the accuracy of specific ICD-10 diagnostic codes in PSP/CBS were sought. Separately, we estimated the positive predictive value (PPV) of specific codes for PSP/CBS in inpatient hospital data (SMR01, SMR04) compared to clinical diagnosis in four regions. Sensitivity was assessed in one region due to a concurrent prevalence study. For PSP, the consistency of the G23.1 code in inpatient and death certificate coding was evaluated across Scotland. RESULTS: No previous ICD-10 validation studies were identified. 14,767 records (SMR01) and 1497 records (SMR04) were assigned the candidate ICD-10 diagnostic codes between February 2011 and July 2019. The best PPV was achieved with G23.1 (1.00, 95% CI 0.93-1.00) in PSP and G23.9 in CBS (0.20, 95% CI 0.04-0.62). The sensitivity of G23.1 for PSP was 0.52 (95% CI 0.33-0.70) and G31.8 for CBS was 0.17 (95% CI 0.05-0.45). Only 38.1% of deceased G23.1 hospital-coded cases also had this coding on their death certificate: the majority (49.0%) erroneously assigned the G12.2 code. DISCUSSION: The high G23.1 PPV in inpatient data shows it is a useful tool for PSP case ascertainment, but death certificate coding is inaccurate. The PPV and sensitivity of existing ICD-10 codes for CBS are poor due to a lack of a specific code.

Validation of diagnosis algorithms for ankylosing spondylitis in claim-based database.

BACKGROUND: Claims-based algorithms using International Classification of Diseases (ICD) codes have become a common approach for researchers to define ankylosing spondylitis (AS) in studies. To address potential misclassification bias caused by the claim-based algorithms, we conducted the current study to validate whether these algorithms of medical claims could accurately represent AS diagnoses. METHODS: Patients diagnosed with AS based on ICD codes were retrieved from the electronic medical records database at a Taiwanese medical center (Chung Shan University Hospital, Taiwan). After random sampling and stratification based on age and sex, the medical information of participants was appraised based on the 2009 ASAS guideline to evaluate the actual status of ICD codes claim-based AS patients. Positive predict values (PPV) of different algorithms of ICD codes were also calculated. RESULTS: Within the 4160 patients with claim-based AS diagnosis, 387 eligible patients were finally included in the study design after random sampling. The PPV of the diagnostic algorithm of having at least 4 outpatient or 1 inpatient ICD record was 72.77 (95% CI, 66.79-78.75), whereas the PPV increased to 85.64 when the diagnoses were restricted to be made by rheumatologists (95% CI, 80.53-90.74). CONCLUSIONS: While performing database studies, researchers should be aware of the low PPV of specific algorithms when defining AS. Algorithms with higher PPV were recommended to be adopted to avoid misclassification biases.

Validation of the recording of idiopathic pulmonary fibrosis in routinely collected electronic healthcare records in England.

BACKGROUND: Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care. METHOD: Using the UK's Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time. RESULT: A total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3-65.3) for a "broad" codeset to 74.9% (95%CI:72.8-76.9) for a "narrow" codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4-81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes. CONCLUSION: High diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation.

Validation of algorithms to identify colorectal cancer patients from administrative claims data of a Japanese hospital.

BACKGROUND: Administrative claims data are a valuable source for clinical studies; however, the use of validated algorithms to identify patients is essential to minimize bias. We evaluated the validity of diagnostic coding algorithms for identifying patients with colorectal cancer from a hospital's administrative claims data. METHODS: This validation study used administrative claims data from a Japanese university hospital between April 2017 and March 2019. We developed diagnostic coding algorithms, basically based on the International Classification of Disease (ICD) 10th codes of C18-20 and Japanese disease codes, to identify patients with colorectal cancer. For random samples of patients identified using our algorithms, case ascertainment was performed using chart review as the gold standard. The positive predictive value (PPV) was calculated to evaluate the accuracy of the algorithms. RESULTS: Of 249 random samples of patients identified as having colorectal cancer by our coding algorithms, 215 were confirmed cases, yielding a PPV of 86.3% (95% confidence interval [CI], 81.5-90.1%). When the diagnostic codes were restricted to site-specific (right colon, left colon, transverse colon, or rectum) cancer codes, 94 of the 100 random samples were true cases of colorectal cancer. Consequently, the PPV increased to 94.0% (95% CI, 87.2-97.4%). CONCLUSION: Our diagnostic coding algorithms based on ICD-10 codes and Japanese disease codes were highly accurate in detecting patients with colorectal cancer from this hospital's claims data. The exclusive use of site-specific cancer codes further improved the PPV from 86.3 to 94.0%, suggesting their desirability in identifying these patients more precisely.

Validating Use of ICD-10 Diagnosis Codes in Identifying Physical Abuse Among Young Children.

OBJECTIVE: Evaluate the positive predictive value of International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes in identifying young children diagnosed with physical abuse. METHODS: We extracted 230 charts of children <24 months of age who had any emergency department, inpatient, or ambulatory care encounters between Oct 1, 2015 and Sept 30, 2020 coded using ICD-10-CM codes suggestive of physical abuse. Electronic health records were reviewed to determine if physical abuse was considered during the medical encounter and assess the level of diagnostic certainty for physical abuse. Positive predictive value of each ICD-10-CM code was assessed. RESULTS: Of 230 charts with ICD-10 codes concerning for physical abuse, 209 (91%) had documentation that a diagnosis of physical abuse was considered during an encounter. The majority of cases, 138 (60%), were rated as definitely or likely abuse, 36 cases (16%) were indeterminate, and 35 (15%) were likely or definitely accidental injury. Other forms of suspected maltreatment were discussed in 16 (7%) charts and 5 (2%) had no documented concerns for child maltreatment. The positive predictive values of the specific ICD-10 codes for encounters rated as definitely or likely abuse varied considerably, ranging from 0.89 (0.80-0.99) for T74.12 "Adult and child abuse, neglect, and other maltreatment, confirmed" to 0.24 (95% CI: 0.06-0.42) for Z04.72 "Encounter for examination and observation following alleged child physical abuse." CONCLUSIONS: ICD-10-CM codes identify young children who experience physical abuse, but certain codes have a higher positive predictive value than others.

Intimate Partner Violence-Related Emergency Department and Hospital Visits in California Following the ICD-10-CM Transition, 2016-2018.

With the transition to the International Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10-CM), additional research is needed to understand which diagnostic codes for intimate partner violence (IPV) are being used. The current study examined characteristics of IPV visits and frequency of diagnostic codes to identify IPV in all emergency department (ED) and inpatient hospital visits for adults in California from 2016-2018, after ICD-10-CM implementation. Five ICD-10-CM codes outlined in the Uniform Data System Reporting Instructions were used to identify IPV. Fewer than 0.1% of visits (17,347 ED visits and 1,430 hospitalizations) included documentation of IPV. Visits with documented IPV were more common among patients who were younger, female, Black, primarily English-speaking, and publicly insured compared to visits with no documented IPV. There were fairly consistent patterns over time in the specific ICD-10-CM codes used for IPV between 2016 and 2018. Physical and sexual abuse were the most common codes for types of abuse. Among the 15 EDs and 15 hospitals in California with the highest volume of IPV visits, there was variability in the use of ICD-10-CM codes for IPV visits. Accurate documentation of IPV in administrative data may improve patient care and increase understanding of the burden and effects of IPV on individuals and communities.

The Measurement of Intimate Partner Violence Using International Classification of Diseases Diagnostic Codes: A Systematic Review.

Intimate partner violence (IPV) is challenging to measure yet systematic surveillance of IPV is critical to informing public health prevention and response efforts. Administrative medical data provide opportunities for such surveillance, and often use the International Classification of Diseases (ICD). The primary purpose of this systematic review was to document which ICD codes have been used in empirical literature to identify IPV, understand the justification used to select specific codes to develop IPV case definitions, and identify the data sources and types of research questions addressed by the existing literature. We searched 11 databases and of the initial 2182 results, 21 empirical studies from 2000 to 2020 met the study inclusion criteria including using ICD codes to measure IPV. The majority of these studies (90.5%) used either national samples of data or population-based administrative data from emergency departments (52.4%) or inpatient hospitalizations (38.1%). We found wide variation of ICD diagnostic codes to measure IPV and categorized the sets of codes used based on the number of codes. The most commonly used ICD-9 codes were E967.3, 995.81, 995.80, 995.85 and the most common ICD-10 codes were T74.1 and Z63.0. Few studies validated the ICD codes used to measure IPV. Most included studies (81.0%) answered epidemiological research questions. The current study provides suggestions for future research, including justifying the selection of ICD codes and providing a range of estimates based on narrow and broad sets of codes. Implications for policy and practice, including enhanced training for healthcare professionals in documenting IPV, are discussed.

Positive Predictive Value of ICD-10 Codes for Cerebral Venous Sinus Thrombosis in Taiwan's National Health Insurance Claims Database.

OBJECTIVE: This study aims to determine the positive predictive value (PPV) of case definitions for cerebral venous sinus thrombosis (CVST) in Taiwan's National Health Insurance claims database based on the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnostic codes. STUDY DESIGN AND SETTING: Inpatient records with ICD-10-CM codes of G08, I629, I636, or I676 were retrieved from the claims data of all hospital branches of Chang Gung Medical Foundation. Manual review of the medical records and images was performed in order to ascertain the diagnosis. The PPV of various case definitions for CVST was estimated. RESULTS: Of the 380 hospitalizations, 166 and 214 were determined to be true-positive and false-positive episodes of acute CVST, respectively. The PPV of the ICD-10-CM codes of G08, I629, I636, and I676 was 88.2%, 2.0%, 100.0%, and 91.3%, respectively. The PPV generally increased when acute CVST was defined as a primary diagnosis or as ICD-10-CM codes plus anticoagulant use. Miscoding in other conditions, tentative diagnosis, and remote episode of CVST were determined as the main reasons for false-positive diagnosis of acute CVST. CONCLUSION: This study determined the PPV of ICD-10-CM codes for identifying CVST, which may offer a reference for future claims-based research.

Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers.

BACKGROUND: Electronic medical records (EMRs) offer the promise of computationally identifying sarcoidosis cases. However, the accuracy of identifying these cases in the EMR is unknown. OBJECTIVE: The aim of this study is to determine the statistical performance of using the International Classification of Diseases (ICD) diagnostic codes to identify patients with sarcoidosis in the EMR. METHODS: We used the ICD diagnostic codes to identify sarcoidosis cases by searching the EMRs of the San Francisco and Palo Alto Veterans Affairs medical centers and randomly selecting 200 patients. To improve the diagnostic accuracy of the computational algorithm in cases where histopathological data are unavailable, we developed an index of suspicion to identify cases with a high index of suspicion for sarcoidosis (confirmed and probable) based on clinical and radiographic features alone using the American Thoracic Society practice guideline. Through medical record review, we determined the positive predictive value (PPV) of diagnosing sarcoidosis by two computational methods: using ICD codes alone and using ICD codes plus the high index of suspicion. RESULTS: Among the 200 patients, 158 (79%) had a high index of suspicion for sarcoidosis. Of these 158 patients, 142 (89.9%) had documentation of nonnecrotizing granuloma, confirming biopsy-proven sarcoidosis. The PPV of using ICD codes alone was 79% (95% CI 78.6%-80.5%) for identifying sarcoidosis cases and 71% (95% CI 64.7%-77.3%) for identifying histopathologically confirmed sarcoidosis in the EMRs. The inclusion of the generated high index of suspicion to identify confirmed sarcoidosis cases increased the PPV significantly to 100% (95% CI 96.5%-100%). Histopathology documentation alone was 90% sensitive compared with high index of suspicion. CONCLUSIONS: ICD codes are reasonable classifiers for identifying sarcoidosis cases within EMRs with a PPV of 79%. Using a computational algorithm to capture index of suspicion data elements could significantly improve the case-identification accuracy.

Diagnostic codes for low back pain, nomenclature or noise? A descriptive study of disease classification system coding of low back pain.

AIM: To compare and contrast the diagnostic codes for spinal causes of low back pain (LBP) in 3 disease classification systems (International Classification of Diseases [ICD]-10, International Classification of Primary Care [ICPC]-2 PLUS and Systematized Nomenclature of Medicine Clinical Terms - Australia [SNOMED CT-AU]) and consider how well they are aligned with the diagnostic approach recommended in contemporary clinical practice guidelines for LBP. METHOD: This was a descriptive study which included 3 disease classification systems: ICD-10, ICPC-2 PLUS and SNOMED CT-AU. Two independent authors extracted relevant LBP codes from each system and mapped the codes to 3 guideline-endorsed categories of spine-related diagnoses for LBP (specific spinal pathology, radicular syndromes, and non-specific LBP) and the various clinical conditions (sub-categories) within each of the 3 categories. RESULTS: ICD-10, ICPC-2 PLUS, and SNOMED CT-AU had 126, 118 and 100 codes for LBP, respectively. All systems provided codes that would cover the 3 guideline-endorsed categories of spine-related diagnoses for LBP. On the basis of contemporary guidelines, the authors developed lists of discrete sub-categories of specific spinal pathology (56 sub-categories), radicular syndromes (7 sub-categories), and non-specific LBP (10 sub-categories). Each of the classification systems was then mapped against these sub-categories to tally redundancy and determine exhaustiveness. However, no system covered all 73 sub-categories of LBP, and within each system, there was substantial redundancy with up to 22 codes for the same clinical condition. CONCLUSION: LBP diagnostic codes used in popular disease classification systems are out of touch with current approaches to diagnosis, as reflected in contemporary LBP guidelines. Our findings suggest these disease classification systems need revision, but precisely how they should be revised is unclear.

Missing diagnoses of congenital cytomegalovirus infection in electronic health records for infants with laboratory-confirmed infection.

Congenital cytomegalovirus (CMV) is a leading cause of non-genetic sensorineural hearing loss and neurodevelopmental disabilities among US children. Studies using administrative healthcare databases have identified infants with congenital CMV using diagnostic codes from the International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification. Using Cerner Health Facts deidentified electronic health records, we assessed the sensitivity of CMV diagnostic codes among infants with laboratory confirmed congenital CMV infection (i.e. a positive CMV laboratory test - polymerase chain reaction, direct fluorescent antibody, or culture from urine, saliva, respiratory secretion or blood samples, or IgM serology - within 21 days of life). During 2010-2017, 668 congenital CMV cases were identified among 7,517,207 infants with encounters within 21 days of life, or 0.89 cases per 10,000 infants. The sensitivity of CMV diagnostic codes assigned within 21 and 90 days of life was 10.3% (95% CI: 8.2-12.9) and 11.1% (95% CI: 8.9-13.7), respectively.

Development and Evaluation of Computable Phenotypes in Pediatric Epilepsy:3 Cases.

INTRODUCTION: Computable phenotypes allow identification of well-defined patient cohorts from electronic health record data. Little is known about the accuracy of diagnostic codes for important clinical concepts in pediatric epilepsy, such as (1) risk factors like neonatal hypoxic-ischemic encephalopathy; (2) clinical concepts like treatment resistance; (3) and syndromes like juvenile myoclonic epilepsy. We developed and evaluated the performance of computable phenotypes for these examples using electronic health record data at one center. METHODS: We identified gold standard cohorts for neonatal hypoxic-ischemic encephalopathy, pediatric treatment-resistant epilepsy, and juvenile myoclonic epilepsy via existing registries and review of clinical notes. From the electronic health record, we extracted diagnostic and procedure codes for all children with a diagnosis of epilepsy and seizures. We used these codes to develop computable phenotypes and evaluated by sensitivity, positive predictive value, and the F-measure. RESULTS: For neonatal hypoxic-ischemic encephalopathy, the best-performing computable phenotype (HIE ICD-9/10 and [brain magnetic resonance imaging (MRI) or electroencephalography (EEG) within 120 days of life] and absence of commonly miscoded conditions) had high sensitivity (95.7%, 95% confidence interval [CI] 85-99), positive predictive value (100%, 95% CI 95-100), and F measure (0.98). For treatment-resistant epilepsy, the best-performing computable phenotype (3 or more antiseizure medicines in the last 2 years or treatment-resistant ICD-10) had a sensitivity of 86.9% (95% CI 79-93), positive predictive value of 69.6% (95% CI 60-79), and F-measure of 0.77. For juvenile myoclonic epilepsy, the best performing computable phenotype (JME ICD-10) had poor sensitivity (52%, 95% CI 43-60) but high positive predictive value (90.4%, 95% CI 81-96); the F measure was 0.66. CONCLUSION: The variable accuracy of our computable phenotypes (hypoxic-ischemic encephalopathy high, treatment resistance medium, and juvenile myoclonic epilepsy low) demonstrates the heterogeneity of success using administrative data to identify cohorts important for pediatric epilepsy research.

Evaluation of the accuracy of diagnostic coding for influenza compared to laboratory results: the availability of test results before hospital discharge facilitates improved coding accuracy.

BACKGROUND: Assessing the accuracy of diagnostic coding is essential to ensure the validity and reliability of administrative coded data. The aim of the study was to evaluate the accuracy of assigned International Classification of Diseases version 10-Australian Modification (ICD-10-AM) codes for influenza by comparing with patients' results of their polymerase chain reaction (PCR)-based laboratory tests. METHOD: A retrospective study was conducted across seven public hospitals in New South Wales, Australia. A total of 16,439 patients who were admitted and tested by either cartridge-based rapid PCR or batched multiplex PCR between January 2016 and December 2017 met the inclusion criteria. We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of ICD-10-AM coding using laboratory results as a gold standard. Separate analyses were conducted to determine whether the availability of test results at the time of hospital discharge influenced diagnostic coding accuracy. RESULTS: Laboratory results revealed 2759 positive influenza cases, while ICD-10-AM coding identified 2527 patients. Overall, 13.7% (n = 378) of test positive patients were not assigned an ICD-10-AM code for influenza. A further 5.8% (n = 146) patients with negative test results were incorrectly assigned an ICD-10-AM code for influenza. The sensitivity, specificity, PPV and NPV of ICD-10-AM coding were 93.1%; 98.9%; 94.5% and 98.6% respectively when test results were received before discharge and 32.7%; 99.2%; 87.8% and 89.8% respectively when test results were not available at discharge. The sensitivity of ICD-10-AM coding varied significantly across hospitals. The use of rapid PCR or hospitalisation during the influenza season were associated with greater coding accuracy. CONCLUSION: Although ICD-10-AM coding for influenza demonstrated high accuracy when laboratory results were received before discharge, its sensitivity was substantially lower for patients whose test results were not available at discharge. The timely availability of laboratory test results during the episode of care could contribute to improved coding accuracy.

A visual analytic approach for the identification of ICU patient subpopulations using ICD diagnostic codes.

A large number of clinical concepts are categorized under standardized formats that ease the manipulation, understanding, analysis, and exchange of information. One of the most extended codifications is the International Classification of Diseases (ICD) used for characterizing diagnoses and clinical procedures. With formatted ICD concepts, a patient profile can be described through a set of standardized and sorted attributes according to the relevance or chronology of events. This structured data is fundamental to quantify the similarity between patients and detect relevant clinical characteristics. Data visualization tools allow the representation and comprehension of data patterns, usually of a high dimensional nature, where only a partial picture can be projected. In this paper, we provide a visual analytics approach for the identification of homogeneous patient cohorts by combining custom distance metrics with a flexible dimensionality reduction technique. First we define a new metric to measure the similarity between diagnosis profiles through the concordance and relevance of events. Second we describe a variation of the Simplified Topological Abstraction of Data (STAD) dimensionality reduction technique to enhance the projection of signals preserving the global structure of data. The MIMIC-III clinical database is used for implementing the analysis into an interactive dashboard, providing a highly expressive environment for the exploration and comparison of patients groups with at least one identical diagnostic ICD code. The combination of the distance metric and STAD not only allows the identification of patterns but also provides a new layer of information to establish additional relationships between patient cohorts. The method and tool presented here add a valuable new approach for exploring heterogeneous patient populations. In addition, the distance metric described can be applied in other domains that employ ordered lists of categorical data.

Disease diagnostic coding to facilitate evidence-based medicine: current and future perspectives.

Technologic advances in information management have rapidly changed laboratory testing and the practice of veterinary medicine. Timely and strategic sampling, same-day assays, and 24-h access to laboratory results allow for rapid implementation of intervention and treatment protocols. Although agent detection and monitoring systems have progressed, and wider tracking of diseases across veterinary diagnostic laboratories exists, such as by the National Animal Health Laboratory Network (NAHLN), the distinction between detection of agent and manifestation of disease is critical to improved disease management. The implementation of a consistent, intuitive, and useful disease diagnosis coding system, specific for veterinary medicine and applicable to multiple animal species within and between veterinary diagnostic laboratories, is the first phase of disease data aggregation. Feedback loops for continuous improvement that could aggregate existing clinical and laboratory databases to improve the value and applications of diagnostic processes and clinical interventions, with interactive capabilities between clinicians and diagnosticians, and that differentiate disease causation from mere agent detection, remain incomplete. Creating an interface that allows aggregation of existing data from clinicians, including final diagnosis, interventions, or treatments applied, and measures of outcomes, is the second phase. Prototypes for stakeholder cooperation, collaboration, and beta testing of this vision are in development and becoming a reality. We focus here on how such a system is being developed and utilized at the Iowa State University Veterinary Diagnostic Laboratory to facilitate evidence-based medicine and utilize diagnostic coding for continuous improvement of animal health and welfare.

Estimation of the prevalence of thyroid dysfunction in Catalonia through two different registries: Pharmaceutical dispensing and diagnostic registration.

Background: Population studies on the prevalence of thyroid dysfunctions are costly. The pharmacy dispensing (PDR) and diagnosis (DR) registers allow us to study the epidemiology of these pathologies in a simpler way. Our aims: 1/Estimate the prevalence of thyroid dysfunction in Catalonia based on data from the PDR and the DR, 2/to evaluate the concordance of the results obtained by both strategies. Methods: The population studied was the one registered with the public health system in Catalonia(Catsalut). In the PDR analysis, the information obtained through the Pharmaceutical Provision file (during 2012, 2013, 2014) was used regarding the number of patients under treatment (NPT) (levothyroxine and antithyroid medication). The DR analysis (2014) was performed by ICD-9 codes (hyperthyroidism 242 and hypothyroidism 243, 244). Results: According to the NPT in the PDR analysis, the prevalence of treated hypothyroidism increased over 3 years: 2.81%(2012), 2.92%(2013) and 3.07%(2014) (P < .00001). The prevalence of hyperthyroidism in treatment was 0.14%(2012), 0.13%(2013) and 0.14%(2014). According to the DR analysis in 2014, the prevalence of hypothyroidism was 2.54% and 0.35% for hyperthyroidism. The PDR analysis estimated a higher hypothyroidism prevalence compared to that estimated by the DR (P < .0001) and vice versa in the case of hyperthyroidism. Conclusion: Both PDR and DR prevalence estimations of thyroid dysfunction show some degree of discordance probably due to undercoding bias in the case of DR and the absence of subclinical pathology in the case of PDR. However, both approaches are valid and complementary for estimating the prevalence of thyroid dysfunction.

Validation of diagnostic codes and epidemiologic trends of Huntington disease: a population-based study in Navarre, Spain.

BACKGROUND: There is great heterogeneity on geographic and temporary Huntington disease (HD) epidemiological estimates. Most research studies of rare diseases, including HD, use health information systems (HIS) as data sources. This study investigates the validity and accuracy of national and international diagnostic codes for HD in multiple HIS and analyses the epidemiologic trends of HD in the Autonomous Community of Navarre (Spain). METHODS: HD cases were ascertained by the Rare Diseases Registry and the reference Medical Genetics Centre of Navarre. Positive predictive values (PPV) and sensitivity with 95% confidence intervals (95% CI) were estimated. Overall and 9-year periods (1991-2017) HD prevalence, incidence and mortality rates were calculated, and trends were assessed by Joinpoint regression. RESULTS: Overall PPV and sensitivity of combined HIS were 71.8% (95% CI: 59.7, 81.6) and 82.2% (95% CI: 70.1, 90.4), respectively. Primary care data was a more valuable resource for HD ascertainment than hospital discharge records, with 66% versus 50% sensitivity, respectively. It also had the highest number of "unique to source" cases. Thirty-five per cent of HD patients were identified by a single database and only 4% by all explored sources. Point prevalence was 4.94 (95% CI: 3.23, 6.65) per 100,000 in December 2017, and showed an annual 6.1% increase from 1991 to 1999. Incidence and mortality trends remained stable since 1995-96, with mean annual rates per 100,000 of 0.36 (95% CI: 0.27, 0.47) and 0.23 (95% CI: 0.16, 0.32), respectively. Late-onset HD patients (23.1%), mean age at onset (49.6 years), age at death (66.6 years) and duration of disease (16.7 years) were slightly higher than previously reported. CONCLUSION: HD did not experience true temporary variations in prevalence, incidence or mortality over 23 years of post-molecular testing in our population. Ascertainment bias may largely explain the worldwide heterogeneity in results of HD epidemiological estimates. Population-based rare diseases registries are valuable instruments for epidemiological studies on low prevalence genetic diseases, like HD, as long as they include validated data from multiple HIS and genetic/family information.

Using ICD-10-CM codes to detect illicit substance use: A comparison with retrospective self-report.

BACKGROUND: Understanding whether International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes can be used to accurately detect substance use can inform their use in future surveillance and research efforts. METHODS: Using 2015-2018 data from a retrospective cohort study of 602 safety-net patients prescribed opioids for chronic non-cancer pain, we calculated the sensitivity and specificity of using ICD-10-CM codes to detect illicit substance use compared to retrospective self-report by substance (methamphetamine, cocaine, opioids [heroin or non-prescribed opioid analgesics]), self-reported use frequency, and type of healthcare encounter. RESULTS: Sensitivity of ICD-10-CM codes for detecting self-reported substance use was highest for methamphetamine (49.5 % [95 % confidence interval: 39.6-59.5 %]), followed by cocaine (44.4 % [35.8-53.2 %]) and opioids (36.3 % [28.8-44.2 %]); higher for participants who reported more frequent methamphetamine (intermittent use: 27.7 % [14.6-42.6 %]; ≥weekly use: 67.2 % [53.7-79.0 %]) and opioid use (intermittent use: 21.4 % [13.2-31.7 %]; ≥weekly use: 52.6 % [40.8-64.2 %]); highest for outpatient visits (methamphetamine: 43.8 % [34.1-53.8 %]; cocaine: 36.8 % [28.6-45.6 %]; opioids: 33.1 % [25.9-41.0 %]) and lowest for emergency department visits (methamphetamine: 8.6 % [4.0-15.6 %]; cocaine: 5.3 % [2.1-10.5 %]; opioids: 6.3 % [3.0-11.2 %]). Specificity was highest for methamphetamine (96.4 % [94.3-97.8 %]), followed by cocaine (94.0 % [91.5-96.0 %]) and opioids (85.0 % [81.3-88.2 %]). CONCLUSIONS: ICD-10-CM codes had high specificity and low sensitivity for detecting self-reported substance use but were substantially more sensitive in detecting frequent use. ICD-10-CM codes to detect substance use, particularly those from emergency department visits, should be used with caution, but may be useful as a lower-bound population measure of substance use or for capturing frequent use among certain patient populations.

Administrative data identify sickle cell disease: A critical review of approaches in U.S. health services research.

To identify people living with sickle cell disease (SCD) and study their healthcare utilization, researchers can either use clinical records linked to administrative data or use billing diagnosis codes in stand-alone administrative databases. Correct identification of individuals clinically managed for SCD using diagnosis codes in claims databases is limited by the accuracy of billing codes in outpatient encounters. In this critical review, we assess the strengths and limitations of claims-based SCD case-finding algorithms in stand-alone administrative databases that contain both inpatient and outpatient records. Validation studies conducted using clinical records and newborn screening for confirmation of SCD case status have found that algorithms that require three or more nonpharmacy claims or one inpatient claim plus two or more outpatient claims with SCD codes show acceptable accuracy (positive predictive value and sensitivity) in children and adolescents. Future studies might seek to assess the accuracy of case-finding algorithms over the lifespan.