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Point-of-care ultrasound (POCUS) has increasingly been used by neonatal providers in neonatal intensive care units in the United States. However, there is a lack of literature addressing the complexities of POCUS coding and billing practices in the United States. This article describes the coding terminology and billing process especially those relevant to neonatal POCUS. We elucidate considerations for neonatal POCUS billing framework and workflow integration. Directions on image storage and supporting documentation to facilitate efficient reimbursement, compliance with billing regulations, and appeal to insurance claim denial are discussed. Key Points Code neonatal POCUS procedure precisely allows accurate reimbursement and reduced errors in billing. Document details to support medical necessity and reimbursement claims effectively. Adhere to regulations to avoid audits, denials, and ensure proper reimbursement. [ABSTRACT FROM AUTHOR]

BACKGROUND: Many states in the United States have progressed towards legalization of marijuana including decriminalization, medicinal and/or recreational use. We studied the impact of legalization on cannabis-related emergency department visits in states with varying degrees of legalization. METHODS: Seventeen healthcare institutions in fifteen states (California, Colorado, Connecticut, Florida, Iowa, Kentucky, Maryland, Massachusetts, Missouri, New Hampshire, Oregon, South Carolina, Tennessee, Texas, Washington) participated. Cannabinoid immunoassay results and cannabis-related International Classification of Diseases (ninth and tenth versions) codes were obtained for emergency department visits over a 3- to 8-year period during various stages of legalization: no state laws, decriminalized, medical approval before dispensaries, medical dispensaries available, recreational approval before dispensaries and recreational dispensaries available. Trends and monthly rates of cannabinoid immunoassay and cannabis-related International Classification of Diseases code positivity were determined during these legalization periods. RESULTS: For most states, there was a significant increase in both cannabinoid immunoassay and International Classification of Diseases code positivity as legalization progressed; however, positivity rates differed. The availability of dispensaries may impact positivity in states with medical and/or recreational approval. In most states with no laws, there was a significant but smaller increase in cannabinoid immunoassay positivity rates. CONCLUSIONS: States may experience an increase in cannabis-related emergency department visits with progression toward marijuana legalization. The differences between states, including those in which no impact was seen, are likely multifactorial and include cultural norms, attitudes of local law enforcement, differing patient populations, legalization in surrounding states, availability of dispensaries, various ordering protocols in the emergency department, and the prevalence of non-regulated cannabis products.

Objectives To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data. Materials and methods We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n  = 244 071), Michigan Genomics Initiative (MGI; n  = 81 243), and UK Biobank (UKB; n  = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results. Results For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. Discussion Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis. Conclusion EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly. [ABSTRACT FROM AUTHOR]

Abstract The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient’s condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.

Abstract Objective To report trends in Australian hospitalisations coded for sepsis and their associated costs. Design Retrospective analysis of Australian national hospitalisation data from 2002 to 2021. Methods Sepsis-coded hospitalisations were identified using the Global Burden of Disease study sepsis-specific ICD-10 codes modified for Australia. Costs were calculated using Australian-Refined Diagnosis Related Group codes and National Hospital Cost Data Collection. Results Sepsis-coded hospitalisations increased from 36,628 in 2002-03 to 131,826 in 2020-21, an annual rate of 7.8%. Principal admission diagnosis codes contributed 13,843 (37.8%) in 2002-03 and 44,186 (33.5%) in 2020-21; secondary diagnosis codes contributed 22,785 (62.2%) in 2002-03 and 87,640 (66.5%) in 2020-21. Unspecified sepsis was the most common sepsis code, increasing from 15,178 hospitalisations in 2002-03 to 68,910 in 2020-21. The population-based incidence of sepsis-coded hospitalisations increased from 18.6 to 10,000 population (2002-03) to 51.3 per 10,000 (2021-21); representing an increase from 55.1 to 10,000 hospitalisations in 2002-03 to 111.4 in 2020-21. Sepsis-coded hospitalisations occurred more commonly in the elderly; those aged 65 years or above accounting for 20,573 (55.6%) sepsis-coded hospitalisations in 2002-03 and 86,135 (65.3%) in 2020-21. The cost of sepsis-coded hospitalisations increased at an annual rate of 20.6%, from AUD199M (€127 M) in financial year 2012 to AUD711M (€455 M) in 2019. Conclusion Hospitalisations coded for sepsis and associated costs increased significantly from 2002 to 2021 and from 2012 to 2019, respectively.

Background: Australia uses the International Classification of Diseases (ICD-10) for mortality coding and its Australian Modification, ICD-10-AM, for morbidity coding. The ICD underpins surveillance (population health, mortality), health planning and research (clinical, epidemiological and others). ICD-10-AM also supports activity-based funding, thereby propelling realignment of the foci of clinical coding and, potentially, coded data's research utility. Objective: To conduct a scoping review of the literature exploring the use of ICD-10 and ICD-10-AM Australian-coded data in research. Research questions addressed herein: (1) What were the applications of ICD-10(-AM) Australian-coded data in published peer-reviewed research, 2012–2022? (2) What were the purposes of ICD-10(-AM) coded data within this context, as classified per a taxonomy of data use framework? Method : Following systematic Medline, Scopus and Cumulative Index to Nursing and Allied Health Literature database searches, a scoping literature review was conducted using PRISMA Extension for Scoping Reviews guidelines. References of a random 5% sample of within-scope articles were searched manually. Results were summarised using descriptive analyses. Results: Multi-stage screening of 2103 imported articles produced 636, including 25 from the references, for extraction and analysis; 54% were published 2019–2022; 50% within the largest five categories were published post-2019; 22% fell within the "Mental health and behavioural" category; 60.3% relied upon an ICD-10 modification. Articles were grouped by: research foci; relevant ICD chapter; themes per the taxonomy; purposes of the coded data. Observational study designs predominated: descriptive (50.6%) and cohort (34.6%). Conclusion: Researchers' use of coded data is extensive, robust and growing. Increasing demand is foreshadowed for ICD-10(-AM) coded data, and HIM-Coders' and Clinical Coders' expert advice to medical researchers. [ABSTRACT FROM AUTHOR]

Background and Aims: International Classification of Diseases (ICD) diagnosis codes are often used in research to identify patients with opioid use disorder (OUD), but their accuracy for this purpose is not fully evaluated. This study describes application of ICD‐10 diagnosis codes for opioid use, dependence and abuse from an electronic health record (EHR) data extraction using data from the clinics' OUD patient registries and clinician/staff EHR entries. Design: Cross‐sectional observational study. Setting: Four rural primary care clinics in Washington and Idaho, USA. Participants: 307 patients. Measurements This study used three data sources from each clinic: (1) a limited dataset extracted from the EHR, (2) a clinic‐based registry of patients with OUD and (3) the clinician/staff interface of the EHR (e.g. progress notes, problem list). Data source one included records with six commonly applied ICD‐10 codes for opioid use, dependence and abuse: F11.10 (opioid abuse, uncomplicated), F11.20 (opioid dependence, uncomplicated), F11.21 (opioid dependence, in remission), F11.23 (opioid dependence with withdrawal), F11.90 (opioid use, unspecified, uncomplicated) and F11.99 (opioid use, unspecified with unspecified opioid‐induced disorder). Care coordinators used data sources two and three to categorize each patient identified in data source one: (1) confirmed OUD diagnosis, (2) may have OUD but no confirmed OUD diagnosis, (3) chronic pain with no evidence of OUD and (4) no evidence for OUD or chronic pain. Findings F11.10, F11.21 and F11.99 were applied most frequently to patients who had clinical diagnoses of OUD (64%, 89% and 79%, respectively). F11.20, F11.23 and F11.90 were applied to patients who had a diagnostic mix of OUD and chronic pain without OUD. The four clinics applied codes inconsistently. Conclusions: Lack of uniform application of ICD diagnosis codes make it challenging to use diagnosis code data from EHR to identify a research population of persons with opioid use disorder. [ABSTRACT FROM AUTHOR]

Purpose: To provide an overview of various sepsis International Classification of Diseases (ICD) coding methods and their diagnostic accuracy. Methods: We undertook a systematic scoping review between 1991 and 2020 (search terms: sepsis, coding, and epidemiology) to include studies reporting the accuracy of a sepsis ICD coding method. Studies were grouped by ICD coding method, number of diagnostic accuracy parameters, ICD version, reference standard, design, country, setting, type of dataset and sepsis definition. ICD coding methods were categorised as explicit or implicit, with the explicit methods further divided into wide and narrow groups. Descriptive statistics were used to present data. Results: We analysed 17 studies, of which 16 (94.1%) used retrospective medical chart review as the reference standard for clinical sepsis, and eight (47.1%) used hospital administrative data to identify sepsis. There were 53 assessments of various ICD coding methods, with 32 (60.4%) of them being explicit and 21 (39.6%) implicit methods. The coding methods had a median sensitivity of <75% but a median specificity of >85%. However, a wide variation was noted in the diagnostic accuracy parameters of all ICD coding methods. Most of the studies showed high methodological quality. Conclusion: None of the current ICD coding methods is optimal for identifying sepsis. [ABSTRACT FROM AUTHOR]

Objective: To report trends in Australian hospitalisations coded for sepsis and their associated costs. Design: Retrospective analysis of Australian national hospitalisation data from 2002 to 2021. Methods: Sepsis-coded hospitalisations were identified using the Global Burden of Disease study sepsis-specific ICD-10 codes modified for Australia. Costs were calculated using Australian-Refined Diagnosis Related Group codes and National Hospital Cost Data Collection. Results: Sepsis-coded hospitalisations increased from 36,628 in 2002-03 to 131,826 in 2020-21, an annual rate of 7.8%. Principal admission diagnosis codes contributed 13,843 (37.8%) in 2002-03 and 44,186 (33.5%) in 2020-21; secondary diagnosis codes contributed 22,785 (62.2%) in 2002-03 and 87,640 (66.5%) in 2020-21. Unspecified sepsis was the most common sepsis code, increasing from 15,178 hospitalisations in 2002-03 to 68,910 in 2020-21. The population-based incidence of sepsis-coded hospitalisations increased from 18.6 to 10,000 population (2002-03) to 51.3 per 10,000 (2021-21); representing an increase from 55.1 to 10,000 hospitalisations in 2002-03 to 111.4 in 2020-21. Sepsis-coded hospitalisations occurred more commonly in the elderly; those aged 65 years or above accounting for 20,573 (55.6%) sepsis-coded hospitalisations in 2002-03 and 86,135 (65.3%) in 2020-21. The cost of sepsis-coded hospitalisations increased at an annual rate of 20.6%, from AUD199M (€127 M) in financial year 2012 to AUD711M (€455 M) in 2019. Conclusion: Hospitalisations coded for sepsis and associated costs increased significantly from 2002 to 2021 and from 2012 to 2019, respectively. [ABSTRACT FROM AUTHOR]

Aim. This study aims to assess the incidence of heart failure (HF) and associated mortality, and also the burden on the healthcare system in the subjects of Russian Federation, based on the HF encoding data.Material and methods. We made a structured request for the number of patients with HF and the number of cases of providing medical care in 2019. HF was understood to mean the presence of at least one of the codes I09.9, I11.0, I13.0, I13.2, I25.5, I42.0, I42.5, I42.6, I42.7, I42.8, I42.9, I43.0, I43.1, I43.2, I43.8, I50.X (expanded encoding) according to International Classification of Diseases 10th Revision. The code I50.X was considered separately (the standard HF enconding).Results. The information about the incidence of HF, associated mortality and burden on the healthcare system was obtained from 15 subjects (53,6% from those that gave the data according to the request; the adult population is 18,9% from total adult population of Russian Federation). We noted significant heterogeneity between the regions. The median of the incidence of HF and associated mortality was 2,6 and 3,2% in accordance with the data of the expanded encoding, and 0,21 and 11,3% — according to the standard HF encoding. The presence of the code I50.x was observed in average in 9,4% of all cases of HF and defined the patients who frequently used emergency medical services and were frequently hospitalized (60 (18, 96) and 48 (20, 137) cases per 100 patients versus 9 (5, 24) and 17 (10, 70) cases in the expanded encoding).Conclusion. According to the encoding, the indicators of the incidence of HF and associated mortality vary greatly between the regions, the median values are 2,6 and 3,2% in expanded and 0,21 and 11,3% in standard approaches. In the standard encoding, there were more frequent use of emergency medical services and less number of outpatient visits. The development and introduction of a unified approach to encoding and recording the cases of HF will provide obtaining objective statistical data and using them for management decisions.

Many states in the United States have progressed towards legalization of marijuana including decriminalization, medicinal and/or recreational use. We studied the impact of legalization on cannabis-related emergency department visits in states with varying degrees of legalization. Seventeen healthcare institutions in fifteen states (California, Colorado, Connecticut, Florida, Iowa, Kentucky, Maryland, Massachusetts, Missouri, New Hampshire, Oregon, South Carolina, Tennessee, Texas, Washington) participated. Cannabinoid immunoassay results and cannabis-related International Classification of Diseases (ninth and tenth versions) codes were obtained for emergency department visits over a 3- to 8-year period during various stages of legalization: no state laws, decriminalized, medical approval before dispensaries, medical dispensaries available, recreational approval before dispensaries and recreational dispensaries available. Trends and monthly rates of cannabinoid immunoassay and cannabis-related International Classification of Diseases code positivity were determined during these legalization periods. For most states, there was a significant increase in both cannabinoid immunoassay and International Classification of Diseases code positivity as legalization progressed; however, positivity rates differed. The availability of dispensaries may impact positivity in states with medical and/or recreational approval. In most states with no laws, there was a significant but smaller increase in cannabinoid immunoassay positivity rates. States may experience an increase in cannabis-related emergency department visits with progression toward marijuana legalization. The differences between states, including those in which no impact was seen, are likely multifactorial and include cultural norms, attitudes of local law enforcement, differing patient populations, legalization in surrounding states, availability of dispensaries, various ordering protocols in the emergency department, and the prevalence of non-regulated cannabis products. [ABSTRACT FROM AUTHOR]

The VA Million Veteran Program (MVP) is a nationwide initiative that seeks to examine how genes influence health and behaviors in military veterans. An article by Merritt et al. analyzing data from the MVP, developing and testing algorithms to query dementia and Alzheimer's disease (AD) diagnoses from the VA's electronic health record system and examining genetic factors, provides an extraordinarily important contribution to the dementia and AD fields. The analyses presented in the article show how large databases can be used to further understand dementia and AD, pointing the way for many more important advances for this field., Competing Interests: Declaration of conflicting interestsDr Ashford has contributed to the development of the MemTrax Continuous Recognition Test which can be used to screen for cognitive impairment and is referenced in this article. Dr Ashford is an Editorial Board Member of this journal but was not involved in the peer-review process of this article nor had access to any information regarding its peer-review.

Background: The national hospital‐acquired complication programme captures complications arising from patient‐related and hospital‐related factors, but the proportion of the two is unclear. Aim: Health services are encouraged to evaluate data from the national hospital‐acquired complications (HAC) programme and identify strategies to mitigate them. Methods: A retrospective chart review compared HAC extracted from administrative data. The setting was a 430‐bed university‐affiliated metropolitan hospital. Records from 260 participants with, and 462 without, reported HAC from 2619 multi‐day stay adults were reviewed. The main outcome measures were prevalence and positive predictive value (PPV) of HAC methodology. Results: No errors of HAC coding or classification were identified. Four hundred and twenty‐three HAC events were reported in 260 records; most commonly delirium (n = 57; 13.4%), pneumonia (n = 46; 10.9%), blood stream infection (n = 39; 9.2%), hypoglycaemia (n = 33; 7.8%) and cardiac arrhythmias (n = 33; 7.8%). One hundred and eight (25.5%) 'HAC' events in 69 separations (95% confidence interval (CI) = 2.05–3.33 per 100 separations) were false positive, and 43 of 462 (95% CI = 6.72–12.22 per 100 separations) were false negative. Prevalence of total (reported plus missing) HAC was 16.06 (95% CI = 14.02–19.52), reported HAC was 9.93 (95% CI = 8.76–11.21), potentially preventable HAC was 1.68 (95% CI = 1.22–2.26) and healthcare errors was 0.31 (95% CI = 0.13–1.30) per 100 separations. PPV of HAC for true clinical events was 0.74 (0.68–0.79), preventable events 0.18 (0.13–0.23) and healthcare error 0.03 (0.01–0.06). Conclusions: Prevalence of HAC events was higher than expected, but PPV for healthcare errors was low, suggesting provision of care is a less common cause of HAC events than patient factors. HAC may be an indicator of hospital admission complexity rather than HAC. [ABSTRACT FROM AUTHOR]

Background: Many studies rely on the International Classification of Diseases, 9th or 10th Revision, Clinical Modification codes to define obesity in electronic health records data. While prior studies found misclassification and low sensitivity of codes for pediatric obesity, it remains unclear whether this misclassification is random and what are the implications of combining different code types to define obesity. Methods: We assessed prevalence, sensitivity, and specificity of obesity codes among 7.4 million children aged 2-19 years over 2014-2021. Among those with obesity in 2021, we estimated the probability of receiving any code or a specific code type by patient characteristics. Results: Obesity code utilization increased in prevalence from 3.9% in 2014 to 9.8% in 2021; prevalence of obesity based on BMI increased from 17.4% to 20.5%. Code sensitivity increased from 19.8% to 40.8%. Among children with obesity in 2021, those with severe obesity (reference: no severe obesity) and chronic disease (reference: no chronic disease) were more likely to get a code, and the highest likelihood was associated with obesity diagnosis codes (vs. status codes). Conclusions: Despite increases, obesity code utilization remained low. Obesity code misclassification is not random and certain child characteristics (e.g., severe obesity or chronic disease) are associated with a higher probability of getting a code. There are also significant differences by code type; thus, caution should be taken before combining obesity codes as a proxy for obesity status, especially in longitudinal analyses. More universal documentation of obesity may improve the quality of care and the use of these data for evaluation and research purposes.

Identifying venous thromboembolism (VTE) from large clinical and administrative databases is important for research and quality improvement. To develop and validate natural language processing (NLP) algorithms to identify VTE from radiology reports among general internal medicine (GIM) inpatients. This cross-sectional study included GIM hospitalizations between April 1, 2010 and March 31, 2017 at 5 hospitals in Toronto, Ontario, Canada. We developed NLP algorithms to identify pulmonary embolism (PE) and deep venous thrombosis (DVT) from radiologist reports of thoracic computed tomography (CT), extremity compression ultrasound (US), and nuclear ventilation-perfusion (VQ) scans in a training dataset of 1551 hospitalizations. We compared the accuracy of our NLP algorithms, the previously-published "simpleNLP" tool, and administrative discharge diagnosis codes (ICD-10-CA) for PE and DVT to the "gold standard" manual review in a separate random sample of 4000 GIM hospitalizations. Our NLP algorithms were highly accurate for identifying DVT from US, with sensitivity 0.94, positive predictive value (PPV) 0.90, and Area Under the Receiver-Operating-Characteristic Curve (AUC) 0.96; and in identifying PE from CT, with sensitivity 0.91, PPV 0.89, and AUC 0.96. Administrative diagnosis codes and the simple NLP tool were less accurate for DVT (ICD-10-CA sensitivity 0.63, PPV 0.43, AUC 0.81; simpleNLP sensitivity 0.41, PPV 0.36, AUC 0.66) and PE (ICD-10-CA sensitivity 0.83, PPV 0.70, AUC 0.91; simpleNLP sensitivity 0.89, PPV 0.62, AUC 0.92). Administrative diagnosis codes are unreliable in identifying VTE in hospitalized patients. We developed highly accurate NLP algorithms to identify VTE from radiology reports in a multicentre sample and have made the algorithms freely available to the academic community with a user-friendly tool (https://lks-chart.github.io/CHARTextract-docs/08-downloads/rulesets.html#venous-thromboembolism-vte-rulesets) • ICD-10 codes do not reliably identify venous thromboembolism (VTE) in hospitalized adults. • We developed algorithms to accurately identify VTE from radiology reports. • This tool is freely available for researchers: https://lks-chart.github.io/CHARTextract-docs/ [ABSTRACT FROM AUTHOR]

Aim. To analyze prevalence of heart failure (HF), clinical and demographic characteristics, health care utilization, and outcomes according to the used International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) codes in regional integrated electronic health record database in Saint Petersburg.Material and methods. The retrospective analysis of the Saint Petersburg regional integrated electronic health record database for 2019 was performed. At least one of the following ICD-10 codes has been considered as HF case: I50.x (standard coding) and/or I11.0, I13.0, I13.2, I25.5, I42.0, I42.9, I09.9, I43.0, I43.1, I43.2, I43.8, I42.5, I42.6, I42.7, I42.8 (extended coding).Results. A total of 64070 adult patients with HF had medical encounters in 2019, 34,5% of whom were identified using standard coding, 65,5% — using extended coding. The combination of codes was observed in 9,9% of cases. HF prevalence/mortality was 1,4%/6,8% in general, as well as 0,49%/15,7% and 0,93%/2,1% with standard and extended coding, respectively. HF patients had high healthcare utilization with the mean number of 14 encounters per patient per year. Actually, 24% of patients had more than 20 both inpatient and outpatient encounters and 54% of patients — at least 1 all-cause hospitalization during the year. Encounters of patients with HF accounted for 4,3% of all visits, 6,5% of all hospitalizations, 4,1% of all outpatient visits and 9,7% of all emergency contacts during the year. Patients identified by the standard coding compared with the extended coding had older age and higher incidence of comorbidities, as well as greater hospitalization and death rates, but lower number of outpatient visits.Conclusion. The prevalence of HF among the adult population of Saint. Petersburg in 2019 was 1,4%. HF was characterized by a high health care utilization and mortality rate reaching 15,7 % per year. The use of different approaches to coding presumably could help to identify different groups of patients with HF, which requires the adaptation of healthcare models and an active monitoring system to reduce the risk of adverse events.

The purpose of this study was to examine patterns of health and developmental outcomes in children with prenatal opioid exposure (POE) and neonatal abstinence syndrome (NAS) compared to children without exposure during the first three years of life. This was a secondary data analysis of the Maternal and Infant Data Hub (MIDH), a de-identified dataset originating from the Midwest region of the United States, consisting of newborn billing records and corresponding maternal and child electronic medical records. For these analyses, the repository included data on more than 20,000 children born between 2013 and 2019. Diagnoses were identified with International Classification of Diseases, ninth and tenth Revision, Clinical Modification codes (ICD-9/10-CM). Firth logistic regression was used to assess whether incidence of each diagnosis code differed by exposure group. Among 20,389 children in the dataset, 13,173 were unexposed; 455 were POE, and 199 were POE + NAS. There were significant differences in frequency of diagnoses between groups, specifically regarding growth and development, infection, mental health, musculoskeletal, neonatal, sensory, and social issues. When comparing exposed groups, children with POE + NAS experienced more negative health outcomes than children with only POE across all years. This study implicates POE as a significant variable associated with many health and developmental outcomes of children during the first three years of life. It is crucial to understand and identify health risks observed more frequently in exposed children during such a critical period of growth and brain development. [ABSTRACT FROM AUTHOR]

Background: The aim of this retrospective study was to demonstrate that irAEs, specifically gastrointestinal and pulmonary, examined through International Classification of Disease (ICD) data leads to underrepresentation of true irAEs and overrepresentation of false irAEs, thereby concluding that ICD claims data are a poor approach to electronic health record (EHR) data mining for irAEs in immunotherapy clinical research. Methods: This retrospective analysis was conducted in 1,063 cancer patients who received ICIs between 2011 and 2017. We identified irAEs by manual review of medical records to determine the incidence of each of our endpoints, namely colitis, hepatitis, pneumonitis, other irAE, or no irAE. We then performed a secondary analysis utilizing ICD claims data alone using a broad range of symptom and disease-specific ICD codes representative of irAEs. Results: 16% (n = 174/1,063) of the total study population was initially found to have either pneumonitis 3% (n = 37), colitis 7% (n = 81) or hepatitis 5% (n = 56) on manual review. Of these patients, 46% (n = 80/174) did not have ICD code evidence in the EHR reflecting their irAE. Of the total patients not found to have any irAEs during manual review, 61% (n = 459/748) of patients had ICD codes suggestive of possible irAE, yet were not identified as having an irAE during manual review. Discussion: Examining gastrointestinal and pulmonary irAEs through the International Classification of Disease (ICD) data leads to underrepresentation of true irAEs and overrepresentation of false irAEs. [ABSTRACT FROM AUTHOR]

Background Health care administrative database research frequently uses standard medical codes to identify diagnoses or procedures. The aim of this review was to establish the diagnostic accuracy of codes used in administrative data research to identify nontuberculous mycobacterial (NTM) disease, including lung disease (NTMLD). Methods We searched Ovid Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from inception to April 2019. We included studies assessing the diagnostic accuracy of International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) diagnosis codes to identify NTM disease and NTMLD. Studies were independently assessed by 2 researchers, and the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess bias and quality. Results We identified 5549 unique citations. Of the 96 full-text articles reviewed, 7 eligible studies of moderate quality (3730 participants) were included in our review. The diagnostic accuracy of ICD-9-CM diagnosis codes to identify NTM disease varied widely across studies, with positive predictive values ranging from 38.2% to 100% and sensitivity ranging from 21% to 93%. For NTMLD, 4 studies reported diagnostic accuracy, with positive predictive values ranging from 57% to 64.6% and sensitivity ranging from 21% to 26.9%. Conclusions Diagnostic accuracy measures of codes used in health care administrative data to identify patients with NTM varied across studies. Overall the positive predictive value of ICD-9-CM diagnosis codes alone is good, but the sensitivity is low; this method is likely to underestimate case numbers, reflecting the current limitations of coding systems to capture NTM diagnoses. [ABSTRACT FROM AUTHOR]

Introduction: The diagnosis of acute myocarditis (AM) is complex due to its heterogeneity and typically is defined by either Electronic Healthcare Records (EHRs) or advanced imaging and endomyocardial biopsy, but there is no consensus. We aimed to investigate the diagnostic accuracy of these approaches for AM., Methods: Data on ICD 10th Revision(ICD-10) codes corresponding to AM were collected from two hospitals and compared to cardiac magnetic resonance (CMR)-confirmed or clinically suspected (CS)-AM cases with respect to diagnostic accuracy, clinical characteristics, and all-cause mortality. Next, we performed a review of published AM studies according to inclusion criteria., Results: We identified 291 unique admissions with ICD-10 codes corresponding to AM in the first three diagnostic positions. The positive predictive value of ICD-10 codes for CMR-confirmed or CS-AM was 36%, and patients with CMR-confirmed or CS-AM had a lower all-cause mortality than those with a refuted diagnosis (P = 0.019). Using an unstructured approach, patients with CMR-confirmed and CS-AM had similar demographics, comorbidity profiles and survival over a median follow-up of 52 months (P = 0.72). Our review of the literature confirmed our findings. Outcomes for patients included in studies using CMR-confirmed criteria were favourable compared to studies with endomyocardial biopsy-confirmed AM cases., Conclusion: ICD-10 codes have poor accuracy in identification of AM cases and should be used with caution in clinical research. There are important differences in management and outcomes of patients according to the selection criteria used to diagnose AM. Potential selection biases must be considered when interpreting AM cohorts and requires standardization of inclusion criteria for AM studies., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Introduction: Research using electronic health care databases continues to grow given widespread adoption of electronic health records (EHR). As manual record review is often not feasible with the increasing amount of data, use of International Statistical Classification of Diseases and Related Health Problems‐Clinical Modification (ICD‐CM) codes are a method of capturing the study population or abstracting outcomes. The accuracy of ICD‐CM coding and methodological validity of its use in medical research has been variable. Objectives: To compare the performance of ICD‐CM code data to pharmacist EHR review to identify patients that developed an in‐hospital thromboembolic event (TE) following four‐factor prothrombin complex concentrate (4F‐PCC) administration for anticoagulation reversal. Methods: A total of 337 patients were evaluated for TE occurrence following 4F‐PCC administration by two separate methods; discharge ICD‐CM code capture and pharmacist EHR review. To adjudicate ICD‐CM code and/or pharmacist‐identified TE cases, a blinded physician investigator reviewed the EHR and categorized the patient as having a TE or not based on extensive study definitions. To quantify the accuracy of ICD‐CM coding for patient outcome event and pharmacist investigator EHR review, positive predictive values (PPV) were calculated using physician review as the gold standard. Results: A total of 76 cases were identified: 57 cases were identified by ICD‐CM codes only, 13 by pharmacist review only, and six by both ICD‐CM code and pharmacist review. Upon review by the physician investigator, 27 (35.5%) were identified as having an in‐hospital TE. There was a high degree of agreement between physician and pharmacist identification (88.2%). ICD‐CM code data demonstrated low PPV (22.2%) while pharmacist EHR review demonstrated high PPV (94.7%) for the identification of TE. Conclusion: ICD‐CM code data may be helpful in identifying study patients in large datasets, however low PPV for identification of TE requires clinician validation before acceptance as outcome data in clinical research. [ABSTRACT FROM AUTHOR]

Abstract Background Telephone consultation and triage are used to limit the workload on emergency departments. Lack of visual cues and clinical tests put telephone consultations to a disadvantage compared to face-to-face consultations increasing the risk of under-triage. Under-triage occurs in telephone triage; however why under-triage happens is not explored yet. The aim of the study was to describe situations of under-triage in context, to assess the quality of under-triaged calls, and to identify communication patterns contributing to under-triage in a regional OOH service in the capital region of Denmark. Methods Explanatory simultaneous mixed method with thematic analysis and descriptive statistics was chosen. The study was carried out in an Out-Of-Hours service (OOH) in the Capital Region of Denmark, Copenhagen. Under-triage was defined as Potentially Under-Triaged Calls (PUTC) by specific criteria to an OOH Hotline, and identification by integration of three databases: Medical Hotline database, Emergency number database, including the Ambulance database, and electronic patient records. Distribution of PUTC were carried out using ICD-10 codes to identify diagnosis and main themes identified by qualitative analysis of audio recorded under-triaged calls. Study period was October 15th to November 30th 2014. Results Three hundred twenty seven PUTC were identified, representing 0.04% of all calls (n = 937.056) to the OOH. Distribution of PUTC according to diagnoses was: digestive (24%), circulatory (19%), respiratory (15%) and all others (42%). Thematic analysis of the voice logs suggested that inadequate communication and non-normative symptom description contributed to under-triage. Discussion The incidence of potentially under-triage is low (0.04%). However, the over-representation of digestive, circulatory, and respiratory diagnoses might suggest that under-triage is related to inadequate symptom description. We recommend that caller and call-handler collaborate systematically on problem identification and negotiate non-normative symptom description. Conclusion The incidence of under-triage is low (0.04%). However, the over-representation of digestive, circulatory, and respiratory diagnoses might suggest that under-triage is related to inadequate symptom description. We recommend that caller and call-handler collaborate systematically on problem identification and negotiate non-normative symptom description.

Child physical abuse is a leading cause of morbidity and mortality in young children. Identification of abused children is challenging, and can affect risk-adjusted benchmarking of trauma center performance. The purpose of this project was to understand diagnosis coding capture rates for child abuse and develop a standardized approach to clinician documentation to improve trauma registry capture. A retrospective cohort was obtained including all admitted trauma patients with injuries from known or suspected abusive mechanism in 2017. Patients who received forensic workup for child physical abuse were classified as "no abuse," "suspected abuse," and "confirmed abuse" using narratives from social work notes. Our trauma registry was used to abstract International Classification of Diseases, Tenth Revision (ICD-10) diagnostic and external cause codes for each patient. Abuse classifications defined by chart review were then compared with coding in the registry using crosstabs. A total of 115 patients were identified as having a forensic workup for child physical abuse. Patients who underwent forensic workup were classified as: 40% no abuse, 37% suspected abuse, and 23% confirmed abuse at the time of discharge. Three patients (6%) with a negative forensic workup were overcoded as suspected abuse in our trauma registry. Among patients with clinically confirmed abuse, our trauma registry identified only 63% by diagnostic codes and only 33% by external cause codes. Child physical abuse is frequently undercoded, and clear clinical documentation of the level of suspicion of abuse at discharge is needed to accurately identify abused patients. [ABSTRACT FROM AUTHOR]

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms' prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm. [ABSTRACT FROM AUTHOR]

In epidemiologic studies, patients with head and neck squamous cell carcinoma (HNSCC) are classified mainly by the International Classification of Diseases (ICD) codes. However, some patients are of an unclear subsite, the "gray zone" cases, which could reflect ICD coding error, absence of primary subsite, or extensive primary tumors that cross over multiple subsites of the oral cavity and oropharynx. Patients with gray zone squamous cell carcinomas were compared with patients with oral cavity squamous cell carcinoma (OSCC) or oropharyngeal squamous cell carcinoma (OPSCC) and stratified by human papillomavirus (HPV) status that was determined by p16 immunostaining or HPV serology. Comparisons consisted of clinicodemographic features and prognostic outcomes presented by Kaplan-Meier curves and Cox proportional hazards regression models, reported as hazard ratios. There were 158 consecutive patients with gray zone HNSCC diagnosed at the Princess Margaret Cancer Center between 2006 and 2017: 66 had subsite coding discrepancies against the clinician's documentation ("discrepant" cases; e.g., the diagnosis by the clinician was OSCC, while the classification by ICD coding was OPSCC), while 92 were squamous cell carcinoma of unknown primary of the head and neck (SCCUPHN) after complete diagnostic workup. Comparators included 721 consecutive OSCC and 938 OPSCC adult cases. All HPV-positive cohorts (OPSCC, discrepant, and SCCUPHN) had similar clinicodemographic characteristics and better 3- and 5-y overall survival and disease-free survival than their HPV-negative counterparts. In contrast, HPV-negative discrepant cases had prognostic outcomes most similar to HPV-negative OPSCC cases, while HPV-negative SCCUPHN had survival outcomes most similar to those of patients with OSCC in this study. HPV-positive status can improve the classification of patients with unclear or discrepant oral/oropharyngeal subsite, an improvement over classification systems that are solely clinician defined or conducted through ICD coding. However, due to clinical practice, we could not make definitive reclassification for patients with HPV-negative gray zone HNSCC. [ABSTRACT FROM AUTHOR]

Objective: To identify early diagnostic profiles such as diagnostic codes and consultation patterns of cancer patients in primary care one year prior to cancer diagnosis. Design: Total population-based case–control study. Setting and subjects: 4562 cancer patients and 17,979 controls matched by age, sex, and primary care unit. Data were collected from the Swedish Cancer Register and the Regional Healthcare Database. Method: We identified cancer patients in the Västra Götaland Region of Sweden diagnosed in 2011 with prostate, breast, colorectal, lung, gynaecological, and skin cancers including malignant melanoma. We studied the symptoms and diagnoses identified by diagnostic codes during a diagnostic interval of 12 months before the cancer diagnosis. Main outcome measures: Consultation frequency, symptom density by cancer type, prevalence and odds ratios (OR) for the diagnostic codes in the cancer population as a whole. Results: The diagnostic codes with the highest OR were unspecified lump in breast, neoplasm of uncertain behaviour, and abnormal serum enzyme levels. The codes with the highest prevalence were hyperplasia of prostate, other skin changes and abdominal and pelvic pain. The frequency of diagnostic codes and consultations in primary care rose in tandem 50 days before diagnosis for breast and gynaecological cancer, 60 days for malignant melanoma and skin cancer, 80 days for prostate cancer and 100 days for colorectal and lung cancer. Conclusion: Eighty-seven percent of patients with the most common cancers consulted a general practitioner (GP) a year before their diagnosis. An increase in consultation frequency and presentation of any symptom should raise the GP’s suspicion of cancer. Key Points Knowledge about the prevalence of early symptoms and other clinical signs in cancer patients in primary care remains insufficient. Eighty-seven percent of the patients with the seven most common cancers consulted a general practitioner 12 months prior to cancer diagnosis. Both the frequency of consultation and the number of symptoms and diseases expressed in diagnostic codes rose in tandem 50–100 days before the cancer diagnosis. Unless it is caused by a previously known disease, an increased consultation rate for any symptom should result in a swift investigation or referral from primary care to confirm or exclude cancer.

Objective: To explore the role of selection bias adjustment by weighting electronic health record (EHR)-linked biobank data for commonly performed analyses., Materials and Methods: We mapped diagnosis (ICD code) data to standardized phecodes from three EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n=244,071), Michigan Genomics Initiative (MGI; n=81,243), and UK Biobank (UKB; n=401,167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to be more representative of the US adult population. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted four common descriptive and analytic tasks comparing unweighted and weighted results., Results: For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB's estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted PheWAS for colorectal cancer, the strongest associations remained unaltered and there was large overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates., Discussion: Weighting had limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation more. Results from untargeted association analyses should be followed by weighted analysis when effect size estimation is of interest for specific signals., Conclusion: EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly., Competing Interests: Competing interests LGF is a Without Compensation (WOC) employee at the VA Ann Arbor, a United States government facility. All other authors declare that they have no competing financial or non-financial interests related to this research.

Aims: ICD codes are used for classification of hospitalizations. The codes are used for administrative, financial, and research purposes. It is known, however, that errors occur. Natural language processing (NLP) offers promising solutions for optimizing the process. To investigate methods for automatic classification of disease in unstructured medical records using NLP and to compare these to conventional ICD coding., Methods and Results: Two datasets were used: the open-source Medical Information Mart for Intensive Care (MIMIC)-III dataset ( n = 55.177) and a dataset from a hospital in Belgium ( n = 12.706). Automated searches using NLP algorithms were performed for the diagnoses 'atrial fibrillation (AF)' and 'heart failure (HF)'. Four methods were used: rule-based search, logistic regression, term frequency-inverse document frequency (TF-IDF), Extreme Gradient Boosting (XGBoost), and Bio-Bidirectional Encoder Representations from Transformers (BioBERT). All algorithms were developed on the MIMIC-III dataset. The best performing algorithm was then deployed on the Belgian dataset. After preprocessing a total of 1438 reports was retained in the Belgian dataset. XGBoost on TF-IDF matrix resulted in an accuracy of 0.94 and 0.92 for AF and HF, respectively. There were 211 mismatches between algorithm and ICD codes. One hundred and three were due to a difference in data availability or differing definitions. In the remaining 108 mismatches, 70% were due to incorrect labelling by the algorithm and 30% were due to erroneous ICD coding (2% of total hospitalizations)., Conclusion: A newly developed NLP algorithm attained a high accuracy for classifying disease in medical records. XGBoost outperformed the deep learning technique BioBERT. NLP algorithms could be used to identify ICD-coding errors and optimize and support the ICD-coding process., Competing Interests: Conflict of interest: None declared, (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Purpose: We assessed the frequency of emergency department (ED) visits by transgender individuals, examined whether these visits were related to gender transition healthcare, and identified longitudinal trends in relevant International Classification of Disease (ICD) codes., Methods: We conducted a retrospective review of aggregated patient records using the California Office of Statewide Health Planning and Development database. ED visits from 2012-2021 that involved ICD-9(2012 to Q3 2015) or ICD-10(Q4 2015 to 2021) codes commonly associated with transgender patients were identified, examining trends in ICD code usage., Results: We identified 393 relevant ED visits (0.0037% of all visits) in 2012, compared to 2642 visits (0.021% of all visits) in 2021. This represents a 570% increase in ED visits by transgender individuals, despite only a 22% increase in ED visits overall. Gender identity disorders were the primary reason for seeking care in 0.76% of relevant visits in 2012, compared to 3.0% in 2019. The use of diagnosis codes for "transsexualism" decreased from 93% of visits in 2012 to 52% in 2021; the use of codes for "gender identity disorder" increased from 43% of visits in 2016 to 47% in 2021., Conclusions: This is the first attempt to assess transgender healthcare needs using a non-LGBT-specific database, providing insights for clinical and policy decision-making. The significant increase in the usage of gender-identity disorder diagnosis codes suggests that the prevalence of gender dysphoria is severely under-estimated. Better documentation practices are needed to improve care and track health and epidemiologic outcomes for transgender patients.

Background: Many patients with common cancers are late diagnosed.Objectives: Identify consultation profiles and clinical features in patients with the seven most common cancers, who had consulted a general practitioner (GP) frequently before their cancer diagnosis.Methods: A case-control study was conducted in Region Västra Götaland, Sweden. A total of 2570 patients, diagnosed in 2011 with prostate, breast, colorectal, lung, gynaecological and skin cancers including malignant melanoma, and 9424 controls were selected from the Swedish Cancer Register and a regional health care database. Diagnostic codes [International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10)] from primary care for patients with ≥4 GP consultations registered in the year before cancer diagnosis were collected. Likelihood ratios (LRs) were calculated for variables associated with the different cancers.Results: Fifty-six percent of the patients had consulted a GP four or more times in the year before cancer diagnosis. Alarm symptoms or signs represented 60% of the codes with the highest LR, but only 40% of the 10 most prevalent codes. Breast lump had the highest LR, 11.9 [95% confidence interval (CI) 8.0-17.8]; abnormalities of plasma proteins had an LR of 5.0 (95% CI 3.0-8.2) and abnormal serum enzyme levels had an LR of 4.6 (95% CI 3.6-5.9). Early clinical features associated with cancer had been registered already at the first two GP consultations.Conclusion: One out of six clinical features associated with cancer were presented by cancer patients with four or more pre-referral consultations already at the two first consultations. These early clinical features that were focal and had benign characteristics might have been missed diagnostic opportunities. [ABSTRACT FROM AUTHOR]

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Objectives: The logging of ICD Diagnostic, Procedure and Drug codes is one means of tracking the experience of medical students' clinical rotations. The goal is to create a web-based computer and mobile application to track the progress of trainees, monitor the effectiveness of their training locations and be a means of sampling public health status.Materials and Methods: We have developed a web-based app in which medical trainees make entries via a simple and quick interface optimized for both mobile devices and personal computers. For each patient interaction, users enter ICD diagnostic, procedure, and drug codes via a hierarchical or search entry interface, as well as patient demographics (age range and gender, but no personal identifiers), and free-text notes. Users and administrators can review and edit input via a series of output interfaces. The user interface and back-end database are provided via dual redundant failover Linux servers.Results and Discussion: Students master the interface in ten minutes, and thereafter complete entries in less than one minute. Five hundred-forty 3rd year VCOM students each averaged 100 entries in the first four week clinical rotation. Data accumulated in various Appalachian clinics and Central American medical mission trips has demonstrated the public health surveillance utility of the application.Conclusion: PC and mobile apps can be used to collect medical trainee experience in real time or near real-time, quickly, and efficiently. This system has collected 75,596 entries to date, less than 2% of trainees have needed assistance to become proficient, and medical school administrators are using the various summaries to evaluate students and compare different rotation sites. [ABSTRACT FROM AUTHOR]

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms’ prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm.

Background Peripherally inserted central catheters (PICCs) are ubiquitous in modern hospitals, but are associated with venous thromboembolism (VTE), which includes deep vein thrombosis (DVT) and/or pulmonary embolism (PE). We retrospectively examined this association in hospitalized patients, highlighting anatomically associated VTEs (those with DVT in the PICC extremity). Methods Charts with an International Classification of Diseases, Ninth Revision (ICD9) code for VTE were collected from a discharge database of PICC-managed patients at a tertiary hospital. A sample (52.3%) of the VTE charts was manually reviewed to verify PICC-associated VTE (unverified charts were excluded), and determine such data as the extremity in which each DVT was diagnosed (using ultrasound reports). VTE rates were calculated using an uncorrected method (from charts with VTE ICD9 code) and a corrected method (from charts with manually verified PICC-associated VTE). Results Our uncorrected VTE rate was 3.9% ( P  < .0001), whereas the corrected rate was 1.5%. Among 125 charts with manually verified PICC-associated VTE, 69 (60.5%) out of 114 patients with a DVT had their DVT occur in the PICC extremity, yielding an anatomically associated VTE rate of 0.84%. The most common reason for a chart being excluded (60.2%) was a VTE occurring before PICC placement. Conclusions We found clinically significant rates of PICC-associated VTE. The majority of patients' DVT occurred in the same extremity as their PICC, lending further evidence that PICCs are an independent risk factor for VTE and require judicious use. There was also a discrepancy in VTE rate derived from ICD codes alone vs. manual chart review. [ABSTRACT FROM AUTHOR]