Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia.

BACKGROUND: Linked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records. METHODS: We analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant RESULTS: Of 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %). CONCLUSION: The extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.

Disability Degree Assessment: A Comprehensive Approach According to Royal Decree 888/2022. / Evaluación del grado de discapacidad: un enfoque integral según el Real Decreto 888/2022.

BACKGORUND AND OBJECTIVE: Royal Decree 888/2022 establishes that the evaluation of disability situations is carried out by multiprofessional teams responsible for assessing and recognizing the degree of disability. The participation of professionals in the healthcare and social fields can be valuable in providing reports from which the necessary data for the proper assessment of disability can be obtained, with the ultimate goal of providing comprehensive assistance to people with disabilities. MATERIALS AND METHODS: An analysis and summary of Royal Decree 888/2022, which has recently come into effect, is performed, focusing on the most relevant aspects for professionals in the healthcare and social fields. RESULTS: The recognition and classification of the degree of disability are the responsibility of the autonomous communities, and the assessments are issued by multiprofessional teams. To do this, four components are evaluated using the criteria outlined in the annexes of the Royal Decree itself. Each criterion generates a score that is combined to obtain a single score, the Final Disability Degree of the Person. CONCLUSIONS: The pathology that causes the disability must have been previously diagnosed by the Healthcare System and considered permanent. Its evaluation is based on the evidence of objective clinical findings that are documented and supported by clinical reports. For this reason, it is important to maintain an accurate medical history, document reviews, and provide all relevant evidence.

Global Trends of Medical Misadventures Using International Classification of Diseases, Tenth Revision Cluster Y62-Y69 Comparing Pre-, Intra-, and Post-COVID-19 Pandemic Phases: Protocol for a Retrospective Analysis Using the TriNetX Platform.

BACKGROUND: The COVID-19 pandemic has sharpened the focus on health care safety and quality, underscoring the importance of using standardized metrics such as the International Classification of Diseases, Tenth Revision (ICD-10). In this regard, the ICD-10 cluster Y62-Y69 serves as a proxy assessment of safety and quality in health care systems, allowing researchers to evaluate medical misadventures. Thus far, extensive research and reports support the need for more attention to safety and quality in health care. The study aims to leverage the pandemic's unique challenges to explore health care safety and quality trends during prepandemic, intrapandemic, and postpandemic phases, using the ICD-10 cluster Y62-Y69 as a key tool for their evaluation. OBJECTIVE: This research aims to perform a comprehensive retrospective analysis of incidence rates associated with ICD-10 cluster Y62-Y69, capturing both linear and nonlinear trends across prepandemic, intrapandemic, and postpandemic phases over an 8-year span. Therefore, it seeks to understand how these trends inform health care safety and quality improvements, policy, and future research. METHODS: This study uses the extensive data available through the TriNetX platform, using an observational, retrospective design and applying curve-fitting analyses and quadratic models to comprehend the relationships between incidence rates over an 8-year span (from 2015 to 2023). These techniques will enable the identification of nuanced trends in the data, facilitating a deeper understanding of the impacts of the COVID-19 pandemic on medical misadventures. The anticipated results aim to outline complex patterns in health care safety and quality during the COVID-19 pandemic, using global real-world data for robust and generalizable conclusions. This study will explore significant shifts in health care practices and outcomes, with a special focus on geographical variations and key clinical conditions in cardiovascular and oncological care, ensuring a comprehensive analysis of the pandemic's impact across different regions and medical fields. RESULTS: This study is currently in the data collection phase, with funding secured in November 2023 through the Ricerca Corrente scheme of the Italian Ministry of Health. Data collection via the TriNetX platform is anticipated to be completed in May 2024, covering an 8-year period from January 2015 to December 2023. This dataset spans pre-pandemic, intra-pandemic, and early post-pandemic phases, enabling a comprehensive analysis of trends in medical misadventures using the ICD-10 cluster Y62-Y69. The final analytics are anticipated to be completed by June 2024. The study's findings aim to provide actionable insights for enhancing healthcare safety and quality, reflecting on the pandemic's transformative impact on global healthcare systems. CONCLUSIONS: This study is anticipated to contribute significantly to health care safety and quality literature. It will provide actionable insights for health care professionals, policy makers, and researchers. It will highlight critical areas for intervention and funding to enhance health care safety and quality globally by examining the incidence rates of medical misadventures before, during, and after the pandemic. In addition, the use of global real-world data enhances the study's strength by providing a practical view of health care safety and quality, paving the way for initiatives that are informed by data and tailored to specific contexts worldwide. This approach ensures the findings are applicable and actionable across different health care settings, contributing significantly to the global understanding and improvement of health care safety and quality. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54838.

Towards uniform case-identification criteria in observational studies on peripheral arterial disease: A scoping review.

OBJECTIVE: The diagnosis peripheral arterial disease (PAD) is commonly applied for symptoms related to atherosclerotic obstructions in the lower extremity, though its clinical manifestations range from an abnormal Ankle Brachial Index to critical limb ischemia. Subsequently, management and prognosis of PAD vary widely with the disease stage. A critical aspect is how this variation is addressed in administrative databases-based studies that rely on diagnosis codes for case identification. The objective of this scoping review is to inventory the identification strategies used in studies on PAD that rely on administrative databases, to map the pros and cons of the ICD codes applied, and propose a first outline for a consensus framework for case identification in administrative databases. METHODS: Registry-based reports published between 2010 to 2021 were identified through a systematic PubMed search. Studies were sub-categorized on the basis of the expressed study focus: claudication, critical limb ischemia, or general peripheral arterial disease and the ICD code(s) applied for case identification mapped. RESULTS: Ninety studies were identified, of which thirty-six (40%) did not specify the grade of PAD studied. Forty-nine (54%) articles specified PAD grade studied. Five (6%) articles specified different PAD subgroups in methods and baseline demographics, but not in further analyses. Mapping of the ICD codes applied for case identification for studies that specified the PAD grade studied indicated a remarkable heterogeneity, overlap, and inconsistency. CONCLUSION: A large proportion of registry-based studies on PAD fails to define the study focus. In addition, inconsistent strategies are used for PAD case-identification in studies that report a focus. These findings challenge study validity, and interfere with inter-study comparison. This scoping review provides a first initiative for a consensus framework for standardized case selection in administrative studies on PAD. It is anticipated that more uniform coding will improve study validity, and facilitate inter-study comparisons.

National Institutes of Health Stroke Scale Reporting in Medicare Claims Data: Reporting in the First 3 Years.

BACKGROUND: Since 2016, hospitals have been able to document International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for the National Institutes of Health Stroke Scale (NIHSS). As of 2023, the Centers for Medicare & Medicaid Services uses NIHSS as a risk adjustment variable. We assessed associations between patient- and hospital-level variables and contemporary NIHSS reporting. METHODS: We performed a retrospective cross-sectional analysis of 2019 acute ischemic stroke admissions using deidentified, national 100% inpatient Medicare Fee-For-Service data sets. We identified index acute ischemic stroke admissions using the ICD-10-CM code I63.x and abstracted demographic information, medical comorbidities, hospital characteristics, and NIHSS. We linked Medicare and Mount Sinai Health System (New York, NY) registry data from 2016 to 2019. We calculated NIHSS documentation at the patient and hospital levels, predictors of documentation, change over time, and concordance with local data. RESULTS: There were 231 383 index acute ischemic stroke admissions in 2019. NIHSS was documented in 44.4% of admissions and by 66.5% of hospitals. Hospitals that documented ≥1 NIHSS were more commonly teaching hospitals (39.0% versus 5.5%; standardized mean difference score, 0.88), stroke certified (37.2% versus 8.0%; standardized mean difference score, 0.75), higher volume (mean, 80.8 [SD, 92.6] versus 6.33 [SD, 14.1]; standardized mean difference score, 1.12), and had intensive care unit availability (84.9% versus 23.2%; standardized mean difference score, 1.57). Adjusted odds of documentation were lower for patients with inpatient mortality (odds ratio, 0.64 [95% CI, 0.61-0.68]; P<0.0001), in nonmetropolitan areas (odds ratio, 0.49 [95% CI, 0.40-0.61]; P<0.0001), and male sex (odds ratio, 0.95 [95% CI, 0.93-0.97]; P<0.0001). NIHSS was documented for 52.9% of Medicare cases versus 93.1% of registry cases, and 74.7% of Medicare NIHSS scores equaled registry admission NIHSS. CONCLUSIONS: Missing ICD-10-CM NIHSS data remain widespread 3 years after the introduction of the ICD-10-CM NIHSS code, and there are systematic differences in reporting at the patient and hospital levels. These findings support continued assessment of NIHSS reporting and caution in its application to risk adjustment models.

Creating a computer assisted ICD coding system: Performance metric choice and use of the ICD hierarchy.

OBJECTIVE: Machine learning methods hold the promise of leveraging available data and generating higher-quality data while alleviating the data collection burden on healthcare professionals. International Classification of Diseases (ICD) diagnoses data, collected globally for billing and epidemiological purposes, represents a valuable source of structured information. However, ICD coding is a challenging task. While numerous previous studies reported promising results in automatic ICD classification, they often describe input data specific model architectures, that are heterogeneously evaluated with different performance metrics and ICD code subsets. This study aims to explore the evaluation and construction of more effective Computer Assisted Coding (CAC) systems using generic approaches, focusing on the use of ICD hierarchy, medication data and a feed forward neural network architecture. METHODS: We conduct comprehensive experiments using the MIMIC-III clinical database, mapped to the OMOP data model. Our evaluations encompass various performance metrics, alongside investigations into multitask, hierarchical, and imbalanced learning for neural networks. RESULTS: We introduce a novel metric, , tailored to the ICD coding task, which offers interpretable insights for healthcare informatics practitioners, aiding them in assessing the quality of assisted coding systems. Our findings highlight that selectively cherry-picking ICD codes diminish retrieval performance without performance improvement over the selected subset. We show that optimizing for metrics such as NDCG and AUPRC outperforms traditional F1-based metrics in ranking performance. We observe that Neural Network training on different ICD levels simultaneously offers minor benefits for ranking and significant runtime gains. However, our models do not derive benefits from hierarchical or class imbalance correction techniques for ICD code retrieval. CONCLUSION: This study offers valuable insights for researchers and healthcare practitioners interested in developing and evaluating CAC systems. Using a straightforward sequential neural network model, we confirm that medical prescriptions are a rich data source for CAC systems, providing competitive retrieval capabilities for a fraction of the computational load compared to text-based models. Our study underscores the importance of metric selection and challenges existing practices related to ICD code sub-setting for model training and evaluation.

Comparison of injury severity scores derived from ICD-10-AM codes with trauma registry derived scores: A study from New Zealand.

INTRODUCTION: Various attempts at automation have been made to reduce the administrative burden of manually assigning Abbreviated Injury Severity (AIS) codes to derive Injury Severity Scores (ISS) in trauma registry data. The accuracy of the resulting measures remains unclear, especially in the New Zealand (NZ) context. The aim of this study was to compare ISS derived from hospital discharge International Classification of Diseases Australian Modification (ICD-10-AM) codes with ISS recorded in the NZ Trauma Registry (NZTR). METHODS: Individuals admitted to hospital and enrolled in the NZTR between 1 December 2016 and 30 November 2018 were included. ISS were calculated using a modified ICD to AIS mapping tool. The agreement between both methods for raw scores was assessed by the Intraclass Correlation Coefficient (ICC), and for categorical scores the Kappa and weighted Kappa index were used. Analysis was conducted by gender, age, ethnicity, and mechanism of injury. RESULTS: 3,156 patients fulfilled the inclusion criteria. The ICC for agreement between the methods was poor (0.40, 95 % CI: 0.37-0.43). The Kappa index indicated slight agreement between both methods when using a cut-off value of 12 (0.06; 95 % CI: 0.01-0.12) and 15 (0.13 6; 95 % CI: 0.09-0.17). CONCLUSION: Although the overall agreement between NZTR-ISS and ICD-ISS was slight, ICD-derived scores may be useful to describe injury patterns and for body region-specific estimations when manually coded ISS are not available.

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial.

BACKGROUND: Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. OBJECTIVE: The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. METHODS: The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. RESULTS: We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence-based CAC innovations to improve coding practice. Expected results to be published summer 2024. CONCLUSIONS: The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. TRIAL REGISTRATION: clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54593.

Prevalence and correlates of ICD-11 prolonged grief disorder among adults living in Ukraine during the war with Russia.

BACKGROUND: Although high rates of bereavement are evident in war-affected populations, no study has investigated the prevalence and correlates of probable ICD-11 prolonged grief disorder (PGD) under these circumstances. METHODS: Participants were 2050 adults who participated in a nationwide survey exploring the effects of the Ukraine-Russia war on the daily lives and mental health of Ukrainian people. RESULTS: Of the total sample, 87.7% (n = 1797) of people indicated a lifetime bereavement. In the full sample, 11.4% met the diagnostic requirements for probable ICD-11 PGD, and amongst those with a lifetime bereavement, the conditional rate of probable ICD-11 PGD was 13.0%. Significant risk factors of ICD-11 PGD included the recent loss of a loved one (6 months to a year ago), being most affected by a partner or spouse's death, loved one dying in the war, no recent contact with the deceased prior to their death, and meeting depression and anxiety diagnostic requirements. CONCLUSION: The study reveals that a significant percentage of Ukrainian bereaved individuals have probable ICD-11 PGD, and identifying risk factors, particularly war-related losses, will aid in the development of intervention and prevention programs for bereaved adults.

Improvements in Neoplasm Classification in the International Classification of Diseases, Eleventh Revision: Systematic Comparative Study With the Chinese Clinical Modification of the International Classification of Diseases, Tenth Revision.

BACKGROUND: The International Classification of Diseases, Eleventh Revision (ICD-11) improved neoplasm classification. OBJECTIVE: We aimed to study the alterations in the ICD-11 compared to the Chinese Clinical Modification of the International Classification of Diseases, Tenth Revision (ICD-10-CCM) for neoplasm classification and to provide evidence supporting the transition to the ICD-11. METHODS: We downloaded public data files from the World Health Organization and the National Health Commission of the People's Republic of China. The ICD-10-CCM neoplasm codes were manually recoded with the ICD-11 coding tool, and an ICD-10-CCM/ICD-11 mapping table was generated. The existing files and the ICD-10-CCM/ICD-11 mapping table were used to compare the coding, classification, and expression features of neoplasms between the ICD-10-CCM and ICD-11. RESULTS: The ICD-11 coding structure for neoplasms has dramatically changed. It provides advantages in coding granularity, coding capacity, and expression flexibility. In total, 27.4% (207/755) of ICD-10 codes and 38% (1359/3576) of ICD-10-CCM codes underwent grouping changes, which was a significantly different change (χ21=30.3; P<.001). Notably, 67.8% (2424/3576) of ICD-10-CCM codes could be fully represented by ICD-11 codes. Another 7% (252/3576) could be fully described by uniform resource identifiers. The ICD-11 had a significant difference in expression ability among the 4 ICD-10-CCM groups (χ23=93.7; P<.001), as well as a considerable difference between the changed and unchanged groups (χ21=74.7; P<.001). Expression ability negatively correlated with grouping changes (r=-.144; P<.001). In the ICD-10-CCM/ICD-11 mapping table, 60.5% (2164/3576) of codes were postcoordinated. The top 3 postcoordinated results were specific anatomy (1907/3576, 53.3%), histopathology (201/3576, 5.6%), and alternative severity 2 (70/3576, 2%). The expression ability of postcoordination was not fully reflected. CONCLUSIONS: The ICD-11 includes many improvements in neoplasm classification, especially the new coding system, improved expression ability, and good semantic interoperability. The transition to the ICD-11 will inevitably bring challenges for clinicians, coders, policy makers and IT technicians, and many preparations will be necessary.

Tumors of the central nervous system. Classification of the World Health Organization 2021. Towards a paradigm shift. / Tumores del sistema nervioso central. Clasificación de la Organización Mundial de la Salud 2021. Hacia un cambio de paradigma.

The study of central nervous system (CNS) tumors is a subject of great interest and such knowledge is of great importance in medical practice. The classifications of CNS neoplasms began in the mid-19th century, until the World Health Organization (WHO) published, in 1979, the first edition of a useful systematic review for the purpose of establishing a common language for all medical specialties. To date, 5 updated editions of neoplastic taxonomy have been published. The fifth edition, from 2021, consolidates the paradigm shift brought about by molecular advances, although the transition between morphological and molecular biological characterization is still in progress. In this article, the new modifications introduced in the different most frequent families of tumors in pediatrics are analyzed, emphasizing useful information for pediatricians in their daily practice and multidisciplinary consultations.

El estudio de los tumores del sistema nervioso central (SNC) resulta ser un tema de gran consideración y su conocimiento reviste una alta importancia en la práctica médica. Las clasificaciones de las neoplasias del SNC comenzaron a mediados del siglo XIX hasta que en 1979 la Organización Mundial de la Salud (OMS) publicó la primera edición de una sistemática útil con el objetivo de establecer un lenguaje común para todas las especialidades médicas. Al día de hoy, 5 ediciones actualizaron la taxonomía neoplásica. La quinta edición del año 2021 consolida el cambio de paradigma dado por los avances moleculares, si bien todavía la transición se encuentra en proceso entre la caracterización morfológica y la biológica molecular. En este artículo, se analizan las nuevas modificaciones incorporadas en las diferentes familias tumorales más frecuentes en pediatría haciendo hincapié en aquella información de utilidad para el médico pediatra en su práctica diaria y la consulta multidisciplinaria.

Validity of ICD-10 diagnosis codes for placenta accreta spectrum disorders.

BACKGROUND: The 10th revision of the International Classification of Diseases, Clinical Modification (ICD-10) includes diagnosis codes for placenta accreta spectrum for the first time. These codes could enable valuable research and surveillance of placenta accreta spectrum, a life-threatening pregnancy complication that is increasing in incidence. OBJECTIVE: We sought to evaluate the validity of placenta accreta spectrum diagnosis codes that were introduced in ICD-10 and assess contributing factors to incorrect code assignments. METHODS: We calculated sensitivity, specificity, positive predictive value and negative predictive value of the ICD-10 placenta accreta spectrum code assignments after reviewing medical records from October 2015 to March 2020 at a quaternary obstetric centre. Histopathologic diagnosis was considered the gold standard. RESULTS: Among 22,345 patients, 104 (0.46%) had an ICD-10 code for placenta accreta spectrum and 51 (0.23%) had a histopathologic diagnosis. ICD-10 codes had a sensitivity of 0.71 (95% CI 0.56, 0.83), specificity of 0.98 (95% CI 0.93, 1.00), positive predictive value of 0.61 (95% CI 0.48, 0.72) and negative predictive value of 1.00 (95% CI 0.96, 1.00). The sensitivities of the ICD-10 codes for placenta accreta spectrum subtypes- accreta, increta and percreta-were 0.55 (95% CI 0.31, 0.78), 0.33 (95% CI 0.12, 0.62) and 0.56 (95% CI 0.31, 0.78), respectively. Cases with incorrect code assignment were less morbid than cases with correct code assignment, with a lower incidence of hysterectomy at delivery (17% vs 100%), blood transfusion (26% vs 75%) and admission to the intensive care unit (0% vs 53%). Primary reasons for code misassignment included code assigned to cases of occult placenta accreta (35%) or to cases with clinical evidence of placental adherence without histopatholic diagnostic (35%) features. CONCLUSION: These findings from a quaternary obstetric centre suggest that ICD-10 codes may be useful for research and surveillance of placenta accreta spectrum, but researchers should be aware of likely substantial false positive cases.

Evaluating MedDRA-to-ICD terminology mappings.

BACKGROUND: In this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD) for example, the mapping between them is essential for drug safety and pharmacovigilance research. Our main objective is to provide a quantitative and qualitative analysis of the mapping status between MedDRA and ICD. We focus on evaluating the current mapping status between MedDRA and ICD through the Unified Medical Language System (UMLS) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We summarized the current mapping statistics and evaluated the quality of the current MedDRA-ICD mapping; for unmapped terms, we used our self-developed algorithm to rank the best possible mapping candidates for additional mapping coverage. RESULTS: The identified MedDRA-ICD mapped pairs cover 27.23% of the overall MedDRA preferred terms (PT). The systematic quality analysis demonstrated that, among the mapped pairs provided by UMLS, only 51.44% are considered an exact match. For the 2400 sampled unmapped terms, 56 of the 2400 MedDRA Preferred Terms (PT) could have exact match terms from ICD. CONCLUSION: Some of the mapped pairs between MedDRA and ICD are not exact matches due to differences in granularity and focus. For 72% of the unmapped PT terms, the identified exact match pairs illustrate the possibility of identifying additional mapped pairs. Referring to its own mapping standard, some of the unmapped terms should qualify for the expansion of MedDRA to ICD mapping in UMLS.

Pilot study using machine learning to improve estimation of physical abuse prevalence.

BACKGROUND: International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes have been shown to underestimate physical abuse prevalence. Machine learning models are capable of efficiently processing a wide variety of data and may provide better estimates of abuse. OBJECTIVE: To achieve proof of concept applying machine learning to identify codes associated with abuse. PARTICIPANTS AND SETTING: Children <5 years, presenting to the emergency department with an injury or abuse-specific ICD-10-CM code and evaluated by the child protection team (CPT) from 2016 to 2020 at a large Midwestern children's hospital. METHODS: The Pediatric Health Information System (PHIS) and the CPT administrative databases were used to identify the study sample and injury and abuse-specific ICD-10-CM codes. Subjects were divided into abused and non-abused groups based on the CPT's evaluation. A LASSO logistic regression model was constructed using ICD-10-CM codes and patient age to identify children likely to be diagnosed by the CPT as abused. Performance was evaluated using repeated cross-validation (CV) and Reciever Operator Characteristic curve. RESULTS: We identified 2028 patients evaluated by the CPT with 512 diagnosed as abused. Using diagnosis codes and patient age, our model was able to accurately identify patients with confirmed PA (mean CV AUC = 0.87). Performance was still weaker for patients without existing ICD codes for abuse (mean CV AUC = 0.81). CONCLUSIONS: We built a model that employs injury ICD-10-CM codes and age to improve accuracy of distinguishing abusive from non-abusive injuries. This pilot modelling endeavor is a steppingstone towards improving population-level estimates of abuse.

The application of the ICD-10 for antepartum stillbirth patients in a referral centre of Eastern China: a retrospective study from 2015 to 2022.

BACKGROUND: The causes of some stillbirths are unclear, and additional work must be done to investigate the risk factors for stillbirths. OBJECTIVE: To apply the International Classification of Disease-10 (ICD-10) for antepartum stillbirth at a referral center in eastern China. METHODS: Antepartum stillbirths were grouped according to the cause of death according to the International Classification of Disease-10 (ICD-10) criteria. The main maternal condition at the time of antepartum stillbirth was assigned to each patient. RESULTS: Antepartum stillbirths were mostly classified as fetal deaths of unspecified cause, antepartum hypoxia. Although more than half of the mothers were without an identified condition at the time of the antepartum stillbirth, where there was a maternal condition associated with perinatal death, maternal medical and surgical conditions and maternal complications during pregnancy were most common. Of all the stillbirths, 51.2% occurred between 28 and 37 weeks of gestation, the main causes of stillbirth at different gestational ages also differed. Autopsy and chromosomal microarray analysis (CMA) were recommended in all stillbirths, but only 3.6% received autopsy and 10.5% underwent chromosomal microarray analysis. CONCLUSIONS: The ICD-10 is helpful in classifying the causes of stillbirths, but more than half of the stillbirths in our study were unexplained; therefore, additional work must be done. And the ICD-10 score may need to be improved, such as by classifying stillbirths according to gestational age. Autopsy and CMA could help determine the cause of stillbirth, but the acceptance of these methods is currently low.

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada. METHODS: The Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Abstract Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians. RESULTS: A total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters. CONCLUSION: Diabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

Chronic Cough: Characterizing and Quantifying Burden in Adults Using a Nationwide Electronic Health Records Database.

Chronic cough is a common condition; until recently, no International Classification of Diseases (ICD) code for chronic cough existed; therefore, the true scope and burden of chronic cough is unclear. Using established algorithms, we examined chronic cough patients and their risk profiles, recurrent cough episodes, and subsequent 1-year health care utilization in the nationwide Cerner EHR data resource, compared with those with acute cough. An ICD-based algorithm was applied to the Cerner Health Facts EHR database to derive a phenotype of chronic cough defined as three ICD-based "cough" encounters 14-days apart over a 56-to-120-day period from 2015 to 2017. Demographics, comorbidities, and outcomes (1-year outpatient, emergency, and inpatient encounters) were collected for the chronic cough cohort and acute cough cohort. The chronic cough cohort was 61.5% female, 70.4% white, and 15.2% African American, with 13.7% being of Asian, Native American, or unknown race. Compared with the acute cough cohort, chronic cough patients were more likely to be older, female, and have chronic pulmonary disease, obesity, and depression. Predictors of recurrent chronic cough were older age and race. Those with chronic cough had more outpatient (2.48 ± 2.10 vs. 1.48 ± 0.99; SMD = 0.94), emergency (1.90 ± 2.26 vs. 1.23 ± 0.68; SMD = 0.82), and inpatient (1.11 ± 0.36 vs. 1.05 ± 0.24, SMD = 0.24) encounters compared with acute cough. While EHR-based data may provide a useful resource to identify chronic cough phenotypes, supplementary data approaches and screening methods for chronic cough can further identify the scope of the problem.

Short report: Transition to International Classification of Diseases, 10th Revision and the prevalence of autism in a cohort of healthcare systems.

LAY ABSTRACT: Currently, the prevalence of autism spectrum disorder (henceforth "autism") is 1 in 36, an increasing trend from previous estimates. In 2015, the United States adopted a new version (International Classification of Diseases, 10th Revision) of the World Health Organization coding system, a standard for classifying medical conditions. Our goal was to examine how the transition to this new coding system impacted autism diagnoses in 10 healthcare systems. We obtained information from electronic medical records and insurance claims data from July 2014 through December 2016 for each healthcare system. We used member enrollment data for 30 consecutive months to observe changes 15 months before and after adoption of the new coding system. Overall, the rates of autism per 1000 enrolled members was increasing for 0- to 5-year-olds before transition to International Classification of Diseases, 10th Revision and did not substantively change after the new coding was in place. There was variation observed in autism diagnoses before and after transition to International Classification of Diseases, 10th Revision for other age groups. The change to the new coding system did not meaningfully affect autism rates at the participating healthcare systems. The increase observed among 0- to 5-year-olds is likely indicative of an ongoing trend related to increases in screening for autism rather than a shift associated with the new coding.

Hyperbolic graph convolutional neural network with contrastive learning for automated ICD coding.

The International Classification of Diseases (ICD) is a widely used criterion for disease classification, health monitoring, and medical data analysis. Deep learning-based automated ICD coding has gained attention due to the time-consuming and costly nature of manual coding. The main challenges of automated ICD coding include imbalanced label distribution, code hierarchy and noisy texts. Recent works have considered using code hierarchy or description for better label representation to solve the problem of imbalanced label distribution. However, these methods are still ineffective and redundant since they only interact with a constant label representation. In this work, we introduce a novel Hyperbolic Graph Convolutional Network with Contrastive Learning (HGCN-CL) to solve the above problems and the shortcomings of the previous methods. We adopt a Hyperbolic graph convolutional network on ICD coding to capture the hierarchical structure of codes, which can solve the problem of large distortions when embedding hierarchical structure with graph convolutional network. Besides, we introduce contrastive learning for automatic ICD coding by injecting code features into text encoder to generate hierarchical-aware positive samples to solve the problem of interacting with constant code features. We conduct experiments on the public MIMIC-III and MIMIC-II datasets. The results on MIMIC III show that HGCN-CL outperforms previous state-of-art methods for automatic ICD coding, which achieves a 2.7% and 3.6% improvement respectively compared to previous best results (Hypercore). We also provide ablation experiments and hierarchy visualization to verify the effectiveness of components in our model.