Unsupervised Feature Selection to Identify Important ICD-10 and ATC Codes for Machine Learning on a Cohort of Patients With Coronary Heart Disease: Retrospective Study.

Background: The application of machine learning in health care often necessitates the use of hierarchical codes such as the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) systems. These codes classify diseases and medications, respectively, thereby forming extensive data dimensions. Unsupervised feature selection tackles the "curse of dimensionality" and helps to improve the accuracy and performance of supervised learning models by reducing the number of irrelevant or redundant features and avoiding overfitting. Techniques for unsupervised feature selection, such as filter, wrapper, and embedded methods, are implemented to select the most important features with the most intrinsic information. However, they face challenges due to the sheer volume of ICD and ATC codes and the hierarchical structures of these systems. Objective: The objective of this study was to compare several unsupervised feature selection methods for ICD and ATC code databases of patients with coronary artery disease in different aspects of performance and complexity and select the best set of features representing these patients. Methods: We compared several unsupervised feature selection methods for 2 ICD and 1 ATC code databases of 51,506 patients with coronary artery disease in Alberta, Canada. Specifically, we used the Laplacian score, unsupervised feature selection for multicluster data, autoencoder-inspired unsupervised feature selection, principal feature analysis, and concrete autoencoders with and without ICD or ATC tree weight adjustment to select the 100 best features from over 9000 ICD and 2000 ATC codes. We assessed the selected features based on their ability to reconstruct the initial feature space and predict 90-day mortality following discharge. We also compared the complexity of the selected features by mean code level in the ICD or ATC tree and the interpretability of the features in the mortality prediction task using Shapley analysis. Results: In feature space reconstruction and mortality prediction, the concrete autoencoder-based methods outperformed other techniques. Particularly, a weight-adjusted concrete autoencoder variant demonstrated improved reconstruction accuracy and significant predictive performance enhancement, confirmed by DeLong and McNemar tests (P<.05). Concrete autoencoders preferred more general codes, and they consistently reconstructed all features accurately. Additionally, features selected by weight-adjusted concrete autoencoders yielded higher Shapley values in mortality prediction than most alternatives. Conclusions: This study scrutinized 5 feature selection methods in ICD and ATC code data sets in an unsupervised context. Our findings underscore the superiority of the concrete autoencoder method in selecting salient features that represent the entire data set, offering a potential asset for subsequent machine learning research. We also present a novel weight adjustment approach for the concrete autoencoders specifically tailored for ICD and ATC code data sets to enhance the generalizability and interpretability of the selected features.

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

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.

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-19 th 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.

Stroke Risk After Emergency Department Treat-and-Release Visit for a Fall.

BACKGROUND: Previous cohort studies of hospitalized patients with a delayed diagnosis of ischemic stroke found that these patients often had an initial emergency department (ED) diagnosis of a fall. We sought to evaluate whether ED visits for a fall resulting in discharge to home (ie, treat-and-release visits) were associated with increased short-term ischemic stroke risk. METHODS: A case-crossover design was used to compare ED visits for falls during case periods (0-15, 16-30, 31-90, and 91-180 days before stroke) and control periods (equivalent time periods exactly 1 year before stroke) using administrative data from the Healthcare Cost and Utilization Project on all hospital admissions and ED visits across 10 states from 2016 to 2020. To identify ED treat-and-release visits for a fall and patients hospitalized for acute ischemic stroke, we used previously validated International Classification of Diseases, Tenth Revision, Clinical Modification codes. Odds ratios and 95% CIs were calculated using conditional logistic regression. RESULTS: Among 90 592 hospitalized patients with ischemic stroke, 5230 (5.8%) had an ED treat-and-release visit for a fall within 180 days before their stroke. Patients with an ED treat-and-release visit for a fall were older (mean age, 74.7 [SD, 14.6] versus 70.8 [SD, 15.1] years), more often female (61.9% versus 53.4%), and had higher rates of vascular comorbidities than other patients with stroke. ED treat-and-release visits for a fall were significantly more common in the 15 days before stroke compared with the 15-day control period 1 year earlier (odds ratio, 2.7 [95% CI, 2.4-3.1]). The association between stroke and a preceding ED treat-and-release visit for a fall decreased in magnitude with increasing temporal distance from stroke. CONCLUSIONS: ED treat-and-release visits for a fall are associated with significantly increased short-term ischemic stroke risk. These visits may be opportunities to improve stroke diagnostic accuracy and treatment in the ED.

Fever of Unknown Origin (FUO) Criteria Influences Diagnostic Outcomes: A Systematic Review and Meta-Analysis.

BACKGROUND: Criteria classifying fever of unknown origin (FUO) patients remains subject to discrepancies. A minimal standardized set of investigative tests serves as the foundation for the qualitative criteria, whereas quantitative incorporates the length of evaluation (7 or 3 days). A systematic review of studies would help physicians anticipate the frequency of illness types that could influence management. METHODS: Prospective studies published in Medline (PubMed), Embase, Scopus, and Web of Science databases from January 1, 1997, to July 31, 2022, were included. A meta-analysis estimated associated pooled proportions between these criteria and diagnostic outcomes adjusted to the International Classification of Diseases, 10th edition (ICD-10) definitions. RESULTS: Five qualitative studies corresponded to an increase of 15.3% (95% CI: 2.3-28.3%, p=0.021) in undiagnosed FUO proportions compared to eleven quantitative studies. Quantitative studies had 19.7% (95% CI: 6.0-33.4%, p=0.005) more in adjusted infectious disease proportions than qualitative studies. No significant differences in proportions between FUO defining criteria were noted for adjusted noninfectious inflammatory disorders (p=0.318), oncology (p=0.901), non-inflammatory miscellaneous disorders (p=0.321), diagnostic evaluation process, gross national income (GNI), or World Health Organization (WHO) geographic region. CONCLUSIONS: Use of either qualitative or quantitative FUO criteria was associated with a statistically significant risk of over- or under-estimating infectious diseases and undiagnosed illnesses when using an ICD-10 adjusted FUO five-category system. Clinicians should anticipate differences depending on which criteria are used. While further research is warranted, qualitative criteria provide the best framework for study comparisons.

Adoption of network and plan-do-check-action in the international classification of disease 10 coding.

BACKGROUND: with the widespread application of computer network systems in the medical field, the plan-do-check-action (PDCA) and the international classification of diseases tenth edition (ICD-10) coding system have also achieved favorable results in clinical medical record management. However, research on their combined application is relatively lacking. Objective: it was to explore the impact of network systems and PDCA management mode on ICD-10 encoding. Material and Method: a retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People's Hospital was conducted. They were divided into a control group (n = 232) and an observation group (n = 536) based on whether the PDCA management mode was implemented. The two sets of coding accuracy, time spent, case completion rate, satisfaction, and other indicators were compared. AIM: To study the adoption of network and PDCA in the ICD-10. METHODS: A retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People's Hospital was conducted. They were divided into a control group (n = 232) and an observation group (n = 536) based on whether the PDCA management mode was implemented. The two sets of coding accuracy, time spent, case completion rate, satisfaction, and other indicators were compared. RESULTS: In the 3, 6, 12, 18, and 24 months of PDCA cycle management mode, the coding accuracy and medical record completion rate were higher, and the coding time was lower in the observation group as against the controls (P < 0.05). The satisfaction of coders (80.22% vs 53.45%) and patients (84.89% vs 51.72%) in the observation group was markedly higher as against the controls (P < 0.05). CONCLUSION: The combination of computer networks and PDCA can improve the accuracy, efficiency, completion rate, and satisfaction of ICD-10 coding.

Linking physiology and demographics, non-ocular pathology and pharmaceutical drug use to standard OCT measures of the inner retina: The PPP project.

PURPOSE: To assess the associations between physiology and demographics, non-ocular pathology and pharmaceutical drug use against peri-papillary retinal nerve fibre layer thickness (pRNFL T) and other optical coherence tomography (OCT) inner retinal measures in normal, healthy eyes. METHODS: A retrospective, cross-sectional study of 705 consecutive participants with bilateral normal, healthy optic nerves and maculae. PRNFL Ts, vertical cup/disc ratio (CDR), cup volume and macular ganglion cell layer-inner plexiform layer (GCL-IPL) Ts were extracted from Cirrus OCT scans, then regressed against predictor variables of participants' physiology and demographics (eye laterality, refraction, intraocular pressure [IOP], age, sex, race/ethnicity, etc.) and non-ocular pathology and pharmaceutical drug use according to the World Health Organisation classifications. Associations were assessed for statistical significance (p < 0.05) and clinical significance (|ß| > 95% limits of agreement for repeated measures). RESULTS: A multitude of non-ocular pathology and pharmaceutical drug use were statistically and clinically significantly associated with deviations in standard OCT inner retinal measures, exceeding the magnitude of other factors such as age, IOP and race/ethnicity. Thinner inner retina and larger optic nerve cup measures were linked to use of systemic corticosteroids, sex hormones/modulators, presence of vasomotor/allergic rhinitis and other diseases and drugs (up to -29.3 [-49.88, -8.72] µm pRNFL T, 0.31 [0.07, 0.54] vertical CDR, 0.29 [0.03, 0.54] mm3 cup volume and -10.18 [-16.62, -3.74] µm macular GCL-IPL T; all p < 0.05). Thicker inner retina and smaller optic nerve cup measures were diffusely associated with use of antineoplastic agents, presence of liver or urinary diseases and other diseases and drugs (up to 67.12 [64.92, 69.31] µm pRNFL T, -0.31 [-0.53, -0.09] vertical CDR, -0.06 [-0.11, 0] mm3 cup volume and 28.84 [14.51, 43.17] µm macular GCL-IPL T; all p < 0.05). CONCLUSION: There are a multitude of systemic diseases and drugs associated with altered OCT inner retinal measures, with magnitudes far exceeding those of other factors such as age, IOP and race/ethnicity. These systemic factors should at least be considered during OCT assessments to ensure precise interpretation of normal versus pathological inner retinal health.

Evaluation of Errors on Death Certificates.

BACKGROUND: Death certificates provide vital data for disease surveillance and health policy. However, errors are common globally, undermining data reliability. This study analyzed prevalence and types of errors in death certificates at a tertiary hospital in Nepal. METHODS: A cross-sectional study reviewed all death certificates issued at Lumbini Medical College, Nepal from April 2020 to April 2022. Certificates were assessed for errors including improper sequencing, absent time intervals, abbreviations, illegible writing, and inaccurate immediate, antecedent, and underlying causes of death as per international guidelines. Errors were classified as major or minor. RESULTS: Of 139 certificates, none were error-free. The most common error was incorrectly or incompletely filling the immediate cause of death (77.7%). Other errors included absent time of death (17.3%), abbreviations (57.6%), illegible writing (22.3%), and omitting the hospital stamp/medical council registration number (8.6%). Based on international criteria, 76.3% had minor errors, 23% had both major and minor errors. CONCLUSIONS: This study found a high rate of errors in death certification at a tertiary hospital in Nepal, undermining data accuracy. Regular training and monitoring with feedback are recommended to improve certification practices. Accurate cause-of-death data is vital for healthcare policy and decision-making in Nepal.

Sensitivity and specificity of International Classification of Diseases algorithms (ICD-9 and ICD-10) used to identify opioid-related overdose cases: A systematic review and an example of estimation using Bayesian latent class models in the absence of gold standards.

OBJECTIVES: This study aimed to summarize validity estimates of International Classification of Diseases (ICD) codes in identifying opioid overdose (OOD) among patient data from emergency rooms, emergency medical services, inpatient, outpatient, administrative, medical claims, and mortality, and estimate the sensitivity and specificity of the algorithms in the absence of a perfect reference standard. METHODS: We systematically reviewed studies published before December 8, 2023, and identified with Medline and Embase. Studies reporting sufficient details to recreate a 2 × 2 table comparing the ICD algorithms to a reference standard in diagnosing OOD-related events were included. We used Bayesian latent class models (BLCM) to estimate the posterior sensitivity and specificity distributions of five ICD-10 algorithms and of the imperfect coroner's report review (CRR) in detecting prescription opioid-related deaths (POD) using one included study. RESULTS: Of a total of 1990 studies reviewed, three were included. The reported sensitivity estimates of ICD algorithms for OOD were low (range from 25.0% to 56.8%) for ICD-9 in diagnosing non-fatal OOD-related events and moderate (72% to 89%) for ICD-10 in diagnosing POD. The last included study used ICD-9 for non-fatal and fatal and ICD-10 for fatal OOD-related events and showed high sensitivity (i.e. above 97%). The specificity estimates of ICD algorithms were good to excellent in the three included studies. The misclassification-adjusted ICD-10 algorithm sensitivity estimates for POD from BLCM were consistently higher than reported sensitivity estimates that assumed CRR was perfect. CONCLUSION: Evidence on the performance of ICD algorithms in detecting OOD events is scarce, and the absence of bias correction for imperfect tests leads to an underestimation of the sensitivity of ICD code estimates.

RéSUMé: OBJECTIFS: Cette étude avait pour objectifs de recenser les estimations de la validité des codes de Classification Internationale des Maladies (CIM) à diagnostiquer les cas de surdose aux opioïdes (SDO) chez des patients en utilisant les données de salles d'urgence, services médicaux d'urgence, hospitalisations, soins ambulatoires, services administratifs, demandes de remboursement de frais médicaux, ainsi que de mortalité, et d'estimer la sensibilité et la spécificité d'algorithmes utilisant la CIM en l'absence d'un test de référence parfait. MéTHODES: Nous avons examiné systématiquement les études publiées avant le 8 décembre 2023, et identifiées dans Medline et Embase. Les études rapportant suffisamment de détails permettant de recréer un tableau 2 × 2 comparant les algorithmes de la CIM à un test de référence pour le diagnostic d'événements liés aux SDO ont été incluses. Les données d'une étude éligible ont été utilisées pour estimer, avec des modèles Bayésiens de classes latentes (MBCL), les distributions a posteriori de la sensibilité et de la spécificité de cinq algorithmes de la CIM-10 et du test imparfait de révision du rapport du coroner (RRC) dans la détection des décès liés aux opioïdes de prescription (DOP). RéSULTATS: Trois parmi les 1 990 études examinées ont été retenues. Les estimations rapportées de la sensibilité des codes CIM étaient faibles (variant de 25,0 % à 56,8 %) pour CIM-9 dans le diagnostic des événements liés aux SDO non-fatales dans une étude, et modérées (72 % à 89 %) pour CIM-10 dans le diagnostic des DOP dans une autre étude. La dernière étude incluse combinait des codes CIM-9 pour les cas non-fatals et fatals et CIM-10 pour les cas fatals et démontrait des estimations de sensibilité élevées (c.à.d. supérieures à 97 %). Les estimations de la spécificité étaient bonnes à excellentes dans les trois études. Les estimations de la sensibilité des algorithmes de la CIM-10 corrigées pour les erreurs de classification pour les décès liés aux opioïdes, obtenues à partir de nos MBCL, étaient systématiquement plus élevées que celles rapportées et qui supposaient que RRC était un test parfait. CONCLUSION: Les évidences sur la performance des algorithmes de la CIM dans la détection des cas de SDO sont rares, et l'absence de correction de biais pour des tests diagnostiques imparfaits conduit à une sous-estimation de la sensibilité des codes de la CIM.

Promoting interoperability between SNOMED CT and ICD-11: lessons learned from the pilot project mapping between SNOMED CT and the ICD-11 Foundation.

OBJECTIVE: To explore the feasibility and challenges of mapping between SNOMED CT and the ICD-11 Foundation in both directions, SNOMED International and the World Health Organization conducted a pilot mapping project between September 2021 and August 2022. MATERIALS AND METHODS: Phase 1 mapped ICD-11 Foundation entities from the endocrine diseases chapter, excluding malignant neoplasms, to SNOMED CT. In phase 2, SNOMED CT concepts equivalent to those covered by the ICD-11 entities in phase 1 were mapped to the ICD-11 Foundation. The goal was to identify equivalence between an ICD-11 Foundation entity and a SNOMED CT concept. Postcoordination was used for mapping to ICD-11. Each map was done twice independently, the results were compared, and discrepancies were reconciled. RESULTS: In phase 1, 59% of 637 ICD-11 Foundation entities had an exact match in SNOMED CT. In phase 2, 32% of 1893 SNOMED CT concepts had an exact match in the ICD-11 Foundation, and postcoordination added 15% of exact match. Challenges encountered included non-synonymous synonyms, mismatch in granularity, composite conditions, and residual categories. CONCLUSION: This pilot project shed light on the tremendous amount of effort required to create a map between the 2 coding systems and uncovered some common challenges. Future collaborative work between SNOMED International and WHO will likely benefit from its findings. It is recommended that the 2 organizations should clarify goals and use cases of mapping, provide adequate resources, set up a road map, and reconsider their original proposal of incorporating SNOMED CT into the ICD-11 Foundation ontology.

Accuracy assessment of patient safety incident (PSI) codes and present-on-admission (POA) indicators: a cross-sectional analysis using the Patient Safety Incidents Inquiry (PSII) in Korea.

BACKGROUND: Among the various methods used, administrative data collected for claims and billing purposes, such as diagnosis codes and present-on-admission (POA) indicators, can easily be employed to assess patient safety status. However, it is crucial that administrative data be accurate to generate valid estimates of adverse event (AE) occurrence. Thus, we aimed to evaluate the accuracy of diagnosis codes and POA indicators in patients with confirmed AEs in the hospital admission setting. METHODS: We analysed the diagnosis codes of 1,032 confirmed AE cases and 6,754 non-AE cases from the 2019 Patient Safety Incidents Inquiry, which was designed as a cross-sectional study, to determine their alignment with the Korean Patient Safety Incidents (PSIs) Code Classification System. The unit of analysis was the individual case rather than the patient, because two or more AEs may occur in one patient. We examined whether the primary and secondary diagnostic codes had PSIs codes matching the AE type and checked each PSI code for whether the POA indicator had an 'N' tag. We reviewed the presence of PSI codes in patients without identified AEs and calculated the correlation between the AE incidence rate and PSI code and POA indicator accuracy across 15 hospitals. RESULTS: Ninety (8.7%) of the AE cases had PSI codes with an 'N' tag on the POA indicator compared to 294 (4.4%) of the non-AE cases. Infection- (20.4%) and surgery/procedure-related AEs (13.6%) had relatively higher instances of correctly tagged PSI codes. We did not identify any PSI codes for diagnosis-related incidents. While we noted significant differences in AE incidence rates, PSI code accuracy, and POA indicator accuracy among the hospitals, the correlations between these variables were not statistically significant. CONCLUSION: Currently, PSI codes and POA indicators in South Korea appear to have low validity. To use administrative data in medical quality improvement activities such as monitoring patient safety levels, improving the accuracy of administrative data should be a priority. Possible strategies include targeted education on PSI codes and POA indicators and introduction of new evaluation indicators regarding the accuracy of administrative data.

Model correction of diagnostic coding-based RSV incidence for children 0-4 years in the US.

BACKGROUND: Although administrative claims data have a high degree of completeness, not all medically attended Respiratory Syncytial Virus-associated lower respiratory tract infections (RSV-LRTIs) are tested or coded for their causative agent. We sought to determine the attribution of RSV to LRTI in claims data via modeling of temporal changes in LRTI rates against surveillance data. METHODS: We estimated the weekly incidence of LRTI (inpatient, outpatient, and total) for children 0-4 years using 2011-2019 commercial insurance claims, stratified by HHS region, matched to the corresponding weekly NREVSS RSV and influenza positivity data for each region, and modelled against RSV, influenza positivity rates, and harmonic functions of time assuming negative binomial distribution. LRTI events attributable to RSV were estimated as predicted events from the full model minus predicted events with RSV positivity rate set to 0. RESULTS: Approximately 42% of predicted RSV cases were coded in claims data. Across all regions, the percentage of LRTI attributable to RSV were 15-43%, 10-31%, and 10-31% of inpatient, outpatient, and combined settings, respectively. However, when compared to coded inpatient RSV-LRTI, 9 of 10 regions had improbable corrected inpatient LRTI estimates (predicted RSV/coded RSV ratio < 1). Sensitivity analysis based on separate models for PCR and antigen-based positivity showed similar results. CONCLUSIONS: Underestimation based on coding in claims data may be addressed by NREVSS-based adjustment of claims-based RSV incidence. However, where setting-specific positivity rates is unavailable, we recommend modeling across settings to mirror NREVSS's positivity rates which are similarly aggregated, to avoid inaccurate adjustments.

Evaluation of SNOMED CT Grouper Accuracy and Coverage in Organizing the Electronic Health Record Problem List by Clinical System: Observational Study.

Background: The problem list (PL) is a repository of diagnoses for patients' medical conditions and health-related issues. Unfortunately, over time, our PLs have become overloaded with duplications, conflicting entries, and no-longer-valid diagnoses. The lack of a standardized structure for review adds to the challenges of clinical use. Previously, our default electronic health record (EHR) organized the PL primarily via alphabetization, with other options available, for example, organization by clinical systems or priority settings. The system's PL was built with limited groupers, resulting in many diagnoses that were inconsistent with the expected clinical systems or not associated with any clinical systems at all. As a consequence of these limited EHR configuration options, our PL organization has poorly supported clinical use over time, particularly as the number of diagnoses on the PL has increased. Objective: We aimed to measure the accuracy of sorting PL diagnoses into PL system groupers based on Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concept groupers implemented in our EHR. Methods: We transformed and developed 21 system- or condition-based groupers, using 1211 SNOMED CT hierarchal concepts refined with Boolean logic, to reorganize the PL in our EHR. To evaluate the clinical utility of our new groupers, we extracted all diagnoses on the PLs from a convenience sample of 50 patients with 3 or more encounters in the previous year. To provide a spectrum of clinical diagnoses, we included patients from all ages and divided them by sex in a deidentified format. Two physicians independently determined whether each diagnosis was correctly attributed to the expected clinical system grouper. Discrepancies were discussed, and if no consensus was reached, they were adjudicated by a third physician. Descriptive statistics and Cohen κ statistics for interrater reliability were calculated. Results: Our 50-patient sample had a total of 869 diagnoses (range 4-59; median 12, IQR 9-24). The reviewers initially agreed on 821 system attributions. Of the remaining 48 items, 16 required adjudication with the tie-breaking third physician. The calculated κ statistic was 0.7. The PL groupers appropriately associated diagnoses to the expected clinical system with a sensitivity of 97.6%, a specificity of 58.7%, a positive predictive value of 96.8%, and an F1-score of 0.972. Conclusions: We found that PL organization by clinical specialty or condition using SNOMED CT concept groupers accurately reflects clinical systems. Our system groupers were subsequently adopted by our vendor EHR in their foundation system for PL organization.

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.

BACKGROUND: Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear. OBJECTIVE: This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions. METHODS: Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic. RESULTS: Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems. CONCLUSIONS: This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association.

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

Physical multimorbidity in psychiatric patients with personality disorders: Insights within the ICD-11 framework.

OBJECTIVE: Recent research has revealed poor physical health among individuals with personality disorders (PDs). We aimed to compare chronic physical illnesses (CPI) and chronic physical multimorbidity (CPM) prevalence between the general population (GEP) and PD patients, and to explore the relationship between CPM and various aspects of PD, predominantly within the ICD-11 framework. METHODS: This cross-sectional study included 126 PD patients and 126 matched controls from the GEP. Patients were evaluated for the ICD-11 PD severity and maladaptive personality domains, subjective emptiness, and reflective functioning. CPI was assessed using a standardized self-report questionnaire. RESULTS: PD patients had a higher mean number of CPIs (2.05 vs. 1.02) and a more frequent CPM occurrence (49.2% vs. 26.2%) compared to the matched controls (p < .001). The ICD-11 PD severity (OR = 1.143, p = .007) and maladaptive domain Negative affectivity (OR = 4.845, p = .002), and poor reflective functioning (OR = 1.694, p = .007) were significant predictors of CPM, independent of sociodemographic, clinical and lifestyle factors. Negative affectivity showed the most robust effect on CPM, while smoking did not significantly mediate these relationships. CONCLUSION: Our study found increased CPM burden in PD patients and a link between CPM and various PD aspects under the ICD-11 framework, highlighting the need for more integrated healthcare.

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.

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.

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.