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The International Classification of Diseases, 11th Revision (ICD-11) has significantly improved the ability to navigate coding challenges beyond prior iterations of the ICD. Commonly encountered sources of complexity in clinical documentation include coding of uncertain and "ruled out" diagnoses. Assessing official international guidelines and rules, this paper documents extensive variation across countries in existing practices for coding and reporting unconfirmed and "ruled out" clinical concepts in ICD-10 (and modifications thereof). The design of ICD-11 is intended to mitigate these coding challenges by introducing postcoordination, expanding the range of codable clinical concepts, and offering clearer guidance in the ICD-11 Reference Guide. ICD-11 offers substantial progress towards more precise capture of uncertain and "ruled out" diagnoses, including international consensus on coding rules for these historically challenging clinical concepts. However, we identify the need for further clarification of the concepts of "provisional diagnosis" and "differential diagnosis."

ObjectiveAccurate identification of lupus nephritis (LN) cases is essential for patient management, research and public health initiatives. However, LN diagnosis codes in electronic health records (EHRs) are underused, hindering efficient identification. We investigated the current performance of International Classification of Diseases (ICD) codes, 9th and 10th editions (ICD9/10), for identifying prevalent LN, and developed scoring systems to increase identification of LN that are adaptable to settings with and without LN ICD codes.MethodsTraining and test sets derived from EHR data from a large health system. An external set comprised data from the EHR of a second large health system. Adults with ICD9/10 codes for SLE were included. LN cases were ascertained through manual chart reviews conducted by rheumatologists. Two definitions of LN were used: strict (definite LN) and inclusive (definite, potential or diagnostic uncertainty). Gradient boosting models including structured EHR fields were used for predictor selection. Two logistic regression-based scoring systems were developed ('LN-Code' included LN ICD codes and 'LN-No Code' did not), calibrated and validated using standard performance metrics.ResultsA total of 4152 patients from University of California San Francisco Medical Center and 370 patients from Zuckerberg San Francisco General Hospital and Trauma Center met the eligibility criteria. Mean age was 50 years, 87% were female. LN diagnosis codes demonstrated low sensitivity (43-73%) but high specificity (92-97%). LN-Code achieved an area under the curve (AUC) of 0.93 and a sensitivity of 0.88 for identifying LN using the inclusive definition. LN-No Code reached an AUC of 0.91 and a sensitivity of 0.95 (0.97 for the strict definition). Both scoring systems had good external validity, calibration and performance across racial and ethnic groups.ConclusionsThis study quantified the underutilisation of LN diagnosis codes in EHRs and introduced two adaptable scoring systems to enhance LN identification. Further validation in diverse healthcare settings is essential to ensure their broader applicability.

OBJECTIVE: Map multimorbidity-weighted index (MWI) conditions to International Classification of Diseases, 10th Revision (ICD-10), expand the conditions and codes to develop a new ICD-10-coded MWI (MWI-ICD10) and updated MWI-ICD9, and assess their consistency. DESIGN: Population-based retrospective cohort. SETTING: Large medical centre between 2013 and 2017. PARTICIPANTS: Adults ≥18 years old with encounters in each of 4 years (2013, 2014, 2016, 2017). MAIN OUTCOME MEASURES: MWI conditions mapped to ICD-10 codes, and additional conditions and codes added to produce a new MWI-ICD10 and updated MWI-ICD9. We compared the prevalence of ICD-coded MWI conditions within the ICD-9 era (2013-2014), within the ICD-10 era (2016-2017) and across the ICD-9-ICD-10 transition in 2015 (washout period) among adults present in both sets of comparison years. We computed the prevalence and change in prevalence of conditions when using MWI-ICD10 versus MWI-ICD9. RESULTS: 88 175 adults met inclusion criteria. Participants were 60.8% female, 50.5% white, with mean age 54.7±17.3 years and baseline MWI-ICD9 4.47±6.02 (range 0-64.33). Of 94 conditions, 65 had

Background: The mechanism for recording International Classification of Diseases (ICD) and diagnosis related groups (DRG) codes in a patient's chart is through a certified medical coder who manually reviews the medical record at the completion of an admission. High-acuity ICD codes justify DRG modifiers, indicating the need for escalated hospital resources. In this manuscript, we demonstrate that value of rules-based computer algorithms that audit for omission of administrative codes and quantifying the downstream effects with regard to financial impacts and demographic findings did not indicate significant disparities. Methods: All study data were acquired via the UCLA Department of Anesthesiology and Perioperative Medicine's Perioperative Data Warehouse. The DataMart is a structured reporting schema that contains all the relevant clinical data entered into the EPIC (EPIC Systems, Verona, WI) electronic health record. Computer algorithms were created for eighteen disease states that met criteria for DRG modifiers. Each algorithm was run against all hospital admissions with completed billing from 2019. The algorithms scanned for the existence of disease, appropriate ICD coding, and DRG modifier appropriateness. Secondarily, the potential financial impact of ICD omissions was estimated by payor class and an analysis of ICD miscoding was done by ethnicity, sex, age, and financial class. Results: Data from 34,104 hospital admissions were analyzed from January 1, 2019, to December 31, 2019. 11,520 (32.9%) hospital admissions were algorithm positive for a disease state with no corresponding ICD code. 1,990 (5.8%) admissions were potentially eligible for DRG modification/upgrade with an estimated lost revenue of $22,680,584.50. ICD code omission rates compared against reference groups (private payors, Caucasians, middle-aged patients) demonstrated significant p-values < 0.05; similarly significant p-value where demonstrated when comparing patients of opposite sexes. Conclusions: We successfully used rules-based algorithms and raw structured EHR data to identify omitted ICD codes from inpatient medical record claims. These missing ICD codes often had downstream effects such as inaccurate DRG modifiers and missed reimbursement. Embedding augmented intelligence into this problematic workflow has the potential for improvements in administrative data, but more importantly, improvements in administrative data accuracy and financial outcomes. [ABSTRACT FROM AUTHOR]

Objective: We aimed to develop an algorithm that accurately identifies children with Down syndrome (DS) using administrative data. Methods: We identified a cohort of children born between 2000 and 2017, enrolled in the Tennessee Medicaid Program (TennCare), who either had DS coded on their birth certificate or had a diagnosis listed using an International Classification of Diseases (ICD) code (suspected DS), and who received care at Vanderbilt University Medical Center, a comprehensive academic medical center, in the United States. Children with suspected DS were defined as having DS if they had (a) karyotype-confirmed DS indicated on their birth certificate; (b) karyotype-pending DS indicated on their birth certificate (or just DS if test type was not specified) and at least two healthcare encounters for DS during the first 6 years of life; or (c) at least three healthcare encounters for DS, with the first and last encounter separated by at least 30 days, during the first six years of life. The positive predictive value (PPV) of the algorithm and 95% confidence interval (CI) were reported. Results: Of the 411 children with suspected DS, 354 (86.1%) were defined as having DS by the algorithm. According to medical chart review, the algorithm correctly identified 347 children with DS (PPV = 98%, 95%CI: 96.0–99.0%). Of the 57 children the algorithm defined as not having DS, 50 (97.7%, 95%CI: 76.8–93.9%) were confirmed as not having DS by medical chart review. Conclusions: An algorithm that accurately identifies individuals with DS using birth certificate data and/or ICD codes provides a valuable tool to study DS using administrative data. [ABSTRACT FROM AUTHOR]

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Introduction Deployment-limiting medical conditions (DLMCs) such as debilitating injuries and conditions may interfere with the ability of military service members (SMs) to deploy. SMs in the United States (U.S.) Department of the Navy (DoN) with DLMCs who are not deployable should be placed in the medically restricted status of limited duty (LIMDU) or referred to the Physical Evaluation Board (PEB) for Service retention determination. It is critical to identify SMs correctly and promptly with DLMCs and predict their return-to-duty (RTD) to ensure the combat readiness of the U.S. Military. In this study, an algorithmic approach was developed to identify DoN SMs with previously unidentified DLMCs and predict whether SMs on LIMDU will be able to RTD. Materials and Methods Five years of historical data (2016–2022) were obtained from inpatient and outpatient datasets across direct and purchased care from the Military Health System (MHS) Data Repository (MDR). Key fields included International Classification of Diseases diagnosis and procedure codes, Current Procedure Terminology codes, prescription medications, and demographics information such as age, rank, gender, and service. The data consisted of 44,580,668 medical encounters across 1,065,224 SMs. To identify SMs with unidentified DLMCs, we developed an ensemble model combining outputs from multiple machine learning (ML) algorithms. When the ML ensemble model predicted a SM to have high risk scores, despite appearing healthy on administrative reports, their case was reviewed by expert clinicians to investigate for previously unidentified DLMCs; and such feedback served to validate the developed algorithms. In addition, leveraging 1,735,422 encounters (60,433 SMs) from LIMDU periods, we developed four separate ML models to estimate RTD probabilities for SMs after each medical encounter and predict the final LIMDU outcome. Results The ensemble model had 0.91 area under the receiver operating characteristic curve (AUROC). Out of 236 (round one) and 314 (round two) SMs reviewed by clinicians, 127 (54%) and 208 (66%) SMs were identified with a previously unidentified or undocumented DLMC, respectively. Regarding predicting RTD for SMs placed on LIMDU, the best performing ML model achieved 0.76 AUROC, 68% sensitivity, and 71% specificity. Conclusion Our research highlighted potential benefits of using predictive analytics in a medical assessment to identify SMs with DLMCs and to predict RTD outcomes once placed on LIMDU. This capability is being deployed for real-time clinical decision support to enhance health care provider's deployability assessment capability, improve accuracy of the DLMC population, and enhance combat readiness of the U.S Military. [ABSTRACT FROM AUTHOR]

Background: In Portugal, trained physicians undertake the clinical coding process, which serves as the basis for hospital reimbursement systems. In 2017, the classification version used for coding of diagnoses and procedures for hospital morbidity changed from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS). Objective: To assess the perceptions of medical coders on the transition of the clinical coding process from ICD-9-CM to ICD-10-CM/PCS in terms of its impact on data quality, as well as the major differences, advantages, and problems they faced. Method: We conducted an observational study using a web-based survey submitted to medical coders in Portugal. Survey questions were based on a literature review and from previous focus group studies. Results: A total of 103 responses were obtained from medical coders with experience in the two versions of the classification system (i.e. ICD-9-CM and ICD-10-CM/PCS). Of these, 82 (79.6%) medical coders preferred the latest version and 76 (73.8%) considered that ICD-10-CM/PCS guaranteed higher quality of the coded data. However, more than half of the respondents (N = 61; 59.2%) believed that more time for the coding process for each episode was needed. Conclusion: Quality of clinical coded data is one of the major priorities that must be ensured. According to the medical coders, the use of ICD-10-CM/PCS appeared to achieve higher quality coded data, but also increased the effort. Implications: According to medical coders, the change off classification systems should improve the quality of coded data. Nevertheless, the extra time invested in this process might also pose a problem in the future. [ABSTRACT FROM AUTHOR]

Introduction There is a longstanding debate about whether health care is more efficiently provided by the public or private sector. The debate is particularly relevant to the Military Health System (MHS), which delivers care through a combination of publicly funded federal facilities and privately contracted providers. This study compares outcomes, treatments, and costs for MHS patients obtaining care for low back pain (LBP) from public versus private providers. Materials and Methods A retrospective cohort study was completed using TRICARE Prime claims data from April 2015 to December 2018. The cohort was identified using International Classification of Diseases Ninth Revision and Tenth Revision diagnostic codes and then followed for 12 months after the index diagnosis to assess treatments, outcomes, and costs. Claims were classified as originating from either public or private providers. The primary outcome measure was resolution of LBP, defined as an absence of LBP diagnoses during the 6-to-12-month window following the index event. Instrumental variable models were used to assess the impact of care setting (i.e. private versus public), conditioning on the covariates. A regional measure of the fraction of private care was used as an instrument. Results Resolution of LBP was achieved for 79.7% of 144,866 patients in the cohort. No significant association was found between resolution of LBP and fraction of privately provided care. Higher fraction of private care was associated with a greater likelihood of opioid treatments (odds ratio, 1.22; 95% CI, 1.02-1.46) and a lower likelihood of benzodiazepine (odds ratio, 0.56; 95% CI, 0.45-0.70) and physical therapy (odds ratio 0.55; 95% CI, 0.42-0.74) treatments; manual therapy was not significantly associated with the fraction of private care. There was a significant negative association between the fraction of private care and cost (coefficient −0.27; 95% CI, −0.44, −0.10). Conclusion This study found that privately provided care was associated with significantly higher opioid prescribing, less use of benzodiazepines and physical therapy, and lower costs. No systematic differences in outcomes (as measured by resolved cases) were identified. The findings suggest that publicly funded health care within the MHS context can attain quality comparable to privately provided care, although differences in treatment choices and costs point to possibilities for improved care within both systems. [ABSTRACT FROM AUTHOR]

Introduction Congenital heart disease (CHD) has an incidence of 0.8% to 1.2% worldwide, making it the most common birth defect. Researchers have compared high-volume to low-volume hospitals and found significant hospital-level variation in major complications, health resource utilization, and mortality after CHD surgery. In addition, researchers found critical CHD patients at low-volume/non-teaching facilities to be associated with higher odds of inpatient mortality when compared to CHD patients at high-volume/teaching hospitals (odds ratio 1.76). We examined the effects of high-quality hospital (HQH) use on health care outcomes and health care costs in pediatric CHD care using an instrumental variable (IV) approach. Materials and Methods Using nationwide representative claim data from the United States Military Health System from 2016 to 2020, TRICARE beneficiaries with a diagnosis of CHD were tabulated based on relevant ICD-10 (International Classification of Diseases, 10th revision) codes. We examined the relationships between annual readmissions, annual emergency room (ER) use, and mortality and HQH use. We applied both the naive linear probability model (LPM), controlling for the observed patient and hospital characteristics, and the two-stage least squares (2SLS) model, accounting for the unobserved confounding factors. The differential distance between the patient and the closest HQH at the index date and the patient and nearest non-HQH was used as the IV. This protocol was approved by the Institutional Review Board at the University of Maryland, College Park (Approval Number: 1576246-2). Results The naive LPM indicated that HQH use was associated with a higher probability of annual readmissions (marginal effect, 18%; 95% CI, 0.12 to 0.23). The naive LPM indicated that HQH use was associated with a higher probability of mortality (marginal effect, 2.2%; 95% CI, 0.01 to 0.03). Using the differential distance of closest HQH and non-HQH, we identified a significant association between HQH use and annual ER use (marginal effect, −14%; 95% CI, −0.24 to −0.03). Conclusions After controlling for patient-level and facility-level covariates and adjusting for endogeneity, (1) HQH use did not increase the probability of more than one admission post 1-year CHD diagnosis, (2) HQH use lowered the probability of annual ER use post 1-year CHD diagnosis, and (3) HQH use did not increase the probability of mortality post 1-year CHD diagnosis. Patients who may have benefited from utilizing HQH for CHD care did not, alluding to potential barriers to access, such as health insurance restrictions or lack of patient awareness. Although we used hospital quality rating for congenital cardiac surgery as reported by the Society of Thoracic Surgeons, the contributing data span a 4-year period and may not reflect real-time changes in center performance. Since this study focused on inpatient care within the first-year post-initial CHD diagnosis, it may not reflect the full range of health system utilization. It is necessary for clinicians and patient advocacy groups to collaborate with policymakers to promote the development of an overarching HQH designation authority for CHD care. Such establishment will facilitate access to HQH for military beneficiary populations suffering from CHD. [ABSTRACT FROM AUTHOR]

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 (|β| > 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. [ABSTRACT FROM AUTHOR]

Abstract Background The mechanism for recording International Classification of Diseases (ICD) and diagnosis related groups (DRG) codes in a patient’s chart is through a certified medical coder who manually reviews the medical record at the completion of an admission. High-acuity ICD codes justify DRG modifiers, indicating the need for escalated hospital resources. In this manuscript, we demonstrate that value of rules-based computer algorithms that audit for omission of administrative codes and quantifying the downstream effects with regard to financial impacts and demographic findings did not indicate significant disparities. Methods All study data were acquired via the UCLA Department of Anesthesiology and Perioperative Medicine’s Perioperative Data Warehouse. The DataMart is a structured reporting schema that contains all the relevant clinical data entered into the EPIC (EPIC Systems, Verona, WI) electronic health record. Computer algorithms were created for eighteen disease states that met criteria for DRG modifiers. Each algorithm was run against all hospital admissions with completed billing from 2019. The algorithms scanned for the existence of disease, appropriate ICD coding, and DRG modifier appropriateness. Secondarily, the potential financial impact of ICD omissions was estimated by payor class and an analysis of ICD miscoding was done by ethnicity, sex, age, and financial class. Results Data from 34,104 hospital admissions were analyzed from January 1, 2019, to December 31, 2019. 11,520 (32.9%) hospital admissions were algorithm positive for a disease state with no corresponding ICD code. 1,990 (5.8%) admissions were potentially eligible for DRG modification/upgrade with an estimated lost revenue of $22,680,584.50. ICD code omission rates compared against reference groups (private payors, Caucasians, middle-aged patients) demonstrated significant p-values

With the widespread adoption of the International Classification of Diseases (ICD) in China's medical evaluation and healthcare payment systems, the importance of disease classification coding has become increasingly prominent, drawing considerable attention from the industry. As ICD is rapidly promoted and applied, various work methods have been developed, with it disagreements in understanding have emerged. Some medical institutions strictly require physicians to select clinical diagnoses from the ICD list of disease and surgical names. This practice undermines and even replaces the coding process conducted by the professional coders, leading to practical issues and affecting the data quality. This article analyzes these issues and proposes that the diagnoses entered by physicians on the first page of medical records are essentially disease nomenclature, while ICD codes represent classified diagnoses. These two elements have fundamentally different purposes andfunctions. It is recommended that physicians understand the requirements of ICD coding for clinical diagnosis documentation, revert to a medical-centric mindset, and accurately record disease diagnoses by using disease nomenclature. For the ICD-coded data, the key to high quality lies in strictly adhering to ICD classification rules and aligning with international standards. Professional medical record coders should be trained in health informatics, master international classification rules to fulfill their responsibilities, and ensure the accurate transformation of clinical diagnoses into ICD codes.

Abstract The International Classification of Diseases, 11th Revision (ICD-11) has significantly improved the ability to navigate coding challenges beyond prior iterations of the ICD. Commonly encountered sources of complexity in clinical documentation include coding of uncertain and “ruled out” diagnoses. Assessing official international guidelines and rules, this paper documents extensive variation across countries in existing practices for coding and reporting unconfirmed and “ruled out” clinical concepts in ICD-10 (and modifications thereof). The design of ICD-11 is intended to mitigate these coding challenges by introducing postcoordination, expanding the range of codable clinical concepts, and offering clearer guidance in the ICD-11 Reference Guide. ICD-11 offers substantial progress towards more precise capture of uncertain and “ruled out” diagnoses, including international consensus on coding rules for these historically challenging clinical concepts. However, we identify the need for further clarification of the concepts of “provisional diagnosis” and “differential diagnosis.”

Background: Mortality statistics serve as a crucial component in community health planning systems. The causes of death can vary across different geographical areas due to factors such as indigenous culture and lifestyle. This study aimed to investigate the mortality trends and their causes from 2015 to 2019 in Hamedan province, located in the west of Iran.Methods: This cross-sectional study examined all deaths (43,515 individuals) in Hamedan province from 2015 to 2019. Death data, including demographics and the underlying cause of death, were extracted from the mortality system of the Hamedan Health Center. Data collection utilized a checklist corresponding to the International Death Certificate issued by the WHO. Data analysis was performed using SPSS ver. 26.Results: In 2019, the average monthly death rate was the highest compared to previous years (766.3±39.1), while this number for 2015 was 682.2±37.7. In 2019, U codes (for Emerging diseases) were used for Covid-19. The mean age of deceased females (73.3±18.2) was higher than that of deceased males (67.0±20.6), showing a significant difference (P

Abstract 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

With the continuous deepening of the reform of the primary care and health system, the standardization of primary health information has received more and more attention. As an important part of the construction of primary health information, primary care diagnostic coding plays an important role in improving the efficiency of primary care management, promoting the quality control of primary care, and effectively collecting primary care data. However, China has used the disease classification of general hospitals as the primary care diagnostic code for a long time, which not only leads to the chaotic state of non-standard and non-standardized primary care diagnosis, but also causes loss and error in the data collection, record and analysis in primary care. By summarizing the historical development of primary care diagnostic coding at home and abroad, this paper points out five main problems faced by China's primary care diagnostic coding: the lack of attention to primary care diagnostic coding, the lack of unified primary care diagnostic coding leads to the limited development of primary care function and quality in China, the disagreements about which international primary care classifications match the primary care diagnostic codes in China, the various challenges faced by the localization of primary care diagnostic coding, and the lack of talents and tools to evaluate and optimize primary care diagnostic coding in China. On this basis, combined with the development process of foreign primary care diagnostic coding, it is proposed that all parties in China should pay attention to the development of primary care diagnostic coding. While further promoting the construction of primary care information in line with international standards, the national unified primary care diagnostic coding standard should be formulated based on the current trial ICD-11 and ICPC-3 classification as soon as possible, and the sound training system for primary care diagnostic coding talents should be established to further implement and optimize primary care diagnostic coding in China.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background Smoking tobacco implies significant health hazards. Digital cessation support can get more smokers in contact with guideline-based cessation. The objective was to test the efficacy of a guideline-based smoking cessation app (NichtraucherHelden®). The hypothesis was a significantly higher cessation rate in the intervention group. Methods The study was a nationwide, multicentric, prospective, parallel, randomized controlled trial in Germany from November 2021 to March 2023. Recruitment took place in medical practices and by telephone via study centers. Eligible participants were adult tobacco-dependent smokers according to ICD-10 (F17.2). Randomization (1:1) was operated by a computer-generated stratified 1:1 block procedure. Intervention (IG; n  = 336) and control group (CG; n  = 325) were briefly advised with regard to stop smoking, IG was additionally treated with the cessation app. The primary endpoint was the self-reported 7-day-point abstinence after 6 months with an intention to treat analysis. Secondary endpoints comprised prolonged abstinence and biochemically verified abstinence. The study was registered at the German Registry of Clinical Trials (DRKS00025933, UTN U1111-1268-2181) and was approved by the competent ethics committees (leading ethic committee Berlin #Eth-52/20). Results Three hundred thirty six participants (IG) and 325 (CG) were analyzed. Seven-day point prevalence was significantly higher in the app group (IG) (20% vs. 10%, OR 2.2 (1.4–3.4)). Additionally, the prolonged abstinence and the objective abstinence rates were significantly higher in the app group. Conclusions The NichtraucherHelden app doubles the abstinence rate. Apps can bridge the gap between the small number of therapeutic offers and the need for modern evidence-based cessation support. Implications The study is the first to provide evidence for the feasibility and efficacy of guideline-based digital smoking cessation provided by a smartphone app for the German statutory health insurance (SHI) system. Smoking cessation support by smartphone apps could be broadly distributed and thus bring more smokers in contact with guideline-based cessation support than to date and increase the number of successful quitters substantially. [ABSTRACT FROM AUTHOR]

Background: Prenatal sonograms frequently show congenital anomalies in fetuses. As expected, families receiving information about severe or multi-system anomalies experience ceaseless distress and may request for termination of the pregnancy. This study was designed to identify the most frequent indications for termination of pregnancy for fetal anomalies (TOPFAs). These data can help in early detection, which can then facilitate informed decisions either for safe terminations or for well-timed fetal procedures for rehabilitation. This information is also important for appropriate genetic testing and assessment of the risk of recurrence in later pregnancies. Objective: To investigate the frequency and epidemiological profile of various fetal abnormalities that have evoked requests for late termination of pregnancy in Mongolia. Materials and methods: This cross-sectional observational study was conducted in a cohort of 45,095 pregnancies. Of these, 156 were terminated because of fetal anomalies. Data pertaining to fetal/congenital anomalies were collected after informed consent from families and compared with 312 healthy controls to evaluate associated maternal risk factors. Results: In this cohort, 34.5 in 10,000 pregnancies were terminated because of fetal anomalies during the study period 2017-2019. A total of 156 terminations were related to congenital anomalies. Down syndrome (DS) was the most frequent reason (25%). The other leading reasons were multiple congenital anomalies (16%), cleft lip/palate (10.9%), and anomalies of the central nervous system (9.6%) or the musculoskeletal system (9.6%). Maternal age >35 years, higher education, less spacing between successive pregnancies, and previous history of abortion(s) were associated with a higher likelihood of birth defects. Conclusion: Down syndrome is the leading indication for late-stage TOPFAs in Mongolia. Multi-system congenital anomalies, clefts, and anomalies of the central nervous system and musculoskeletal system were other reasons that led to requests for termination of pregnancy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Data harmonization is vital for secondary electronic health record data analysis, especially when combining data from multiple sources. Currently, there is a gap in knowledge as to how studies identify cohorts of patients with age-related macular degeneration (AMD), a leading cause of blindness. We hypothesize that there is variation in using medical condition codes to define cohorts of AMD patients that can lead to either the under- or overrepresentation of such cohorts. This study identified articles studying AMD using the International Classification of Diseases (ICD-9, ICD-9-CM, ICD-10, and ICD-10-CM). The data elements reviewed included the year of publication; dataset origin (Veterans Affairs, registry, national or commercial claims database, and institutional EHR); total number of subjects; and ICD codes used. A total of thirty-seven articles were reviewed. Six (16%) articles used cohort definitions from two ICD terminologies. The Medicare database was the most used dataset (14, 38%), and there was a noted increase in the use of other datasets in the last few years. We identified substantial variation in the use of ICD codes for AMD. For the studies that used ICD-10 terminologies, 7 (out of 9, 78%) defined the AMD codes correctly, whereas, for the studies that used ICD-9 and 9-CM terminologies, only 2 (out of 30, 7%) defined and utilized the appropriate AMD codes (p = 0.0001). Of the 43 cohort definitions used from 37 articles, 31 (72%) had missing or incomplete AMD codes used, and only 9 (21%) used the exact codes. Additionally, 13 articles (35%) captured ICD codes that were not within the scope of AMD diagnosis. Efforts to standardize data are needed to provide a reproducible research output. [ABSTRACT FROM AUTHOR]

BACKGROUND: Low-value use of screening colonoscopy is wasteful and potentially harmful to patients. Decreasing low-value colonoscopy prevents procedural complications, saves patient time and reduces patient discomfort, and can improve access by reducing procedural demand. The objective of this study was to develop and validate an electronic measure of screening colonoscopy overuse using International Classification of Diseases, Tenth Edition codes and then apply this measure to estimate facility-level overuse to target quality improvement initiatives to reduce overuse in a large integrated healthcare system. METHODS: Retrospective national observational study of US Veterans undergoing screening colonoscopy at 119 Veterans Health Administration (VHA) endoscopy facilities in 2017. A measure of screening colonoscopy overuse was specified by an expert workgroup, and electronic approximation of the measure numerator and denominator was performed (electronic measure). The electronic measure was then validated via manual record review (n=511). Reliability statistics (n=100) were calculated along with diagnostic test characteristics of the electronic measure. The measure was then applied to estimate overall rates of overuse and facility-level variation in overuse among all eligible patients. RESULTS: The electronic measure had high specificity (99%) and moderate sensitivity (46%). Adjusted positive predictive value and negative predictive value were 33% and 95%, respectively. Inter-rater reliability testing revealed near perfect agreement between raters (k=0.81). 269 572 colonoscopies were performed in VHA in 2017 (88 143 classified as screening procedures). Applying the measure to these 88 143 screening colonoscopies, 24.5% were identified as potential overuse. Median facility-level overuse was 22.5%, with substantial variability across facilities (IQR 19.1%-27.0%). CONCLUSIONS: An International Classification of Diseases, Tenth Edition based electronic measure of screening colonoscopy overuse has high specificity and improved sensitivity compared with a previous International Classification of Diseases, Ninth Edition based measure. Despite increased focus on reducing low-value care and improving access, a quarter of VHA screening colonoscopies in 2017 were identified as potential low-value procedures, with substantial facility-level variability.

PurposeTo develop models for progression of nonproliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR) and determine if incorporating updated information improves model performance.DesignRetrospective cohort study.ParticipantsElectronic health record (EHR) data from a tertiary academic center, University of California San Francisco (UCSF), and a safety-net hospital, Zuckerberg San Francisco General (ZSFG) Hospital were used to identify patients with a diagnosis of NPDR, age ≥ 18 years, a diagnosis of type 1 or 2 diabetes mellitus, ≥ 6 months of ophthalmology follow-up, and no prior diagnosis of PDR before the index date (date of first NPDR diagnosis in the EHR).MethodsFour survival models were developed: Cox proportional hazards, Cox with backward selection, Cox with LASSO regression and Random Survival Forest. For each model, three variable sets were compared to determine the impact of including updated clinical information: Static0 (data up to the index date), Static6m (data updated 6 months after the index date), and Dynamic (data in Static0 plus data change during the 6-month period). The UCSF data were split into 80% training and 20% testing (internal validation). The ZSFG data were used for external validation. Model performance was evaluated by the Harrell's concordance index (C-Index).Main outcome measuresTime to PDR.ResultsThe UCSF cohort included 1130 patients and 92 (8.1%) patients progressed to PDR. The ZSFG cohort included 687 patients and 30 (4.4%) patients progressed to PDR. All models performed similarly (C-indices ∼ 0.70) in internal validation. The random survival forest with Static6m set performed best in external validation (C-index 0.76). Insurance and age were selected or ranked as highly important by all models. Other key predictors were NPDR severity, diabetic neuropathy, number of strokes, mean Hemoglobin A1c, and number of hospital admissions.ConclusionsOur models for progression of NPDR to PDR achieved acceptable predictive performance and validated well in an external setting. Updating the baseline variables with new clinical information did not consistently improve the predictive performance.Financial disclosuresProprietary or commercial disclosure may be found after the references.

The article presents a study which explored the extent to which anaphylaxis events are missed by algorithms that require the presence of an anaphylaxis diagnosis code. Topics include indicators based on procedure codes and concepts extracted from unstructured clinical notes using natural language processing, findings on uncoded anaphylaxis events, and characteristics of emergency department encounters in the electronic health records of a midsized healthcare system in the Pacific Northwest.

A significant proportion of perinatal deaths are preventable. The most critical factor contributing to the failure of international programs aimed at reducing preventable perinatal deaths is the lack of sufficient and accurate information about the causes of these deaths. This deficiency hinders countries' ability to develop effective interventions to reduce perinatal mortality rates. Various classification systems have been developed to categorize the causes of perinatal deaths, aiming to enhance understanding and prevention efforts. However, these systems face challenges, including assigning a considerable portion of perinatal deaths to unknown causes and limitations in usability in developing countries with high perinatal mortality rates. The need for a comprehensive and unified international system for classifying the causes of perinatal mortality is crucial due to the diverse classification systems currently in use, which hinder data comparison at national and international levels. The purpose of this editorial study is to introduce the first classification system of causes of perinatal mortality with international applicability developed by the World Health Organization.

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. 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. [ABSTRACT FROM AUTHOR]

The International Classification of Diseases, 11th Edition (ICD-11) includes a new personality disorder (PD) severity diagnosis that may be further characterized using up to five trait domain specifiers. Most of the previous studies have investigated the ICD-11 trait domains using self-report measures. The present study aimed to validate ICD-11 PD trait domains using a multimethod design in a community mental health sample (n = 336). We conducted two confirmatory factor analyses to examine the factor structure of the ICD-11 PD trait model, utilizing clinician-rating, self-report, and informant-report measures. Finally, we examined associations between clinician-rated, self-reported, and informant-reported ICD-11 trait domains with external criteria, specifically traditional PD symptoms and the five-factor model of normal personality. All clinician-rated, self-reported, and informant-reported domain scores loaded meaningfully on their expected factors when controlling for nontrivial method factors. Generally, the trait domains exhibited meaningful associations with conceptually relevant external criteria, although the anankastia domain exhibited more variability in its pattern of correlations across methods. Overall, the ICD-11 trait domain model shows promising reliability and validity, indicating good progress within the field of PD assessment toward a more useful PD operationalization. Public Significance Statement: The International Classification of Diseases, 11th Edition, has a new diagnostic system for personality disorder, which includes dimensional personality traits. Further validation of this system is necessary to ensure that it works well in practice. The present study provides support for validity of the International Classification of Diseases, 11th Edition personality trait model. [ABSTRACT FROM AUTHOR]

Introduction Shoulder injuries account for approximately 8% to 24% of all musculoskeletal injuries in the military. Recently, a change was made to service-specific physical fitness tests. Knowledge of relative shoulder labral injury rates before and after this change would help guide future directions and preventive strategies. However, we found no previous literature evaluating the rates of labral injury among United States Military branch personnel by enlistment status (enlisted versus officer), gender, age, or race. Materials and Methods In this retrospective epidemiological study, we queried the Defense Medical Epidemiology Database for International Classification of Disease, Tenth Revision (ICD-10) code S43.43 (superior glenoid labrum lesion) to determine the total number of patients with a shoulder labral injury from 2016 to 2019. Results were assessed for demographic associations of shoulder labral injury with branch, service occupation, rank, gender, race, and age. Queries were limited to first-time occurrences and ambulatory data only. Results Overall, our study found the incidence of shoulder labral injuries to be largely conserved each year from 2016 to 2019 (3.22–3.35/1000/year). Incidence of labral injury was highest in males, White service members, the junior enlisted, the Army service branch, ages 20 to 29, and enlisted non-combat personnel. Conclusions With knowledge of injury patterns in specific military populations, initiatives may be taken to identify at-risk service members with the goal of informing future preventive strategies. [ABSTRACT FROM AUTHOR]

Introduction Finger and hand injuries are among the most common musculoskeletal conditions presenting to emergency departments and primary care providers. Many rural and community hospitals may not have immediate access to an orthopedic surgeon on-site. Furthermore, military treatment facilities, both within the continental United States and in austere deployment environments, face similar challenges. Therefore, knowing how to treat basic finger and hand injuries is paramount for patient care. Materials and Methods The Armed Forces Health Surveillance Branch operates the Defense Medical Surveillance System, a database that serves as the central repository of medical surveillance data for the armed forces. The Defense Medical Surveillance System was queried for ICD-10 codes associated with finger injuries from 2015 to 2019 among active duty service members across the major branches of the military. Results The most commonly reported finger injuries were open wounds to fingers without damage to nails, metacarpal fractures, phalanx fractures, and finger subluxation/dislocation. Emergency departments were the most commonly reported treatment facility type accounting for 35% of initial finger injuries, followed by 32.2% at orthopedic surgery clinics, 22.2% at family medicine clinics, and 10.8% at urgent care centers. Conclusions Finger injuries are common in the military setting and presenting directly to an orthopedic surgeon does not appear the norm. Fingertip injuries, fractures within the hand, and finger dislocations can often be managed without the need for a subspecialist. By following simple guidelines with attention to "red flags," primary care providers can manage most of these injuries with short-term follow-up with orthopedics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The percentage of total body surface area (%TBSA) burned and burn depth provide valuable information on burn injury severity. Objective: This study investigated the concordance between The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) codes and expert burn clinicians in assessing burn injury severity. Method: We conducted a retrospective population-based review of all patients who sustained a burn injury between July 1, 2009, and June 30, 2019, requiring admission into a specialist burn service across Australia and New Zealand. The %TBSA burned (including the percentage of full thickness burns) recorded by expert burn clinicians within the Burns Registry of Australia and New Zealand (BRANZ) were compared to ICD-10-AM coding. Results: 20,642 cases (71.5%) with ICD-10-AM code data were recorded. Overall, kappa scores (95% confidence interval [CI]) for burn size ranged from 0.64 (95% CI 0.63–0.66) to 0.86 (95% CI 0.78–0.94) indicating substantial to almost perfect agreement across all %TBSA groups. When stratified by depth, the lowest agreement was observed for < 10% TBSA and < 10% full thickness (kappa 0.03; 95% CI 0.02–0.04) and the highest agreement was observed for burns of ≥ 90% TBSA and ≥ 90% full thickness (kappa 0.72; 95% CI 0.58–0.85). Conclusion: Overall, there was substantial agreement between the BRANZ and ICD-10-AM coded data for %TBSA classification. When %TBSA classification was stratified by burn depth, greater agreement was observed for larger and deeper burns compared with smaller and superficial burns. Implications: Greater consistency in the classification of burns is needed. [ABSTRACT FROM AUTHOR]

Background: The International Classification of Diseases (ICD) is widely used by clinical coders worldwide for clinical coding morbidity data into administrative health databases. Accordingly, hospital data quality largely depends on the coders' skills acquired during ICD training, which varies greatly across countries. Objective: To characterise the current landscape of international ICD clinical coding training. Method: An online questionnaire was created to survey the 194 World Health Organization (WHO) member countries. Questions focused on the training provided to clinical coding professionals. The survey was distributed to potential participants who met specific criteria, and to organisations specialised in the topic, such as WHO Collaborating Centres, to be forwarded to their representatives. Responses were analysed using descriptive statistics. Results: Data from 47 respondents from 26 countries revealed disparities in all inquired topics. However, most participants reported clinical coders as the primary person assigning ICD codes. Although training was available in all countries, some did not mandate training qualifications, and those that did differed in type and duration of training, with college or university degree being most common. Clinical coding certificates most frequently entailed passing a certification exam. Most countries offered continuing training opportunities, and provided a range of support resources for clinical coders. Conclusion: Variability in clinical coder training could affect data collection worldwide, thus potentially hindering international comparability of health data. Implications: These findings could encourage countries to improve their resources and training programs available for clinical coders and will ultimately be valuable to the WHO for the standardisation of ICD training. [ABSTRACT FROM AUTHOR]

Background: The child mortality rate is one of the important indicators that is influenced by various factors such as the health, economic, political, and cultural status of societies. Objectives: The purpose of this study is to determine the causes related to the death of children aged 1 to 59 months in Zahedan University of Medical Sciences (ZAUMS) in the years 2018 to 2020 using the 10th edition of the International Classification of Diseases. Methods: The population of this descriptive study was children aged 1 - 59 months who died in the population covered by ZAUMS from 2018 to 2020. The research data were collected from the mortality surveillance system for children aged 1 to 59 months of the Vice-Chancellors for Health and Treatment Affairs of ZAUMS. The data collection tool was a checklist. The collected data was analyzed using descriptive statistics. Results: The death rates of children aged 1 to 59 months in 2018, 2019, and 2020 were 7.6, 7.9, and 6.9 per thousand live births, respectively. The highest percentage of deaths was related to boys ages 1 to 12 months. Most of the deceased were residents of urban areas. The most common causes of death were related to respiratory system diseases, injuries, poisonings, and other specific consequences of external causes, such as congenital malformations, deformations, and chromosomal abnormalities. Conclusions: Educating parents on timely treatment of respiratory system diseases in children, improving the knowledge of families to control accidents and take better care of children in high-risk environments where there is a possibility of burns, respiratory obstruction, poisoning, and drowning, reforming and promoting policies related to genetic screening plans to identify congenital abnormalities during pregnancy, and premarital counseling to prevent high-risk family marriages can improve children’s health index. [ABSTRACT FROM AUTHOR]

Background England and Wales experienced a stagnation of previously improving life expectancy during the 2010s. Public bodies cited influenza as an important cause. Sources of data We used data from the Office for National Statistics to examine mortality attributed directly to influenza and to all influenza-like diseases for the total population of England and Wales 2010–19. Several combinations of ICD-10 codes were used to address the possibility of under-counting influenza deaths. Areas of agreement Deaths from influenza and influenza-like diseases declined between 2010 and 2019, while earlier improvements in mortality from all causes of death were stalling and, with some causes, worsening. Our findings support existing research showing that influenza is not an important cause of the stalling of mortality rates 2010–19. Areas of controversy Influenza was accepted by many as an important cause of stalling life expectancy for much of the 2010s, while few in public office have accepted austerity as a key factor in the changes seen during that time. Growing points This adds to the mounting evidence that austerity damaged health prior to COVID-19 and left the population more vulnerable when it arrived. Areas for developing timely research Future research should explore why so many in public office were quick to attribute the change in trends in overall mortality in the UK in this period to influenza, and why many continue to do so through to 2023 and to deny the key role of austerity in harming population health. [ABSTRACT FROM AUTHOR]

Aim. To study the opinion of doctors about the applicability, frequency and criteria for practical use of codes of the International Classification of Diseases, 10th revision (ICD-10) from the group of chronic coronary artery disease (CAD) — I25.0, I25.1, I25.8, I25.9.Material and methods. This cross-sectional study was conducted in the period from February 27, 2024 to March 11, 2024 on a continuous sample of medical workers who agreed to participate in an anonymous survey as respondents. The questionnaire consisted of an introductory, personal and main part, which consisted of 19 questions devoted to various aspects of chronic CAD coding at the doctor’s appointment and during preparing medical death certificate. This article presents the results of a survey of cardiologists (group 1) and internists/general practitioners/family physicians (group 2). The χ2 test was used to compare two groups. The Friedman test was used to test the hypothesis that the physicians' opinion about the use of different chronic CAD codes was random. The Kendall test was used to assess the agreement between physicians. Calculations were carried out using Microsoft Office Excel 2021 and SPSS-26.0.Results. A total of 883 doctors from 47 constituent entities of the Russian Federation took part in the survey. Half of the respondents believed that there are differences between the codes I25.0, I25.1, I25.8, I25.9 and that they are necessary for statistical recording. Although cardiologists (59,2%) were less likely than internists/general practitioners/family doctors (65,9%) to believe that one or more of the 4 codes analyzed could be used only on the basis of risk factors, differences did not reach significance (p=0,1). The need to confirm chronic CAD using paraclinical research methods varied from 57,3% for code I25.9 to 83,1% for code I25.1. In case of death of a patient from chronic CAD, almost a third of respondents do not see the differences between the 4 specified codes, but are ready to use them in different clinical setting. In addition, 14,6% responded that when filling out medical death certificates they would not use any of the 4 specified codes. There is no agreement among physicians regarding the use of codes in clinical practice (Kendall's coefficient of concordance 0,084; p

Abstract 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.

Abstract 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.

Current efforts to better understand alcohol use disorder (AUD) have led to revisions of the most used classification systems, the DSM and the ICD. There is scarce information regarding how the latest versions of those two classification systems (DSM-5 and ICD-11) relate to functional characteristics (functional impairment (FI) and subjective distress (SD)) associated with AUD. Aim: To examine how the primary diagnostic system’s criteria (DSM) and guidelines (ICD) were related to two functional characteristics (FI and SD) as evidence of these systems' concurrent validity in Argentineans with AUD. Methods: We conducted a cross-sectional correlational study with a clinical sample (n=34) in 2018. Results: AUD's severity was more strongly related to SD than FI. FI was weakly related to the criterion of much time spent using it. We found weak associations between SD and role impairment, interpersonal problems, tolerance, and physical or psychological problems due to use, withdrawal, and much time spent using. Only one of the ICD guidelines was weakly related to SD, and we found moderate positive correlations between DSM-5 and FI and between DSM-5 and SD. Conclusion: DSM-5 was more accurate than ICD-11 in identifying those with higher levels of FI and SD and, thus, had a greater concurrent validity among a clinical sample of Argentineans with AUD. Our results contribute to a better understanding of the detection of alcohol-related conditions.

A significant proportion of perinatal deaths are preventable. The most critical factor contributing to the failure of international programs aimed at reducing preventable perinatal deaths is the lack of sufficient and accurate information about the causes of these deaths. This deficiency hinders the ability of countries to develop effective interventions for reducing perinatal mortality rates. Various classification systems have been developed to categorize the causes of perinatal death, aiming to enhance understanding and prevention. However, these systems face several challenges, such as assigning a considerable portion of perinatal deaths to unknown causes and usability limitations in developing countries with high perinatal mortality rates. The need for a comprehensive and unified international system for classifying the causes of perinatal mortality is crucial due to the variety of current classification systems, which hinder data comparison at national and global levels. This editorial study aims to introduce the first classification system for the causes of perinatal mortality with international applicability developed by the World Health Organization. [ABSTRACT FROM AUTHOR]

Objective: We aimed to develop an algorithm that accurately identifies children with Down syndrome (DS) using administrative data. Methods: We identified a cohort of children born between 2000 and 2017, enrolled in the Tennessee Medicaid Program (TennCare), who either had DS coded on their birth certificate or had a diagnosis listed using an International Classification of Diseases (ICD) code (suspected DS), and who received care at Vanderbilt University Medical Center, a comprehensive academic medical center, in the United States. Children with suspected DS were defined as having DS if they had (a) karyotype-confirmed DS indicated on their birth certificate; (b) karyotype-pending DS indicated on their birth certificate (or just DS if test type was not specified) and at least two healthcare encounters for DS during the first 6 years of life; or (c) at least three healthcare encounters for DS, with the first and last encounter separated by at least 30 days, during the first six years of life. The positive predictive value (PPV) of the algorithm and 95% confidence interval (CI) were reported. Results: Of the 411 children with suspected DS, 354 (86.1%) were defined as having DS by the algorithm. According to medical chart review, the algorithm correctly identified 347 children with DS (PPV = 98%, 95%CI: 96.0–99.0%). Of the 57 children the algorithm defined as not having DS, 50 (97.7%, 95%CI: 76.8–93.9%) were confirmed as not having DS by medical chart review. Conclusions: An algorithm that accurately identifies individuals with DS using birth certificate data and/or ICD codes provides a valuable tool to study DS using administrative data.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]