Diagnosis coding of neurocognitive disorders.

Objective: Diagnosis coding is a core clinical competency. A basic understanding of the structure of the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), the conventions and rules for diagnosis coding, and what constitutes accurate coding, is fundamental to the clinician's knowledge base. This commentary seeks to provide a practical framework for clinicians to perform accurate diagnosis coding of neurocognitive disorders. Method: This paper: (1) summarizes the structure of the ICD-10-CM, (2) describes the rules and conventions of diagnosis coding for diagnostic categories relevant to neurocognitive disorders, (3) presents clinical examples and pragmatic recommendations to help readers improve their day-to-day use of diagnosis codes, and (4) describes limitations and discrepancies in the diagnosis coding advice for neurocognitive disorders presented within the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) and the DSM-5-TR Neurocognitive Disorders Supplement. Its content originates from the ICD-10-CM itself and its companion document, the ICD-10-CM Official Guidelines for Coding and Reporting. Conclusion: The ICD-10-CM classification scheme is logically organized and easy to navigate for users who understand its structure and rules. Many neuropsychologists rely on the DSM-5-TR diagnosis coding advice, however that advice is limited with respect to the range of diagnosis codes relevant to neurocognitive disorders and their underlying causes. Relying on the ICD-10-CM directly for diagnosis coding of neurocognitive disorders, rather than the DSM-5-TR or other secondary sources, is therefore preferable and aids clinicians in accurate diagnosis coding.

Validation of Diagnostic Codes to Identify Glaucoma in Taiwan's Claims Data: A Multi-Institutional Study.

Background: Healthcare databases play a crucial role in improving our understanding of glaucoma epidemiology, which is the leading cause of irreversible blindness globally. However, the accuracy of diagnostic codes used in these databases to detect glaucoma is still uncertain. Aim: To assess the accuracy of ICD-9-CM and ICD-10-CM codes in identifying patients with glaucoma, including two distinct subtypes, primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG). Methods: We analyzed electronic medical records data from a 2% random sample of patients who newly underwent visual field examination in Taiwan's largest multi-institutional healthcare system from 2011 to 2020. The diagnosis of glaucoma was confirmed by two ophthalmologists, based on the glaucoma diagnostic criteria. The positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for ICD-9-CM codes 365.1X and 365.2X, and ICD-10-CM codes H4010X, H4011X, H4012X, H4020X, H4021X, H4022X, H4023X and H4024X for glaucoma were calculated. Results: We randomly selected 821 patients (mean age: 56.9 years old; female: 50.5%) from the original cohort of 41,050 newly receiving visual field examination in the study. Among 464 cases with an ICD-9-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 86.5, 96.5, 91.9, and 90.9%, respectively. Among 357 cases with an ICD-10-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 87.0, 92.8, 92.2 and 87.9%, respectively. The accuracy of diagnostic codes to identify POAG and PACG remained consistent. Conclusion: The diagnostic codes were highly reliable for identifying cases of glaucoma in Taiwan's routine healthcare practice. These results provide confidence when using ICD-9-CM and ICD-10-CM codes to define glaucoma cases in healthcare database research in Taiwan.

Validity of ICD-10-based algorithms to identify patients with influenza in inpatient and outpatient settings.

PURPOSE: To evaluate the validity of ICD-10-CM code-based algorithms as proxies for influenza in inpatient and outpatient settings in the USA. METHODS: Administrative claims data (2015-2018) from the largest commercial insurer in New Jersey (NJ), USA, were probabilistically linked to outpatient and inpatient electronic health record (EHR) data containing influenza test results from a large NJ health system. The primary claims-based algorithms defined influenza as presence of an ICD-10-CM code for influenza, stratified by setting (inpatient/outpatient) and code position for inpatient encounters. Test characteristics and 95% confidence intervals (CIs) were calculated using test-positive influenza as a reference standard. Test characteristics of alternative outpatient algorithms incorporating CPT/HCPCS testing codes and anti-influenza medication pharmacy claims were also calculated. RESULTS: There were 430 documented influenza test results within the study period (295 inpatient, 135 outpatient). The claims-based influenza definition had a sensitivity of 84.9% (95% CI 72.9%-92.1%), specificity of 96.3% (95% CI 93.1%-98.0%), and PPV of 83.3% (95% CI 71.3%-91.0%) in the inpatient setting, and a sensitivity of 76.7% (95% CI 59.1%-88.2%), specificity of 96.2% (95% CI 90.6%-98.5%), PPV of 85.2% (95% CI 67.5%-94.1%) in the outpatient setting. Primary inpatient discharge diagnoses had a sensitivity of 54.7% (95% CI 41.5%-67.3%), specificity of 99.6% (95% CI 97.7%-99.9%), and PPV of 96.7% (95% CI 83.3%-99.4%). CPT/HCPCS codes and anti-influenza medication claims were present for few outpatient encounters (sensitivity 3%-10%). CONCLUSIONS: In a large US healthcare system, inpatient ICD-10-CM codes for influenza, particularly primary inpatient diagnoses, had high predictive value for test-positive influenza. Outpatient ICD-10-CM codes were moderately predictive of test-positive influenza.

Opportunities for Nurses to Decrease the Stigma Associated with Housing Instability and Homelessness.

The number of people experiencing homelessness (PEH) in the United States has increased in the past 5 years. PEH have a higher disease burden and early mortality compared to people who are housed. Stigma adds to the burden of disease and disease management for PEH. In this article the authors review stigma, define housing and homelessness, describe the health and health care disparities PEH experience, and using the socio-ecological model as a framework, offer opportunities for nurses to intervene in efforts to decrease the stigma that PEH and housing instability encounter to improve health outcomes.

A compressed large language model embedding dataset of ICD 10 CM descriptions.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements, download and use without any other setup. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

Validation of ICD-10-CM Diagnostic Codes for Identifying Patients with ST-Elevation and Non-ST-Elevation Myocardial Infarction in a National Health Insurance Claims Database.

Purpose: Distinguishing ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) is crucial in acute myocardial infarction (AMI) research due to their distinct characteristics. However, the accuracy of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for STEMI and NSTEMI in Taiwan's National Health Insurance (NHI) database remains unvalidated. Therefore, we developed and validated case definition algorithms for STEMI and NSTEMI using ICD-10-CM and NHI billing codes. Patients and Methods: We obtained claims data and medical records of inpatient visits from 2016 to 2021 from the hospital's research-based database. Potential STEMI and NSTEMI cases were identified using diagnostic codes, keywords, and procedure codes associated with AMI. Chart reviews were then conducted to confirm the cases. The performance of the developed algorithms for STEMI and NSTEMI was assessed and subsequently externally validated. Results: The algorithm that defined STEMI as any STEMI ICD code in the first three diagnosis fields had the highest performance, with a sensitivity of 93.6% (95% confidence interval [CI], 91.7-95.2%), a positive predictive value (PPV) of 89.4% (95% CI, 87.1-91.4%), and a kappa of 0.914 (95% CI, 0.900-0.928). The algorithm that used the NSTEMI ICD code listed in any diagnosis field performed best in identifying NSTEMI, with a sensitivity of 82.6% (95% CI, 80.7-84.4%), a PPV of 96.5% (95% CI, 95.4-97.4), and a kappa of 0.889 (95% CI, 0.878-0.901). The algorithm that included either STEMI or NSTEMI ICD codes listed in any diagnosis field showed excellent performance in defining AMI, with a sensitivity of 89.4% (95% CI, 88.2-90.6%), a PPV of 95.6% (95% CI, 94.7-96.4%), and a kappa of 0.923 (95% CI, 0.915-0.931). External validation confirmed these algorithms' efficacy. Conclusion: Our results provide valuable reference algorithms for identifying STEMI and NSTEMI cases in Taiwan's NHI database.

Social Determinants of Health-Related Z Codes and Health Care Among Patients With Hypertension.

Introduction: Tracking social needs can provide information on barriers to controlling hypertension and the need for wraparound services. No recent studies have examined ICD-10-CM social determinants of health-related Z codes (Z55-Z65) to indicate social needs with a focus on patients with hypertension. Methods: Three cohorts were identified with a diagnosis of hypertension during 2016-2017 and continuously enrolled in fee-for-service insurance through June 2021: (1) commercial, age 18-64 years (n=1,024,012); (2) private insurance to supplement Medicare (Medicare Supplement), age ≥65 years (n=296,340); and (3) Medicaid, age ≥18 years (n=146,484). Both the proportion of patients and healthcare encounters or visits with social determinants of health-related Z code were summarized annually. Patient and visit characteristics were summarized for 2019. Results: In 2020, the highest annual documentation of social determinants of health-related Z codes was among Medicaid beneficiaries (3.02%, 0.46% commercial, 0.42% Medicare Supplement); documentation was higher among inpatient than among outpatient visits for all insurance types. Z63 (related to primary support group) was more common among commercial and Medicare Supplement beneficiaries, and Z59 (housing and economic circumstances) was more common among Medicaid beneficiaries. The 2019 total unadjusted medical expenditures were 1.85, 1.78, and 1.61 times higher for those with social determinants of health-related Z code than for those without commercial, Medicare Supplement, and Medicaid, respectively. Patients with social determinants of health-related Z code also had higher proportions of diagnosed chronic conditions. Among Medicaid beneficiaries, differences in the presence of social determinants of health-related Z code by race or ethnicity were observed. Conclusions: Although currently underreported, social determinants of health-related Z codes provide an opportunity to integrate social and medical data and may help decision makers understand the need for additional services among individuals with hypertension.

Positive Predictive Values of Anaphylaxis Diagnosis in Claims Data: A Multi-Institutional Study in Taiwan.

Real-world data sources can facilitate essential understanding of the epidemiological features of anaphylaxis. However, the accuracy of case-identifying definitions based on diagnosis codes for anaphylaxis in healthcare databases remains understudied. We conducted a cross-sectional study analyzing claims data from the largest multi-institutional healthcare system in Taiwan during 2017-2021. We included patients with incident anaphylaxis identified by either ICD-10-CM codes for anaphylaxis (Group 1) or ICD-10-CM codes for severe allergic or drug adverse events and additional modifier codes for acute allergy events (Group 2). We randomly selected 20% of the cases to determine the positive predictive value (PPV) of anaphylaxis case-identifying definitions in Groups 1 and 2 after review of electronic medical records by two physicians. From the original cohort (n = 2,176), we randomly selected 433 patients with either a diagnosis of anaphylaxis (Group 1), or a diagnosis of severe allergic and drug adverse events with additional modifier codes for acute allergy events (Group 2). In Group 1, we judged 135 / 170 patients as true anaphylaxis cases, giving a PPV of 79.4% (95% CI: 73.3-85.5). In Group 2, we judged 47 / 263 patients as true anaphylaxis cases, giving a PPV of 17.9% (95% CI: 13.3-22.5). In conclusion, acceptable PPVs were observed when anaphylaxis cases were identified by ICD-10-CM codes for anaphylaxis, but not by ICD-10-CM codes for severe allergic or drug adverse event with additional modifier codes for acute allergy events. Our multi-institutional findings could serve as a fundamental reference for further studies of anaphylaxis based on real-world healthcare databases.

Positive Predictive Value of International Classification of Diseases, Ninth Revision, Clinical Modification, and International Classification of Diseases, Tenth Revision, Clinical Modification, Codes for Identification of Congenital Heart Defects.

Background Administrative data permit analysis of large cohorts but rely on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), and International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes that may not reflect true congenital heart defects (CHDs). Methods and Results CHDs in 1497 cases with at least 1 encounter between January 1, 2010 and December 31, 2019 in 2 health care systems, identified by at least 1 of 87 ICD-9-CM/ICD-10-CM CHD codes were validated through medical record review for the presence of CHD and CHD native anatomy. Interobserver and intraobserver reliability averaged >95%. Positive predictive value (PPV) of ICD-9-CM/ICD-10-CM codes for CHD was 68.1% (1020/1497) overall, 94.6% (123/130) for cases identified in both health care systems, 95.8% (249/260) for severe codes, 52.6% (370/703) for shunt codes, 75.9% (243/320) for valve codes, 73.5% (119/162) for shunt and valve codes, and 75.0% (39/52) for "other CHD" (7 ICD-9-CM/ICD-10-CM codes). PPV for cases with >1 unique CHD code was 85.4% (503/589) versus 56.3% (498/884) for 1 CHD code. Of cases with secundum atrial septal defect ICD-9-CM/ICD-10-CM codes 745.5/Q21.1 in isolation, PPV was 30.9% (123/398). Patent foramen ovale was present in 66.2% (316/477) of false positives. True positives had younger mean age at first encounter with a CHD code than false positives (22.4 versus 26.3 years; P=0.0017). Conclusions CHD ICD-9-CM/ICD-10-CM codes have modest PPV and may not represent true CHD cases. PPV was improved by selecting certain features, but most true cases did not have these characteristics. The development of algorithms to improve accuracy may improve accuracy of electronic health records for CHD surveillance.

A practical strategy to use the ICD-11 for morbidity coding in the United States without a clinical modification.

OBJECTIVE: The aim of this study was to derive and evaluate a practical strategy of replacing ICD-10-CM codes by ICD-11 for morbidity coding in the United States, without the creation of a Clinical Modification. MATERIALS AND METHODS: A stepwise strategy is described, using first the ICD-11 stem codes from the Mortality and Morbidity Statistics (MMS) linearization, followed by exposing Foundation entities, then adding postcoordination (with existing codes and adding new stem codes if necessary), with creating new stem codes as the last resort. The strategy was evaluated by recoding 2 samples of ICD-10-CM codes comprised of frequently used codes and all codes from the digestive diseases chapter. RESULTS: Among the 1725 ICD-10-CM codes examined, the cumulative coverage at the stem code, Foundation, and postcoordination levels are 35.2%, 46.5% and 89.4% respectively. 7.1% of codes require new extension codes and 3.5% require new stem codes. Among the new extension codes, severity scale values and anatomy are the most common categories. 5.5% of codes are not one-to-one matches (1 ICD-10-CM code matched to 1 ICD-11 stem code or Foundation entity) which could be potentially challenging. CONCLUSION: Existing ICD-11 content can achieve full representation of almost 90% of ICD-10-CM codes, provided that postcoordination can be used and the coding guidelines and hierarchical structures of ICD-10-CM and ICD-11 can be harmonized. The various options examined in this study should be carefully considered before embarking on the traditional approach of a full-fledged ICD-11-CM.

Validation of an Algorithm to Identify Venous Thromboembolism in Health Insurance Claims Data Among Patients with Rheumatoid Arthritis.

Purpose: Health insurance claims databases provide an opportunity to study uncommon events, such as venous thromboembolism (VTE), in large patient populations. This study evaluated case definitions for identifying VTE among patients treated for rheumatoid arthritis (RA) using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes in claims data. Patients and Methods: Study participants were insured adults who received treatment for and had a diagnosis of RA between 2016 and 2020. After a 6-month covariate assessment window, patients were observed for ≥1 month until health plan disenrollment, occurrence of a presumptive VTE, or end of the study (12/31/2020). Presumptive VTEs were identified using predefined algorithms based on ICD-10-CM diagnosis codes, anticoagulant use, and care setting. Medical charts were abstracted to confirm the VTE diagnosis. Performance of primary and secondary (less stringent) algorithms was assessed by calculating the positive predictive value (PPV; primary and secondary objectives). Additionally, a linked electronic health record (EHR) claims database and abstracted provider notes were used as a novel alternative source to validate claims-based outcome definitions (exploratory objective). Results: A total of 155 charts identified with the primary VTE algorithm were abstracted. The majority of patients were female (73.5%), with mean (standard deviation) age 66.4 (10.7) years and Medicare insurance (80.6%). Obesity (46.8%), ever smoking (55.8%), and prior evidence of VTE (28.4%) were commonly reported in medical charts. The PPV for the primary VTE algorithm was 75.5% (117/155; 95% confidence interval [CI], 68.7%, 82.3%). A less stringent secondary algorithm had a PPV of 52.6% (40/76; 95% CI, 41.4%, 63.9%). Using an alternative EHR-linked claims database, the primary VTE algorithm PPV was lower, potentially due to the unavailability of relevant records for validation. Conclusion: Administrative claims data can be used to identify VTE among patients with RA in observational studies.

A Compressed Language Model Embedding Dataset of ICD 10 CM Descriptions.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

Trends and impact of intravascular ultrasound and optical coherence tomography on percutaneous coronary intervention for myocardial infarction.

Background: Intravascular imaging with either intravascular ultrasound (IVUS) or optical coherence tomography (OCT) during percutaneous coronary intervention (PCI) is associated with improved outcomes, but these techniques have previously been underutilized in the real world. We aimed to examine the change in utilization of intravascular imaging-guided PCI over the past decade in the United States and assess the association between intravascular imaging and clinical outcomes following PCI for myocardial infarction (MI). Methods: We surveyed the National Inpatient Sample from 2008 to 2019 to calculate the number of PCIs for MI guided by IVUS or OCT. Temporal trends were analyzed using Cochran-Armitage trend test or simple linear regression for categorical or continuous outcomes, respectively. Multivariable logistic regression was used to compare outcomes following PCI with and without intravascular imaging. Results: A total of 2,881,746 PCIs were performed for MI. The number of IVUS-guided PCIs increased by 309.9 % from 6,180 in 2008 to 25,330 in 2019 (P-trend < 0.001). The percentage of IVUS use in PCIs increased from 3.4 % in 2008 to 8.7 % in 2019 (P-trend < 0.001). The number of OCT-guided PCIs increased 548.4 % from 246 in 2011 to 1,595 in 2019 (P-trend < 0.001). The percentage of OCT guidance in all PCIs increased from 0.0 % in 2008 to 0.6 % in 2019 (P-trend < 0.001). Intravascular imaging-guided PCI was associated with lower odds of in-hospital mortality (adjusted odds ratio 0.66, 95 % confidence interval 0.60-0.72, p < 0.001). Conclusions: Although the number of intravascular imaging-guided PCIs have been increasing, adoption of intravascular imaging remains poor despite an association with lower mortality.

Safety of measles, mumps, and rubella vaccine in adolescents and adults in the vaccine safety Datalink.

Background: Measles, mumps, and rubella vaccine (MMR) is routinely administered to children; however, adolescents and adults may receive MMR for various reasons. Safety studies in adolescents and adults are limited. We report on safety of MMR in this age group in the Vaccine Safety Datalink. Methods: We included adolescents (aged 9-17 years) and adults (aged ≥ 18 years) who received ≥ 1 dose of MMR from January 1, 2010-December 31, 2018. Pre-specified outcomes were identified by diagnosis codes. Clinically serious outcomes included anaphylaxis, encephalitis/myelitis, Guillain-Barré syndrome, immune thrombocytopenia, meningitis, and seizure. Non-serious outcomes were allergic reaction, arthropathy, fever, injection site reaction, lymphadenopathy, non-specific reaction, parotitis, rash, and syncope. All serious outcomes underwent medical record review. Outcome-specific incidence was calculated in pre-defined post-vaccination windows. A self-controlled risk interval design was used to determine the relative risk of each outcome in a risk window after vaccination compared to a more distal control window. Results: During the study period, 276,327 MMR doses were administered to adolescents and adults. Mean age of vaccinees was 34.8 years; 65.8 % were female; 53.2 % of doses were administered simultaneously with ≥ 1 other vaccine. Serious outcomes were rare, with incidence ≤ 6 per 100,000 doses for each outcome assessed, and none had a significant elevation in incidence during the risk window compared to the control window. Incidence of non-serious outcomes per 100,000 doses ranged from 3.4 for parotitis to 263.0 for arthropathy. Other common outcomes included injection site reaction and rash (157.0 and 112.9 per 100,000 doses, respectively). Significantly more outcomes were observed during the risk window compared to the control window for all non-serious outcomes except parotitis. Some variability was observed by sex and age group. Conclusion: Serious outcomes after MMR are rare in adolescents and adults, but vaccinees should be counseled regarding anticipated local and systemic non-serious adverse events.

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

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

ICD-10-CM codes infrequently used to document human trafficking in 2019 Nationwide Emergency Department Sample.

INTRODUCTION: People who experience human trafficking (HT) visit emergency departments (ED). The International Classification of Diseases, Clinical Modification (ICD-10-CM) introduced codes to document HT in June 2018. The aim of this study is to identify characteristics of ED patients who experienced forced labor or sexual exploitation as a documented external cause of morbidity in US visits. METHODS: Nationally representative surveillance based on patient visits to 989 hospital-owned EDs in the Nationwide Emergency Department Sample in 2019 became available in 2021. Eight ICD-10-CM codes to classify HT as an external cause of morbidity were combined into one HT variable for analysis in 2021-2022. RESULTS: A weighted count of 517 of 33.1 million ED visits (0.0016%) documented HT as an external cause of morbidity. Of them, sexual exploitation (71.6%) was documented more frequently than labor exploitation (28.4%). Most HT-related codes were visits by females (87.3%) from large metropolitan areas, and identified as white. Approximately 40% of visits were from ZIP codes with a median household income less than $48,000 annually. Relative to all other ED visits, patients with HT as an external cause of morbidity had higher odds of being female (OR = 6.54, 95% CI:3.59, 11.92) and being a minor (OR = 1.76, 95% CI:1.02, 3.04). CONCLUSION: HT was rarely documented as an external cause of morbidity in 989 hospitals' ED visits from a nationally representative sample in 2019. Documentation of recently added HT ICD-10-CM codes does not appear to have been implemented sufficiently to yield an unbiased representation of those who experienced HT and presented in the ED. Efforts to enhance the utility of ICD-10-CM HT codes for surveillance and documentation must first address ED personnel training on identification and response to HT. In doing so, ED personnel also need to address ethical concerns (e.g. stigma, confidentiality, risk of patient harm) and allow for informed consent among trafficked patients in order to be scaled up responsibly.

ICD-10-CM Codes for the Identification of Abusive Head Trauma in Administrative Datasets.

OBJECTIVE: We sought to develop and validate a list of ICD-10-CM codes identifying abusive head trauma (AHT). METHODS: Subjects included all children under 2 years with head trauma seen in the emergency department or admitted to one of 5 medical centers. Cases were classified as AHT, accidental head injury, or indeterminate based on chart review of the medical record. ICD-10-CM code list to identify cases of AHT was developed based on prior head injury code lists. Sensitivity and specificity of the final code list were calculated. RESULTS: There were 2883 patients in the study population of whom 524 had AHT, 2123 had accidental injury, and 236 were indeterminate cases. The final list of AHT codes had a sensitivity of 76.1% (95% CI 72.5-79.8) and a specificity of 98.5% (95% CI 98.0-99.0) when limiting analyses to the groups with identified cause of injury (accidental vs abusive). Misclassification of cases based on codes resulting in false positives and false negatives was due to coding errors. CONCLUSIONS: The list of ICD-10-CM codes can be utilized to identify and track cases of AHT at a national level in large administrative datasets though likely underestimates true injury burden.

Technical Report: Protocol for Characterizing Phenotype Variants Using Phenome-Wide Association Study (PheWAS) Utilizing the Nationwide Inpatient Sample 2020 in Individuals With Pancreatic Cysts and Lung Cancer.

This technical report serves as a comprehensive guide for conducting a phenome-wide association study (PheWAS) utilizing data extracted from the Nationwide Inpatient Sample 2020. Specifically tailored to individuals diagnosed with pancreatic cysts and lung cancer, the report establishes a step-by-step workflow designed to assist researchers in uncovering potential associations within this specific cohort. The methodology outlined in the report ensures clarity and reproducibility by employing a curated cohort sourced from the GitHub repository and executed using R for robust data analysis. The code encompasses pivotal steps, including the utilization of a QQ plot as a crucial diagnostic tool aimed at identifying systematic biases or associations. Additionally, the report incorporates the creation of a Manhattan plot, delving into essential mathematical considerations to enhance the interpretability of the results. Notably, the report elucidates the handling of the International Classification of Disease version 10 (ICD-10) codes, providing a sample approach for their segmentation to analyze associations by diagnostic categories. The segmentation aligns with the guidelines outlined in the American Medical Association's ICD-10-CM 2022, the Complete Official Codebook with Guidelines (American Medical Association Press, 2021), ensuring a standardized and rigorous analytical process. This comprehensive guide equips researchers with the tools and insights needed to navigate the complexities of PheWAS within the context of pancreatic cysts and lung cancer, fostering transparency, reproducibility, and meaningful scientific exploration.

Different Renal Function Equations and Dosing of Direct Oral Anticoagulants in Atrial Fibrillation.

Background: Randomized trials of direct oral anticoagulants (DOACs) adopted the Cockcroft-Gault (CG) formula to calculate estimated glomerular filtration rate (eGFR) to determine the dosages of DOACs. Objectives: The authors aimed to investigate the agreements/disagreements of eGFRs calculated using different equations (CG, Modified Diet in Renal Disease [MDRD], and Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] formulas), and their impacts on the dosages of DOACs and clinical outcomes. Methods: Medical data from a multicenter health care provider in Taiwan including 39,239 patients with atrial fibrillation were used. Among these patients, there were 11,185 and 2,323 patients treated with DOACs and warfarin, respectively. Results: At the cutoff values of eGFR of <15, 15-50, and >50 mL/min, the agreements were 78% between MDRD and CG and 81% between CKD-EPI and CG. The disagreements among the different equations were largely due to overestimations, especially for patients aged >75 years and with a body weight of <50 kg (58.8% for MDRD and 50.9% for CKD-EPI). Among patients receiving DOACs whose dosages were defined as "on label" based on MDRD or CKD-EPI, only those whose dosages were "truly on label" based on CG were associated with a lower risk of major bleeding (adjusted HR: 0.34; 95% CI: 0.26-0.45) compared to warfarin. Conclusions: The adoptions of MDRD or CKD-EPI rather than CG would result in inappropriate dosing of DOACs (mainly overdosing), which would attenuate the advantages of DOACs compared to warfarin. The CG equation should be used as the gold standard to calculate eGFRs and guide the DOAC dosages.