International Classification Of Disease Codes Research Articles

REFINING THE DEFINITION OF MILIARY/DISSEMINATED TUBERCULOSIS IN CANADA

While a multi-site definition of disseminated tuberculosis (DTB) exists, there is limited evidence to support its use. Herein we sought to generate that evidence. We evaluated treatment outcomes and reporting requirements against two distinct definitions of DTB in a 15-year population-based cohort of consecutively-diagnosed TB patients in Canada. Definitions were combined in a multi-variable logistic regression to determine risk factors for TB-related death in DTB. We applied two mutually exclusive definitions of DTB to our dataset: 1. 'strict' - TB disease associated with a positive TB culture in blood/bone marrow or TB disease associated with a miliary pattern on chest imaging and a positive TB culture or, 2. 'multisite' - TB disease in two or more non-contiguous sites. Among 2877 notified TB patients, 110 (3.8%) met the 'strict' definition, while 168 (5.8%) met the 'multisite' definition. Of all 278 DTB patients only 135 (48.6%) were notified as DTB using International Classification of Disease codes, and only 66 (23.7%) were classified as DTB by Canada's Public Health Agency. DTB patients, by either definition, were less likely to achieve cure/treatment completion and more likely to die. Risk factors for a fatal outcome included extremes of age, Canadian birth, central nervous system involvement, and HIV co-infection. Our findings support the combination of both a strict and multisite definition of DTB for purposes of reporting consistency and investigational comparability.

Association Between Congenital Heart Disease-Related Diagnosis Codes and Trauma Surgery Outcomes.

Congenital heart disease (CHD) is one the most common congenital anomalies, with a prevalence of 8-10 cases per 1000 live births in the United States. Congenital heart disease has been recognized as a risk factor for poor perioperative and postoperative outcomes in non-cardiac surgery. We aimed to determine if documentation of CHD-related diagnosis codes was associated with similar risks for trauma surgery. Data were acquired from the 2010-2019 American College of Surgeons' Trauma Quality Programs Participant Use Files. This study included trauma patients of all ages with one or more surgical procedures and at least one documented non-trauma (comorbidity) International Classification of Diseases code. Patients were stratified based on presence of CHD-related comorbidity codes vs any other comorbidity. Outcomes included mortality, hospital length of stay (LOS), discharge disposition, and in-hospital complications. Using 1:1 propensity score matching, we matched 215 cases with CHD-related comorbid diagnoses to non-CHD controls. Compared to patients with other comorbidities, patients with CHD-related comorbidites were less likely to be discharged home to self-care (odds ratio: 0.44, 95% confidence interval [CI]: 0.25, 078 P = .005) and tended to have prolonged hospital LOS (incidence rate ratio [IRR]: 1.06, 95% CI: 1.001, 1.13, P = .046). We present the first quantitative multicenter analysis correlating documentation of comorbid CHD-related diagnoses with higher risk of adverse outcomes after trauma surgery. These results support the need to routinely acknowledge and document CHD as comorbidity in trauma admissions that could lead to surgical intervention and for trauma centers to prepare for patients with a possible CHD comorbidity.

Insights into the aetiology and temporal trends in cardiac mortality in the young: a 21-year review of national and registry data in the united kingdom

Abstract Background An accurate representation of the incidence and aetiology of cardiac and sudden cardiac death (SCD) is vital for healthcare policymakers to effectively allocate resources for preventative strategies. Purpose We aimed to report on the incidence and causes of cardiac and SCD among individuals under 35-years-old in England and Wales for 21 years (2001-2021), with a focus on identifying any temporal trends. Methods Annual mortality data from the Office for National Statistics (ONS) pertaining to cardiovascular (CV) and possible CV deaths in individuals under 35-years-old were analyzed using International Classification of Diseases-10 codes. Deaths were categorized into four-classes: A1- definitive cardiac deaths without identifiable structural heart disease (consistent with sudden arrhythmic death syndrome, SADS); A2- definitive cardiac deaths with identified structural heart disease; A3- definitive cardiac deaths with indeterminate cause; and B- potential cardiac deaths. Incidence rates were computed based on annual ONS census data. Additionally, insights were gleaned from the Cardiac Risk in the Young Centre for Cardiac Pathology (CRYCCP) nationwide registry. Results The mean annual number of definitive cardiac deaths (classes A1+A2+A3) was 414 (SD 27.4), resulting in an incidence of young cardiac and SCD of 1.68/100,000 individuals/year. Ischaemic heart disease (28.4%), cardiomyopathies (25.9%), and SADS (21.8%) were the most prevalent cardiac conditions. Cardiomyopathies and SADS related mortality peaked in the 10-to-19 age group, while ischaemic heart disease peaked in the 30-to-35 age group. A mean of 573 (SD 76.6) possible cardiac deaths (class B) occurred, these primarily included ill-defined causes (33.5%), epilepsy (32.4%), sudden infant death syndrome (24.2%), and drowning (7%). Definitive and possible cardiac deaths showed a male preponderance (male to female ratios: 2.3:1 and 1.6:1, respectively). A declining trend in cardiac mortality was observed with a 1.3% (95%CI: 1.0%-1.7%) incidence rate reduction/year (p<0.001). This was associated with a 2.5% annual incidence rate reduction for deaths attributed to structural heart disease (p<0.001), but an 8.5% annual incidence rate increase for deaths attributed to SADS (p<0.001). These trends corelated with increasing numbers of deaths referred to the CRYCCP where an expert cardiac pathologist diagnosed SADS in over 52% of cases. Conclusions Over 21 years in England and Wales, national datasets suggest the incidence of young cardiac and SCD is 1.68/100,000/year, with a small but appreciable declining mortality trend. The rise in SADS rates and contemporaneous decline in deaths attributed to structural heart disease may signify increased awareness and accessibility to expert cardiac pathologists. Ensuring accurate mortality coding is crucial for thoroughly assessing first-degree family members in cases of suspected inherited cardiac conditions.

Health impact of the Serious Accidents Punishment Act in Korean occupational settings

Abstract Background Interrupted time series analysis serves as a valuable study design for assessing the efficacy of public health interventions implemented at a specific moment in time at the population level. We aim to elucidate the effects of the Serious Accidents Punishment Act implemented in February 2021 in Korea by analyzing changes in the incidence rates of occupational diseases. Methods The monthly data from patients with occupational diseases was extracted from the National Health Insurance Service Cohort database from 2011 to 2023. An Interrupted time series analysis with Poisson segmented regression was used to compare the rates before and after the Serious Accidents Punishment Act introduction in both outpatients and inpatients. Adjustments or stratification variables included sex, age, company size, and injury-related international classification of diseases codes (Injury, poisoning, and certain other consequences of external causes, S00-T88). Results Based on injury rates, men’s inpatient injury rates remained stable, but outpatient rates decreased after the act implementation, while women’s injury rates consistently declined in both inpatient and outpatient settings. The Interrupted time series regression analysis indicates that the Serious Accidents Punishment Act led to a significant reduction in both outpatient and inpatient injury rates for men and women, with a marked decline in outpatient rates and hospital admissions among women post-intervention. Conclusions The Interrupted time series regression results suggest that the Serious Accidents Punishment Act has been effective in reducing injury-related healthcare usage, highlighting the necessity of gender-specific interventions and the potential impact on lowering inpatient visits, although causality remains uncertain without further analysis. Key messages • The Serious Accidents Punishment Act significantly reduced occupational injury rates in Korea, especially among women, demonstrating effective public health intervention. • Interrupted time series analysis reveals Serious Accidents Punishment Act’s impact on reducing healthcare usage for occupational injuries, highlighting the need for gender-specific approaches.

Twenty-year trends in excess costs of chronic obstructive pulmonary disease.

Several major risk factors for chronic obstructive pulmonary disease (COPD), such as population aging, smoking rates, and air pollution levels are rapidly changing, causing inevitable changes in the population burden of COPD. We determined the excess direct costs of COPD and their trend from 2001 to 2020. Using administrative health data from British Columbia, Canada, we created a retrospective matched cohort of physician-diagnosed COPD patients and non-COPD individuals. Excess direct medical costs (2020 $CAD) were estimated by analysing hospital records, outpatient services, medications, and community-care services. Comorbidity classes were assessed using the International Classification of Diseases codes. Excess COPD costs were estimated as the adjusted difference in direct medical costs between COPD and non-COPD cohorts. There were 208 554 and 404 703 individuals in the COPD and non-COPD cohorts, respectively (47.8% female; mean baseline age 69.1 and 68.2 years, respectively). Direct medical costs for COPD were $9224 per patient-year (/PY), compared to $3396/PY for non-COPD, giving rise to excess costs of $5828/PY (95% confidence interval [CI] 5759-5897). Excess costs increased by 48% over the study period. Excess costs due to comorbidities were $3588/PY (95% CI 3554-3622), with cardiovascular-related conditions alone exceeding the costs attributed to COPD ($1375/PY versus $904/PY). Despite multifaceted prevention and management initiatives, COPD-related economic burden is increasing, with the majority of costs due to comorbid conditions. Rising per-patient costs, combined with the flat or increasing prevalence of COPD in many jurisdictions, indicates a significant increase in COPD burden.

Validation Study of the Claims-Based Algorithm Using the International Classification of Diseases Codes to Identify Patients With Coronavirus Disease in Japan From 2020 to 2022: The VENUS Study.

We validated claims-based algorithms using the International Classification of Diseases, Tenth Revision (ICD-10) to identify patients with the first-ever coronavirus disease (COVID-19) onset between May 2020 and August 2022. The study cohort was comprised of residents of one municipality enrolled in a public insurance program. This study used data provided by the municipality, including residents' insurer-based medical claims data linked to the Health Center Real-time Information-Sharing System (HER-SYS). The HER-SYS data included positive results from COVID-19 tests and were used as reference standards. Claims-based algorithms #1 and #2 were U07.1, B34.2, with and without suspicious diagnoses, respectively. Claims-based algorithms #3 and #4 were U07.1 with and without suspicious diagnoses, respectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm. The study cohort included 165 038 residents, including 13 402 residents were the reference standard. For the entire period, the sensitivity, specificity, PPV, and NPV were 55.7% (95% confidence interval: 54.8%-56.5%), 65.4% (65.2%-65.6%), 11.5% (11.3%-11.8%), and 98.9% (98.8%-99.0%) for Algorithm #1, and 67.0% (66.2%-67.8%), 88.1% (87.9%-88.3%), 31.6% (31.1%-32.2%), and 97.8% (97.7%-97.8%) for Algorithm #2, and 52.9% (52.0%-53.7%), 67.1% (66.9%-67.3%), 11.5% (11.2%-11.8%), and 98.3% (98.3%-98.4%) for Algorithm #3, 62.6% (61.8%-63.4%), 88.5% (88.3%-88.7%), 30.9% (30.3%-31.4%), and 97.3% (97.2%-97.4%) for Algorithm #4, respectively. Our study showed that the validity of claims-based algorithms consisting of COVID-19-related ICD-10 codes to identify patients with first-onset COVID-19 is limited.

Antepartum risk factors and outcomes of massive transfusion for elective cesarean section: a nationwide retrospective cohort study

BackgroundPostpartum hemorrhage is one of the leading causes of maternal mortality and morbidity. The etiology of postpartum hemorrhage exhibits variations in relation to the mode of birth; consequently, risk factors for massive transfusion in elective cesarean section might diverge from those encountered in vaginal birth or emergency cesarean section. The main purpose of this study was to investigate antepartum risk factors of massive transfusion for elective cesarean section.MethodsWe conducted a retrospective cohort study based on data from a nationwide system that collected inpatient medical records from tertiary hospitals in mainland China. We included women who had undergone elective cesarean section from January 2013 to August 2018. Primary outcome was massive transfusion defined as transfusion of more than eight units of red blood cells on the day of childbirth. Candidate risk factors were identified by the 10th revision of International Classification of Diseases Codes of admission diagnoses. The relationship between each factor and massive transfusion was assessed using multivariable logistic regression.ResultsA total of 294,695 women were included and 572 of them received massive transfusion (incidence: 194 per 100,000 elective cesarean sections). Maternal age [adjusted odds ratio (aOR) 1.22; 95% confidence interval (CI) 1.10–1.48], anemia (aOR 1.66; 95% CI 1.34–2.05), thrombocytopenia (aOR 3.54; 95% CI 2.39–5.05), coagulopathy (aOR 25.92; 95% CI 8.59–69.50), hypoalbuminemia (aOR 2.97; 95% CI 1.86–4.53), hepatic dysfunction (aOR 1.65; 95% CI 1.04–2.47), uterine scar (aOR 1.39; 95% CI 1.15–1.67), multiple pregnancy (aOR 2.84; 95% CI 1.74–4.38), polyhydramnios (aOR 2.52; 95% CI 1.19–4.68) and placenta previa (aOR 25.03; 95% CI 21.04–29.77) were associated with massive blood transfusion for elective cesarean section. Among the women receiving massive blood transfusion, 7 (1.2%) died during hospitalization, 126 (22.0%) needed hysterectomy, 25 (4.4%) uterine packing and 57 (10.0%) uterine artery ligation.ConclusionsTen risk factors of massive transfusion were identified in women undergoing elective cesarean section. Our findings may facilitate blood products preparation and provide opportunities for applying prophylactic strategies prior to cesarean section for women at high risk of massive transfusion.

Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes.

We aimed to develop an algorithm that accurately identifies children with Down syndrome (DS) using administrative data. 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. 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. 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.

Polycystic ovary syndrome underdiagnosis patterns by individual-level and spatial social vulnerability measures.

To use electronic health records (EHR) data at Boston Medical Center (BMC) to identify individual-level and spatial predictors of missed diagnosis, among those who meet diagnostic criteria for PCOS. The BMC Clinical Data Warehouse was used to source patients who presented between October 1, 2003 and September 30, 2015 for any of the following: androgen blood tests, hirsutism, evaluation of menstrual regularity, pelvic ultrasound for any reason, or PCOS. Algorithm PCOS cases were identified as those with International Classification of disease (ICD) codes for irregular menstruation and either an ICD code for hirsutism, elevated testosterone lab, or polycystic ovarian morphology as identified using natural language processing on pelvic ultrasounds. Logistic regression models were used to estimate odds ratios (ORs) of missed PCOS diagnosis by age, race/ethnicity, education, primary language, body mass index (BMI), insurance type and social vulnerability index (SVI) score. In the 2003-2015 BMC-EHR PCOS at-risk cohort (n=23,786), there were 1,199 physician-diagnosed PCOS cases and 730 algorithm PCOS cases. In logistic regression models controlling for age, year, education, and SVI scores, Black/African American patients were more likely to have missed a PCOS diagnosis (OR = 1.69 [95% CI, 1.28, 2.24]) compared to non-Hispanic White patients, and relying on Medicaid or charity for insurance was associated with an increased odds of missed diagnosis when compared to private insurance (OR = 1.90 [95% CI, 1.47, 2.46], OR = 1.90 [95% CI, 1.41, 2.56], respectively). Higher SVI scores were associated with increased odds of missed diagnosis in univariate models. We observed individual-level and spatial disparities within the PCOS diagnosis. Further research should explore drivers of disparities for earlier intervention.

ACUTE PANCREATITIS IN AKMOLA REGION ACCORDING TO EMERGENCY SURGERY

Background: Acute pancreatitis is a major issue in surgical practice, with high complications and mortality rates. It severely affects patient quality of life and places a significant economic strain on healthcare systems, making its study crucial for public health. This study aims to analyze trends in acute pancreatitis incidence in the Akmola region from 2009 to 2022. Materials and methods: The study is based on a retrospective analysis of data from the Ministry of Health of the Republic of Kazakhstan according to the International Classification of Diseases-10 code: K85. Methods of health statistics and time series analysis were used to assess the dynamics of morbidity. Results: There was an increase in the total number of hospitalizations from 865 cases in 2009 to 1.061 in 2022. The percentage of surgical interventions decreased from 5.7% to 4.2%, while the share of conservative treatment increased to 95.8%. The mortality rate among operated patients was 20.4% in 2019 and increased to 33.3% in 2022. The incidence of hospitalization increased from 117.1 per 100000 population in 2009 to 144.6 per 100000 population in 2022, with a decrease in the rate of late visits from 42.6 to 36.1 per 100000 population, respectively. Conclusion: It has been established that for patients in the second group according to the Enhanced Recovery After Surgery strategy, hemodynamic support and effective pain management can contribute to early patient mobilization after surgery. Early mobilization, in turn, can expedite recovery and reduce the length of hospital stay, ultimately leading to potential cost savings. Conclusion. The study highlights the need to improve medical approaches to acute pancreatitis in the Akmola region. The results indicate the critical importance of early diagnosis and timely hospitalization to reduce complications and mortality. Additional research is needed to develop more effective clinical protocols.

PheW2P2V: a phenome-wide prediction framework with weighted patient representations using electronic health records.

Electronic health records (EHRs) provide opportunities for the development of computable predictive tools. Conventional machine learning methods and deep learning methods have been widely used for this task, with the approach of usually designing one tool for one clinical outcome. Here we developed PheW2P2V, a Phenome-Wide prediction framework using Weighted Patient Vectors. PheW2P2V conducts tailored predictions for phenome-wide phenotypes using numeric representations of patients' past medical records weighted based on their similarities with individual phenotypes. PheW2P2V defines clinical disease phenotypes using Phecode mapping based on International Classification of Disease codes, which reduces redundancy and case-control misclassification in real-life EHR datasets. Through upweighting medical records of patients that are more relevant to a phenotype of interest in calculating patient vectors, PheW2P2V achieves tailored incidence risk prediction of a phenotype. The calculation of weighted patient vectors is computationally efficient, and the weighting mechanism ensures tailored predictions across the phenome. We evaluated prediction performance of PheW2P2V and baseline methods with simulation studies and clinical applications using the MIMIC-III database. Across 942 phenome-wide predictions using the MIMIC-III database, PheW2P2V has median area under the receiver operating characteristic curve (AUC-ROC) 0.74 (baseline methods have values ≤0.72), median max F1-score 0.20 (baseline methods have values ≤0.19), and median area under the precision-recall curve (AUC-PR) 0.10 (baseline methods have values ≤0.10). PheW2P2V can predict phenotypes efficiently by using medical concept embeddings and upweighting relevant past medical histories. By leveraging both labeled and unlabeled data, PheW2P2V reduces overfitting and improves predictions for rare phenotypes, making it a useful screening tool for early diagnosis of high-risk conditions, though further research is needed to assess the transferability of embeddings across different databases. PheW2P2V is fast, flexible, and has superior prediction performance for many clinical disease phenotypes across the phenome of the MIMIC-III database compared to that of several popular baseline methods.

Identifying Patients With Primary Biliary Cholangitis and Cirrhosis Using Administrative Data in a National Cohort.

The accuracy of administrative codes to capture patients with both primary biliary cholangitis (PBC) and cirrhosis could be challenging because of the potential for incorrect coding due to the old nomenclature "Primary Biliary Cirrhosis." Therefore, the aim of this study was to examine the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for PBC and cirrhosis. This was a retrospective cohort study using data from the VA Corporate Data Warehouse. Eligibility criteria included adult patients diagnosed to have PBC and cirrhosis based on one inpatient or two outpatient ICD 9 or 10 codes, and were validated against chart review of each participant. We identified 1408 patients who were found to have ICD codes for both cirrhosis and PBC. The ICD 9/10 codes for PBC and cirrhosis had a PPV of 0.75 (95% CI 0.73-0.75) for cirrhosis, 0.75 for PBC (95% CI 0.73-0.78), and 0.52 (0.50-0.55) for PBC and cirrhosis. When portal hypertension was combined with ICD 9/10 codes, the PPV of cirrhosis improved to 0.92 (0.90-0.94), and that of PBC cirrhosis improved to 0.64 (0.60-0.67). By combining ICD 9/10 codes for portal hypertension and receipt of ursodeoxycholic acid (UDCA), the PPV for cirrhosis improved to 0.91 (0.88-0.94), PBC increased to 0.78 (0.74-0.82), and that for PBC cirrhosis to 0.69 (0.65-0.74). In a large national cohort, the use of ICD 9/10 codes had modest reliability for identifying participants with PBC and cirrhosis. The PPV for cirrhosis can be improved by incorporating ICD 9/10 codes for portal hypertension with receipt of UDCA.

Association Between Admission Systolic Blood Pressure and Outcomes in Patients with Isolated Traumatic Brain Injury: A Cross-National Multicenter Cohort Study.

The optimal prehospital blood pressure in patients following traumatic brain injury (TBI) remains controversial. We aimed to assess the association between the systolic blood pressure (SBP) at emergency department triage and patient outcomes following isolated moderate-to-severe TBI. We conducted a cross-national multicenter retrospective cohort study using the Pan-Asia Trauma Outcomes Study database from January 1, 2016, to November 30, 2018. The enrollees were adult patients with isolated moderate-to-severe TBI defined by the International Classification of Diseases code, a Glasgow Coma Scale (GCS) <13 at triage, and a nonhead Abbreviated Injury Scale ≤3. The studied variables were SBPs at triage categorized into different ranges. The primary outcome was 30-day mortality, and the secondary outcome was poor functional status at hospital discharge defined by the modified Rankin Scale ≥4. Multivariable logistic regression was applied to adjust for confounders including country, sex, age, mechanism of injury, prehospital vascular access, respiratory rate, GCS, oxygen saturation, intubation, Injury Severity Score, head surgery, intensive care unit admission, and length of hospital stay. Subgroup analyses were performed on different severity of TBI. A total of 785 patients (median age, 42 years; male patients 77.5%; mean SBP at triage, 136.3 ± 33.1 mmHg) were included in the primary analysis. The lowest 30-day mortality rate existed in patients with SBP of 100-119 mmHg. Taking it as baseline, the adjusted odds ratios (aORs) and 95% confidence intervals (CIs) of SBP <100 mmHg, 120-139 mmHg, 140-159 mmHg, and ≥160 mmHg were 7.05 (2.51-19.78), 3.14 (1.14-8.65), 2.91 (1.04-8.17), and 3.28 (1.14-9.42). As for the secondary outcome, the aORs and 95% CIs were 1.36 (0.68-2.68) of <100 mmHg, 0.99 (0.57-1.70) of 120-139 mmHg, 1.23 (0.67-2.25) of 140-159 mmHg, and 1.52 (0.78-2.95) of ≥160 mmHg. Subgroup analyses revealed trends of the best outcomes in both moderate and severe TBI patients with SBP 100-119 mmHg, whereas statistical significance appeared only in patients with severe TBI. SBP of 110-119 mmHg at triage is associated with the lowest 30-day mortality in patients following isolated moderate-to-severe TBI and possibly related to a better functional outcome.

Dermatologic Conditions in Down Syndrome: A Multi-Site Retrospective Review of International Classification of Diseases Codes.

As the population and life expectancy of people with Down syndrome increases, identifying common skin conditions throughout the lifespan will help inform clinical care and research. We sought to evaluate dermatologic conditions diagnosed in pediatric and adult patients with Down syndrome. This multicenter retrospective study analyzed demographic and outpatient visit International Classification of Diseases codes of patients with Down syndrome evaluated at outpatient dermatology clinics in the United States or Canada between 2011 and 2021. A total of 1529 patients with Down syndrome were identified from eight academic medical centers: 50.8% were children (0-12 years), 25.2% were adolescents (13-17 years), and 24% were adults (18 years and older). Eczematous dermatitis was the most common diagnosis overall (26%), followed by folliculitis (19.3%) and seborrheic dermatitis (15.6%). Other notable diagnoses included dermatophyte infections (13%), alopecia areata (11.6%), and psoriasis (6.7%). About 4.3% of visits included a code for high-risk medication use. Eczematous dermatitis, alopecia areata, and folliculitis were the most common diagnoses observed in children; folliculitis, hidradenitis suppurativa, and eczematous dermatitis in adolescents; and seborrheic dermatitis, eczematous dermatitis, and folliculitis in adults. Dermatologic conditions in patients with Down syndrome vary by age, but are most often eczematous, adnexal, and cutaneous autoimmune disorders. This multicenter retrospective review identifies skin diseases that should be prioritized for clinical care guideline development and research in the Down syndrome community.

Contemporary nationwide trends in major adverse cardiovascular events in young cannabis users without concomitant tobacco, alcohol, cocaine use.

Cannabis use has increased among young individuals in recent years. Although dependent cannabis use disorder (CUD) has been associated with various cardiac events, its effects on young adults without concurrent substance use remain understudied. To examine trends in hospitalizations for major adverse cardiac and cerebrovascular events (MACCE) in this cohort. We used the National Inpatient Sample (2016-2019) to identify hospitalized young individuals (18-44 years), excluding those with concurrent substance usage (tobacco, alcohol, and cocaine). They were divided into CUD+ and CUD-. Using International Classification of Diseases-10 codes, we examined the trends in MACCE hospitalizations, including all-cause mortality (ACM), acute myocardial infarction (AMI), cardiac arrest (CA), and acute ischemic stroke (AIS). Of 27.4 million hospitalizations among young adults without concurrent substance abuse, 4.2% (1.1 million) had co-existent CUD. In CUD+ group, hospitalization rates for MACCE (1.71% vs 1.35%), AMI (0.86% vs 0.54%), CA (0.27% vs 0.24%), and AIS (0.49% vs 0.35%) were higher than in CUD- group (P < 0.001). However, rate of ACM hospitalizations was lower in CUD+ group (0.30% vs 0.44%). From 2016 to 2019, CUD+ group experienced a relative rise of 5% in MACCE and 20% in AMI hospitalizations, compared to 22% and 36% increases in CUD- group (P < 0.05). The CUD+ group had a 13% relative decrease in ACM hospitalizations, compared to a 10% relative rise in CUD- group (P < 0.05). However, when adjusted for confounders, MACCE odds among CUD+ cohort remain comparable between 2016 and 2019. The CUD+ group had higher rates of MACCE, but the rising trends were more apparent in the CUD- group over time. Interestingly, the CUD+ group had lower ACM rates than the CUD- group.

Nontobacco Nicotine Dependence and Rates of Periprosthetic Joint Infection and Other Postoperative Complications in Shoulder Arthroplasty: A Retrospective Analysis.

Nontobacco nicotine products, including e-cigarettes and vaping, are marketed as healthier alternatives to tobacco. However, the literature on nontobacco nicotine dependence (NTND) is scarce. It is important to analyze the influence of these products as they pertain to medical and surgical postoperative complications. This study hypothesizes that patients with NTND will experience more postoperative complications. Using the TriNetX database, which aggregates deidentified medical records from 89 healthcare organizations in the Research Network, Current Procedural Terminology and 10th revision of the International Classification of Diseases codes were used to identify patients undergoing primary shoulder arthroplasty (SA) from January 2012 to February 2024. Patients were divided into cohorts based on their NTND status before surgery. 90-day major medical complications and 2-year implant-related complications were assessed. Statistical analyses involved calculating risk ratios for postoperative complications. This study analyzed a total of 89,910 SA patients, of which 6,756 were 1:1 propensity matched into NTND or control cohorts. Within the 90-day postoperative period, the NTND cohort exhibited significantly higher rates of sepsis (1.80 vs. 1.20, P = 0.012), surgical site infection (1.20 vs. 0.70%, P = 0.007), and wound disruptions (0.70 vs. 0.40%, P = 0.048), average opioids prescribed (4.46 vs. 3.338, P < 0.001), readmission (10.20% vs. 6.20%, P 0.001) compared with the non-NTND cohort. At the 2-year follow-up, mechanical loosening was notably higher in the NTND group (1.10 vs. 0.30%, P 0.001), as were rates of prosthetic joint infections (2.20 vs. 1.20%, P 0.001). No significant difference was observed for revision rates (3.20% vs. 2.90%, P = 0.269). NTND is associated with higher 90-day rates of wound distruptions, infections, sepsis, as well as increased rates of mechanical loosening and prosthetic joint infection at 2 years postoperatively after SA. These results highlight the need for comprehensive NTND preoperative screening and tailored patient counseling in this patient population.

Machine learning approach to predict acute kidney injury among patients undergoing multi-level spinal posterior instrumented fusion.

Acute kidney injury (AKI) after spinal fusion is a significant morbidity that can lead to poor post-surgical outcomes. Identifying AKI risk factors and developing a risk model can raise surgeons' awareness and allow them to take actions to mitigate the risks. The objective of the current study is to develop machine learning (ML) models to assess patient risk factors predisposing to AKI after posterior spinal instrumented fusion. Data was collected from the IBM MarketScan Database (2009-2021) for patients >18 years old who underwent spinal fusion with posterior instrumentation (3-6 levels). AKI incidence (defined by the International Classification of Diseases codes) was recorded 90-day post-surgery. Risk factors for AKI were investigated and compared through several ML models including logistic regression, linear support vector machine (LSVM), random forest, extreme gradient boosting (XGBoost), and neural networks. Among the 141,697 patients who underwent fusion with posterior instrumentation (3-6 levels), the overall rate of 90-day AKI was 2.96%. We discovered that the logistic regression model and LSVM demonstrated the best predictions with area under the curve (AUC) values of 0.75. The most important AKI prediction features included chronic renal disease, hypertension, diabetes mellitus ± complications, older age (>50 years old), and congestive heart failure. Patients who did not have these five key risk factors had a 90-day AKI rate of 0.29%. Patients who had an increasing number of key risk factors subsequently had higher risks of postoperative AKI. The analysis of the data with different ML models identified 5 key variables that are most closely associated with AKI: chronic renal disease, hypertension, diabetes mellitus ± complications, older age (>50 years old), and congestive heart failure. These variables constitute a simple risk calculator with additive odds ratio ranging from 3.38 (1 risk factor) to 91.10 (5 risk factors) over 90 days after posterior spinal fusion surgery. These findings can help surgeons risk-stratify their patients for AKI risk, and potentially guide post-operative monitoring and medical management.

Evaluating a Natural Language Processing-Driven, AI-Assisted International Classification of Diseases, 10th Revision, Clinical Modification, Coding System for Diagnosis Related Groups in a Real Hospital Environment: Algorithm Development and Validation Study.

International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs). This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs). By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023. Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F1-score, 0.667 (F1-score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F1-score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs. An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.

Real-world data to support post-market safety and performance of embolization coils: evidence generation from a medical device manufacturer and data institute partnership

BackgroundRecognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils.MethodsThrough a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils’ use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana’s largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints.ResultsA total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method.ConclusionsGenerating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems.

Physician–patient racial concordance and newborn mortality

The racial gap in infant mortality is a pressing public-health concern, and [B. N. Greenwood et al., Proc. Natl. Acad. Sci. U.S.A. 117, 21194-21200 (2020), 10.1073/pnas.1913405117] suggest that Black newborns are more likely to survive if cared for by Black physicians after birth, even in models that control for numerous variables, including hospital and physician fixed effects, and the 65 most common comorbidities affecting newborns (as described by International Classification of Disease codes). We acquired the data used in the study, covering Florida hospital discharges from 1992 through the third quarter of 2015, to replicate and extend the analysis. We find that the magnitude of the concordance effect is substantially reduced after controlling for diagnoses indicating very low birth weight (<1,500 g), which are a strong predictor of neonatal mortality but not among the 65 most common comorbidities. In fact, the estimated effect is near zero and statistically insignificant in the expanded specifications that control for very low birth weight and include hospital and physician fixed effects.