International Classification Of Disease Codes Research Articles

External Validation of the Surgical Intervention for Traumatic Injuries Scale in Children.

The Surgical Intervention for Traumatic Injuries (SITI) scale is intended to predict the likelihood of needing surgical decompression among patients with traumatic brain injury (TBI). We sought to examine the performance of the SITI score to predict likelihood of acute neurosurgical intervention for children with TBI. We conducted a cross-sectional, retrospective, observational study of children diagnosed with TBI as determined by International Classification of Diseases codes, presenting to a single level 1 pediatric trauma center, between June 1, 2003, and May 31, 2018. The main outcome was decompressive craniotomy or craniectomy within 24 hours of arrival. Data for SITI scoring were abstracted by research assistants, and all cases were scored by a physician who was blinded to the outcome. The SITI scale performance was evaluated using receiver operating characteristic curve and by calculating the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). There were 656 encounters with TBI, of which 39 (5.9%) underwent surgical decompression. The mean SITI scores were 4.15 for the operative group and 0.40 for the nonoperative group (P < 0.001). A cutoff of 2 or greater for a positive score gave the best performance with a sensitivity of 0.79, specificity of 0.90, PPV of 0.34, and NPV of 0.99. The area under the receiver operating characteristic curve was 0.89 (95% confidence interval, 0.83-0.96). In sensitivity analysis excluding 75 cases with depressed skull fractures, a score of 2 or greater had a sensitivity of 0.96, specificity of 0.91, PPV of 0.31, and NPV of 1.00. The area under the receiver operating characteristic curve was 0.98 (95% confidence interval, 0.97-1.00). A SITI score of less than 2 is associated with nonoperative management. However, clinicians should not be falsely reassured by a low score in patients with depressed skull fractures.

Validation of Parkinson's Disease Ascertainment in the Veterans Administration Electronic Medical Record.

Electronic medical record (EMR)-based studies hold great potential for epidemiologic investigations of Parkinson's disease (PD) causal factors and phenomenology, but diagnostic misclassification may obscure or bias inferences. The aims were to determine the validity of PD diagnostic codes in the Veterans Administration (VA) national electronic medical databases and develop recommendations for maximizing ascertainment accuracy. We investigated a cohort of 146,776 veterans who utilized VA healthcare between 1999 and 2021. We reviewed the medical records of individuals with a PD International Classification of Diseases (ICD) code in outpatient, inpatient, or community care encounters to assign a gold-standard diagnosis. We determined diagnostic accuracy based on provider type, coding frequency, medications, and potentially exclusionary ICD codes overall and by race. A total of 377 of 810 (46.5%) with a PD ICD code had PD. Veterans whose PD was coded by a PD-specialist neurologist were most likely to have PD (83.6%), but sensitivity was low (15.0%). Diagnostic accuracy decreased for PD coded by any neurologist (66.9%), but sensitivity improved (69.4%). Requiring two or more PD codes in combination with two or more levodopa prescriptions improved accuracy, particularly among nonneurologists. Neuroleptic-induced parkinsonism was the most frequent diagnosis in those without PD (15.6%). Accuracy was lower in Black (29.0%) than White (50.5%) veterans regardless of provider type (miscoding odds ratio 2.5, 95% confidence interval 1.7-3.6). These results highlight the limitations of EMR-based PD ascertainment. Researchers can maximize accuracy by considering provider specialty, coding frequency, pharmacy data, and exclusionary diagnoses, but some degree of record review is required to ensure high accuracy. Higher miscoding among Black veterans warrants further study. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Trends in Korean Medicine Utilization for Chronic Rhinosinusitis Using National Health Insurance Service Data (2012-2022)

Objectives: Chronic rhinosinusitis (CRS) is often treated with Korean medicine (KM), but comprehensive utilization data is lacking. This study aimed to analyze the patterns of KM use for CRS using National Health Insurance Service data from 2012 to 2022.Methods: We conducted a retrospective analysis of claims data for CRS patients (International Classification of Diseases code: J32) who visited KM institutions. We examined demographic characteristics, visiting patterns, medical costs, treatment modalities, and concurrent use of Western medicine.Results: Of 138,647 patients (52.2% male, mean age 23.8 years), 41.8% were under 10 years old. Annual patient numbers ranged from 14,595-17,242 (2012-2019), decreasing sharply from 2020. Average costs increased 5% annually. Acupuncture was the most common treatment (661,455, 50.5%). The most frequently prescribed insured herbal medicine was Hyeonggaeyeongyo-tang (48,494, 48.5%). Notably, 87.0% (120,610 patients) concurrently received Western medical treatment, indicating a prevalent integrative approach in CRS management. 77.8% received their initial diagnosis at Western institutions.Conclusion: This comprehensive analysis of KM utilization for CRS in Korea reveals significant patterns in patient demographics, treatment preferences, and integration with Western medicine. The high rate of concurrent KM and Western treatment highlights the integrative nature of CRS management in Korea, providing valuable insights for health policy and clinical practice.

Performance of ICD-10-based injury severity scores in pediatric trauma patients using the ICD-AIS map and survival rate ratios.

The performance of injury severity scores, used widely to quantify injury severity and predict outcomes, has not been investigated in German pediatric cases. This study aims to identify the most feasible and accurate injury score predictor of mortality in German children with trauma using International Classification of Diseases 10 (ICD-10). Between 2014 and 2020, a retrospective observational cohort study of hospital admissions cases aged < 18 years with injury-related ICD-10 codes using the German hospital database (GHD) was conducted. The maximum abbreviated injury scale and injury severity score were calculated using the ICD-AIS map provided by the Association for the Advancement of Automotive Medicine, adjusted to the German modification of the ICD-10 classification. The survival risk ratio was used to calculate the single worst ICD-derived injury (single ICISS) and a multiplicative injury severity score (multiplicative ICISS). Logistic regressions were conducted for each of the four above mentioned scores (predictors) to predict in-hospital mortality(outcome), in the selected trauma population and within four clinically relevant subgroups, using discrimination and calibration. 1,720,802 were trauma patients and ICD-AIS mapping was possible in 1,328,377 cases. Cases with mapping failure (n = 392,425;22.8%) were younger and had a higher mortality rate were excluded from the performance analysis. ICISS-derived scores had a better discrimination and calibration than ICD-AIS based scores in the overall cohort and all four subgroups (AUC ranges between 0.985-0.998 vs 0.886- 0.972 respectively). Empirically derived measures of injury severity were superior to ICD-AIS mapped scores in the GHD to predict mortality in pediatric trauma patients. Given the high percentage of mapping failure and high mortality among cases with single coded injury, the single ICISS may be the most suitable measure of injury severity in this group of patients.

Extracting International Classification of Diseases Codes from Clinical Documentation using Large Language Models.

Large language models (LLMs) have shown promise in various professional fields, including medicine and law. However, their performance in highly specialized tasks, such as extracting ICD-10-CM codes from patient notes, remains underexplored. The primary objective was to evaluate and compare the performance of ICD-10-CM code extraction by different LLMs with that of human coder. We evaluated performance of six LLMs (GPT-3.5, GPT-4, Claude 2.1, Claude 3, Gemini Advanced, and Llama 2-70b) in extracting ICD-10-CM codes against human coder. We used deidentified inpatient notes from American Health Information Management Association Vlab authentic patient cases for this study. We calculated percent agreement and Cohen's kappa values to assess the agreement between LLMs and human coder. We then identified reasons for discrepancies in code extraction by LLMs in a 10% random subset. Among 50 inpatient notes, human coder extracted 165 unique ICD-10-CM codes. LLMs extracted significantly higher number of unique ICD-10-CM codes than human coder, with Llama 2-70b extracting most (658) and Gemini Advanced the least (221). GPT-4 achieved highest percent agreement with human coder at 15.2%, followed by Claude 3 (12.7%) and GPT-3.5 (12.4%). Cohen's kappa values indicated minimal to no agreement, ranging from -0.02 to 0.01. When focusing on primary diagnosis, Claude 3 achieved highest percent agreement (26%) and kappa value (0.25). Reasons for discrepancies in extraction of codes varied amongst LLMs and included extraction of codes for diagnoses not confirmed by providers (60% with GPT-4), extraction of non-specific codes (25% with GPT-3.5), extraction of codes for signs and symptoms despite presence of more specific diagnosis (22% with Claude-2.1) and hallucinations (35% with Claude-2.1). Current LLMs have poor performance in extraction of ICD-10-CM codes from inpatient notes when compared against the human coder.

Maltreatment, homicide and access to specialised palliative care among children and young adults with a life-limiting condition: a nationwide population-based study

ObjectiveThis study compared the differences in the rates of maltreatment and homicide deaths between children and young adults with and without a life-limiting condition (LLC) and determined whether this affects...

Axial Spondyloarthritis in Black Americans: An Observational Study From Five Centers in Shelby County, Tennessee.

Axial spondyloarthritis (axSpA) is an immune-mediated disease that predominantly affects the axial skeleton. The objective of this study was to describe the frequency and clinical characteristics of axSpA in Black Americans in Shelby County. We also aimed to report diagnostic delays, disease severity, and health disparities in this population. This was a retrospective observational study in which electronic records of patients with axSpA were reviewed across five centers in Shelby County. Patients were identified using International Classification of Diseases codes. Data on demographics, clinical characteristics, and imaging were collected. The frequency was reported. Two-group comparisons were done using the chi-square test and two-sample t-test. Multivariable logistic and linear regression methods were used to compare sacroiliitis grades and delay in diagnosis, respectively, after adjusting for confounders. Black Americans constituted 32% (n = 78) of the 244 patients with axSpA identified. Black Americans had significantly more hip involvement, more elevations in C-reactive protein, less HLA-B27 positivity, and less family history positivity when compared with White patients. After adjusting for age, sex, and HLA-B27, Black Americans had twice as higher odds of having advanced grades of sacroiliitis on x-rays compared to White Americans (odds ratio 2.32 [95% confidence interval 1.23-4.44]). AxSpA was associated with significant diagnostic delays in both races. A significant proportion of patients with axSpA in Shelby County were Black Americans. The study identified that Black Americans have more odds of having advanced sacroiliitis on x-rays, more hip involvement, and higher markers of inflammation. It also showed that, regardless of race or ethnicity, there is a significant diagnostic delay.

Estimated clinical utility of multi-gene pharmacogenetic testing in a retrospective cohort of gynecology patients.

This study aimed to estimate the clinical utility of performing multi-gene pharmacogenetic testing on patients undergoing gynecologic surgery/procedure by evaluating the prescribing rate of Clinical Pharmacogenetics Implementation Consortium (CPIC) level A medications and frequency of drug-gene interactions (DGIs). The electronic health record was queried for 76 current procedural terminology codes to identify gynecologic surgeries/procedures that occurred between 1 January 2015 to 31 December 2020 in patients with at least one of 152 international classification of disease codes. Prescription data for CPIC level A medications was extracted. Those enrolled in the Penn Medicine Biobank were assessed for DGIs. The cohort consisted of 7798 female patients and 682 were in the biobank. Up to 6 years following their surgery or procedure, 80% were ordered ≥1 CPIC level A medication. Over half (54%) of these medications were ordered within 3 days after their surgery or procedure. The most common CPIC level A medications ordered were ibuprofen (57%) and ondansetron (42%). Overall, 7% of the cohort had ≥1 known or predicted DGI with medications they were prescribed. Multi-gene pharmacogenetic testing may be beneficial to gynecologic surgery/procedure patients by assisting clinicians with prescribing postoperative analgesics and future medications.

Occurrence of stroke in children and young adults in Indonesia: a multicenter private hospital study.

Most studies that estimate the occurrence of childhood stroke use heterogeneous methods and rely on International Classification of Diseases codes, a strategy that may be unreliable. This study aimed to estimate the occurrence of childhood stroke in Indonesia using imaging and clinical data from a private hospital network. This cross-sectional study used consecutive retrospective multi-center data samples collected in 2019-2023. The study cohort included children aged >28 days to young adults aged 24 years with confirmed ischemic or hemorrhagic stroke on computed tomography (CT) or magnetic resonance imaging (MRI). The 1-year occurrence was calculated, and the data analysis was performed using SPSS version 26. Over 5 years, the performance of 8,987 CT and 6,133 MRI scans resulted in the identification of 1,074 stroke cases. The average patient age was 14.8 ± 7 years. Stroke occurrence was highest in 2021 (9.08%) and lowest in 2022 (5.91%). Male patients accounted for 67.9% of cases, with hemorrhagic strokes accounting for 83.4% of the total, primarily resulting from accidents (73.2%) and predominantly occurring in the frontal region (37.1%). A significant majority of cases (66.7%) were reported in Java. Males had a relative risk of 1.93 (95% confidence interval, 1.48-2.52; p < 0.0001) for hemorrhagic stroke versus females. The incidence of childhood stroke revealed critical epidemiological trends and disparities, emphasizing the need for targeted public health interventions and enhanced stroke prevention strategies in Indonesia.

Knowledge Aware Automated Health Claims Processing with Medical Ontologies and Large Language Models

One of the major challenges in automating health insurance claims processing lies in the complexity involved in validating an incoming claim's medical diagnoses against its policy Underwriting (UW) exclusions. Termed UW Exclusion Detection, this process ensures claims are only paid out if their diagnoses are not medically associated with conditions excluded under the policy. Medical diagnoses in health insurance claims are typically represented by the International Classification of Disease (ICD) codes, established by the World Health Organization. For example, given a policy that excludes "all respiratory illness". A claim with the ICD code J45 (Asthma) will be subject to rejection as J45 is a respiratory-related diagnosis that falls within the scope of the policy's UW exclusion. The key challenge in automating this process lies in the wide range of available ICD codes. The ICD-10-CM coding scheme consists of over 40,000 codes, which often results in scenarios where codes encountered during inference are absent from the training data. These unseen ICD codes limit the effectiveness of data-driven approaches, which depend on the training data to discern medically relevant associations between UW exclusions and ICD codes. This underscores the need to supplement data-driven approaches with additional domain knowledge. We hypothesize that integrating implicit medical domain knowledge inherent in Large Language Models (LLMs) with explicit domain knowledge from medical ontologies, will enhance data-driven approaches for UW Exclusion Detection. Thoroughly validated on real-world health insurance claims data, our proposed approach proved effective in accurately establishing medically relevant associations between UW exclusions and ICD codes.

Automated Medical Records Review for Mild Cognitive Impairment and Dementia.

Objectives : Unstructured and structured data in electronic health records (EHR) are a rich source of information for research and quality improvement studies. However, extracting accurate information from EHR is labor-intensive. Here we introduce an automated EHR phenotyping model to identify patients with Alzheimer's Disease, related dementias (ADRD), or mild cognitive impairment (MCI). Methods : We assembled medical notes and associated International Classification of Diseases (ICD) codes and medication prescriptions from 3,626 outpatient adults from two hospitals seen between February 2015 and June 2022. Ground truth annotations regarding the presence vs. absence of a diagnosis of MCI or ADRD were determined through manual chart review. Indicators extracted from notes included the presence of keywords and phrases in unstructured clinical notes, prescriptions of medications associated with MCI/ADRD, and ICD codes associated with MCI/ADRD. We trained a regularized logistic regression model to predict the ground truth annotations. Model performance was evaluated using area under the receiver operating curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, specificity, precision/positive predictive value, recall/sensitivity, and F1 score (harmonic mean of precision and recall). Results : Thirty percent of patients in the cohort carried diagnoses of MCI/ADRD based on manual review. When evaluated on a held-out test set, the best model using clinical notes, ICDs, and medications, achieved an AUROC of 0.98, an AUPRC of 0.98, an accuracy of 0.93, a sensitivity (recall) of 0.91, a specificity of 0.96, a precision of 0.96, and an F1 score of 0.93 The estimated overall accuracy for patients randomly selected from EHRs was 99.88%. Conclusion : Automated EHR phenotyping accurately identifies patients with MCI/ADRD based on clinical notes, ICD codes, and medication records. This approach holds potential for large-scale MCI/ADRD research utilizing EHR databases.

The Right ICD Code, Right Now: A Call to Action for Pragmatic Language Disorders After Right Hemisphere Stroke.

Diagnosis of language impairments after stroke is important to optimizing stroke outcomes. After right hemisphere brain damage (RHD), apragmatism can impact the comprehension and production of pragmatic language. However, despite decades of empirical evidence, there is no International Classification of Diseases (ICD) code for RHD pragmatic language impairments. The absence of an ICD code has far reaching ramifications that impact patient outcomes, including reduced clinical and public awareness, limited curricular content, and underdiagnosis. This viewpoint justifies the need to appropriately classify the pragmatic language symptomology after RHD with an ICD code. An ICD code can positively influence health care practitioner knowledge, education, and practice while informing public health considerations vital to epidemiological analyses.

Rates of buprenorphine prescribing and racial disparities among patients with opioid overdose

BackgroundAwareness of the relationship between real-world buprenorphine prescribing and overdose frequency is limited, especially in the Southeastern United States. We described buprenorphine prescribing rates for patients experiencing nonfatal opioid overdoses in the context of overdose frequency. MethodsElectronic medical records review was conducted at an urban, academic hospital in Alabama from January 1 through December 31, 2021. Patients with opioid use disorder (OUD) and nonfatal opioid overdoses, dispositioned from either the emergency department (ED), inpatient, or outpatient affiliated clinics, were identified by International Classification of Diseases-10 codes. ResultsThe study included 358 unique patients. Many patients were white (71.5 %), male (59.2 %), and uninsured (54.2 %), with a mean age of 42.0±12.8 years. The majority (85.5 %) experienced one to three overdoses, and 14.5 % of patients had more than three overdoses. The buprenorphine prescription rate increased to 55.8 % when patients had more than three overdoses, compared to one overdose (34.5 %) and two to three overdoses (37.4 %) (p=0.025). Compared to females, more males overdosed more than once (p=0.004). Black patients were less likely to receive buprenorphine prescriptions than white patients (27.3 % vs. 44.5 %, p=0.004). Compared to patients with multiple overdoses, more patients with one overdose had public insurance (p=0.028) and were less likely to present to the ED (p<0.001). ConclusionUnder-prescribing of buprenorphine is high among patients with OUD and opioid overdoses, even in patients with multiple overdoses, and there appear to be racial disparities in prescribing. Our findings indicate clinical opportunities for improving buprenorphine prescribing and reducing the current disparities.

Multimodal Attention Network for Dementia Prediction.

The early identification of an individual's dementia risk is crucial for disease prevention and the design of insurance products in an aging society. This study aims to accurately predict the future incidence risk of dementia in individuals by leveraging the advantages of neural networks. This is, however, complicated by the high dimensionality and sparsity of the International Classification of Diseases (ICD) codes when utilizing data from Taiwan's National Health Insurance, which includes individual profiles and medical records. Inspired by the click-through rate (CTR) problem in recommendation systems, where future user behavior is predicted based on their past consumption records, we address these challenges with a multimodal attention network for dementia (MAND), which incorporates an ICD code embedding layer and multihead self-attention to encode ICD codes and capture interactions among diseases. Additionally, we investigate the applicability of several CTR methods to the dementia prediction problem. MAND achieves an AUC of 0.9010, surpassing traditional CTR models and demonstrating its effectiveness. The highly flexible pipelined design allows for module replacement to meet specific requirements. Furthermore, the analysis of attention scores reveals diseases highly correlated with dementia, aligning with prior research and emphasizing the interpretability of the model. This research deepens our understanding of the diseases associated with dementia, and the accurate prediction provided can serve as an early warning for dementia occurrence, aiding in its prevention.

REFINING THE DEFINITION OF MILIARY/DISSEMINATED TUBERCULOSIS IN CANADA

Although a "multisite" 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 patients with tuberculosis (TB) in Canada. Definitions were combined in a multi-variable logistic regression to determine the risk factors for TB-related death in DTB. We applied two mutually exclusive definitions of DTB to our data set: 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 patients with TB, 110 (3.8%) met the strict definition, whereas 168 (5.8%) met the multisite definition. Of all 278 patients with DTB, 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. Patients with DTB by either definition were less likely to achieve cure/treatment completion and more likely to die. The 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 a strict and multisite definition of DTB for purposes of reporting consistency and investigational comparability.

Validating International Classification of Diseases Code 10th Revision algorithms for accurate identification of pulmonary embolism

BackgroundMany research investigations for pulmonary embolism (PE) rely on the International Classification of Diseases 10th Revision (ICD-10) codes for analyses of electronic databases. The validity of ICD-10 codes in identifying PE remains uncertain. ObjectivesThe objective of this study was to validate an algorithm to efficiently identify pulmonary embolism using ICD-10 codes. MethodsUsing a prespecified protocol, patients in the Mass General-Brigham hospitals (2016-2021) with ICD-10 principal discharge codes for PE, those with secondary codes for PE, and those without PE codes were identified (n = 578 from each group). Weighting was applied to represent each group proportionate to their true prevalence. The accuracy of ICD-10 codes for identifying PE was compared with adjudication by independent physicians. The F1 score, which incorporates sensitivity and positive predictive value (PPV), was assessed. Subset validation was performed at Yale-New Haven Health System. ResultsA total of 1712 patients were included (age: 60.6 years; 52.3% female). ICD-10 PE codes in the principal discharge position had sensitivity and PPV of 58.3% and 92.1%, respectively. Adding secondary discharge codes to the principal discharge codes improved the sensitivity to 83.2%, but the PPV was reduced to 79.1%. Using a combination of ICD-10 PE principal discharge codes or secondary codes plus imaging codes for PE led to sensitivity and PPV of 81.6% and 84.7%, respectively, and the highest F1 score (83.1%; P < .001 compared with other methods). Validation yielded largely similar results. ConclusionAlthough the principal discharge codes for PE show excellent PPV, they miss 40% of acute PEs. A combination of principal discharge codes and secondary codes plus PE imaging codes led to improved sensitivity without severe reduction in PPV.

Bisphosphonate Use in Patients Who Have Osteoporosis Does Not Increase the Risk of Periprosthetic Fracture Following Total Knee Arthroplasty

BackgroundThe purpose of this study was to evaluate the effect of preoperative bisphosphonate use in patients who have osteoporosis on the risk of complications following primary total knee arthroplasty (TKA). MethodsAn administrative claims database was queried for patients who have osteoporosis undergoing primary TKA between 2010 and 2019 with a minimum of 2-year follow-up. Bisphosphonate-naive patients and bisphosphonate users, defined as patients who had a continuous prescription for bisphosphonates for a minimum of 6 months preoperatively, were matched 1:1 based on age, sex, and comorbidity burden. Patients undergoing nonelective TKA on chronic glucocorticoid therapy or receiving any other pharmacologic treatment for osteoporosis were excluded. The final cohort included 21,058 matched pairs of patients. The incidence of postoperative complications was identified via International Classification of Disease coding and compared between matched groups. A subgroup analysis was performed to examine outcomes among patients who underwent cemented and cementless TKA. ResultsThere was no difference in the incidence of periprosthetic fracture on univariate (0.7 versus 0.8%, P = 0.068) or multivariate testing (OR [odds ratio]: 1.24, 95% confidence interval [0.99 to 1.56]; P = 0.060). Bisphosphonate users were statistically less likely to undergo all-cause revision TKA at 2 years (OR: 0.84 [0.72 to 0.97]; P = 0.021). Patients who had osteoporosis were found to have an increased risk of periprosthetic fracture when TKA was performed with cementless implants (1.6 versus 0.4%; P = 0.033). However, when treated with bisphosphonates, patients who have osteoporosis demonstrated equivalent fracture rates regardless of implant type (1.3 versus 1.0%; P = 1.000). ConclusionsWhile bisphosphonate use in patients who have osteoporosis did not decrease the risk of periprosthetic fracture, it did significantly lower the incidence of all-cause revision at 2 years, although the difference identified was small. Consideration should be given to performing cemented TKA in patients who have untreated osteoporosis, given the higher rate of periprosthetic fracture when cementless implants were utilized.

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.

Quantifying Plastic and Reconstructive Surgery Engagement in the Evolution of ICD-10 Codes.

International Classification of Diseases (ICD)-10 code specificity is important for detailed diagnostic documentation. Insufficient coding granularity in plastic surgery may hinder accurate clinical documentation, impacting education, research, and patient care. This study examines ICD-10 code modification efforts by plastic surgeons and plastic surgery organizations within the last decade, subsequently detailing the formal proposal process and highlighting potential areas where coding modifications may be explored. A retrospective review of all modification proposals presented to the ICD Coordination and Maintenance Committee from March 2013 to March 2023 was conducted. Plastic surgery-related proposals were identified and evaluated for successful implementation. These results were compared to those of other specialties using Fisher exact tests and 2-tailed t tests. A total of 472 unique proposals were identified and reviewed. Surgeons and/or surgical organizations contributed to 111 (23.5%) novel proposals, 41 (36.9%) of which were initiated by surgeons. Nine surgical specialties and 14 supporting organizations were represented. The success rate for all surgical-related proposals was 85.6%, with an average time to implementation of 24.7 months. Only 2 (1.8%) of all surgical-related proposals pertained to plastic surgery, both of which were successfully implemented. Though a few plastic surgeons have taken advantage of the ICD code revision process within the last decade, the high overall success rate for surgical-related proposals suggests that this seems to be a feasible method by which plastic surgeons and plastic surgery organizations can bring about useful coding changes that meaningfully impact clinical practice.

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&amp;lt;0.001). This was associated with a 2.5% annual incidence rate reduction for deaths attributed to structural heart disease (p&amp;lt;0.001), but an 8.5% annual incidence rate increase for deaths attributed to SADS (p&amp;lt;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.