UNASSIGNED: Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation.
UNASSIGNED: A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394).
UNASSIGNED: Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported.
UNASSIGNED: Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.

UNASSIGNED: ICD codes are used for classification of hospitalizations. The codes are used for administrative, financial, and research purposes. It is known, however, that errors occur. Natural language processing (NLP) offers promising solutions for optimizing the process. To investigate methods for automatic classification of disease in unstructured medical records using NLP and to compare these to conventional ICD coding.
UNASSIGNED: Two datasets were used: the open-source Medical Information Mart for Intensive Care (MIMIC)-III dataset (n = 55.177) and a dataset from a hospital in Belgium (n = 12.706). Automated searches using NLP algorithms were performed for the diagnoses \'atrial fibrillation (AF)\' and \'heart failure (HF)\'. Four methods were used: rule-based search, logistic regression, term frequency-inverse document frequency (TF-IDF), Extreme Gradient Boosting (XGBoost), and Bio-Bidirectional Encoder Representations from Transformers (BioBERT). All algorithms were developed on the MIMIC-III dataset. The best performing algorithm was then deployed on the Belgian dataset. After preprocessing a total of 1438 reports was retained in the Belgian dataset. XGBoost on TF-IDF matrix resulted in an accuracy of 0.94 and 0.92 for AF and HF, respectively. There were 211 mismatches between algorithm and ICD codes. One hundred and three were due to a difference in data availability or differing definitions. In the remaining 108 mismatches, 70% were due to incorrect labelling by the algorithm and 30% were due to erroneous ICD coding (2% of total hospitalizations).
UNASSIGNED: A newly developed NLP algorithm attained a high accuracy for classifying disease in medical records. XGBoost outperformed the deep learning technique BioBERT. NLP algorithms could be used to identify ICD-coding errors and optimize and support the ICD-coding process.

暂无摘要。

BACKGROUND: Osteoporosis is a global issue with a worldwide prevalence of 18.3%, and the presence of coexisting fragility fractures can reduce the survival rate by approximately 20%. In Japan, the prevalence of osteoporosis is estimated to be 12.8 million, and the annual occurrence of hip fractures is approximately 193,400. Remarkably, coexisting hip or spinal fragility fractures caused by slight external force meet the Japanese diagnostic criterion for osteoporosis regardless of bone mineral density. However, only 191 deaths due to osteoporosis were published in 2021 in Japan. With the concern that some cases of hip and spinal fragility fractures were assigned an underlying cause of death of traumatic fracture instead of osteoporosis, this study aimed to elucidate the actual number of deaths due to osteoporosis in Japan.
METHODS: We used the data from Japan in 2018. First, the number of deaths due to osteoporosis and hip or spinal fractures was reviewed using published vital statistics. Second, we calculated the number of elderly deaths (age ≥80 years) resulting from hip or spinal fractures caused by falls on the same level using data from approximately 1.4 million annual individual death certificates. Combining the above data, the actual number of deaths due to osteoporosis was estimated.
RESULTS: Only 190 deaths due to osteoporosis were reported in the published data. The individual certificate data revealed 3437 elderly deaths due to hip or spinal fractures caused by falls on the same level, which could meet the criteria of osteoporotic fragility fractures. Accordingly, the estimated number of deaths caused by osteoporosis was calculated as 3,627, approximately 19 times the published value.
CONCLUSIONS: After researching the individual death certificate data focusing on the coexisting hip or spinal fragility fracture, it was implied that osteoporosis may have a higher mortality rate in Japan than what is published.

Within the trauma system, the emergency department (ED) is the hospital\'s first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED.
The aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED.
We used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED.
Our proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320).
Our proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.

The utilization of International Classification of Diseases, Ninth or Tenth Revision, (ICD-9/10) coding to identify the incidence of disease is frequently performed in medical research. This study attempts to assess the validity of using ICD-9/10 codes to identify patients with shoulder dystocia (SD) with concurrent neonatal brachial plexus palsy (NBPP).
This retrospective cohort study examined patients evaluated at the University of Michigan Brachial Plexus and Peripheral Nerve Program (UM-BP/PN) from 2004 to 2018. We reported the percentage of patients with reported NBPP ICD-9/10 and SD ICD-9/10 discharged at birth who were later diagnosed with NBPP by a specialty clinic by interdisciplinary faculty and staff utilizing physical evaluations and ancillary testing such as such as electrodiagnostics and imaging. The relationship of reported NBPP ICD-9/10, SD ICD-9/10, extent of NBPP nerve involvement, and NBPP persistence at age two years were examined via chi-square or Fischer exact test.
Of the 51 mother-infant dyads with complete birth discharge records evaluated at the UM-BP/PN, 26 (51%) were discharged without an ICD-9/10 code documenting NBPP; of these 26 patients, only four had ICD-9/10 documentation of SD at discharge, which left 22 patients with no ICD-9/10 code documentation of either SD or NBPP (43%). Patients with pan-plexopathy were more likely to be discharged with an NBBP ICD-9/10 code than those infants with upper nerve involvement (77% vs 39%, P < 0.02).
Use of ICD-9/10 codes for the identification of NBPP appears to undercount the true incidence. This underestimation is more pronounced for milder forms of NBPP.

The World Health Organization\'s International Classification of Diseases (ICD) has become the international standard diagnostic classification for reporting morbidity and mortality. In 2015, the United States transitioned from the 9th to 10th Revision. The update was necessary due to major structural limitations of the ICD-9 system. Concerns of the transition mainly centered around clinical usage and cost; however, there were concerns for overlapping codes with the same classification but different meanings between the two versions. Duplicate codes could pose an issue for big data retrospective studies that overlap between the two systems. Therefore, the goals of this study are to further explore and identify duplicate ICD codes between the systems. ICD-9-CM and ICD-10-CM code files were obtained from the Centers for Medicare & Medicaid Services. There were 14,567 ICD-9-CM codes and 91,737 unique ICD-10-CM codes tabulated. Duplicated items between the files were isolated. Four hundred sixty-nine duplicate codes were identified, consisting of 39 E Codes and 430 V Codes. These twin codes contain classifications for external causes of injury and factors influencing health status and contact with health services. Therefore, special attention should be drawn to retrospective research involving methods of injury spanning ICD-9 and ICD-10 systems.

Immune checkpoint inhibitors (ICIs) are a rapidly expanding class of targeted therapies effective in the treatment of various cancers. However, while efficacious, ICIs have been associated with treatment complications, namely immune-related adverse events (irAEs). IrAEs of the endocrine system are among the most commonly reported irAEs, but despite their high incidence, standardized disease definitions and endocrine IrAE-specific International Classification of Diseases (ICD) codes remain lacking. This dearth of standardized nomenclature and ICD codes has in many ways impeded both the clinical care of patients and the progress of endocrine irAE-related research. ICD codes are used internationally and are essential for medical claims reporting in the health care setting, and they provide a universal language system for recording, reporting, and monitoring diseases. These codes are also a well-accepted form of electronic health record data capture that facilitates the collection, storage, and sharing of data. Therefore, the lack of standardized disease definitions and ICD codes has been associated with misclassification and suboptimal management of individuals with endocrine irAEs and has also been associated with reduced data availability, comparability, and quality. Harmonized and clinically relevant disease definitions along with the subsequent development of endocrine-irAE-specific ICD codes will provide a systematic approach to understanding the spectrum and burden of endocrine irAE diseases, and will have a positive effect across clinical, public health, and research settings.

In the US, over 200 lives are lost from opioid overdoses each day. Accurate and prompt diagnosis of opioid use disorders (OUD) may help prevent overdose deaths. However, international classification of disease (ICD) codes for OUD are known to underestimate prevalence, and their specificity and sensitivity are unknown. We developed and validated algorithms to identify OUD in electronic health records (EHR) and examined the validity of OUD ICD codes.
Through four iterations, we developed EHR-based OUD identification algorithms among patients who were prescribed opioids from 2014 to 2017. The algorithms and OUD ICD codes were validated against 169 independent \"gold standard\" EHR chart reviews conducted by an expert adjudication panel across four healthcare systems. After using 2014-2020 EHR for validating iteration 1, the experts were advised to use 2014-2017 EHR thereafter.
Of the 169 EHR charts, 81 (48%) were reviewed by more than one expert and exhibited 85% expert agreement. The experts identified 54 OUD cases. The experts endorsed all 11 OUD criteria from the Diagnostic and Statistical Manual of Mental Disorders-5, including craving (72%), tolerance (65%), withdrawal (56%), and recurrent use in physically hazardous conditions (50%). The OUD ICD codes had 10% sensitivity and 99% specificity, underscoring large underestimation. In comparison our algorithm identified OUD with 23% sensitivity and 98% specificity.
This is the first study to estimate the validity of OUD ICD codes and develop validated EHR-based OUD identification algorithms. This work will inform future research on early intervention and prevention of OUD.

Physical trauma-related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the Injury Severity Score (ISS), which is based on the Abbreviated Injury Scale (AIS), an anatomical injury severity scoring system. However, the AIS requires specialists to code the injury scale by reviewing a patient\'s medical record; therefore, applying the model to every hospital is impossible.
We aimed to develop an artificial intelligence (AI) model to predict in-hospital mortality in physical trauma patients using the International Classification of Disease 10th Revision (ICD-10), triage scale, procedure codes, and other clinical features.
We used the Korean National Emergency Department Information System (NEDIS) data set (N=778,111) compiled from over 400 hospitals between 2016 and 2019. To predict in-hospital mortality, we used the following as input features: ICD-10, patient age, gender, intentionality, injury mechanism, and emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and procedure codes. We proposed the ensemble of deep neural networks (EDNN) via 5-fold cross-validation and compared them with other state-of-the-art machine learning models, including traditional prediction models. We further investigated the effect of the features.
Our proposed EDNN with all features provided the highest area under the receiver operating characteristic (AUROC) curve of 0.9507, outperforming other state-of-the-art models, including the following traditional prediction models: Adaptive Boosting (AdaBoost; AUROC of 0.9433), Extreme Gradient Boosting (XGBoost; AUROC of 0.9331), ICD-based ISS (AUROC of 0.8699 for an inclusive model and AUROC of 0.8224 for an exclusive model), and KTAS (AUROC of 0.1841). In addition, using all features yielded a higher AUROC than any other partial features, namely, EDNN with the features of ICD-10 only (AUROC of 0.8964) and EDNN with the features excluding ICD-10 (AUROC of 0.9383).
Our proposed EDNN with all features outperforms other state-of-the-art models, including the traditional diagnostic code-based prediction model and triage scale.