Developing and testing a framework for coding general practitioners' free-text diagnoses in electronic medical records - a reliability study for generating training data in natural language processing.

BACKGROUND: Diagnoses entered by general practitioners into electronic medical records have great potential for research and practice, but unfortunately, diagnoses are often in uncoded format, making them of little use. Natural language processing (NLP) could assist in coding free-text diagnoses, but NLP models require local training data to unlock their potential. The aim of this study was to develop a framework of research-relevant diagnostic codes, to test the framework using free-text diagnoses from a Swiss primary care database and to generate training data for NLP modelling. METHODS: The framework of diagnostic codes was developed based on input from local stakeholders and consideration of epidemiological data. After pre-testing, the framework contained 105 diagnostic codes, which were then applied by two raters who independently coded randomly drawn lines of free text (LoFT) from diagnosis lists extracted from the electronic medical records of 3000 patients of 27 general practitioners. Coding frequency and mean occurrence rates (n and %) and inter-rater reliability (IRR) of coding were calculated using Cohen's kappa (Κ). RESULTS: The sample consisted of 26,980 LoFT and in 56.3% no code could be assigned because it was not a specific diagnosis. The most common diagnostic codes were, 'dorsopathies' (3.9%, a code covering all types of back problems, including non-specific lower back pain, scoliosis, and others) and 'other diseases of the circulatory system' (3.1%). Raters were in almost perfect agreement (Κ ≥ 0.81) for 69 of the 105 diagnostic codes, and 28 codes showed a substantial agreement (K between 0.61 and 0.80). Both high coding frequency and almost perfect agreement were found in 37 codes, including codes that are particularly difficult to identify from components of the electronic medical record, such as musculoskeletal conditions, cancer or tobacco use. CONCLUSION: The coding framework was characterised by a subset of very frequent and highly reliable diagnostic codes, which will be the most valuable targets for training NLP models for automated disease classification based on free-text diagnoses from Swiss general practice.

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

PURPOSE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline. METHODS: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline. RESULTS: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%). CONCLUSIONS: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.

Prospective validation of a mobile health application for blood pressure management in patients with hypertensive disorders of pregnancy: study protocol for a randomized controlled trial.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) pose significant risks to both maternal and fetal health, contributing to global morbidity and mortality. Management of HDP is complex, particularly because of concerns regarding potential negative effects on utero-placental circulation and limited therapeutic options due to fetal safety. Our study investigates whether blood pressure monitoring through a mobile health (mHealth) application can aid in addressing the challenges of blood pressure management in pregnant individuals with HDP. Additionally, we aim to assess whether this intervention can improve short-term maternal and fetal outcomes and potentially mitigate long-term cardiovascular consequences. METHODS: This prospective, randomized, single-center trial will include 580 pregnant participants who meet the HDP criteria or who have a heightened risk of pregnancy-related hypertension due to factors such as multiple pregnancies, obesity, diabetes, or a history of HDP in prior pregnancies leading to preterm birth. Participants will be randomized to either the mHealth intervention group or the standard care group. The primary endpoint is the difference in systolic blood pressure from enrollment to 1 month after childbirth. The secondary endpoints include various blood pressure parameters, obstetric outcomes, body mass index trajectory, step counts, mood assessment, and drug adherence. CONCLUSIONS: This study emphasizes the potential of mHealth interventions, such as the Heart4U application, to improve blood pressure management in pregnant individuals with HDP. By leveraging technology to enhance engagement, communication, and monitoring, this study aims to positively impact maternal, fetal, and postpartum outcomes associated with HDP. This innovative approach demonstrates the potential of personalized technology-driven solutions for managing complex health conditions. TRIAL REGISTRATION: ClinicalTrials.gov NCT05995106. Registered on 16 August 2023.

Outcome evaluation of technical strategies on reduction of patient waiting time in the outpatient department at Kilimanjaro Christian Medical Centre-Northern Tanzania.

BACKGROUND: The Tanzania healthcare system is beset by prolonged waiting time in its hospitals particularly in the outpatient departments (OPD). Previous studies conducted at Kilimanjaro Christian Medical Centre (KCMC) revealed that patients typically waited an average of six hours before receiving the services at the OPD making KCMC have the longest waiting time of all the Zonal and National Referral Hospitals. KCMC implemented various interventions from 2016 to 2021 to reduce the waiting time. This study evaluates the outcome of the interventions on waiting time at the OPD. METHODS: This is an analytical cross-sectional mixed method using an explanatory sequential design. The study enrolled 412 patients who completed a structured questionnaire and in-depth interviews (IDI) were conducted among 24 participants (i.e., 12 healthcare providers and 12 patients) from 3rd to 14th July, 2023. Also, a documentary review was conducted to review benchmarks with regards to waiting time. Quantitative data analysis included descriptive statistics, bivariable and multivariable. All statistical tests were conducted at 5% significance level. Thematic analysis was used to analyse qualitative data. RESULTS: The findings suggest that post-intervention of technical strategies, the overall median OPD waiting time significantly decreased to 3 h 30 min IQR (2.51-4.08), marking a 45% reduction from the previous six-hour wait. Substantial improvements were observed in the waiting time for registration (9 min), payment (10 min), triage (14 min for insured patients), and pharmacy (4 min). Among the implemented strategies, electronic medical records emerged as a significant predictor to reduced waiting time (AOR = 2.08, 95% CI, 1.10-3.94, p-value = 0.025). IDI findings suggested a positive shift in patients' perceptions of OPD waiting time. Problems identified that still need addressing include, ineffective implementation of block appointment and extension of clinic days was linked to issues of ownership, organizational culture, insufficient training, and ineffective follow-up. The shared use of central modern diagnostic equipment between inpatient and outpatient services at the radiology department resulted in delays. CONCLUSION: The established technical strategies have been effective in reducing waiting time, although further action is needed to attain the global standard of 30 min to 2 h OPD waiting time.

Improve the efficiency and accuracy of ophthalmologists' clinical decision-making based on AI technology.

BACKGROUND: As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy. METHODS: In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors. RESULTS: The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors. CONCLUSION: The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

Effect of electronic records on mortality among patients in hospital and primary healthcare settings: a systematic review and meta-analyses.

Background: Electronic medical records or electronic health records, collectively called electronic records, have significantly transformed the healthcare system and service provision in our world. Despite a number of primary studies on the subject, reports are inconsistent and contradictory about the effects of electronic records on mortality. Therefore, this review examined the effect of electronic records on mortality. Methods: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guideline. Six databases: PubMed, EMBASE, Scopus, CINAHL, Cochrane Library, and Google Scholar, were searched from February 20 to October 25, 2023. Studies that assessed the effect of electronic records on mortality and were published between 1998 and 2022 were included. Joanna Briggs Institute quality appraisal tool was used to assess the methodological quality of the studies. Narrative synthesis was performed to identify patterns across studies. Meta-analysis was conducted using fixed effect and random-effects models to estimate the pooled effect of electronic records on mortality. Funnel plot and Egger's regression test were used to assess for publication bias. Results: Fifty-four papers were found eligible for the systematic review, of which 42 were included in the meta-analyses. Of the 32 studies that assessed the effect of electronic health record on mortality, eight (25.00%) reported a statistically significant reduction in mortality, 22 (68.75%) did not show a statistically significant difference, and two (6.25%) studies reported an increased risk of mortality. Similarly, among the 22 studies that determined the effect of electronic medical record on mortality, 12 (54.55%) reported a statistically significant reduction in mortality, and ten (45.45%) studies didn't show a statistically significant difference. The fixed effect and random effects on mortality were OR = 0.95 (95% CI: 0.93-0.97) and OR = 0.94 (95% CI: 0.89-0.99), respectively. The associated I-squared was 61.5%. Statistical tests indicated that there was no significant publication bias among the studies included in the meta-analysis. Conclusion: Despite some heterogeneity among the studies, the review indicated that the implementation of electronic records in inpatient, specialized and intensive care units, and primary healthcare facilities seems to result in a statistically significant reduction in mortality. Maturity level and specific features may have played important roles. Systematic Review Registration: PROSPERO (CRD42023437257).

Delabelling beta-lactam allergy.

Background: Hypersensitivity to beta-lactam (BL) antibiotics is one of the most frequent reported drug allergies. In our population, it is common to find labels of BL allergy in electronic medical records (EMRs) that have not been assessed. The objective of our study was to detect patients with beta-lactam allergy labels in their EMRs and to assess how many of them are false after a correct diagnostic evaluation. Methods: A multicentre prospective study was performed with patients labelled as allergic to BLs in their EMRs in the previous 5 years. Demographical and clinical data, as well as variables regarding the BL allergy label and the characteristics of the index reaction from clinical history and EMRs, were recorded. Then, diagnostic assessments including clinical history, skin tests (STs), and drug provocation tests (DPTs) were conducted in order to confirm or exclude the diagnosis of BL allergy. Results: A total of 249 patients completed the study, of which 160 (64.3%) were women with a median age of 57 years (interquartile range [IQR], 45-68). The most frequent BL allergy labels detected were for penicillin (124), amoxicillin/clavulanic acid (61), and amoxicillin (54). Of the 204 patients who underwent STs, 20.1% were positive. DPTs were performed in 224 patients, showing good tolerance in 87.1% of cases. After the allergy diagnosis work-up, 186 patients (74.7%) were diagnosed as non-allergic to BL antibiotics. Conclusion: In our study population, the number of patients labelled as allergic to BLs in their EMRs was similar to that in previously published studies, with proportions near to 75%-80% being falsely labelled as allergic to BLs.

Real-world evidence for vitiligo using an electronic medical records database in Spain: the REVEAL-ES study.

The European prevalence of vitiligo diagnosis is 0.2%-0.8%, with country-specific and methodological differences. Although vitiligo profoundly impacts quality of life, limited studies have evaluated disease burden and treatment patterns. This real-world study describes the prevalence, incidence, characteristics, and treatment patterns of vitiligo among patients in Spain during 2015-2021. This retrospective observational study using the IQVIA Electronic Medical Records database in Spain included patients with vitiligo (International Classification of Diseases, Ninth Revision codes 709.01/374.53). Incident and prevalent cohorts comprised registered patients with vitiligo diagnoses during and before 2015-2021, respectively. Patient characteristics and treatment data were extracted. Vitiligo incidence was 0.016 (95% CI: 0.014-0.018) per 100 person-years, and prevalence was 0.19% (95% CI: 0.18%-0.19%) in 2021. Females were more affected than males (0.16% vs 0.13%, respectively). Among 1,400 incident patients, mean (SD) age was 40.7 (19.7) years; most were female (53.9%). The most common comorbidities after vitiligo diagnosis were eczema (20.8%), hypercholesterolaemia/hypertriglyceridaemia (17.9%), anxiety (10.9%), thyroid disorders (9.1%), and diabetes (6.4%). In 2021, 78.6% of prevalent patients did not receive vitiligo-related treatments. The most prescribed vitiligo-related treatments were topical calcineurin inhibitors (13.9%) and topical corticosteroids (13.0%); 11.9% had a record of psychiatric medications. This study confirms the association between vitiligo and comorbidities (e.g., eczema, thyroid disorders) and high disease burden. The prevalence in Spain in 2021 (0.19%) was within the lower band of European estimates based on surveys/medical screenings. Most patients are not receiving vitiligo-related treatment and could benefit from new, effective treatments.

Trajectories of BMI before and after diagnosis of type 2 diabetes in a real-world population.

AIMS/HYPOTHESIS: Few studies have examined the clinical characteristics associated with changes in weight before and after diagnosis of type 2 diabetes. Using a large real-world cohort, we derived trajectories of BMI before and after diabetes diagnosis, and examined the clinical characteristics associated with these trajectories, including assessing the impact of pre-diagnosis weight change on post-diagnosis weight change. METHODS: We performed an observational cohort study using electronic medical records from individuals in the Scottish Care Information Diabetes Collaboration database. Two trajectories were calculated, based on observed BMI measurements between 3 years and 6 months before diagnosis and between 1 and 5 years after diagnosis. In the post-diagnosis trajectory, each BMI measurement was time-dependently adjusted for the effects of diabetes medications and HbA1c change. RESULTS: A total of 2736 individuals were included in the study. There was a pattern of pre-diagnosis weight gain, with 1944 individuals (71%) gaining weight overall, and 875 (32%) gaining more than 0.5 kg/m2 per year. This was followed by a pattern of weight loss after diagnosis, with 1722 individuals (63%) losing weight. Younger age and greater social deprivation were associated with increased weight gain before diagnosis. Pre-diagnosis weight change was unrelated to post-diagnosis weight change, but post-diagnosis weight loss was associated with older age, female sex, higher BMI, higher HbA1c and weight gain during the peri-diagnosis period. When considering the peri-diagnostic period (defined as from 6 months before to 12 months after diagnosis), we identified 986 (36%) individuals who had a high HbA1c at diagnosis but who lost weight rapidly and were most aggressively treated at 1 year; this subgroup had the best glycaemic control at 5 years. CONCLUSIONS/INTERPRETATION: Average weight increases before diagnosis and decreases after diagnosis; however, there were significant differences across the population in terms of weight changes. Younger individuals gained weight pre-diagnosis, but, in older individuals, type 2 diabetes is less associated with weight gain, consistent with other drivers for diabetes aetiology in older adults. We have identified a substantial group of individuals who have a rapid deterioration in glycaemic control, together with weight loss, around the time of diagnosis, and who subsequently stabilise, suggesting that a high HbA1c at diagnosis is not inevitably associated with a poor outcome and may be driven by reversible glucose toxicity.

Utilizing EMR Data for Smoking Behavior Surveillance in Hospitalized Patients With Chronic Respiratory Diseases and Its Impact on Clinical Outcomes - Beijing Municipality, China, 2014-2023.

What is already known on this topic?: Smoking is the primary risk factor for a poor prognosis in chronic respiratory disease (CRD). Current tobacco surveillance efforts in China focus on the general population and do not adequately cover CRD patients. What is added by this report?: We employed electronic medical records (EMR) to track smoking habits in 28,334 hospitalized CRD patients at Beijing Chao-Yang Hospital. The rates of former and current smokers were 30.7% and 18.0%, respectively. Both former and current smokers exhibited an increased risk of respiratory symptoms and extended hospital stays. What are the implications for public health practice?: These results underscore the importance of implementing smoking monitoring and targeted cessation interventions for hospitalized patients with CRDs.

TREATMENT OF HIP AND KNEE PERIPROSTHETIC JOINT INFECTION REQUIRES EXTENSIVE ADMINISTRATIVE WORK.

INTRODUCTION: Treatment of periprosthetic joint infections (PJI) typically requires more resource utilization than primary total joint arthroplasty (TJA). This study quantifies the amount of time spent in the electronic medical record (EMR) for patients who have PJI requiring surgical intervention. METHODS: A retrospective analysis of EMR activity for 165 hip and knee PJI was performed to capture work during the preoperative and postoperative time periods. Independent sample t tests were conducted to compare total time based on procedure, age, insurance, health literacy, sex, race, and ethnicity. RESULTS: The EMR work performed by the orthopaedic team was 338.4 minutes (min) (SD [standard deviation] 130.3), with 119.4 minutes (SD 62.8) occurring preoperatively and 219.0 minutes (SD 112.9) postoperatively. Preoperatively, the surgeon's work accounted for 35.7 minutes (SD 25.4), mid-level providers 21.3 minutes (SD 15.9), nurses 38.6 minutes (SD 36.8), and office staff 32.7 minutes (SD 29.9). Infectious Disease (ID) colleagues independently performed 158.9 minutes (SD 108.5) of postoperative work. Overall, PJI of the knees required more postoperative work. Secondary analysis revealed that patients who have hip PJI and a BMI < 30 and patients < 65 years required more work when compared to the PJI of heavier and older individuals. There was no difference in total work based on insurance, health literacy, race, or ethnicity. CONCLUSION: Over 8 hours of administrative work is required for surgical management of PJI. Surgeons alone performed 451% more work for PJI during the preoperative period (7.9 versus 35.7min) compared to primary TJA. In efforts to provide best care for our sickest patients, much work is required perioperatively. This work is necessary to consider when assigning value and physician reimbursement.

TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease.

OBJECTIVE: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. MATERIALS AND METHODS: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. RESULTS: The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. DISCUSSION: The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. CONCLUSION: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.

It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine.

Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.

New-Onset Hidradenitis Suppurativa in Psoriasis Patients: A Multi-Center, Retrospective Cohort Study.

BACKGROUND: Previous research has indicated a potential correlation between hidradenitis suppurativa (HS) and psoriasis (PSO), two chronic inflammatory dermatological diseases. However, there is a lack of comprehensive evaluations that consider a variety of clinical and demographic factors, and the risk of developing HS in PSO patients remains unclear. Our study aims to examine HS risk over time among PSO patients versus matched controls while considering the influence of confounders to provide insights into the potential link between these two diseases. METHOD: In this multi-institutional cohort study using the TriNetX database, we matched 202,318 patients with PSO with an equivalent number of individuals without PSO, using propensity score matching. The study period extended from 1 January 2005 to 31 December 2018. We computed hazard ratios and their respective 95% confidence intervals (CIs) to evaluate the probability of HS manifestation over a period of 5 years in patients with PSO in comparison to those without PSO. RESULTS: PSO patients demonstrated a consistently higher risk of developing HS than matched controls across all analytic models with the hazard ratios (HR) ranging from 1.43 (95% CI 1.30-1.56) to 5.91 (95% CI 2.49-14.04). Stratified analyses showed the increased HS risk was observed in both genders but only significant in those aged 18-64 years. Kaplan-Meier analysis indicated PSO patients had a higher cumulative probability of developing HS over time (HR 1.77, 95% CI 1.49-1.89). CONCLUSIONS: PSO was associated with increased HS risk, highlighting the importance of considering HS as a potential comorbidity in PSO patients and may have implications for early detection, prevention, and management strategies for both conditions. Shared inflammatory pathways, genetic components, and skin dysbiosis may contribute. Further research should elucidate underlying mechanisms.

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease.

Introduction: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve patient prognosis. Methods: Electronic medical records from patients with CKD at a single center from 2015 to 2020 were used to develop machine learning models for the prediction of CVD. Least absolute shrinkage and selection operator (LASSO) regression was used to select important features predicting the risk of developing CVD. Seven machine learning classification algorithms were used to build models, which were evaluated by receiver operating characteristic curves, accuracy, sensitivity, specificity, and F1-score, and Shapley Additive explanations was used to interpret the model results. CVD was defined as composite cardiovascular events including coronary heart disease (coronary artery disease, myocardial infarction, angina pectoris, and coronary artery revascularization), cerebrovascular disease (hemorrhagic stroke and ischemic stroke), deaths from all causes (cardiovascular deaths, non-cardiovascular deaths, unknown cause of death), congestive heart failure, and peripheral artery disease (aortic aneurysm, aortic or other peripheral arterial revascularization). A cardiovascular event was a composite outcome of multiple cardiovascular events, as determined by reviewing medical records. Results: This study included 8,894 patients with CKD, with a composite CVD event incidence of 25.9%; a total of 2,304 patients reached this outcome. LASSO regression identified eight important features for predicting the risk of CKD developing into CVD: age, history of hypertension, sex, antiplatelet drugs, high-density lipoprotein, sodium ions, 24-h urinary protein, and estimated glomerular filtration rate. The model developed using Extreme Gradient Boosting in the test set had an area under the curve of 0.89, outperforming the other models, indicating that it had the best CVD predictive performance. Conclusion: This study established a CVD risk prediction model for patients with CKD, based on routine clinical diagnostic and treatment data, with good predictive accuracy. This model is expected to provide a scientific basis for the management and treatment of patients with CKD.

Residuals in space: Potential pitfalls and applications from single-institution survival analysis.

In practice, survival analyses appear in pharmaceutical testing, procedural recovery environments, and registry-based epidemiological studies, each reasonably assuming a known patient population. Less commonly discussed is the additional complexity introduced by non-registry and spatially-referenced data with time-dependent covariates in observational settings. In this short report we discuss residual diagnostics and interpretation from an extended Cox proportional hazard model intended to assess the effects of wildfire evacuation on risk of a secondary cardiovascular events for patients of a specific healthcare system on the California's central coast. We describe how traditional residuals obscure important spatial patterns indicative of true geographical variation, and their impacts on model parameter estimates. We briefly discuss alternative approaches to dealing with spatial correlation in the context of Bayesian hierarchical models. Our findings/experience suggest that careful attention is needed in observational healthcare data and survival analysis contexts, but also highlights potential applications for detecting observed hospital service areas.

Development of Recommendations for the Digital Sharing of Notes With Adolescents in Mental Health Care: Delphi Study.

Background: In many countries, health care professionals are legally obliged to share information from electronic health records with patients. However, concerns have been raised regarding the sharing of notes with adolescents in mental health care, and health care professionals have called for recommendations to guide this practice. Objective: The aim was to reach a consensus among authors of scientific papers on recommendations for health care professionals' digital sharing of notes with adolescents in mental health care and to investigate whether staff at child and adolescent specialist mental health care clinics agreed with the recommendations. Methods: A Delphi study was conducted with authors of scientific papers to reach a consensus on recommendations. The process of making the recommendations involved three steps. First, scientific papers meeting the eligibility criteria were identified through a PubMed search where the references were screened. Second, the results from the included papers were coded and transformed into recommendations in an iterative process. Third, the authors of the included papers were asked to provide feedback and consider their agreement with each of the suggested recommendations in two rounds. After the Delphi process, a cross-sectional study was conducted among staff at specialist child and adolescent mental health care clinics to assess whether they agreed with the recommendations that reached a consensus. Results: Of the 84 invited authors, 27 responded. A consensus was reached on 17 recommendations on areas related to digital sharing of notes with adolescents in mental health care. The recommendations considered how to introduce digital access to notes, write notes, and support health care professionals, and when to withhold notes. Of the 41 staff members at child and adolescent specialist mental health care clinics, 60% or more agreed with the 17 recommendations. No consensus was reached regarding the age at which adolescents should receive digital access to their notes and the timing of digitally sharing notes with parents. Conclusions: A total of 17 recommendations related to key aspects of health care professionals' digital sharing of notes with adolescents in mental health care achieved consensus. Health care professionals can use these recommendations to guide their practice of sharing notes with adolescents in mental health care. However, the effects and experiences of following these recommendations should be tested in clinical practice.

Epidemiology of Injuries Among National Basketball Association Players: 2013-2014 Through 2018-2019.

BACKGROUND: Understanding the epidemiology of injuries to athletes is essential to informing injury prevention efforts. HYPOTHESIS: The incidence and impact of basketball-related injuries among National Basketball Association (NBA) players from 2013-2014 through 2018-2019 is relatively stable over time. STUDY DESIGN: Descriptive epidemiology study. LEVEL OF EVIDENCE: Level 3. METHODS: Injuries from 2013-2014 through 2018-2019 were analyzed using the NBA Injury and Illness Database from an electronic medical record system. Descriptive statistics were calculated for injuries by season, game-loss, and onset. Incidence rates were estimated using Poisson models and linear trend tests. RESULTS: Between 552 and 606 players participated in ≥1 game per season during the study. Annual injury incidence ranged from 1550 to 1892, with 33.6% to 38.5% resulting in a missed NBA game. Game-loss injury rates ranged from 5.6 to 7.0 injuries per 10,000 player-minutes from 2014-2015 through 2018-2019 (P = 0.19); the rate was lower in 2013-2014 (5.0 injuries per 10,000 player-minutes), partly due to increased preseason injury rates and transition of reporting processes. The 6-year game-loss injury rate in preseason and regular season games was 6.9 (95% CI 6.0, 8.0) and 6.2 (95% CI 6.0, 6.5) injuries per 10,000 player-minutes; the rate in playoff games was lower (P < 0.01) at 2.8 (95% CI 2.2, 3.6). Most (73%) game-loss injuries had acute onset; 44.4% to 52.5% of these involved contact with another player. CONCLUSION: From 2013-2014 through 2018-2019, over one-third of injuries resulted in missed NBA games, with highest rates of game-loss injuries in preseason games and lowest rates in playoff games. Most game-loss injuries had acute onset, and half of those involved contact with another player. CLINICAL RELEVANCE: These findings - through reliable data reporting by team medical staff in an audited system - can guide evidence-based injury reduction strategies and inform player health priorities.

New-onset Fibromyalgia After Total Knee Replacement in Patients With Osteoarthritis: A Propensity-score-matched Cohort Study in the United States.

BACKGROUND/AIM: The risk of new-onset fibromyalgia after total knee replacement (TKR) in osteoarthritis patients is not well-established. This study aimed to assess the risk of developing fibromyalgia post-TKR, considering potential variations across age and sex. PATIENTS AND METHODS: Utilizing a multicenter retrospective cohort design and data from the TriNetX research network, electronic health records of osteoarthritis patients who underwent TKR and the same number of matched controls were analyzed. Propensity-score matching was performed by matching critical confounders. Hazard ratios were evaluated to assess fibromyalgia risk in the TKR cohort compared to non-TKR controls. RESULTS: The hazard ratio of future fibromyalgia for the TKR cohort was 2.08 (95% confidence interval=1.74-2.49) for 1 year after the index date, 1.81 (95% confidence interval=1.62-2.02) for 3 years, and 1.69 (95% confidence interval=1.54-1.86) for 5 years compared with non-TKR controls. The significant association remained in sensitivity models and stratification analyses in different age and sex subgroups. CONCLUSION: Clinicians should be vigilant about the potential for fibromyalgia development post-TKR and consider tailored interventions; our findings emphasize the need for further research to elucidate underlying mechanisms and identify modifiable risk factors.

Prescriptions of opioid-containing drugs in patients with chronic cough.

BACKGROUND: Chronic cough (CC) affects about 10% of adults, but opioid use in CC is not well understood. OBJECTIVES: To determine the use of opioid-containing cough suppressant (OCCS) prescriptions in patients with CC using electronic health records. DESIGN: Retrospective cohort study. METHODS: Through retrospective analysis of Midwestern U.S. electronic health records, diagnoses, prescriptions, and natural language processing identified CC - at least three medical encounters with cough, with 56-120 days between first and last encounter - and a 'non-chronic cohort'. Student's t-test, Pearson's chi-square, and zero-inflated Poisson models were used. RESULTS: About 20% of 23,210 patients with CC were prescribed OCCS; odds of an OCCS prescription were twice as great in CC. In CC, OCCS drugs were ordered in 38% with Medicaid insurance and 15% with commercial insurance. CONCLUSION: Findings identify an important role for opioids in CC, and opportunity to learn more about the drugs' effectiveness.