Characterization of sleep disturbance in established rheumatoid arthritis patients: exploring the relationship with central nervous system pain regulation.

BACKGROUND: To characterize sleep disturbance in patients with established rheumatoid arthritis (RA) and explore the relationship between sleep and mechanisms of central nervous system pain regulation. METHODS: Forty-eight RA participants completed wrist-worn actigraphy monitoring and daily sleep diaries for 14 days to assess sleep-wake parameters. Participants underwent quantitative sensory testing to assess pressure pain thresholds, temporal summation, and conditioned pain modulation. Data were analyzed using descriptive statistics, Spearman's correlation, and multivariable median regression analyses. RESULTS: Median actigraphy and sleep diary derived sleep duration was 7.6 h (interquartile range (IQR) 7.0, 8.2) and 7.1 h (IQR 6.7, 7.6), respectively. Actigraphy based sleep fragmentation (rho = 0.34), wake after sleep onset (rho = 0.36), and sleep efficiency (rho = -0.32) were each related to higher temporal summation values in unadjusted analyses, but these relationships did not persist after controlling for age, body mass index, disease duration, and swollen joint count. No significant relationships were observed between sleep with pressure pain thresholds and conditioned pain modulation. CONCLUSION: Actigraphy and sleep diary monitoring are well tolerated in established RA patients. Future investigations should include both subjective and objective assessments, as they may provide information relating to different components and mechanisms.

An Atlas Characterizing the Shared Genetic Architecture of Inflammatory Bowel Disease with Clinical and Behavioral Traits.

BACKGROUND: Inflammatory bowel disease (IBD) development is a complex, multifactorial process that involves extrinsic and intrinsic factors such as host genetics, the immune system, the gut microbiome, and environmental risks. To help understand the genetic contribution of clinical, behavioral, psychiatric, and diet-related traits, we aim to provide a deep and comprehensive characterization of the shared genetic architecture between IBD and hundreds of potentially related traits. METHODS: Utilizing publicly available summary statistics from a previously published IBD genome-wide association study and hundreds of traits from the United Kingdom BioBank (UKBB), we performed linkage disequilibrium score regression (LDSR) analysis to estimate cross-trait genetic correlations between Crohn's disease (CD), ulcerative colitis (UC), and IBD summary statistics with the UKBB traits of interest. RESULTS: Nominally significant (P < .05) genetic correlations were observed for 181 traits in overall IBD, 239 traits in CD, and 94 traits in UC. We replicate the known association between smoking behavior and CD/UC, namely that current tobacco smoking has a positive genetic correlation with CD (rg = 0.12, P = 4.2 × 10-4), while "ever smoking" has a negative genetic correlation with UC (rg = -0.07, P = .042). Globally, all 3 strata (IBD, CD, and UC) demonstrated increased genetic correlations for psychiatric-related traits related to anxiety and depression. CONCLUSION: The present analysis reveals the shared genetic architecture between multiple traits and IBD, CD, and UC. Understanding the relevance of joint occurrences of IBD with psychiatric diseases may moderate management of these diseases for individuals jointly affected by them.

This study provides an atlas of the genetic correlation between hundreds of United Kingdom Biobank (UKBB) traits with inflammatory bowel disease (IBD), Crohn's disease (CD), and ulcerative colitis (UC). Notable strong correlations are seen between IBD and various psychiatric traits.

Heritable Traits and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study.

INTRODUCTION: Lung cancer is a complex polygenic disorder. Analysis with Mendelian randomization (MR) allows for genetically predicted risks to be estimated between exposures and outcomes. METHODS: We analyzed 345 heritable traits from the United Kingdom Biobank and estimated their associated effects on lung cancer outcomes using two sample MR. In addition to estimating effects with overall lung cancer, adenocarcinoma, small cell lung cancer, and squamous cell lung cancers, we performed conditional effect modeling with multivariate MR (MVMR) and the traits of alcohol use, smoking initiation, average pre-tax income, and educational attainment. RESULTS: Univariate MR provided evidence for increased age at first sexual intercourse (OR, 0.55; P = 6.15 × 10-13), educational attainment (OR, 0.24; P = 1.07 × 10-19), average household income (OR, 0.58; P = 7.85 × 10-05), and alcohol usually taken with meals (OR, 0.19; P = 1.06 × 10-06) associating with decreased odds of overall lung cancer development. In contrast, a lack of additional educational attainment (OR, 8.00; P = 3.48 × 10-12), body mass index (OR, 1.28; P = 9.00 × 10-08), pack years smoking as a proportion of life span (OR, 9.93; P = 7.96 × 10-12), and weekly beer intake (OR, 3.48; P = 4.08 × 10-07) were associated with an increased risk of overall lung cancer development. CONCLUSIONS: Many heritable traits associated with an increased or inverse risk of lung cancer development. Effects vary based on histologic subtype and conditional third trait exposures. IMPACT: We identified several heritable traits and presented their genetically predictable impact on lung cancer development, providing valuable insights for consideration.

A comprehensive analysis of lung cancer highlighting epidemiological factors and psychiatric comorbidities from the All of Us Research Program.

Lung cancer is the leading cause of cancer-related mortality in the United States. Investigating epidemiological and clinical parameters can contribute to an improved understanding of disease development and management. In this cross-sectional, case-control study, we used the All of Us database to compare healthcare access, family history, smoking-related behaviors, and psychiatric comorbidities in light smoking controls, matched smoking controls, and primary and secondary lung cancer patients. We found a decreased odds of primary lung cancer patients versus matched smoking controls reporting inability to afford follow-up or specialist care. Additionally, we found a significantly increased odds of secondary lung cancer patients having comorbid anxiety and insomnia when compared to matched smoking controls. Our study provides a profile of the psychiatric disease burden in lung cancer patients and reports key epidemiological factors in patients with primary and secondary lung cancer. By using two controls, we were able to separate smoking behavior from lung cancer and identify factors that were mediated by heavy smoking alone or by both smoking and lung cancer.

Modifiable physical and behavioural factors associated with widespread pain in older adults with radiographic evidence of knee osteoarthritis.

OBJECTIVE: To identify modifiable physical and behavioural factors associated with widespread pain (WSP) in older adults with radiographic evidence of knee osteoarthritis (OA). METHODS: Cross-sectional initial visit data of participants with radiographic knee OA (Kellgren-Lawrence grade of ≥2) from the Osteoarthritis Initiative Study were analysed. WSP was defined as pain on both sides of the body, above and below the waist, and in the axial skeleton. Time (hrs/d) spent participating in sitting and moderate-strenuous physical activities were calculated from the Physical Activity Scale for the Elderly questionnaire. Physical function was quantified using gait speed and the chair stand test. Restless sleep was assessed using an item on the CES-D Scale. Logistic regression models were constructed to examine the strength of the associations between primary exposures and WSP in unadjusted and adjusted analyses. RESULTS: Among the 2637 participants (mean age 62.6 years, 58.6% female), 16.8% met the criteria for WSP. All primary measures of interest were related to WSP in unadjusted analyses. In adjusted multivariable analysis, slow gait speed (adjusted odds ratio [aOR] 1.43; 95% CI 1.01, 2.02), lower chair stand rate (aOR 0.98; 95% CI 0.97-0.99), and restless sleep (aOR 1.61; 95% CI 1.25-2.08) maintained significant associations with WSP. CONCLUSION: Poor sleep behaviours and low physical function capacity are associated with WSP in adults with radiographic knee OA. These findings highlight the importance of assessing sleep, physical function, and pain distribution in this population. Interventions to improve physical function and sleep behaviours should be investigated as potential strategies to mitigate WSP.

The British Society for Sexual Medicine Guidelines on Male Adult Testosterone Deficiency, with Statements for Practice.

Testosterone deficiency (TD) is an increasingly common problem with significant health implications, but its diagnosis and management can be challenging. A multi-disciplinary panel from BSSM reviewed the available literature on TD and provide evidence-based statements for clinical practice. Evidence was derived from Medline, EMBASE and Cochrane searches on hypogonadism, testosterone therapy (T Therapy) and cardiovascular safety from May 2017 to September 2022. This revealed 1,714 articles, including 52 clinical trials and 32 placebo-controlled randomised controlled trials. A total of twenty-five statements are provided, relating to five key areas: screening, diagnosis, initiating T Therapy, benefits and risks of T Therapy, and follow-up. Seven statements are supported by level 1 evidence, eight by level 2, five by level 3, and five by level 4. Recent studies have demonstrated that low levels of testosterone in men are associated with increased risk of incident type 2 diabetes mellitus, worse outcomes in chronic kidney disease and COVID 19 infection with increased all-cause mortality, along with significant quality of life implications. These guidelines should help practitioners to effectively diagnose and manage primary and age-related TD.

The utility of machine learning for predicting donor discard in abdominal transplantation.

BACKGROUND: Increasing access and better allocation of organs in the field of transplantation is a critical problem in clinical care. Limitations exist in accurately predicting allograft discard. Potential exists for machine learning to provide a balanced assessment of the potential for an organ to be used in a transplantation procedure. METHODS: We accessed and utilized all available deceased donor United Network for Organ Sharing data from 1987 to 2020. With these data, we evaluated the performance of multiple machine learning methods for predicting organ use. The machine learning methods trialed included XGBoost, random forest, Naïve Bayes (NB), logistic regression, and fully connected feedforward neural network classifier methods. The top two methods, XGBoost and random forest, were fully developed using 10-fold cross-validation and Bayesian optimization of hyperparameters. RESULTS: The top performing model at predicting liver organ use was an XGBoost model which achieved an AUC-ROC of .925, an AUC-PR of .868, and an F1 statistic of .756. The top performing model for predicting kidney organ use classification was an XGBoost model which achieved an AUC-ROC of .952, and AUC-PR of .883, and an F1 statistic of .786. CONCLUSIONS: The XGBoost method demonstrated a significant improvement in predicting donor allograft discard for both kidney and livers in solid organ transplantation procedures. Machine learning methods are well suited to be incorporated into the clinical workflow; they can provide robust quantitative predictions and meaningful data insights for clinician consideration and transplantation decision-making.

Comparison of Patient-Reported Outcomes Measurement Information System Computerized Adaptive Testing Versus Fixed Short Forms in Juvenile Myositis.

OBJECTIVE: Patient-Reported Outcomes Measurement Information System (PROMIS) measures can be administered via computerized adaptive testing (CAT) or fixed short forms (FSFs), but the empirical benefits of CAT versus FSFs are unknown in juvenile myositis (JM). The present study was undertaken to assess whether PROMIS CAT is feasible, precise, correlated with FSFs, and less prone to respondent burden and floor/ceiling effects than FSFs in JM. METHODS: Patients 8-17 years of age (self-report and parent proxy) and parents of patients 5-7 years of age (only parent proxy) completed PROMIS fatigue, pain interference, upper extremity function, mobility, anxiety, and depressive symptoms measures. Pearson correlations, paired t-tests, and Cohen's d were calculated between PROMIS CAT and FSFs. McNemar's test assessed floor/ceiling effects between CAT and FSFs. Precision and respondent burden were examined across the T score range. RESULTS: Data from 67 patient-parent dyads were analyzed. CAT and FSF mean scores did not significantly differ except in parent proxy anxiety and fatigue (effect size 0.23 and 0.19, respectively). CAT had less pronounced floor/ceiling effects at the less symptomatic extreme in all domains except self-report anxiety. Increased item burden and higher SEs were seen in less symptomatic scorers for CAT. Modified stopping rules limiting CAT item administration did not decrease precision. CONCLUSION: PROMIS CAT appears to be feasible and correlated with FSFs. CAT had less pronounced floor/ceiling effects, allowing detection of individual differences in less symptomatic patients. Modified stopping rules for CAT may decrease respondent burden. CAT can be considered for long-term follow-up of JM patients.

Incorporating Expected Outcomes Into Clinical Decision-Making for Total Knee Arthroplasty.

OBJECTIVE: Expected outcomes (e.g., expected survivorship after a cancer treatment) have improved decision-making around treatment options in many clinical fields. Our objective was to evaluate the effect of expected values of 3 widely available total knee arthroplasty (TKA) outcomes (risk of serious complications, time to revision, and improvement in pain and function at 2 years after surgery) on clinical recommendation of TKA. METHODS: The RAND/University of California Los Angeles appropriateness criteria method was used to evaluate the role of the 3 expected outcomes in clinical recommendation of TKA. The expected outcomes were added to 5 established preoperative factors from the modified Escobar appropriateness criteria. The 8 indication factors were used to develop 279 clinical scenarios, and a panel of 9 clinicians rated the appropriateness of TKA for each scenario as inappropriate, inconclusive, and appropriate. Classification tree analysis was applied to these ratings to identify the most influential of the 8 factors in discriminating TKA appropriateness classifications. RESULTS: Ratings for the 279 appropriateness scenarios deemed 34.4% of the scenarios as appropriate, 40.1% as inconclusive, and 25.5% as inappropriate. Classification tree analyses showed that expected improvement in pain and function and expected time to revision were the most influential factors that discriminated among the TKA appropriateness classification categories. CONCLUSION: Our results showed that clinicians would use expected postoperative outcome factors in determining appropriateness for TKA. These results call for further work in this area to incorporate estimates of expected pain/function and revision outcomes into clinical practice to improve decision-making for TKA.

Risk Stratification by Percent Liver Herniation in Congenital Diaphragmatic Hernia.

INTRODUCTION: Congenital diaphragmatic hernia is associated with pulmonary hypoplasia, pulmonary hypertension, and significant neonatal morbidity. Although intrathoracic liver herniation (LH) >20% is associated with adverse outcomes, the relationship between LH <20% and outcomes is poorly characterized. METHODS: A single-center retrospective cohort study was performed from 2011 to 2020 of 80 fetuses with left-sided congenital diaphragmatic hernia that were delivered and repaired at our institution. Perinatal, perioperative, and postoperative data were collected. We evaluated the association of %LH with outcomes as a stratified ordinal variable (0%-10% LH, 10%-19% LH, and >20% LH) and as a continuous variable. Data were analyzed by analysis of variance with Bonferroni post hoc analysis, chi-square analyses, and univariate logistic regression. RESULTS: Extracorporeal membrane oxygenation (ECMO) (P < 0.001), repair on ECMO (P = 0.002), repair with patch (P < 0.001), length of stay (P = 0.002), inhaled nitric oxide use (P < 0.001), and sildenafil use at discharge (P < 0.001), showed significant differences among LH groups. There were no differences among the groups concerning survival (at discharge, 6 mo, and 1 y) and tracheostomy. On further analysis there was no difference between 10% and 19% LH and ≥20% LH patients concerning ECMO (P = 0.55), repair on ECMO (P = 0.54), repair with patch (P = 1.00), length of stay (P = 1.00), and inhaled nitric oxide use (P = 0.33). Logistic regression analysis displayed a significant association with LH and ECMO, repair on ECMO, repair with patch, inhaled nitric oxide use, and sildenafil use. CONCLUSIONS: Our analysis displays no significant difference in perinatal management between patients with 10%-19% and ≥20% LH. These findings suggest that the historical cutoff of ≥20% LH may not be sufficient alone to guide perinatal counseling and decision-making.

nnU-Net Deep Learning Method for Segmenting Parenchyma and Determining Liver Volume From Computed Tomography Images.

Background: Recipient donor matching in liver transplantation can require precise estimations of liver volume. Currently utilized demographic-based organ volume estimates are imprecise and nonspecific. Manual image organ annotation from medical imaging is effective; however, this process is cumbersome, often taking an undesirable length of time to complete. Additionally, manual organ segmentation and volume measurement incurs additional direct costs to payers for either a clinician or trained technician to complete. Deep learning-based image automatic segmentation tools are well positioned to address this clinical need. Objectives: To build a deep learning model that could accurately estimate liver volumes and create 3D organ renderings from computed tomography (CT) medical images. Methods: We trained a nnU-Net deep learning model to identify liver borders in images of the abdominal cavity. We used 151 publicly available CT scans. For each CT scan, a board-certified radiologist annotated the liver margins (ground truth annotations). We split our image dataset into training, validation, and test sets. We trained our nnU-Net model on these data to identify liver borders in 3D voxels and integrated these to reconstruct a total organ volume estimate. Results: The nnU-Net model accurately identified the border of the liver with a mean overlap accuracy of 97.5% compared with ground truth annotations. Our calculated volume estimates achieved a mean percent error of 1.92% + 1.54% on the test set. Conclusions: Precise volume estimation of livers from CT scans is accurate using a nnU-Net deep learning architecture. Appropriately deployed, a nnU-Net algorithm is accurate and quick, making it suitable for incorporation into the pretransplant clinical decision-making workflow.

Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer.

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

Association of red blood cell transfusion volume with postoperative complications and mortality in neonatal surgery.

INTRODUCTION: Red blood cell transfusion (RBCT) is commonly administered in neonatal surgical care in the absence of clear clinical indications such as active bleeding or anemia. We hypothesized that higher RBCT volumes are associated with worse postoperative outcomes. METHODS: Neonates within the National Surgical Quality Improvement Program-Pediatric database who underwent inpatient surgery (2012-2016) were stratified by weight-based RBCT volume: <20cc/kg, 20-40cc/kg, and >40cc/kg. Postoperative complications were categorized as wound, systemic infection, central nervous system (CNS), renal, pulmonary, and cardiovascular. Multivariable logistic regression and cubic spline analysis were used to evaluate the association between RBCT volume, postoperative complications, and 30-day mortality. Sensitivity analysis was conducted by performing propensity score matching. RESULTS: Among 9,877 neonates, 1,024 (10%) received RBCTs. Of those who received RBCT, 53% received <20cc/kg, 27% received 20-40cc/kg, and 20% received >40cc/kg. Relative to neonates who were not transfused, RBCT volume was associated with a dose-dependent increase in renal complications, CNS complications, cardiovascular complications, and 30-day mortality. With cubic spline analysis, a lone inflection point for 30-day mortality was identified at a RBCT volume of 30 - 35 cc/kg. After propensity score matching, the dose-dependent relationship was still present for 30-day mortality. CONCLUSION: Total RBCT volume is associated with worse postoperative outcomes in neonates with a significant increase in 30-day mortality at a RBCT volume of 30 - 35 cc/kg. Future prospective studies are needed to better understand the association between large RBCT volumes and poor outcomes after neonatal surgery. LEVEL OF EVIDENCE: Level IV, Retrospective cohort study.

Industry Payments to Plastic Surgeons: What Has Changed Over the Last 6 Years Following Implementation of the Physician Payments Sunshine Act?

BACKGROUND: The Open Payments Program, as designated by the Physician Payments Sunshine Act, is the single largest repository of industry payments made to licensed physicians within the United States. Though sizeable in its dataset, the database and user interface are limited in their ability to permit expansive data interpretation and summarization. OBJECTIVES: The authors sought to comprehensively compare industry payments made to plastic surgeons with payments made to all surgeons and all physicians to elucidate industry relationships since implementation. METHODS: The Open Payments Database was queried between 2014 and 2019, and inclusion criteria were applied. These data were evaluated in aggregate and for yearly totals, payment type, and geographic distribution. RESULTS: A total 61,000,728 unique payments totaling $11,815,248,549 were identified over the 6-year study period; 9089 plastic surgeons, 121,151 surgeons, and 796,260 total physicians received these payments. Plastic surgeons annually received significantly less payment than all surgeons (P = 0.0005). However, plastic surgeons did not receive significantly more payment than all physicians (P = 0.0840). Cash and cash equivalents proved to be the most common form of payment; stock and stock options were least commonly transferred. Plastic surgeons in Tennessee received the most in payments between 2014 and 2019 (mean $76,420.75). California had the greatest number of plastic surgeons who received payments (1452 surgeons). CONCLUSIONS: Plastic surgeons received more in industry payments than the average of all physicians but received less than all surgeons. The most common payment was cash transactions. Over the past 6 years, geographic trends in industry payments have remained stable.

Association of prematurity with complications and failure to rescue in neonatal surgery.

BACKGROUND: The majority of failure to rescue (FTR), or death after a postoperative complication, in pediatric surgery occurs among infants and neonates. The purpose of this study is to evaluate the association between gestational age (GA) and FTR in infants and neonates. METHODS: National cohort study of 46,452 patients < 1 year old within the National Surgical Quality Improvement Program-Pediatric database who underwent inpatient surgery. Patients were categorized as preterm neonates, term neonates, or infants. Neonates were stratified based on GA. Surgical procedures were classified as low- (< 1% mortality) or high-risk (≥ 1%). Multivariable logistic regression and cubic splines were used to evaluate the association between GA and FTR. RESULTS: Preterm neonates had the highest FTR (28%) rates. Among neonates, FTR increased with decreasing GA (≥ 37 weeks, 12%; 33-36 weeks, 15%; 29-32 weeks, 30%; 25-28 weeks 41%; ≤ 24 weeks, 57%). For both low- and high-risk procedures, FTR significantly (trend test, p < 0.01) increased with decreasing GA. When stratifying preterm neonates by GA, all GAs ≤ 28 weeks were associated with significantly higher odds of FTR for low- (OR 2.47, 95% CI [1.38-4.41]) and high-risk (OR 2.27, 95% CI [1.33-3.87]) procedures. A lone inflection point for FTR was identified at 31-32 weeks with cubic spline analysis. CONCLUSIONS: The dose-dependent relationship between decreasing GA and FTR as well as the FTR inflection point noted at GA 31-32 weeks can be used by stakeholders in designing quality improvement initiatives and directing perioperative care. LEVEL OF EVIDENCE: Level IV, Retrospective cohort study.

Anterior Urethroplasty for the Management of Urethral Strictures in Males: A Systematic Review.

OBJECTIVE: To identify the currently utilised techniques of anterior urethroplasty described in literature for treatment of urethral strictures, assess the effectiveness of the identified techniques based on re-stricture and complication rates, evaluate, and suggest treatment options based on current evidence for urethral strictures at different locations and of different lengths. METHODS: A systematic review of the MEDLINE, EMBASE, Scopus and Cochrane Library databases from conception up to September 2020 was performed. Primary outcomes included success rates measured via re-stricture rates and the post-operative maximum urinary flow rate (Qmax). Secondary outcomes included patient reported complication rates. RESULTS: A total of 52 papers, including 7 RCTs, met the inclusion criteria. Forty studies described the use of free graft urethroplasty with a median success rate of 86.5% (IQR = 8.1). The best outcomes were found in dorsal onlay buccal mucosa grafting in the penile urethra (86.6%). Twelve described the use of pedicled flap urethroplasty with a median success rate of 76% (IQR = 14.4). Excision and Primary Anastomosis results were reported in 5 studies and showed an overall highest success rate of 89.7% (IQR = 7.0) but involved the shortest strictures of median lengths of 2.1 cm (IQR = 0.48). CONCLUSION: Graft urethroplasty showed optimal outcomes when utilised in penile and bulbar strictures, with dorsal onlay buccal mucosa grafting presenting with the largest evidence base and best outcomes overall. Flap urethroplasty had the highest success rates in panurethral and bulbar strictures, while anastomotic urethroplasty had the greatest success in bulbar and penobulbar strictures.

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction.

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

Population simulations of COVID-19 outbreaks provide tools for risk assessment and continuity planning.

OBJECTIVES: We developed COVID-19 Outbreak Simulator (https://ictr.github.io/covid19-outbreak-simulator/) to quantitatively estimate the effectiveness of preventative and interventive measures to prevent and battle COVID-19 outbreaks for specific populations. MATERIALS AND METHODS: Our simulator simulates the entire course of infection and transmission of the virus among individuals in heterogeneous populations, subject to operations and influences, such as quarantine, testing, social distancing, and community infection. It provides command-line and Jupyter notebook interfaces and a plugin system for user-defined operations. RESULTS: The simulator provides quantitative estimates for COVID-19 outbreaks in a variety of scenarios and assists the development of public health policies, risk-reduction operations, and emergency response plans. DISCUSSION: Our simulator is powerful, flexible, and customizable, although successful applications require realistic estimation and robustness analysis of population-specific parameters. CONCLUSION: Risk assessment and continuity planning for COVID-19 outbreaks are crucial for the continued operation of many organizations. Our simulator will be continuously expanded to meet this need.

The shared genetic architecture between epidemiological and behavioral traits with lung cancer.

The complex polygenic nature of lung cancer is not fully characterized. Our study seeks to identify novel phenotypes associated with lung cancer using cross-trait linkage disequilibrium score regression (LDSR). We measured pairwise genetic correlation (rg) and SNP heritability (h2) between 347 traits and lung cancer risk using genome-wide association study summary statistics from the UKBB and OncoArray consortium. Further, we conducted analysis after removing genomic regions previously associated with smoking behaviors to mitigate potential confounding effects. We found significant negative genetic correlations between lung cancer risk and dietary behaviors, fitness metrics, educational attainment, and other psychosocial traits. Alcohol taken with meals (rg = - 0.41, h2 = 0.10, p = 1.33 × 10-16), increased fluid intelligence scores (rg = - 0.25, h2 = 0.22, p = 4.54 × 10-8), and the age at which full time education was completed (rg = - 0.45, h2 = 0.11, p = 1.24 × 10-20) demonstrated negative genetic correlation with lung cancer susceptibility. The body mass index was positively correlated with lung cancer risk (rg = 0.20, h2 = 0.25, p = 2.61 × 10-9). This analysis reveals shared genetic architecture between several traits and lung cancer predisposition. Future work should test for causal relationships and investigate common underlying genetic mechanisms across these genetically correlated traits.