External Validation and Comparison of Statistical and Machine Learning-Based Models in Predicting Outcomes Following Out-of-Hospital Cardiac Arrest: A Multicenter Retrospective Analysis.

BACKGROUND: The aim of this study was to validate and compare the performance of statistical (Utstein-Based Return of Spontaneous Circulation and Shockable Rhythm-Witness-Age-pH) and machine learning-based (Prehospital Return of Spontaneous Circulation and Swedish Cardiac Arrest Risk Score) models in predicting the outcomes following out-of-hospital cardiac arrest and to assess the impact of the COVID-19 pandemic on the models' performance. METHODS AND RESULTS: This retrospective analysis included adult patients with out-of-hospital cardiac arrest treated at 3 academic hospitals between 2015 and 2023. The primary outcome was neurological outcomes at hospital discharge. Patients were divided into pre- (2015-2019) and post-2020 (2020-2023) subgroups to examine the effect of the COVID-19 pandemic on out-of-hospital cardiac arrest outcome prediction. The models' performance was evaluated using the area under the receiver operating characteristic curve and compared by the DeLong test. The analysis included 2161 patients, 1241 (57.4%) of whom were resuscitated after 2020. The cohort had a median age of 69.2 years, and 1399 patients (64.7%) were men. Overall, 69 patients (3.2%) had neurologically intact survival. The area under the receiver operating characteristic curves for predicting neurological outcomes were 0.85 (95% CI, 0.83-0.87) for the Utstein-Based Return of Spontaneous Circulation score, 0.82 (95% CI, 0.81-0.84) for the Shockable Rhythm-Witness-Age-pH score, 0.79 (95% CI, 0.78-0.81) for the Prehospital Return of Spontaneous Circulation score, and 0.79 (95% CI, 0.77-0.81) for the Swedish Cardiac Arrest Risk Score model. The Utstein-Based Return of Spontaneous Circulation score significantly outperformed both the Prehospital Return of Spontaneous Circulation score (P<0.001) and the Swedish Cardiac Arrest Risk Score model (P=0.007). Subgroup analysis indicated no significant difference in predictive performance for patients resuscitated before versus after 2020. CONCLUSIONS: In this external validation, both statistical and machine learning-based models demonstrated excellent and fair performance, respectively, in predicting neurological outcomes despite different model architectures. The predictive performance of all evaluated clinical scoring systems was not significantly influenced by the COVID-19 pandemic.

Maternal autoimmune disease and offspring risk of haematological malignancies: a case-control study.

Background: Autoimmune diseases are known to be associated with an increased risk of cancer. Whether maternal immune dysregulation can have an impact on the development of haematological malignancies in offspring remains uncertain. Therefore, we explored the association between offspring risk of haematological malignancies and maternal autoimmune disease using a real-world nationwide population-based study. Methods: In this case-control study, we identified 2172 children with haematological malignancies between 2004 and 2019 from Taiwan's National Health Insurance program and compared them with population-based controls without haematologic malignancies, who were matched with each individual at a ratio of 1:4. The medical information of the autoimmune mothers were obtained from the Taiwan Maternal and Child Health Database. Conditional logistic regression was used to estimate the odds ratio for haematologic malignancy in offspring. Furthermore, subgroup and stratified analyses were conducted. Findings: Among the rheumatologic diseases in our study, Crohn's disease was the most common disease both in the haematological malignancy group (1.1%) and the control group (0.9%). In multivariable analysis, the odds ratio for haematological malignancy in offspring with maternal autoimmune diseases was 1.2 (95% confidence interval [CI] 0.91-1.58). The overall risk of haematologic malignancy was not significantly higher when adjusted for specific risk factors, including neonatal age, maternal age, family income, urbanization, maternal occupation, birth weight, or maternal comorbidity, except for prematurity. When comparing different autoimmune diseases among haematological malignancies and the control group, maternal psoriatic arthritis/psoriasis had the highest adjusted overall risk for haematological malignancies (adjusted OR 2.11, CI 0.89-5), followed by ankylosing spondylitis (adjusted OR 1.45, CI 0.7-3), autoimmune thyroiditis (OR 1.26, CI 0.57-2.81), systemic lupus erythematosus (OR 1.21, CI 0.48-3.02), Crohn's disease (OR 1.19, CI 0.75-1.9), and Sjogren's syndrome (OR 1.18, CI 0.65-2.15), but no significance was reached in these analyses. Multivariable analysis of risk factors associated with haematological malignancy subtypes was done. It showed no associations between maternal autoimmune disease and childhood haematological malignancies. Interpretation: We found no significant relationship between maternal autoimmune disease and childhood haematological malignancies. The influence of maternal immune dysregulation on the next generation with respect to haematological malignancies development may be limited. Funding: There was no funding source for this study.

Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.

Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retrospectively retrieved from two hospitals between 2015 and 2020. All CXRs were annotated with pixel-level labels for NGT localization and image-level labels for NGT presence and malposition. In the CAD system, DeepLabv3 + with backbone ResNeSt50 and DenseNet121 served as the model architecture for segmentation and classification models, respectively. The CAD system was tested on images from chronologically different datasets (National Taiwan University Hospital (National Taiwan University Hospital)-20), geographically different datasets (National Taiwan University Hospital-Yunlin Branch (YB)), and the public CLiP dataset. For the segmentation model, the Dice coefficients indicated accurate delineation of the NGT course (National Taiwan University Hospital-20: 0.665, 95% confidence interval (CI) 0.630-0.696; National Taiwan University Hospital-Yunlin Branch: 0.646, 95% CI 0.614-0.678). The distance between the predicted and ground-truth NGT tips suggested accurate tip localization (National Taiwan University Hospital-20: 1.64 cm, 95% CI 0.99-2.41; National Taiwan University Hospital-Yunlin Branch: 2.83 cm, 95% CI 1.94-3.76). For the classification model, NGT presence was detected with high accuracy (area under the receiver operating characteristic curve (AUC): National Taiwan University Hospital-20: 0.998, 95% CI 0.995-1.000; National Taiwan University Hospital-Yunlin Branch: 0.998, 95% CI 0.995-1.000; CLiP dataset: 0.991, 95% CI 0.990-0.992). The CAD system also detected NGT malposition with high accuracy (AUC: National Taiwan University Hospital-20: 0.964, 95% CI 0.917-1.000; National Taiwan University Hospital-Yunlin Branch: 0.991, 95% CI 0.970-1.000) and detected abnormal nasoenteric tube positions with favorable performance (AUC: 0.839, 95% CI 0.807-0.869). The CAD system accurately localized NGTs and detected NGT malposition, demonstrating excellent potential for external generalizability.

End-tidal Carbon Dioxide Trajectory-based Prognostication of Out-of-hospital Cardiac Arrest.

Background: During cardiopulmonary resuscitation (CPR), end-tidal carbon dioxide (EtCO2) is primarily determined by pulmonary blood flow, thereby reflecting the blood flow generated by CPR. We aimed to develop an EtCO2 trajectory-based prediction model for prognostication at specific time points during CPR in patients with out-of-hospital cardiac arrest (OHCA). Methods: We screened patients receiving CPR between 2015-2021 from a prospectively collected database of a tertiary-care medical center. The primary outcome was survival to hospital discharge. We used group-based trajectory modeling to identify the EtCO2 trajectories. Multivariable logistic regression analysis was used for model development and internally validated using bootstrapping. We assessed performance of the model using the area under the receiver operating characteristic curve (AUC). Results: The primary analysis included 542 patients with a median age of 68.0 years. Three distinct EtCO2 trajectories were identified in patients resuscitated for 20 minutes (min): low (average EtCO2 10.0 millimeters of mercury [mm Hg]; intermediate (average EtCO2 26.5 mm Hg); and high (average EtCO2: 51.5 mm Hg). Twenty-min EtCO2 trajectory was fitted as an ordinal variable (low, intermediate, and high) and positively associated with survival (odds ratio 2.25, 95% confidence interval [CI] 1.07-4.74). When the 20-min EtCO2 trajectory was combined with other variables, including arrest location and arrest rhythms, the AUC of the 20-min prediction model for survival was 0.89 (95% CI 0.86-0.92). All predictors in the 20-min model remained statistically significant after bootstrapping. Conclusion: Time-specific EtCO2 trajectory was a significant predictor of OHCA outcomes, which could be combined with other baseline variables for intra-arrest prognostication. For this purpose, the 20-min survival model achieved excellent discriminative performance in predicting survival to hospital discharge.

Association of maternal constipation and risk of atopic dermatitis in offspring.

Objectives: Atopic dermatitis (AD) is a chronic and relapsing dermatologic disease that can affect individuals of all ages, including children and adults. The prevalence of AD has increased dramatically over the past few decades. AD may affect children's daily activities, increase their parents' stress, and increase health expenditure. Constipation is a worldwide issue and may affect the gut microbiome. Some research has indicated that constipation might be associated with risk of atopic disease. The primary objective of this retrospective cohort study was to extend and to explore the link between maternal constipation and risk of atopic dermatitis in offspring. Methods: Using the Longitudinal Health Insurance Database, a subset of Taiwan's National Health Insurance Research Database, we identified 138,553 mothers with constipation and 138,553 matched controls between 2005 and 2016. Propensity score analysis was used matching birth year, child's sex, birth weight, gestational weeks, mode of delivery, maternal comorbidities, and antibiotics usage, with a ratio of 1:1. Multiple Cox regression and subgroup analyses were used to estimate the adjusted hazard ratio of child AD. Results: The incidence of childhood AD was 66.17 per 1,000 person-years in constipated mothers. By adjusting child's sex, birth weight, gestational weeks, mode of delivery, maternal comorbidities, and received antibiotics, it was found that in children whose mother had constipation, there was a 1.26-fold risk of AD compared to the children of mothers without constipation (adjusted hazard ratio [aHR]: 1.26; 95% CI, 1.25-1.28). According to subgroup analyses, children in the maternal constipation group had a higher likelihood of AD irrespective of child's sex, birth weight, gestational weeks, mode of delivery, and with or without comorbidities, as well as usage of antibiotics during pregnancy. Compared to the non-constipated mothers, the aHR for the constipated mothers with laxative prescriptions <12 and ≥12 times within one year before the index date were 1.26; 95% CI, 1.24 -1.28 and 1.40; 95% CI, 1.29-1.52, respectively. Conclusion: Maternal constipation was associated with an elevated risk of AD in offspring. Clinicians should be aware of the potential link to atopic dermatitis in the children of constipation in pregnant women and should treat gut patency issues during pregnancy. More study is needed to investigate the mechanisms of maternal constipation and atopic diseases in offspring.

Clinical application and feasibility of capsule endoscopy in children at a medical center in central Taiwan.

BACKGROUND PURPOSE: Capsule endoscopy (CE) is a noninvasive examination for excellent visualization of small bowel mucosal lesions. We aimed to evaluate the clinical efficacy and safety of CE in pediatric patients. METHODS: From April 2014 to December 2022, CE procedures performed in children younger than 18 years of age at Taichung Veteran General Hospital were analyzed retrospectively. RESULTS: Among 136 procedures, the completion rate was 95.6% (n = 130), with a median age of 14 years old. Suspicion or evaluation of inflammatory bowel diseases (IBD) (41%) was the most common indication for CE. Other common indications of CE were chronic unexplained abdominal pain (35%) and obscure gastrointestinal bleeding or iron deficiency anemia (21%). No procedure-related complications occurred. The diagnosis of those patients with incomplete study were CD with small bowel stricture, graft-versus-host disease and duodenal ulcers. A total of 86 CE procedures showed positive findings, and the overall diagnostic yield rate was 63.2%. Small bowel ulcers (65.12%) were the most common findings. Overall, 26.5% of CE examinations resulted in a new diagnosis and 44.9% of CE exams led to a change in therapy. For patients with IBD, CE findings resulted in an even higher therapeutic change rate of 48.1%. CONCLUSIONS: CE is a safe and feasible diagnostic method to study the small intestine in children, especially for IBD. Incomplete study could be an indicator of positive finding and can potentially be a guide to identify the site of possible strictures.

Prognostic Impact of Heart Rhythm Shockability Trajectory in Out-of-Hospital Cardiac Arrest: A Multicenter Retrospective Study.

BACKGROUND: This study aimed to investigate the association between the temporal transitions in heart rhythms during cardiopulmonary resuscitation (CPR) and outcomes after out-of-hospital cardiac arrest. METHODS: This was an analysis of the prospectively collected databases in 3 academic hospitals in northern and central Taiwan. Adult patients with out-of-hospital cardiac arrest transported by emergency medical service between 2015 and 2022 were included. Favorable neurological recovery and survival to hospital discharge were the primary and secondary outcomes, respectively. Time-specific heart rhythm shockability was defined as the probability of shockable rhythms at a particular time point during CPR. The temporal changes in the time-specific heart rhythm shockability were calculated by group-based trajectory modeling. Multivariable logistic regression analyses were performed to examine the association between the trajectory group and outcomes. Subgroup analyses examined the effects of extracorporeal CPR in different trajectories. RESULTS: The study comprised 2118 patients. The median patient age was 69.1 years, and 1376 (65.0%) patients were male. Three distinct trajectories were identified: high-shockability (52 patients; 2.5%), intermediate-shockability (262 patients; 12.4%), and low-shockability (1804 patients; 85.2%) trajectories. The median proportion of shockable rhythms over the course of CPR for the 3 trajectories was 81.7% (interquartile range, 73.2%-100.0%), 26.7% (interquartile range, 16.7%-37.5%), and 0% (interquartile range, 0%-0%), respectively. The multivariable analysis indicated both intermediate- and high-shockability trajectories were associated with favorable neurological recovery (intermediate-shockability: adjusted odds ratio [aOR], 4.98 [95% CI, 2.34-10.59]; high-shockability: aOR, 5.40 [95% CI, 2.03-14.32]) and survival (intermediate-shockability: aOR, 2.46 [95% CI, 1.44-4.18]; high-shockability: aOR, 2.76 [95% CI, 1.20-6.38]). The subgroup analysis further indicated extracorporeal CPR was significantly associated with favorable neurological outcomes (aOR, 4.06 [95% CI, 1.11-14.81]) only in the intermediate-shockability trajectory. CONCLUSIONS: Heart rhythm shockability trajectories were associated with out-of-hospital cardiac arrest outcomes, which may be a supplementary factor in guiding the allocation of medical resources, such as extracorporeal CPR.

Hidradenitis suppurativa as a potential risk factor of periodontitis: a multi-center, propensity-score-matched cohort study.

Background: Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease associated with systemic symptoms. Periodontitis, a prevalent dental disease, shares immune-mediated inflammatory characteristics with HS. This cohort study aims to evaluate the association between HS and periodontitis. Methods: Using the TriNetX research network, a global-federated database of electronic health records, we conducted a retrospective cohort study. People being diagnosed of HS were identified and propensity score matching was performed to identify proper control group, via balancing critical covariates Within the follow-up time of 1 year, 3 year and 5 years, hazard ratios were calculated to assess the risk of periodontitis in HS patients compared to controls. Results: Within the 53,968 HS patients and the same number of matched controls, the HS patients exhibited a significantly increased risk of developing periodontitis compared to controls after 3 years of follow-up (HR: 1.64, 95% CI: 1.11, 2.44) and 5 years of follow-up (HR: 1.64, 95% CI: 1.21, 2.24) of follow-up. Sensitivity analyses supported these findings under various matching models and washout periods. While comparing with patients with psoriasis, the association between HS and periodontitis remained significant (HR: 1.73, 95% CI: 1.23, 2.44). Conclusion: The observed increased risk suggests the need for heightened awareness and potential interdisciplinary care for individuals with HS to address periodontal health.

Deep Learning-based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study.

Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning-based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854-0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912-0.965, p-value < 0.001) compared with anterior-posterior (AUC 0.782, 95% CI 0.644-0.897) or portable anterior-posterior (AUC 0.869, 95% CI 0.814-0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823-0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765-0.904) and Shenzhen (AUC 0.806, 95% CI 0.771-0.839). A deep learning-based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.

Application of Machine Learning to Ultrasonography in Identifying Anatomical Landmarks for Cricothyroidotomy Among Female Adults: A Multi-center Prospective Observational Study.

We aimed to develop machine learning (ML)-based algorithms to assist physicians in ultrasound-guided localization of cricoid cartilage (CC) and thyroid cartilage (TC) in cricothyroidotomy. Adult female volunteers were prospectively recruited from two hospitals between September and December, 2020. Ultrasonographic images were collected via a modified longitudinal technique. You Only Look Once (YOLOv5s), Faster Regions with Convolutional Neural Network features (Faster R-CNN), and Single Shot Detector (SSD) were selected as the model architectures. A total of 488 women (mean age: 36.0 years) participated in the study, contributing to a total of 292,053 frames of ultrasonographic images. The derived ML-based algorithms demonstrated excellent discriminative performance for the presence of CC (area under the receiver operating characteristic curve [AUC]: YOLOv5s, 0.989, 95% confidence interval [CI]: 0.982-0.994; Faster R-CNN, 0.986, 95% CI: 0.980-0.991; SSD, 0.968, 95% CI: 0.956-0.977) and TC (AUC: YOLOv5s, 0.989, 95% CI: 0.977-0.997; Faster R-CNN, 0.981, 95% CI: 0.965-0.991; SSD, 0.982, 95% CI: 0.973-0.990). Furthermore, in the frames where the model could correctly indicate the presence of CC or TC, it also accurately localized CC (intersection-over-union: YOLOv5s, 0.753, 95% CI: 0.739-0.765; Faster R-CNN, 0.720, 95% CI: 0.709-0.732; SSD, 0.739, 95% CI: 0.726-0.751) or TC (intersection-over-union: YOLOv5s, 0.739, 95% CI: 0.722-0.755; Faster R-CNN, 0.709, 95% CI: 0.687-0.730; SSD, 0.713, 95% CI: 0.695-0.730). The ML-based algorithms could identify anatomical landmarks for cricothyroidotomy in adult females with favorable discriminative and localization performance. Further studies are warranted to transfer this algorithm to hand-held portable ultrasound devices for clinical use.

Prognostic implication of heart failure stage and left ventricular ejection fraction for patients with in-hospital cardiac arrest: a 16-year retrospective cohort study.

BACKGROUND: The 2022 AHA/ACC/HFSA guidelines for the management of heart failure (HF) makes therapeutic recommendations based on HF status. We investigated whether the prognosis of in-hospital cardiac arrest (IHCA) could be stratified by HF stage and left ventricular ejection fraction (LVEF). METHODS: This single-center retrospective study analyzed the data of patients who experienced IHCA between 2005 and 2020. Based on admission diagnosis, past medical records, and pre-arrest echocardiography, patients were classified into general IHCA, at-risk for HF, pre-HF, HF with preserved ejection fraction (HFpEF), and HF with mildly reduced ejection fraction or HF with reduced ejection fraction (HFmrEF-or-HFrEF) groups. RESULTS: This study included 2,466 patients, including 485 (19.7%), 546 (22.1%), 863 (35.0%), 342 (13.9%), and 230 (9.3%) patients with general IHCA, at-risk for HF, pre-HF, HFpEF, and HFmrEF-or-HFrEF, respectively. A total of 405 (16.4%) patients survived to hospital discharge, with 228 (9.2%) patients achieving favorable neurological recovery. Multivariable logistic regression analysis indicated that pre-HF and HFpEF were associated with better neurological (pre-HF, OR: 2.11, 95% confidence interval [CI]: 1.23-3.61, p = 0.006; HFpEF, OR: 1.90, 95% CI: 1.00-3.61, p = 0.05) and survival outcomes (pre-HF, OR: 2.00, 95% CI: 1.34-2.97, p < 0.001; HFpEF, OR: 1.91, 95% CI: 1.20-3.05, p = 0.007), compared with general IHCA. CONCLUSION: HF stage and LVEF could stratify patients with IHCA into different prognoses. Pre-HF and HFpEF were significantly associated with favorable neurological and survival outcomes after IHCA. Further studies are warranted to investigate whether HF status-directed management could improve IHCA outcomes.

Association between hidradenitis suppurativa and atopic diseases: a multi-center, propensity-score-matched cohort study.

Background: Cross-sectional evidence has suggested a high prevalence of atopic diseases in patients with hidradenitis suppurativa (HS). However, there is a lack of evidence based on longitudinal studies. This study aimed to assess the risk of different atopic diseases, including asthma, atopic dermatitis, and allergic rhinitis, in patients with HS. Methods: In this retrospective cohort study, data from the TriNetX research network were obtained. Patients with HS were enrolled, and a 1:1 propensity score matching was performed to select a non-HS control group. Matching covariates included age, sex, race, comorbidities, comedications, socioeconomic status, lab data, and medical utilization status. Hazard ratios (HR) for atopic diseases were assessed. Results: Over a 15-year follow-up period, patients with HS were found to be at a higher risk for atopic dermatitis (HR = 1.65; 95% CI, 1.44-1.90), asthma (HR = 1.41; 95% CI, 1.33-1.49), and allergic rhinitis (HR = 1.08; 95% CI, 1.03-1.13). A similar trend was observed in shorter follow-up periods. The association between HS, atopic dermatitis, and asthma was consistent across different age and sex subgroups. Conclusion: Atopic diseases including atopic dermatitis, asthma and allergic rhinitis are associated with HS. Further investigation is needed to assess the necessity of early screening for atopic diseases in patients with HS.

The prevalence of obstructive sleep apnea syndrome after COVID-19 infection.

Obstructive sleep apnea is a well-known risk factor regarding the severity of COVID-19 infection. However, to date, relatively little research performed on the prevalence of obstructive sleep apnea in COVID-19 survivors. The purpose of this study was to investigate the risk of obstructive sleep apnea after COVID-19 infection. This study was based on data collected from the US Collaborative Network in TriNetX. From January 1, 2020 to June 30, 2022, participants who underwent the SARS-CoV-2 test were included in the study. Based on their positive or negative results of the COVID-19 test results (the polymerase chain reaction [PCR] test), we divided the study population into two groups. The duration of follow-up began when the PCR test was administered and continued for 12 months. Hazard ratios (HRs) and 95% confidence intervals (CIs) for newly recorded COVID-19 positive subjects for obstructive sleep apnea were calculated using the Cox proportional hazards model and compared to those without COVID-19 infection. Subgroup analyses were performed for the age, sex, and race, groups. The COVID-19 group was associated with an increased risk of obstructive sleep apnea, at both 3 months of follow-up (HR: 1.51, 95% CI: 1.48-1.54), and 1 year of follow-up (HR: 1.57, 95% CI: 1.55-1.60). Kaplan-Meier curves regarding the risk of obstructive sleep apnea revealed a significant difference of probability between the two cohorts in the follow-up periods of 3 months and 1 year (Log-Rank test, p < 0.001). The risks of obstructive sleep apnea among COVID-19 patients were significant in the less than 65 year of age group (HR: 1.50, 95% CI: 1.47-1.52), as well as in the group older than or equal to 65 years (HR:1.69, 95% CI: 1.64-1.73). Furthermore, the risks of obstructive sleep apnea were evident in both the male and female COVID-19 groups. Compared to the control group, the risks of obstructive sleep apnea in the COVID-19 participants increased in the subgroups of White (HR: 1.62, 95% CI: 1.59-1.64), Blacks/African Americans (HR: 1.50, 95% CI: 1.45-1.55), Asian (HR: 1.46, 95% CI: 1.32-1.62) and American Indian/Alaska Native (HR: 1.36, 95% CI: 1.07-1.74). In conclusion, the incidence of new diagnosis obstructive sleep apnea could be substantially higher after COVID-19 infection than non-COVID-19 comparison group. Physicians should evaluate obstructive sleep apnea in patients after COVID-19 infection to help prevent future long-term adverse effects from occurring in the future, including cardiovascular and neurovascular disease.

Deep Learning-Based Localization and Detection of Malpositioned Endotracheal Tube on Portable Supine Chest Radiographs in Intensive and Emergency Medicine: A Multicenter Retrospective Study.

OBJECTIVES: We aimed to develop a computer-aided detection (CAD) system to localize and detect the malposition of endotracheal tubes (ETTs) on portable supine chest radiographs (CXRs). DESIGN: This was a retrospective diagnostic study. DeepLabv3+ with ResNeSt50 backbone and DenseNet121 served as the model architecture for segmentation and classification tasks, respectively. SETTING: Multicenter study. PATIENTS: For the training dataset, images meeting the following inclusion criteria were included: 1) patient age greater than or equal to 20 years; 2) portable supine CXR; 3) examination in emergency departments or ICUs; and 4) examination between 2015 and 2019 at National Taiwan University Hospital (NTUH) (NTUH-1519 dataset: 5,767 images). The derived CAD system was tested on images from chronologically (examination during 2020 at NTUH, NTUH-20 dataset: 955 images) or geographically (examination between 2015 and 2020 at NTUH Yunlin Branch [YB], NTUH-YB dataset: 656 images) different datasets. All CXRs were annotated with pixel-level labels of ETT and with image-level labels of ETT presence and malposition. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: For the segmentation model, the Dice coefficients indicated that ETT would be delineated accurately (NTUH-20: 0.854; 95% CI, 0.824-0.881 and NTUH-YB: 0.839; 95% CI, 0.820-0.857). For the classification model, the presence of ETT could be accurately detected with high accuracy (area under the receiver operating characteristic curve [AUC]: NTUH-20, 1.000; 95% CI, 0.999-1.000 and NTUH-YB: 0.994; 95% CI, 0.984-1.000). Furthermore, among those images with ETT, ETT malposition could be detected with high accuracy (AUC: NTUH-20, 0.847; 95% CI, 0.671-0.980 and NTUH-YB, 0.734; 95% CI, 0.630-0.833), especially for endobronchial intubation (AUC: NTUH-20, 0.991; 95% CI, 0.969-1.000 and NTUH-YB, 0.966; 95% CI, 0.933-0.991). CONCLUSIONS: The derived CAD system could localize ETT and detect ETT malposition with excellent performance, especially for endobronchial intubation, and with favorable potential for external generalizability.

Deep Learning-based Diagnosis and Localization of Pneumothorax on Portable Supine Chest X-ray in Intensive and Emergency Medicine: A Retrospective Study.

PURPOSE: To develop two deep learning-based systems for diagnosing and localizing pneumothorax on portable supine chest X-rays (SCXRs). METHODS: For this retrospective study, images meeting the following inclusion criteria were included: (1) patient age ≥ 20 years; (2) portable SCXR; (3) imaging obtained in the emergency department or intensive care unit. Included images were temporally split into training (1571 images, between January 2015 and December 2019) and testing (1071 images, between January 2020 to December 2020) datasets. All images were annotated using pixel-level labels. Object detection and image segmentation were adopted to develop separate systems. For the detection-based system, EfficientNet-B2, DneseNet-121, and Inception-v3 were the architecture for the classification model; Deformable DETR, TOOD, and VFNet were the architecture for the localization model. Both classification and localization models of the segmentation-based system shared the UNet architecture. RESULTS: In diagnosing pneumothorax, performance was excellent for both detection-based (Area under receiver operating characteristics curve [AUC]: 0.940, 95% confidence interval [CI]: 0.907-0.967) and segmentation-based (AUC: 0.979, 95% CI: 0.963-0.991) systems. For images with both predicted and ground-truth pneumothorax, lesion localization was highly accurate (detection-based Dice coefficient: 0.758, 95% CI: 0.707-0.806; segmentation-based Dice coefficient: 0.681, 95% CI: 0.642-0.721). The performance of the two deep learning-based systems declined as pneumothorax size diminished. Nonetheless, both systems were similar or better than human readers in diagnosis or localization performance across all sizes of pneumothorax. CONCLUSIONS: Both deep learning-based systems excelled when tested in a temporally different dataset with differing patient or image characteristics, showing favourable potential for external generalizability.

Maternal constipation is associated with allergic rhinitis in the offspring: A nationwide retrospective cohort study.

Allergic rhinitis (AR) is a common atopic disease worldwide, and it was found that babies with constipation in their early life might have an increased risk of atopic diseases, including AR. Furthermore, recent studies also indicate that the maternal gut microbiota may influence babies. Thus, we extended the definition of early life in utero and evaluated the association between maternal constipation and the risk of AR in their babies. Using the Longitudinal Health Insurance Database, a subset of Taiwan's National Health Insurance Research Database, we identified 102,820 constipated mothers and 102,820 matched controls between 2005 and 2015. Propensity score analysis was used to match birth year, child sex, birth weight, gestational age, mode of delivery, maternal comorbidities, and children antibiotics taken. Multiple Cox regression and subgroup analyzes were conducted to estimate the adjusted hazard ratio of childhood AR. The incidence of childhood AR was 83.47 per 1,000 person-years in constipated mothers. Adjusting children's sex, birth weight, gestational age, mode of delivery, maternal comorbidities, and children antibiotic use, the results showed that the children whose mothers had constipation had a 1.20-fold risk of AR compared to children of mothers without constipation. Maternal constipation was associated with an increased risk of AR. Therefore, it is important to pay close attention to pregnant mothers with constipation.

Influence of Helicobacter pylori infection on risk of rheumatoid arthritis: a nationwide population-based study.

The relationship between Helicobacter pylori infection and rheumatoid arthritis has been investigated, but the results remain controversial. This study aims to determine the association between the two diseases via a 17-year retrospective cohort study. Using the National Health Insurance Research Database, a nationwide population based in Taiwan, we identified 97,533 individuals with H. pylori infection and matched controls between 2000 and 2017 using propensity score matching at a 1:1 ratio. The adjusted hazard ratio of rheumatoid arthritis was determined by multiple Cox regression. The incidence rate of rheumatoid arthritis was 1.28 per 10,000 person-months in the H. pylori cohort, with a higher risk compared to the control group. In the < 30 years old subgroup, the risk was highest, especially in women < 30 years old with H. pylori infection. Patients with < 1 year follow-up showed 1.58 times higher susceptibility to rheumatoid arthritis. Individuals with follow-ups of 1-5 years and over 5 years demonstrated 1.43 and 1.44 times higher risks of rheumatoid arthritis, respectively. Our study showed H. pylori infection was associated with the development of rheumatoid arthritis. Clinicians should note higher risk, especially < 30 years old. More research needed to understand underlying mechanism.

End-tidal Carbon Dioxide + Return of Spontaneous Circulation After Cardiac Arrest (RACA) Score to Predict Outcomes After Out-of-hospital Cardiac Arrest.

INTRODUCTION: The return of spontaneous circulation after cardiac arrest (RACA) score is a well-validated model for estimating the probability of return of spontaneous circulation (ROSC) in patients with out-of-hospital cardiac arrest (OHCA) by incorporating several variables, including gender, age, arrest aetiology, witness status, arrest location, initial cardiac rhythms, bystander cardiopulmonary resuscitation (CPR), and emergency medical services (EMS) arrival time. The RACA score was initially designed for comparisons between different EMS systems by standardising ROSC rates. End-tidal carbon dioxide (EtCO2) is a quality indicator of CPR. We aimed to improve the performance of the RACA score by adding minimum EtCO2 measured during CPR to develop the EtCO2 + RACA score for OHCA patients transported to an emergency department (ED). METHODS: This was a retrospective analysis using prospectively collected data for OHCA patients resuscitated at an ED during 2015-2020. Adult patients with advanced airways inserted and available EtCO2 measurements were included. We used the EtCO2 values recorded in the ED for analysis. The primary outcome was ROSC. In the derivation cohort, we used multivariable logistic regression to develop the model. In the temporally split validation cohort, we assessed the discriminative performance of the EtCO2 + RACA score by the area under the receiver operating characteristic curve (AUC) and compared it with the RACA score using the DeLong test. RESULTS: There were 530 and 228 patients in the derivation and validation cohorts, respectively. The median measurements of EtCO2 were 8.0 times (interquartile range [IQR] 3.0-12.0 times), with the median minimum EtCO2 of 15.5 millimeters of mercury (mm Hg) (IQR 8.0-26.0 mm Hg). The median RACA score was 36.4% (IQR 28.9-48.0%), and a total of 393 patients (51.8%) achieved ROSC. The EtCO2 + RACA score was validated with good discriminative performance (AUC, 0.82, 95% CI 0.77-0.88), outperforming the RACA score (AUC, 0.71, 95% CI 0.65-0.78) (DeLong test: P < 0.001). CONCLUSION: The EtCO2 + RACA score may facilitate the decision-making process regarding allocations of medical resources in EDs for OHCA resuscitation.