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.

The Force-Frequency Characteristics of Quartz Wafers under a Cantilever Beam Structure.

This study investigated the force-frequency characteristics of quartz wafers inside a cantilever beam frame. Firstly, the force-frequency coefficient formula of quartz wafers with fixed ends under axial force was analyzed. Firstly, the formula for the force-frequency coefficient of quartz wafers with fixed ends under axial force was analyzed. A force-frequency coefficient formula suitable for cantilever beam structures was derived by considering the changes in surface stress and stiffness of quartz wafers with fixed ends and one end under force on the other. Subsequently, the formula's accuracy was verified by experiments, and the accuracy was more than 92%. In addition, strain simulation analysis was performed on three different shapes of quartz wafers, and experimental verification was carried out on two of them. The results revealed that trapezoidal quartz wafers and cantilever beam structures exhibited superior stress distribution to rectangular chips. Furthermore, by positioning electrodes at various locations on the surface of the quartz chip, it was observed that, as the electrodes moved closer to the fixed end, the force-frequency coefficient of the rectangular quartz chip increased, along with an increase in chip strain under the cantilever structure. In summary, this study provides a new approach for designing cantilever quartz resonator sensors in the future.

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.

Ultrasound-Guided Sciatic Nerve Hydrodissection Can Improve the Clinical Outcomes of Patients with Deep Gluteal Syndrome: A Case-Series Study.

Deep gluteal syndrome (DGS) is caused by sciatic nerve entrapment. Because fascial entrapment neuropathies may occur in multiple locations, ultrasound-guided nerve hydrodissection is a key component of DGS treatment. In this study, we examined the clinical outcomes of patients with DGS undergoing ultrasound-guided sciatic nerve hydrodissection. A 10 mL mixture consisting of 5% dextrose, 0.2% lidocaine (Xylocaine), and 4 mg betamethasone (Rinderon) was used for nerve hydrodissection. Clinical outcomes were evaluated using Numeric Rating Scale (NRS) scores of pain, the proportion of patients with favorable outcomes (reduction of ≥50% in pain), the duration for which patients exhibited favorable outcomes (percentage of follow-up duration), and the occurrence of major complications and minor side effects. A total of 53 patients were consecutively included and followed up for 3 to 19 months. After the initial injection, the NRS scores significantly improved at 1 week, 1 month, 3 months, and the final follow-up. Specifically, 73.6%, 71.7%, 64.2%, and 62.3% of the patients exhibited favorable outcomes at 1 week, 1 month, 3 months, and the final follow-up, respectively. The median duration for which the patients exhibited favorable outcomes was 84.7% of the follow-up period. Three patients (5.7%) experienced transient dizziness and vomiting, which resolved without further treatment. No vessel or nerve puncture was observed. Overall, ultrasound-guided sciatic nerve hydrodissection is a safe procedure that mitigates the pain associated with DGS. To achieve favorable outcomes, three consecutive injections 3 weeks apart are required.

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.

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.

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.

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.

Percutaneous endoscopic lumbar discectomy versus open lumbar microdiscectomy for treating lumbar disc herniation: Using the survival analysis.

Objectives: This study compared the risk of symptomatic recurrent disc herniation and clinical outcomes of percutaneous endoscopic lumbar discectomy (PELD) versus open lumbar microdiscectomy (OLM) for lumbar disc herniation with 2 years of follow-up. Materials and Methods: We analyzed 23 patients who underwent PELD and 32 patients who underwent OLM for lumbar disc herniation. The numeric rating scale of back and leg pain, Oswestry Disability Index (ODI), and Roland-Morris Disability Questionnaire (RMDQ) were assessed before and at 12 and 24 months after the surgery. The wound pain and complications were also recorded. Survival analysis was performed to estimate the risk of symptomatic recurrent disc herniation. Results: In the comparison of groups, the reductions in back and leg pain, ODI, and RMDQ were not significantly different at 12 and 24 months. For patients who underwent PELD, the wound pain was significant lower at the day of surgery. The survival rate of patients who were free from symptomatic recurrent disc herniation at 24 months was 0.913 in PELD and 0.875 in OLM, and the log-rank test revealed no significant difference between the two survival curves. The incidence of complication was not significantly different between groups. Conclusion: Both PELD and OLM are effective treatments for lumbar disc herniation because they have similar clinical outcomes. PELD provided patients with less painful wounds. The survival analysis revealed that the risk of symptomatic recurrent disc herniation in 2 years of follow-up was not different between PELD and OLM.

Ultrasound-guided nerve hydrodissection of cervical nerve roots for cervical radicular pain in patients with mild and moderate to severe stenosis: a retrospective cohort study.

Because fascial entrapment neuropathy can occur in multiple locations, ultrasound-guided nerve hydrodissection has become a key component of the treatment of cervical radicular pain. In this paper, we propose a combination of injectates used for nerve hydrodissection of the cervical nerve roots and compare the clinical outcomes of this treatment among patients with different severities of stenosis. This is a retrospective cohort study designed to compare outcomes between patients with mild stenosis and moderate to severe stenosis. Forty-four patients with mild cervical stenosis and 30 patients with moderate to severe cervical stenosis were consecutively enrolled into two groups. A 10-mL mixture in a single level consisting of 5% in Dextrose, 0.2% lidocaine (Xylocaine), and 4 mg betamethasone (Rinderon) was used for nerve roots hydrodissection. The two groups were compared with regard to their numeric rating scales (NRS) of pain, proportion of patients who exhibited a favorable outcome (a reduction of pain ≥ 50%), duration of patient exhibited a favorable outcome, and occurrence of serious complications and minor side effects. The follow-up period ranged from 3 to 20 months. The NRS of both groups improved significantly by 1 week, 1 month, 3 months, and final follow-up after the initial injection. Differences in the groups' NRS, proportion of patients who exhibited a favorable outcome, duration of patient exhibited a favorable outcome, and occurrence of serious complications and minor side effects were nonsignificant. There were 4 patients (5.4%) experienced dizziness in that resolved without further treatment. Ultrasound-guided nerve hydrodissection of cervical nerve roots is a safe procedure that reduces pain associated with cervical radicular pain, even in patients with moderate to severe stenosis.

Utilizing a nomogram to predict the one-year postoperative mortality risk for geriatric patients with a hip fracture.

Despite the abundance of research on the risk factors for mortality following hip fracture surgery, there has been a dearth of studies on prediction models in this population. The objective of this research was to explore the influencing factors and construct a clinical nomogram to predict one-year postoperative mortality in patients with hip fracture surgeries. Using the Ditmanson Research Database (DRD), we included 2333 subjects, aged ≥ 50 years who underwent hip fracture surgery between October, 2008 and August, 2021. The endpoint was all-cause mortality. A least absolute shrinkage and selection operator (LASSO) derived Cox regression was performed to select the independent predictors of one-year postoperative mortality. A nomogram was built for predicting one-year postoperative mortality. The prognostic performance of nomogram was evaluated. On the basis of tertiary points in a nomogram, the patients were divided into low, middle and high risk groups, and compared by the Kaplan-Meier analysis. Within 1 year after hip fracture surgery, 274 patients (11.74%) died. Variables retained in the final model comprised age, sex, length of stay, RBC transfusions, hemoglobin, platelet, and eGFR. The AUC for one-year mortality predictions were 0.717 (95% CI = 0.685-0.749). The Kaplan-Meier curves were significantly different among the three risk groups (p < 0.001). The nomogram showed good calibration. In summary, we explored the one-year postoperative mortality risk in geriatric patients with a hip fracture and developed a prediction model that could help clinicians identify patients at high risk of postoperative mortality.

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.

Point-of-care testing for adult out-of-hospital cardiac arrest resuscitated at the ED to predict ROSC: Development and external validation of POC-ED-ROSC model.

BACKGROUND AND IMPORTANCE: Most prediction models, like return of spontaneous circulation (ROSC) after cardiac arrest (RACA) or Utstein-based (UB)-ROSC score, were developed for prehospital settings to predict the probability of ROSC in patients with out-of-hospital cardiac arrest (OHCA). A prediction model has been lacking for the probability of ROSC in patients with OHCA at emergency departments (EDs). OBJECTIVE: In the present study, a point-of-care (POC) testing-based model, POC-ED-ROSC, was developed and validated for predicting ROSC of OHCA at EDs. DESIGN, SETTINGS AND PARTICIPANTS: Prospectively collected data for adult OHCA patients between 2015 and 2020 were analysed. POC blood gas analysis obtained within 5 min of ED arrival was used. OUTCOMES MEASURE AND ANALYSIS: The primary outcome was ROSC. In the derivation cohort, multivariable logistic regression was used to develop the POC-ED-ROSC model. In the temporally split validation cohort, the discriminative performance of the POC-ED-ROSC model was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with RACA or UB-ROSC score using DeLong test. MAIN RESULTS: The study included 606 and 270 patients in the derivation and validation cohorts, respectively. In the total cohort, 471 patients achieved ROSC. Age, initial cardiac rhythm at ED, pre-hospital resuscitation duration, and POC testing-measured blood levels of lactate, potassium and glucose were significant predictors included in the POC-ED-ROSC model. The model was validated with fair discriminative performance (AUC: 0.75, 95% confidence interval [CI]: 0.69-0.81) with no significant differences from RACA (AUC: 0.68, 95% CI: 0.62-0.74) or UB-ROSC score (AUC: 0.74, 95% CI: 0.68-0.79). CONCLUSION: Using only six easily accessible variables, the POC-ED-ROSC model can predict ROSC for OHCA resuscitated at ED with fair accuracy.

Safety and efficacy of intracoronary artery administration of human bone marrow-derived mesenchymal stem cells in STEMI of Lee-Sung pigs-A preclinical study for supporting the feasibility of the OmniMSC-AMI phase I clinical trial.

Background: This study tested whether early left intracoronary arterial (LAD) administration of human bone marrow-derived mesenchymal stem cells (hBMMSCs, called OmniMSCs) in acute ST-segment elevation myocardial infarction (STEMI) of Lee-Sung pigs induced by 90 min balloon-occluded LAD was safe and effective. Methods and results: Young male Lee-Sung pigs were categorized into SC (sham-operated control, n = 3), AMI-B (STEMI + buffer/21 cc/administered at 90 min after STEMI, n = 6), and AMI-M [acute myocardial infarction (AMI) + hBMMSCs/1.5 × 107/administered at 90 min after STEMI, n = 6] groups. By 2 and 5 months after STEMI, the cardiac magnetic resonance imaging demonstrated that the muscle scar score (MSS) and abnormal cardiac muscle exercise score in the infarct region were significantly increased in the AMI-B than in the SC group that were significantly reversed in the AMI-M group, whereas the left ventricular ejection function by each month (from 1 to 5) displayed an opposite pattern of MSS among the groups (all p < 0.001). By 5 months, histopathological findings of infarct and fibrosis areas and isolectin-B4 exhibited an identical pattern, whereas the cellular expressions of troponin-I/troponin-T/von Willebrand factor exhibited an opposite pattern of MSS among the groups (all p < 0.001). The ST-segment resolution (>80%) was significantly earlier (estimated after 6-h AMI) in the AMI-M group than in the AMI-B group (p < 0.001). The protein expressions of inflammation (IL-1ß/TNF-α/NF-κB)/oxidative stress (NOX-1/NOX-2/oxidized protein)/apoptosis (cleaved caspase-3/cleaved PARP)/DNA damage (γ-H2AX) displayed an identical pattern to MSS among the groups, whereas the protein expressions of angiogenesis factors (SDF-1α/VEGF) were significantly and progressively increased from SC, AMI-B, to AMI-M groups (all p < 0.001). Conclusion: Early intra-LAD transfusion of OmniMSC treatment effectively reduced the infarct size and preserved LV function in porcine STEMI.

Preparation of Medical Supply for Prehospital Emergencies and Disasters: An Internet-Based Simulation Drill.

Background: Mass casualties caused by natural disasters and man-made events may overwhelm local emergency medical services and healthcare systems. Logistics is essential to a successful emergency medical response. Drills have been used in disaster preparedness to validate plans, policies, procedures, and agreements, and identify resource gaps. The application of the internet to facilitate the conduct of exercise was still limited. This study aimed to investigate the optimal preparation of medical supplies by medical emergency response teams (MERTs) during emergencies and disasters using an internet-based drill. Methods: An internet-based drill based on real-life mass casualty incidents (MCIs) was developed and conducted in Taiwan from June 2017 to July 2018. The drill involved an MCI with 50 events delivered under two scenarios: (1) reduced transfer capacity and well-functioning local healthcare facilities (emergency module); (2) severely reduced transfer capacity and dysfunctional local healthcare facilities (disaster module). For each event, medical supplies commonly prepared by local MERTs in Taiwan were listed in structured questionnaires and participants selected the supplies they would use. Results: Forty-three senior medical emergency responders participated in the survey (responding rate of 47.3%). Resuscitation-related supplies increased from emergency to disaster module (e.g., intubation from 9.1% to 13.9%; dopamine from 3.2% to 5.0%; all p < 0.001). In the subgroup analysis of events with life-threatening injuries, the utilization of resuscitation-related supplies (e.g., intubation from 46.6% to 65.3%; p < 0.001) remained higher in the disaster than in the emergency module. Compared to emergency medical technicians, physicians and nurses are more likely to use intravenous/intramuscular analgesics. Conclusions: The severity of scenarios and the professional background of emergency responders have a different utilization of medical supplies in the simulation drill. The internet-based drill may contribute to optimizing the preparedness of medical response to prehospital emergencies and disasters.

Preoperative overweight and obesity do not cause inferior outcomes following open-wedge high tibial osteotomy: A retrospective cohort study of 123 patients.

Open-wedge high tibial osteotomy (OWHTO) is effective in treating medial compartment osteoarthritis. The association between body mass index (BMI) and outcomes following OWHTO is being debated. This study compared radiographic and clinical outcomes between patients with preoperative overweight, obesity, and normal BMI following OWHTO for medial compartment osteoarthritis. In total, 123 patients (123 knees) who underwent OWHTO for medial compartment osteoarthritis were enrolled and were divided into normal-BMI (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2), and obese (>30 kg/m2) groups based on body mass index. The numeric rating scale for pain, mechanical tibiofemoral angle (mTFA), tibia tilting angle (TTA), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for function were evaluated preoperatively and at postoperative follow-ups. The improvements of clinical and radiological outcomes in normal-BMI, overweight, and obese groups were not significantly different. The incidence of soft tissue irritation, wound infection, nonunion, and conversion to total knee arthroplasty were not significantly different between groups.The clinical and radiological outcomes in patients with preoperative overweight, obesity, and normal-BMI were not significantly different. Preoperative overweight and obesity thus has no effect on outcomes following OWHTO during the two years follow-up period. These findings cannot be generalized to patients with morbid obesity.

The effect of e-learning on point-of-care ultrasound education in novices.

BACKGROUND: Current studies assessed the learning efficacy of e-learning in ultrasound (US) training using questionnaires, or simulation in well-controlled conditions. This study investigates the effect of e-learning on the clinical US performance of the first postgraduate year (PGY-1) residents. METHODS: In this prospective observational study, we enrolled PGY-1 and second postgraduate year (PGY-2) residents. The e-learning was introduced on the first day and each PGY-1 was authorized to access the e-learning platform. The point-of-care ultrasound (PoCUS) curriculum for the focused assessment of sonography for trauma (FAST) was conducted on the 7th day for PGY-1 and the objective structured clinical examination (OSCE) followed. The PGY-2 received bedside one-to-one random learning before the study and did not have the authorization to access the e-learning. The FAST examinations performed by the PGY-1 and PGY-2 were collected on the 30th day. The clinical FAST performance was assessed by the instructor not involved in the curriculum and blinded to the use of e-learning, including numbers, image quality, and diagnostic accuracy between PGY-1 e-learning users, non-users, and the PGY-2. RESULTS: One hundred and seventy PGY-1 with 736 FAST examinations and 53 PGY-2 residents with 134 examinations were included. Seventy PGY-1 used e-learning with a median time spent of 13.2 mins (IQR, 6.5-21.1 mins) at the first access. The PGY-2 had more PoCUS experience than the PGY-1, however, the 70 e-learning users performed more FAST examinations than the PGY-2 (median [IQR], 4 [2-6] vs. 2 [1-3], p = 0.0004) and had better image quality than the PGY-2 (3 [3-3.2] vs. 3 [2.7-3], p = 0.044). There were no significant differences in the diagnostic accuracy between the PGY-1 and PGY-2. CONCLUSIONS: E-learning has a positive effect on US learning. The PGY-1 users had comparable performance with the PGY-2 and even better image acquisition although the PGY-2 had more PoCUS experience. TRIAL REGISTRATION: NCT03738033 at ClinicalTrials.gov.

Using a telemedicine-assisted airway model to improve the communication and teamwork of tracheal intubation during the coronavirus disease 2019 pandemic.

INTRODUCTION: Isolated spaces impair communication and teamwork during tracheal intubation (TI) in suspected coronavirus disease 2019 patients. We thus aimed to evaluate the telemedicine-assisted airway model (TAM) to improve communication and teamwork during the pandemic. METHODS: This two-stage prospective study included adult patients intubated in the emergency department of the National Taiwan University Hospital between 1 August 2020 and 31 July 2021. First, we randomised patients receiving TI in the standard setting into the conventional group (Con-G) and the isolation area into the isolation group (Iso-G). We evaluated the obstacles to communication and teamwork in an isolation scenario. Second, we developed the TAM to facilitate communication and teamwork between staff in separate spaces during TI and assigned patients to the TAM group (TAM-G). Communication and teamwork were evaluated using the Team Emergency Assessment Measure (TEAM). Subjective evaluations were conducted using a questionnaire administered to medical staff. RESULTS: Eighty-nine patients were enrolled: 17, 34, and 38 in the Con-G, Iso-G, and TAM-G, respectively. The communication frequency (CF) of the Con-G and Iso-G was the highest and lowest, respectively. The CF of the TAM-G increased and approached that of the Con-G. The overall TEAM score was the highest in the Con-G and the lowest in the Iso-G, while the overall score in the TAM-G was comparable to that of the Con-G. DISCUSSION: The TAM may improve communication and teamwork for TIs without compromising efficacy during the pandemic. This study was registered at ClinicalTrials.gov; registration numbers: NCT04479332 and NCT04591873.