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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.

To develop a prediction model for emergency medical technicians (EMTs) to identify trauma patients at high risk of deterioration to emergency medical service (EMS)-witnessed traumatic cardiac arrest (TCA) on the scene or en route. We developed a prediction model using the classical cross-validation method from the Pan-Asia Trauma Outcomes Study (PATOS) database from 1 January 2015 to 31 December 2020. Eligible patients aged ≥18 years were transported to the hospital by the EMS. The primary outcome (EMS-witnessed TCA) was defined based on changes in vital signs measured on the scene or en route. We included variables that were immediately measurable as potential predictors when EMTs arrived. An integer point value system was built using multivariable logistic regression. The area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow (HL) test were used to examine discrimination and calibration in the derivation and validation cohorts. In total, 74,844 patients were eligible for database review. The model comprised five prehospital predictors: age <40 years, systolic blood pressure <100 mmHg, respiration rate >20/minute, pulse oximetry <94%, and levels of consciousness to pain or unresponsiveness. The AUROC in the derivation and validation cohorts was 0.767 and 0.782, respectively. The HL test revealed good calibration of the model (p = 0.906). We established a prediction model using variables from the PATOS database and measured them immediately after EMS personnel arrived to predict EMS-witnessed TCA. The model allows prehospital medical personnel to focus on high-risk patients and promptly administer optimal treatment. [ABSTRACT FROM AUTHOR]

Unilateral lower limb swelling is a common complaint among emergency department (ED) patients. However, an isolated intramuscular hematoma is an uncommon cause of lower limb swelling. We present a case of left thigh swelling after a traffic accident in which an intramuscular hematoma was diagnosed using point-of-care ultrasound. A literature review was also conducted. [ABSTRACT FROM AUTHOR]

Background: Since first reported in 2002, the rate of methicillin-resistant Staphylococcus aureus (MRSA) among childhood community-associated (CA) S. aureus infection in Taiwan increased significantly up to 2005. There have been no reports on this issue since then.Methods: We prospectively collected clinical S. aureus isolates from the patients <19 years of age in a university-affiliated hospital in 2012. Only first isolate from each patient was included. The medical records were retrospectively reviewed and the patients were classified as CA or healthcare-associated (HA) by the standard epidemiologic criteria. Isolates as CA-MRSA were further characterized by pulsed-field gel electrophoresis, staphylococcal cassette chromosome (SCCmec) typing, and multilocus sequence typing.Results: A total of 409 S. aureus isolates were included, and 260 (63.6%) were MRSA. The proportion of MRSA among all S. aureus isolates in 2012 increased significantly (p < 0.001) compared to that in 2004-2005. Of the 181 CA-MRSA isolates, 86.2% were identified from pus or wound. Nine pulsotypes were identified with two major types (type D, 119 (65.7%); type C, 27 (14.9%). Most of the isolates carried either SCCmec IV (66 isolates, 36%) or VT (112 isolates, 62%). 128 isolates (71%) carried Panton-Valentine leukocidin (PVL) genes. Clonal complex (CC) 59 accounted for 146 isolates (80.7%) of two major pulsotypes, CC45 for 19 isolates, ST30 for 6 isolates and ST8 (USA 300) for 4 isolates. In addition to penicillin (100%), most isolates were resistant to erythromycin (81%) and clindamycin (79.3%).Conclusions: Around two-thirds of childhood community-associated S. aureus infections in northern Taiwan were MRSA. Though CC59 is still the prevalent community clone, several new clones emerged in northern Taiwan. [ABSTRACT FROM AUTHOR]

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.

Coronavirus disease 2019 (COVID-19) had caused a worldwide pandemic with public health emergencies since 2020. For the symptomatic patients, high mortality rate was observed if without timely and optimized management. In this study, we aimed to investigate the predictive and prognostic roles of hematologic and biochemical parameters obtained in the emergency department (ED) for COVID-19 patients. We conducted a retrospective study in a dedicated COVID-19 medical center, recruiting a total of 228 COVID-19 patients with 86 severe and 142 non-severe cases. Both the hematologic and biochemical parameters obtained in the ED upon arrival were analyzed to evaluate the association of the biomarkers with disease severity and prognosis among COVID-19 patients. Among these parameters, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), ferritin, and D-dimer were significantly higher in the severe group than the non-severe one, whereas the platelet count and lymphocyte-to-monocyte ratio were significantly lower. Receiver operating characteristic curve analysis revealed that the areas under curve of CRP, PCT, LDH, ferritin, D-dimer, and NLR for differentiating the severity of COVID-19 were 0.713, 0.755, 0.763, 0.741, 0.733, and 0.683, respectively, whereas the areas under curve of CRP, PCT, LDH, ferritin, D-dimer, and NLR for differentiating the mortality of COVID-19 were 0.678, 0.744, 0.680, 0.676, 0.755, and 0.572, respectively. Logistic regression analysis revealed that CRP, PCT, LDH, ferritin, D-dimer, and NLR were independent indicators for prediction of severe COVID-19, and LDH and ferritin were independent factors associated with the mortality in COVID-19. In conclusion, higher CRP, PCT, LDH, ferritin, D-dimer, and NLR were associated with severe COVID-19, whereas higher LDH and ferritin were associated with the mortality in COVID-19. These findings could help early risk stratification in the ED and contribute to optimized patient management., Competing Interests: None