Search

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.

An expression of Rayleigh‐Taylor (R‐T) instability growth rate based on the field‐line integrated theory is newly established. This expression can be directly utilized in ionosphere models with magnetic flux tube structure based on Modified Apex Coordinates. The R‐T instability growth rates are calculated using the thermospheric and ionospheric conditions based on the coupled Whole Atmosphere Model and Ionosphere Plasmasphere Electrodynamic model (WAM‐IPE). The parameters used in this calculation include the field‐line integrated conductivities and currents, which consider the Quasi‐Dipole Coordinates and the modifications to the equations of electrodynamics. Detailed description of the new formulas and comprehensive analyses of diurnal, longitudinal, and seasonal variations of the R‐T instability growth rate are carried out. The dependencies of growth rates on pre‐reversal enhancement (PRE) vertical drifts and solar activity are also examined. The results show that pronounced R‐T growth rates are captured between 18 and 22 local time (LT) when strong PRE occurs in the equatorial ionosphere. The simulated R‐T growth rate increases with increasing solar activity levels and demonstrates strong correlations with the angle between the sunset terminator and the geomagnetic field line. These results are consistent with plasma irregularity occurrence rates shown in various satellite observations, suggesting that the newly developed R‐T growth rate calculation can effectively capture the probability of irregularities by considering the changes along magnetic flux‐tubes in the ionosphere. Since the WAM‐IPE is running in operation at National Oceanic and Atmospheric Administration Space Weather Prediction Center, the new calculations can be potentially implemented in the near future to provide forecasted information of the R‐T growth rate. Key Points: A new expression incorporating Modified Apex coordinates for calculating the Rayleigh‐Taylor (R‐T) instability growth rate has been establishedThe simulation of R‐T growth rate using Whole Atmosphere Model and Ionosphere Plasmasphere Electrodynamic model closely resembles the bubble occurrence observed by satellitesThe R‐T instability is primarily driven by the eastward electric field, which is correlated with the pre‐reversal enhancement vertical drift [ABSTRACT FROM AUTHOR]

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]

This study assessed the clinical efficacy of nirmatrelvir plus ritonavir (NMV-r) in treating patients with COVID-19 who have preexisting cardiovascular diseases (CVDs). Patients with underlying CVDs and COVID-19 were included from the TriNetX network. We employed a 1:1 propensity score matching to create two comparable cohorts: patients receiving NMV-r and those not receiving NMV-r. The primary outcome was the composite outcome of all-cause hospitalization or death within 30 days. Propensity score matching yielded two matched cohorts of 10,847 patients each. The composite outcomes of all-cause hospitalization or death within 30 days were 2.2% (239 patients) in the NMV-r cohort and 4.7% (512 patients) in the control cohort, indicating reduced risk in the NMV-r cohort (hazard ratio [HR], 0.475; 95% confidence interval [CI], 0407–0.533). The NMV-r cohort exhibited lower risks of all-cause hospitalization (HR, 0.525; 95% CI, 0.449–0.615) and mortality (HR, 0.113; 95% CI, 0.052–0.246) compared with the control group. A similar trend was observed across most of the subgroups. Our findings indicate that NMV-r to treat COVID-19 could reduce all-cause hospitalization and death in patients with CVDs. [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.