scholarly journals Early Prediction Model of Brain Death in Out-of-hospital Cardiac Arrest Patients: a Single-centre Retrospective and Internal Validation Analysis

2021 ◽  
Author(s):  
Yuki Itagaki ◽  
Mineji Hayakawa ◽  
Kunihiko Maekawa ◽  
Akira Kodate ◽  
Koyo Moriki ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Brain Death ◽  
Prediction Model ◽  
Organ Procurement ◽  
Low Flow ◽  
Stepwise Logistic Regression ◽  
Early Prediction ◽  
Organ Donors ◽  
Hospital Cardiac Arrest

Abstract Background A shortage of donor organs amid a high demand for transplantable organs is a worldwide problem, and an increase in organ donation would be welcomed by the global healthcare system. Patients with brain death (BD) are potential organ donors, and early prediction of BD patients may facilitate the process of organ procurement. Therefore, we developed a model for early prediction of BD in patients who survived the initial phase of out-of-hospital cardiac arrest (OHCA). Methods We retrospectively analysed data of patients aged, who were aged < 80 years, experienced OHCA with return of spontaneous circulation (ROSC), and were admitted to our hospital between 2006 and 2018. We categorised the patients into either a non-BD or BD group. Demographic and laboratory data on emergency department admission were used for stepwise logistic regression. Prediction scores of BD after OHCA were based on β-coefficients of prognostic factors identified in the multivariable logistic model. Results Overall, 419 OHCA patients with ROSC were admitted to our hospital during the study period. Seventy-seven patients showed BD (18.3%). Age and OHCA aetiology were significantly different between the groups. Logistic regression analysis confirmed that age, low-flow time, pH, and aetiology were independent predictors of BD. The area under the receiver operating characteristic curve for this model was 0.831 (95% CI, 0.786–0.876). Conclusion We developed and internally validated a new prediction model for BD after OHCA, which could aid in early identification of potential organ donors for early donor organ procurement.

Abstract 12769: Amplitude-Spectral Area of the Ventricular Fibrillation Waveform and Chest Compression Quality in Witnessed and Unwitnessed Out-Of-Hospital Cardiac Arrest: Can We Predict Who is Unlikely to Survive?

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Julia Indik ◽  
Zacherie Conover ◽  
Meghan McGovern ◽  
Annemarie Silver ◽  
Daniel Spaite ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Hospital Discharge ◽  
Chest Compression ◽  
Stepwise Logistic Regression ◽  
Individual Subject ◽  
Spectral Area ◽  
Initial Rhythm ◽  
Hospital Cardiac Arrest

Background: Previous investigations in human out of hospital cardiac arrest (OHCA) due to ventricular fibrillation (VF) have shown that the frequency-based waveform characteristic, amplitude spectral area (AMSA) predicts defibrillation success and is associated with survival to hospital discharge. We evaluated the relative strength of factors associated with hospital discharge including witnessed/unwitnessed status, chest compression (CC) quality and AMSA. We then investigated if there is a threshold value for AMSA that can identify patients who are unlikely to survive. Methods: Adult OHCA patients (age ≥18), with initial rhythm of VF from an Utstein-Style database (collected from 2 EMS systems) were analyzed. AMSA was measured from the waveform immediately prior to each shock, and averaged for each individual subject (AMSA-ave). Univariate and stepwise multivariable logistic regression, and receiver-operator-characteristic (ROC) analyses were performed. Factors analyzed: age, sex, witnessed status, time from dispatch to monitor/defibrillator application, number of shocks, mean CC rate, depth, and release velocity (RV). Results: 140 subjects were analyzed, [104 M (74%), age 62 ± 14 yrs, witnessed 65%]. Survival was 38% in witnessed and 16% in unwitnessed arrest. In univariate analyses, age (P=0.001), witnessed status (P=0.009), AMSA-ave (P<0.001), mean CC depth (P=0.025), and RV (P< 0.001) were associated with survival. Stepwise logistic regression identified AMSA-ave (P<0.001), RV (P=0.001) and age (P=0.018) as independently associated with survival. The area under the curve (ROC analysis) was 0.849. The probability of survival was < 5% in witnessed arrest for AMSA-ave < 5 mV-Hz, and in unwitnessed arrest for AMSA-ave < 15 mV-Hz. Conclusion: In OHCA with an initial rhythm of VF, AMSA-ave and CC RV are highly associated with survival. Further study is needed to evaluate whether AMSA-ave may be useful to identify patients highly unlikely to survive.

5228Identification of reliable clinical variables for early prediction of outcome after out-of-hospital cardiac arrest using algorithm-based machine learning statistical methods

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J Johnsson ◽  
S Hoerberg ◽  
A Holm ◽  
S Gustafzelius ◽  
J Dankiewicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Low Flow ◽  
Temperature Management ◽  
Target Temperature Management ◽  
Extended Model ◽  
Early Prediction ◽  
Prediction Of Outcome ◽  
Clinical Variables ◽  
Hospital Cardiac Arrest

Abstract Background Several factors are known to influence both survival and long-term neurologic function after out-of-hospital cardiac arrest (OHCA). Previous studies have indicated that both pre-hospital circumstances as well as patients' history and clinical status on hospital admission are variables strongly associated with later outcome. This study aimed to identify and evaluate clinical variables for early prediction of outcome for unconscious survivors after OHCA using machine learning statistics analysis. Methods The Target Temperature Management (TTM) trial randomized 939 international patients with OHCA of presumed cardiac cause to TTM at 33°C or 36 °C for 24 h in intensive care units (ICUs). Patient outcome were survival and neurological function defined by the Cerebral Performance Category (CPC) scale. This multicentre cohort was used for a post hoc analysis using machine learning statistical analysis. A Conditional Interference decision forest algorithm was designed for training on the TTM-dataset to perform early prediction of outcome at 180 days. Results After ranking all available variables in the TTM-dataset based on their importance for the algorithm to make predictions, we could identify a slimmed list with eleven clinical predictors of a poor outcome including older age, low motor score on Glasgow Coma Scale (GCS), increasing doses of adrenaline, first monitored rhythm not shockable, longer duration of low flow, longer time from cardiac arrest to advanced life support, high BMI (Body Mass Index), low pH, bilateral absence of corneal and pupillary reflex, low initial body temperature and cardiac arrest location at home. Age was overall the most important variable for prediction. Our slimmed prediction model performed slightly worse with an AUC of 0.813 (0.741–0.916) compared to an extended model with all available variables included, AUC = 0.839 (0.778 – 0.886). When using all variables in a comparing logistic regression analysis the mean AUC was a corresponding 0.830 (0.792–0.882). Conclusion This algorithm with eleven clinical variables predicted outcome almost as good as a corresponding large model with cardiac arrest patients from the TTM-trial and could be a powerful clinical decision tool for early prediction of outcome after cardiac arrest.

scholarly journals Association of school hours with outcomes of out-of-hospital cardiac arrest in schoolchildren

Heart Asia ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. e011236
Author(s):  
Akira Yamashita ◽  
Hisanori Kurosaki ◽  
Kohei Takada ◽  
Yoshio Tanaka ◽  
Yoshitaka Hamada ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Survival Rate ◽  
Public Access ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Bystander Cardiopulmonary Resuscitation ◽  
Initial Rhythm ◽  
Major Factors ◽  
Hospital Cardiac Arrest

ObjectiveTo investigate the association of school hours with outcomes of schoolchildren with out-of-hospital cardiac arrest (OHCA).MethodsFrom the 2005–2014 nationwide databases, we extracted the data for 1660 schoolchildren (6–17 years) with bystander-witnessed OHCA. Univariate analyses followed by propensity-matching procedures and stepwise logistic regression analyses were applied. School hours were defined as 08:00 to 18:00.ResultsThe neurologically favourable 1-month survival rate during school hours was better than that during non-school hours only on school days: 18.4% and 10.5%, respectively. During school hours on school days, patients with OHCA more frequently received bystander cardiopulmonary resuscitation (CPR) and public access defibrillation (PAD), and had a shockable initial rhythm and presumed cardiac aetiology. The neurologically favourable 1-month survival rate did not significantly differ between school hours on school days and all other times of day after propensity score matching: 16.4% vs 16.1% (unadjusted OR 1.02; 95% CI 0.69 to 1.51). Stepwise logistic regression analysis during school hours on school days revealed that shockable initial rhythm (adjusted OR 2.44; 95% CI 1.12 to 5.42), PAD (adjusted OR 3.32; 95% CI 1.23 to 9.10), non-exogenous causes (adjusted OR 5.88; 95% CI 1.85 to 20.0) and a shorter emergency medical service (EMS) response time (adjusted OR 1.15; 95% CI 1.02 to 1.32) and witness-to-first CPR interval (adjusted OR 1.08; 95% CI 1.01 to 1.15) were major factors associated with an improved neurologically favourable 1-month survival rate.ConclusionsSchool hours are not an independent factor associated with improved outcomes of OHCA in schoolchildren. The time delays in CPR and EMS arrival were independently associated with poor outcomes during school hours on school days.

scholarly journals Prediction of Endometrial Hyperplasia and Cancer among Premenopausal Women with Abnormal Uterine Bleeding

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Luca Giannella ◽  
Lillo Bruno Cerami ◽  
Tiziano Setti ◽  
Ezio Bergamini ◽  
Fausto Boselli
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Endometrial Hyperplasia ◽  
Premenopausal Women ◽  
Abnormal Uterine Bleeding ◽  
Uterine Bleeding ◽  
Stepwise Logistic Regression ◽  
Simultaneous Presence ◽  
Endometrial Pathology

Objective. To create a prediction model including clinical variables for the prediction of premalignant/malignant endometrial pathology in premenopausal women with abnormal uterine bleeding (AUB). Methods. This is an observational retrospective study including 240 premenopausal women with AUB referred to diagnostic hysteroscopy. Based on the presence of endometrial hyperplasia (EH) or cancer (EC), the women were divided into cases (EH/EC) and controls (no EH/EC). Univariate, stepwise logistic regression and ROC curve analysis were performed. Results. 12 women had EH/EC (5%). Stepwise logistic regression analysis showed that EH/EC associated significantly with BMI ≥ 30 (OR=7.70, 95% CI 1.90 to 31.17), diabetes (OR=9.71, 95% CI 1.63 to 57.81), and a thickened endometrium (OR=1.20, 95% CI 1.08 to 1.34, criterion > 11 mm). The AUC was 0.854 (95% confidence intervals 0.803 to 0.896, p<0.0001). Considering the pretest probability for EH/EC of 5%, the prediction model with a positive likelihood ratio of 8.14 showed a posttest probability of 30%. The simultaneous presence of two or three risk factors was significantly more common in women with EH/EC than controls (50% vs. 6.6 and 25% vs. 0%, respectively, p<0.0001). Conclusion. When premenopausal vaginal bleeding occurs in diabetic obese women with ET > 11 mm, the percentage of premalignant/malignant endometrial pathology increases by 25%. It is likely that the simultaneous presence of several risk factors is necessary to significantly increase the probability of endometrial pathology.

scholarly journals Mechanical circulatory support for refractory out-of-hospital cardiac arrest: a Danish nationwide multicenter study

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Sivagowry Rasalingam Mørk ◽  
Carsten Stengaard ◽  
Louise Linde ◽  
Jacob Eifer Møller ◽  
Lisette Okkels Jensen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Survival Rate ◽  
Neurological Outcome ◽  
Mechanical Circulatory Support ◽  
Circulatory Support ◽  
Good Neurological Outcome ◽  
Factors Associated ◽  
National Consensus ◽  
Hospital Cardiac Arrest

Abstract Background Mechanical circulatory support (MCS) with either extracorporeal membrane oxygenation or Impella has shown potential as a salvage therapy for patients with refractory out-of-hospital cardiac arrest (OHCA). The objective of this study was to describe the gradual implementation, survival and adherence to the national consensus with respect to use of MCS for OHCA in Denmark, and to identify factors associated with outcome. Methods This retrospective, observational cohort study included patients receiving MCS for OHCA at all tertiary cardiac arrest centers (n = 4) in Denmark between July 2011 and December 2020. Logistic regression and Kaplan–Meier survival analysis were used to determine association with outcome. Outcome was presented as survival to hospital discharge with good neurological outcome, 30-day survival and predictors of 30-day mortality. Results A total of 259 patients were included in the study. Thirty-day survival was 26%. Sixty-five (25%) survived to hospital discharge and a good neurological outcome (Glasgow–Pittsburgh Cerebral Performance Categories 1–2) was observed in 94% of these patients. Strict adherence to the national consensus showed a 30-day survival rate of 30% compared with 22% in patients violating one or more criteria. Adding criteria to the national consensus such as signs of life during cardiopulmonary resuscitation (CPR), pre-hospital low-flow < 100 min, pH > 6.8 and lactate < 15 mmol/L increased the survival rate to 48%, but would exclude 58% of the survivors from the current cohort. Logistic regression identified asystole (RR 1.36, 95% CI 1.18–1.57), pulseless electrical activity (RR 1.20, 95% CI 1.03–1.41), initial pH < 6.8 (RR 1.28, 95% CI 1.12–1.46) and lactate levels > 15 mmol/L (RR 1.16, 95% CI 1.16–1.53) as factors associated with increased risk of 30-day mortality. Patients presenting signs of life during CPR had reduced risk of 30-day mortality (RR 0.63, 95% CI 0.52–0.76). Conclusions A high survival rate with a good neurological outcome was observed in this Danish population of patients treated with MCS for OHCA. Stringent patient selection for MCS may produce higher survival rates but potentially withholds life-saving treatment in a significant proportion of survivors.

Abstract 16924: Initial AMSA and Increased AMSA After CPR Predict Second Shock Success in Initially Shock-resistant VF During Out-of-hospital Cardiac Arrest

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Ulrich Herken ◽  
Weilun Quan
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Success Rate ◽  
Fourier Transformation ◽  
Amplitude Spectrum ◽  
Fast Fourier Transformation ◽  
Shock Delivery ◽  
Spectrum Area ◽  
Hospital Cardiac Arrest

Purpose: Amplitude spectrum area (AMSA), which is calculated from the ventricular fibrillation (VF) waveform using fast Fourier transformation, has been recognized as a predictor of successful defibrillation (DF) and as an index of myocardial perfusion and viability during resuscitation. In this study, we investigated whether a change in AMSA occurring during CPR would predict DF outcome for subsequent DF attempts after a failed DF. We hypothesized that a patient responding to CPR with an increase in AMSA would have an increased likelihood of DF success. Methods: This was a retrospective analysis of out-of-hospital cardiac arrest patients who received a second DF due to initially shock-resistant VF. A total of 193 patients with an unsuccessful first DF were identified in a manufacturer database of electrocardiographic defibrillator records. AMSA was calculated for the first DF (AMSA1) and the second DF (AMSA2) during a 2.1 sec window ending 0.5 sec prior to DF. A successful DF attempt was defined as the presence of an organized rhythm with a rate ≥ 40 / min starting within 60 sec from the DF and lasting for > 30 sec. After the failed first DF, all patients received CPR for 2 to 3 minutes before delivery of the second DF. Change in AMSA (dAMSA) was calculated as dAMSA = AMSA2 - AMSA1. Results: The overall second DF success rate was 14.5%. Multivariable logistic regression showed that both AMSA1 and dAMSA were independent predictors of second DF success with odds ratios of 1.24 (95% CI 1.12 - 1.38, p<0.001) and 1.27 (95% CI 1.16 - 1.41, p<0.001) for each mVHz change in AMSA or dAMSA, respectively. Conclusions: In initially DF-resistant VF, a high initial AMSA value predicted an increased likelihood of second shock success. An increase of AMSA in response to CPR also predicted a higher second shock success rate. Monitoring of AMSA during resuscitation therefore may be useful to guide CPR efforts, possibly including timing of second shock delivery. These findings also further support the value of AMSA as indicator of myocardial viability.

Abstract 354: A Prediction Model for Patients With Emergency Medical Technicians Witnessed Out-of-Hospital Cardiac Arrest

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Ming-Ju Hsieh ◽  
Wen-Chu Chiang ◽  
Wei-Tien Chang ◽  
Chih-Wei Yang ◽  
Yu-Chun Chien ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Prediction Model ◽  
Situation Awareness ◽  
Blood Pressure Level ◽  
Emergency Medical Technicians ◽  
Validation Dataset ◽  
Systolic Blood Pressure Level ◽  
Value System ◽  
Emergency Medical ◽  
Hospital Cardiac Arrest

Introduction: In-hospital early warning system scores for prediction of clinical deterioration have been well-developed. However, such prediction tools in prehospital setting remain unavailable. Hypothesis: To develop a model for predicting patients with emergency medical technicians witnessed out-of-hospital cardiac arrest (EMT-witnessed OHCA) . Methods: We used the fire-based emergency medical service (EMS) data from Taipei city to develop the prediction model. Patients included in this study were those initially alive, non-traumatic, and aged ≧20 years. Data were extracted from records of ambulance run sheets and OHCA registry in Taipei. The primary outcome (i.e. EMT-witnessed OHCA) was defined as cardiac arrest occurring during EMT services before arrival at the receiving hospital. The prediction model was developed through the standard cross-validation method (i.e. divided dataset for training group and validation group). Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow (HL) test were used to test discrimination and calibration. The point value system with Youden’s J Index was used to find the best cut-off value for practical application. Results: From 2011 to 2015, a total of 252,771 patients were included. Of them, 660 (0.26%) were EMT-witnessed OHCA. The prediction model, including gender, respiratory rate, heart rate, systolic blood pressure, level of consciousness and oxygen saturation, showed excellent discrimination (AUC 0.94) and calibration ( p =0.42 for HL test). When applied to the validation dataset, it maintained good discriminatory ability (AUC 0.94) and calibration ( p =0.11). The optimal cut-off value (≧13) of the point value system of the tool showed high sensitivity (87.84%) and specificity (86.20%). Conclusions: The newly developed prediction model will help identify high-risk patients with EMT-witnessed OHCA and indicate potential prevention by situation awareness in EMS.

Effects of Targeted Temperature Management in Patients with Post-Cardiac Arrest Syndrome after an Out-Of-Hospital Cardiac Arrest Caused by Hanging: A Prospective Observational Cohort Study in Japan

2021 ◽  
Author(s):  
Ryuichiro Kakizaki ◽  
Naofumi Bunya ◽  
Shuji Uemura ◽  
Takehiko Kasai ◽  
Keigo Sawamoto ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Propensity Score ◽  
Multivariate Logistic Regression Analysis ◽  
Targeted Temperature Management ◽  
Temperature Management ◽  
Matched Cohort ◽  
Neurological Outcomes ◽  
Post Cardiac Arrest Syndrome ◽  
Hospital Cardiac Arrest

Abstract Background: Targeted temperature management (TTM) is recommended for unconscious patients after a cardiac arrest. However, its effectiveness in patients with post-cardiac arrest syndrome (PCAS) by hanging remains unclear. Therefore, this study aimed to investigate the relationship between TTM and favorable neurological outcomes in patients with PCAS by hanging.Methods: This study was a retrospective analysis of the Japanese Association for Acute Medicine out-of-hospital cardiac arrest (OHCA) registry between June 2014 and December 2017 among patients with PCAS admitted to the hospitals after an OHCA caused by hanging. A multivariate logistic regression analysis was performed to estimate the propensity score and to predict whether patients with PCAS by hanging receive TTM. We compared patients with PCAS by hanging who received TTM (TTM group) and those who did not (non-TTM group) using propensity score analysis.Results: A total of 199 patients with PCAS by hanging were enrolled in this study. Among them, 43 were assigned to the TTM group and 156 to the non-TTM group. Logistic regression model adjusted for propensity score revealed that TTM was not associated with favorable neurological outcome at 1-month (adjusted odds ratio [OR]: 1.38, 95% confidence interval [CI]: 0.27–6.96). Moreover, no difference was observed in the propensity score-matched cohort (adjusted OR: 0, 73, 95% CI: 0.10–4.71) and in the inverse probability of treatment weighting-matched cohort (adjusted OR: 0.63, 95% CI: 0.15–2.69).Conclusions: TTM was not associated with increased favorable neurological outcomes at 1-month in patients with PCAS after OHCA by hanging.

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