Dataset Information

Variability in functional outcome and treatment practices by treatment center after out-of-hospital cardiac arrest: analysis of International Cardiac Arrest Registry.

ABSTRACT: PURPOSE:Functional outcomes vary between centers after out-of-hospital cardiac arrest (OHCA) and are partially explained by pre-existing health status and arrest characteristics, while the effects of in-hospital treatments on functional outcome are less understood. We examined variation in functional outcomes by center after adjusting for patient- and arrest-specific characteristics and evaluated how in-hospital management differs between high- and low-performing centers. METHODS:Analysis of observational registry data within the International Cardiac Arrest Registry was used to perform a hierarchical model of center-specific risk standardized rates for good outcome, adjusted for demographics, pre-existing functional status, and arrest-related factors with treatment center as a random effect variable. We described the variability in treatments and diagnostic tests that may influence outcome at centers with adjusted rates significantly above and below registry average. RESULTS:A total of 3855 patients were admitted to an ICU following cardiac arrest with return of spontaneous circulation. The overall prevalence of good outcome was 11-63% among centers. After adjustment, center-specific risk standardized rates for good functional outcome ranged from 0.47 (0.37-0.58) to 0.20 (0.12-0.26). High-performing centers had faster time to goal temperature, were more likely to have goal temperature of 33 °C, more likely to perform unconscious cardiac catheterization and percutaneous coronary intervention, and had differing prognostication practices than low-performing centers. CONCLUSIONS:Center-specific differences in outcomes after OHCA after adjusting for patient-specific factors exist. This variation could partially be explained by in-hospital management differences. Future research should address the contribution of these factors to the differences in outcomes after resuscitation.

SUBMITTER: May TL

PROVIDER: S-EPMC6486427 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Variability in functional outcome and treatment practices by treatment center after out-of-hospital cardiac arrest: analysis of International Cardiac Arrest Registry.

May Teresa L TL Lary Christine W CW Riker Richard R RR Friberg Hans H Patel Nainesh N Søreide Eldar E McPherson John A JA Undén Johan J Hand Robert R Sunde Kjetil K Stammet Pascal P Rubertsson Stein S Belohlvaek Jan J Dupont Allison A Hirsch Karen G KG Valsson Felix F Kern Karl K Sadaka Farid F Israelsson Johan J Dankiewicz Josef J Nielsen Niklas N Seder David B DB Agarwal Sachin S

Intensive care medicine 20190308 5

<h4>Purpose</h4>Functional outcomes vary between centers after out-of-hospital cardiac arrest (OHCA) and are partially explained by pre-existing health status and arrest characteristics, while the effects of in-hospital treatments on functional outcome are less understood. We examined variation in functional outcomes by center after adjusting for patient- and arrest-specific characteristics and evaluated how in-hospital management differs between high- and low-performing centers.<h4>Methods</h4> ...[more]

PMID: 30848327

Similar Datasets

Project description:Background and aimsCardiac arrest (CA) in children is a major public health problem. Thanks to advances in cardiopulmonary resuscitation (CPR) guidelines and teaching skills, results in children have improved. However, pediatric CA has a very high mortality. In the treatment of in-hospital CA there are still multiple controversies. The objective of this study is to develop a multicenter and international registry of in-hospital pediatric cardiac arrest including the diversity of management in different clinical and social contexts. Participation in this register will enable the evaluation of the diagnosis of CA, CPR and post-resuscitation care and its influence in survival and neurological prognosis.MethodsAn intrahospital CA data recording protocol has been designed following the Utstein model. Database is hosted according to European legislation regarding patient data protection. It is drafted in English and Spanish. Invitation to participate has been sent to Spanish, European and Latinamerican hospitals. Variables included, asses hospital characteristics, the resuscitation team, patient's demographics and background, CPR, post-resuscitation care, mortality, survival and long-term evolution. Survival at hospital discharge will be evaluated as a primary outcome and survival with good neurological status as a secondary outcome, analyzing the different factors involved in them. The study design is prospective, observational registry of a cohort of pediatric CA.ConclusionsThis study represents the development of a registry of in-hospital CA in childhood. Its development will provide access to CPR data in different hospital settings and will allow the analysis of current controversies in the treatment of pediatric CA and post-resuscitation care. The results may contribute to the development of further international recommendations. Trial register: ClinicalTrials.gov Identifier: NCT04675918. Registered 19 December 2020 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT04675918?cond=pediatric+cardiac+arrest&draw=2&rank=10.

Project description:BackgroundResuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information.Methods and findingsPatient-level data were extracted from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients who had experienced a cardiac arrest within 24 hours prior to admission to an intensive care unit (ICU) during the period January 2006 to December 2016. The primary outcome was in-hospital mortality. The models were trained and tested on a dataset (split 90:10) including age, lowest and highest physiologic variables during the first 24 hours, and key past medical history. LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neural network [ANN], and an ensemble) were compared to the APACHE III and Australian and New Zealand Risk of Death (ANZROD) predictions. In all, 39,566 patients from 186 ICUs were analysed. Mean (±SD) age was 61 ± 17 years; 65% were male. Overall in-hospital mortality was 45.5%. Models were evaluated in the test set. The APACHE III and ANZROD scores demonstrated good discrimination (area under the receiver operating characteristic curve [AUROC] = 0.80 [95% CI 0.79-0.82] and 0.81 [95% CI 0.8-0.82], respectively) and modest calibration (Brier score 0.19 for both), which was slightly improved by LR (AUROC = 0.82 [95% CI 0.81-0.83], DeLong test, p < 0.001). Discrimination was significantly improved using ML models (ensemble and GBM AUROCs = 0.87 [95% CI 0.86-0.88], DeLong test, p < 0.001), with an improvement in performance (Brier score reduction of 22%). Explainability models were created to assist in identifying the physiologic features that most contributed to an individual patient's survival. Key limitations include the absence of pre-hospital data and absence of external validation.ConclusionsML approaches significantly enhance predictive discrimination for mortality following cardiac arrest compared to existing illness severity scores and LR, without the use of pre-hospital data. The discriminative ability of these ML models requires validation in external cohorts to establish generalisability.

Dataset Information

Variability in functional outcome and treatment practices by treatment center after out-of-hospital cardiac arrest: analysis of International Cardiac Arrest Registry.

Publications

Variability in functional outcome and treatment practices by treatment center after out-of-hospital cardiac arrest: analysis of International Cardiac Arrest Registry.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure