Guideline for resuscitation in cardiac arrest after cardiac surgery

Department of Cardiothoracic Surgery, James Cook University Hospital, Middlesbrough, UK.
European journal of cardio-thoracic surgery: official journal of the European Association for Cardio-thoracic Surgery (Impact Factor: 2.81). 04/2009; 36(1):3-28. DOI: 10.1016/j.ejcts.2009.01.033
Source: PubMed

ABSTRACT The Clinical Guidelines Committee of the European Association for Cardio-Thoracic Surgery provides this professional view on resuscitation in cardiac arrest after cardiac surgery. This document was created using a multimodal methodology for evidence generation including the extrapolation of existing guidelines from the International Liaison Committee on Resuscitation where possible, our own structured literature reviews on issues particular to cardiac surgery, an international survey on resuscitation hosted by CTSNet and manikin simulations of potential protocols. This protocol differs from existing generic guidelines in a number of areas, the most import of which are the following: successful treatment of cardiac arrest after cardiac surgery is a multi-practitioner activity with six key roles that should be allocated and rehearsed on a regular basis; in ventricular fibrillation, three sequential attempts at defibrillation (where immediately available) should precede external cardiac massage; in asystole or extreme bradycardia, pacing (where immediately available) should precede external cardiac massage; where the above measures fail, and in pulseless electrical activity, early resternotomy is advocated; adrenaline should not be routinely given; protocols for excluding reversible airway and breathing complications and for safe emergency resternotomy are given. This guideline is subject to continuous informal review, and when new evidence becomes available.


Available from: Alessandro Fabbri, Jun 07, 2015
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    ABSTRACT: IntroductionIn medical and surgical intensive care units, clinical risk prediction models for readmission have been developed; however studies reporting the risks for cardiovascular intensive care unit (CVICU) readmission have been methodologically limited by small numbers of outcomes, unreported measures of calibration or discrimination, or a lack of information spanning the entire perioperative period. The purpose of this study was to derive and validate a clinical prediction model for CVICU readmission in cardiac surgical patients.MethodsA total of 10,799 patients more than or equal to 18 years in the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) registry who underwent cardiac surgery (coronary artery bypass or valvular surgery) between 2004 and 2012 and were discharged alive from the first CVICU admission were included. The full cohort was used to derive the clinical prediction model and the model was internally validated with bootstrapping. Discrimination and calibration were assessed using the AUC c-index and the Hosmer-Lemeshow tests, respectively.ResultsA total of 479 (4.4%) patients required CVICU readmission. The mean CVICU length of stay (19.9 versus 3.3 days, P <0.001) and in-hospital mortality (14.4 % versus 2.2%, P <0.001) were higher among patients readmitted to the CVICU. In the derivation cohort, a total of three preoperative (Age ¿70, ejection fraction, chronic lung disease), two intraoperative (Single valve repair or replacement¿+¿non-CABG surgery, multivalve repair or replacement), and seven postoperative variables (cardiac arrest, pneumonia, pleural effusion, deep sternal wound infection, leg graft harvest site infection, gastrointestinal bleed, neurologic complications) were independently associated with CVICU readmission. The clinical prediction model had robust discrimination and calibration in the derivation cohort (AUC c index =0.799; Hosmer-Lemeshow P =0.192). The validation point estimates and confidence intervals were similar to derivation model.Conclusion In a large population based dataset incorporating a comprehensive set of perioperative variables, we have derived a clinical prediction model with excellent discrimination and calibration. This model identifies opportunities for targeted therapeutic interventions aimed at reducing CVICU readmissions in high risk patients.
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