Early chest tube removal after coronary artery bypass graft surgery

Abstract Background: There is no clear data about the optimum time for chest tube removal after coronary artery bypass surgery. Aim: The aim of this study was to assess the impact of the chest tube removal time following coronary artery bypass grafting surgery on the clinical outcome of the patients. Material and Methods: An analysis of data from 307 patients was performed. The patients were randomized into two groups: in group 1 (N=107) chest tubes were removed within the first 24 hours after surgery, whereas in group 2 (N=200), chest tubes were removed in the second 24 hours after surgery. Demographics, lactate and pH at the beginning, during and after the operation, creatinine, left ventricular ejection fraction, inotropic drugs administration, length of ICU stay, and mortality data were collected. Respiratory rate and pain level was assessed. Results: In these surgeries, the mean± standard deviation for the aortic clamping time was 49.18±17.59 minutes and cardiopulmonary bypass time was 78.39±25.12 minutes. The amount of heparin consumed by the second group was higher (P <0.001) which could be considered as an important factor in increasing the drainage time after the surgery (P =0.047). The pain level evaluated 24 hours post-operation was lower in the first group, and the difference in the pain level between the 2 groups evaluated 30 hours post-operation was significant (P=0.016). The mean time of intensive care unit stay was longer in the second group but it was not statistically significant. Conclusion: Early extracting of chest tubes after coronary artery bypass graft surgery when there is no significant drainage can lead to pain reduction and consuming oxygen is an effective measure after surgery toward healing; it doesn't increase the risk of creation of plural effusion and pericardial effusion

Overcoming challenges of implementing chronic care model in diabetes management: An action research approach

Background: Reforming the health care system to improve suitable health care model for diabetic patients is essential. This study aimed to implement, identify, and overcome the challenges of implementing the Chronic Care Model in diabetes management in a clinic. Methods: This study is a qualitative technical action research with the Kemmis and McTaggart model including planning, action, reflection, observation, and revision plan which was conducted in the specialized polyclinic from 2015 to 2017 in Isfahan city – Iran. Data were gathered through qualitative and quantitative methods. Diabetes team and 17 patients with type 2 diabetes participated in semi-structured interviews that were purposively chosen. Qualitative data were analyzed using content analysis and then quantitative data collected. Results: The qualitative findings of this research are in five main categories: System design upgrade, self-management upgrade, decision support, health care organization, and clinical information system upgrade. Results of quantitative data showed that most metabolic indicators like HbA1c have statistical meaningful changes (P value < 0.05). Conclusions: Implementing the Chronic Care Model became feasible despite serious challenges and two groups of ready and active team and active patients were developed. The study showed that one important lost link of diabetes management is underestimating the nurses' capabilities in the management of this disease. Inevitably, serious investment on maximum use of nurses' knowledge and skills in improving diabetes management will help diabetes care upgrade significantly

Evaluation of accuracy of Euroscore risk model in prediction of perioperative mortality after coronary bypass graft surgery in Isfahan*

Background: This study aimed to evaluate the accuracy of Euroscore (European System for Cardiac Operative Risk Evaluation) in predicting perioperative mortality after cardiac surgery in Iranian patient population. Methods: Data on 1362 patients undergoing coronary bypass graft surgery (CABG) from 2007 to 2009 were collected. Calibration was assessed by Hosmer-Lemeshow goodness-of-fit. Area under the curve (AUC) was used to assess score validity. Odds ratios were measured to evaluate the predictive value of each risk factor on mortality rate. Results: The overall perioperative in hospital mortality was 3.6% whereas the Euroscore predicted a mortality of 3.96%. Euroscore model fitted well in the validation databases. The mean AUC was 66%. Mean length of intensive care unit (ICU) stay was 2.5 ± 2.5 days. Among risk factors, only left ventricular dysfunction, age and neurologic dysfunction were found to be related to mortality rate. Conclusions: Euroscore did not have acceptable discriminatory ability in perioperative in hospital mortality in Iranian patients. It seems that development of a local mortality risk scores corresponding to our patients epidemiologic characteristics may improve prediction of outcome