Preoperative calculation of risk for prolonged intensive care unit stay following coronary artery bypass grafting

Ghotkar, Sanjay V; Grayson, Antony D.; Fabri, Brian M.; Dihmis, Walid C.; Pullan, D. Mark

doi:10.1186/1749-8090-1-14

Cited by 76 publications

(64 citation statements)

References 17 publications

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“…In February 2008, the MEDLINE and PreMEDLINE databases were searched for studies on prediction models for PICULOS after cardiac 2,11,13,[15][16][17][18][19][20][21][22][23][24][25][26][27][28] we further restricted our analysis to prediction models that used a threshold for PICULOS within the bounds of 24 to 72 hours.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery

et al. 2010

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Background-Several models have been developed to predict prolonged stay in the intensive care unit (ICU) after cardiac surgery. However, no extensive quantitative validation of these models has yet been conducted. This study sought to identify and validate existing prediction models for prolonged ICU length of stay after cardiac surgery. Methods and Results-After a systematic review of the literature, the identified models were applied on a large registry database comprising 11 395 cardiac surgical interventions. The probabilities of prolonged ICU length of stay based on the models were compared with the actual outcome to assess the discrimination and calibration performance of the models. Literature review identified 20 models, of which 14 could be included. Of the 6 models for the general cardiac surgery population, the Parsonnet model showed the best discrimination (area under the receiver operating characteristic curveϭ0.75 [95% confidence interval, 0.73 to 0.76]), followed by the European system for cardiac operative risk evaluation (EuroSCORE) (0.71 [0.70 to 0.72]) and a model by Huijskes and colleagues (0.71 [0.70 to 0.73]). Most of the models showed good calibration. Conclusions-In this validation of prediction models for prolonged ICU length of stay, 2 widely implemented models (Parsonnet, EuroSCORE), although originally designed for prediction of mortality, were superior in identifying patients with prolonged ICU length of stay. (Circulation. 2010;122:682-689.)Key Words: cardiovascular diseases Ⅲ complications Ⅲ epidemiology Ⅲ risk factors Ⅲ surgery I n the past decades, mortality during or shortly after cardiac surgery has decreased. 1 However, morbidity has increased, 2 mainly because cardiac surgery is increasingly utilized in older and more vulnerable patients. This often results in more complications after surgery and potential reduction in quality of life. [3][4][5] One method of assessing complications occurring directly after cardiac surgery is a prolonged stay in the intensive care unit (ICU). 6 -9 Prolonged ICU stay also leads to incremental use of resources. In practice, prediction models are being used for efficient use of ICU resources. Patients with a low risk of complications are being scheduled for surgery before patients with a high risk. [5][6][7][8][9][10][11][12][13] Various prediction models have been developed to preoperatively identify patients with an increased risk for postoperative complications and prolonged ICU stay. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] Interestingly, all of these prediction models were derived from samples including different patients, as reflected by the different distributions of patient and outcome characteristics. Hence, which model should be preferred in which situation is still unclear. Recently, in a qualitative review, Messaoudi and colleagues 14 reviewed 13 of these prediction models by comparing their published prognostic values for predicting ICU stay. They found that the 13 different prediction models indeed used...

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Section: Systematic Literature Reviewmentioning

confidence: 99%

Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery

et al. 2010

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“…Although both models were originally developed to predict mortality, authors found that those scores were also superior in identifying patients with an increased risk of prolonged LOSI [18]. The explanation given by the authors was that, in current practice, mortality has decreased over the last decades but morbidity has increased [19,20]. Because of advances in perioperative care in cardiac surgery [21], most of the patients who were likely to die time ago, will now survive, but they still have a higher probability of developing complications and in fact, prolonged LOSI.…”

Section: Discussionmentioning

confidence: 99%

Predictive Model for Length of Stay in Intensive Care Unit Based on Preoperative and Perioperative Variables. Four-Year Analysis of Mitral Valve Surgery in One Single Center

Carmona¹

2016

JACCOA

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Submit Manuscript | http://medcraveonline.com IntroductionThe cost of intensive care unit (ICU) stay is very high for patients after cardiac surgery in all settings. The costs of cardiac valve surgery depends mainly on the expenses of the operation and length of hospital (LOSH) and ICU (LOSI) stay.As ICUs are the most expensive part during hospital admission, it is important to assess which are the risk factors contributing to a prolonged stay in the ICU. Several risk models such as the Cleveland model, EuroSCORE, CORRADscore, and Parsonnet model have been developed originally to predict postoperative morbidity and mortality of patients undergoing cardiac surgery [1][2][3]. High preoperative risk scores have been associated with longer LOSH and increased total costs in coronary artery bypass surgery [4]. Those scores have been successfully validated also for prediction of prolonged LOSI. Parsonnet model and EuroSCORE were superior in identifying patients with prolonged LOSI [5].Predicting LOSI after mitral valve surgery is challenging due to the wide spectrum of comorbidities these patients sustain, and to the cardiovascular procedures and potential complications related to the surgical procedures. In addition, patients with a AbstractStudy objective: Assessing length of stay in Intensive Care Units (LOSI) after mitral valve surgery is challenging due to wide spectrum of comorbidities and cardiovascular procedures. The study aim was to elaborate a predictive model in order to identify risk factors and to predict the expected resources consumption for better monitoring for both patients and caregivers.Design: Retrospective study of prospectively collected data in the period 2009-2013. Setting: University teaching hospital.Patients: We studied all consecutive adult patients (≥ 18 years) who underwent elective mitral valve surgery. We enrolled 212 patients. Measurements:The outcome variable was LOSI. Comorbidities, postoperative morbidity and mortality were collected during the perioperative period. Multivariate analysis was used and data were studied as an additive model to assess the associations between the variables and correlations between them. A generalized additive model was fitted with least squared means estimated by the additive model due the non linearity of Euroscore I. Histograms of the residuals were examined visually to assess the fit of the models above. The model was selected using the statistical software R. Results:Median LOSI was 4 days (Interquantile range 2 to 4 days). The model for LOSI includes: infection post surgery (p<0.05), cardiogenic shock (p<0.05), Euroscore I (p<0.05), valvular replacement (p<0.05), double valvular disease (p<0.05) and associated tricuspid valve repair (p<0.05). The adjusted r squared for this model was 0.74, which implies a high predictive value for LOSI. In this model the ponderate weights for the main variables were 36.3% for infection post surgery, 15.2% for aortic clamp time, 6.9% for cardiogenic shock and 5.4% for the Euroscore I. Conclusion:Longer LOSI is st...

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“…Prolonged hospitalization in ICU increased costs of treatment significantly and consumed a lot of medical resources and equipments which can lead to the lack of bed for new patients to be admitted in the ICU after cardiac surgery. So sometimes it can lead to postpone some operations [12]. In addition, patients with prolonged ICU stay after heart surgery can lead to develop poor quality of life after discharge from hospital [13].…”

Section: Discussionmentioning

confidence: 99%

Factors of Prolonged Intensive Care Unit Stay After Surgery in Patients with Type A Acute Aortic Dissection

Khan¹,

Fa²,

Yu³

2017

Biol Med (Aligarh)

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Objective: To identify the risk factors of prolonged Intensive Care Unit stay after surgery in patients with the type A acute aortic dissection. Methods:Retrospective analysis of 80 patients, 54 males (67.5%) and 26 females (32.5%) who underwent surgery for the treatment of Stanford type A acute aortic dissection in the cardiovascular department of 2nd affiliated hospital of Zhengzhou university through December 2014 to July 2016 were done. The mean age of the patients was 48.9 ± 12.5 years. The patients were divided into two groups, group A, prolonged (n=13) and group B, nonprolonged (n=67) based on the duration of ICU stay time either above or below 5 days. Univariate and multivariate analysis were done to find out the predictive risk factors. Results:The mean length of ICU stay time was 206.9 ± 25.4 hours in group A and 63.2 ± 17.4 hours in group B respectively. Mortality rate was 15.4% in group A and 3.0% in group B while overall mortality was 5%. ICU stay time was significantly influenced by the following factors; age (P=0.

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Preoperative calculation of risk for prolonged intensive care unit stay following coronary artery bypass grafting

Cited by 76 publications

References 17 publications

Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery

Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery

Predictive Model for Length of Stay in Intensive Care Unit Based on Preoperative and Perioperative Variables. Four-Year Analysis of Mitral Valve Surgery in One Single Center

Factors of Prolonged Intensive Care Unit Stay After Surgery in Patients with Type A Acute Aortic Dissection

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