Parsonnet score is a good predictor of the duration of intensive care unit stay following cardiac surgery

Outcomes After Prolonged ICU Stays in Postoperative Cardiac Surgery Patients

Background

Prolonged postoperative intensive care unit (ICU) stays are common after cardiac surgery and are associated with poor outcomes. There are few studies evaluating how risk factors associated with mortality may change during prolonged ICU stays or how mortality may vary with length of stay. We evaluated operative and long-term mortality in post-cardiac surgery patients after prolonged ICU stays at 7, 14, 21, and 28 days and factors associated with mortality.

Methods

We included University of Michigan Medical Center cardiac surgery patients with ≥ 7 postoperative days in the ICU. We determined factors associated with hospital mortality at 7, 14, 21, and 28 days of ICU stay using logistic regression, and among hospital survivors, we determined the factors associated with long-term mortality using Cox regression.

Results

Of 8309 ICU admissions from cardiac surgery, 1174 (14%) had ICU stays > 7 days. Operative mortality was 11%, 18%, 22%, and 35% for the 7-, 14-, 21-, and 28-day groups, respectively. Mechanical ventilation on the day of assessment was associated with increased odds ratios of operative mortality in all models. Of the 1049 (89%) hospital survivors, 420 (40%) died by late follow-up. Median (IQR) Cox model survival was 10.7 (0.7) years. Longer ICU stays, postoperative pneumonia, and elevated discharge blood urea nitrogen were associated with increased hazard of dying; whereas higher discharge platelet count and cardiac transplant were protective.

Conclusions

Both operative and late mortality increased as the duration of a ICU stay increased after cardiac surgery.

Predicting Intensive Care Unit Length of Stay After Acute Type A Aortic Dissection Surgery Using Machine Learning

Background: Patients with acute type A aortic dissection are usually transferred to the intensive care unit (ICU) after surgery. Prolonged ICU length of stay (ICU-LOS) is associated with higher level of care and higher mortality. We aimed to develop and validate machine learning models for predicting ICU-LOS after acute type A aortic dissection surgery.

Methods: A total of 353 patients with acute type A aortic dissection transferred to ICU after surgery from September 2016 to August 2019 were included. The patients were randomly divided into the training dataset (70%) and the validation dataset (30%). Eighty-four preoperative and intraoperative factors were collected for each patient. ICU-LOS was divided into four intervals (<4, 4–7, 7–10, and >10 days) according to interquartile range. Kendall correlation coefficient was used to identify factors associated with ICU-LOS. Five classic classifiers, Naive Bayes, Linear Regression, Decision Tree, Random Forest, and Gradient Boosting Decision Tree, were developed to predict ICU-LOS. Area under the curve (AUC) was used to evaluate the models' performance.

Results: The mean age of patients was 51.0 ± 10.9 years and 307 (87.0%) were males. Twelve predictors were identified for ICU-LOS, namely, D-dimer, serum creatinine, lactate dehydrogenase, cardiopulmonary bypass time, fasting blood glucose, white blood cell count, surgical time, aortic cross-clamping time, with Marfan's syndrome, without Marfan's syndrome, without aortic aneurysm, and platelet count. Random Forest yielded the highest performance, with an AUC of 0.991 (95% confidence interval [CI]: 0.978–1.000) and 0.837 (95% CI: 0.766–0.908) in the training and validation datasets, respectively.

Conclusions: Machine learning has the potential to predict ICU-LOS for acute type A aortic dissection. This tool could improve the management of ICU resources and patient-throughput planning, and allow better communication with patients and their families.

Comparison of the prognostic value of SYNTAX score and clinical SYNTAX score on outcomes of Chinese patients underwent percutaneous coronary intervention

Background

Previous studies have validated the capability of SYNTAX score (SS) and clinical SYNTAX score (CSS) in the prediction of clinical outcomes in patients who have undergone PCI; however, studies on comparison of these two scoring systems in Chinese population have been sparse.

Methods

To study the ability of SS and CSS in prediction of clinical outcomes of Chinese patients underwent percutaneous coronary intervention (PCI). We retrospectively calculated SS and CSS for 547 Chinese patients from a single center who underwent PCI. Patients were stratified into tertiles according to their SS and CSS. We compared the 2-year clinical outcomes in these patients stratified separately by SS and CSS tertiles.

Results

The incidence of major adverse cardiac and cerebro-vascular events (MACCE) was the highest in patients with SS_HIGH (13.5%), comparing to 6.8% in SS_MED and 0% in SS_LOW (p < 0.0001). The Cox multivariable analysis showed that the SS and CSS were both strong independent predictors for MACCE [1.100 (1.069–1.133), 1.017 (1.010–1.025), both p < 0.0001]. The receiver operating characteristic (ROC) curves showed the areas-under-the-curves for all-cause death by CSS was slightly larger comparing to SS but not significantly (AUC SS, 0.64; AUC CSS, 0.71; p = 0.23).

Conclusion

We concluded that both the SS and CSS were capable of risk stratification of clinical outcomes in all-comers population as well as in low and moderate risk Chinese patients undergoing PCI with CSS showing slightly better advantage.

Scoring Systems for Risk Stratification in Patients Undergoing Cardiac Surgery

Cardiac surgery is associated with significant mortality rates. Careful selection of surgical candidates is, therefore, vital to optimize morbidity and mortality outcomes. Risk scores can be used to inform this decision-making process. The European System for Cardiac Operative Risk Evaluation Score and the Society of Thoracic Surgeons score are among the most commonly used risk scores. There are many other scoring systems in existence; however, no perfect scoring system exists, therefore, additional research is needed as clinicians strive toward a more idealized risk stratification model. The purpose of this review is to discuss the advantages and limitations of some of the most commonly used risk stratification systems and use this to determine what an ideal scoring system might look like. This includes not only the generalizability of available scores but also their ease of use and predictive power.

Impaired right ventricular ejection fraction after cardiac surgery is associated with a complicated ICU stay

Background

Right ventricular (RV) dysfunction is a known risk factor for increased mortality in cardiac surgery. However, the association between RV performance and ICU morbidity is largely unknown.

Methods

We performed a single-centre, retrospective study including cardiac surgery patients equipped with a pulmonary artery catheter, enabling continuous right ventricular ejection fraction (RVEF) measurements. Primary endpoint of our study was ICU morbidity (as determined by ICU length of stay, duration of mechanical ventilation, usage of inotropic drugs and fluids, and kidney dysfunction) in relation to RVEF. Patients were divided into three groups according to their RVEF; < 20%, 20-30%, and > 30%.

Results

We included 1109 patients. Patients with a RVEF < 20% had a significantly longer stay in ICU, a longer duration of mechanical ventilation, higher fluid balance, a higher incidence of inotropic drug usage, and more increase in postoperative creatinine levels in comparison to the other subgroups. In a multivariate analysis, RVEF was independently associated with increased ICU length of stay (OR 0.934 CI 0.908-0.961, p < 0.001), prolonged duration of mechanical ventilation (OR 0.969, CI 0.942-0.998, p = 0.033), usage of inotropic drugs (OR 0.944, CI 0.917-0.971, p < 0.001), and increase in creatinine (OR 0.962, CI 0.934-0.991, p = 0.011).

Conclusions

A decreased RVEF is independently associated with a complicated ICU stay.

Predictive factors associated with mortality and discharge in intensive care units: a retrospective cohort study

Background and aim

Accurate prediction of prognosis of patients admitted to intensive care units (ICUs) is very important for the clinical management of the patients. The present study aims to identify independent factors affecting death and discharge in ICUs using competing risk modeling.

Methods

This retrospective cohort study was conducted on enrolling 880 patients admitted to emergency ICU in Namazi hospital, Shiraz University of Medical Sciences, Shiraz, Iran during 2013-2015. The data was collected from patients' medical records using a researcher-made checklist by a trained nurse. Competing risk regression models were fitted for the factors affecting the occurrence of death and discharge in ICU. Data analysis was conducted using STATA 13 and R 3.3.3 software.

Results

Among these patients, 682 (77.5%) were discharged and 157 (17.8%) died in the ICU. The patients' mean ± SD age was 48.90±19.52 yr. Among the study patients, 45.57% were female and 54.43% were male. In the competing risk model, age (Sub-distribution Hazard Ratio (SHR)) =1.02, 95% CI: 1.007-1.032), maximum heart rate (SHR=1.009, 95% CI: 1.001-1.019), minimum sodium level (SHR=1.035, 95% CI: 1.007-1.064), PH (SHR=7.982, 95% CI: 1.259-50.61), and bilirubin (SHR=1.046, 95% CI: 1.015-1.078) increased the risk of death, while maximum sodium level (SHR=0.946, 95% CI: 0.908-0.986) and maximum HCT (SHR=0.938, 95% CI: 0.882-0.998) reduced the risk of death.

Conclusion

In conclusion, the results of this study revealed several variables that were effective in ICU length of stay (LOS). The variables that independently influenced time-to-discharge were age, maximum systolic blood pressure, minimum HCT, maximum WBC, and urine output, maximum HCT and Glasgow coma score. The results also showed that age, maximum heart rate, maximum sodium level, PH, urine output, and bilirubin, minimum sodium level and maximum HCT were the predictors of death. Furthermore, our findings indicated that the competing risk model was more appropriate than the Cox model in evaluating the predictive factors associated with the occurrence of death and discharge in patients hospitalized in ICUs. Hence, this model could play an important role in managers' and clinicians' decision-making and improvement of the standard of care in ICUs.

Factors associated with prolonged length of stay following cardiac surgery in a major referral hospital in Oman: a retrospective observational study

OBJECTIVES

Two objectives were set for this study. The first was to identify factors influencing prolonged postoperative length of stay (LOS) following cardiac surgery. The second was to devise a predictive model for prolonged LOS in the cardiac intensive care unit (CICU) based on preoperative factors available at admission and to compare it against two existing cardiac stratification systems.

DESIGN

Observational retrospective study.

SETTINGS

A tertiary hospital in Oman.

PARTICIPANTS

All adult patients who underwent cardiac surgery at a major referral hospital in Oman between 2009 and 2013.

RESULTS

30.5% of the patients had prolonged LOS (≥11 days) after surgery, while 17% experienced prolonged ICU LOS (≥5 days). Factors that were identified to prolong CICU LOS were non-elective surgery, current congestive heart failure (CHF), renal failure, combined coronary artery bypass graft (CABG) and valve surgery, and other non-isolated valve or CABG surgery. Patients were divided into three groups based on their scores. The probabilities of prolonged CICU LOS were 11%, 26% and 28% for group 1, 2 and 3, respectively. The predictive model had an area under the curve of 0.75. Factors associated with prolonged overall postoperative LOS included the body mass index, the type of surgery, cardiopulmonary bypass machine use, packed red blood cells use, non-elective surgery and number of complications. The latter was the most important determinant of postoperative LOS.

CONCLUSIONS

Patient management can be tailored for individual patient based on their treatments and personal attributes to optimise resource allocation. Moreover, a simple predictive score system to enable identification of patients at risk of prolonged CICU stay can be developed using data that are routinely collected by most hospitals.

Outcomes of Patients With Prolonged Intensive Care Unit Length of Stay After Cardiac Surgery

OBJECTIVE

To determine in-hospital and post-discharge long-term survival in patients with prolonged intensive care unit (ICU) stays after cardiac surgery.

DESIGN

Retrospective, cohort study of cardiac surgery patients from May 2007 to June 2012.

SETTING

Single-center cardiac surgery ICU.

PARTICIPANTS

Patients were grouped according to length of ICU stay: between 1 and 2 weeks, between 2 and 4 weeks, and>4 weeks.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of 4,963 patients, 3.3%, 1.6%, and 2.9% of patients stayed 1 to 2 weeks, 2 to 4 weeks, and>4 weeks in the ICU, respectively. In-hospital mortality was 11.1%, 26.6%, and 31.0% for patients with 1 to 2 weeks, 2 to 4 weeks, and>4 weeks ICU stay, respectively. Patients with ICU stays between 1 and 2 weeks had 6 months, 1 year, and 2 year survival rates of 84.4%, 80.0%, and 75.3% after discharge, respectively. Patients with ICU stay between 2 and 4 weeks had similar 6 months, 1 year, and 2 year survival rates of 84.7%, 79.9%, and 74.1%, respectively. In contrast, patients with>4 week ICU stays had significantly lower postdischarge survival rates of 63.3%, 56.4%, and 41.1% at 6 months, 1 year, and 2 years, respectively. Postoperative stroke conferred the greatest risk of death within 1 year after discharge (odds ratio 7.6, p = 0.0140).

CONCLUSIONS

In-hospital mortality rates post-cardiac surgery correlate with length of ICU stay but appear to plateau after 4 weeks. However, a>4 week ICU length of stay confers a worse long-term outcome post-hospital discharge, especially in patients with postoperative stroke.

Does Parsonnet scoring model predict mortality following adult cardiac surgery in India?

Aims and Objectives:

To validate the Parsonnet scoring model to predict mortality following adult cardiac surgery in Indian scenario.

Materials and Methods:

A total of 889 consecutive patients undergoing adult cardiac surgery between January 2010 and April 2011 were included in the study. The Parsonnet score was determined for each patient and its predictive ability for in-hospital mortality was evaluated. The validation of Parsonnet score was performed for the total data and separately for the sub-groups coronary artery bypass grafting (CABG), valve surgery and combined procedures (CABG with valve surgery). The model calibration was performed using Hosmer–Lemeshow goodness of fit test and receiver operating characteristics (ROC) analysis for discrimination. Independent predictors of mortality were assessed from the variables used in the Parsonnet score by multivariate regression analysis.

Results:

The overall mortality was 6.3% (56 patients), 7.1% (34 patients) for CABG, 4.3% (16 patients) for valve surgery and 16.2% (6 patients) for combined procedures. The Hosmer–Lemeshow statistic was <0.05 for the total data and also within the sub-groups suggesting that the predicted outcome using Parsonnet score did not match the observed outcome. The area under the ROC curve for the total data was 0.699 (95% confidence interval 0.62–0.77) and when tested separately, it was 0.73 (0.64–0.81) for CABG, 0.79 (0.63–0.92) for valve surgery (good discriminatory ability) and only 0.55 (0.26–0.83) for combined procedures. The independent predictors of mortality determined for the total data were low ejection fraction (odds ratio [OR] - 1.7), preoperative intra-aortic balloon pump (OR - 10.7), combined procedures (OR - 5.1), dialysis dependency (OR - 23.4), and re-operation (OR - 9.4).

Conclusions:

The Parsonnet score yielded a good predictive value for valve surgeries, moderate predictive value for the total data and for CABG and poor predictive value for combined procedures.

Factors influencing prolonged ICU stay after open heart surgery

Background:

There are different risk factors that affect the intensive care unit (ICU) stay after cardiac surgery.

Objectives:

The aim of this study was to evaluate possible risk factors influencing prolonged ICU stay in a large referral hospital.

Patients and Methods:

We conducted a case-control study to determinate causes of prolonged ICU stay in 280 adult patients undergoing cardiac surgery in a tertiary care center for cardiovascular patients, Tehran, Iran. These patients were divided into two groups according to ICU stay ≤ 96 and > 96 hours. We evaluated perioperative risk factors of ICU stay > 96 hours.

Results:

Among the 280 patients studied, 184 (65.7%) had stayed ≤ 96 hours and 96 (34.3%) had stayed > 96 hours in ICU. Frequency of prolonged ICU stay was 34.2% in patients undergoing coronary artery bypass graft (CABG), 30.8% in patients with valve surgery, and 44.8% in patients with CABG plus valve surgery. Patients with > 96 hours of ICU stay received more blood transfusion and intravenous inotropes. They also had longer anesthesia, cardiopulmonary bypass, and postoperative intubation time. There were higher incidence of postoperative tamponade, re-exploration, re-intubation, hemodialysis, and hypotension in this group (P < 0.05 for all comparisons).

Conclusions:

In this study, about one-third of patients had prolonged ICU stay. Factors influencing prolonged ICU stay were medical and some non-medical factors. In the present study, up to 30% of the patients had a prolonged ICU stay of > 96 hours. Additional data from well-designed investigations are needed for further assessment of the factors influencing prolonged ICU stay after cardiac surgery.

Very prolonged stay in the intensive care unit after cardiac operations: early results and late survival

BACKGROUND

Prolonged intensive care unit (ICU) stay is a surrogate for advanced morbidity or perioperative complications, and resource utilization may become an issue. It is our policy to continue full life support in the ICU, even for patients with a seemingly grim outlook. We examined the effect of duration of ICU stay on early outcomes and late survival.

METHODS

Between 1993 and 2011, 6,385 patients were admitted to the ICU after cardiac surgery. Patients were grouped according to length of stay in the ICU: group 1, 2 days or less (n = 4,631; 73%); group 2, 3 to 14 days (n = 1,423; 22%); group 3, more than 14 days (n = 331; 5%). Length of stay in ICU for group 3 patients was 38 ± 24 days (range, 15 to 160; median 31). Clinical profile and outcomes were compared between groups.

RESULTS

Patients requiring prolonged ICU stay were older, underwent more complex surgery, had greater comorbidity, and a higher predicted operative mortality (p < 0.0001). They had a higher incidence of adverse events and increased mortality (p < 0.0001). Of the 331 group 3 patients, 60% were discharged: survival of these patients at 1, 3, and 5 years was 78%, 65%, and 52%, respectively. Operative mortality as well as late survival of discharged patients was proportional to duration of ICU stay.

CONCLUSIONS

Current technology enables keeping sick patients alive for extended periods of time. Nearly two thirds of patients requiring prolonged ICU leave hospital, and of these, 50% attain 5-year survival. These data support offering full and continued support even for patients requiring very prolonged ICU stay.

Risk model of prolonged intensive care unit stay in Chinese patients undergoing heart valve surgery

BACKGROUND

The aim of this study was to develop a preoperative risk prediction model and an scorecard for prolonged intensive care unit length of stay (PrlICULOS) in adult patients undergoing heart valve surgery.

METHODS

This is a retrospective observational study of collected data on 3925 consecutive patients older than 18 years, who had undergone heart valve surgery between January 2000 and December 2010. Data were randomly split into a development dataset (n=2401) and a validation dataset (n=1524). A multivariate logistic regression analysis was undertaken using the development dataset to identify independent risk factors for PrlICULOS. Performance of the model was then assessed by observed and expected rates of PrlICULOS on the development and validation dataset. Model calibration and discriminatory ability were analysed by the Hosmer-Lemeshow goodness-of-fit statistic and the area under the receiver operating characteristic (ROC) curve, respectively.

RESULTS

There were 491 patients that required PrlICULOS (12.5%). Preoperative independent predictors of PrlICULOS are shown with odds ratio as follows: (1) age, 1.4; (2) chronic obstructive pulmonary disease (COPD), 1.8; (3) atrial fibrillation, 1.4; (4) left bundle branch block, 2.7; (5) ejection fraction, 1.4; (6) left ventricle weight, 1.5; (7) New York Heart Association class III-IV, 1.8; (8) critical preoperative state, 2.0; (9) perivalvular leakage, 6.4; (10) tricuspid valve replacement, 3.8; (11) concurrent CABG, 2.8; and (12) concurrent other cardiac surgery, 1.8. The Hosmer-Lemeshow goodness-of-fit statistic was not statistically significant in both development and validation dataset (P=0.365 vs P=0.310). The ROC curve for the prediction of PrlICULOS in development and validation dataset was 0.717 and 0.700, respectively.

CONCLUSION

We developed and validated a local risk prediction model for PrlICULOS after adult heart valve surgery. This model can be used to calculate patient-specific risk with an equivalent predicted risk at our centre in future clinical practice.

Clinical outcomes in patients with prolonged intensive care unit length of stay after cardiac surgical procedures

BACKGROUND

Advances in critical care medicine have allowed for improved care of patients requiring prolonged intensive care unit length of stay (prICULOS) after cardiac operations, yet little is known regarding their eventual outcomes. The purpose of this study was to examine short- and long-term outcomes in patients undergoing cardiac operations with prICULOS.

METHODS

All cases of coronary artery bypass grafting (CABG), aortic valve, mitral valve, and combined CABG/valve surgical procedures performed at a single institution from July 2002 to July 2007 were identified. All-cause mortality in patients discharged alive from the hospital was determined until December 2007 through linkage with the Social Security Death Index. Patients who experienced intraoperative death or those with missing or invalid social security numbers were excluded. The definition of prICULOS was total ICULOS greater than 7 days.

RESULTS

A total of 3,478 patients met inclusion criteria. One hundred thirty-seven of three thousand four hundred seventy-eight patients (3.9%) experienced prICULOS. These patients were more likely to be older than 70 years (55.5% versus 30.5%; p<0.0001) and to have had recent myocardial infarction (28.5% versus 20.1%; p=0.02), previous cardiac operation (18.3% versus 6.9%; p<0.0001), and emergent status (9.5% versus 1.6%; p<0.0001). They experienced greater in-hospital mortality (37.2% versus 1.7%; p<0.0001) and those who were discharged alive had worse long-term survival (log-rank, p<0.0001). After risk adjustment, prICULOS emerged as a significant predictor of in-hospital death (odds ratio [OR] 20.9; 95% confidence interval [CI], 12.9-33.7) and decreased long-term survival (hazard ratio [HR] 2.9; 95% CI, 2.0-4.3).

CONCLUSIONS

Patients with prICULOS after cardiac operations have worse overall outcomes. These data may be used to inform these patients and their families of realistic expectations regarding their clinical course.

Predicting prolonged intensive care unit stays in older cardiac surgery patients: a validation study

PURPOSE

In cardiac surgery prediction models identifying patients at risk of prolonged stay at the Intensive Care Unit (ICU) are used to optimize treatment and use of ICU resources. A recent systematic validation study of 14 of these models identified three models with a good predictive performance across patients of all ages. It is however unclear how these models perform in older patients, who nowadays form a considerable part of this patient population. The current study specifically validates the performance of these three models in older cardiac surgery patients and quantifies how their performance changes with increasing age of patients.

METHODS

The Parsonnet model, the EuroSCORE, and a model by Huijskes and colleagues were validated using prospectively collected data of 11,395 cardiac surgery patients. Performance of the models was described by discrimination (area under the ROC curve, AUC) and calibration.

RESULTS

For the Parsonnet model, the EuroSCORE and the Huijskes model discrimination clearly decreased with increasing age (AUCs of 0.76, 0.71 and 0.72 for ages 70-75 and 0.72, 0.70 and 0.72, respectively, for ages 75-80 and 0.68, 0.64 and 0.69, respectively, above 80 years). The models showed poor calibration in patients aged >70 (p values for fit of the models <0.006).

CONCLUSIONS

To optimize treatment and ICU resources, risk prediction for prolonged ICU stay after cardiac surgery using the existing models should be done with great care for older patients.

Major predictors of long-term clinical outcomes after coronary revascularization in patients with unprotected left main coronary disease: analysis from the MAIN-COMPARE study

BACKGROUND

The clinical characteristics that identify high-risk subsets of patients with unprotected left main coronary artery disease undergoing percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) have not been well established.

METHODS AND RESULTS

Between January 2000 and June 2006, 2240 patients with unprotected left main coronary artery disease underwent PCI (n=1102) or CABG (n=1138). Twenty-six preprocedural parameters were evaluated by univariate and multivariate Cox regression analysis to identify independent predictors of all-cause mortality and target-vessel revascularization. Interaction tests were performed to compare heterogeneities of effects of preprocedural parameters depending on the revascularization methods. During follow-up (median of 3.1 years), 187 patients died (78 PCI and 109 CABG) and 149 patients had target-vessel revascularization (121 PCI and 28 CABG). EuroSCORE > or =6 was an independent predictor of death in both groups. Additional independent predictors were chronic renal failure and previous congestive heart failure in the PCI group and age > or =75 years, atrial fibrillation, right coronary artery disease, and left main distal bifurcation disease in the CABG group. Interaction analysis showed no heterogeneities of the effects of variables depending on the revascularization methods. Independent predictors of target-vessel revascularization were acute coronary syndrome and left main distal bifurcation disease in the PCI group and history of coronary intervention in the CABG group. The interaction between previous PCI and treatment remained after adjustment for all independent predictors of target-vessel revascularization (interaction P=0.0345).

CONCLUSIONS

Several clinical characteristics were identified as important preprocedural predictors of long-term adverse outcomes after percutaneous or surgical revascularization in patients with unprotected left main coronary artery disease.

Prediction models for prolonged intensive care unit stay after cardiac surgery: systematic review and validation study

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 curve=0.75 [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.

Prediction of clinical conditions after coronary bypass surgery using dynamic data analysis

This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes according to the time they need to reach a stable state after coronary bypass surgery: less or more than 9 h. On the basis of five physiological variables (heart rate, systolic arterial blood pressure, systolic pulmonary pressure, blood temperature and oxygen saturation), different dynamic features were extracted, namely the means and standard deviations at different moments in time, coefficients of multivariate autoregressive models and cepstral coefficients. These sets of features served subsequently as inputs for a Gaussian process and the prediction results were compared with the case where only admission data was used for the classification. The dynamic features, especially the cepstral coefficients (aROC: 0.749, Brier score: 0.206), resulted in higher performances when compared to static admission data (aROC: 0.547, Brier score: 0.247). The differences in performance are shown to be significant. In all cases, the Gaussian process classifier outperformed to logistic regression.

Preoperative and intraoperative risk factors for prolonged intensive care unit stay after aortic arch surgery

OBJECTIVE

The present study was performed to evaluate preoperative and intraoperative risk factors for prolonged intensive care unit (ICU) stay after aortic arch surgery.

DESIGN

A retrospective study. Prolonged ICU stay was defined as >5 days (120 hours).

SETTING

Cardiovascular operating rooms and the ICU.

PARTICIPANTS

Adults requiring aortic arch surgery with deep hypothermic circulatory arrest plus antegrade selective cerebral perfusion.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

After 11 patients who underwent 1-stage total or subtotal aortic replacement were excluded, 298 patients were enrolled in the study. The average age of patients was 44.9 +/- 10.7 years with male predominance (74.8%). Sixty-one patients (20.5%) stayed longer than 5 days in the ICU. Univariate analyses found age, body mass index, New York Heart Association classification, preoperative serum creatinine, creatinine clearance, emergency, inotropes, cardiopulmonary bypass time, myocardial ischemia time, and fresh-frozen plasma transfused intraoperatively were significantly associated with prolonged ICU stay (p < 0.05). Independent risk factors for prolonged ICU stay were found to be New York Heart Association classification (class III and IV), emergency, inotropes used intraoperatively, and prolonged cardiopulmonary bypass time (p < 0.05).

CONCLUSION

The authors identified 4 preoperative and intraoperative risk factors for prolonged ICU stay. This is helpful to identify patients with increased risk for prolonged ICU stay, implement specific strategies, and allocate medical resources.

Novel polymorphism of interleukin-18 associated with greater inflammation after cardiac surgery

Introduction

Interleukin (IL)-18 is a key modulator of the cytokine response that leads to organ dysfunction and prolonged intensive care unit (ICU) stay after cardiopulmonary bypass surgery. We hypothesised that variation in the pro-inflammatory gene IL-18 is associated with adverse clinical outcome because of a more intense inflammatory response.

Methods

Haplotypes of the IL-18 gene were inferred from genotypes of 23 Coriell Registry subjects. Four haplotype tag single nucleotide polymorphisms (-607 C/A, -137 G/C, 8148 C/T and 9545 T/G) identified four major haplotype clades. These polymorphisms were genotyped in 658 Caucasian patients undergoing cardiopulmonary bypass surgery. Clinical phenotypes were collected by retrospective chart review.

Results

Patients homozygous for the T allele of the 9545 T/G polymorphism had an increased occurrence of prolonged ICU stay (6.8% for TT genotype versus 2.7% for GG or GT genotype; p = 0.015). Patients homozygous for the T allele also had increased occurrence of low systemic vascular resistance index (62%) compared with the GG and GT genotypes (53%; p = 0.045). Patients homozygous for the T allele had increased serum IL-18 concentrations 24 hours post-surgery (p = 0.018), increased pro-inflammatory tumour necrosis factor alpha concentrations (p = 0.014) and decreased anti-inflammatory serum IL-10 concentrations (p = 0.018) 24 hours post-surgery.

Conclusions

The TT genotype of the IL-18 9545 T/G polymorphism is associated with an increased occurrence of prolonged ICU stay post-surgery and greater post-surgical inflammation. These results may be explained by greater serum IL-18, leading to greater pro-versus anti-inflammatory cytokine expression.

Risk stratification models for cardiac surgery

A wide variety of risk stratification systems have been developed to quantify the risk of cardiac surgery. Generally, the focus has been on mortality; however, more recently models have been developed that allow the preoperative prediction of the incidence of morbidity, including renal failure, infection, prolonged ventilation, and neurologic deficit. Many of these risk stratification models are developed from large databases of cardiac surgical patients. Patient and surgical factors that are present preoperatively are assessed for their predictive value for postoperative complications. Risk factors that are found to be significant are assigned a specific weight in the overall summation of risk. These models have been used as tools to compare surgeon's results, institutional outcomes, individual patient risk, and within quality improvement programs. This article will focus on the European System for Cardiac Operative Risk Evaluation, the Society of Thoracic Surgeons score, the Parsonnet score, Cleveland Clinic Model, the Bayes model, and the Northern New England Score.

Increased incidence of acute kidney injury with aprotinin use during cardiac surgery detected with urinary NGAL

BACKGROUND

Use of aprotinin has been associated with acute kidney injury after cardiac surgery. Neutrophil gelatinase-associated lipocalin (NGAL) is a novel, very sensitive marker for renal injury. Urinary NGAL may be able to detect renal injury caused by aprotinin. This study determined if the use of aprotinin is associated with an increased incidence of acute kidney injury and increased levels of urinary NGAL.

METHODS

In this prospective, observational study 369 patients undergoing cardiac surgery were enrolled. 205 patients received aprotinin and 164 received epsilon amino-caproic acid intraoperatively. Urinary NGAL was measured before and immediately after cardiac surgery and 3, 18 and 24 h later. The association of aprotinin use with the incidence of acute kidney injury (increase of serum creatinine >0.5 mg/dl) and NGAL levels was determined using logistic and linear regression models.

RESULTS

51 of 205 patients (25%) who received aprotinin developed acute kidney injury compared to 19 of 164 patients (12%) who received epsilon amino-caproic acid (p = 0.0013). Aprotinin use was associated with a two-fold higher risk of acute kidney injury when adjusted for potential confounders (age, Parsonnet score, preoperative serum creatinine, cardiopulmonary bypass and cross-clamp times; multiple logistic regression: OR = 2.164; CI (95%) = 1.102 to 4.249; p = 0.0249. Urinary NGAL was 19 times higher immediately after cardiopulmonary bypass and 18 times higher 3 h later in patients who had received aprotinin (postoperative: 19.23; CI (95%) = 12.60 to 29.33; p < 0.0001; 3 h post-cardiopulmonary bypass 18.67; CI (95%) = 11.45 to 30.43; p < 0.0001).

CONCLUSIONS

Postoperative urinary NGAL - a novel marker for renal injury - is increased in cardiac surgical patients receiving aprotinin compared to patients receiving epsilon amino-caproic acid. These results further support the hypothesis that aprotinin may cause renal injury. The substantial rise of urinary NGAL associated with aprotinin use may in part be due to aprotinin blocking the uptake of NGAL by megalin/gp330 receptors in the proximal tubules.

Epidural anesthesia improves outcome and resource use in cardiac surgery: a single-center study of a 1293-patient cohort

Thoracic epidural anesthesia (TEA) combined with general anesthesia in cardiac surgery has the potential to initiate earlier spontaneous ventilation and extubation, improved hemodynamics, less arrhythmia or myocardial ischemia, and an attenuated neurohormonal response. The aim of the current study was to characterize the correlation between TEA and postoperative resource use or outcome in a consecutive-patient cohort. The study was performed in a tertiary care, 3-surgeon, university-affiliated hospital that performs 350 to 400 cardiac surgeries per year. All 1293 adult patients who underwent cardiac surgery between July 1, 2002, and February 1, 2006, were included. Patients were assigned to anesthesiologists practicing TEA (TEA group, n = 506) or not (control group, n = 787) for cardiac surgery. The preoperative parameter values and Parsonnet scores for the 2 groups were similar. The 2 groups had the same distribution of surgery types. The TEA group presented with fewer intensive care unit (ICU) complications, such as delirium, pneumonia, and acute renal failure, and presented with better myocardial protection. The TEA group presented with a higher proportion of immediately postoperative extubations and with shorter ventilation times and ICU stays. Total ICU costs decreased from US $18,700 to $9900 per patient. Combining TEA and general anesthesia for cardiac surgery allows a significant change in anesthesia strategy. This change improves immediate postoperative outcomes and reduces the use and costs of ICU resources.

Application of the Parsonnet scoring system for a Canadian cardiac surgery program

BACKGROUND

In the past two decades, cases involving patients requiring cardiac surgery have become more complex, presenting with more comorbidities. Outcome analysis has become very important in assessing the quality of cardiac surgical care in these patients. The latest version of the Parsonnet scoring system was developed in 2000 and is the most recent system available.

OBJECTIVE

To evaluate the accuracy of the Parsonnet scoring system in a major Canadian university-based cardiac surgery centre with a population of high-risk patients.

METHODS

Data on 4883 consecutive patients operated on between 2000 and 2005 were prospectively collected, and a standardized mortality rate was calculated using the Parsonnet score as the ratio of observed deaths to expected deaths. Analyses were conducted on the whole group and on subgroups, based on Parsonnet score distribution quantiles, age and surgery status.

RESULTS

The mean Parsonnet score was 18.8+/-13.7 (range 0 to 83). The overall mortality rate was 6.4%. The overall standardized mortality ratio was 0.52 (95% CI 0.420 to 0.568), which was statistically significant (P=0.01). The observed mortality rate was significantly lower than expected in all categories.

CONCLUSIONS

Despite more complex cases with multiple comorbidities, the results of cardiac surgery in a Canadian university hospital show better results than expected when using the Parsonnet score.

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

Objective

Patients who have prolonged stay in intensive care unit (ICU) are associated with adverse outcomes. Such patients have cost implications and can lead to shortage of ICU beds. We aimed to develop a preoperative risk prediction tool for prolonged ICU stay following coronary artery surgery (CABG).

Methods

5,186 patients who underwent CABG between 1st April 1997 and 31st March 2002 were analysed in a development dataset. Logistic regression was used with forward stepwise technique to identify preoperative risk factors for prolonged ICU stay; defined as patients staying longer than 3 days on ICU. Variables examined included presentation history, co-morbidities, catheter and demographic details. The use of cardiopulmonary bypass (CPB) was also recorded. The prediction tool was tested on validation dataset (1197 CABG patients between 1^st April 2003 and 31^st March 2004). The area under the receiver operating characteristic (ROC) curve was calculated to assess the performance of the prediction tool.

Results

475(9.2%) patients had a prolonged ICU stay in the development dataset. Variables identified as risk factors for a prolonged ICU stay included renal dysfunction, unstable angina, poor ejection fraction, peripheral vascular disease, obesity, increasing age, smoking, diabetes, priority, hypercholesterolaemia, hypertension, and use of CPB. In the validation dataset, 8.1% patients had a prolonged ICU stay compared to 8.7% expected. The ROC curve for the development and validation datasets was 0.72 and 0.74 respectively.

Conclusion

A prediction tool has been developed which is reliable and valid. The tool is being piloted at our institution to aid resource management.

Validation of the 2000 Bernstein-Parsonnet Score Versus the EuroSCORE as a Prognostic Tool in Cardiac Surgery

BACKGROUND

Intradepartmental and interdepartmental benchmarking requires scoring systems with reliability (calibration) and stability over the complete spectrum of periprocedural risk. The aim of this single-center study was to assess the performance of the 2000 Bernstein-Parsonnet risk stratification model in cardiac surgery, by itself and against the EuroSCORE.

METHODS

A prospective observational design was used. The study group consisted of 1,639 consecutive patients of mean age 64.6 +/- 12.04 years who underwent elective or emergency cardiac surgery from January 2003 to June 2004. The probabilities of hospital death were estimated with the 2000 Bernstein-Parsonnet and EuroSCORE algorithms. The correlation of predicted and observed mortality was compared between the two models, and score validity was assessed by calculating the area under the receiver operating characteristic (ROC) curve.

RESULTS

The patients were stratified into five risk groups according to their scores in the two models. For the 2000 Bernstein-Parsonnet model, findings were as follows: score 0-10: predicted mortality 0%-2.2%, observed mortality 0.6%; score 10.5-20: predicted 2.3%-4.7%, observed 2.3%; score 20.5-30: predicted 4.8%-10%, observed 6.7%; score 30.5-40: predicted 10.1%-23%, observed 11.5%; and score greater than 40: predicted 23.1%-80%, observed 29.9%. For the EuroSCORE, findings were as follows: score 0%-2%: predicted mortality 1.1%, observed mortality 0.6%; score 3%-5%: predicted 2.1%, observed 3.0%; score 6%-8%: predicted 4.1%, observed 3.5%; score 9-11: predicted 7.6%, observed 6.6.%; and score greater than 12: predicted 13.8%, observed 14.0%. There was good agreement between the observed and expected number of deaths, with both models. The area under the ROC curve was higher for the Bernstein-Parsonnet model (0.83, odds ratio [OR] 2.01, 95% confidence interval [CI] 1.75-2.31, p < 0.0001) than for the EuroSCORE (0.73, OR 1.05, 95% CI 1.04-1.07, p < 0.001).

CONCLUSIONS

The 2000 Bernstein-Parsonnet model is a simple, objective system for the estimation of hospital mortality in patients undergoing cardiac surgery, with slightly higher calibration and discrimination than the EuroSCORE additive model.

Tracheostomy in infants and children after cardiothoracic surgery: indications, associated risk factors, and timing

BACKGROUND

Respiratory insufficiency in children after cardiothoracic surgery delays weaning from the ventilator and prolongs intensive care unit stay. There is little consensus as to the indications for tracheostomy and its safety in this population.

METHODS

We reviewed our institutional experience in 37 consecutive infants and children (median age, 8.6 months; weight, 7.2 kg) requiring a tracheostomy after cardiothoracic surgery between January 1998 and December 2001, with follow-up to June 2003.

RESULTS

Twenty-four children underwent tracheostomy after corrective (n = 15) or palliative (n = 9) surgery for congenital heart disease, 8 had undergone thoracic transplantation, and 5 had undergone thoracic surgery. Median duration of pretracheostomy ventilation was 30 days, and median total duration of ventilation was 73 days. Tracheostomy was performed earlier in patients undergoing transplantation (median of 20 days postoperatively), with a duration of ventilation of 34 days. No patient experienced mediastinitis, and a wound infection in 1 child was the only identified complication. Twenty-two children survived to hospital discharge, of whom 15 have since been decannulated; 6 still have a tracheostomy in situ and 1 has been lost to follow-up. A number of preoperative and postoperative factors were identified in this cohort. These were preoperative respiratory insufficiency, a history of neonatal ventilation, the need for cardiac reoperations, diaphragmatic paralysis, tracheobronchomalacia, neurological comorbidity, and associated chromosomal abnormalities.

CONCLUSION

Tracheostomy can be performed safely and without increased risk of complications in infants and children early after cardiothoracic surgery. The presence of identifiable factors in patients in whom weaning has been unsuccessful should alert clinicians to early consideration of tracheostomy.

EuroSCORE Predicts Intensive Care Unit Stay and Costs of Open Heart Surgery

BACKGROUND

This study aimed to determine whether the preoperative risk stratification model EuroSCORE predicts the different components of resource utilization in open heart surgery.

METHODS

Data for all adult patients undergoing heart surgery at the University Hospital of Lund, Sweden, between 1999 and 2002 were prospectively collected. Costs were calculated for the surgery and intensive care and ward stay for each patient (excluding transplant cases and patients who died intraoperatively). Regression analysis was applied to evaluate the correlation between EuroSCORE and costs. The predictive accuracy for prolonged postoperative intensive care unit (ICU) stay was assessed by the Hosmer-Lemeshow goodness-of-fit test. The discriminatory power was evaluated by calculating the areas under receiver operating characteristics curves.

RESULTS

The study included 3,404 patients. The mean cost for the surgery was 7,300 dollars, in the ICU 3,746 dollars, and in the ward 3,500 dollars. Total cost was significantly correlated with EuroSCORE, with a correlation coefficient of 0.47 (p < 0.0001); the correlation coefficient was 0.31 for the surgery cost, 0.46 for the ICU cost, and 0.11 for the ward cost. The Hosmer-Lemeshow p value for EuroSCORE prediction of more than 2 days' stay in the ICU was 0.40, indicating good accuracy. The area under the receiver operating characteristics curve was 0.78. The probability of an ICU stay exceeding 2 days was more than 50% at a EuroSCORE of 14 or more.

CONCLUSIONS

In this single-institution study, the additive EuroSCORE algorithm could be used to predict ICU cost and also an ICU stay of more than 2 days after open heart surgery.

Postoperative troponin-T predicts prolonged intensive care unit length of stay following cardiac surgery*

OBJECTIVE

To evaluate the use of postoperative cardiac troponin T (cTnT) for the prediction of prolonged intensive care unit length of stay following cardiac surgery.

DESIGN

Prospective, single-center, observational cohort study of patients following cardiac surgical procedures. The enrollment period was from October through December 2000. Patients were enrolled on admission to the intensive care unit and followed until hospital discharge.

SETTING

The cardiac surgical intensive care unit of the Massachusetts General Hospital.

PATIENTS

A total of 222 consecutive patients were enrolled.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Perioperative clinical factors and serum concentrations of cTnT measured every 8 hrs after surgery were recorded. These clinical factors and the results of serum cTnT measurement were correlated with the need for prolonged intensive care unit length of stay (defined as >24 hrs). Univariable analysis identified factors predictive of prolonged intensive care unit length of stay. Stepwise logistic regression identified independent predictors of prolonged intensive care unit length of stay. Multiple linear regression was used to explore the direct relationship between cTnT concentrations at several postoperative time points and intensive care unit length of stay. At each time point assessed, cTnT concentrations from patients requiring a prolonged intensive care unit length of stay were significantly higher (all p <.001) than in those individuals with normal length of stay. In contrast, creatine kinase isoenzymes were not significantly different between patients with normal or prolonged intensive care unit length of stay. Multivariable analysis demonstrated that an immediate postoperative cTnT concentration > or =1.58 ng/mL was the strongest predictor of a prolonged intensive care unit length of stay (odds ratio, 5.6; 95% confidence interval, 2.9-10.8). Multiple linear regression analysis revealed that intensive care unit length of stay increased by 0.32 days with each incremental 1.0 ng/mL increase in cTnT measured at 18-24 hrs postprocedure.

CONCLUSIONS

Elevated postoperative cTnT concentrations can prospectively identify patients requiring prolonged intensive care unit length of stay after cardiac surgery.

Comparative analysis of risk-adjusted bypass surgery stratification models in a community hospital

BACKGROUND

Quality improvement in health care, which relies on appropriate strategies to evaluate and compare provider performance, has spawned the propagation of many public report cards or provider profiles for cardiac surgery. These risk-adjusted prediction models allow for the computation of a provider's expected outcome event rate compared with observed outcome events. The aim of this study was to assess the accuracy and reliability of 5 risk-adjusted predictive models for mortality in an independent population of patients in a community hospital who underwent coronary artery bypass graft surgery.

METHODS

Five nonproprietary models were selected for evaluation (Parsonnet, Canadian, Cleveland, New York, and the Northern New England).

RESULTS

The C-statistic for the 5 models was 0.752, 0.693, 0.748, 0.735, and 0.722 for the Parsonnet, Canadian, Cleveland, New York, and Northern New England models respectively. The H-L c2 calibration statistics were 4.948, P =.763; 1.616, P =.899; 11.96, P =.035; 10.23, P =.249; and c2 = 12.14, P =.145 for the Parsonnet, Canadian, Cleveland, New York, and Northern New England models respectively.

CONCLUSIONS

Comparing hospital-specific or surgeon-specific mortality/morbidity rates will remain a challenge. This analysis reaffirms the concept of risk-adjusting outcomes and emphasizes the importance of the risk-adjustment process for CABG surgery in a community hospital.

Risk stratification for adverse outcome in cardiac surgery

Risk-adjusted outcome prediction is mainly important in two separate fields. The first is quality monitoring: measuring actual versus predicted mortality in an institution allows assessment of the clinical surgical and anaesthesia performance while adjusting for the risk profile of the patients. Without risk stratification, surgeons and hospitals treating high-risk patients will appear to have worse results than others. This may prejudice referral patterns, affect the allocation of resources and even discourage the treatment of high-risk patients. The second field is that of informed consent and clinical decision-making. Risk-adjusted predicted mortality should form an important part of patient and surgeon decisions on whether or not to proceed with surgery. Clearly, no 'perfect' model can be produced as some aspects of mortality will always be related to risk factors not included in the model (e.g. the quality of the distal coronary artery vessels in coronary artery surgery) or due to chance happenings not related to preoperative patient characteristics (such as surgical error). An individual patient will either survive or die after cardiac surgery. Clearly, no scoring system will predict the specific outcome for every patient. However, risk stratification will inform patients and clinicians of the likely risk of death for a group of patients with a similar risk profile undergoing the proposed operation. This information is useful and should form part of the basis on which the patient and surgeon decide whether to proceed.

[Intensive care unit hospitalization after colorectal surgery]

OBJECTIVE

Intensive care units (ICU) support critically ill patients during the perioperative period. Few studies exist focusing on ICU hospitalisation after colorectal surgery. The objective of the study was to 1) detect predictive factors of mortality and length of stay in ICU after colorectal procedures, and 2) compare the autonomy status of the patients before and 30 days after their ICU stay.

PATIENTS AND METHODS

This study followed a prospective non randomized cohort in our colorectal surgery unit. During a period of one year (January 1st to December 31th, 2000) 351 colorectal procedures were performed and 54 patients were admitted to ICU after surgery. For each patient, 37 parameters were collected on a standardized register. Predictive factors of mortality (30 days after the procedure) and ICU stay (up to 3 days) were studied by univariate and multivariate statistical analysis. Self autonomy before surgery and 30 days after was also investigated.

RESULTS

"Multiple-intervention" was the only independent factor influencing mortality. Both "low autonomy status before surgery" and "pulmonary comorbidity" increased the length of stay. Regarding the 48 survivors, 45 (94%) recovered the same autonomy index as in the preoperative period 30 days after the procedure.

CONCLUSION

This study highlights the poor predictive factors influencing mortality during or after ICU stay following colorectal surgery, and emphasizes two preoperative parameters increasing the length of stay up to 3 days. This should guide the informations given to the patients families. Finally, this study confirms the good quality of self-sufficiency after ICU stay even for a long time (over 3 days).

Simplified Parsonnet risk scale identifies limits to early patient discharge

BACKGROUND

Fast-track recovery after coronary artery bypass surgery has influenced patient care positively. Predicting patients who fall off track and require prolonged (> or =7 days) hospitalization remains uncertain. The Parsonnet risk assessment score is effective in predicting length of stay, but is limited by inaccurate subdivision of risk categories. We simplified the Parsonnet risk scale to better identify patients eligible for fast-track recovery.

METHOD

The cases of 604 consecutive patients who underwent isolated coronary artery bypass grafting (CABG) using cardiopulmonary bypass (CPB) were reviewed retrospectively. A rapid recovery protocol emphasizing reduced CPB time, preoperative intra-aortic balloon pump (IABP) criteria, and atrial fibrillation prophylaxis was applied to all patients. The five original divisions of the Parsonnet risk scale were reduced to three risk categories: Low (0-10; Group A), Intermediate (11-20; Group B), High (> 20; Group C). Comparisons of progressive risk categories were analyzed to identify predictive factors associated with fast-track outcomes.

RESULTS

The thirty-day operative mortality for the entire group was 3.6%. Three clinical features were identified that distinguished risk progression-female gender, reoperative CABG, and increased age. Additionally, the presence of diabetes (p < 0.05), congestive heart failure (p < 0.01), and peripheral vascular disease (p < 0.001) distinguished Groups A and B, while acute myocardial infarction (p < 0.05) influenced outcomes in Group C. Group A (48%) mean risk score 5.9+/-3.2 was compared to Group B (34%) 14.8+/-2.6, which was further compared to Group C (18%) 26.4+/-2.8. The mean length of stay for Group A (5.3+/-4.1 days) was notably less than Group B (6.1+/-4.7 days; p < 0.05); however, both groups responded favorably to fast-track techniques. Group C did not respond comparably (9.2+/-9.2 vs 6.1+/-4.7 days; p < 0.001) and experienced prolonged recovery. The simplified Parsonnet risk scale did not identify differences in operative mortality and revealed only pneumonia (p < 0.05) and atrial fibrillation (p < 0.01) to be greater in Group C. As risk increased, significantly less revascularization was performed (Group A 3.6+/-1.2 grafts/patient vs Group B 3.3+/-1.2 [p < 0.01]; Group B 3.3+/-1.2 vs Group C 2.5+/-1.0 [p < 0.001]).

CONCLUSION

A simplified Parsonnet risk scale (three categories) is an effective tool in identifying factors limiting fast-track recovery. Low- and intermediate-risk patients represent the majority (82%) and respond well to fast-track methods. High-risk patients (18%) are limited by a greater percentage of female patients, reoperative CABG, and the very elderly, resulting in fast-track failure. Strategies to improve recovery in high-risk patients may include evolving off-pump techniques.