Risk stratification for adverse outcome in cardiac surgery

UK Trainee Cardiothoracic Surgeons' Perceptions of Public Outcome Reporting in Surgery: A Mixed-Methods Study

Background Since 2004, the Society for Cardiothoracic Surgery in Great Britain and Ireland has reported outcomes of named surgeons. In 2013, the National Health Service England published outcome data for 10 specialties, including cardiothoracic surgery. Before this, no consistent and major stakeholder feedback had occurred. This is the first study to assess UK trainee cardiothoracic surgeons' perceptions of public outcome reporting (POR) in surgery. Methodology In this study, first, an online survey was sent to all trainee cardiothoracic surgeons (n = 257) in the UK. The survey had a response rate of 17%. Second, 10 semi-structured, one-to-one interviews were conducted with trainee cardiothoracic surgeons who had completed the survey. Results The majority of respondents opposed the public release of surgeon-specific mortality data in adult cardiac surgery. It is believed to be associated with several consequences, including risk aversion, 'gaming', and detriments to the training and development of surgeons. Despite this, the majority of respondents favoured the POR of alternative outcome measures, including unit mortality, which provides a better indicator for the overall quality of care provided to patients. Conclusions Trainee cardiothoracic surgeons accept and approve of POR. However, policymakers should refine the current strategy if they are to receive full support from the future of the specialty.

Left ventricular function outcome after coronary artery bypass grafting, King Abdullah Medical City (KAMC)- single-center experience

Background

Coronary artery bypass grafting is known to be associated with better outcome in ischemic heart disease patients with low ejection fraction. We aim to demonstrate the effect of coronary artery bypass grafting (CABG) on left ventricle (LV) systolic function and to identify the predictors that adversely lead to postoperative poor outcome.

Result

This is a cross-sectional prospective study; we included 110 patients with left ventricular ejection fraction (LVEF) < 50% who underwent CABG with a mean age of 56.1 ± 12.2 years old. Those patients were classified into two groups: group I, 76 (69%) patients with LVEF > 35%, and group II, 34 (31%) patients with LVEF < 35%. Our results as regards demographic and clinical data revealed that group II patients had a significantly higher prevalence of diabetes mellitus (DM) and Euro SCORE II compared to group I patients (p = 0.05 and < 0.001 respectively); otherwise, all other clinical predictors did not differ between the two studied groups. There was a significant improvement in LVEF post-surgery (p = 0.05) in both groups with observed no significant difference recorded for in-hospital mortality rate among patients with different groups. DM, significant diastolic dysfunction, and insertion of IABP are predictors of in-hospital mortality of the patients (p = 0.001, 0.03 and < 0.001, respectively)

Conclusion

We concluded that there is a significant improvement of LV systolic function after CABG and hence better survival rate. DM, significant diastolic dysfunction, and perioperative insertion of IABP are predictors of mortality after cardiac surgery. Special care should be provided to such patients to improve their outcome

Emergent aortic surgery in octogenarians: is the advanced age a contraindication?

Surgery of both the ascending and descending aortic segments in the context of an acute aortic syndrome is one of the greatest challenges for the cardiac surgeon. In the case of surgery of the descending aorta, surgical risk increases due to the technical complexity, the required aggressive approach and because surgical indication is usually established as a result of complications and therefore involves, almost always, critically ill patients. The aging of the population is causing such surgery to be considered in an increasing number of octogenarians. The present review analyzes the available scientific evidence on the surgical indications and outcomes of these complex procedures in this population, particularly in the emergent scenario. Ascending and descending thoracic aortic diseases are reviewed separately, and the role of both the current risk scores and frailty assessments are comprehensively discussed.

Outcome of cardiac surgery in patients with low preoperative ejection fraction

Background

In patients undergoing cardiac surgery, a reduced preoperative left ventricular ejection fraction (LVEF) is common and is associated with a worse outcome. Available outcome data for these patients address specific surgical procedures, mainly coronary artery bypass graft (CABG). Aim of our study was to investigate perioperative outcome of surgery on patients with low pre-operative LVEF undergoing a broad range of cardiac surgical procedures.

Methods

Data from patients with pre-operative LVEF ≤40 % undergoing cardiac surgery at a university hospital were reviewed and analyzed. A subgroup analysis on patients with pre-operative LVEF ≤30 % was also performed.

Results

A total of 7313 patients underwent cardiac surgery during the study period. Out of these, 781 patients (11 %) had a pre-operative LVEF ≤40 % and were included in the analysis. Mean pre-operative LVEF was 33.9 ± 6.1 % and in 290 patients (37 %) LVEF was ≤30 %. The most frequently performed operation was CABG (31 % of procedures), followed by mitral valve surgery (22 %) and aortic valve surgery (19 %). Overall perioperative mortality was 5.6 %. Mitral valve surgery was more frequent among patients who did not survive, while survivors underwent more frequently CABG. Post-operative myocardial infarction occurred in 19 (2.4 %) of patients, low cardiac output syndrome in 271 (35 %). Acute kidney injury occurred in 195 (25 %) of patients. Duration of mechanical ventilation was 18 (12–48) hours. Incidence of complications was higher in patients with LVEF ≤30 %. Stepwise multivariate analysis identified chronic obstructive pulmonary disease, pre-operative insertion of intra-aortic balloon pump, and pre-operative need for inotropes as independent predictors of mortality among patients with LVEF ≤40 %.

Conclusions

We confirmed that patients with low pre-operative LVEF undergoing cardiac surgery are at higher risk of post-operative complications. Cardiac surgery can be performed with acceptable mortality rates; however, mitral valve surgery, was found to be associated with higher mortality rates in this population. Accurate selection of patients, risk/benefit evaluation, and planning of surgical and anesthesiological management are mandatory to improve outcome.

Electronic supplementary material

The online version of this article (doi:10.1186/s12871-016-0271-5) contains supplementary material, which is available to authorized users.

Cardiac surgery risk-stratification models

Risk models are widely used to predict outcomes after cardiac surgery. Not only is risk modelling applied in the assessment of the relative impact of specific risk factors on surgical outcomes, but also in patient counselling, the selection of treatment options, comparison of postoperative results, and quality-improvement programmes. At least 19 risk-stratification models exist for open-heart surgery. The focus of risk models was originally on pre-operative prediction of mortality. However, major morbidity is in general more common than mortality and the ability to predict only operative mortality is not an adequate method of determining surgical outcome. Multiple intra- and postoperative variables have been excluded in the majority of models and the possible effect of their future inclusion remains to be seen. The unique patient population of sub-Saharan Africa requires a unique risk model that reflects the patient population and levels of care.

Discovering the impact of preceding units' characteristics on the wait time of cardiac surgery unit from statistic data

INTRODUCTION

Prior research shows that clinical demand and supplier capacity significantly affect the throughput and the wait time within an isolated unit. However, it is doubtful whether characteristics (i.e., demand, capacity, throughput, and wait time) of one unit would affect the wait time of subsequent units on the patient flow process. Focusing on cardiac care, this paper aims to examine the impact of characteristics of the catheterization unit (CU) on the wait time of cardiac surgery unit (SU).

METHODS

This study integrates published data from several sources on characteristics of the CU and SU units in 11 hospitals in Ontario, Canada between 2005 and 2008. It proposes a two-layer wait time model (with each layer representing one unit) to examine the impact of CU's characteristics on the wait time of SU and test the hypotheses using the Partial Least Squares-based Structural Equation Modeling analysis tool.

RESULTS

Results show that: (i) wait time of CU has a direct positive impact on wait time of SU (β = 0.330, p < 0.01); (ii) capacity of CU has a direct positive impact on demand of SU (β = 0.644, p < 0.01); (iii) within each unit, there exist significant relationships among different characteristics (except for the effect of throughput on wait time in SU).

CONCLUSION

Characteristics of CU have direct and indirect impacts on wait time of SU. Specifically, demand and wait time of preceding unit are good predictors for wait time of subsequent units. This suggests that considering such cross-unit effects is necessary when alleviating wait time in a health care system. Further, different patient risk profiles may affect wait time in different ways (e.g., positive or negative effects) within SU. This implies that the wait time management should carefully consider the relationship between priority triage and risk stratification, especially for cardiac surgery.

A comparative study of four intensive care outcome prediction models in cardiac surgery patients

Background

Outcome prediction scoring systems are increasingly used in intensive care medicine, but most were not developed for use in cardiac surgery patients. We compared the performance of four intensive care outcome prediction scoring systems (Acute Physiology and Chronic Health Evaluation II [APACHE II], Simplified Acute Physiology Score II [SAPS II], Sequential Organ Failure Assessment [SOFA], and Cardiac Surgery Score [CASUS]) in patients after open heart surgery.

Methods

We prospectively included all consecutive adult patients who underwent open heart surgery and were admitted to the intensive care unit (ICU) between January 1^st 2007 and December 31^st 2008. Scores were calculated daily from ICU admission until discharge. The outcome measure was ICU mortality. The performance of the four scores was assessed by calibration and discrimination statistics. Derived variables (Mean- and Max- scores) were also evaluated.

Results

During the study period, 2801 patients (29.6% female) were included. Mean age was 66.9 ± 10.7 years and the ICU mortality rate was 5.2%. Calibration tests for SOFA and CASUS were reliable throughout (p-value not < 0.05), but there were significant differences between predicted and observed outcome for SAPS II (days 1, 2, 3 and 5) and APACHE II (days 2 and 3). CASUS, and its mean- and maximum-derivatives, discriminated better between survivors and non-survivors than the other scores throughout the study (area under curve ≥ 0.90). In order of best discrimination, CASUS was followed by SOFA, then SAPS II, and finally APACHE II. SAPS II and APACHE II derivatives had discrimination results that were superior to those of the SOFA derivatives.

Conclusions

CASUS and SOFA are reliable ICU mortality risk stratification models for cardiac surgery patients. SAPS II and APACHE II did not perform well in terms of calibration and discrimination statistics.

Predicting acute renal failure after cardiac surgery: external validation of two new clinical scores

BACKGROUND AND OBJECTIVES

Different scores to predict acute kidney injury after cardiac surgery have been developed recently. The purpose of this study was to validate externally two clinical scores developed at Cleveland and Toronto.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

A retrospective analysis was conducted of a prospectively maintained database of all cardiac surgeries performed during a 5-yr period (2002 to 2006) at a University Hospital in Madrid, Spain. Acute kidney injury was defined as the need for renal replacement therapy. For evaluation of the performance of both models, discrimination and calibration were measured.

RESULTS

Frequency of acute kidney injury after cardiac surgery was 3.7% in the cohort used to validate the Cleveland score and 3.8% in the cohort used to validate the Toronto score. Discrimination of both models was excellent, with values for the areas under the receiving operator characteristics curves of 0.86 (95% confidence interval 0.81 to 0.9) and 0.82 (95% confidence interval 0.76 to 0.87), respectively. Calibration was poor, with underestimation of the risk for acute kidney injury except for patients within the very-low-risk category. The performance of both models clearly improved after recalibration.

CONCLUSIONS

Both models were found to be very useful to discriminate between patients who will and will not develop acute kidney injury after cardiac surgery; however, before using the scores to estimate risk probabilities at a specific center, recalibration may be needed.

The role of depression and anxiety symptoms in hospital readmissions after cardiac surgery

The objective of this study was to determine the association between depression, anxiety and general stress symptoms with hospital readmissions after coronary artery bypass graft surgery. Two hundred and twenty six coronary artery bypass graft patients completed baseline self-report measures of depression, anxiety and stress and 222 patients completed these measures after surgery on the hospital ward. The hospital readmission outcomes at six months were analyzed using multivariable proportional hazard models. When analyzed as continuous variables in multivariable analyses, preoperative anxiety and postoperative depression predicted readmissions independent of medical covariates. In multivariable analyses with dichotomized anxiety, depression and stress, more than two-fold increase in readmission risk was attributable to preoperative anxiety and postoperative depression, independent of covariates. These results lend further support to previous research that has shown the symptoms of depression and anxiety are associated with morbidity following coronary artery bypass graft surgery. The findings highlight the need to develop suitable interventions for anxiety and depression among coronary artery bypass graft surgery patients.

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example

BACKGROUND

Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.

METHODS

Eight models were developed: Bayes linear and quadratic models, k-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.

RESULTS

Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and k-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, k-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.

CONCLUSION

Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.

A multivariate Bayesian model for assessing morbidity after coronary artery surgery

Introduction

Although most risk-stratification scores are derived from preoperative patient variables, there are several intraoperative and postoperative variables that can influence prognosis. Higgins and colleagues previously evaluated the contribution of preoperative, intraoperative and postoperative predictors to the outcome. We developed a Bayes linear model to discriminate morbidity risk after coronary artery bypass grafting and compared it with three different score models: the Higgins' original scoring system, derived from the patient's status on admission to the intensive care unit (ICU), and two models designed and customized to our patient population.

Methods

We analyzed 88 operative risk factors; 1,090 consecutive adult patients who underwent coronary artery bypass grafting were studied. Training and testing data sets of 740 patients and 350 patients, respectively, were used. A stepwise approach enabled selection of an optimal subset of predictor variables. Model discrimination was assessed by receiver operating characteristic (ROC) curves, whereas calibration was measured using the Hosmer-Lemeshow goodness-of-fit test.

Results

A set of 12 preoperative, intraoperative and postoperative predictor variables was identified for the Bayes linear model. Bayes and locally customized score models fitted according to the Hosmer-Lemeshow test. However, the comparison between the areas under the ROC curve proved that the Bayes linear classifier had a significantly higher discrimination capacity than the score models. Calibration and discrimination were both much worse with Higgins' original scoring system.

Conclusion

Most prediction rules use sequential numerical risk scoring to quantify prognosis and are an advanced form of audit. Score models are very attractive tools because their application in routine clinical practice is simple. If locally customized, they also predict patient morbidity in an acceptable manner. The Bayesian model seems to be a feasible alternative. It has better discrimination and can be tailored more easily to individual institutions.