• Calibration
• Coronary artery bypass graft
• Discrimination
• EuroSCORE II
• Risk-adjusted mortality index

• ICU stay
• RDW value
• RLR ratio
• cardiovascular surgery
• lymphocyte count
• older adults

• All-cause mortality
• Cardiac surgery
• Coronary artery bypass grafting
• Handgrip strength
• Phase angle

• Cardiac Surgical Procedures
• Electric Impedance
• Hand Strength
• Humans
• Length of Stay
• Prognosis

• Private Universität Witten/Herdecke gGmbH (3128)

Reimbursement for cardiac surgical procedures in Israel is uniform and does not account for diversity in costs of various procedures or for diversity in patient mix. In an era of new and costly technology coupled with higher risk patients needing more complex surgery, these tariffs may not adequately reflect the true financial burden on the caregivers. In the present study we attempt to determine whether case mix and complexity of procedures significantly affect cost to justify differential tariffs.

We included all patients undergoing cardiac surgery at Shaare Zedek Medical Center between the years 1993–2016. Patients were stratified according to (1) type of surgery and (2) clinical profile as reflected by the predicted operative risk according to the European System for Cardiac Operative Risk Evaluation (EuroSCORE). Approximate cost of each group of patients was estimated by the average number of days in the Intensive Care Unit and days in the postoperative ward multiplied by the respective daily costs as determined by the Ministry of Health. We then added the fixed cost of the components used in the operating room (manpower and disposables). The final estimated cost (the outcome variable) was then evaluated as it relates to type of surgery and clinical profile. ANOVA was used to analyze cost variability between groups, and backward regression analysis to determine the respective effect of the abovementioned variables on cost. Because of non-normal distribution, both costs and lengths of stay were Log-transformed.

Altogether there were 5496 patients: 3863, 836, 685 and 112 in the isolated CABG, CABG + valve, 1 valve and 2 valves replacement groups. By ANOVA, the costs in all EuroSCORE subgroups were significantly different from each other, increasing with increased EuroSCORE subgroup. Cost was also significantly different among procedure groups, increasing from simple CABG to single valve surgery to CABG + valve surgery to 2-valve surgery. In backward stepwise multiple regression analysis, both type of procedure and EuroSCORE group significantly impacted cost. ICU stay and Ward stay were significantly but weakly related while EuroSCORE subgroup was highly predictive of both ICU stay and ward stay.

The cost of performing heart surgery today is directly influenced by both patient profile as well as type of surgery, both of which can be quantified. Modern day technology is costly yet has become mandatory. Thus reimbursement for heart surgery should be based on differential criteria, namely clinical risk profile as well as type of surgery. Our results suggest an urgent need for design and implementation of a differential tariff model in the Israeli reimbursement system. We suggest that a model using a fixed, average price according to the type of procedure costs, in addition to a variable hospitalization cost (ICU + ward) determined by the patient EuroSCORE or EuroSCORE subgroup should enable an equitable reimbursement to hospitals, based on their case mix.

• Cardiac surgery
• Cream skimming
• EuroSCORE
• PROCEDURE related group (PRG)
• Reimbursement

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.

• acute type A aortic dissection
• intensive care unit
• length of stay
• machine learning
• surgery

Coronary artery bypass grafting (CABG) with cardiopulmonary bypass (CPB) improved symptoms and increased survival and quality of life in patients with coronary artery disease. However, it should be the main cause of a complex organic systemic inflammatory response that greatly contributes to several postoperative adverse effects.

We aimed to evaluate heat-shock protein 70 (HSP 70) expression as a morbimortality predictor in patients with preserved ventricular function undergoing coronary artery bypass grafting (CABG) with cardiopulmonary bypass (CPB) and to determine their association with the lactate as a marker of tissue hypoperfusion and the EuroSCORE risk score. This is a prospective, observational study including 46 patients and occurring between May and July 2016. Patients without ventricular dysfunction undergoing myocardial revascularization with extracorporeal circulation were included. They were divided into (1) complicated and (2) uncomplicated postoperative evolution groups. EuroSCORE, lactate levels, and HSP 70 expression and their correlations were determined.

Statistical analysis showed that the group with complicated evolution had higher EuroSCORE values than the other group. HSP 70 protein levels were significantly increased in the group with uncomplicated evolution and showed similar results. According to our results, HSP family proteins may be independent predictors of uncomplicated evolution in patients without ventricular dysfunction undergoing CABG with CPB.

HSP 70 should be a good discriminator and protection marker for complications in cardiac surgery.

• Cardiac surgery
• Intensive care
• Morbidity
• Pulmonary artery catheter
• Right ventricle
• Right ventricular function
• Thermodilution

• Aortic stenosis
• Balloon-expandable valve
• Cost
• Resource
• TAVI

• Aged
• Aged, 80 and over
• Aortic Valve Stenosis/surgery
• Female
• Humans
• Intensive Care Units/statistics & numerical data
• Length of Stay/statistics & numerical data
• Male
• Operative Time
• Retrospective Studies
• Risk Factors
• Severity of Illness Index
• Transcatheter Aortic Valve Replacement/economics
• Transcatheter Aortic Valve Replacement/methods

• Biomarkers
• Cardiac Surgical Procedures/statistics & numerical data
• Humans
• Risk Assessment/methods

• ICU prolonged stay
• determinants of hospital stay
• hospital resource planning
• prediction model

• Aged
• Cardiac Surgical Procedures/adverse effects
• Cardiac Surgical Procedures/classification
• Cardiopulmonary Bypass
• Female
• Humans
• Intensive Care Units
• Length of Stay/statistics & numerical data
• Logistic Models
• Male
• Middle Aged
• Oman
• Postoperative Complications/epidemiology
• Postoperative Period
• Referral and Consultation
• Retrospective Studies
• Risk Factors

• cardiac surgical procedures
• complication
• hospital mortality
• intensive care units
• length of stay
• outcome
• stroke
• survival rate

Previous studies around the world indicated validity and accuracy of European System for Cardiac Operative Risk Evaluation (EuroSCORE) risk scoring system we evaluated the EuroSCORE risk scoring system for patients undergoing coronary artery bypass graft (CABG) surgery in a group of Iranian patients.

In this cohort 2220 patients more than 18 years, who were performed CABG surgery in Massih Daneshvari Hospital, from January 2004 to March 2010 were recruited. Predicted mortality risk scores were calculated using logistic EuroSCORE and Acute Physiology and Chronic Health Evaluation II (APACHE II) and compared with observed mortality. Calibration was measured by the Hosmer–Lemeshow (HL) test and discrimination by using the receiver operating characteristic (ROC) curve area.

Of the 2220 patients, in hospital deaths occurred in 270 patients (mortality rate of 12.2%). The accuracy of mortality prediction in the logistic EuroSCORE and APACHE II model was 89.1%; in the local EuroSCORE (logistic) was 91.89%; and in the local EuroSCORE support vector machines (SVM) was 98.6%. The area under curve for ROC curve, was 0.724 (95% confidence interval [CI]: 0.57–0.88) for logistic EuroSCORE; 0.836 (95% CI: 0.731–0.942) for local EuroSCORE (logistic); 0.978 (95% CI: 0.937–1) for Local EuroSCORE (SVM); and 0.832 (95% CI: 0.723–0.941) for APACHE II model. The HL test showed good calibration for the local EuroSCORE (SVM), APACHE II model and local EuroSCORE (logistic) (P = 0.823, P = 0.748 and P = 0.06 respectively); but there was a significant difference between expected and observed mortality according to EuroSCORE model (P = 0.033).

We detected logistic EuroSCORE risk model is not applicable on Iranian patients undergoing CABG surgery.

• Coronary artery bypass graft
• European System for Cardiac Operative Risk Evaluation
• mortality
• risk stratification
• scoring system
• validity

Epidemiological differences can be found between Brazilian and European valvular heart disease patients. The prevalence of heart valve diseases due to rheumatic disease is significantly higher in the Brazilian compared with the European population. Therefore, they could have different risks during and after cardiac surgery. The aim of this study was to evaluate the applicability of the additive and logistic EuroSCORE and EuroSCORE II in a cohort of high-risk patients with valvular heart disease of predominantly rheumatic aetiology submitted to surgery.

Between 1 February and 30 December 2009, 540 consecutive patients scheduled for valvular heart surgery were included in this study. In this set of patients, we examined the performance of the additive, logistic, and EuroSCORE II models for predicting in-hospital mortality. Calibration of each model was assessed by comparing predicted and observed in-hospital mortality and by the goodness of fit of the Hosmer-Lemeshow chi-square test. Discrimination performance of the model was evaluated with the receiver operating characteristic (ROC) curve analysis.

The mean age was 56 ± 16 years, 50.6% were female, and the mortality rate was 16.0% (6.0% in elective surgery and 34.0% in emergency/urgency surgery). Mortality rates were estimated according to the additive and logistic EuroSCORE and EuroSCORE II at 6.1%, 8.7%, and 4.3%, respectively. The AUC was 0.76 (95% confidence interval [95% CI] 0.70–0.81) for the additive EuroSCORE, 0.76 (95% CI 0.70–0.81) for the logistic EuroSCORE and 0.81 (95% CI 0.76–0.86) for EuroSCORE II. Hosmer-Lemeshow goodness-of-fit statistics were P = 0.52, P = 0.07, and P = 0.12 for additive, logistic EuroSCORE, and EuroSCORE II.

In this cohort of Brazilian patients with valvular heart disease submitted to surgical procedure, the EuroSCORE models had a good discriminatory capacity; however, the calibration was compromised because of an underestimation of the mortality rate.

• Adult
• Aged
• Brazil/epidemiology
• Cohort Studies
• Comorbidity
• Europe/epidemiology
• Female
• Heart Valve Diseases/epidemiology
• Heart Valve Diseases/etiology
• Heart Valve Diseases/surgery
• Humans
• Male
• Middle Aged
• Prevalence
• ROC Curve
• Rheumatic Fever/complications
• Risk Factors

• Cardiac surgery
• Intensive care
• Octogenarians
• Outcomes
• Quality of life

Preoperative risk stratification models have previously been suggested to predict cardiac surgery unit costs. However, there is a lack of consistency in their reliability in this field. In this study we aim to test the correlation between the values of six commonly known preoperative scoring systems and evaluate their reliability at predicting unit costs of cardiac surgery patients.

Over a period of 14 months all consecutive adult patients undergoing cardiac surgery on cardiopulmonary bypass were prospectively classified using six preoperative scoring models (EuroSCORE, Parsonnet, Ontario, French, Pons and CABDEAL). Transplantation patients were the only patients we excluded. Total hospital costs for each patient were calculated independently on a daily basis using the bottom up method. The full unit costs were calculated including preoperative diagnostic tests, operating room cost, disposable materials, drugs, blood components as well as costs for personnel and fixed hospital costs. The correlation between hospital cost and the six models was determined by linear regression analysis. Both Spearman’s and Pearson’s correlation coefficients were calculated from the regression lines. An analysis of residuals was performed to determine the quality of the regression.

A total of 887 patients were operated on for CABG (n = 608), valve (n = 142), CABG plus valve (n = 100), thoracic aorta (n = 33) and ventricular assist devices (n = 4). Mean age of the patients was 68.3±9.9 years, 27.6% were female. 30-day mortality rate was 4.1%. Correlation between the six models and hospital cost was weak (Pearson’s: r < 0.30; Spearman’s: r < 0.40).

The risk stratification models in this study are not reliable at predicting total costs of cardiac surgical patients. We therefore do not recommend their use for this purpose.

The prediction of intensive care unit length of stay (ICU-LOS) could contribute to more efficient ICU resources' allocation and better planning of care among cardiac surgery patients. The aim of this study was to identify the preoperative and intraoperative predictors for prolonged cardiac surgery ICU-LOS. An observational cohort study was conducted among 150 consecutive patients, who were admitted to the cardiac surgery ICU of a tertiary hospital of Athens, Greece from September 2010 to January 2011. Multivariate regression analysis revealed that patients with increased creatinine levels preoperatively (odds ratio (OR) 3.0, P = 0.049), history of atrial fibrillation (AF) (OR 6.3, P = 0.012) and high EuroSCORE values (OR 2.6, P = 0.017) had a significant greater probability to stay in the ICU for more than 2 days. In addition, intraoperative hyperglycemia (OR 3.0, P = 0.004) was strongly associated with longer ICU-LOS. In conclusion, the high perioperative risk, the history of AF and renal dysfunction, and the intraoperative hyperglycemia are significant predictors of prolonged ICU stay. The early identification of patients at risk could allow the efficient ICU resources' allocation and the reduction of healthcare costs. This would contribute to nursing care planning depending on the availability of healthcare personnel and ICU bed capacity.

The intensive care unit (ICU) length of stay (LOS) of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP), a machine learning technique.

Non-interventional study. Predictive modeling, separate development (n = 461) and validation (n = 499) cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task), and to predict the day of ICU discharge as a discrete variable (regression task). GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF) ((actual-predicted)/actual) and calculating root mean squared relative errors (RMSRE).

Median (P25-P75) ICU length of stay was 3 (2-5) days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%), which was significantly better than the EuroSCORE (p < 0.001) and nurses (p = 0.044) but equivalent to physicians. GP had the lowest RMSRE (0.408) of all predictive models.

A GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a classification as well as a regression task. The GP model demonstrated a significantly better discriminative power than the EuroSCORE and the ICU nurses, and at least as good as predictions done by ICU physicians. The GP model was the only well calibrated model.

• Coronary Artery Bypass/adverse effects
• Female
• Humans
• Intensive Care Units
• Length of Stay
• Male
• Middle Aged
• Risk Factors