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

Development and Validation of Scores to Predict Prolonged Mechanical Ventilation after Cardiac Surgery

OBJECTIVES

To optimize the early prediction of prolonged postoperative mechanical ventilation after cardiac surgery (>24 hours postoperatively).

DESIGN

The authors performed a retrospective analysis.

SETTING

The Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) database was utilized.

PARTICIPANTS

All patients included in the ANZSCTS database between January 2015 and December 2018 were analyzed.

INTERVENTIONS

No interventions were performed in this observational study.

MEASUREMENTS AND MAIN RESULTS

A previously developed model was modified to allow retrospective risk calculation and model assessment (Modified Hessels score). The database was split into development and validation sets. A new risk model was developed using forward and backward stepwise elimination (ANZ-PreVent score). The authors assessed 48,382 patients, of whom 5004 (10.3%) were ventilated mechanically for >24 hours post-operatively. The Modified Hessels score demonstrated good performance in this database, with a c-index of 0.78 (95% CI 0.77-0.78) and a Brier score of 0.08. The newly developed ANZ-PreVent score demonstrated better performance (validation cohort, n = 12,229), with a c-index of 0.84 (95% CI 0.83-0.85) (p < 0.0001) and a Brier score of 0.07. Both scores performed better than the severity of illness scores commonly used to predict outcomes in intensive care.

CONCLUSIONS

The authors validated a modified version of an existing prediction score and developed the ANZ-PreVent score, with improved performance for identifying patients at risk of ventilation for >24 hours. The improved score can be used to identify high-risk patients for targeted interventions in future randomized controlled trials.

Evaluation of Vasoactive-Inotropic Score and Survival to Decannulation in Adult Patients on Venoarterial Extracorporeal Life Support: An Observational Cohort Study

Extracorporeal life support with venoarterial extracorporeal membrane oxygenation (VA-ECMO) is used to assist circulation in patients with severe cardiogenic shock or cardiac arrest. The vasoactive-inotropic score (VIS) is a standardized calculation of vasoactive medication support which uses coefficients for each medication that converts them to an equivalent value. The purpose of this study was to assess the VIS as an early prognostication tool for survival to decannulation patients on adult VA-ECMO support. This was a single-center, observational cohort study of adult patients who received VA-ECMO support compared based on their survival to decannulation. The primary endpoint was the VIS at hour 24 postcannulation. Among the 265 patients included in this study, 140 patients (52.8%) survived to decannulation of VA-ECMO. At 24 hours postcannulation, a lower VIS was observed in the group that survived decannulation (6.5 ± 7.5 vs. 12.3 ± 16.9; p < 0.001). Multivariate analysis performed also demonstrates an association between 24-hour VIS and survival to decannulation (odds ratio 0.95; 95% confidence interval, 0.91-0.95). This study suggests that the 24-hour VIS may be an early prognostic indicator in patients on VA-ECMO patients. http://links.lww.com/ASAIO/B39.

CASUS and APACHE II score in predicting mortality after coronary artery bypass grafting

Background

This study aims to compare Cardiac Surgery Score (CASUS) and the Acute Physiology and Chronic Health Evaluation (APACHE II) scoring systems for predicting mortality in patients undergoing isolated coronary artery bypass grafting.

Methods

Between January 2019 and March 2019, a total of 204 patients (166 males, 38 females; mean age: 60.5±0.7 years; range, 59.2 to 61.9 years) who underwent isolated coronary artery bypass grafting and were monitored at least for 24 h in the intensive care unit postoperatively were included. Pre-, intra-, and postoperative data were recorded. The CASUS and APACHE II scores were calculated using the most abnormal values for each variable during the first 24 h, postoperatively. Clinical outcomes were seven-day mortality and 30-day mortality, need for reintubation, readmission to the intensive care unit, length of intensive care unit stay and length of hospital stay.

Results

The 30-day overall mortality was 4.9% (n=10). The CASUS scores were significantly higher for patients developing mortality within 30 days postoperatively (p=0.030) and for patients needing reintubation (p=0.003). In the receiver operating characteristic curve analysis predicting seven-day mortality and prolonged intensive care unit stay, the area under curve was higher for CASUS scoring compared to APACHE II (0.90 vs. 0.72 and 0.82 vs. 0.76).

Conclusion

The CASUS may prove to be a more reliable scoring system than APACHE II for predicting mortality and morbidity in patients undergoing isolated coronary artery bypass grafting.

A comparative study of four thoracic mortality scores

BACKGROUND

The percentage of patients in resectable stages at initial diagnosis of non-small cell lung cancer (NSCLC) raises due to better screening programs. Therefore, risk prediction models are becoming more critical. Here, we validated and compared four established scoring models, the Thoracoscore, Epithor, Eurloung 2, and the simplified Eurolung 2 (2b), in their ability to predict 30-day mortality.

METHODS

All consecutive patients undergoing anatomical pulmonary resection were included. The performance of the four scoring systems was assessed with Hosmer-Lemeshow goodness-of-fit test (calibration) and receiver operating characteristic (ROC) curves (discrimination). We compared the area under the curve (AUC) of the ROC curves by DeLong's method.

RESULTS

A total of 624 patients underwent surgery for NSCLC at our institution between 2012 and 2018 30-day mortality was 2.2% (14 patients). The AUC for Eurolung 2 and the simplified Eurolung 2 (0.82) were greater than those of the other scoring systems, Epithor (0.71) and Thoracoscore (0.65). In addition, the DeLong analysis showed a significant superiority of Eurolung 2 and Eurolung 2b over the Thoracoscore (p = 0.04); there were no significant differences compared to Epithor.

CONCLUSION

Eurolung 2 and the simplified Eurolung 2 were the favorable scoring systems for predicting 30-day mortality compared to Thoracoscore and Epithor. Therefore, we recommend using Eurolung 2 or the simplified Eurolung 2 for preoperative risk stratification.

Construction and validation of a nomogram for predicting survival in elderly patients with cardiac surgery

Background

In recent years, the number of elderly patients undergoing cardiac surgery has rapidly increased and is associated with poor outcomes. However, there is still a lack of adequate models for predicting the risk of death after cardiac surgery in elderly patients. This study sought to identify independent risk factors for 1-year all-cause mortality in elderly patients after cardiac surgery and to develop a predictive model.

Methods

A total of 3,752 elderly patients with cardiac surgery were enrolled from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset and randomly divided into training and validation sets. The primary outcome was the all-cause mortality at 1 year. The Least absolute shrinkage and selection operator (LASSO) regression was used to decrease data dimensionality and select features. Multivariate logistic regression was used to establish the prediction model. The concordance index (C-index), receiver operating characteristic curve (ROC), and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram.

Results

Our results demonstrated that age, sex, Sequential Organ Failure Assessment (SOFA), respiratory rate (RR), creatinine, glucose, and RBC transfusion (red blood cell) were independent factors for elderly patient mortality after cardiac surgery. The C-index of the training and validation sets was 0.744 (95%CI: 0.707-0.781) and 0.751 (95%CI: 0.709-0.794), respectively. The area under the curve (AUC) and decision curve analysis (DCA) results substantiated that the nomogram yielded an excellent performance predicting the 1-year all-cause mortality after cardiac surgery.

Conclusions

We developed a novel nomogram model for predicting the 1-year all-cause mortality for elderly patients after cardiac surgery, which could be an effective and useful clinical tool for clinicians for tailored therapy and prognosis prediction.

Accuracy of Quick Sequential Organ Failure Assessment Score & Systemic Inflammatory Response Syndrome Criteria in Predicting Adverse Outcomes in Emergency Surgical Patients With Suspected Sepsis: A Prospective Observational Study

Purpose: Due to the mixed population enrolled in different studies i.e., medical and surgical cases, conflicting data exists about the accuracy of quick sequential organ failure assessment (qSOFA) and systemic inflammatory response syndrome (SIRS) scores in predicting adverse outcomes in patients with suspected sepsis presenting to the surgical emergency.

Method: A prospective observational study was done in the department of surgery of a tertiary teaching hospital, India from June 2018 to July 2019. Consecutive patients who visited the surgical emergency department with suspected sepsis were included. Patients were followed up until hospital discharge or death.

Results: Of the 410 patients screened, 287 were included in the analysis. The median age was 52 years (interquartile range, 41 to 61years) and 208 (72.8%) were men. Around 56.8% of patients had intra-abdominal pathology, and 43.2% had skin and soft -tissue infection. Sixty-nine (24%) patients died during their hospitalization, 98 (34.1%) patients had organ dysfunction, and 168 (58.5%) patients needed admission to the intensive care unit (ICU). A higher qSOFA score (≥2) was associated with organ dysfunction, ICU admission, and in-hospital mortality. The specificity, positive predictive value and diagnostic accuracy of qSOFA for organ dysfunction (85.7%, 67.8%, 76.3%), ICU admission (92.4%, 89.3%, 64.5%), and in-hospital mortality (81.6%, 52.4%, 77.4%) was higher than SIRS. The area under the receiver operating characteristic curve for qSOFA for these variables was also higher than for SIRS (0.826 vs. 0.524, 0.823 vs. 0.577, and 0.823 vs. 0.555, respectively).

Conclusion: qSOFA is a better model for predicting adverse outcomes and mortality, organ dysfunction, and ICU admission in surgical patients. However, SIRS indicates intervention requirements in a surgical patient better than qSOFA.

Performance of Sequential Organ Failure Assessment and Simplified Acute Physiology Score II for Post-Cardiac Surgery Patients in Intensive Care Unit

Background: The aim of this study is to assess the performance of Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score (SAPS II) on outcomes of patients with cardiac surgery and identify the cutoff values to provide a reference for early intervention.

Methods: All data were extracted from MIMIC-III (Medical Information Mart for Intensive Care-III) database. Cutoff values were calculated by the receiver-operating characteristic curve and Youden indexes. Patients were grouped, respectively, according to the cutoff values of SOFA and SAPS II. A non-adjusted model and adjusted model were established to evaluate the prediction of risk. Comparison of clinical efficacy between two scoring systems was made by decision curve analysis (DCA). The primary outcomes of this study were in-hospital mortality, 28-day mortality, 90-day mortality, and 1-year mortality after cardiac surgery. The secondary outcomes included length of hospital stay and intensive care unit (ICU) stay and the incidence of acute kidney injury (AKI) within 7 days after ICU admission.

Results: A total of 6,122 patients were collected and divided into the H-SOFA group (SOFA ≥ 7) and L-SOFA group (SOFA < 7) or H-SAPS II group (SAPS II ≥ 43) and L-SAPS II group (SAPS II < 43). In-hospital mortality, 28-day mortality, 90-day mortality, and 1-year mortality were higher, the length of hospital and ICU stay were longer in the H-SOFA group than in the L-SOFA group (p < 0.05), while the incidence of AKI was not significantly different. In-hospital mortality, 28-day mortality, 90-day mortality, 1-year mortality, and the incidence of AKI were all significantly higher in the H-SAPS II group than in the L-SAPS II group (p < 0.05). Hospital stay and ICU stay were longer in the H-SAPS II group than in the L-SAPS II group (p < 0.05). According to DCA, the SAPS II scoring system had more net benefits on assessing the long-term mortality compared with the SOFA scoring system.

Conclusion: Exceeding the cutoff values of SOFA and SAPS II scores could lead to increased mortality and extended length of ICU and hospital stay. The SAPS II scoring system had a better discriminative performance of 90-day mortality and 1-year mortality in post-cardiac surgery patients than the SOFA scoring system. Emphasizing the critical value of the scoring system is of significance for timely treatment.

External Validation of the Cardiac Surgery Score in a Quaternary Hospital in the United States of America

BACKGROUND

The Cardiac Surgery Score (CASUS) was developed to assist in predicting post-cardiac surgery mortality using parameters measured in the intensive care unit. It is calculated by assigning points to ten physiologic variables and adding them to obtain a score (additive CASUS), or by logistic regression to weight the variables and estimate the probability of mortality (logistic CASUS). Both additive and logistic CASUS have been externally validated elsewhere, but not yet in the United States of America (USA). This study aims to validate CASUS in a quaternary hospital in the USA and compare the predictive performance of additive to logistic CASUS in this setting.

METHODS

Additive and logistic CASUS (postoperative days 1-5) were calculated for 7098 patients at Cleveland Clinic from January 2015 to February 2017. 30-day mortality data were abstracted from institutional records and the Death Registries for Ohio State and the Centers for Disease Control. Given a low event rate, model discrimination was assessed by area under the curve (AUROC), partial AUROC (pAUC), and average precision (AP). Calibration was assessed by curves and quantified using Harrell's Emax, and Integrated Calibration Index (ICI).

RESULTS

30-day mortality rate was 1.37%. For additive CASUS, odds ratio for mortality was 1.41 (1.35-1.46, P <0.001). Additive and logistic CASUS had comparable pAUC and AUROC (all >0.83). However, additive CASUS had greater AP, especially on postoperative day 1 (0.22 vs. 0.11). Additive CASUS had better calibration curves, and lower Emax, and ICI on all days.

CONCLUSIONS

Additive and logistic CASUS discriminated well for postoperative 30-day mortality in our quaternary center in the USA, however logistic CASUS under-predicted mortality in our cohort. Given its ease of calculation, and better predictive accuracy, additive CASUS may be the preferred model for postoperative use. Validation in more typical cardiac surgery centers in the USA is recommended.

Analysis of the Performance of Daily Surgery Score (CASUS) in Patients with Mixed Racial Profile after Cardiac Surgery: A Single-Center Retrospective Study

OBJECTIVE

The aim was to look at the Cardiac Surgery Score (CASUS) assessment after cardiac surgery, and compare it with the intensive care unit (ICU) mortality and morbidity, in a racially diverse group of patients, in a single center.

DESIGN

Clinical retrospective study analyzing data from 319 patients over a 1-year duration.

SETTING

Cardiothoracic intensive care unit (CTICU) of a tertiary care center.

PARTICIPANTS

All patients who underwent cardiac surgery between January 1 and December 31, 2017.

INTERVENTIONS

Review of electronic patient records.

MEASUREMENTS AND RESULTS

Daily CASUS assessments (calculated on an online application and recorded on patient electronic records) were retrieved. The variables of CASUS used for the study were CASUS value on postoperative day 1 (POD1-CASUS), on death/discharge from CTICU (Dis-CASUS), mean of all CASUS values during CTICU stay (M-CASUS), and differential CASUS (Dif- CASUS) [CASUS POD 1 - CASUS on discharge]. The receiver operating characteristic (ROC) curve for the diagnostic level of POD 1-CASUS, indicating mortality, was calculated. A value of >6.5 for POD 1 CASUS had 80% sensitivity and 84% specificity, with area under the curve value 0.756 (95% confidence interval: 0.46 to 1). The mean values of POD1-CASUS (8.6 ± 6), M-CASUS (8.2 ± 5.2), and Dis-CASUS (7.8 ± 5.7) were significantly higher in cases of mortality, compared to the others. POD1-CASUS, M-CASUS, and Dis-CASUS were found to be statistically significantly elevated in patients with acute kidney injury (AKI) and postoperative stroke, and in those who were readmitted to the CTICU after initial discharge. Patients with POD1-CASUS ≥6.5 had a statistically significant association with mortality and postoperative morbidity (p < 0.05). Findings from multivariate logistic regression indicated that body mass index (BMI), ICU readmission, length of mechanical ventilation, and length of ICU stay remained associated significantly with POD1 CASUS ≥6.5.

CONCLUSION

This study found that CASUS on POD 1, mean values of CASUS during CTICU stay, and CASUS at death/discharge from CTICU predicted ICU mortality after cardiac surgery in this racially diverse group. The CASUS derivatives can be used to predict unfavorable outcomes after cardiac surgery. A POD1-CASUS value of 6.5 or more could signify mortality and postoperative morbidity.

Preoperative and ICU Scoring Models for Predicting the In-Hospital Mortality of Patients With Ruptured Abdominal Aortic Aneurysms

OBJECTIVES

This study's objective was to compare several preoperative and intensive care unit (ICU) prognostic scoring systems for predicting the in-hospital mortality of ruptured abdominal aortic aneurysms (RAAAs).

DESIGN

Retrospective cohort study.

SETTING

Single tertiary university center.

PARTICIPANTS

The study comprised 157 patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 157 patients (82% male) presented with RAAA at Charité University Hospital from January 2011 to December 2020. The mean age was 74 years (standard deviation ten years). In-hospital mortality was 29% (n = 45), of whom nine patients (6%) died en route to the operating room, 13 (8%) on the operating table, and 23 (15%) in the ICU. A total of 135 patients (86%) were admitted to the ICU. All six models demonstrated good discriminating performance between survivors and nonsurvivors. Overall, the area under the curve (AUC) for RAAA preoperative scores was greater than those for ICU scores. The largest AUC was achieved with the Vascular Study Group of New England (VSGNE) RAAA risk score (AUC = 0.87 for all patients, AUC = 0.84 for patients admitted to the ICU), followed by Hardman Index (AUC = 0.83 for all patients, AUC = 0.81 for patients admitted to the ICU), and Glasgow Aneurysm Score (AUC = 0.74 for all patients, AUC = 0.83 for patients admitted to the ICU). The largest AUC for ICU scores (only patients admitted to the ICU) was achieved with Simplified Acute Physiology Score II (0.75), followed by Sepsis-related Organ Failure Assessment (0.73), and Acute Physiology and Chronic Health Evaluation II (0.71).

CONCLUSIONS

Preoperative and ICU scores can predict the mortality of patients presenting with RAAA. In addition, the discriminatory ability of preoperative scores between survivors and nonsurvivors was larger than that for ICU scores.

Machine learning applied to a Cardiac Surgery Recovery Unit and to a Coronary Care Unit for mortality prediction

Most established severity-of-illness systems used for prediction of intensive care unit (ICU) mortality were developed targeted at the general ICU population, based on logistic regression (LR). To date, no dynamic predictive tool for ICU mortality has been developed targeted at the Cardiac Surgery Recovery Unit (CSRU) and Coronary Care Unit (CCU) using machine learning (ML). CSRU and CCU adult patients from the MIMIC-III critical care database were studied. The ML methods developed extract ICU data during a 5-h window and demographic features to produce mortality predictions and were compared to six established severity-of-illness systems and LR. In a secondary experiment, additional procedure/surgery and ICU features were added to the models. The ML models developed were the Tree Ensemble (TE), Random Forest, XGBoost Tree Ensemble (XGB), Naive Bayes (NB), and Bayesian network. The discrimination, calibration and accuracy statistics were assessed. The AUROC values were superior for the ML models reaching 0.926 and 0.924 for the XGB, and 0.904 and 0.908 for the TE for ICU mortality prediction in the primary and secondary experiments respectively. Among the conventional systems, the serial SOFA obtained the highest AUROC (0.8405). The Brier score was better for the ML models except the NB over the conventional systems. The accuracy statistics less sensitive to unbalanced cohorts were higher for all the ML models. In conclusion, the predictive power of XGB was excellent, substantially outperforming the conventional systems and LR. The ML models developed in this work offer promising results that could benefit CSRU and CCU.

Perioperative care in cardiac surgery

As mortality is now low for many cardiac surgical procedures, there has been an increasing focus on patient centered outcomes such as recovery and quality of life. The Enhanced Recovery After Surgery (ERAS) cardiac society recently published the first set of guidelines for cardiac surgery which will be useful as a starting point to help translate this philosophy for the benefit of those undergoing cardiac surgery. At the same time there are many advances in other areas such as mechanical circulation, diagnostics and quality metrics. We intend here to present a balanced and evidenced based review of selected aspects of current practice, encompassing both UK and international perioperative care with a focus on recent advances. For the convenience of the reader we will adopt the conventional perioperative preoperative, intraoperative and postoperative phases of care. The focus of cardiac surgical practice needs to evolve from mortality to recovery. Those specialists who work in cardiac anaesthesia and critical care are well placed to contribute to these changes. Accompanying this work is the development of technologies to improve recognition of and intervention to prevent early organ dysfunction. Measuring, benchmarking and publishing quality outcomes from cardiac surgical centres is likely to improve services and benefit our patients.

Discriminatory power of scoring systems for outcome prediction in patients with extracorporeal membrane oxygenation following cardiovascular surgery†

OBJECTIVES

Although extracorporeal membrane oxygenation (ECMO) represents a rapidly evolving treatment option in patients with refractory heart or lung failure, survival remains poor and appropriate risk stratification challenging because established risk prediction models have not been validated for this specific population.

METHODS

This observational single-centre registry included a total of 240 patients treated with venoarterial ECMO therapy following cardiovascular surgery and analysed the discriminatory power of the European System of Cardiac Operative Risk Evaluation (EuroSCORE) additive, the EuroSCORE II, the Sequential Organ Failure Assessment (SOFA) score, the Simplified Acute Physiology Score (SAPS) II, the SAPS III, the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Risk of renal failure, Injury to the kidney, Failure of kidney function, Loss of kidney function and End-stage renal failure (RIFLE) classification, the survival after venoarterial ECMO (SAVE) score, the prEdictioN of Cardiogenic shock OUtcome foR AMI patients salvaGed by VA-ECMO (ENCOURAGE) score and the Society of Thoracic Surgeons (STS) risk model for outcome prediction.

RESULTS

During a median follow-up time of 37 months (interquartile range 19-67), 65% of the patients died. Only the SAVE score and the SAPS II were significantly associated with the 30-day mortality rate with a hazard ratio (HR) of 1.06 [95% confidence interval (CI) 1.02-1.11; P = 0.002] for the SAVE score and an HR of 1.02 (95% CI 1.01-1.03; P = 0.004) for the SAPS II with a modest discriminatory power displayed by a C-index of 0.61 and 0.57, respectively. Seven out of 10 scoring systems revealed significant association with long-term mortality, with the SAVE score and the SAPS II remaining the strongest predictors of long-term mortality with an HR of 1.06 (95% CI 1.03-1.09; P < 0.001, C-index 0.61) for the SAVE score and an HR of 1.02 (95% CI 1.01-1.03; P < 0.001, C-index 0.58) for the SAPS II.

CONCLUSIONS

Risk assessment based on established risk models in patients with ECMO remains difficult. Only the SAPS II and the SAVE score were exclusively found to be suitable for short- and long-term outcome prediction in this specific vulnerable patient population.

The initial validation of a novel outcome measure in severe burns- the Persistent Organ Dysfunction +Death: Results from a multicenter evaluation

INTRODUCTION

A need exists to improve the efficiency of clinical trials in burn care. The objective of this study was to validate "Persistent Organ Dysfunction" plus death as endpoint in burn patients and to demonstrate its statistical efficiency.

METHODS

This secondary outcome analysis of a dataset from a prospective international multicenter RCT (RE-ENERGIZE) included patients with burned total body surface area >20% and a 6-month follow-up. Persistent organ dysfunction was defined as persistence of organ dysfunction with life-supportiing technologies and ICU care.

RESULTS

In the 539 included patients, the prevalence of 0p p+ pdeath was 40% at day 14 and of 27% at day 28. At both timepoints, survivors with POD (vs. survivors without POD) had a higher mortality rate, longer ICU- and hospital-stays, and a reduced quality of life. POD + death as an endpoint could result in reduced sample size requirements for clinical trials. Detecting a 25% relative risk reduction in 28-day mortality would require a sample size of 4492 patients, whereas 1236 patients would be required were 28-day POD + death used.

CONCLUSIONS

POD + death represents a promising composite outcome measure that may reduce the sample size requirements of clinical trials in severe burns patients. Further validation in larger clinical trials is warranted.

STUDY TYPE

Prospective cohort study, level of evidence: II.

Validation of Cardiac Surgery Score (CASUS) in Postoperative Cardiac Patients

Introduction. Cardiac Surgery Score (CASUS) was introduced in 2005 as the first postoperative scoring system specific for patients who had cardiac surgery. Prior to this, European System for Cardiac Operative Risk Evaluation (EuroSCORE) has been used preoperatively, while Intensive Care National Audit and Research Centre Score (ICNARC) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, which are widely used in general intensive care unit population, have been used to score cardiac patients postoperatively. The development of CASUS by Hekmat and colleagues for use in postoperative cardiac patients aims to change this. We wanted to validate CASUS against the well-established preoperative Logistic EuroSCORE, and postoperative APACHE II and ICNARC scores. Method. Institutional approval for this study was granted by the Audit and Clinical Governance Committee. We analyzed prospectively collected data of patients who had cardiac surgery in Castle Hill Hospital between January 2016 and September 2018. All patients who underwent surgery in the unit would have had Logistic EuroSCORE, APACHE, and ICNARC scores calculated as standard. CASUS was then calculated for these patients based on their day 1 postoperative variables. The scoring systems were compared and data presented as area under the receiver operating characteristic curve. Result. Our study shows that CASUS is the best predictor of mortality followed by ICNARC, Logistic EuroSCORE, and APACHE II. ICNARC score remains the most accurate predictor of renal and pulmonary complication followed by CASUS. Conclusion. CASUS is a useful scoring system in post-cardiac surgery patients. The accuracy of CASUS and ICNARC scores in predicting mortality, pulmonary, and renal complications are comparable.

Mortality prediction by SOFA score in ICU-patients after cardiac surgery; comparison with traditional prognostic-models

Background

There are many prognostic models and scoring systems in use to predict mortality in ICU patients. The only general ICU scoring system developed and validated for patients after cardiac surgery is the APACHE-IV model. This is, however, a labor-intensive scoring system requiring a lot of data and could therefore be prone to error. The SOFA score on the other hand is a simpler system, has been widely used in ICUs and could be a good alternative.

The goal of the study was to compare the SOFA score with the APACHE-IV and other ICU prediction models.

Methods

We investigated, in a large cohort of cardiac surgery patients admitted to Dutch ICUs, how well the SOFA score from the first 24 h after admission, predict hospital and ICU mortality in comparison with other recalibrated general ICU scoring systems. Measures of discrimination, accuracy, and calibration (area under the receiver operating characteristic curve (AUC), Brier score, R², and Ĉ-statistic) were calculated using bootstrapping. The cohort consisted of 36,632 Patients from the Dutch National Intensive Care Evaluation (NICE) registry having had a cardiac surgery procedure for which ICU admission was necessary between January 1st, 2006 and June 31st, 2018.

Results

Discrimination of the SOFA-, APACHE-IV-, APACHE-II-, SAPS-II-, MPM₂₄-II - models to predict hospital mortality was good with an AUC of respectively: 0.809, 0.851, 0.830, 0.850, 0.801. Discrimination of the SOFA-, APACHE-IV-, APACHE-II-, SAPS-II-, MPM₂₄-II - models to predict ICU mortality was slightly better with AUCs of respectively: 0.809, 0.906, 0.892, 0.919, 0.862. Calibration of the models was generally poor.

Conclusion

Although the SOFA score had a good discriminatory power for hospital- and ICU mortality the discriminatory power of the APACHE-IV and SAPS-II was better. The SOFA score should not be preferred as mortality prediction model above traditional prognostic ICU-models.

Mortality Prediction in Rural Kenya: A Cohort Study of Mechanical Ventilation in Critically Ill Patients

Critical care is expanding in low- and middle-income countries. Yet, due to factors such as missing data and different disease patterns, predictive scores often fail to adequately predict the high rates of mortality observed.

Prediction of Postoperative Outcomes and Long-Term Survival in Cardiac Surgical Patients Using the Intensive Care National Audit & Research Centre Score

OBJECTIVES

Scoring systems used in cardiac surgery, such as the European System for Cardiac Operative Risk Evaluation and the Society of Thoracic Surgeons scoring systems, do not adjust for events that take place intraoperatively. The authors hypothesized that intensive care unit scoring systems such as the Intensive Care National Audit & Research Centre (ICNARC) could predict accurately not only in-hospital mortality, but also other significant complications, as well as long-term survival after cardiac surgery.

DESIGN

Prospective cohort study using perioperative data from the ICNARC Audit and Dendrite database.

SETTING

Single tertiary referral cardiac surgery center.

PARTICIPANTS

A total of 4,446 consecutive cardiac surgical patients who had surgery between January 2011 and April 2018.

INTERVENTIONS

Comparison of scoring systems to predict postoperative outcomes.

MEASUREMENTS AND RESULTS

Receiver operating curves (ROCs) were used to evaluate how well the ICNARC scores predicted in-hospital mortality and postoperative complications (renal failure, pulmonary complications, gastrointestinal complications, and multiorgan failure). Cox regression analysis was used to determine factors affecting long-term survival. The C-indices for the ROC graphs for the ICNARC score were 0.840 for in-hospital mortality, 0.858 for renal failure, 0.665 for pulmonary complications, 0.764 for gastrointestinal complications, 0.702 for neurological complications in general and 0.654 for confusion, and 0.885 for multiorgan failure. From Cox regression analysis, the significant (p < 0.05) predictors of midterm mortality (5 years) were a higher ICNARC score, a higher age at surgery, chronic obstructive pulmonary disease, preoperative renal failure, preoperative neurological comorbidity, arteriopathy, and non-coronary artery bypass graft surgery.

CONCLUSION

The ICNARC scoring system is simple and can be used as an early warning screening tool to predict which patients are at higher risk for postoperative organ failure.

Variation between hospitals in outcomes following cardiac surgery in the UK

INTRODUCTION

We examine the influence of variations in provision of cardiac surgery in the UK at hospital level on patient outcomes and also to assess whether there is an inequality of access and delivery of healthcare. Cardiothoracic surgery has pioneered the reporting of surgeon-specific outcomes, which other specialties have followed. We set out to identify factors other than the individual surgeon, which can affect outcomes and enable other surgical specialties to adopt a similar model.

MATERIALS AND METHODS

A retrospective analysis of prospectively collected data of patient and hospital level factors between 2013 and 2016 from 16 cardiac surgical units in the UK were analysed through the Society for Cardiothoracic Surgery of Great Britain and Ireland and the Royal College of Surgeons Research Collaborative. Patient demographic data, risks factors, postoperative complications and in-hospital mortality, as well as hospital-level factors such as number of beds and operating theatres, were collected. Correlation between outcome measures was assessed using Pearson's correlation coefficient. Associations between hospital-level factors and outcomes were assessed using univariable and multivariable regression models.

RESULTS

Of 50,871 patients (60.5% of UK caseload), 25% were older than 75 years and 29% were female. There was considerable variation between units in patient comorbidities, bed distribution and staffing. All hospitals had dedicated cardiothoracic intensive care beds and consultants. Median survival was 97.9% (range 96.3-98.6%). Postoperative complications included re-sternotomy for bleeding (median 4.8%; range 3.5-6.9%) and mediastinitis (0.4%; 0.1-1.0%), transient ischaemic attack/cerebrovascular accident (1.7%; range 0.3-3.0%), haemofiltration (3.7%; range 0.8-6.8%), intra-aortic balloon pump use (3.3%; range 0.4-7.4%), tracheostomy (1.6%; range 1.3-2.6%) and laparotomy (0.3%; range 0.2-0.6%). There was variation in outcomes between hospitals. Univariable analysis showed a small number of positive associations between hospital-level factors and outcomes but none remained significant in multivariable models.

CONCLUSIONS

Variations among hospital level factors exists in both delivery of, and outcomes, following cardiac surgery in the UK. However, there was no clear association between these factors and patient outcomes. This negative finding could be explained by differences in outcome definition, differences in risk factors between centres that are not captured by standard risk stratification scores or individual surgeon/team performance.

Vasoactive-inotropic score and the prediction of morbidity and mortality after cardiac surgery

BACKGROUND

The vasoactive-inotropic score (VIS) predicts mortality and morbidity after paediatric cardiac surgery. Here we examined whether VIS also predicted outcome in adults after cardiac surgery, and compared predictive capability between VIS and three widely used scoring systems.

METHODS

This single-centre retrospective cohort study included 3213 cardiac surgery patients. Maximal VIS (VIS_max) was calculated using the highest doses of vasoactive and inotropic medications administered during the first 24 h post-surgery. We established five VIS_max categories: 0-5, >5-15, >15-30, >30-45, and >45 points. The predictive accuracy of VIS_max was evaluated for a composite outcome, which included 30-day mortality, mediastinitis, stroke, acute kidney injury, and myocardial infarction.

RESULTS

VIS_max showed good prediction accuracy for the composite outcome [area under the curve (AUC), 0.72; 95% confidence interval (CI), 0.69-0.75]. The incidence of the composite outcome was 9.6% overall and 43% in the highest VIS_max group (>45). VIS_max predicted 30-day mortality (AUC, 0.76; 95% CI, 0.69-0.83) and 1-yr mortality (AUC, 0.70; 95% CI, 0.65-0.74). Prediction accuracy for unfavourable outcome was significantly better with VIS_max than with Acute Physiology and Chronic Health Evaluation II (P=0.01) and Simplified Acute Physiological Score II (P=0.048), but not with the Sequential Organ Failure Assessment score (P=0.32).

CONCLUSIONS

In adults after cardiac surgery, VIS_max predicted a composite of unfavourable outcomes and predicted mortality up to 1 yr after surgery.

Non-utility of sepsis scores for identifying infection in surgical intensive care unit patients

BACKGROUND

The Sequential Organ Failure Assessment (SOFA) and quick SOFA (qSOFA) scores replaced the Systemic Inflammatory Response System (SIRS) criteria for defining sepsis, and are often utilized to identify infection, however remain understudied in surgical populations.

METHODS

Daily SOFA, qSOFA, and SIRS scores were prospectively collected in a surgical/trauma intensive care unit (ICU), comparing scores between patients with and without new infection. Multivariable analysis controlled for ICU type and pre-existing infection.

RESULTS

Scores were recorded for 1942 patient-days, including 1385 (71%) with no infection, 439 (23%) with existing/treated infection, and 120 (6.2%) with new infection. Scores were globally elevated, with 98% having SOFA score ≥2, 82% with qSOFA score ≥2, and 92% meeting ≥2 SIRS criteria. Neither univariate nor multivariate analysis revealed a correlation between SOFA, qSOFA, or SIRS score and infection.

CONCLUSION

No scores correlated with new infection, potentially related to increased existing inflammation in this population.

SUMMARY

The Sequential Organ Failure Assessment (SOFA) and quick SOFA (qSOFA) have replaced the Systemic Inflammatory Response System (SIRS) criteria for sepsis, however are not well investigated in surgical populations or for identifying infections, as they are often used in practice. In this study, neither daily SOFA, qSOFA, nor SIRS criteria correlated with new infection in a population of critically ill surgical patients. Scores were globally elevated in non-infected patients, potentially related to high levels of existing inflammation in this population.

Vasopressor magnitude predicts poor outcome in adults with congenital heart disease after cardiac surgery

BACKGROUND

High levels of vasoactive inotrope support (VIS) after congenital heart surgery are predictive of morbidity in pediatric patients. We sought to discern if this relationship applies to adults with congenital heart disease (ACHD).

METHODS

We retrospectively studied adult patients (≥18 years old) admitted to the intensive care unit after cardiac surgery for congenital heart disease from 2002 to 2013 at Mayo Clinic. Vasoactive medication dose values within 96 hours of admission were examined to determine the relationship between VIS score and poor outcome of early mortality, early morbidity, or complication related morbidity.

RESULTS

Overall, 1040 ACHD patients had cardiac surgery during the study time frame; 243 (23.4%) met study inclusion criteria. Sixty-two patients (25%), experienced composite poor outcome [including eight deaths within 90 days of hospital discharge (3%)]. Thirty-eight patients (15%) endured complication related early morbidity. The maximum VIS (maxVIS) score area under the curve was 0.92 (95% CI: 0.86-0.98) for in-hospital mortality; and 0.82 (95% CI: 0.76-0.89) for combined poor clinical outcome. On univariate analysis, maxVIS score ≥3 was predictive of composite adverse outcome (OR: 14.2, 95% CI: 7.2-28.2; P < 0.001), prolonged ICU LOS ICU LOS (OR: 19.2; 95% CI: 8.7-42.1; P < 0.0001), prolonged mechanical ventilation (OR: 13.6; 95% CI: 4.4-41.8; P < 0.0001) and complication related morbidity (OR: 7.3; 95% CI: 3.4-15.5; P < 0.0001).

CONCLUSIONS

MaxVIS score strongly predicted adverse outcomes and can be used as a risk prediction tool to facilitate early intervention that may improve outcome and assist with clinical decision making for ACHD patients after cardiac surgery.

The APACHE II Score as a Predictor of Mortality After Open Heart Surgery

Objective

The Acute Physiology and Chronic Health Evaluation (APACHE) severity of disease classification system, which is one of the most widely used scoring systems to predict mortality, is used for intensive care units (ICU) patients. This study aimed to evaluate the predictive ability of APACHE II for mortality in patients after undergoing cardiac surgery. We studied if APACHE II could successfully predict the outcome in post-cardiac surgery patients.

Methods

This study involved retrospective data collection of all adult patients who were admitted to Ordu State Hospital cardiovascular surgery ICU following cardiac surgery from August 2013 to December 2015. Area under the receiver operating characteristic (ROC) curve (AUC) values were calculated for the APACHE II model.

Results

During the two years of data collection, we included 600 patients with a mean age of 64.77±10.148 years. Of these, 180 (30.0%) were females. The ICU mortality rate was 8.33%, and the mean length of ICU stay was 4.210±6.913 days. The mean pre-operative EuroSCORE was 3.890±2.565, and the mean pre-operative APACHE II score was 6.790±3.617. The AUC values for APACHE II and EuroSCORE were 0.743 and 0.767, respectively.

Conclusion

The APACHE II model can be used to predict mortality in a Turkish population of patients who have undergone cardiac surgery.

Changes in biopsychosocial outcomes for a mixed cohort of ICU survivors

Background

Prolonged inflammation and infection associated with being critically ill and the ensuing physical inactivity has proven negative effects on the recovery of physical function, psychological health and reintegration into society for intensive care unit (ICU) survivors. Limited evidence is available on changes in biopsychosocial outcomes for South Africans recovering from an episode of critical illness.

Objectives

To determine changes in biopsychosocial outcomes for a mixed cohort of ICU survivors in hospital and at 1 month and 6 months after discharge.

Method

A prospective, observational, longitudinal study was conducted. Severity of illness, mechanical ventilation (MV) duration and ICU and hospital length of stay (LOS) were recorded. Physical function in ICU test-scored (PFIT-s) was performed at discharge from ICU and hospital. At 1 month and 6 months, peripheral muscle strength, exercise endurance, health-related quality of life (HRQOL), depression status and return to work were assessed. Descriptive and inferential statistics were used.

Results

Participants (n = 24) had a median age of 51.5 years, majority were male (n = 19; 79%) and most were employed before admission (n = 20; 83%). At 6 months, 11 participants (n = 11) were part of the final sample. Median PFIT-s changed significantly (0.3 points; p = 0.02) between ICU and hospital discharge. Peripheral muscle strength improved significantly for upper and lower limbs over 6 months (p = 0.00–0.03) but change in median 6-minute walk test distance (65m) was not significantly different. Significant improvements occurred in mean Medical Outcomes Short Form-36 (SF-36) physical health component scores (8.8 ± 7.6; p = 0.00). Mean SF-36 mental health component scores had a strong negative relationship with MV duration (r = −0.7; p = 0.01), LOS (r = −0.56; p = 0.04) and Patient Health Questionnaire 9 scores (r = −0.72; p = 0.01). Six participants (55%) returned to employment.

Conclusion

Clinically important improvements in biopsychosocial outcomes related to physical function and social factors were observed. Limitations in mental aspects of HRQOL were present at 6 months and some reported mild depressive symptoms.

Clinical implications

Intensive care unit survivors with a history of prolonged MV duration and hospital LOS who exhibit limitations in mental HRQOL, and signs of depressive symptoms should be referred to a psychologist for evaluation.

Predictive Factors of Long-Term Stay in the ICU after Cardiac Surgery: Logistic CASUS Score, Serum Bilirubin Dosage and Extracorporeal Circulation Time

Objective

To test the capacity of the Logistic CASUS Score on the second postoperative day, the total serum bilirubin dosage on the second postoperative day and the extracorporeal circulation time, as possible predictive factors of long-term stay in Intensive Care Unit after cardiac surgery.

Methods

Eight-two patients submitted to cardiac surgery with extracorporeal circulation were selected. The Logistic CASUS Score on the second postoperative day was calculated and bilirubin dosage on the second postoperative day was measured. The extracorporeal circulation time was also registered. Patients were divided into two groups: Group A, those who were discharged up to the second day of postoperative care; Group B, those who were discharged after the second day of postoperative care.

Results

In this study, 40 cases were listed in Group A and 42 cases in Group B. The mean extracorporeal circulation time was 83.9±29.4 min in Group A and 95.8±29.31 min in Group B. Extracorporeal circulation time was not significant in this study (P=0.0735). The level of P significance of bilirubin dosage on the second postoperative day was 0.0003 and an area under the ROC curve of 0.708 with a cut-off point at 0.51 mg/dl was registered. The level of P significance of Logistic CASUS Score on the second postoperative day was 0.0001 and an area under the ROC curve of 0.723 with a cut-off point at 0.40% was registered.

Conclusion

The Logistic CASUS Score on the second postoperative day has shown to be better than the bilirubin dosage on the second postoperative day as a predictive tool for calculating the length of stay in intensive care unit during the postoperative care period of patients. Notwithstanding, extracorporeal circulation time has failed to prove itself as an efficient tool to predict an extended length of stay in intensive care unit.

A Comparison of Acute Physiology and Chronic Health Evaluation III and Simplified Acute Physiology Score II in Predicting Sepsis Outcome in Intensive Care Unit

Context

Acute Physiology and Chronic Health Evaluation (APACHE) III and Simplified Acute Physiology Score (SAPS) II are frequently used to predict the outcome of Intensive Care Unit (ICU) patients of sepsis.

Aim

The aim of the study was to compare the predictability of outcome with APACHE III and SAPS II score in ICU patients of sepsis, severe sepsis, and septic shock and the 28-day mortality.

Settings and Design

This study was an observational, prospective cohort study.

Materials and Methods

A total of 100 consecutive patients of sepsis were studied over 20 months. The worst physiological and biochemical parameters during the first 24 h were recorded for the scores and the patient's 28-day outcome followed up.

Statistical Analysis Used

Continuous data were expressed as mean ± standard deviation or median. Receivers operating characteristic (ROC) curve was used to find the cutoff value, area under the curve, sensitivity and specificity of APACHE III score, and SAPS II score. Binary logistic regression with response variable as the outcome was utilized. P < 0.05 was considered statistically significant.

Results

The mean APACHE III score in the survivor group was 66.49 ± 18.56 as opposed to 80.67 ± 19.03 for nonsurvivors. The mean SAPS II score for the survivor group was 43.32 ± 13.02 as against the nonsurvivor group at 51.92 ± 12.34. The area under the ROC curve for APACHE III was 0.711 with 95% confidence interval as against 0.686 for SAPS II. The best cutoff value obtained for mortality prediction using the ROC curve was 69 for APACHE III while that for SAPS II was 49.

Conclusions

APACHE III was found to be a better predictor of mortality as compared to SAPS II though the margin of difference in mortality prediction was not high.

Sepsis: A Review of Advances in Management

Infections represent a common health problem in people of all ages. Usually, the response given to them is appropriate and so little treatment is needed. Sometimes, however, the response to the infection is inadequate and may lead to organ dysfunction; this is the condition known as sepsis. Sepsis can be caused by bacteria, fungi or viruses and at present there is no specific treatment; its management basically focuses on containing the infection through source control and antibiotics plus organ function support. This article reviews key elements of sepsis management, focusing on diagnosis, biomarkers and therapy. The main recent advance in therapy is the strategy of personalized medicine, based on a precise approach using biomarkers to identify specific individuals who are likely to benefit from more personalized attention.

Cardiothoracic Critical Care

High-value CCC is rapidly evolving to meet the demands of increased patient acuity and to incorporate advances in technology. The high-performing CCC system and culture should aim to learn quickly and continuously improve. CCC demands a proactive, interactive, precise, an expert team, and continuity.

Is 'gut feeling' by medical staff better than validated scores in estimation of mortality in a medical intensive care unit? - The prospective FEELING-ON-ICU study

PURPOSE

The aim of the FEELING-ON-ICU study was to compare mortality estimations of critically ill patients based on 'gut feeling' of medical staff and by Acute Physiology And Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II and Sequential Organ Failure Assessment (SOFA).

MATERIALS AND METHODS

Medical staff estimated patients' mortality risks via questionnaires. APACHE II, SAPS II and SOFA were calculated retrospectively from records. Estimations were compared with actual in-hospital mortality using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC).

RESULTS

66 critically ill patients (60.6% male, mean age 63±15years (range 30-86)) were evaluated each by a nurse (n=66, male 32.4%) and a physician (n=66, male 67.6%). 15 (22.7%) patients died on the intensive care unit. AUC was largest for estimations by physicians (AUC 0.814 (95% CI 0.705-0.923)), followed by SOFA (AUC 0.749 (95% CI 0.629-0.868)), SAPS II (AUC 0.723 (95% CI 0.597-0.849)), APACHE II (AUC 0.721 (95% CI 0.595-0.847)) and nursing staff (AUC 0.669 (95% CI 0.529-0.810)) (p<0.05 for all results).

CONCLUSIONS

The concept of physicians' 'gut feeling' was comparable to classical objective scores in mortality estimations of critically ill patients. Concerning practicability physicians' evaluations were advantageous to complex score calculation.

Effect of high-dose sodium selenite in cardiac surgery patients: A randomized controlled bi-center trial

BACKGROUND & AIMS

Cardiac surgery is accompanied by oxidative stress and systemic inflammatory response, which may be associated with organ dysfunction and increased mortality. Selenium and selenoenzymes are important constituents of anti-oxidative defense. We hypothesized that high-dose sodium selenite supplementation can attenuate the postoperative inflammation and might, therefore, improve clinical outcome.

METHODS

Randomized, placebo-controlled, double-blinded, bi-center study on 411 adult patients undergoing elective cardiac surgery. Patients received an intravenous bolus of 4000 μg selenium (in the form of sodium selenite) or placebo after induction of anesthesia and 1000 μg/d selenium or placebo during their intensive care unit (ICU) stay. Primary outcome measure was the Sequential Organ Failure Assessment (SOFA) score on the second postoperative day. Secondary endpoints included the change in perioperative selenium levels, change of inflammatory and cardiac markers, use of vasoactive medication, incidence of acute kidney injury, ICU and hospital length of stay, and mortality.

RESULTS

The perioperative administration of high-dose sodium selenite prevented the postoperative drop of blood and serum selenium levels, reduced the number of patients depending on postoperative vasoactive support but failed to reduce the postoperative SOFA score and its related organ-specific scores compared to placebo. Except for an increase of postoperative procalcitonin and bilirubin levels in the sodium selenite group, other inflammatory markers, organ function variables and clinical endpoints remained unchanged.

CONCLUSIONS

The perioperative administration of high-dose sodium selenite in cardiac surgery patients prevented the postoperative fall of blood selenium levels and reduced the need for postoperative vasoactive support by a yet unknown mechanism.

TRIAL REGISTRATION

Registered under ClinicalTrials.gov Identifier no. NCT01141556.

The Assessment of Risk in Cardiothoracic Intensive Care (ARCtIC): prediction of hospital mortality after admission to cardiothoracic critical care

The models used to predict outcome after adult general critical care may not be applicable to cardiothoracic critical care. Therefore, we analysed data from the Case Mix Programme to identify variables associated with hospital mortality after admission to cardiothoracic critical care units and to develop a risk-prediction model. We derived predictive models for hospital mortality from variables measured in 17,002 patients within 24 h of admission to five cardiothoracic critical care units. The final model included 10 variables: creatinine; white blood count; mean arterial blood pressure; functional dependency; platelet count; arterial pH; age; Glasgow Coma Score; arterial lactate; and route of admission. We included additional interaction terms between creatinine, lactate, platelet count and cardiac surgery as the admitting diagnosis. We validated this model against 10,238 other admissions, for which the c index (95% CI) was 0.904 (0.89-0.92) and the Brier score was 0.055, while the slope and intercept of the calibration plot were 0.961 and -0.183, respectively. The discrimination and calibration of our model suggest that it might be used to predict hospital mortality after admission to cardiothoracic critical care units.

Urinary Output Predicts Survival in Patients Undergoing Extracorporeal Membrane Oxygenation Following Cardiovascular Surgery

OBJECTIVES

Extracorporeal membrane oxygenation represents a valuable and rapidly evolving therapeutic option in patients with severe heart or lung failure following cardiovascular surgery. However, survival remains poor and accurate risk stratification challenging. Therefore, we evaluated the predictive value of urinary output within 24 hours after extracorporeal membrane oxygenation initiation on mortality in patients undergoing venoarterial extracorporeal membrane oxygenation support following cardiovascular surgery and aimed to improve established risk prediction models.

DESIGN

Single-center, observational registry.

SETTING

University-affiliated tertiary care center.

PATIENTS

We included 205 patients undergoing veno-arterial extracorporeal membrane oxygenation therapy following cardiovascular surgery at a university-affiliated tertiary-care center into our single-centre registry.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

During a median follow-up time of 35 months (interquartile range, 19-69), 64% of patients died. Twenty-four-hour urinary output was the strongest predictor of outcome among renal function variables with an adjusted hazard ratio per 1 SD of 0.55 (95% CI, 0.40-0.76; p < 0.001) for 30-day mortality and of 0.65 (95% CI, 0.53-0.86; p = 0.002) for 2-year long-term mortality. Most remarkably, 24-hour urinary output showed additional prognostic value beyond that achievable with the simplified acute physiology score-3 and sequential organ failure assessment score indicated by improvements in the category-free net reclassification index for 30-day mortality (simplified acute physiology score-3: 36%, p = 0.015; sequential organ failure assessment score: 36%, p = 0.02), as well as for 2-year mortality (simplified acute physiology score-3: 33%, p = 0.02; sequential organ failure assessment score: 43%, p = 0.005).

CONCLUSIONS

We identified 24-hour urinary output as a strong and easily available predictor of mortality in patients undergoing extracorporeal membrane oxygenation therapy following cardiovascular surgery. Implementation of 24-hour urinary output leads to a substantial improvement of established risk prediction models in this vulnerable patient population. These results are particularly compelling because measurement of urinary output is inexpensive and routinely performed in all critical care units.

Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients

Background

National clinical audit has a key role in ensuring quality in health care. When comparing outcomes between providers, it is essential to take the differing case mix of patients into account to make fair comparisons. Accurate risk prediction models are therefore required.

Objectives

To improve risk prediction models to underpin quality improvement programmes for the critically ill (i.e. patients receiving general or specialist adult critical care or experiencing an in-hospital cardiac arrest).

Design

Risk modelling study nested within prospective data collection.

Setting

Adult (general/specialist) critical care units and acute hospitals in the UK.

Participants

Patients admitted to an adult critical care unit and patients experiencing an in-hospital cardiac arrest attended by the hospital-based resuscitation team.

Interventions

None.

Main outcome measures

Acute hospital mortality (adult critical care); return of spontaneous circulation (ROSC) greater than 20 minutes and survival to hospital discharge (in-hospital cardiac arrest).

Data sources

The Case Mix Programme (adult critical care) and National Cardiac Arrest Audit (in-hospital cardiac arrest).

Results

The current Intensive Care National Audit & Research Centre (ICNARC) model was externally validated using data for 29,626 admissions to critical care units in Scotland (2007–9) and outperformed the Acute Physiology And Chronic Health Evaluation (APACHE) II model in terms of discrimination (c-index 0.848 vs. 0.806) and accuracy (Brier score 0.140 vs. 0.157). A risk prediction model for cardiothoracic critical care was developed using data from 17,002 admissions to five units (2010–12) and validated using data from 10,238 admissions to six units (2013–14). The model included prior location/urgency, blood lactate concentration, Glasgow Coma Scale (GCS) score, age, pH, platelet count, dependency, mean arterial pressure, white blood cell (WBC) count, creatinine level, admission following cardiac surgery and interaction terms, and it had excellent discrimination (c-index 0.904) and accuracy (Brier score 0.055). A risk prediction model for admissions to all (general/specialist) adult critical care units was developed using data from 155,239 admissions to 232 units (2012) and validated using data from 90,017 admissions to 216 units (2013). The model included systolic blood pressure, temperature, heart rate, respiratory rate, partial pressure of oxygen in arterial blood/fraction of inspired oxygen, pH, partial pressure of carbon dioxide in arterial blood, blood lactate concentration, urine output, creatinine level, urea level, sodium level, WBC count, platelet count, GCS score, age, dependency, past medical history, cardiopulmonary resuscitation, prior location/urgency, reason for admission and interaction terms, and it outperformed the current ICNARC model for discrimination and accuracy overall (c-index 0.885 vs. 0.869; Brier score 0.108 vs. 0.115) and across unit types. Risk prediction models for in-hospital cardiac arrest were developed using data from 14,688 arrests in 122 hospitals (2011–12) and validated using data from 7791 arrests in 143 hospitals (2012–13). The models included age, sex (for ROSC > 20 minutes), prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between rhythm and location. Discrimination for hospital survival exceeded that for ROSC > 20 minutes (c-index 0.811 vs. 0.720).

Limitations

The risk prediction models developed were limited by the data available within the current national clinical audit data sets.

Conclusions

We have developed and validated risk prediction models for cardiothoracic and adult (general and specialist) critical care units and for in-hospital cardiac arrest.

Future work

Future development should include linkage with other routinely collected data to enhance available predictors and outcomes.

Funding details

The National Institute for Health Research Health Services and Delivery Research programme.

Combination of European System for Cardiac Operative Risk Evaluation (EuroSCORE) and Cardiac Surgery Score (CASUS) to Improve Outcome Prediction in Cardiac Surgery

Background

We hypothesized that the combination of a preoperative and a postoperative scoring system would improve the accuracy of mortality prediction and therefore combined the preoperative ‘additive EuroSCORE‘ (European system for cardiac operative risk evaluation) with the postoperative ‘additive CASUS’ (Cardiac Surgery Score) to form the ‘modified CASUS’.

Material/Methods

We included all consecutive adult patients after cardiac surgery during January 2007 and December 2010 in our prospective study. Our single-centre study was conducted in a German general referral university hospital. The original additive and the ‘modified CASUS’ were tested using calibration and discrimination statistics. We compared the area under the curve (AUC) of the receiver characteristic curves (ROC) by DeLong’s method and calculated overall correct classification (OCC) values.

Results

The mean age among the total of 5207 patients was 67.2±10.9 years. Whilst the ICU mortality was 5.9% we observed a mean length of ICU stay of 4.6±7.0 days. Both models demonstrated excellent discriminatory power (mean AUC of ‘modified CASUS’: ≥0.929; ‘additive CASUS’: ≥0.920), with no significant differences according to DeLong. Neither model showed a significant p-value (<0.05) in calibration. We detected the best OCC during the 2^nd day (modified: 96.5%; original: 96.6%).

Conclusions

Our ‘additive’ and ‘modified’ CASUS are reasonable overall predictors. We could not detect any improvement in the accuracy of mortality prediction in cardiac surgery by combining a preoperative and a postoperative scoring system. A separate calculation of the two individual elements is therefore recommended.

Evaluation of Persistent Organ Dysfunction Plus Death As a Novel Composite Outcome in Cardiac Surgical Patients

OBJECTIVES

Validated composite outcomes after complicated cardiac surgery are poorly established. Therefore, the authors evaluated a novel composite endpoint, persistent organ dysfunction (POD)+death, which is defined as any need for life-sustaining therapies or death at any time within 28 days from surgery.

DESIGN

Secondary analysis extracted from a large-scale prospective randomized trial of critically ill cardiac surgery patients.

SETTING

Multi-institutional, university hospitals.

PARTICIPANTS

Ninety-five cardiac surgery patients with complicated postoperative courses.

INTERVENTIONS

Cardiac surgery with cardiopulmonary bypass.

MEASUREMENTS AND MAIN RESULTS

At 28 days following surgery, the prevalence of POD was 15%, and 23% of patients had died (POD+death = 38%). Patients alive with POD at day 28 exhibited a significantly higher extent of organ injury and longer ICU (33 v 7 days; p<0.001) and hospital lengths of stay (49 v 21 days; p<0.001) compared to patients without POD at day 28. At 3 and 6 months, quality-of-life scores (by Short Form 36 questionnaire) showed a significantly reduced rating for most components in patients with POD at day 28 compared to those without POD. The 6-month mortality rate was 21% among patients alive with POD at day 28 compared to 5% among patients alive without POD (p = 0.05). The calculated number of patients needed per arm to detect a 25% relative risk reduction for mortality alone was 762 compared to 386 per arm for POD+ death.

CONCLUSIONS

POD+death at day 28 following cardiac surgery may be a valid composite endpoint and offers statistical efficiencies in terms of sample size calculations for cardiac surgical trials.

Inclusion of 'ICU-Day' in a Logistic Scoring System Improves Mortality Prediction in Cardiac Surgery

Background

Prolonged intensive care unit (ICU) stay is a predictor of mortality. The length of ICU stay has never been considered as a variable in an additive scoring system. How could this variable be integrated into a scoring system? Does this integration improve mortality prediction?

Material/Methods

The ‘modified CArdiac SUrgery Score’ (CASUS) was generated by implementing the length of stay as a new variable to the ‘additive CASUS’. The ‘logistic CASUS’ already considers this variable. We defined outcome as ICU mortality and statistically compared the three CASUS models. Discrimination, comparison of receiver operating characteristic curves (DeLong’s method), and calibration (observed/expected ratio) were analyzed on days 1–13.

Results

Between 2007 and 2010, we included 5207 cardiac surgery patients in this prospective study. The mean age was 67.2±10.9 years. The mean length of ICU stay was 4.6±7.0 days and ICU mortality was 5.9%. All scores had good discrimination, with a mean area under the curve of 0.883 for the additive and modified, and 0.895 for the ‘logistic CASUS’. DeLong analysis showed superiority in favor of the logistic model as from day 5. The calibration of the logistic model was good. We identified overestimation (days 1–5) and accurate (days 6–9) calibration for the additive and ‘modified CASUS’. The ‘modified CASUS’ remained accurate but the ‘additive CASUS’ tended to underestimate the risk of mortality (days 10–13).

Conclusions

The integration of length of ICU stay as a variable improves mortality prediction significantly. An ‘ICU-day’ variable should be included into a logistic but not an additive model.

Scoring Systems for Outcome Prediction in a Cardiac Surgical Intensive Care Unit: A Comparative Study

BACKGROUND

Most scoring systems used to predict clinical outcome in critical care were not designed for application in cardiac surgery patients.

OBJECTIVES

To compare the predictive ability of the most widely used scoring systems (Acute Physiology and Chronic Health Evaluation [APACHE] II, Simplified Acute Physiology Score [SAPS] II, and Sequential Organ Failure Assessment [SOFA]) and of 2 specialized systems (European System for Cardiac Operative Risk Evaluation [EuroSCORE] II and the cardiac surgery score [CASUS]) for clinical outcome in patients after cardiac surgery.

METHODS

Consecutive patients admitted to a cardiac surgical intensive care unit (CSICU) were prospectively studied. Data on the preoperative condition, intraoperative parameters, and postoperative course were collected. EuroSCORE II, CASUS, and scores from 3 general severity-scoring systems (APACHE II, SAPS II, and SOFA) were calculated on the first postoperative day. Clinical outcome was defined as 30-day mortality and in-hospital morbidity.

RESULTS

A total of 150 patients were included. Thirty-day mortality was 6%. CASUS was superior in outcome prediction, both in relation to discrimination (area under curve, 0.89) and calibration (Brier score = 0.043, χ(2) = 2.2, P = .89), followed by EuroSCORE II for 30-day mortality (area under curve, 0.87) and SOFA for morbidity (Spearman ρ= 0.37 and 0.35 for the CSICU length of stay and duration of mechanical ventilation, respectively; Wilcoxon W = 367.5, P = .03 for probability of readmission to CSICU).

CONCLUSIONS

CASUS can be recommended as the most reliable and beneficial option for benchmarking and risk stratification in cardiac surgery patients.

Evaluation of Serial Arterial Lactate Levels as a Predictor of Hospital and Long-Term Mortality in Patients After Cardiac Surgery

OBJECTIVES

Although hyperlactatemia is common after cardiac surgery, its value as a prognostic marker is unclear. The aim of the present study was to determine whether postoperative serial arterial lactate (AL) measurements after cardiac surgery could predict outcome.

DESIGN

Prospective, observational study.

SETTING

Surgical intensive care unit in a tertiary-level university hospital.

PARTICIPANTS

Participants included 2,935 consecutive patients.

INTERVENTIONS

AL was measured on admission to the intensive care unit and 6, 12, and 24 hours after surgery, and evaluated together with clinical data and outcomes including in-hospital and long-term mortality.

MEASUREMENTS AND MAIN RESULTS

In-hospital and long-term mortality (mean follow-up 6.3±1.7 years) were 5.9% and 8.7%, respectively. Compared with survivors, nonsurvivors showed higher mean AL values in all measurements (p<0.001). Hyperlactatemia (AL>3.0 mmol/L) was a predictor for in-hospital mortality (odds ratio = 1.468; 95% confidence interval = 1.239-1.739; p<0.001) and long-term mortality (hazard ratio = 1.511; 95% confidence interval = 1.251-1.825; p<0.001). Recent myocardial infarction and longer cardiopulmonary bypass time were predictors of hyperlactatemia. The pattern of AL dynamics was similar in both groups, but nonsurvivors showed higher AL values, as confirmed by repeated measures analysis of variance (p<0.001). The area under the curve also showed higher levels of AL in nonsurvivors (80.9±68.2 v 49.71±25.8 mmol/L/h; p = 0.038). Patients with hyperlactatemia were divided according to their timing of peak AL, with higher mortality and worse survival in patients in whom AL peaked at 24 hours compared with other groups (79.1% v 86.7%-89.2%; p = 0.03).

CONCLUSIONS

The dynamics of the postoperative AL curve in patients undergoing cardiac surgery suggests a similar mechanism of hyperlactatemia in survivors and nonsurvivors, albeit with a higher production or lower clearance of AL in nonsurvivors. The presence of a peak of hyperlactatemia at 24 hours is associated with higher in-hospital and long-term mortality.

Which risk-adjustment index performs better in predicting 30-day mortality? A systematic review and meta-analysis

RATIONALE, AIMS AND OBJECTIVES

Individual comparisons of the performance of risk-adjustment indices have been widely conducted. Few reviews have been conducted to summarize the performance of different risk-adjustment indices. A 30-day mortality rate is widely used to evaluate the quality of care in hospitals by federal agencies like the Centers for Medicare and Medicaid Services. This study examined relative performance of risk-adjustment indices that predict 30-day mortality.

METHODS

Databases including Medline, PubMed and PsycINFO were searched for studies that compared risk-adjustment indices. The search protocol included comparative studies in which the performance of risk-adjustment indices were compared across any defined cohort to compare 30-day mortality, including mortality within 30 days and intensive care unit mortality, which lasts less than 30 days. Data were extracted using a structured form and abstract data included author and publication year, population studied (including location, sample size, study time period), comparison indices, outcome studied, results and conclusions from the results. A meta-analytical approach was used to summarize all the studies. Scaled ranking score was used to estimate the relative superiority of any given risk-adjustment indices. A hypergeometric test was carried out to evaluate the performance of risk-adjustment measures.

RESULTS

Out of 2805 studies identified, 23 studies met the eligibility criteria. Main risk-adjustment indices used for comparison included Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment score, Charlson co-morbidity index, Model for End-Stage Liver Disease score and Simplified Acute Physiology Score (SAPS). Based on scaled ranking score, SAPS performed best (score 0.510) among all the risk-adjustment indices. However, based on hypergeometric test, the five measures performed equally well.

CONCLUSIONS

Although all the selected risk-adjustment indices perform equally well, SAPS seems better than other indices for short-term mortality based on scaled ranking score.

SAPS 3 is not superior to SAPS 2 in cardiac surgery patients

OBJECTIVES

Cardiac surgery patients are excluded from SAPS2 but included in SAPS3. Neither score is evaluated for this exclusive population; however, they are used daily. We hypothesized that SAPS3 may be superior to SAPS2 in outcome prediction in cardiac surgery patients.

DESIGN

All consecutive patients undergoing cardiac surgery between January 2007 and December 2010 were included in our prospective study. Both models were tested with calibration and discrimination statistics. We compared the AUC of the ROC curves by DeLong's method and calculated OCC values.

RESULTS

A total of 5207 patients with mean age of 67.2 ± 10.9 years were admitted to the ICU. The mean length of ICU stay was 4.6 ± 7.0 days and the ICU mortality was 5.9%. The two tested models had acceptable discriminatory power (AUC: SAPS2: 0.777-0.875; SAPS3: 0.757-893). SAPS3 had a low AUC and poor calibration on admission day. SAPS2 had poor calibration on Days 1-6 and 8.

CONCLUSIONS

Despite including cardiac surgery patients, SAPS3 was not superior to SAPS2 in our analysis. In this large cohort of ICU cardiac surgery patients, performance of both SAPS models was generally poor. In this subset of patients, neither scoring system is recommended.

The APACHE II measured on patients' discharge from the Intensive Care Unit in the prediction of mortality

OBJECTIVE

to analyze the performance of the Acute Physiology and Chronic Health Evaluation (APACHE II), measured based on the data from the last 24 hours of hospitalization in ICU, for patients transferred to the wards.

METHOD

an observational, prospective and quantitative study using the data from 355 patients admitted to the ICU between January and July 2010, who were transferred to the wards.

RESULTS

the discriminatory power of the AII-OUT prognostic index showed a statistically significant area beneath the ROC curve. The mortality observed in the sample was slightly greater than that predicted by the AII-OUT, with a Standardized Mortality Ratio of 1.12. In the calibration curve the linear regression analysis showed the R2 value to be statistically significant.

CONCLUSION

the AII-OUT could predict mortality after discharge from ICU, with the observed mortality being slightly greater than that predicted, which shows good discrimination and good calibration. This system was shown to be useful for stratifying the patients at greater risk of death after discharge from ICU. This fact deserves special attention from health professionals, particularly nurses, in managing human and technological resources for this group of patients.

Development of the Post Cardiac Surgery (POCAS) prognostic score

Introduction

The risk of mortality in cardiac surgery is generally evaluated using preoperative risk-scale models. However, intraoperative factors may change the risk factors of patients, and the organism functionality parameters determined upon ICU admittance could therefore be more relevant in deciding operative mortality. The goals of this study were to find associations between the general parameters of organism functionality upon ICU admission and the operative mortality following cardiac operations, to develop a Post Cardiac Surgery (POCAS) Scale to define operative risk categories and to validate an operative mortality risk score.

Methods

We conducted a prospective study, including 920 patients who had undergone cardiac surgery with cardiopulmonary bypass. Several parameters recorded on their ICU admission were explored, looking for a univariate and multivariate association with in-hospital mortality (90 days). In-hospital mortality was 9%. Four independent factors were included in the POCAS mortality risk model: mean arterial pressure, bicarbonate, lactate and the International Normalized Ratio (INR). The POCAS scale was compared with four other risk scores in the validation series.

Results

In-hospital mortality (90 days) was 9%. Four independent factors were included in the POCAS mortality risk model: mean arterial pressure, bicarbonate ratio, lactate ratio and the INR. The POCAS scale was compared with four other risk scores in the validation series. Discriminatory power (accuracy) was defined with a receiver-operating characteristics (ROC) analysis. The best accuracy in predicting in-hospital mortality (90 days) was achieved by POCAS. The areas under the ROC curves of the different systems analyzed were 0.890 (POCAS), followed by 0.847 (Simplified Acute Physiology Score (SAP II)), 0.825 (Sepsis-related Organ Failure Assessment (SOFA)), 0.768 (Acute Physiology and Chronic Health Evaluation (APACHE II)), 0.754 (logistic EuroSCORE), 0.714 (standard EuroSCORE) and 0.699 (Age, Creatinine, Ejection Fraction (ACEF) score).

Conclusions

Our new system to predict the operative mortality risk of patients undergoing cardiac surgery is better than others used for this purpose (SAP II, SOFA, APACHE II, logistic EuroSCORE, standard EuroSCORE, and ACEF score). Moreover, it is an easy-to-use tool since it only requires four risk factors for its calculation.

Rapid clinical evaluation: an early warning cardiac surgical scoring system for hand-held digital devices

OBJECTIVES

The purpose of this study was to develop a new scoring system for the prompt recognition of clinical deterioration and early treatment in postoperative cardiac surgical patients.

METHODS

All consecutive adult patients undergoing cardiac surgery between 1st January 2007 and 31st December 2010 were included. The new score was calculated daily until intensive care unit (ICU) discharge. The score consists of 11 variables representing six different organ systems. Performance was assessed using receiver-operating characteristic (ROC) curves and calibration tests.

RESULTS

A total of 5207 patients with a mean age of 67.2 ± 10.9 years were admitted to the ICU after cardiac surgery. The operations performed covered the whole spectrum of cardiac surgery. ICU mortality was 5.9%. The mean length of ICU stay was 4.6 ± 7.0 days. The new score had an excellent discrimination with areas under the ROC curves between 0.91 and 0.96. Calibration was also excellent reflected by observed/expected mortality ratios ranging between 1.0 and 1.26.

CONCLUSIONS

The new score is a simple and reliable scoring system to assess organ dysfunction in cardiac intensive care patients. It is designed especially for personal digital assistants to simplify and accelerate the process of risk stratification in cardiac surgical ICUs.