A score to estimate 30-day mortality after intensive care admission after cardiac surgery

A systematic review of cardiac surgery clinical prediction models that include intra-operative variables

BACKGROUND

Most cardiac surgery clinical prediction models (CPMs) are developed using pre-operative variables to predict post-operative outcomes. Some CPMs are developed with intra-operative variables, but none are widely used. The objective of this systematic review was to identify CPMs with intra-operative variables that predict short-term outcomes following adult cardiac surgery.

METHODS

Ovid MEDLINE and EMBASE databases were searched from inception to December 2022, for studies developing a CPM with at least one intra-operative variable. Data were extracted using a critical appraisal framework and bias assessment tool. Model performance was analysed using discrimination and calibration measures.

RESULTS

A total of 24 models were identified. Frequent predicted outcomes were acute kidney injury (9/24 studies) and peri-operative mortality (6/24 studies). Frequent pre-operative variables were age (18/24 studies) and creatinine/eGFR (18/24 studies). Common intra-operative variables were cardiopulmonary bypass time (16/24 studies) and transfusion (13/24 studies). Model discrimination was acceptable for all internally validated models (AUC 0.69-0.91). Calibration was poor (15/24 studies) or unreported (8/24 studies). Most CPMs were at a high or indeterminate risk of bias (23/24 models). The added value of intra-operative variables was assessed in six studies with statistically significantly improved discrimination demonstrated in two.

CONCLUSION

Weak reporting and methodological limitations may restrict wider applicability and adoption of existing CPMs that include intra-operative variables. There is some evidence that CPM discrimination is improved with the addition of intra-operative variables. Further work is required to understand the role of intra-operative CPMs in the management of cardiac surgery patients.

Incorporating preoperative and intraoperative data to predict postoperative pneumonia in elderly patients undergoing non-cardiothoracic surgery: The online two-stage prediction tool

BACKGROUND

Prior research on postoperative pneumonia (POP) risk models focused on preoperative factors but overlooked intraoperative variables vital for precision. These models also neglected the higher-risk elderly population. This study seeks to develop and evaluate preoperative and combined models to predict POP risk in elderly patients undergoing non-cardiothoracic surgery.

METHODS

A retrospective cohort of 444 patients who underwent non-cardiothoracic surgery at Nanjing First Hospital from March 2021 to April 2022 was included. Univariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression were employed to develop preoperative and combined logistic regression models.

RESULTS

The area under the receiver operating characteristic curve for both models exceeded 0.80, indicating excellent discriminatory ability. Furthermore, the combined model demonstrated superior predictive accuracy compared to the preoperative model.

CONCLUSION

This study developed preoperative and combined nomograms that offer practical and innovative tools for clinicians to predict POP risk and improve patient care.

Current Status of Adult Post-Cardiac Surgery Critical Care in Saudi Arabia

OBJECTIVE

The field of cardiac surgery in Saudi Arabia has developed significantly over the years, with more advanced procedures being performed for high-risk patients with multiple comorbidities. This poses challenging postoperative management issues requiring multidisciplinary, highly organized expert care in cardiovascular critical care. This survey aimed to describe the current state of postoperative critical care for cardiac surgeries in Saudi Arabia.

DESIGN

This e-mail survey developed by the Chapter of Adult Cardiovascular Critical Care of the Saudi Critical Care Society included 61 questions pertaining to the geographic distribution of adult cardiac surgery centers in Saudi Arabia, including what types of operations and how many operations are being performed, and information on intensive care units such as data on staffing, equipment, protocols, and outcome assessment in these units.

SETTING

The study was conducted in Saudi Arabia.

PARTICIPANTS

Participating physicians included representatives of adult intensive care units in all cardiac centers (N = 42).

INTERVENTIONS

There were no interventions in this study.

MEASUREMENTS AND MAIN RESULTS

Of the study cardiac centers, 71.4% have specialized cardiovascular critical care units for the postoperative care of cardiac patients and 42.9% are managed in a closed design by expert in-house physicians on a 24-hour basis. The estimated cardiac surgery intensive care unit bed capacity in Saudi Arabia is 7.3 (ranging from 3.0 in Qasim Region to 11.6 in Mecca Region) beds/1 million population, with 1.3 cardiac centers/1 million and 79 centers/1 million cardiovascular surgical patients. Several protocols are implemented in these critical care units with key performance indicators to meet the best quality of care.

CONCLUSIONS

Cardiac surgery intensive care units in Saudi Arabia have varying management structures, care practices, and healthcare provider staffing models, although most of the large-volume centers are adopting the intensivist-led team model of care. Guidelines are needed to standardize practice in all cardiac surgery centers regarding processes and protocols, intensive care unit staffing models, and reporting of outcomes and key performance indicators. Further studies are needed to study cardiac surgery intensive care unit factors related to patient outcomes after cardiac surgery.

Comparing ensemble learning algorithms and severity of illness scoring systems in cardiac intensive care units: a retrospective study

BACKGROUND

Beatriz Nistal-Nuño designed a machine learning system type of ensemble learning for patients undergoing cardiac surgery and intensive care unit cardiology patients, based on sequences of cardiovascular physiological measurements and other intensive care unit physiological measurements in addition to static features, which generates a score for prediction of mortality of cardiac intensive care unit patients.

BACKGROUND

■ Gradient Boosting Machine and Random Forest models were built for prediction of mortality at cardiac intensive care units.

BACKGROUND

■ A total of 9,761 intensive care unit stays of patients admitted under a Cardiac Surgery and Cardiac Medical services were studied.

BACKGROUND

■ The AUROC and AUPRC values were significantly superior to seven conventional systems compared.

BACKGROUND

■ The machine learning models' calibration curves were substantially closer to the ideal line.

OBJECTIVE

Logistic Regression has been used traditionally for the development of most predictor tools of intensive care unit mortality. The purpose of this study is to combine shared risk factors between patients undergoing cardiac surgery and intensive care unit cardiology patients to develop a risk score for prediction of mortality in cardiac intensive care unit patients, using machine learning.

METHODS

Gradient Boosting Machine and Distributed Random Forest models were developed based on 9,761 intensive care unit-stays from the MIMIC-III database. Sequential and static features were collected. The primary endpoint was intensive care unit mortality prediction. Discrimination, calibration, and accuracy statistics were evaluated. The predictive performance of traditional scoring systems was compared.

RESULTS

Machine learning models' AUROC and AUPRC were significantly superior to all conventional systems for the primary endpoint (p<0.05), with AUROC of 0.9413 for Gradient Boosting Machine and 0.9311 for Distributed Random Forest. Sensitivity was 0.6421 for Gradient Boosting Machine, 0.6 for Distributed Random Forest, and <0.3 for all conventional systems except for serial SOFA (0.6316). Precision was 0.574 for Gradient Boosting Machine, 0.566 for Distributed Random Forest, and <0.5 for all conventional systems. Diagnostic odds ratio was 58.8144 for Gradient Boosting Machine, 51.2926 for Distributed Random Forest and <34 for all conventional systems. Brier score was 0.025 for Gradient Boosting Machine and 0.028 for Distributed Random Forest, being worse for the traditional systems. Calibration curves of Gradient Boosting Machine and Distributed Random Forest were substantially closer to the ideal line.

CONCLUSION

The machine learning models showed superiority over the traditional scoring systems compared, with Gradient Boosting Machine having the best performance. Discrimination and calibration were excellent for Gradient Boosting Machine, followed by Distributed Random Forest. The machine learning methods exhibited better capacity for most accuracy statistics.

ARISCAT and LAS VEGAS risk scores for predicting postoperative pulmonary complications after cardiac surgery: a cohort study

Background

Postoperative pulmonary complications (PPCs) could lead to morbidity, mortality, and prolonged hospital stay. Different risk-scoring systems are used to predict the identification of patients at risk of developing PPCs. The diagnostic accuracies of the Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) and Local Assessment of Ventilatory Management During General Anaesthesia for Surgery (LAS VEGAS) risk scores are compared in prediction of PPCs taking pulmonary complication as the gold standard in cardiac surgery.

Materials and methods

A prospective cohort study with consecutive sampling technique. A total of 181 patients were included. Quantitative data is presented as simple descriptive statistics giving mean and standard deviation, and qualitative variables are presented as frequency and percentages. Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracies are also calculated.

Results

Total 181 post-cardiac surgery patients were analyzed. The median [interquartile range] of age, height, weight, and BMI were 60.0 [52.0-67.0] years, 163.0 [156.0-168.0] cm, 71.0 [65.0-80.0] kg and 27.3 [24.2-30.4] kg/m2. 127 (70.2%) were male, and 54 (29.8%) were female. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of ARISCAT for the prediction of PPCs were (94.9%, 4.65%, 76.1%, 22.9% and 73.4%), whereas LAS VEGAS were (97.1%, 4.65%, 76.5%, 33.3% and 75.1%), respectively.

Conclusion

Both the ARISCAT and LAS VEGAS risk scores are of limited value in cardiac surgery patients for the prediction of postoperative pulmonary complications, based on the predicted scores in this study.

Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach

BACKGROUND

Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create digital phenotypes among heterogenous populations, representing distinct patient subpopulations grouped by shared characteristics, from which we can personalize care, anticipate clinical care trajectories, and explore therapies. We hypothesized that digital phenotypes in preoperative settings are associated with postoperative adverse events including in-hospital and 30-day mortality, 30-day surgical redo, intensive care unit (ICU) admission, and hospital length of stay (LOS).

METHODS

We identified all laminectomies, colectomies, and thoracic surgeries performed over a 9-year period from a large hospital system. Seventy-seven readily extractable preoperative features were first selected from clinical consensus, including demographics, medical history, and lab results. Three surgery-specific datasets were built and split into derivation and validation cohorts using chronological occurrence. Consensus k -means clustering was performed independently on each derivation cohort, from which phenotypes' characteristics were explored. Cluster assignments were used to train a random forest model to assign patient phenotypes in validation cohorts. We reconducted descriptive analyses on validation cohorts to confirm the similarity of patient characteristics with derivation cohorts, and quantified the association of each phenotype with postoperative adverse events by using the area under receiver operating characteristic curve (AUROC). We compared our approach to American Society of Anesthesiologists (ASA) alone and investigated a combination of our phenotypes with the ASA score.

RESULTS

A total of 7251 patients met inclusion criteria, of which 2770 were held out in a validation dataset based on chronological occurrence. Using segmentation metrics and clinical consensus, 3 distinct phenotypes were created for each surgery. The main features used for segmentation included urgency of the procedure, preoperative LOS, age, and comorbidities. The most relevant characteristics varied for each of the 3 surgeries. Low-risk phenotype alpha was the most common (2039 of 2770, 74%), while high-risk phenotype gamma was the rarest (302 of 2770, 11%). Adverse outcomes progressively increased from phenotypes alpha to gamma, including 30-day mortality (0.3%, 2.1%, and 6.0%, respectively), in-hospital mortality (0.2%, 2.3%, and 7.3%), and prolonged hospital LOS (3.4%, 22.1%, and 25.8%). When combined with the ASA score, digital phenotypes achieved higher AUROC than the ASA score alone (hospital mortality: 0.91 vs 0.84; prolonged hospitalization: 0.80 vs 0.71).

CONCLUSIONS

For 3 frequently performed surgeries, we identified 3 digital phenotypes. The typical profiles of each phenotype were described and could be used to anticipate adverse postoperative events.

Temporal Changes in Quality Indicators in a Regional System of Care After Surgical and Transcatheter Aortic Valve Replacement

Background

Historically, quality-of-care monitoring was performed separately for transcatheter and surgical aortic valve replacement (TAVR, SAVR). Using consensus indicators, we provide a global report on the quality of care for treatment of aortic stenosis across the highest-volume treatments: transfemoral (TF) TAVR, isolated SAVR, and SAVR combined with coronary artery bypass graft.

Methods

Retrospective observational cohort study of consecutive patients in a regional system of care. Primary endpoint was 30-day and 1-year mortality (2015-2019). Secondary endpoints included rate of new pacemaker, rate of readmission, and length of stay (2012-2019). Following multivariable logistic regressions, we developed mortality case-mix adjustment models to report risk estimates.

Results

The proportion of patients receiving TAVR grew from 32% to 53% (2015-2019). Those receiving TF TAVR were significantly older, with higher rates of comorbidities. Observed 30-day and 1-year all-cause mortality after TF TAVR decreased from 3.1% to 0.6% (P = 0.03), and 13.6% to 6.6% (P = 0.09), respectively; surgical mortality rates for isolated SAVR and SAVR combined with coronary artery bypass graft were low and did not change significantly over time, ranging from 0.3% to 1.4% and from 0.9% to 3.4%, respectively at 30 days, and from 0.9% to 3.4% and from 4.7% to 6.7 at 1 year. In the TF TAVR cohort, the observed vs expected ratio for 30-day and 1-year mortality decreased significantly from 1.9 (95% confidence interval [CI] 0.9, 3.5) to 0.3 (95% CI 0.1, 0.8), and from 1.3 (95% CI 0.9, 1.7) to 0.7 (95% CI 0.5, 0.99), respectively; no change occurred in risk-adjusted surgical mortality.

Conclusions

Consensus quality indicators provide unique insights on the quality of care for patients receiving treatment for aortic stenosis.

Delirium in the intensive care unit and its importance in the post-operative context: A review

The burden of delirium in the intensive care setting is a global priority. Delirium affects up to 80% of patients in intensive care units; an episode of delirium is often distressing to patients and their families, and delirium in patients within, or outside of, the intensive care unit (ICU) setting is associated with poor outcomes. In the short term, such poor outcomes include longer stay in intensive care, longer hospital stay, increased risk of other hospital-acquired complications, and increased risk of hospital mortality. Longer term sequelae include cognitive impairment and functional dependency. While medical category of admission may be a risk factor for poor outcomes in critical care populations, outcomes for surgical ICU admissions are also poor, with dependency at hospital discharge exceeding 30% and increased risk of in-hospital mortality, particularly in vulnerable groups, with high-risk procedures, and resource-scarce settings. A practical approach to delirium prevention and management in the ICU setting is likely to require a multi-faceted approach. Given the good evidence for the prevention of delirium among older post-operative outside of the intensive care setting, simple non-pharmacological interventions should be effective among older adults post-operatively who are cared for in the intensive care setting. In response to this, the future ICU environment will have a range of organizational and distinct environmental characteristics that are directly targeted at preventing delirium.

Homelessness and Race are Mortality Predictors in US Veterans Undergoing CABG

Coronary artery disease requiring surgical revascularization is prevalent in United States Veterans. We aimed to investigate preoperative predictors of 30-day mortality following coronary artery bypass grafting (CABG) in the Veteran population. The Veterans Affairs Surgical Quality Improvement (VASQIP) national database was queried for isolated CABG cases between 2008 and 2018. The primary outcome was 30-day mortality. A multivariable logistic regression was performed to assess for independent predictors of the primary outcome. A P-value of <0.05 was considered statistically significant. A total of 32,711 patients were included. The 30-day mortality rate was 1.37%. Multivariable analysis identified the following predictors of 30-day mortality: African-American race (OR 1.46, 95% CI 1.09-1.96); homelessness (OR 6.49, 95% CI 3.39-12.45); female sex (OR 2.15, 95% CI 1.08-4.30); preoperative myocardial infarction within 7 days (OR 1.49, 95% CI 1.06-2.10) or more than 7 days before CABG (OR 1.34, 95% CI 1.04-1.72); partially/fully dependent functional status (OR 1.44, 95% CI 1.07-1.93); chronic obstructive pulmonary disease (OR 1.54, 95% CI 1.24-1.92); mild (OR 1.48, 95% CI 1.04-2.11) and severe aortic stenosis (OR 2.06, 95% CI 1.37-3.09); moderate (OR 1.88, 95% CI 1.31-2.72), or severe (OR 2.99, 95% CI 1.71-5.22) mitral regurgitation; cardiomegaly (OR 1.73, 95% CI 1.35-2.22); NYHA Class III/IV heart failure (OR 2.05, 95% CI 1.10-3.83); and urgent/emergent operation (OR 1.42, 95% CI 1.08-1.87). The 30-day mortality rate in US Veterans undergoing isolated CABG between 2008 and 2018 was 1.37%. In addition to established clinical factors, African-American race and homelessness were independent demographic predictors of 30-day mortality.

Benchmark of Intraoperative Activity in Cardiac Surgery: A Comparison between Pre- and Post-Operative Prognostic Models

Objectives: Despite its large diffusion and improvements in safety, the risks of complications after cardiac surgery remain high. Published predictive perioperative scores (EUROSCORE, STS, ACEF) assess risk on preoperative data only, not accounting for the intraopertive period. We propose a double-fold model, including data collected before surgery and data collected at the end of surgery, to evaluate patient risk evolution over time and assess the direct contribution of surgery. Methods: A total of 15,882 cardiac surgery patients from a Margherita-Prosafe cohort study were included in the analysis. Probability of death was estimated using two logistic regression models (preoperative data only vs. post-operative data, also including information at discharge from the operatory theatre), testing calibration and discrimination of each model. Results: Pre-operative and post-operative models were built and demonstrate good discrimination and calibration with AUC = 0.81 and 0.87, respectively. Relative difference in pre- and post-operative mortality in separate centers ranged from −0.36 (95% CI: −0.44–−0.28) to 0.58 (95% CI: 0.46–0.71). The usefulness of this two-fold preoperative model to benchmark medical care in single hospital is exemplified in four cases. Conclusions: Predicted post-operative mortality differs from predicted pre-operative mortality, and the distance between the two models represent the impact of surgery on patient outcomes. A double-fold model can assess the impact of the intra-operative team and the evolution of patient risk over time, and benchmark different hospitals on patients subgroups to promote an improvement in medical care in each center.

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

BACKGROUND

Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients.

METHODS

We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted.

RESULTS

Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results.

CONCLUSIONS

ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.

Transparency, Public Reporting and a Culture of Change to Quality and Safety in Cardiac Surgery

Academic medical centers have a duty to serve as hospitals of last resort for advanced cardiac surgical care and therefore manage patients at elevated risk of post-operative morbidity and mortality. They must also meet state and professional quality targets devised to protect the public. The tension between these imperatives can be managed by a multi-dimensional quality improvement program which aims to manage risk, optimize outcomes and exclude futile operations. We here share our approach to this process, its impact on our institution and discuss pertinent issues relevant to institutions in a similar situation.

Machine Learning Models with Preoperative Risk Factors and Intraoperative Hypotension Parameters Predict Mortality After Cardiac Surgery

OBJECTIVES

Machine learning models used to predict postoperative mortality rarely include intraoperative factors. Several intraoperative factors like hypotension (IOH), vasopressor-inotropes, and cardiopulmonary bypass (CPB) time are significantly associated with postoperative outcomes. The authors explored the ability of machine learning models incorporating intraoperative risk factors to predict mortality after cardiac surgery.

DESIGN

Retrospective study.

SETTING

Tertiary hospital.

PARTICIPANTS

A total of 5,015 adults who underwent cardiac surgery from 2008 to 2016.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

The intraoperative phase was divided into the following: (1) CPB, (2) outside CPB, and (3) total surgery for quantifying IOH only. Phase-specific IOH parameters (area under the curve for mean arterial pressure <65 mmHg), vasopressor-inotropes (norepinephrine equivalents), duration, and cross-clamp time, along with preoperative risk factors ,were incorporated into the models. The primary outcome was mortality. The following 5 models were applied to 3 intraoperative phases separately: (1) logistic regression, (2) random forests, (3) neural networks, (4) support vector machines, and (5) extreme gradient boosting (XGB). Mortality was predicted using area under the receiver operating characteristic curve. Of 5,015 patients included, 112 (2.2%) died. XGB model from the outside-CPB phase predicted mortality better with area under the receiver operating characteristic curve, 95% confidence interval (CI): 0.88(0.83-0.94); positive predictive value, 0.10(0.06-0.15); specificity 0.85 (0.83-0.87) and sensitivity 0.75 (0.57-0.90).

CONCLUSION

XGB machine learning model from IOH outside the CPB phase seemed to offer a better discrimination, sensitivity, specificity, and positive predictive value compared with other models. Machine learning models incorporating intraoperative adverse factors might offer better predictive ability for risk stratification and triaging of patients after cardiac surgery.

Mortality Prediction After Cardiac Surgery: Higgins' Intensive Care Unit Admission Score Revisited

BACKGROUND

This study was performed to develop and validate a cardiac surgical intensive care risk adjustment model for mixed cardiac surgery based on a few preoperative laboratory tests, extracorporeal circulation time, and measurements at arrival to the intensive care unit.

METHODS

This was a retrospective study of admissions to 5 cardiac surgical intensive care units in Sweden that submitted data to the Swedish Intensive Care Registry. Admissions from 2008 to 2014 (n = 21,450) were used for model development, whereas admissions from 2015 to 2016 (n = 6463) were used for validation. Models were built using logistic regression with transformation of raw values or categorization into groups.

RESULTS

The final model showed good performance, with an area under the receiver operating characteristics curve of 0.86 (95% confidence interval, 0.83-0.89), a Cox calibration intercept of -0.16 (95% confidence interval, -0.47 to 0.19), and a slope of 1.01 (95% confidence interval, 0.89-1.13) in the validation cohort.

CONCLUSIONS

Eleven variables available on admission to the intensive care unit can be used to predict 30-day mortality after cardiac surgery. The model performance was better than those of general intensive care risk adjustment models used in cardiac surgical intensive care and also avoided the subjective assessment of the cause of admission. The standardized mortality ratio improves over time in Swedish cardiac surgical intensive care.

Comparison of Glycemic Variability Indices: Blood Glucose, Risk Index, and Coefficient of Variation in Predicting Adverse Outcomes for Patients Undergoing Cardiac Surgery

OBJECTIVES

Fluctuations in blood glucose (glycemic variability) increase the risk of adverse outcomes. No universally accepted tool for glycemic variability exists during the perioperative period. The authors compared 2 measures of glycemic variability-(1) coefficient of variation (CV) and (2) the Blood Glucose Risk Index (BGRI)-in predicting adverse outcomes after cardiac surgery.

DESIGN

Prospective, observational study.

SETTING

Single-center, teaching hospital.

PARTICIPANTS

A total of 1,963 adult patients undergoing cardiac surgery.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Postoperative blood glucose levels were measured hourly for the first 24 hours and averaged every 4 hours (4, 8, 12, 16, 20, and 24 hours). Glycemic variability was measured by CV and the BGRI. The primary outcome, major adverse events (MAEs), was a predefined composite of postoperative complications (death, reoperation, deep sternal infection, stroke, pneumonia, renal failure, tamponade, and myocardial infarction). Logistic regression models were constructed to evaluate the association. Predictive ability was measured using C-statistics. Major adverse events were seen in 170 (8.7%) patients. Only the fourth quartile of CV showed association (odds ratio [OR] 1.91; 95% confidence interval [CI] [1.19-3.14]; p = 0.01), whereas BGRI was related significantly to MAE (OR 1.20; 95% CI [1.10-1.32]; p < 0.0001). The predictive ability of CV and BGRI increased on adding the standard Society of Thoracic Surgeons (STS) risk index. The C-statistic for STS was 0.68, whereas STS + CV was 0.70 (p = 0.012) and STS + BGRI was 0.70 (p = 0.012).

CONCLUSION

Both CV and the BGRI had good predictive ability. The BGRI being a continuous variable could be a preferred measure of glycemic variability in predicting adverse outcomes (cutoff value 2.24) after cardiac surgery.

Incidence and Risk Factors for Mortality Following Bariatric Surgery: a Nationwide Registry Study

BACKGROUND

Although bariatric surgery (BS) is considered safe, concern remains regarding severe post-operative adverse events and mortality. Using a national BS registry, the aim of this study was to assess the incidence, etiologies, and risk factors for mortality following BS.

METHODS

Prospective data from the National Registry of Bariatric Surgery in Israel (NRBS) including age, gender, BMI, comorbidities, and surgical procedure information were collected for all patients who underwent BS in Israel between June 2013 and June 2016. The primary study outcome was the 3.5-year post-BS mortality rate, obtained by cross-referencing with the Israel population registry.

RESULTS

Of the 28,755 patients analyzed (67.3% females, mean age 42.0 ± 12.5 years, and preoperative BMI 42.14 ± 5.21 kg/m²), 76% underwent sleeve gastrectomy (SG), 99.1% of the surgeries were performed laparoscopically, and 50.8% of the surgeries were performed in private medical centers. Overall, 95 deaths occurred during the study period (146.9/100,000 person years). The 30-day rate of post-operative mortality was 0.04% (n = 12). Male gender (HR = 1.94, 95%CI 1.16-3.25), age (HR = 1.06, 95%CI 1.04-1.09), BMI (HR = 1.08, 95%CI 1.05-1.11), and depression (HR = 2.38, 95%CI 1.25-4.52) were independently associated with an increased risk of all-cause 3.5-year mortality, while married status (HR = 0.43, 95%CI 0.26-0.71) was associated with a decreased risk.

CONCLUSION

Mortality after BS is low. Nevertheless, a variety of risk factors including male gender, advanced age, unmarried status, higher BMI, and preoperative depressive disorder were associated with higher mortality rates. Special attention should be given to these "at-risk" BS patients.

Intensive Care Unit Readmission After Left Ventricular Assist Device Implantation: Causes, Associated Factors, and Association With Patient Mortality

BACKGROUND

Previous studies on readmissions after left ventricular assist device (LVAD) implantation have focused on hospital readmissions after dismissal from the index hospitalization. Because few data exist, the purpose of this study was to examine intensive care unit (ICU) readmissions in patients during their initial hospitalization for LVAD implantation to determine reasons for, factors associated with, and incidence of mortality after ICU readmission.

METHODS

A retrospective analysis was performed from February 2007 to March 2015 of patients at our institution receiving first-time LVAD implantation. After LVAD implantation, patients dismissed from the ICU who then required ICU readmission before hospital dismissal were compared to those not requiring ICU readmission before hospital dismissal with respect to preoperative, intraoperative, and postoperative factors.

RESULTS

Among 287 LVAD patients, 266 survived their initial ICU admission, of which 49 (18.4%) required ICU readmission. The most common reasons for readmission were bleeding and respiratory failure. Factors found to be univariably associated with ICU readmission were preoperative hemoglobin, preoperative aspartate aminotransferase, preoperative atrial fibrillation, preoperative dialysis, longer cardiopulmonary bypass times, and higher intraoperative allogeneic blood transfusion requirements. Multivariable analysis revealed ICU readmission to be independently associated with preoperative dialysis (odds ratio, 12.86; 95% confidence interval, 3.16-52.28; P < .001). Overall mortality at 1 year was 22.6%. Survival after hospital dismissal was worse for patients who required ICU readmission during the index hospitalization (adjusted hazard ratio, 2.35; 95% confidence interval, 1.15-4.81; P = .019).

CONCLUSIONS

ICU readmission after LVAD implantation occurred relatively frequently and was significantly associated with 1-year mortality after hospital dismissal. These data can perhaps be used to identify subsets of LVAD patients at risk for ICU readmission and may lead to implementation of practice changes to mitigate ICU readmissions. Future larger and prospective studies are warranted.

The LAS VEGAS risk score for prediction of postoperative pulmonary complications: An observational study

BACKGROUND

Currently used pre-operative prediction scores for postoperative pulmonary complications (PPCs) use patient data and expected surgery characteristics exclusively. However, intra-operative events are also associated with the development of PPCs.

OBJECTIVE

We aimed to develop a new prediction score for PPCs that uses both pre-operative and intra-operative data.

DESIGN

This is a secondary analysis of the LAS VEGAS study, a large international, multicentre, prospective study.

SETTINGS

A total of 146 hospitals across 29 countries.

PATIENTS

Adult patients requiring intra-operative ventilation during general anaesthesia for surgery.

INTERVENTIONS

The cohort was randomly divided into a development subsample to construct a predictive model, and a subsample for validation.

MAIN OUTCOME MEASURES

Prediction performance of developed models for PPCs.

RESULTS

Of the 6063 patients analysed, 10.9% developed at least one PPC. Regression modelling identified 13 independent risk factors for PPCs: six patient characteristics [higher age, higher American Society of Anesthesiology (ASA) physical score, pre-operative anaemia, pre-operative lower SpO2 and a history of active cancer or obstructive sleep apnoea], two procedure-related features (urgent or emergency surgery and surgery lasting ≥ 1 h), and five intra-operative events [use of an airway other than a supraglottic device, the use of intravenous anaesthetic agents along with volatile agents (balanced anaesthesia), intra-operative desaturation, higher levels of positive end-expiratory pressures > 3 cmH2O and use of vasopressors]. The area under the receiver operating characteristic curve of the LAS VEGAS risk score for prediction of PPCs was 0.78 [95% confidence interval (95% CI), 0.76 to 0.80] for the development subsample and 0.72 (95% CI, 0.69 to 0.76) for the validation subsample.

CONCLUSION

The LAS VEGAS risk score including 13 peri-operative characteristics has a moderate discriminative ability for prediction of PPCs. External validation is needed before use in clinical practice.

TRIAL REGISTRATION

The study was registered at Clinicaltrials.gov, number NCT01601223.

Gender and intraoperative blood transfusion: analysis of 54,122 non-reoperative coronary revascularization procedures

Background:

Previous studies have shown that women undergoing isolated coronary artery bypass graft (CABG) surgery have an increased risk for postoperative morbidity and mortality when compared to men. Additionally, recent evidence suggests that blood transfusions are independently associated with an increased risk of adverse outcome.

Methods:

We evaluated gender differences in the risk of intraoperative red blood cell (RBC) transfusion during CABG surgery. Consecutive, non-reoperative CABG procedures performed across 196 institutions between April 2012 and May 2015 were retrospectively reviewed. Gender differences for intraoperative transfusion were evaluated with a multi-variable binary logistic regression model, adjusting for age, blood volume (Nadler formula to normalize for height and weight), body mass index, procedure acuity, net extracorporeal circuit prime volume, use of autologous priming, first hematocrit (Hct) in the operating room (OR), nadir Hct on cardiopulmonary bypass (CPB), volume added on CPB, ultrafiltration volume, urine output on CPB and procedure duration.

Results:

Among 54,122 patients (25.3% female), 21.6% (n = 11,701) received a RBC transfusion. Compared to men, female patients were older (66 years vs. 64 years, p<0.001), had lower blood volumes (4.3L vs. 5.6L, p<0.001) and a lower preoperative Hct (32.9% vs. 37.2%, p<0.001). Transfusion rates were three-fold higher in women versus men (45.1% vs. 13.7%, p<0.001). After adjustment for independent predictors of intraoperative transfusion, women remained at increased risk versus men (OR = 1.30, 95%CI = 1.19−1.43).

Conclusions:

Women have an increased risk of intraoperative RBC transfusion versus men. After adjusting for height and weight, much of this risk is due to gender differences in preoperative Hct and blood volume; however, a residual significant risk remained after adjustment. Perfusion strategies aimed at gender differences may minimize unnecessary transfusions. Future study on the impact of gender on transfusion practice in cardiac surgery is warranted.