Development of an open-heart intraoperative risk scoring model for predicting a prolonged intensive care unit stay

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.

Association between triglyceride-glucose index and atrial fibrillation: A retrospective observational study

BACKGROUND

Insulin resistance is associated with atrial remodeling as well as atrial fibrillation (AF). However, there was limited evidence on the relationship of triglyceride-glucose index (TyG) index, a simple, valuable marker of insulin resistance, with AF. Thus, we aimed to investigate the association between TyG index and AF among hospitalized patients.

METHODS

A retrospective observational study was conducted in Daping Hospital, which included 356 hospitalized patients from the Department of Cardiology. Clinical and biochemical parameters were collected from electronic medical records and AF was diagnosed from electrocardiogram (ECG) findings.

RESULTS

We found that the TyG index was significantly higher in the AF group than in the group without AF. Multivariate logistic regression revealed that hypertension (OR = 1.756, 95%CI 1.135-2.717, P = 0.011) and TyG index (OR = 2.092, 95%CI 1.412-3.100, P<0.001) were positively associated with AF. The analysis of the area under the ROC curve was performed and revealed that area under curve (AUC) of TyG index was 0.600 (95%CI, 0.542-0.659, P = 0.001), the optimal critical value was 8.35, the sensitivity was 65.4%, and the specificity was 52.0%. Additional subgroup analyses of diabetic and non-diabetic subjects were also performed and found the TyG index was increased in non-diabetic subjects with AF. Furthermore, a logistic regression analysis showed TyG index was associated with AF (OR = 3.065, 95% CI, 1.819-5.166, P<0.001) in non-diabetic subjects. However, TyG index was not associated with AF in diabetic subjects.

CONCLUSION

Elevated TyG index is an independent risk factor for AF among non-diabetic hospitalized patients.

Predictors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Grafting: Development a Risk Scoring System

Introduction

To determine predictors of length of stay (LOS) in the intensive care unit (ICU) after coronary artery bypass grafting (CABG) and to develop a risk scoring system were the objectives of this study.

Methods

In this retrospective study, 1202 patients' medical records after CABG were evaluated by a research-made checklist. Tarone-Ware test was used to determine the predictors of patients' LOS in the ICU. Cox regression model was used to determine the risk factors and risk ratios associated with ICU LOS.

Results

The mean ICU LOS after CABG was 55.27±17.33 hours. Cox regression model showed that having more than two chest tubes (95% confidence interval [CI] 1.005-1.287, Relative Risk [RR]=1.138), occurrence of atelectasis (95% CI 1.000-3.007, RR=1.734), and occurrence of atrial fibrillation after CABG (95% CI 1.428-2.424, RR=1.861) were risk factors associated with longer ICU LOS. The discrimination power of this set of predictors was demonstrated with an area under the receiver operating characteristic curve and it was 0.69. A simple risk scoring system was developed based on three identified predictors that can raise ICU LOS.

Conclusion

The simple risk scoring system developed based on three identified predictors can help to plan more accurately a patient's LOS in hospital for CABG and can be useful in managing human and financial resources.

Predictive value of intraoperative factors for complications after oesophagectomy

OBJECTIVES

Oesophagectomy for malignancy is a highly complex and difficult procedure associated with considerable postoperative complications. In this study, we aimed to identify the ability of an intraoperative factor (IPFs)-based classifier to predict complications after oesophagectomy.

METHODS

This retrospective review included 251 patients who underwent radical oesophagectomy from October 2015 to December 2017. Using the least absolute shrinkage and selection operator regression model, we extracted IPFs that were associated with postoperative morbidity and then built a classifier. Preoperative variables and the IPF-based classifier were analysed using univariable and multivariable logistic regression analysis. A nomogram to predict the risk of postoperative morbidity was constructed and validated using bootstrap resampling.

RESULTS

Following the least absolute shrinkage and selection operator regression analysis, we discovered that those 4 IPF (surgical approach, lowest heart rate, lowest mean arterial blood pressure and estimated blood loss) were associated with postoperative morbidity. After stratification into low-and high-risk groups with the IPF-based classifier, the differences in 30-day morbidity (7.2% vs 70.1%, P < 0.001, respectively) and mortality (0% vs 4.7%, P = 0.029, respectively) were found to be statistically significant. The multivariable analysis demonstrated that the IPF-based classifier was an independent risk factor for predicting postoperative morbidity for patients with oesophageal cancer. The performance of the nomogram was evaluated and proven to be clinically useful.

CONCLUSIONS

We demonstrated that an IPF-based nomogram could reliably predict the risk of postoperative morbidity. It has the potential to facilitate the individual perioperative management of patients with oesophageal cancer.

Validation of Open-Heart Intraoperative Risk score to predict a prolonged intensive care unit stay for adult patients undergoing cardiac surgery with cardiopulmonary bypass

Background

A prolonged stay in an intensive care unit (ICU) after cardiac surgery with cardiopulmonary bypass (CPB) increases the cost of care as well as morbidity and mortality. Several predictive models aim at identifying patients at risk of prolonged ICU stay after cardiac surgery with CPB, but almost all of them involve a preoperative assessment for proper resource management, while one - the Open-Heart Intraoperative Risk (OHIR) score - focuses on intra-operative manipulatable risk factors for improving anesthetic care and patient outcome.

Objective

We aimed to revalidate the OHIR score in a different context.

Materials and methods

The ability of the OHIR score to predict a prolonged ICU stay was assessed in 123 adults undergoing cardiac surgery (both coronary bypass graft and valvular surgery) with CPB at two tertiary university hospitals between January 2013 and December 2014. The criteria for a prolonged ICU stay matched a previous study (ie, a stay longer than the median).

Results

The area under the receiver operating characteristic curve of the OHIR score to predict a prolonged ICU stay was 0.95 (95% confidence interval 0.90-1.00). The respective sensitivity, specificity, positive predictive value, and accuracy of an OHIR score of ≥3 to discriminate a prolonged ICU stay was 93.10%, 98.46%, 98.18%, and 95.9%.

Conclusion

The OHIR score is highly predictive of a prolonged ICU stay among intraopera-tive patients undergoing cardiac surgery with CPB. The OHIR comprises of six risk factors, five of which are manipulatable intraoperatively. The OHIR can be used to identify patients at risk as well as to improve the outcome of those patients.

Systematic review of factors influencing length of stay in ICU after adult cardiac surgery

BACKGROUND

Intensive care unit (ICU) care is associated with costly and often scarce resources. In many parts of the world, ICUs are being perceived as major bottlenecks limiting downstream services such as operating theatres. There are many clinical, surgical and contextual factors that influence length of stay. Knowing these factors can facilitate resource planning. However, the extent at which this knowledge is put into practice remains unclear. The aim of this systematic review was to identify factors that impact the duration of ICU stay after cardiac surgery and to explore evidence on the link between understanding these factors and patient and resource management.

METHODS

We conducted electronic searches of Embase, PubMed, ISI Web of Knowledge, Medline and Google Scholar, and reference lists for eligible studies.

RESULTS

Twenty-nine papers fulfilled inclusion criteria. We recognised two types of objectives for identifying influential factors of ICU length of stay (LOS) among the reviewed studies. These were general descriptions of predictors and prediction of prolonged ICU stay through statistical models. Among studies with prediction models, only two studies have reported their implementation. Factors most commonly associated with increased ICU LOS included increased age, atrial fibrillation/ arrhythmia, chronic obstructive pulmonary disease (COPD), low ejection fraction, renal failure/ dysfunction and non-elective surgery status.

CONCLUSION

Cardiac ICUs are major bottlenecks in many hospitals around the world. Efforts to optimise resources should be linked to patient and surgical characteristics. More research is needed to integrate patient and surgical factors into ICU resource planning.

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

OBJECTIVES

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

DESIGN

Observational retrospective study.

SETTINGS

A tertiary hospital in Oman.

PARTICIPANTS

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

RESULTS

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

CONCLUSIONS

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