Prediction of fluid responsiveness using respiratory variations in left ventricular stroke area by transoesophageal echocardiographic automated border detection in mechanically ventilated patients.

BackgroundLeft ventricular stroke area by transoesophageal echocardiographic automated border detection has been shown to be strongly correlated to left ventricular stroke volume. Respiratory variations in left ventricular stroke volume or its surrogates are good predictors of fluid responsiveness in mechanically ventilated patients. We hypothesised that respiratory variations in left ventricular stroke area (DeltaSA) can predict fluid responsiveness.MethodsEighteen mechanically ventilated patients undergoing coronary artery bypass grafting were studied immediately after induction of anaesthesia. Stroke area was measured on a beat-to-beat basis using transoesophageal echocardiographic automated border detection. Haemodynamic and echocardiographic data were measured at baseline and after volume expansion induced by a passive leg raising manoeuvre. Responders to passive leg raising manoeuvre were defined as patients presenting a more than 15% increase in cardiac output.ResultsCardiac output increased significantly in response to volume expansion induced by passive leg raising (from 2.16 +/- 0.79 litres per minute to 2.78 +/- 1.08 litres per minute; p < 0.01). DeltaSA decreased significantly in response to volume expansion (from 17% +/- 7% to 8% +/- 6%; p < 0.01). DeltaSA was higher in responders than in non-responders (20% +/- 5% versus 10% +/- 5%; p < 0.01). A cutoff DeltaSA value of 16% allowed fluid responsiveness prediction with a sensitivity of 92% and a specificity of 83%. DeltaSA at baseline was related to the percentage increase in cardiac output in response to volume expansion (r = 0.53, p < 0.01).ConclusionDeltaSA by transoesophageal echocardiographic automated border detection is sensitive to changes in preload, can predict fluid responsiveness, and can quantify the effects of volume expansion on cardiac output. It has potential clinical applications

Outcome prediction in intensive care with special reference to cardiac surgery

The development, use, and understanding of severity of illness scoring systems has advanced rapidly in the last decade; their weaknesses and limitations have also become apparent. This work follows some of this development and explores some of these aspects. It was undertaken in three stages and in two countries. The first study investigated three severity of illness scoring systems in a general Intensive Care Unit (ICU) in Cape Town, namely the Acute Physiology and Chronic Health Evaluation (APACHE II) score, the Therapeutic Intervention Scoring System (TISS), and a locally developed organ failure score. All of these showed a good relationship with mortality, with the organ failure score the best predictor of outcome. The TISS score was felt to be more likely to be representative of intensiveness of medical and nursing management than severity of illness. The APACHE II score was already becoming widely used world-wide and although it performed less well in some diagnostic categories (for example Adult Respiratory Distress Syndrome) than had been hoped, it clearly warranted further investigation. Some of the diagnosis-specific problems were eliminated in the next study which concentrated on the application of the APACHE II score in a cardiothoracic surgical ICU in London. Although group predictive ability was statistically impressive, the predictive ability of APACHE II in the individual patient was limited as only very high APACHE II scores confidently predicted death and then only in a small number of patients. However, there were no deaths associated with an APACHE II score of less than 5 and the mortality was less than 1 % when the APACHE II score was less than 10. Finally, having recognised the inadequacies in mortality prediction of the APACHE II score in this scenario, a study was undertaken to evaluate a novel concept: a combination of preoperative, intraoperative, and postoperative (including APACHE II and III) variables in cardiac surgery patients admitted to the same ICU. The aim was to develop a more precise method of predicting length of stay, incidence of complications, and ICU and hospital outcome for these patients. There were 1008 patients entered into the study. There was a statistically significant relationship between increasing Parsonnet (a cardiac surgery risk prediction score), APACHE II, and APACHE III scores and mortality. By forward stepwise logistic regression a model was developed for the probability of hospital death. This model included bypass time, need for inotropes, mean arterial pressure, urea, and Glasgow Coma Scale. Predictive performance was evaluated by calculating the area under the receiver operating characteristic (ROC) curve. The derived model had an area under the ROC curve 0.87, while the Parsonnet score had an area of 0.82 and the APACHE II risk of dying 0.84. It was concluded that a combination of intraoperative and postoperative variables can improve predictive ability

Characterization of Postoperative Recovery After Cardiac Surgery- Insights into Predicting Individualized Recovery Pattern

Understanding the patterns of postoperative recovery after cardiac surgery is important from several perspectives: to facilitate patient-centered treatment decision making, to inform health care policy targeted to improve postoperative recovery, and to guide patient care after cardiac surgery. Our works aimed to address the following: 1) to summarize existing approaches to measuring and reporting postoperative recovery after cardiac surgery, 2) to develop a framework to efficiently measure patient-reported outcome measures to understand longitudinal recovery process, and 3) to explore ways to summarize the longitudinal recovery data in an actionable way, and 4) to evaluate whether addition of patient information generated through different phases of care would improve the ability to predict patient’s outcome. We first conducted a systematic review of the studies reporting on postoperative recovery after cardiac surgery using patient-reported outcome measures. Our systematic review demonstrated that the current approaches to measuring and reporting recovery as a treatment outcome varied widely across studies. This made synthesis of collective knowledge challenging and highlighted key gaps in knowledge, which we sought to address in our prospective cohort study. We conducted a prospective single-center cohort study of patients after cardiac surgery to measure their recovery trajectory across multiple domains of recovery. Using a digital platform, we measured patient recovery in various domains over 30 days after surgery to visualize a granular evolution of patient recovery after cardiac surgery. We used a latent class analysis to facilitate identification of dominant trajectory patterns that had been obscured in a conventional way of reporting such time-series data using group-level means. For the pain domain, we identified 4 trajectory classes, one of which was a group of patients with persistently high pain trajectory that only became distinguishable from less concerning group after 10 days. Therefore, we obtained a potentially actionable insights to tailoring individualized follow-up timing after surgery to improve the pain control. The prospective study embodied several important features to successfully conducting such studies of patient-reported outcomes. This included the use of digital platform to facilitate efficient data collection extending after hospital discharge, iteratively improving the protocol to optimize patient engagement including evaluation of potential barriers to survey completion, and using latent class analysis to identify dominant patterns of recovery trajectories. We outlined these insights in the protocol manuscript to inform subsequent studies aiming to leverage such a digital platform to measure longitudinal patient-centered outcome. Finally, we evaluated the potential value of incorporating health care data generated in the different phases of patient care in improving the prediction of postoperative outcomes after cardiac surgery. The current standard of risk prediction in cardiac surgery is the Society of Thoracic Surgeons’ (STS) risk model, which only uses patient information available preoperatively. We demonstrated through prediction models fitted on the national STS risk model for coronary artery bypass graft surgery that the addition of intraoperative variables to the conventional preoperative variable set improved the performance of prediction models substantially. Using machine learning approach to such a high-dimensional dataset proved to be marginally important. This work demonstrated the potential value and importance of being able to leverage health care data to continuously update the prediction to inform patient outcomes and guide clinical care. Our work collectively advanced knowledge in several key aspects of postoperative recovery. First, we highlighted the knowledge gap in the existing literature through characterizing the variability in the ways such studies had been conducted. Second, we designed and described a framework to measure postoperative recovery and an analytical approach to informatively characterize longitudinal patient recovery. Third, we employed these designs in a prospective cohort study to measure and analyze recovery trajectories and described clinical insights obtained from the study. Finally, we demonstrated the potential value of a dynamic risk model to iteratively improve its predictive performance by incorporating new data generated as the patient progresses through the phase of care. Such a platform has the potential to individualize patient’s post-acute care in a data-driven manner

Goal-directed therapy in intraoperative fluid and hemodynamic management.

Intraoperative fluid management is pivotal to the outcome and success of surgery, especially in high-risk procedures. Empirical formula and invasive static monitoring have been traditionally used to guide intraoperative fluid management and assess volume status. With the awareness of the potential complications of invasive procedures and the poor reliability of these methods as indicators of volume status, we present a case scenario of a patient who underwent major abdominal surgery as an example to discuss how the use of minimally invasive dynamic monitoring may guide intraoperative fluid therapy

Optimising cardiac services using routinely collected data and discrete event simulation

Background: The current practice of managing hospital resources, including beds, is very much driven by measuring past or expected utilisation of resources. This practice, however, doesn’t reflect variability among patients. Consequently, managers and clinicians cannot make fully informed decisions based upon these measures which are considered inadequate in planning and managing complex systems. Aim: to analyse how variation related to patient conditions and adverse events affect resource utilisation and operational performance. Methods: Data pertaining to cardiac patients (cardiothoracic and cardiology, n=2241) were collected from two major hospitals in Oman. Factors influential to resource utilisation were assessed using logistic regressions. Other analysis related to classifying patients based on their resource utilisation was carried out using decision tree to assist in predicting hospital stay. Finally, discrete event simulation modelling was used to evaluate how patient factors and postoperative complications are affecting operational performance. Results: 26.5% of the patients experienced prolonged Length of Stay (LOS) in intensive care units and 30% in the ward. Patients with prolonged postoperative LOS had 60% of the total patient days. Some of the factors that explained the largest amount of variance in resource use following cardiac procedure included body mass index, type of surgery, Cardiopulmonary Bypass (CPB) use, non-elective surgery, number of complications, blood transfusion, chronic heart failure, and previous angioplasty. Allocating resources based on patient expected LOS has resulted in a reduction of surgery cancellations and waiting times while overall throughput has increased. Complications had a significant effect on perioperative operational performance such as surgery cancellations. The effect was profound when complications occurred in the intensive care unit where a limited capacity was observed. Based on the simulation model, eliminating some complications can enlarge patient population. Conclusion: Integrating influential factors into resource planning through simulation modelling is an effective way to estimate and manage hospital capacity.Open Acces

Quantitative Cardiac Magnetic Resonance Imaging Biomarkers for the Characterisation of Ischaemic Cardiomyopathy

Our understanding of the processes that determine outcomes in patients with ischaemic cardiomyopathy is based on conventional physiological concepts such as ischaemia and viability. Qualitative methods for characterising these processes tend to be binary and often fail to capture the complexity of the underlying biology. Importantly, these are perhaps inadequate to evaluate treatment effects, including the impact of coronary revascularisation. The aim of this thesis was to deploy novel quantitative cardiac magnetic resonance (CMR) techniques to evaluate and distinguish between the pathophysiological processes that determine outcomes in patients with ischaemic cardiomyopathy, through integration of anatomical, functional, perfusion and tissue characterisation information. The work is centred around the use of coronary artery bypass graft (CABG) surgery as the method for revascularisation, and focuses on the impact of myocardial blood flow alterations on cardiac physiology and clinical outcomes. In this work, I first evaluate the impact of surgical revascularisation on myocardial structure and function in patients with impaired left ventricular (LV) systolic function, using paired assessments before and after CABG. I found that at 6 months following revascularisation, despite improvement in functional capacity, more than a third of total myocardial segments examined are no longer considered revascularised. As a result, the overall augmentation in global myocardial blood flow (MBF) following CABG surgery is significantly blunted. There are however technical concerns regarding the quantitative estimation of myocardial blood flow in patients with coronary artery grafts, particularly in relation to the impact of long coronary grafts on contrast kinetics. I therefore evaluated the impact of arterial contrast delay on myocardial blood flow estimation in patients with left internal mammary artery (LIMA) grafts. I showed that absolute MBF estimation is minimally affected by delayed contrast arrival in patients with LIMA grafts, and that irrespective of graft patency, residual native disease severity is a key determinant of myocardial blood flow. Following these findings, I then assessed the prognostic impact of myocardial blood flow in a large cohort of patients with prior CABG. The only imaging study to date examining the prognostic role of quantitative perfusion indices in this population, it demonstrated that both stress MBF and myocardial perfusion reserve (MPR) independently predict adverse cardiovascular outcomes and all cause-mortality. Finally, using the existing quantitative perfusion technique and its associated framework, I co-developed and implemented a non-invasive, in-line method of measuring pulmonary transit time (PTT) and pulmonary blood volume (PBV) during routine CMR scanning. I then found that both imaging parameters can be used as independent quantitative prognostic biomarkers in patients with known or suspected coronary artery disease

Clinical review: Practical recommendations on the management of perioperative heart failure in cardiac surgery

Acute cardiovascular dysfunction occurs perioperatively in more than 20% of cardiosurgical patients, yet current acute heart failure (HF) classification is not applicable to this period. Indicators of major perioperative risk include unstable coronary syndromes, decompensated HF, significant arrhythmias and valvular disease. Clinical risk factors include history of heart disease, compensated HF, cerebrovascular disease, presence of diabetes mellitus, renal insufficiency and high-risk surgery. EuroSCORE reliably predicts perioperative cardiovascular alteration in patients aged less than 80 years. Preoperative B-type natriuretic peptide level is an additional risk stratification factor. Aggressively preserving heart function during cardiosurgery is a major goal. Volatile anaesthetics and levosimendan seem to be promising cardioprotective agents, but large trials are still needed to assess the best cardioprotective agent(s) and optimal protocol(s). The aim of monitoring is early detection and assessment of mechanisms of perioperative cardiovascular dysfunction. Ideally, volume status should be assessed by 'dynamic' measurement of haemodynamic parameters. Assess heart function first by echocardiography, then using a pulmonary artery catheter (especially in right heart dysfunction). If volaemia and heart function are in the normal range, cardiovascular dysfunction is very likely related to vascular dysfunction. In treating myocardial dysfunction, consider the following options, either alone or in combination: low-to-moderate doses of dobutamine and epinephrine, milrinone or levosimendan. In vasoplegia-induced hypotension, use norepinephrine to maintain adequate perfusion pressure. Exclude hypovolaemia in patients under vasopressors, through repeated volume assessments. Optimal perioperative use of inotropes/vasopressors in cardiosurgery remains controversial, and further large multinational studies are needed. Cardiosurgical perioperative classification of cardiac impairment should be based on time of occurrence (precardiotomy, failure to wean, postcardiotomy) and haemodynamic severity of the patient's condition (crash and burn, deteriorating fast, stable but inotrope dependent). In heart dysfunction with suspected coronary hypoperfusion, an intra-aortic balloon pump is highly recommended. A ventricular assist device should be considered before end organ dysfunction becomes evident. Extra-corporeal membrane oxygenation is an elegant solution as a bridge to recovery and/or decision making. This paper offers practical recommendations for management of perioperative HF in cardiosurgery based on European experts' opinion. It also emphasizes the need for large surveys and studies to assess the optimal way to manage perioperative HF in cardiac surgery

Cardiopulmonary Predicators of Dysfunctional Ventilator Weaning Response after Coronary Artery Bypass Graft

Although the majority of coronary artery bypass graft (CABG) surgery patients are extubated within 6 to 8 hours following surgery, 20% to 40% of patients remain intubated 12 hours after surgery due to dysfunctional ventilator weaning response (DVWR). DVWR associated with increased morbidity and mortality (30% to 43%) following CABG surgery. Finding significant antecedence to predict DVWR could help to identify and prevent the complications from DVWR after CABG surgery. Literature review revealed that there is an association between cardiopulmonary indicators (CPI) and DVWR after CABG surgery. Cardiopulmonary indicators are the selected hemodynamic parameters that have an association with DVWR. The association of CPI with DVWR may be used to predict DVWR. Therefore, this study set out to find a predictive model for DVWR using CPI and significant antecedence. The purposes of this research study were to describe the characteristics of CPI among patients with normal ventilator weaning response (NVWR) and dysfunctional ventilator weaning response (DVWR) after coronary artery bypass graft (CABG) surgery, to find the differences in characteristics of cardiopulmonary indicators between patients with NVWR and DVWR after CABG surgery, and to build a prediction model for DVWR with significant antecedence. A retrospective case control study with time series design was utilized. An inclusion criteria guided purposive sampling technique was used to recruit 300 subjects from a retrospective audit of electronic medical records of patients who underwent CABG surgery between May 2003 and February 2006. Among the 300 subjects, 100 subjects constituted the case group and 200 constituted the control group. This study utilized descriptive and inferential statistical analysis, which was performed through SAS programs including PROC UNIVARIATE, PROC FREQ, PROC GLM, PROC REG, PROC MIXED REPEATED MEASURE ANOVA, and PROC LOGISTIC. The study included such demographic variables as age and sex and clinical variables COPD, CHF, renal failure, number of grafts, and BSA, which were used for the description of the study sample as well as included in the analysis as covariates. The outcome variables of this study were DVWR and NVWR. The independent variable of the study was CPI, which included heart rate (HR), mean arterial pressure (MAP), central venous pressure (CVP), cardiac output (CO), respiratory rate (RR), mixed venous oxygen saturation (SVO2), oxygen saturation (SPO2), pulmonary artery diastolic pressure (PAD) and pulmonary artery systolic pressure (PASP). An hourly time series measurement of selected CPI for 12 consecutive hours after CABG surgery was used to predict DVWR. Findings revealed that several antecedence including COPD, CHF, MAP, RR, CO, PAD, and PASP were significantly associated with DVWR. In addition, findings revealed that the odds in favor of DVWR for patients with COPD were 5.466 times higher as compared to patients without COPD, holding all other variables constant. The odds in favor of DVWR for patients with CHF were 3.930 times higher than for patients without CHF, holding all other variables constant. The odds in favor of DVWR for patients with decrease 10mm/Hg mean MAP were 1.915 times the probability of NVWR, holding all other variables constant. This implies that hypotension increases risk of developing DVWR after CABG surgery. The odds in favor of DVWR for patients with decrease 5 points of mean RR were 2.978 times the probability of NVWR, holding all other variables constant. This implies that patients with lower RR are at risk of developing DVWR after CABG surgery. The odds in favor of DVWR for patients with decrease in mean CO by 2 points were 1.943 times the probability of NVWR, holding all other variables constant. This implies that patients with low CO are at the risk of developing DVWR after CABG surgery. The odds in favor of DVWR for patients with increase in mean PAD by 5mm/hg were 3.640 times the probability of NVWR, holding all other variables constant. This implies that patients with high PAD pressure are at risk of developing DVWR after CABG surgery. The odds in favor of DVWR for patients with decrease in mean PASP by 10mm/hg were 3.053 times the probability of NVWR, holding all other variables constant. This implies that the patients with low PASP are at risk of developing DVWR after CABG surgery. In conclusion, the results of this study revealed significant antecedence to predict DVWR after CABG surgery, including COPD, CHF, MAP, RR, CO, PAD, and PASP. Therefore, this study concluded that the above-mentioned significant antecedence may be used to predict DVWR after CABG surgery in critical care. The implications from the conclusion are that the weaning protocols after CABG surgery may be tailored using these significant predictors. In addition, the study findings imply that patients with a history of COPD and CHF have significant risk of developing DVWR after CABG surgery. Therefore, this researcher recommends that weaning criteria be developed considering the above risk factors for high risk patients