Dataset Information

Bayesian networks identify determinants of outcomes following cardiac surgery in a UK population.

ABSTRACT:

Background

Traditional risk stratification tools do not describe the complex principle determinant relationships that exist amongst pre-operative and peri-operative factors and their influence on cardiac surgical outcomes. This paper reports on the use of Bayesian networks to investigate such outcomes.

Methods

Data were prospectively collected from 4776 adult patients undergoing cardiac surgery at a single UK institute between April 2012 and May 2019. Machine learning techniques were used to construct Bayesian networks for four key short-term outcomes including death, stroke and renal failure.

Results

Duration of operation was the most important determinant of death irrespective of EuroSCORE. Duration of cardiopulmonary bypass was the most important determinant of re-operation for bleeding. EuroSCORE was predictive of new renal replacement therapy but not mortality.

Conclusions

Machine-learning algorithms have allowed us to analyse the significance of dynamic processes that occur between pre-operative and peri-operative elements. Length of procedure and duration of cardiopulmonary bypass predicted mortality and morbidity in patients undergoing cardiac surgery in the UK. Bayesian networks can be used to explore potential principle determinant mechanisms underlying outcomes and be used to help develop future risk models.

SUBMITTER: Mazhar K

PROVIDER: S-EPMC9903419 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Bayesian networks identify determinants of outcomes following cardiac surgery in a UK population.

Mazhar Khurum K Mohamed Saifullah S Patel Akshay J AJ Veith Sarah Berger SB Roberts Giles G Warwick Richard R Balacumaraswami Lognathen L Abid Qamar Q Raseta Marko M

BMC cardiovascular disorders 20230206 1

<h4>Background</h4>Traditional risk stratification tools do not describe the complex principle determinant relationships that exist amongst pre-operative and peri-operative factors and their influence on cardiac surgical outcomes. This paper reports on the use of Bayesian networks to investigate such outcomes.<h4>Methods</h4>Data were prospectively collected from 4776 adult patients undergoing cardiac surgery at a single UK institute between April 2012 and May 2019. Machine learning techniques w ...[more]

PMID: 36747123

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Project description:AimsOperability of type A acute aortic dissections (TAAAD) is currently based on non-standardized decision-making process, and it lacks a disease-specific risk evaluation model that can predict mortality. We investigated patient, intraoperative data, surgeon, and centre-related variables for patients who underwent TAAAD in the UK.Methods and resultsWe identified 4203 patients undergoing TAAAD surgery in the UK (2009-18), who were enrolled into the UK National Adult Cardiac Surgical Audit dataset. The primary outcome was operative mortality. A multivariable logistic regression analysis was performed with fast backward elimination of variables and the bootstrap-based optimism-correction was adopted to assess model performance. Variation related to hospital or surgeon effects were quantified by a generalized mixed linear model and risk-adjusted funnel plots by displaying the individual standardized mortality ratio against expected deaths. Final variables retained in the model were: age [odds ratio (OR) 1.02, 95% confidence interval (CI) 1.02-1.03; P < 0.001]; malperfusion (OR 1.79, 95% CI 1.51-2.12; P < 0.001); left ventricular ejection fraction (moderate: OR 1.40, 95% CI 1.14-1.71; P = 0.001; poor: OR 2.83, 95% CI 1.90-4.21; P < 0.001); previous cardiac surgery (OR 2.29, 95% CI 1.71-3.07; P < 0.001); preoperative mechanical ventilation (OR 2.76, 95% CI 2.00-3.80; P < 0.001); preoperative resuscitation (OR 3.36, 95% CI 1.14-9.87; P = 0.028); and concomitant coronary artery bypass grafting (OR 2.29, 95% CI 1.86-2.83; P < 0.001). We found a significant inverse relationship between surgeons but not centre annual volume with outcomes.ConclusionsPatient characteristics, intraoperative factors, cardiac centre, and high-volume surgeons are strong determinants of outcomes following TAAAD surgery. These findings may help refining clinical decision-making, supporting patient counselling and be used by policy makers for quality assurance and service provision improvement.

Project description:ObjectivesEarly extubation following pediatric cardiac surgery is common, but debate exists whether location affects outcome, with some centers performing routine early extubations in the operating room (odds ratio) and others in the cardiac ICU. We aimed to define early extubation practice variation across hospitals and assess impact of location on hospital length-of-stay and other outcomes.DesignSecondary analysis of the Pediatric Cardiac Critical Care Consortium registry.SettingTwenty-eight Pediatric Cardiac Critical Care Consortium hospitals.PatientsPatients undergoing Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery mortality category 1-3 operations between August 2014 and February 2018.InterventionsNone.Measurements and main resultsWe defined early extubation as extubation less than 6 hours after postoperative admission. Hospitals were categorized based on the proportion of their early extubation patients who underwent an odds ratio extubation. Categories included low- (< 50% of early extubation, n = 12), medium- (50%-90%, n = 8), or high- (> 90%, n = 8) frequency odds ratio early extubation centers. The primary outcome of interest was postoperative hospital length-of-stay. We analyzed 16,594 operations (9,143 early extubation, 55%). Rates of early extubation ranged from 16% to 100% across hospitals. Odds ratio early extubation rates varied from 16% to 99%. Patient characteristics were similar across hospital odds ratio early extubation categories. Early extubation rates paralleled the hospital odds ratio early extubation rates-77% patients underwent early extubation at high-frequency odds ratio extubation centers compared with 39% at low-frequency odds ratio extubation centers (p < 0.001). High- and low-frequency odds ratio early extubation hospitals had similar length-of-stay, cardiac arrest rates, and low mortality. However, high-frequency odds ratio early extubation hospitals used more noninvasive ventilation than low-frequency hospitals (15% vs. 9%; p < 0.01), but had fewer extubation failures (3.6% vs. 4.5%; p = 0.02).ConclusionsConsiderable variability exists in early extubation practices after low- and moderate-complexity pediatric cardiac surgery. In this patient population, hospital length-of-stay did not differ significantly between centers with different early extubation strategies based on location or frequency.

Dataset Information

Bayesian networks identify determinants of outcomes following cardiac surgery in a UK population.

Background

Methods

Results

Conclusions

Publications

Bayesian networks identify determinants of outcomes following cardiac surgery in a UK population.

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