scholarly journals Explainable machine-learning predictions for complications after pediatric congenital heart surgery

2021 ◽  
Author(s):  
Xian Zeng ◽  
Yaoqin Hu ◽  
Liqi Shu ◽  
Jianhua Li ◽  
Huilong Duan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Time Series Data ◽  
Model Performance ◽  
Congenital Heart Surgery ◽  
Series Data ◽  
Complication Risk

Abstract The quality of treatment and prognosis after pediatric congenital heart surgery remains unsatisfactory. A reliable prediction model for postoperative complications of congenital heart surgery patients is essential to enable prompt initiation of therapy and improve the quality of prognosis. Here, we develop an interpretable machine-learning-based model that integrates patient demographics, surgery-specific features and intraoperative blood pressure data for accurately predicting complications after pediatric congenital heart surgery. We used blood pressure variability and the k-means algorithm combined with a smoothed formulation of dynamic time wrapping to extract features from time series data. In addition, SHAP framework was used to provide explanations of the prediction. Our model achieved the best performance both in binary and multi-label classification compared with other consensus-based risk models. In addition, this explainable model explains why a prediction was made to help improve the clinical understanding of complication risk and generate actionable knowledge in practice. The combination of model performance and interpretability is easy for clinicians to trust and provide insight into how they should respond before the condition worsens after pediatric congenital heart surgery.

scholarly journals Explainable machine-learning predictions for complications after pediatric congenital heart surgery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xian Zeng ◽  
Yaoqin Hu ◽  
Liqi Shu ◽  
Jianhua Li ◽  
Huilong Duan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Time Series Data ◽  
Model Performance ◽  
Congenital Heart Surgery ◽  
Series Data ◽  
Complication Risk

AbstractThe quality of treatment and prognosis after pediatric congenital heart surgery remains unsatisfactory. A reliable prediction model for postoperative complications of congenital heart surgery patients is essential to enable prompt initiation of therapy and improve the quality of prognosis. Here, we develop an interpretable machine-learning-based model that integrates patient demographics, surgery-specific features and intraoperative blood pressure data for accurately predicting complications after pediatric congenital heart surgery. We used blood pressure variability and the k-means algorithm combined with a smoothed formulation of dynamic time wrapping to extract features from time-series data. In addition, SHAP framework was used to provide explanations of the prediction. Our model achieved the best performance both in binary and multi-label classification compared with other consensus-based risk models. In addition, this explainable model explains why a prediction was made to help improve the clinical understanding of complication risk and generate actionable knowledge in practice. The combination of model performance and interpretability is easy for clinicians to trust and provide insight into how they should respond before the condition worsens after pediatric congenital heart surgery.

Cardiac Arrest in Infants After Congenital Heart Surgery

Circulation ◽  
1999 ◽  
Vol 100 (suppl_2) ◽  
Author(s):  
John F. Rhodes ◽  
Andrew D. Blaufox ◽  
Howard S. Seiden ◽  
Jeremy D. Asnes ◽  
Ronda P. Gross ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Intensive Care Unit ◽  
Cardiac Arrest ◽  
Intensive Care ◽  
Pediatric Intensive Care Unit ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Pediatric Intensive Care ◽  
Congenital Heart Surgery ◽  
Inotropic Support

Background —The survival rate to discharge after a cardiac arrest in a patient in the pediatric intensive care unit is reported to be as low as 7%. The survival rates and markers for survival strictly regarding infants with cardiac arrest after congenital heart surgery are unknown. Methods and Results —Infants in our pediatric cardiac intensive care unit database were identified who had a postoperative cardiac arrest between January 1994 and June 1998. Parameters from the perioperative, prearrest, and resuscitation periods were analyzed for these patients. Comparisons were made between survivors and nonsurvivors. Of 575 infants who underwent congenital heart surgery, 34 (6%) sustained a documented cardiac arrest; of these, 14 (41%) survived to discharge. Perioperative parameters, ventricular physiology, and primary rhythm at the time of arrest did not influence outcome. Prearrest blood pressure was lower in nonsurvivors than in survivors ( P <0.001). A high level of inotropic support prearrest was associated with death ( P =0.06). Survivors had a shorter duration of resuscitation ( P <0.001) and higher minimal arterial pH ( P <0.02) and received a smaller total dose of medication during the resuscitation. Although survivors had an overall shorter duration of resuscitation, 5 of 22 patients (23%) survived to discharge despite resuscitation of >30 minutes. Conclusions —The outcome of cardiac arrest in infants after congenital heart surgery was better than that for pediatric intensive care unit populations as a whole. Univentricular physiology did not increase the risk of death after cardiac arrest. Infants with more hemodynamic compromise before the arrest as demonstrated with lower mean arterial blood pressure and higher inotropic support were less likely to survive. The use of predetermined resuscitation end points in this subpopulation may not be justified.

scholarly journals Verification of data in congenital cardiac surgery

2008 ◽  
Vol 18 (S2) ◽  
pp. 177-187 ◽  
Author(s):  
David R. Clarke ◽  
Linda S. Breen ◽  
Marshall L. Jacobs ◽  
Rodney C.G. Franklin ◽  
Zdzislaw Tobota ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
United Kingdom ◽  
Thoracic Surgery ◽  
European Association ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Congenital Heart Surgery ◽  
Congenital Cardiac Surgery ◽  
The United Kingdom

AbstractAccurate, complete data is now the expectation of patients, families, payers, government, and even media. It has become an obligation of those practising congenital cardiac surgery. Appropriately, major professional organizations worldwide are assuming responsibility for the data quality in their respective registry databases.The purpose of this article is to review the current strategies used for verification of the data in the congenital databases of The Society of Thoracic Surgeons, The European Association for Cardio-Thoracic Surgery, and The United Kingdom Central Cardiac Audit Database. Because the results of the initial efforts to verify data in the congenital databases of the United Kingdom and Europe have been previously published, this article provides a more detailed look at the current efforts in North America, which prior to this article have not been published. The discussion and presentation of the strategy for the verification of data in the congenital heart surgery database of The Society of Thoracic Surgeons is then followed by a review of the strategies utilized in the United Kingdom and Europe. The ultimate goal of sharing the information in this article is to provide information to the participants in the databases that track the outcomes of patients with congenitally malformed hearts. This information should help to improve the quality of the data in all of our databases, and therefore increase the utility of these databases to function as a tool to optimise the management strategies provided to our patients.The need for accurate, complete and high quality Congenital Heart Surgery outcome data has never been more pressing. The public interest in medical outcomes is at an all time high and “pay for performance” is looming on the horizon. Information found in administrative databases is not risk or complexity adjusted, notoriously inaccurate, and far too imprecise to evaluate performance adequately in congenital cardiac surgery. The Society of Thoracic Surgeons and European Association for Cardio-Thoracic Surgery databases contain the elements needed for assessment of quality of care provided that a mechanism exists within these organizations to guarantee the completeness and accuracy of the data. The Central Cardiac Audit Database in the United Kingdom has an advantage in this endeavour with the ability to track and verify mortality independently, through their National Health Service.A combination of site visits with “Source Data Verification”, in other words, verification of the data at the primary source of the data, and external verification of the data from independent databases or registries, such as governmental death registries, may ultimately be required to allow for optimal verification of data. Further research in the area of verification of data is also necessary. Data must be verified for both completeness and accuracy.

Evaluation of the Quality of Care in Congenital Heart Surgery: Contribution of the Aristotle Complexity Score

2007 ◽  
Vol 54 (1) ◽  
pp. 67-83 ◽  
Author(s):  
Francois Lacour-Gayet ◽  
Jeffrey P. Jacobs ◽  
David R. Clarke ◽  
Bohdan Maruszewski ◽  
Marshall L. Jacobs ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Congenital Heart Surgery ◽  
Complexity Score

Benchmarking in Congenital Heart Surgery Using Machine Learning-Derived Optimal Classification Trees

2021 ◽  
pp. 215013512110512
Author(s):  
Dimitris Bertsimas ◽  
Daisy Zhuo ◽  
Jordan Levine ◽  
Jack Dunn ◽  
Zdzislaw Tobota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Ventilatory Support ◽  
Classification Trees ◽  
Length Of Hospital Stay ◽  
Congenital Heart Surgery ◽  
Case Mix ◽  
Specific Patient ◽  
Optimal Classification

Background: We have previously shown that the machine learning methodology of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery (CHS). We have now applied this methodology to define benchmarking standards after CHS, permitting case-adjusted hospital-specific performance evaluation. Methods: The European Congenital Heart Surgeons Association Congenital Database data subset (31 792 patients) who had undergone any of the 10 “benchmark procedure group” primary procedures were analyzed. OCT models were built predicting hospital mortality (HM), and prolonged postoperative mechanical ventilatory support time (MVST) or length of hospital stay (LOS), thereby establishing case-adjusted benchmarking standards reflecting the overall performance of all participating hospitals, designated as the “virtual hospital.” These models were then used to predict individual hospitals’ expected outcomes (both aggregate and, importantly, for risk-matched patient cohorts) for their own specific cases and case-mix, based on OCT analysis of aggregate data from the “virtual hospital.” Results: The raw average rates were HM = 4.4%, MVST = 15.3%, and LOS = 15.5%. Of 64 participating centers, in comparison with each hospital's specific case-adjusted benchmark, 17.0% statistically (under 90% confidence intervals) overperformed and 26.4% underperformed with respect to the predicted outcomes for their own specific cases and case-mix. For MVST and LOS, overperformers were 34.0% and 26.4%, and underperformers were 28.3% and 43.4%, respectively. OCT analyses reveal hospital-specific patient cohorts of either overperformance or underperformance. Conclusions: OCT benchmarking analysis can assess hospital-specific case-adjusted performance after CHS, both overall and patient cohort-specific, serving as a tool for hospital self-assessment and quality improvement.

scholarly journals Follow-up after neonatal heart disease repair: watch out for chronic kidney disease and hypertension!

2020 ◽  
Vol 35 (11) ◽  
pp. 2137-2145
Author(s):  
Louis Huynh ◽  
Sara Rodriguez-Lopez ◽  
Kelly Benisty ◽  
Adrian Dancea ◽  
Daniel Garros ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Chronic Kidney Disease ◽  
Heart Disease ◽  
Kidney Disease ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Congenital Heart Surgery ◽  
Clinical Tool ◽  
Prospective Longitudinal

Abstract Background With advances in care, neonates undergoing cardiac repairs are surviving more frequently. Our objectives were to 1) estimate the prevalence of chronic kidney disease (CKD) and hypertension 6 years after neonatal congenital heart surgery and 2) determine if cardiac surgery-associated acute kidney injury (CS-AKI) is associated with these outcomes. Methods Two-center prospective, longitudinal single-visit cohort study including children with congenital heart disease surgery as neonates between January 2005 and December 2012. CKD (estimated glomerular filtration rate < 90 mL/min/1.73m2 or albumin/creatinine ≥3 mg/mmol) and hypertension (systolic or diastolic blood pressure ≥ 95th percentile for age, sex, and height) prevalence 6 years after surgery was estimated. The association of CS-AKI (Kidney Disease: Improving Global Outcomes definition) with CKD and hypertension was determined using multiple regression. Results Fifty-eight children with median follow-up of 6 years were evaluated. CS-AKI occurred in 58%. CKD and hypertension prevalence were 17% and 30%, respectively; an additional 15% were classified as having elevated blood pressure. CS-AKI was not associated with CKD or hypertension. Classification as cyanotic postoperatively was the only independent predictor of CKD. Postoperative days in hospital predicted hypertension at follow-up. Conclusions The prevalence of CKD and hypertension is high in children having neonatal congenital heart surgery. This is important; early identification of CKD and hypertension can improve outcomes. These children should be systematically followed for the evolution of these negative outcomes. CS-AKI defined by current standards may not be a useful clinical tool to decide who needs follow-up and who does not.

Stratification of complexity: The Risk Adjustment for Congenital Heart Surgery-1 Method and The Aristotle Complexity Score – past, present, and future

2008 ◽  
Vol 18 (S2) ◽  
pp. 163-168 ◽  
Author(s):  
Marshall Lewis Jacobs ◽  
Jeffrey Phillip Jacobs ◽  
Kathy J. Jenkins ◽  
Kimberlee Gauvreau ◽  
David R. Clarke ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Quality Of Care ◽  
Risk Adjustment ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Congenital Heart Surgery ◽  
Case Mix ◽  
Objective Data ◽  
Complexity Score

AbstractMeaningful evaluation of quality of care must account for variations in the population of patients receiving treatment, or “case-mix”. In adult cardiac surgery, empirical clinical data, initially from tens of thousands, and more recently hundreds of thousands of operations, have been used to develop risk-models, to increase the accuracy with which the outcome of a given procedure on a given patient can be predicted, and to compare outcomes on non-identical patient groups between centres, surgeons and eras.In the adult cardiac database of The Society of Thoracic Surgeons, algorithms for risk-adjustment are based on over 1.5 million patients undergoing isolated coronary artery bypass grafting and over 100,000 patients undergoing isolated replacement of the aortic valve or mitral valve. In the pediatric and congenital cardiac database of The Society of Thoracic Surgeons, 61,014 operations are spread out over greater than 100 types of primary procedures. The problem of evaluating quality of care in the management of pediatric patients with cardiac diseases is very different, and in some ways a great deal more challenging, because of the smaller number of patients and the higher number of types of operations.In the field of pediatric cardiac surgery, the importance of the quantitation of the complexity of operations centers on the fact that outcomes analysis using raw measurements of mortality, without adjustment for complexity, is inadequate. Case-mix can vary greatly from program to program. Without stratification of complexity, the analysis of outcomes for congenital cardiac surgery will be flawed. Two major multi-institutional efforts have attempted to measure the complexity of pediatric cardiac operations: the Risk Adjustment in Congenital Heart Surgery-1 method and the Aristotle Complexity Score. Both systems were derived in large part from subjective probability, or expert opinion. Both systems are currently in wide use throughout the world and have been shown to correlate reasonably well with outcome.Efforts are underway to develop the next generation of these systems. The next generation will be based more on objective data, but will continue to utilize subjective probability where objective data is lacking. A goal, going forward, is to re-evaluate and further refine these tools so that, they can be, to a greater extent, derived from empirical data. During this process, ideally, the mortality elements of both the Aristotle Complexity Score and the Risk Adjustment in Congenital Heart Surgery-1 methodology will eventually unify and become one and the same. This review article examines these two systems of stratification of complexity and reviews the rationale for the development of each system, the current use of each system, the plans for future enhancement of each system, and the potential for unification of these two tools.

scholarly journals Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach

2021 ◽  
pp. 215013512110071
Author(s):  
Dimitris Bertsimas ◽  
Daisy Zhuo ◽  
Jack Dunn ◽  
Jordan Levine ◽  
Eugenio Zuccarelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Classification Trees ◽  
Congenital Heart Surgery ◽  
Assessment Tools ◽  
Hospital Length Of Stay ◽  
Total Quality ◽  
Optimal Classification

Objective: Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence techniques, we sought to develop nonlinear models for predicting outcomes in CHS. Methods: We built machine learning (ML) models to predict mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) for patients who underwent CHS, based on data of more than 235,000 patients and 295,000 operations provided by the European Congenital Heart Surgeons Association Congenital Database. We used optimal classification trees (OCTs) methodology for its interpretability and accuracy, and compared to logistic regression and state-of-the-art ML methods (Random Forests, Gradient Boosting), reporting their area under the curve (AUC or c-statistic) for both training and testing data sets. Results: Optimal classification trees achieve outstanding performance across all three models (mortality AUC = 0.86, prolonged MVST AUC = 0.85, prolonged LOS AUC = 0.82), while being intuitively interpretable. The most significant predictors of mortality are procedure, age, and weight, followed by days since previous admission and any general preoperative patient risk factors. Conclusions: The nonlinear ML-based models of OCTs are intuitively interpretable and provide superior predictive power. The associated risk calculator allows easy, accurate, and understandable estimation of individual patient risks, in the theoretical framework of the average performance of all centers represented in the database. This methodology has the potential to facilitate decision-making and resource optimization in CHS, enabling total quality management and precise benchmarking initiatives.

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