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Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease.
This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients’ final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve.
Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved.
Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.
Optimising short- and long-term outcomes for children and patients with CHD depends on continued scientific discovery and translation to clinical improvements in a coordinated effort by multiple stakeholders. Several challenges remain for clinicians, researchers, administrators, patients, and families seeking continuous scientific and clinical advancements in the field. We describe a new integrated research and improvement network – Cardiac Networks United – that seeks to build upon the experience and success achieved to-date to create a new infrastructure for research and quality improvement that will serve the needs of the paediatric and congenital heart community in the future. Existing gaps in data integration and barriers to improvement are described, along with the mission and vision, organisational structure, and early objectives of Cardiac Networks United. Finally, representatives of key stakeholder groups – heart centre executives, research leaders, learning health system experts, and parent advocates – offer their perspectives on the need for this new collaborative effort.
As pediatric cardiac critical care becomes more sub-specialized it is reasonable to assume that dedicated units may provide a better infrastructure for improved multidisciplinary care, cardiac-specific patient safety initiatives, and dedicated training of fellows and residents. The knowledge base required to optimally manage pediatric patients with critical cardiac disease has evolved sufficiently to consider a standardized training curriculum and board certification for pediatric cardiac critical care. This strategy would potentially provide consistency of training and healthcare and improve quality of care and patient safety.
With improvements in early survival following congenital heart surgery, it has become increasingly important to understand longer-term outcomes; however, routine collection of these data is challenging and remains very limited. We describe the development and initial results of a collaborative programme incorporating standardised longitudinal follow-up into usual care at the Children’s Hospital of Philadelphia (CHOP) and University of Michigan (UM).
We included children undergoing benchmark operations of the Society of Thoracic Surgeons. Considerations regarding personnel, patient/parent engagement, funding, regulatory issues, and annual data collection are described, and initial follow-up rates are reported.
The present analysis included 1737 eligible patients undergoing surgery at CHOP from January 2007 to December 2014 and 887 UM patients from January 2010 to December 2014. Overall, follow-up data, of any type, were obtained from 90.8% of patients at CHOP (median follow-up 4.3 years, 92.2% survival) and 98.3% at UM (median follow-up 2.8 years, 92.7% survival), with similar rates across operations and institutions. Most patients lost to follow-up at CHOP had undergone surgery before 2010. Standardised questionnaires assessing burden of disease/quality of life were completed by 80.2% (CHOP) and 78.4% (UM) via phone follow-up. In subsequent pilot testing of an automated e-mail system, 53.4% of eligible patients completed the follow-up questionnaire through this system.
Standardised follow-up data can be obtained on the majority of children undergoing benchmark operations. Ongoing efforts to support automated electronic systems and integration with registry data may reduce resource needs, facilitate expansion across centres, and support multi-centre efforts to understand and improve long-term outcomes in this population.
Despite many advances in recent years for patients with critical paediatric and congenital cardiac disease, significant variation in outcomes remains across hospitals. Collaborative quality improvement has enhanced the quality and value of health care across specialties, partly by determining the reasons for variation and targeting strategies to reduce it. Developing an infrastructure for collaborative quality improvement in paediatric cardiac critical care holds promise for developing benchmarks of quality, to reduce preventable mortality and morbidity, optimise the long-term health of patients with critical congenital cardiovascular disease, and reduce unnecessary resource utilisation in the cardiac intensive care unit environment. The Pediatric Cardiac Critical Care Consortium (PC4) has been modelled after successful collaborative quality improvement initiatives, and is positioned to provide the data platform necessary to realise these objectives. We describe the development of PC4 including the philosophical, organisational, and infrastructural components that will facilitate collaborative quality improvement in paediatric cardiac critical care.
The complexity of the modern systems providing health care presents a unique challenge in delivering care of the required quality in a safe environment. Issues of safety have been thrust into the limelight because of adverse events highly publicized in the general media.
In the United States of America, improving the safety and quality in health care has been set forth as a priority for improvements in the 21st century in the report from the Institute of Medicine. Many measures have now been initiated for improving the safety of patients at hospital, regional, and national level, and through initiatives sponsored by governments and private organizations. In this review, we summarize known concepts and current issues on the safety of patients, and their applicability to children with congenital cardiac disease. Prior to examining the issues of medical error and safety, it is important to define the terminology.
An error is defined as the failure of a planned action to be completed as intended, also known as an execution error, or the use of a wrong plan to achieve an aim, this representing a planning error. An active error is an error that occurs at the level of the frontline operator, and the effects of which are felt immediately. A latent error is an error in the design, organization, training and maintenance, that leads to operator errors, and the effects of which are typically dormant in the system for lengthy periods of time. Latent errors may cause harm given the right circumstances and environment.
An adverse event is defined as an injury resulting from medical intervention. A preventable adverse event is an adverse event that occurs due to medical error. Negligent adverse events are a subset of preventable adverse events where the care provided did not meet the standard of care expected of that practitioner.
The study of improving the delivery of safe care for our patients is a rapidly growing field. Important components for development of programmes to improve the safety of patients include the leadership for the programme, the implementation of process design based on human limitations, the promotion of teamwork and function, the anticipation of unexpected events, and the creation of a learning environment.
Much is yet to be learned about the risk and incidence of adverse events during hospitalization of children with congenital cardiac disease. Errors due to human factors, such as poor communication, poor coordination, and suboptimal team work, have shown to be important causes of adverse outcomes in children undergoing cardiac surgery, and should be a focus for improvement. Future research on evaluating causes and prevention of medical errors and adverse events in this population at high risk, and consuming high resources, is essential.
Issues of inadequate safeguards for patients have been prominent in the media, and have been highlighted in reports from the Institute of Medicine. Our review discusses research on the causes of medical error, and proposes concepts to design successful programmes to improve safety for the patients on a local level.
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