Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit.

@article{Olive2018CurrentMA,
  title={Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit.},
  author={Mary K Olive and Gabe E. Owens},
  journal={Translational pediatrics},
  year={2018},
  volume={7 2},
  pages={
          120-128
        }
}
The objectives of this review are (I) to describe the challenges associated with monitoring patients in the pediatric cardiac intensive care unit (PCICU) and (II) to discuss the use of innovative statistical and artificial intelligence (AI) software programs to attempt to predict significant clinical events. Patients cared for in the PCICU are clinically fragile and at risk for fatal decompensation. Current monitoring modalities are often ineffective, sometimes inaccurate, and fail to detect a… 

Figures and Tables from this paper

A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine.
TLDR
This narrative review covers recent efforts to leverage analytics in pediatric cardiac anesthesia and critical care to improve the care of children with CHD.
Augmented intelligence in pediatric anesthesia and pediatric critical care.
PURPOSE OF REVIEW Acute care technologies, including novel monitoring devices, big data, increased computing capabilities, machine-learning algorithms and automation, are converging. This enables the
A deep learning model for real-time mortality prediction in critically ill children
TLDR
A deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients is developed and validated.
Artificial intelligence in pediatric cardiology and cardiac surgery: Irrational hype or paradigm shift?
  • A. Chang
  • Medicine
    Annals of pediatric cardiology
  • 2019
TLDR
You are in the serene cardiac intensive care pod where real‐time analytics are displayed (rather than the de rigueur vital signs) andDeep learning and deep learning are now routinely used for personalized intensive care unit decision support and to mitigate the stress of families and physicians/ nurses.
Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review.
TLDR
Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients, and prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.
Prediction of extubation failure in the paediatric cardiac ICU using machine learning and high-frequency physiologic data.
TLDR
The utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease and whether these markers can improve clinical decision-making is evaluated.
A primer on artificial intelligence for the paediatric cardiologist
TLDR
This review paper briefly discusses the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.
Artificial intelligence in paediatrics: A checkup
TLDR
Artificial intelligence (AI) has demonstrated the ability to positively impact patient care across many broad areas of paediatrics, including imaging interpretation, diagnostics, disease prediction, and treatment decisions.
Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology.
TLDR
The top 10 lessons on AI and digital health summarized in this expert review are relevant broadly beyond CHD in cardiology and medical innovations as digital health continues to evolve worldwide.
...
...

References

SHOWING 1-10 OF 64 REFERENCES
Predictive monitoring for early detection of sepsis in neonatal ICU patients
TLDR
Harnessing and analyzing the vast amounts of physiologic data constantly displayed in ICU patients will lead to improved algorithms for early detection, prognosis, and therapy of critical illnesses.
Predictive monitoring for early detection of subacute potentially catastrophic illnesses in critical care
  • J. R. Moorman, C. Rusin, D. Lake
  • Medicine
    2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 2011
TLDR
The fundamental precepts are some potentially catastrophic medical and surgical illnesses have subclinical phases during which early diagnosis and treatment might have life-saving effects and teams of clinicians and quantitative scientists can work together to identify clinically important abnormalities of monitoring data.
Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model
TLDR
A neural network-based prediction model for clinical deterioration in patients hospitalized in the hematologic malignancy unit outperformed an existing model, substantially increasing the positive predictive value, allowing the clinician to be confident in the alarm raised.
Next generation patient monitor powered by in-silico physiology
TLDR
Results for applying the approach to the hemodynamic monitoring of infants immediately following cardiac surgery are presented and its efficacy of estimating the probability of inadequate systemic oxygen delivery is demonstrated.
Validation of the Cardiac Children's Hospital Early Warning Score: an early warning scoring tool to prevent cardiopulmonary arrests in children with heart disease.
TLDR
C-CHEWS has excellent discrimination to identify deterioration in children with cardiac disease and performed significantly better than PEWS both as an ordinal variable and when choosing cut points to maximize AUROC.
Pediatric Index of Cardiac Surgical Intensive Care Mortality Risk Score for Pediatric Cardiac Critical Care*
TLDR
This newly developed mortality score, PICSIM, consisting of 13 risk variables encompassing physiology, cardiovascular condition, and time of admission to the ICU showed better discrimination than Pediatric Index of Mortality-2, Pediatric risk of mortality-3, and STAT score and category for mortality in a multisite cohort of pediatric cardiac surgical patients.
Poor prognosis for existing monitors in the intensive care unit.
TLDR
Efforts to develop intelligent monitoring systems have more potential to deliver significantly improved patient care by initially targeting especially weak areas in ICU monitoring, such as pulse oximetry reliability.
Advanced analytics for outcome prediction in intensive care units
TLDR
A new expert knowledge based clinical decision support system for prediction of intensive care units outcome based on the physiological measurements collected during the first 48 hours of the patient's admission to the ICU, which outperforms widely used acuity scoring systems, SOFA and SAPS-III.
Alarm Algorithms in Critical Care Monitoring
TLDR
An overview of the current clinical situation and the underlying problems of alarm algorithms is given and different methods from statistics and computational science and their potential for clinical application are discussed.
Management of the postoperative pediatric cardiac surgical patient
TLDR
The practice of pediatric cardiac intensive care has evolved considerably over the last several years and innovations include the extension of cerebral oximetry from the operating room into the critical care setting; mechanical circulatory devices designed for pediatric patients; and surgery in very low birth weight neonates.
...
...