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Shah SP, Loomba RS. Clinical parameters to predict adverse outcomes in patients with shunt-dependent physiology with a Blalock-Taussig-Thomas shunt. Ann Pediatr Cardiol 2023; 16:345-348. [PMID: 38766460 PMCID: PMC11098288 DOI: 10.4103/apc.apc_135_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/17/2023] [Accepted: 01/23/2024] [Indexed: 05/22/2024] Open
Abstract
In patients with shunt-dependent physiology, early risk factor identification can facilitate the prevention of adverse outcomes. This study aims to determine a scoring system to estimate the risk for adverse outcomes after Blalock-Taussig-Thomas shunt placement. Of the 39 neonates with Blalock-Taussig-Thomas shunt placement, 10 experienced the composite outcome. The resulting risk score from clinical and hemodynamic variables attributed 1 point for each of the following: central venous pressure >7.8, serum lactate >1.8 mmol/L, renal oxygen extraction ratio >32, and vasoactive-inotrope score >8.7. A score of 0 was associated with a 0% risk of the composite outcome, a score of 1 or 2 with a 15% risk, and a score of 3 or 4 with a 60% risk. A combination of increased central venous pressure, increased serum lactate, increased renal oxygen extraction ratio, and increased vasoactive-inotrope score are highly accurately associated with the risk of cardiopulmonary arrest, need for extracorporeal membrane oxygenation, or inpatient mortality after a Blalock-Taussig-Thomas shunt in patients with shunt-dependent physiology.
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Affiliation(s)
- Saloni P. Shah
- Division of Pediatric Cardiology, Advocate Children’s Hospital, Oak Lawn, IL, USA
| | - Rohit S. Loomba
- Division of Pediatric Cardiology, Advocate Children’s Hospital, Oak Lawn, IL, USA
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Abstract
BACKGROUND Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. We hypothesise that blood lactate in paediatric ICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics. METHODS Forty-eight post-operative children, median age 4 months (2.9-11.8 interquartile range), mean baseline heart rate of 131 beats per minute (range 33-197), mean lactate level at admission of 22.3 mg/dL (range 6.3-71.1), were included. Morphological arterial waveform characteristics were acquired and analysed. Predicting lactate levels was accomplished using regression-based supervised learning algorithms, evaluated with hold-out cross-validation, including, basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. Algorithms were assessed with mean absolute error, the average of the absolute differences between actual and predicted lactate concentrations. Low values represent superior model performance. RESULTS The best performing algorithm was the tuned random forest, which yielded a mean absolute error of 3.38 mg/dL when predicting blood lactate with updated ground truth from the most recent blood draw. CONCLUSIONS The random forest is capable of predicting serum lactate levels by analysing perioperative variables, including the arterial pressure waveform. Thus, machine learning can predict patient blood lactate levels, a proxy for haemodynamic instability, non-invasively, continuously and with accuracy that may demonstrate clinical utility.
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Kenet AL, Pemmaraju R, Ghate S, Raghunath S, Zhang Y, Yuan M, Wei TY, Desman JM, Greenstein JL, Taylor CO, Ruchti T, Fackler J, Bergmann J. A pilot study to predict cardiac arrest in the pediatric intensive care unit. Resuscitation 2023; 185:109740. [PMID: 36805101 DOI: 10.1016/j.resuscitation.2023.109740] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND Cardiac arrest is a leading cause of mortality prior to discharge for children admitted to the pediatric intensive care unit. To address this problem, we used machine learning to predict cardiac arrest up to three hours in advance. METHODS Our data consists of 240 Hz ECG waveform data, 0.5 Hz physiological time series data, medications, and demographics from 1,145 patients in the pediatric intensive care unit at the Johns Hopkins Hospital, 15 of whom experienced a cardiac arrest. The data were divided into training, validating, and testing sets, and features were generated every five minutes. 23 heart rate variability (HRV) metrics were determined from ECG waveforms. 96 summary statistics were calculated for 12 vital signs, such as respiratory rate and blood pressure. Medications were classified into 42 therapeutic drug classes. Binary features were generated to indicate the administration of these different drugs. Next, six machine learning models were evaluated: logistic regression, support vector machine, random forest, XGBoost, LightGBM, and a soft voting ensemble. RESULTS XGBoost performed the best, with 0.971 auROC, 0.797 auPRC, 99.5% sensitivity, and 69.6% specificity on an independent test set. CONCLUSION We have created high-performing models that identify signatures of in-hospital cardiac arrest (IHCA) that may not be evident to clinicians. These signatures include a combination of heart rate variability metrics, vital signs data, and therapeutic drug classes. These machine learning models can predict IHCA up to three hours prior to onset with high performance, allowing clinicians to intervene earlier, improving patient outcomes.
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Affiliation(s)
- Adam L Kenet
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States.
| | - Rahul Pemmaraju
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Sejal Ghate
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Shreeya Raghunath
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Yifan Zhang
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Mordred Yuan
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Tony Y Wei
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Jacob M Desman
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Joseph L Greenstein
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Casey O Taylor
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Institute for Computational Medicine, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Timothy Ruchti
- Nihon Kohden Digital Health Solutions Inc, Irvine, CA, United States
| | - James Fackler
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jules Bergmann
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Walker SB, Badke CM, Carroll MS, Honegger KS, Fawcett A, Weese-Mayer DE, Sanchez-Pinto LN. Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children. Pediatr Res 2023; 93:396-404. [PMID: 36329224 DOI: 10.1038/s41390-022-02359-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/16/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Continuous cardiorespiratory physiological monitoring is a cornerstone of care in hospitalized children. The data generated by monitoring devices coupled with machine learning could transform the way we provide care. This scoping review summarizes existing evidence on novel approaches to continuous cardiorespiratory monitoring in hospitalized children. We aimed to identify opportunities for the development of monitoring technology and the use of machine learning to analyze continuous physiological data to improve the outcomes of hospitalized children. We included original research articles published on or after January 1, 2001, involving novel approaches to collect and use continuous cardiorespiratory physiological data in hospitalized children. OVID Medline, PubMed, and Embase databases were searched. We screened 2909 articles and performed full-text extraction of 105 articles. We identified 58 articles describing novel devices or approaches, which were generally small and single-center. In addition, we identified 47 articles that described the use of continuous physiological data in prediction models, but only 7 integrated multidimensional data (e.g., demographics, laboratory results). We identified three areas for development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using continuous cardiorespiratory data. IMPACT: We performed a comprehensive scoping review of novel approaches to capture and use continuous cardiorespiratory physiological data for monitoring, diagnosis, providing care, and predicting events in hospitalized infants and children, from novel devices to machine learning-based prediction models. We identified three key areas for future development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using cardiorespiratory data.
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Affiliation(s)
- Sarah B Walker
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. .,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
| | - Colleen M Badke
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Michael S Carroll
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Kyle S Honegger
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Andrea Fawcett
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Debra E Weese-Mayer
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - L Nelson Sanchez-Pinto
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
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Risk Management In Intensive Care Units With Artificial Intelligence Technologies: Systematic Review of Prediction Models Using Electronic Health Records. JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES 2022. [DOI: 10.30621/jbachs.993798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background and aim: Clinical risk assessments should be made to protect patients from negative outcomes, and the definition, frequency and severity of the risk should be determined. The information contained in the electronic health records (EHRs) can use in different areas such as risk prediction, estimation of treatment effect ect. Many prediction models using artificial intelligence (AI) technologies that can be used in risk assessment have been developed. The aim of this study is to bring together the researches on prediction models developed with AI technologies using the EHRs of patients hospitalized in the intensive care unit (ICU) and to evaluate them in terms of risk management in healthcare.
Methods: The study restricted the search to the Web of Science, Pubmed, Science Direct, and Medline databases to retrieve research articles published in English in 2010 and after. Studies with a prediction model using data obtained from EHRs in the ICU are included. The study focused solely on research conducted in ICU to predict a health condition that poses a significant risk to patient safety using artificial intellegence (AI) technologies.
Results: Recognized prediction subcategories were mortality (n=6), sepsis (n=4), pressure ulcer (n=4), acute kidney injury (n=3), and other areas (n=10). It has been found that EHR-based prediction models are good risk management and decision support tools and adoption of such models in ICUs may reduce the prevalence of adverse conditions.
Conclusions: The article results remarks that developed models was found to have higher performance and better selectivity than previously developed risk models, so they are better at predicting risks and serious adverse events in ICU. It is recommended to use AI based prediction models developed using EHRs in risk management studies. Future work is still needed to researches to predict different health conditions risks.
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Riley CM, Murphy LD, Mastropietro CW. Cardiac Arrest in Children Following Cardiac Surgery: A Scoping Review of Contributing Factors. World J Pediatr Congenit Heart Surg 2022; 13:475-481. [PMID: 35757944 DOI: 10.1177/21501351221100791] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Nearly half of children experiencing cardiac arrest following cardiac surgery do not survive hospital discharge and patients who survive often experience significant neurological impairment. Additionally, increased resource utilization following cardiac arrest translates into adverse logistical and financial consequences. Although some studies have identified patient characteristics that increase the risk of cardiac arrest after pediatric cardiac surgery, modifiable risk factors, which could provide a foundation for effective prevention strategies, have been elusive. This scoping review explores the current knowledge surrounding risk factors associated with cardiac arrest in children following cardiac surgery and provides recommendations for future research.
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Affiliation(s)
| | - Lee D Murphy
- Indiana University School of Medicine, Riley 548952Hospital for Children at Indiana University Health, Indianapolis, IN, USA
| | - Christopher W Mastropietro
- Indiana University School of Medicine, Riley 548952Hospital for Children at Indiana University Health, Indianapolis, IN, USA
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Abstract
Clinical informatics can support quality improvement and patient safety in the pediatric intensive care unit (PICU) in several ways including data extraction, analysis, and decision support enabled by electronic health records (EHRs), and databases and registries. Clinical decision support (CDS), embedded in EHRs, now an integral part of the workflow in the PICU, includes several tools and is increasingly leveraging artificial intelligence (AI). Understanding the opportunities and challenges can improve the engagement of clinicians with the design, validation, and implementation of CDS, improve satisfaction with CDS, and improve patient safety, care quality, and value.
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Simpao AF, Nelson O, Ahumada LM. Automated anesthesia artifact analysis: can machines be trained to take out the garbage? J Clin Monit Comput 2020; 35:225-227. [PMID: 32920701 DOI: 10.1007/s10877-020-00589-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 09/08/2020] [Indexed: 11/24/2022]
Affiliation(s)
- Allan F Simpao
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104-4399, USA.
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
| | - Olivia Nelson
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104-4399, USA
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Luis M Ahumada
- Department of Health Informatics, Predictive Analytics Core, Johns Hopkins All Children's Hospital, 501 6th Ave S, St. Petersburg, FL, 33701, USA
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore, MD, 21205, USA
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Ehrmann DE, Leopold DK, Phillips R, Shahi N, Campbell K, Ross M, Zablah JE, Moulton SL, Morgan G, Kim JS. The Compensatory Reserve Index Responds to Acute Hemodynamic Changes in Patients with Congenital Heart Disease: A Proof of Concept Study. Pediatr Cardiol 2020; 41:1190-1198. [PMID: 32474738 DOI: 10.1007/s00246-020-02374-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 05/22/2020] [Indexed: 12/17/2022]
Abstract
Patients with congenital heart disease (CHD) who undergo cardiac procedures may become hemodynamically unstable. Predictive algorithms that utilize dense physiologic data may be useful. The compensatory reserve index (CRI) trends beat-to-beat progression from normovolemia (CRI = 1) to decompensation (CRI = 0) in hemorrhagic shock by continuously analyzing unique sets of features in the changing pulse photoplethysmogram (PPG) waveform. We sought to understand if the CRI accurately reflects changing hemodynamics during and after a cardiac procedure for patients with CHD. A transcatheter pulmonary valve replacement (TcPVR) model was used because left ventricular stroke volume decreases upon sizing balloon occlusion of the right ventricular outflow tract (RVOT) and increases after successful valve placement. A single-center, prospective cohort study was performed. The CRI was continuously measured to determine the change in CRI before and after RVOT occlusion and successful TcPVR. Twenty-six subjects were enrolled with a median age of 19 (interquartile range (IQR) 13-29) years. The mean (± standard deviation) CRI decreased from 0.66 ± 0.15 1-min before balloon inflation to 0.53 ± 0.16 (p = 0.03) 1-min after balloon deflation. The mean CRI increased from a pre-valve mean CRI of 0.63 [95% confidence interval (CI) 0.56-0.70] to 0.77 (95% CI 0.71-0.83) after successful TcPVR. In this study, the CRI accurately reflected acute hemodynamic changes associated with TcPVR. Further research is justified to determine if the CRI can be useful as an early warning tool in patients with CHD at risk for decompensation during and after cardiac procedures.
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Affiliation(s)
- Daniel E Ehrmann
- Division of Cardiology, Department of Pediatrics, Children's Hospital Colorado, University of Colorado School of Medicine, 13123 East 16th Avenue, B100, Aurora, CO, 80045, USA.
| | - David K Leopold
- Department of Anesthesia, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ryan Phillips
- Division of Pediatric Surgery, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Niti Shahi
- Division of Pediatric Surgery, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kristen Campbell
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Michael Ross
- Division of Pediatric Cardiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jenny E Zablah
- Division of Cardiology, Department of Pediatrics, Children's Hospital Colorado, University of Colorado School of Medicine, 13123 East 16th Avenue, B100, Aurora, CO, 80045, USA
| | - Steven L Moulton
- Division of Pediatric Surgery, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Gareth Morgan
- Division of Cardiology, Department of Pediatrics, Children's Hospital Colorado, University of Colorado School of Medicine, 13123 East 16th Avenue, B100, Aurora, CO, 80045, USA
| | - John S Kim
- Division of Cardiology, Department of Pediatrics, Children's Hospital Colorado, University of Colorado School of Medicine, 13123 East 16th Avenue, B100, Aurora, CO, 80045, USA
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Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care. J Pediatr 2020; 221S:S3-S10. [PMID: 32482232 DOI: 10.1016/j.jpeds.2020.02.039] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 02/12/2020] [Accepted: 02/19/2020] [Indexed: 01/21/2023]
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