1
|
Kurita T, Oami T, Tochigi Y, Tomita K, Naito T, Atagi K, Fujitani S, Nakada TA. Machine learning algorithm for predicting 30-day mortality in patients receiving rapid response system activation: A retrospective nationwide cohort study. Heliyon 2024; 10:e32655. [PMID: 38961987 PMCID: PMC11219993 DOI: 10.1016/j.heliyon.2024.e32655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 06/06/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024] Open
Abstract
This study investigated the accuracy of a machine learning algorithm for predicting mortality in patients receiving rapid response system (RRS) activation. This retrospective cohort study used data from the In-Hospital Emergency Registry in Japan, which collects nationwide data on patients receiving RRS activation. The missing values in the dataset were replaced using multiple imputations (mode imputation, BayseRidge sklearn. linear model, and K-nearest neighbor model), and the enrolled patients were randomly assigned to the training and test cohorts. We established prediction models for 30-day mortality using the following four types of machine learning classifiers: Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting, random forest, and neural network. Fifty-two variables (patient characteristics, details of RRS activation, reasons for RRS initiation, and hospital capacity) were used to construct the prediction algorithm. The primary outcome was the accuracy of the prediction model for 30-day mortality. Overall, the data from 4,997 patients across 34 hospitals were analyzed. The machine learning algorithms using LightGBM demonstrated the highest predictive value for 30-day mortality (area under the receiver operating characteristic curve, 0.860 [95 % confidence interval, 0.825-0.895]). The SHapley Additive exPlanations summary plot indicated that hospital capacity, site of incidence, code status, and abnormal vital signs within 24 h were important variables in the prediction model for 30-day mortality.
Collapse
Affiliation(s)
- Takeo Kurita
- Chiba University Graduate School of Medicine, Department of Emergency and Critical Care Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
| | - Takehiko Oami
- Chiba University Graduate School of Medicine, Department of Emergency and Critical Care Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
| | - Yoko Tochigi
- Smart119 Inc., 7th Floor, Chiba Chuo Twin Building No. 2, 2-5-1 Chuo, Chiba, 260-0013, Japan
| | - Keisuke Tomita
- Chiba University Graduate School of Medicine, Department of Emergency and Critical Care Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
| | - Takaki Naito
- Department of Emergency and Critical Care Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae-ku, Kawasaki-shi, Kanagawa, 216-8511, Japan
| | - Kazuaki Atagi
- Intensive Care Unit, Nara General Medical Center, 2-897-5, Shichijonishi, Nara-shi, Nara, 630-8581, Japan
| | - Shigeki Fujitani
- Department of Emergency and Critical Care Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae-ku, Kawasaki-shi, Kanagawa, 216-8511, Japan
| | - Taka-aki Nakada
- Chiba University Graduate School of Medicine, Department of Emergency and Critical Care Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
- Smart119 Inc., 7th Floor, Chiba Chuo Twin Building No. 2, 2-5-1 Chuo, Chiba, 260-0013, Japan
| | - IHER-J collaborators
- Chiba University Graduate School of Medicine, Department of Emergency and Critical Care Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
- Smart119 Inc., 7th Floor, Chiba Chuo Twin Building No. 2, 2-5-1 Chuo, Chiba, 260-0013, Japan
- Department of Emergency and Critical Care Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae-ku, Kawasaki-shi, Kanagawa, 216-8511, Japan
- Intensive Care Unit, Nara General Medical Center, 2-897-5, Shichijonishi, Nara-shi, Nara, 630-8581, Japan
| |
Collapse
|
2
|
Cho KJ, Kim KH, Choi J, Yoo D, Kim J. External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study. Crit Care Med 2024; 52:e110-e120. [PMID: 38381018 PMCID: PMC10876170 DOI: 10.1097/ccm.0000000000006137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
OBJECTIVES The limitations of current early warning scores have prompted the development of deep learning-based systems, such as deep learning-based cardiac arrest risk management systems (DeepCARS). Unfortunately, in South Korea, only two institutions operate 24-hour Rapid Response System (RRS), whereas most hospitals have part-time or no RRS coverage at all. This study validated the predictive performance of DeepCARS during RRS operation and nonoperation periods and explored its potential beyond RRS operating hours. DESIGN Retrospective cohort study. SETTING In this 1-year retrospective study conducted at Yonsei University Health System Severance Hospital in South Korea, DeepCARS was compared with conventional early warning systems for predicting in-hospital cardiac arrest (IHCA). The study focused on adult patients admitted to the general ward, with the primary outcome being IHCA-prediction performance within 24 hours of the alarm. PATIENTS We analyzed the data records of adult patients admitted to a general ward from September 1, 2019, to August 31, 2020. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Performance evaluation was conducted separately for the operational and nonoperational periods of the RRS, using the area under the receiver operating characteristic curve (AUROC) as the metric. DeepCARS demonstrated a superior AUROC as compared with the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS), both during RRS operating and nonoperating hours. Although the MEWS and NEWS exhibited varying performance across the two periods, DeepCARS showed consistent performance. CONCLUSIONS The accuracy and efficiency for predicting IHCA of DeepCARS were superior to that of conventional methods, regardless of whether the RRS was in operation. These findings emphasize that DeepCARS is an effective screening tool suitable for hospitals with full-time RRS, part-time RRS, and even those without any RRS.
Collapse
Affiliation(s)
- Kyung-Jae Cho
- Department of Research and Development, VUNO, Seoul, Republic of Korea
| | - Kwan Hyung Kim
- Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jaewoo Choi
- Department of Research and Development, VUNO, Seoul, Republic of Korea
| | - Dongjoon Yoo
- Department of Research and Development, VUNO, Seoul, Republic of Korea
- Department of Critical Care Medicine and Emergency Medicine, Inha University Hospital, Incheon, Republic of Korea
| | - Jeongmin Kim
- Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea
| |
Collapse
|
3
|
Jeon Y, Kim YS, Jang W, Park JD, Lee B. Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards. Sci Rep 2024; 14:4707. [PMID: 38409469 PMCID: PMC10897152 DOI: 10.1038/s41598-024-55528-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 02/24/2024] [Indexed: 02/28/2024] Open
Abstract
Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables. This retrospective observational study included patients under the age of 18 who were admitted to the general ward of a tertiary children's hospital between 2020 and 2022. A critical event was defined as cardiopulmonary resuscitation, unplanned transfer to the intensive care unit, or mortality. The vital signs measured during hospitalization, their measurement intervals, sex, and age were used to train a critical event prediction model. Age-specific z-scores were used to normalize the variability of the normal range by age. The entire dataset was classified into a training dataset and a test dataset at an 8:2 ratio, and model learning and testing were performed on each dataset. The predictive performance of the developed model showed excellent results, with an area under the receiver operating characteristics curve of 0.986 and an area under the precision-recall curve of 0.896. We developed a deep learning model with outstanding predictive power using simplified variables to effectively predict critical events while reducing the workload of medical staff. Nevertheless, because this was a single-center trial, no external validation was carried out, prompting further investigation.
Collapse
Affiliation(s)
- Yonghyuk Jeon
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - You Sun Kim
- Department of Pediatrics, National Medical Center, Seoul, Republic of Korea
| | - Wonjin Jang
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - June Dong Park
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Bongjin Lee
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
| |
Collapse
|
4
|
Rusin CG, Acosta SI, Brady KM, Vu E, Scahill C, Fonseca B, Barrett C, Simsic J, Yates AR, Klepczynski B, Gaynor WJ, Penny DJ. Automated prediction of cardiorespiratory deterioration in patients with single-ventricle parallel circulation: A multicenter validation study. JTCVS OPEN 2023; 15:406-411. [PMID: 37808061 PMCID: PMC10556807 DOI: 10.1016/j.xjon.2023.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/13/2023] [Accepted: 05/02/2023] [Indexed: 10/10/2023]
Abstract
Objectives Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first- and second-stage palliation surgeries. Detection of deterioration episodes may allow for early intervention and improved outcomes. Methods A prospective study was executed at Nationwide Children's Hospital, Children's Hospital of Philadelphia, and Children's Hospital Colorado to collect physiologic data of subjects with single ventricle physiology during all hospitalizations between neonatal palliation and II surgeries using the Sickbay software platform (Medical Informatics Corp). Timing of cardiorespiratory deterioration events was captured via chart review. The predictive algorithm previously developed and validated at Texas Children's Hospital was applied to these data without retraining. Standard metrics such as receiver operating curve area, positive and negative likelihood ratio, and alert rates were calculated to establish clinical performance of the predictive algorithm. Results Our cohort consisted of 58 subjects admitted to the cardiac intensive care unit and stepdown units of participating centers over 14 months. Approximately 28,991 hours of high-resolution physiologic waveform and vital sign data were collected using the Sickbay. A total of 30 cardiorespiratory deterioration events were observed. the risk index metric generated by our algorithm was found to be both sensitive and specific for detecting impending events one to two hours in advance of overt extremis (receiver operating curve = 0.927). Conclusions Our algorithm can provide a 1- to 2-hour advanced warning for 53.6% of all cardiorespiratory deterioration events in children with single ventricle physiology during their initial postop course as well as interstage hospitalizations after stage I palliation with only 2.5 alarms being generated per patient per day.
Collapse
Affiliation(s)
- Craig G. Rusin
- Department of Pediatrics—Cardiology, Baylor College of Medicine, Texas Children's Hospital, Houston, Tex
| | - Sebastian I. Acosta
- Department of Pediatrics—Cardiology, Baylor College of Medicine, Texas Children's Hospital, Houston, Tex
| | - Kennith M. Brady
- Department of Anesthesiology, Northwestern University, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill
| | - Eric Vu
- Department of Anesthesiology, Northwestern University, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill
| | - Carly Scahill
- Department of Pediatrics—Cardiology, Children's Hospital Colorado, Aurora, Colo
| | - Brian Fonseca
- Department of Pediatrics—Cardiology, Children's Hospital Colorado, Aurora, Colo
| | - Cindy Barrett
- Department of Pediatrics—Cardiology, Children's Hospital Colorado, Aurora, Colo
| | - Janet Simsic
- Department of Pediatrics—Cardiology, Nationwide Children's Hospital, Columbus, Ohio
| | - Andrew R. Yates
- Department of Pediatrics—Cardiology, Nationwide Children's Hospital, Columbus, Ohio
| | - Brenna Klepczynski
- Department of Cardiovascular Surgery, Children's Hospital of Philadelphia, Philadelphia, Pa
| | - William J. Gaynor
- Department of Cardiovascular Surgery, Children's Hospital of Philadelphia, Philadelphia, Pa
| | - Daniel J. Penny
- Department of Pediatrics—Cardiology, Baylor College of Medicine, Texas Children's Hospital, Houston, Tex
| |
Collapse
|
5
|
Zoodsma RS, Bosch R, Alderliesten T, Bollen CW, Kappen TH, Koomen E, Siebes A, Nijman J. Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development. JMIR Cardio 2023; 7:e45190. [PMID: 37191988 DOI: 10.2196/45190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/16/2023] [Accepted: 04/24/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Critical congenital heart disease (cCHD)-requiring cardiac intervention in the first year of life for survival-occurs globally in 2-3 of every 1000 live births. In the critical perioperative period, intensive multimodal monitoring at a pediatric intensive care unit (PICU) is warranted, as their organs-especially the brain-may be severely injured due to hemodynamic and respiratory events. These 24/7 clinical data streams yield large quantities of high-frequency data, which are challenging in terms of interpretation due to the varying and dynamic physiology innate to cCHD. Through advanced data science algorithms, these dynamic data can be condensed into comprehensible information, reducing the cognitive load on the medical team and providing data-driven monitoring support through automated detection of clinical deterioration, which may facilitate timely intervention. OBJECTIVE This study aimed to develop a clinical deterioration detection algorithm for PICU patients with cCHD. METHODS Retrospectively, synchronous per-second data of cerebral regional oxygen saturation (rSO2) and 4 vital parameters (respiratory rate, heart rate, oxygen saturation, and invasive mean blood pressure) in neonates with cCHD admitted to the University Medical Center Utrecht, the Netherlands, between 2002 and 2018 were extracted. Patients were stratified based on mean oxygen saturation during admission to account for physiological differences between acyanotic and cyanotic cCHD. Each subset was used to train our algorithm in classifying data as either stable, unstable, or sensor dysfunction. The algorithm was designed to detect combinations of parameters abnormal to the stratified subpopulation and significant deviations from the patient's unique baseline, which were further analyzed to distinguish clinical improvement from deterioration. Novel data were used for testing, visualized in detail, and internally validated by pediatric intensivists. RESULTS A retrospective query yielded 4600 hours and 209 hours of per-second data in 78 and 10 neonates for, respectively, training and testing purposes. During testing, stable episodes occurred 153 times, of which 134 (88%) were correctly detected. Unstable episodes were correctly noted in 46 of 57 (81%) observed episodes. Twelve expert-confirmed unstable episodes were missed in testing. Time-percentual accuracy was 93% and 77% for, respectively, stable and unstable episodes. A total of 138 sensorial dysfunctions were detected, of which 130 (94%) were correct. CONCLUSIONS In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations and simultaneous parameter-shifting (ie, population-specific) proofs would be promising with respect to enhancing applicability to heterogeneous critically ill pediatric populations. After prospective validation, the current-and comparable-models may, in the future, be used in the automated detection of clinical deterioration and eventually provide data-driven monitoring support to the medical team, allowing for timely intervention.
Collapse
Affiliation(s)
- Ruben S Zoodsma
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Rian Bosch
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Thomas Alderliesten
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Casper W Bollen
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Teus H Kappen
- Department of Anaesthesiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Erik Koomen
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Arno Siebes
- Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
| | - Joppe Nijman
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| |
Collapse
|
6
|
The use of machine learning and artificial intelligence within pediatric critical care. Pediatr Res 2023; 93:405-412. [PMID: 36376506 PMCID: PMC9660024 DOI: 10.1038/s41390-022-02380-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 09/15/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022]
Abstract
The field of pediatric critical care has been hampered in the era of precision medicine by our inability to accurately define and subclassify disease phenotypes. This has been caused by heterogeneity across age groups that further challenges the ability to perform randomized controlled trials in pediatrics. One approach to overcome these inherent challenges include the use of machine learning algorithms that can assist in generating more meaningful interpretations from clinical data. This review summarizes machine learning and artificial intelligence techniques that are currently in use for clinical data modeling with relevance to pediatric critical care. Focus has been placed on the differences between techniques and the role of each in the clinical arena. The various forms of clinical decision support that utilize machine learning are also described. We review the applications and limitations of machine learning techniques to empower clinicians to make informed decisions at the bedside. IMPACT: Critical care units generate large amounts of under-utilized data that can be processed through artificial intelligence. This review summarizes the machine learning and artificial intelligence techniques currently being used to process clinical data. The review highlights the applications and limitations of these techniques within a clinical context to aid providers in making more informed decisions at the bedside.
Collapse
|
7
|
Tang Q, Cen X, Pan C. Explainable and efficient deep early warning system for cardiac arrest prediction from electronic health records. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:9825-9841. [PMID: 36031970 DOI: 10.3934/mbe.2022457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Cardiac arrest (CA) is a fatal acute event. The development of new CA early warning system based on time series of vital signs from electronic health records (EHR) has great potential to reduce CA damage. In this process, recursive architecture-based deep learning, as a powerful tool for time series data processing, enables automatically extract features from various monitoring clinical parameters and to further improve the performance for acute critical illness prediction. However, the unexplainable nature and excessive time caused by black box structure with poor parallelism are the limitations of its development, especially in the CA clinical application with strict requirement of emergency treatment and low hidden dangers. In this study, we present an explainable and efficient deep early warning system for CA prediction, which features are captured by an efficient temporal convolutional network (TCN) on EHR clinical parameters sequence and explained by deep Taylor decomposition (DTD) theoretical framework. To demonstrate the feasibility of our method and further evaluate its performance, prediction and explanation experiments were performed. Experimental results show that our method achieves superior CA prediction accuracy compared with standard national early warning score (NEWS), in terms of overall AUROC (0.850 Vs. 0.476) and F1-Score (0.750 Vs. 0.450). Furthermore, our method improves the interpretability and efficiency of deep learning-based CA early warning system. It provides the relevance of prediction results for each clinical parameter and about 1.7 times speed enhancement for system calculation compared with the long short-term memory network.
Collapse
Affiliation(s)
- Qinhua Tang
- Shanghai Chest Hospital, Shanghai Jiaotong University, 241 West Huaihai Road, Shanghai, China
| | - Xingxing Cen
- Shanghai Chest Hospital, Shanghai Jiaotong University, 241 West Huaihai Road, Shanghai, China
| | - Changqing Pan
- Shanghai Chest Hospital, Shanghai Jiaotong University, 241 West Huaihai Road, Shanghai, China
| |
Collapse
|
8
|
Kattner AA. About gladiators and a sacred disease. Biomed J 2022; 45:1-8. [PMID: 35339730 PMCID: PMC9133364 DOI: 10.1016/j.bj.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 12/01/2022] Open
Abstract
In this special edition of the Biomedical Journal the reader gains an insight into drug-resistant epilepsy and according treatment approaches involving deep brain stimulation, the ketogenic diet and fecal microbiota transplant. Another emphasis is put on personalized medicine strategies, and covered in articles about the use of natriuretic peptides against cancer, along with an article about companion diagnostics involving extracellular vesicles. Recurrent infection with Clostridium difficile, associated risk factors and therapeutic options are discussed. We learn about a mechanism that helps Leishmania evade a host control mechanism, receive an update about human adenovirus and are presented with characteristic magnetic resonance neuroimaging in COVID-19 pediatric patients. An advanced assessment in pediatric septic shock and an improved model for a pediatric early warning system are proposed. Some of the genetic causes of renal hypomagnesemia are explored, the impact of air pollution on children is examined, and an antisiphon device is described for surgical treatment of hydrocephalus. The relation between energy metabolism, circadian rhythm and its influence on the ATPase in the SCN are investigated, and among others some of the genetics influencing smoking duration and lung cancer. Finally it is discussed how embryo quality can be improved in in vitro fertilization, and what impact high estradiol has on blastocyst implantation. The outcome of surgery to correct mandibular deficiency is assessed, and in two letters the inclusion of observational studies in the evaluation of clinical trials related to COVID-19 is elaborated.
Collapse
|
9
|
Garcia-Canadilla P, Isabel-Roquero A, Aurensanz-Clemente E, Valls-Esteve A, Miguel FA, Ormazabal D, Llanos F, Sanchez-de-Toledo J. Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery. Front Pediatr 2022; 10:930913. [PMID: 35832588 PMCID: PMC9271800 DOI: 10.3389/fped.2022.930913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/06/2022] [Indexed: 11/18/2022] Open
Abstract
Pediatric congenital heart disease (CHD) patients are at higher risk of postoperative complications and clinical deterioration either due to their underlying pathology or due to the cardiac surgery, contributing significantly to mortality, morbidity, hospital and family costs, and poor quality of life. In current clinical practice, clinical deterioration is detected, in most of the cases, when it has already occurred. Several early warning scores (EWS) have been proposed to assess children at risk of clinical deterioration using vital signs and risk indicators, in order to intervene in a timely manner to reduce the impact of deterioration and risk of death among children. However, EWS are based on measurements performed at a single time point without incorporating trends nor providing information about patient's risk trajectory. Moreover, some of these measurements rely on subjective assessment making them susceptible to different interpretations. All these limitations could explain why the implementation of EWS in high-resource settings failed to show a significant decrease in hospital mortality. By means of machine learning (ML) based algorithms we could integrate heterogeneous and complex data to predict patient's risk of deterioration. In this perspective article, we provide a brief overview of the potential of ML technologies to improve the identification of pediatric CHD patients at high-risk for clinical deterioration after cardiac surgery, and present the CORTEX traffic light, a ML-based predictive system that Sant Joan de Déu Barcelona Children's Hospital is implementing, as an illustration of the application of an ML-based risk stratification system in a relevant hospital setting.
Collapse
Affiliation(s)
- Patricia Garcia-Canadilla
- BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Sant Joan de Déu and Hospital Clínic, University of Barcelona, Barcelona, Spain.,Cardiovascular Diseases and Child Development, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Alba Isabel-Roquero
- Department of Pediatric Cardiology, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain.,BCNMedTech, Universitat Pompeu Fabra, Barcelona, Spain
| | - Esther Aurensanz-Clemente
- Cardiovascular Diseases and Child Development, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain.,Department of Pediatric Cardiology, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain
| | - Arnau Valls-Esteve
- Innovation in Health Technologies, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Francesca Aina Miguel
- Department of Engineering, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain
| | - Daniel Ormazabal
- Department of Informatics, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain
| | - Floren Llanos
- Department of Informatics, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain
| | - Joan Sanchez-de-Toledo
- Cardiovascular Diseases and Child Development, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain.,Department of Pediatric Cardiology, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain.,Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| |
Collapse
|
10
|
Alamgir A, Mousa O, Shah Z. Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review. JMIR Med Inform 2021; 9:e30798. [PMID: 34927595 PMCID: PMC8726033 DOI: 10.2196/30798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/07/2021] [Accepted: 10/10/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). CONCLUSIONS AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.
Collapse
Affiliation(s)
- Asma Alamgir
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Osama Mousa
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| |
Collapse
|