1
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Wang L, Wu YH, Ren Y, Sun FF, Tao SH, Lin HX, Zhang CS, Tang W, Chen ZG, Chen C, Zhang LD. Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis. Pediatr Infect Dis J 2024; 43:736-742. [PMID: 38717173 DOI: 10.1097/inf.0000000000004376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/19/2024]
Abstract
BACKGROUND Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU). STUDY DESIGN This retrospective observational study was conducted in the PICUs of the First Affiliated Hospital of Sun Yat-sen University from December 2016 to June 2019 and Shenzhen Children's Hospital from January 2019 to July 2020. The children were divided into a death group and a survival group. Different machine language (ML) models were used to predict the risk of death in children with sepsis. RESULTS A total of 671 children with sepsis were enrolled. The accuracy (ACC) of the artificial neural network model was better than that of support vector machine, logical regression analysis, Bayesian, K nearest neighbor method and decision tree models, with a training set ACC of 0.99 and a test set ACC of 0.96. CONCLUSIONS The AI model can be used to predict the risk of death due to sepsis in children in the PICU, and the artificial neural network model is better than other AI models in predicting mortality risk.
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Affiliation(s)
- Li Wang
- From the Pediatric Intensive Care Unit, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Yu-Hui Wu
- Pediatric Intensive Care Unit, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Yong Ren
- Scientific Research Project Department, Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Pazhou Lab, Guangzhou, Guangdong, China
- Shensi lab, Shenzhen Institute for Advanced Study, UESTC, Shenzhen, Guangdong, China
- Center for Digestive Disease, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Fan-Fan Sun
- From the Pediatric Intensive Care Unit, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Shao-Hua Tao
- Pediatric Intensive Care Unit, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China
| | - Hong-Xin Lin
- From the Pediatric Intensive Care Unit, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Chuang-Sen Zhang
- From the Pediatric Intensive Care Unit, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Wen Tang
- Pediatric Intensive Care Unit, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Zhuang-Gui Chen
- Pediatric Intensive Care Unit, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Chun Chen
- Pediatric Hematology Laboratory, Division of Hematology/Oncology, Department of Pediatrics, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Li-Dan Zhang
- From the Pediatric Intensive Care Unit, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
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Kittrell HD, Shaikh A, Adintori PA, McCarthy P, Kohli-Seth R, Nadkarni GN, Sakhuja A. Role of artificial intelligence in critical care nutrition support and research. Nutr Clin Pract 2024. [PMID: 39073166 DOI: 10.1002/ncp.11194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 06/06/2024] [Accepted: 06/28/2024] [Indexed: 07/30/2024] Open
Abstract
Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.
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Affiliation(s)
- Hannah D Kittrell
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ahmed Shaikh
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Peter A Adintori
- Food and Nutrition Services Department, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Program in Rehabilitation Sciences, New York University Steinhardt, New York, New York, USA
| | - Paul McCarthy
- Department of Cardiovascular and Thoracic Surgery, Division of Cardiovascular Critical Care, West Virginia University, Morgantown, West Virginia, USA
| | - Roopa Kohli-Seth
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ankit Sakhuja
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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3
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Horiguchi D, Shin S, Pepino JA, Peterson JT, Kehoe IE, Goldstein JN, Lee J, Kwon BK, Hahn JO, Reisner AT. Hypotension During Vasopressor Infusion Occurs in Predictable Clusters: A Multicenter Analysis. J Intensive Care Med 2024; 39:683-692. [PMID: 38282376 DOI: 10.1177/08850666241226893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Background: Published evidence indicates that mean arterial pressure (MAP) below a goal range (hypotension) is associated with worse outcomes, though MAP management failures are common. We sought to characterize hypotension occurrences in ICUs and consider the implications for MAP management. Methods: Retrospective analysis of 3 hospitals' cohorts of adult ICU patients during continuous vasopressor infusion. Two cohorts were general, mixed ICU patients and one was exclusively acute spinal cord injury patients. "Hypotension-clusters" were defined where there were ≥10 min of cumulative hypotension over a 60-min period and "constant hypotension" was ≥10 continuous minutes. Trend analysis was performed (predicting future MAP using 14 min of preceding MAP data) to understand which hypotension-clusters could likely have been predicted by clinician awareness of MAP trends. Results: In cohorts of 155, 66, and 16 ICU stays, respectively, the majority of hypotension occurred within the hypotension-clusters. Failures to keep MAP above the hypotension threshold were notable in the bottom quartiles of each cohort, with hypotension durations of 436, 167, and 468 min, respectively, occurring within hypotension-clusters per day. Mean arterial pressure trend analysis identified most hypotension-clusters before any constant hypotension occurred (81.2%-93.6% sensitivity, range). The positive predictive value of hypotension predictions ranged from 51.4% to 72.9%. Conclusions: Across 3 cohorts, most hypotension occurred in temporal clusters of hypotension that were usually predictable from extrapolation of MAP trends.
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Affiliation(s)
- Daisuke Horiguchi
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
- Nihon Kohden Innovation Center, LLC, Cambridge, MA, USA
| | - Sungtae Shin
- Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
| | - Jeremy A Pepino
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jeffrey T Peterson
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Iain E Kehoe
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Joshua N Goldstein
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jarone Lee
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston MA, USA
| | - Brian K Kwon
- Department of Orthopaedics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
| | - Andrew T Reisner
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
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4
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Liu L, Hang Y, Chen R, He X, Jin X, Wu D, Li Y. LDSG-Net: an efficient lightweight convolutional neural network for acute hypotensive episode prediction during ICU hospitalization. Physiol Meas 2024; 45:065003. [PMID: 38772397 DOI: 10.1088/1361-6579/ad4e92] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 05/21/2024] [Indexed: 05/23/2024]
Abstract
Objective. Acute hypotension episode (AHE) is one of the most critical complications in intensive care unit (ICU). A timely and precise AHE prediction system can provide clinicians with sufficient time to respond with proper therapeutic measures, playing a crucial role in saving patients' lives. Recent studies have focused on utilizing more complex models to improve predictive performance. However, these models are not suitable for clinical application due to limited computing resources for bedside monitors.Approach. To address this challenge, we propose an efficient lightweight dilated shuffle group network. It effectively incorporates shuffling operations into grouped convolutions on the channel and dilated convolutions on the temporal dimension, enhancing global and local feature extraction while reducing computational load.Main results. Our benchmarking experiments on the MIMIC-III and VitalDB datasets, comprising 6036 samples from 1304 patients and 2958 samples from 1047 patients, respectively, demonstrate that our model outperforms other state-of-the-art lightweight CNNs in terms of balancing parameters and computational complexity. Additionally, we discovered that the utilization of multiple physiological signals significantly improves the performance of AHE prediction. External validation on the MIMIC-IV dataset confirmed our findings, with prediction accuracy for AHE 5 min prior reaching 93.04% and 92.04% on the MIMIC-III and VitalDB datasets, respectively, and 89.47% in external verification.Significance. Our study demonstrates the potential of lightweight CNN architectures in clinical applications, providing a promising solution for real-time AHE prediction under resource constraints in ICU settings, thereby marking a significant step forward in improving patient care.
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Affiliation(s)
- Longfei Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Yujie Hang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- College of Artificial Intelligence University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Rongqin Chen
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Xianliang He
- Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, Guangdong, People's Republic of China
| | - Xingliang Jin
- Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, Guangdong, People's Republic of China
| | - Dan Wu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Ye Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
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5
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Soliman MM, Marshall C, Kimball JP, Choudhary T, Clermont G, Pinsky MR, Buchman TG, Coopersmith CM, Inan OT, Kamaleswaran R. Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock. Biomed Signal Process Control 2024; 92:105974. [PMID: 38559667 PMCID: PMC10977921 DOI: 10.1016/j.bspc.2024.105974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importance. However, earlier recognition of shock requires active monitoring, which may be delayed due to subclinical manifestations of the disease at the early phase of onset. Machine learning systems can increase timely detection of shock onset by exploiting complex interactions among continuous physiological waveforms. We use a dataset consisting of high-resolution physiological waveforms from intensive care unit (ICU) of a tertiary hospital system. We investigate the use of mean arterial blood pressure (MAP), pulse arrival time (PAT), heart rate variability (HRV), and heart rate (HR) for the early prediction of shock onset. Using only five minutes of the aforementioned vital signals from 239 ICU patients, our developed models can accurately predict septic shock onset 6 to 36 hours prior to clinical recognition with area under the receiver operating characteristic (AUROC) of 0.84 and 0.8 respectively. This work lays foundations for a robust, efficient, accurate and early prediction of septic shock onset which may help clinicians in their decision-making processes. This study introduces machine learning models that provide fast and accurate predictions of septic shock onset times up to 36 hours in advance. BP, PAT and HR dynamics can independently predict septic shock onset with a look-back period of only 5 mins.
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Affiliation(s)
- Moamen M. Soliman
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA
| | - Curtis Marshall
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, 30322, GA, USA
| | - Jacob P. Kimball
- School of Biomedical and Electrical Engineering, University of Portland, Portland, 97203, OR, USA
| | - Tilendra Choudhary
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, 30322, GA, USA
| | - Gilles Clermont
- School of Medicine, University of Pittsburgh, Pittsburgh, 15213, PA, USA
| | - Michael R. Pinsky
- School of Medicine, University of Pittsburgh, Pittsburgh, 15213, PA, USA
| | - Timothy G. Buchman
- Department of Surgery and Emory Critical Care Center, Emory University School of Medicine, Atlanta, 30322, GA, USA
| | - Craig M. Coopersmith
- Department of Surgery and Emory Critical Care Center, Emory University School of Medicine, Atlanta, 30322, GA, USA
| | - Omer T. Inan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, 30322, GA, USA
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA
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6
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Nakanishi T, Tsuji T, Tamura T, Fujiwara K, Sobue K. Development and Validation of a Prediction Model for Acute Hypotensive Events in Intensive Care Unit Patients. J Clin Med 2024; 13:2786. [PMID: 38792329 PMCID: PMC11122431 DOI: 10.3390/jcm13102786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Background: Persistent hypotension in the intensive care unit (ICU) is associated with increased mortality. Predicting acute hypotensive events can lead to timely intervention. We aimed to develop a prediction model of acute hypotensive events in patients admitted to the ICU. Methods: We included adult patients admitted to the Nagoya City University (NCU) Hospital ICU between January 2018 and December 2021 for model training and internal validation. The MIMIC-III database was used for external validation. A hypotensive event was defined as a mean arterial pressure < 60 mmHg for at least 5 min in 10 min. The input features were age, sex, and time-series data for vital signs. We compared the area under the receiver-operating characteristic curve (AUROC) of three machine-learning algorithms: logistic regression, the light gradient boosting machine (LightGBM), and long short-term memory (LSTM). Results: Acute hypotensive events were found in 1325/1777 (74.6%) and 2691/5266 (51.1%) of admissions in the NCU and MIMIC-III cohorts, respectively. In the internal validation, the LightGBM model had the highest AUROC (0.835), followed by the LSTM (AUROC 0.834) and logistic regression (AUROC 0.821) models. Applying only blood pressure-related features, the LSTM model achieved the highest AUROC (0.843) and consistently showed similar results in external and internal validation. Conclusions: The LSTM model using only blood pressure-related features had the highest AUROC with comparable performance in external validation.
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Affiliation(s)
- Toshiyuki Nakanishi
- Department of Anesthesiology and Intensive Care Medicine, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya 467-8601, Japan
- Department of Materials Process Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
| | - Tatsuya Tsuji
- Department of Anesthesiology and Intensive Care Medicine, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya 467-8601, Japan
| | - Tetsuya Tamura
- Department of Anesthesiology and Intensive Care Medicine, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya 467-8601, Japan
| | - Koichi Fujiwara
- Department of Materials Process Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
| | - Kazuya Sobue
- Department of Anesthesiology and Intensive Care Medicine, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya 467-8601, Japan
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7
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Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne) 2024; 10:1174429. [PMID: 38264049 PMCID: PMC10804886 DOI: 10.3389/fmed.2023.1174429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 11/09/2023] [Indexed: 01/25/2024] Open
Abstract
The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chaodong Chen
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Kai Chen
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Ming Chen
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yaolong Chen
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yating Cui
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qi Ding
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Renyu Ding
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tao Gao
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xiaohua Gu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongli He
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jiawei He
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Huang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaobo Huang
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Huizhen Jiang
- Department of Information Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Jiang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Yunping Lan
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jun Li
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Linfeng Li
- Medical Data Research Institute, Chongqing Medical University, Chongqing, China
| | - Lu Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenxiong Li
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Yongzai Li
- Information Network Center, QiLu Hospital, ShanDong University, Jinan, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xufei Luo
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Feng Lyu
- Department of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Miao
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwen Shen
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Yinghuan Shi
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Qihang Sun
- British Chinese Society of Health Informatics, Beijing, China
| | - Weijun Sun
- Faculty of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhiyun Tang
- Department of Intensive Care Unit, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Emergency and Intensive Care Unit Center, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bo Wang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Haijun Wang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongliang Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Luhao Wang
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Sicong Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhanwen Wang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Zhong Wang
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Dong Wei
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Jianfeng Wu
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xuezhong Xing
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jin Yang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Xianghong Yang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangquan Yu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenkui Yu
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yuan Yu
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Hao Yuan
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Qian Zhai
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Hao Zhang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Meng Zhang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunguang Zhao
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Ruiqiang Zheng
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lei Zhong
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguo Zhu
- Department of General Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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8
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Rooney SR, Kaufman R, Murugan R, Kashani KB, Pinsky MR, Al-Zaiti S, Dubrawski A, Clermont G, Miller JK. Forecasting imminent atrial fibrillation in long-term electrocardiogram recordings. J Electrocardiol 2023; 81:111-116. [PMID: 37683575 PMCID: PMC10841237 DOI: 10.1016/j.jelectrocard.2023.08.011] [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: 05/12/2023] [Revised: 08/03/2023] [Accepted: 08/12/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND Despite the morbidity associated with acute atrial fibrillation (AF), no models currently exist to forecast its imminent onset. We sought to evaluate the ability of deep learning to forecast the imminent onset of AF with sufficient lead time, which has important implications for inpatient care. METHODS We utilized the Physiobank Long-Term AF Database, which contains 24-h, labeled ECG recordings from patients with a history of AF. AF episodes were defined as ≥5 min of sustained AF. Three deep learning models incorporating convolutional and transformer layers were created for forecasting, with two models focusing on the predictive nature of sinus rhythm segments and AF epochs separately preceding an AF episode, and one model utilizing all preceding waveform as input. Cross-validated performance was evaluated using area under time-dependent receiver operating characteristic curves (AUC(t)) at 7.5-, 15-, 30-, and 60-min lead times, precision-recall curves, and imminent AF risk trajectories. RESULTS There were 367 AF episodes from 84 ECG recordings. All models showed average risk trajectory divergence of those with an AF episode from those without ∼15 min before the episode. Highest AUC was associated with the sinus rhythm model [AUC = 0.74; 7.5-min lead time], though the model using all preceding waveform data had similar performance and higher AUCs at longer lead times. CONCLUSIONS In this proof-of-concept study, we demonstrated the potential utility of neural networks to forecast the onset of AF in long-term ECG recordings with a clinically relevant lead time. External validation in larger cohorts is required before deploying these models clinically.
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Affiliation(s)
- Sydney R Rooney
- Department of Pediatrics, Children's Hospital of Pittsburgh, 4401 Penn Ave, Pittsburgh, PA 15224, USA.
| | - Roman Kaufman
- Auton Lab, Carnegie Mellon University, Newell Simon Hall 3128, Forbes Ave, Pittsburgh, PA 15213, USA.
| | - Raghavan Murugan
- Program for Critical Care Nephrology, Department of Critical Care Medicine. University of Pittsburgh School of Medicine, 3550 Terrace Street, Alan Magee Scaife Hall, Suite 600, Pittsburgh, PA 15213, USA.
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA.
| | - Michael R Pinsky
- Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street Alan Magee Scaife Hall, Suite 600, Pittsburgh, PA, 15213 Pittsburgh, PA, USA.
| | - Salah Al-Zaiti
- Department of Acute & Tertiary Care, University of Pittsburgh Medical Center, School of Nursing, 3500 Victoria Street, Victoria Building, Pittsburgh, PA 15261, USA.
| | - Artur Dubrawski
- Auton Lab, Carnegie Mellon University, Newell Simon Hall 3128, Forbes Ave, Pittsburgh, PA 15213, USA.
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street Alan Magee Scaife Hall, Suite 600, Pittsburgh, PA, 15213 Pittsburgh, PA, USA.
| | - J Kyle Miller
- Auton Lab, Carnegie Mellon University, Newell Simon Hall 3128, Forbes Ave, Pittsburgh, PA 15213, USA.
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9
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Rooney SR, Clermont G. Forecasting algorithms in the ICU. J Electrocardiol 2023; 81:253-257. [PMID: 37883866 DOI: 10.1016/j.jelectrocard.2023.09.015] [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: 05/16/2023] [Revised: 08/18/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023]
Abstract
Despite significant advances in modeling methods and access to large datasets, there are very few real-time forecasting systems deployed in highly monitored environment such as the intensive care unit. Forecasting models may be developed as classification, regression or time-to-event tasks; each could be using a variety of machine learning algorithms. An accurate and useful forecasting systems include several components beyond a forecasting model, and its performance is assessed using end-user-centered metrics. Several barriers to implementation and acceptance persist and clinicians will play an active role in the successful deployment of this promising technology.
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Affiliation(s)
- Sydney R Rooney
- Department of Pediatrics, Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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10
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Makonnen N, Layng T, Hartka T. Comparison of mortality in emergency department patients with immediate versus delayed hypotension. Am J Emerg Med 2023; 72:1-6. [PMID: 37437384 DOI: 10.1016/j.ajem.2023.06.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 06/16/2023] [Accepted: 06/19/2023] [Indexed: 07/14/2023] Open
Abstract
BACKGROUND Hypotension in the emergency department (ED) is known to be associated with increased mortality, however, the relationship between timing of hypotension and mortality has not been investigated. The objective of the study was to compare the mortality rate of patients presenting with hypotension with those who develop hypotension while in the ED. METHODS This was a retrospective cohort study in a large academic medical center collected from January 2018-December 2021. Patients were included if they were ≥ 18 years old and had at least one recorded systolic blood pressure (SBP) ≤ 90 in the ED. Patients were separated into medical and trauma presentations by chief compliant. The primary outcome was in-hospital mortality, which included any deaths between ED arrival and hospital discharge. Further analysis examined the association of time to the first hypotensive SBP measurement with mortality. RESULTS There were 212,085 adult patients who presented to the ED during the study period, with 4053 (1.9%) patients having at least one hypotensive blood pressure measurement. The mortality rate was 0.8% for all patients and 10.0% for patients with hypotension. There were 676 unique chief complaints, of which 86 (12.7%) were determined to be trauma related. This grouping resulted in 176,947(83.4%) patients classified as medical and 35,138(16.6%) patients as trauma. For patients presenting with medical complaints, there was not a significant difference in mortality for patients who were hypotensive on arrival and those who developed hypotension during their ED stay (RR 1.19 [95% CI:0.97-1.39]). Similarly, there was no difference for patients with trauma (RR 0.6 [95% CI: 0.31-1.24]). However, for all patients, there was a significant trend toward decreased mortality for every hour after arrival until the development of hypotension, and increased mortality with increasing number of hypotensive measurements recorded. CONCLUSION This study demonstrated hypotension in the ED was associated with a very significantly increased risk of in-hospital mortality. However, there was no significant increase in mortality between those patients with hypotension on arrival those who develop hypotension while in the ED. These finding underscore the importance of careful hemodynamic monitoring for patients in the ED throughout their stay.
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Affiliation(s)
- Nardos Makonnen
- International Emergency Medicine and Global Public Health Fellow, George Washington University Hospital, 900 23rd St NW, Washington, DC 20037, United States of America.
| | - Timothy Layng
- Emergency Medicine, University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903, United States of America
| | - Thomas Hartka
- Emergency Medicine, University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903, United States of America
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11
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Chang H, Jung W, Ha J, Yu JY, Heo S, Lee GT, Park JE, Lee SU, Hwang SY, Yoon H, Cha WC, Shin TG, Kim T. EARLY PREDICTION OF UNEXPECTED LATENT SHOCK IN THE EMERGENCY DEPARTMENT USING VITAL SIGNS. Shock 2023; 60:373-378. [PMID: 37523617 PMCID: PMC10510834 DOI: 10.1097/shk.0000000000002181] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/20/2023] [Accepted: 07/02/2023] [Indexed: 08/02/2023]
Abstract
ABSTRACT Objective/Introduction : Sequential vital-sign information and trends in vital signs are useful for predicting changes in patient state. This study aims to predict latent shock by observing sequential changes in patient vital signs. Methods : The dataset for this retrospective study contained a total of 93,194 emergency department (ED) visits from January 1, 2016, and December 31, 2020, and Medical Information Mart for Intensive Care (MIMIC)-IV-ED data. We further divided the data into training and validation datasets by random sampling without replacement at a 7:3 ratio. We carried out external validation with MIMIC-IV-ED. Our prediction model included logistic regression (LR), random forest (RF) classifier, a multilayer perceptron (MLP), and a recurrent neural network (RNN). To analyze the model performance, we used area under the receiver operating characteristic curve (AUROC). Results : Data of 89,250 visits of patients who met prespecified criteria were used to develop a latent-shock prediction model. Data of 142,250 patient visits from MIMIC-IV-ED satisfying the same inclusion criteria were used for external validation of the prediction model. The AUROC values of prediction for latent shock were 0.822, 0.841, 0.852, and 0.830 with RNN, MLP, RF, and LR methods, respectively, at 3 h before latent shock. This is higher than the shock index or adjusted shock index. Conclusion : We developed a latent shock prediction model based on 24 h of vital-sign sequence that changed with time and predicted the results by individual.
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Affiliation(s)
- Hansol Chang
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
| | - Weon Jung
- Smart Health Lab, Research Institute of Future Medicine, Samsung Medical Center, Seoul, South Korea
| | - Juhyung Ha
- Department of Computer Science, Indiana University Bloomington, Bloomington, Indiana
| | - Jae Yong Yu
- Department of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Sejin Heo
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
| | - Gun Tak Lee
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jong Eun Park
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Se Uk Lee
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sung Yeon Hwang
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hee Yoon
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Won Chul Cha
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
- Smart Health Lab, Research Institute of Future Medicine, Samsung Medical Center, Seoul, South Korea
- Digital Innovation Center, Samsung Medical Center, Seoul, South Korea
| | - Tae Gun Shin
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Taerim Kim
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
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12
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Lafuente JL, González S, Puertas E, Gómez-Tello V, Avilés E, Albo N, Mateo C, Beunza JJ. Development of a urinometer for automatic measurement of urine flow in catheterized patients. PLoS One 2023; 18:e0290319. [PMID: 37651353 PMCID: PMC10470914 DOI: 10.1371/journal.pone.0290319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023] Open
Abstract
Urinary flow measurement and colorimetry are vital medical indicators for critically ill patients in intensive care units. However, there is a clinical need for low-cost, continuous urinary flow monitoring devices that can automatically and in real-time measure urine flow. This need led to the development of a non-invasive device that is easy to use and does not require proprietary disposables. The device operates by detecting urine flow using an infrared barrier that returns an unequivocal pattern, and it is capable of measuring the volume of liquid in real-time, storing the history with a precise date, and returning alarms to detect critical trends. The device also has the ability to detect the color of urine, allowing for extended data and detecting problems in catheterized patients such as hematuria. The device is proposed as an automated clinical decision support system that utilizes the concept of the Internet of Medical Things. It works by using a LoRa communication method with the LoRaWAN protocol to maximize the distance to access points, reducing infrastructure costs in massive deployments. The device can send data wirelessly for remote monitoring and allows for the collection of data on a dashboard in a pseudonymous way. Tests conducted on the device using a gold standard medical grade infusion pump and fluid densities within the 1.005 g/ml to 1.030 g/ml urine density range showed that droplets were satisfactorily captured in the range of flows from less than 1 ml/h to 500 ml/h, which are acceptable ranges for urinary flow. Errors ranged below 15%, when compared to the values obtained by the hospital infusion pump used as gold standard. Such values are clinically adequate to detect changes in diuresis patterns, specially at low urine output ranges, related to renal disfunction. Additionally, tests carried out with different color patterns indicate that it detects different colors of urine with a precision in detecting RGB values <5%. In conclusion, the results suggest that the device can be useful in automatically monitoring diuresis and colorimetry in real-time, which can facilitate the work of nursing and provide automatic decision-making support to intensive care physicians.
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Affiliation(s)
- José-Luis Lafuente
- IASalud, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Engineering Department, School of Architecture, Engineering, & Design, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
| | - Samuel González
- IASalud, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Intensive Care Unit, Hospital Universitario HLA Moncloa, Madrid, Spain
- Department of Medicine, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
| | - Enrique Puertas
- IASalud, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Science, Computing and Technology, School of Engineering, Architecture & Design, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
| | - Vicente Gómez-Tello
- IASalud, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Department of Medicine, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Emergency Department, Hospital Universitario HLA Moncloa, Madrid, Spain
| | - Eva Avilés
- IASalud, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Engineering Department, School of Architecture, Engineering, & Design, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
| | - Niza Albo
- IASalud, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Engineering Department, School of Architecture, Engineering, & Design, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
| | - Claudia Mateo
- IASalud, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Engineering Department, School of Architecture, Engineering, & Design, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
| | - Juan-Jose Beunza
- IASalud, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Department of Medicine, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
- Research and Doctorate School, Universidad Europea de Madrid, Villaviciosa de Odón, Madrid, Spain
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13
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Helman S, Terry MA, Pellathy T, Hravnak M, George E, Al-Zaiti S, Clermont G. Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support. Appl Clin Inform 2023; 14:789-802. [PMID: 37793618 PMCID: PMC10550364 DOI: 10.1055/s-0043-1775565] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/26/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care. OBJECTIVES Our objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score. METHODS Intensive care unit (ICU) clinicians participated in focus groups seeking input on instability risk forecast presented in a prototype GUI. Two stratified rounds (three focus groups [only nurses, only providers, then combined]) were moderated by a focus group methodologist. After round 1, GUI design changes were made and presented in round 2. Focus groups were recorded, transcribed, and deidentified transcripts independently coded by three researchers. Codes were coalesced into emerging themes. RESULTS Twenty-three ICU clinicians participated (11 nurses, 12 medical providers [3 mid-level and 9 physicians]). Six themes emerged: (1) analytics transparency, (2) graphical interpretability, (3) impact on practice, (4) value of trend synthesis of dynamic patient data, (5) decisional weight (weighing AI output during decision-making), and (6) display location (usability, concerns for patient/family GUI view). Nurses emphasized having GUI objective information to support communication and optimal GUI location. While providers emphasized need for recommendation interpretability and concern for impairing trainee critical thinking. All disciplines valued synthesized views of vital signs, interventions, and risk trends but were skeptical of placing decisional weight on AI output until proven trustworthy. CONCLUSION Gaining input from all clinical users is important to consider when designing AI-derived GUIs. Results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisional support components need to be used as an adjunct to human decision-making.
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Affiliation(s)
- Stephanie Helman
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Martha Ann Terry
- Department of Behavioral and Community Health Sciences, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Tiffany Pellathy
- Veterans Administration Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States
| | - Marilyn Hravnak
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Elisabeth George
- Department of Nursing, University of Pittsburgh Medical Center, Presbyterian Hospital, Pittsburgh, Pennsylvania, United States
| | - Salah Al-Zaiti
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
- Division of Cardiology at University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
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14
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Cánovas-Segura B, Morales A, Juarez JM, Campos M. Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework. J Biomed Inform 2023:104397. [PMID: 37245656 DOI: 10.1016/j.jbi.2023.104397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/11/2023] [Accepted: 05/15/2023] [Indexed: 05/30/2023]
Abstract
Alerts are a common functionality of clinical decision support systems (CDSSs). Although they have proven to be useful in clinical practice, the alert burden can lead to alert fatigue and significantly reduce their usability and acceptance. Based on a literature review, we propose a unified framework consisting of a set of meaningful timestamps that allows the use of state-of-the-art measures for alert burden, such as alert dwell time, alert think time, and response time. In addition, it can be used to investigate other measures that could be relevant as regards dealing with this problem. Furthermore, we provide a case study concerning three different types of alerts to which the framework was successfully applied. We consider that our framework can easily be adapted to other CDSSs and that it could be useful for dealing with alert burden measurement thus contributing to its appropriate management.
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Affiliation(s)
| | - Antonio Morales
- AIKE Research Group (INTICO), University of Murcia, Murcia, Spain.
| | - Jose M Juarez
- AIKE Research Group (INTICO), University of Murcia, Murcia, Spain.
| | - Manuel Campos
- AIKE Research Group (INTICO), University of Murcia, Murcia, Spain; Murcian Bio-Health Institute (IMIB-Arrixaca), Murcia, Spain.
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15
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Saqib M, Iftikhar M, Neha F, Karishma F, Mumtaz H. Artificial intelligence in critical illness and its impact on patient care: a comprehensive review. Front Med (Lausanne) 2023; 10:1176192. [PMID: 37153088 PMCID: PMC10158493 DOI: 10.3389/fmed.2023.1176192] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/04/2023] [Indexed: 05/09/2023] Open
Abstract
Artificial intelligence (AI) has great potential to improve the field of critical care and enhance patient outcomes. This paper provides an overview of current and future applications of AI in critical illness and its impact on patient care, including its use in perceiving disease, predicting changes in pathological processes, and assisting in clinical decision-making. To achieve this, it is important to ensure that the reasoning behind AI-generated recommendations is comprehensible and transparent and that AI systems are designed to be reliable and robust in the care of critically ill patients. These challenges must be addressed through research and the development of quality control measures to ensure that AI is used in a safe and effective manner. In conclusion, this paper highlights the numerous opportunities and potential applications of AI in critical care and provides guidance for future research and development in this field. By enabling the perception of disease, predicting changes in pathological processes, and assisting in the resolution of clinical decisions, AI has the potential to revolutionize patient care for critically ill patients and improve the efficiency of health systems.
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Affiliation(s)
- Muhammad Saqib
- Khyber Medical College, Peshawar, Khyber Pakhtunkhwa, Pakistan
| | | | - Fnu Neha
- Ghulam Muhammad Mahar Medical College, Sukkur, Sindh, Pakistan
| | - Fnu Karishma
- Jinnah Sindh Medical University, Karachi, Sindh, Pakistan
| | - Hassan Mumtaz
- Health Services Academy, Islamabad, Pakistan
- *Correspondence: Hassan Mumtaz,
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16
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Abstract
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .
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Affiliation(s)
- Joo Heung Yoon
- grid.21925.3d0000 0004 1936 9000Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA USA
| | - Michael R. Pinsky
- grid.21925.3d0000 0004 1936 9000Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA USA
| | - Gilles Clermont
- grid.21925.3d0000 0004 1936 9000Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA USA
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17
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Pinsky MR, Dubrawski A, Clermont G. Intelligent Clinical Decision Support. SENSORS (BASEL, SWITZERLAND) 2022; 22:1408. [PMID: 35214310 PMCID: PMC8963066 DOI: 10.3390/s22041408] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/04/2022] [Accepted: 02/06/2022] [Indexed: 06/14/2023]
Abstract
Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.
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Affiliation(s)
- Michael R. Pinsky
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA;
| | - Artur Dubrawski
- Auton Laboratory, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA;
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18
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Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms. SENSORS 2022; 22:s22020442. [PMID: 35062401 PMCID: PMC8781307 DOI: 10.3390/s22020442] [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: 11/05/2021] [Revised: 12/14/2021] [Accepted: 12/30/2021] [Indexed: 11/16/2022]
Abstract
Hypovolemia is a physiological state of reduced blood volume that can exist as either (1) absolute hypovolemia because of a lower circulating blood (plasma) volume for a given vascular space (dehydration, hemorrhage) or (2) relative hypovolemia resulting from an expanded vascular space (vasodilation) for a given circulating blood volume (e.g., heat stress, hypoxia, sepsis). This paper examines the physiology of hypovolemia and its association with health and performance problems common to occupational, military and sports medicine. We discuss the maturation of individual-specific compensatory reserve or decompensation measures for future wearable sensor systems to effectively manage these hypovolemia problems. The paper then presents areas of future work to allow such technologies to translate from lab settings to use as decision aids for managing hypovolemia. We envision a future that incorporates elements of the compensatory reserve measure with advances in sensing technology and multiple modalities of cardiovascular sensing, additional contextual measures, and advanced noise reduction algorithms into a fully wearable system, creating a robust and physiologically sound approach to manage physical work, fatigue, safety and health issues associated with hypovolemia for workers, warfighters and athletes in austere conditions.
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Song Y, Liu J, Lei M, Wang Y, Fu Q, Wang B, Guo Y, Mi W, Tong L. An External-Validated Algorithm to Predict Postoperative Pneumonia Among Elderly Patients With Lung Cancer After Video-Assisted Thoracoscopic Surgery. Front Oncol 2022; 11:777564. [PMID: 34970491 PMCID: PMC8712479 DOI: 10.3389/fonc.2021.777564] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/09/2021] [Indexed: 12/15/2022] Open
Abstract
The aim of the study was to develop an algorithm to predict postoperative pneumonia among elderly patients with lung cancer after video-assisted thoracoscopic surgery. We analyzed 3,009 patients from the Thoracic Perioperative Database for Geriatrics in our hospital and finally enrolled 1,585 elderly patients (age≧65 years) with lung cancer treated with video-assisted thoracoscopic surgery. The included patients were randomly divided into a training group (n = 793) and a validation group (n = 792). Patients in the training group were used to develop the algorithm after screening up to 30 potential risk factors, and patients in the validation group were used to internally validate the algorithm. External validation of the algorithm was achieved in the external validation dataset after enrolling 165 elderly patients with lung cancer treated with video-assisted thoracoscopic surgery from two hospitals in China. Of all included patients, 9.15% (145/1,585) of patients suffered from postoperative pneumonia in the Thoracic Perioperative Database for Geriatrics, and 10.30% (17/165) of patients had postoperative pneumonia in the external validation dataset. The algorithm consisted of seven variables, including sex, smoking, history of chronic obstructive pulmonary disease (COPD), surgery duration, leukocyte count, intraoperative injection of colloid, and intraoperative injection of hormone. The C-index from the receiver operating characteristic curve (AUROC) was 0.70 in the training group, 0.67 in the internal validation group, and 0.71 in the external validation dataset, and the corresponding calibration slopes were 0.88 (95% confident interval [CI]: 0.37–1.39), 0.90 (95% CI: 0.46–1.34), and 1.03 (95% CI: 0.24–1.83), respectively. The actual probabilities of postoperative pneumonia were 5.14% (53/1031) in the low-risk group, 15.07% (71/471) in the medium-risk group, and 25.30% (21/83) in the high-risk group (p < 0.001). The algorithm can be a useful prognostic tool to predict the risk of developing postoperative pneumonia among elderly patients with lung cancer after video-assisted thoracoscopic surgery.
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Affiliation(s)
- Yanping Song
- Anesthesia and Operation Center, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.,Department of Anesthesia, 922 Hospital of People's Liberation Army (PLA), Hengyang, China
| | - Jingjing Liu
- Anesthesia and Operation Center, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.,Department of Anesthesia, Beijing Corps Hospital of Chinese People's Armed Police Force, Beijing, China
| | - Mingxing Lei
- The National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.,Department of Orthopedic Surgery, Hainan Hospital of Chinese People's Liberation Army (PLA) General Hospital, Sanya, China.,Chinese People's Liberation Army (PLA) Medical School, Beijing, China
| | - Yanfeng Wang
- Department of Anesthesia, Xiangya Hospital, Central South University, Changsha, China
| | - Qiang Fu
- Anesthesia and Operation Center, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Bailin Wang
- Department of Thoracic Surgery, Hainan Hospital of Chinese People's Liberation Army (PLA) General Hospital, Sanya, China
| | - Yongxin Guo
- Anesthesia and Operation Center, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Weidong Mi
- Anesthesia and Operation Center, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Li Tong
- Anesthesia and Operation Center, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
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Gadhoumi K, Beltran A, Scully CG, Xiao R, Nahmias DO, Hu X. Technical considerations for evaluating clinical prediction indices: a case study for predicting code blue events with MEWS. Physiol Meas 2021; 42. [PMID: 33902012 DOI: 10.1088/1361-6579/abfbb9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 04/26/2021] [Indexed: 11/11/2022]
Abstract
Objective.There have been many efforts to develop tools predictive of health deterioration in hospitalized patients, but comprehensive evaluation of their predictive ability is often lacking to guide implementation in clinical practice. In this work, we propose new techniques and metrics for evaluating the performance of predictive alert algorithms and illustrate the advantage of capturing the timeliness and the clinical burden of alerts through the example of the modified early warning score (MEWS) applied to the prediction of in-hospital code blue events.Approach. Different implementations of MEWS were calculated from available physiological parameter measurements collected from the electronic health records of ICU adult patients. The performance of MEWS was evaluated using conventional and a set of non-conventional metrics and approaches that take into account the timeliness and practicality of alarms as well as the false alarm burden.Main results. MEWS calculated using the worst-case measurement (i.e. values scoring 3 points in the MEWS definition) over 2 h intervals significantly reduced the false alarm rate by over 50% (from 0.19/h to 0.08/h) while maintaining similar sensitivity levels as MEWS calculated from raw measurements (∼80%). By considering a prediction horizon of 12 h preceding a code blue event, a significant improvement in the specificity (∼60%), the precision (∼155%), and the work-up to detection ratio (∼50%) could be achieved, at the cost of a relatively marginal decrease in sensitivity (∼10%).Significance. Performance aspects pertaining to the timeliness and burden of alarms can aid in understanding the potential utility of a predictive alarm algorithm in clinical settings.
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Affiliation(s)
- Kais Gadhoumi
- School of Nursing, Duke University, Durham, NC, United States of America
| | - Alex Beltran
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States of America
| | - Christopher G Scully
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, United States of America
| | - Ran Xiao
- School of Nursing, Duke University, Durham, NC, United States of America
| | - David O Nahmias
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, United States of America
| | - Xiao Hu
- School of Nursing, Duke University, Durham, NC, United States of America
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