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Gao T, Nong Z, Luo Y, Mo M, Chen Z, Yang Z, Pan L. Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury. Ren Fail 2024; 46:2316267. [PMID: 38369749 PMCID: PMC10878338 DOI: 10.1080/0886022x.2024.2316267] [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: 11/23/2023] [Accepted: 02/03/2024] [Indexed: 02/20/2024] Open
Abstract
OBJECTIVES This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms. METHODS Patients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (n = 2440) and development (n = 9756, 80%) queues. Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. Ten-fold cross-validation was used to verify the performance of the algorithm in the development queue. The area under the receiver operating characteristic curve (ROC-AUC) was used to evaluate the differentiation accuracy and performance of the prediction model in the validation queue. The best-performing model was interpreted by Shapley additive explanations (SHAP). RESULTS A total of 12,196 patients were enrolled in this study. Eleven variables were finally chosen to develop the prediction model. The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774-0.821). According to the SHAP plots, old age, low Glasgow Coma Scale (GCS) score, high AKI stage, reduced urine output, high Simplified Acute Physiology Score (SAPS II), high respiratory rate, low temperature, low absolute lymphocyte count, high creatinine level, dysnatremia, and low body mass index (BMI) increased the risk of poor prognosis. CONCLUSIONS The RF model developed in this study is a good predictor of in-hospital mortality for patients with SA-AKI in the intensive care unit (ICU), which may have potential applications in mortality prediction.
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Affiliation(s)
- Tianyun Gao
- Department of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning City, PR China
| | - Zhiqiang Nong
- Department of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning City, PR China
| | - Yuzhen Luo
- Department of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning City, PR China
| | - Manqiu Mo
- Department of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning City, PR China
| | - Zhaoyan Chen
- Department of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning City, PR China
| | - Zhenhua Yang
- Department of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning City, PR China
| | - Ling Pan
- Department of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning City, PR China
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2
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Alge OP, Pickard J, Zhang W, Cheng S, Derksen H, Omenn GS, Gryak J, VanEpps JS, Najarian K. Continuous sepsis trajectory prediction using tensor-reduced physiological signals. Sci Rep 2024; 14:18155. [PMID: 39103488 PMCID: PMC11300462 DOI: 10.1038/s41598-024-68901-x] [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: 08/28/2023] [Accepted: 07/29/2024] [Indexed: 08/07/2024] Open
Abstract
The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual's risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08, p = 0.01 ). Adding arterial line features can also improve performance (AUROC 0.69 ± 0.07, p < 0.01 ), and benefit from tensor decomposition (AUROC 0.71 ± 0.07, p = 0.01 ). Adding EHR data features to a tensor-reduced signal model further improves performance (AUROC 0.77 ± 0.06, p < 0.01 ). Despite reduction in performance going from an EHR data-informed model to a tensor-reduced waveform data model, the signals-informed model offers distinct advantages. The first is that predictions can be made on a continuous basis in real-time, and second is that these predictions are not limited by the availability of EHR data. Additionally, structuring the waveform features as a tensor conserves structural and temporal information that would otherwise be lost if the data were presented as flat vectors.
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Affiliation(s)
- Olivia P Alge
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Joshua Pickard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Winston Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Shuyang Cheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Harm Derksen
- Department of Mathematics, Northeastern University, Boston, MA, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Departments of Internal Medicine, Human Genetics, and Environmental Health, Ann Arbor, MI, USA
| | - Jonathan Gryak
- Department of Computer Science, Queens College, CUNY, Queens, NY, USA
| | - J Scott VanEpps
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Macromolecular Science and Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA
- Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
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Schmulevich D, Hynes AM, Murali S, Benjamin AJ, Cannon JW. Optimizing damage control resuscitation through early patient identification and real-time performance improvement. Transfusion 2024; 64:1551-1561. [PMID: 39075741 DOI: 10.1111/trf.17806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/05/2024] [Accepted: 02/16/2024] [Indexed: 07/31/2024]
Affiliation(s)
- Daniela Schmulevich
- Division of Traumatology, Surgical Critical Care & Emergency Surgery, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Allyson M Hynes
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Emergency Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
| | - Shyam Murali
- Division of Traumatology, Surgical Critical Care & Emergency Surgery, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrew J Benjamin
- Trauma and Acute Care Surgery, Department of Surgery, The University of Chicago, Chicago, Illinois, USA
| | - Jeremy W Cannon
- Division of Traumatology, Surgical Critical Care & Emergency Surgery, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Surgery, Uniformed Services University F. Edward Hébert School of Medicine, Bethesda, Maryland, USA
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Scott IA, De Guzman KR, Falconer N, Canaris S, Bonilla O, McPhail SM, Marxen S, Van Garderen A, Abdel-Hafez A, Barras M. Evaluating automated machine learning platforms for use in healthcare. JAMIA Open 2024; 7:ooae031. [PMID: 38863963 PMCID: PMC11165368 DOI: 10.1093/jamiaopen/ooae031] [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: 12/21/2023] [Revised: 03/06/2024] [Accepted: 04/22/2024] [Indexed: 06/13/2024] Open
Abstract
Objective To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models. Materials and Methods Evaluation criteria for selecting an Auto ML platform suited to ML needs of a local health district were developed in 3 steps: (1) identification of key requirements, (2) a market scan, and (3) an assessment process with desired outcomes. Results The final checklist comprising 21 functional and 6 non-functional criteria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case. Discussion A team of clinicians, data scientists, and key stakeholders developed a checklist which can be adapted to ML needs of healthcare organizations, the use case providing a relevant example. Conclusion An evaluative checklist was developed for selecting Auto ML platforms which requires validation in larger multi-site studies.
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Affiliation(s)
- Ian A Scott
- Centre for Health Services Research, University of Queensland, Brisbane, 4102, Australia
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, 4102, Australia
| | - Keshia R De Guzman
- Department of Pharmacy, Princess Alexandra Hospital, Brisbane, 4102, Australia
- School of Pharmacy, The University of Queensland, Brisbane, 4102, Australia
| | - Nazanin Falconer
- Department of Pharmacy, Princess Alexandra Hospital, Brisbane, 4102, Australia
- School of Pharmacy, The University of Queensland, Brisbane, 4102, Australia
| | - Stephen Canaris
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
| | - Oscar Bonilla
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
| | - Steven M McPhail
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia
| | - Sven Marxen
- Pharmacy Service, Logan and Beaudesert Hospitals, Logan, 4131, Australia
| | - Aaron Van Garderen
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
- Pharmacy Service, Logan and Beaudesert Hospitals, Logan, 4131, Australia
| | - Ahmad Abdel-Hafez
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia
| | - Michael Barras
- Department of Pharmacy, Princess Alexandra Hospital, Brisbane, 4102, Australia
- School of Pharmacy, The University of Queensland, Brisbane, 4102, Australia
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Pérez-Tome JC, Parrón-Carreño T, Castaño-Fernández AB, Nievas-Soriano BJ, Castro-Luna G. Sepsis mortality prediction with Machine Learning Tecniques. Med Intensiva 2024:S2173-5727(24)00131-0. [PMID: 38876921 DOI: 10.1016/j.medine.2024.05.009] [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: 01/04/2024] [Accepted: 04/30/2024] [Indexed: 06/16/2024]
Abstract
OBJECTIVE To develop a sepsis death classification model based on machine learning techniques for patients admitted to the Intensive Care Unit (ICU). DESIGN Cross-sectional descriptive study. SETTING The Intensive Care Units (ICUs) of three Hospitals from Murcia (Spain) and patients from the MIMIC III open-access database. PATIENTS 180 patients diagnosed with sepsis in the ICUs of three hospitals and a total of 4559 patients from the MIMIC III database. MAIN VARIABLES OF INTEREST Age, weight, heart rate, respiratory rate, temperature, lactate levels, partial oxygen saturation, systolic and diastolic blood pressure, pH, urine, and potassium levels. RESULTS A random forest classification model was calculated using the local and MIMIC III databases. The sensitivity of the model of our database, considering all the variables classified as important by the random forest, was 95.45%, the specificity was 100%, the accuracy was 96.77%, and an AUC of 95%. . In the case of the model based on the MIMIC III database, the sensitivity was 97.55%, the specificity was 100%, and the precision was 98.28%, with an AUC of 97.3%. CONCLUSIONS According to random forest classification in both databases, lactate levels, urine output and variables related to acid.base equilibrium were the most important variable in mortality due to sepsis in the ICU. The potassium levels were more critical in the MIMIC III database than the local database.
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Affiliation(s)
| | - Tesifón Parrón-Carreño
- Department of Nursing: Physiotherapy and Medicine, University of Almeria, 04120 Almeria, Spain
| | | | | | - Gracia Castro-Luna
- Department of Nursing: Physiotherapy and Medicine, University of Almeria, 04120 Almeria, Spain.
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Choi DH, Choi SW, Kim KH, Choi Y, Kim Y. Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department. Am J Emerg Med 2024; 80:67-76. [PMID: 38507849 DOI: 10.1016/j.ajem.2024.03.006] [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: 04/10/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024] Open
Abstract
OBJECTIVE To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation. METHODS This retrospective study included adults who visited the ED afebrile at initial presentation. We developed four models based on artificial neural networks to identify suspected serious infection. Patient demographics, vital signs, laboratory test results and information extracted from initial ED physician notes using term frequency-inverse document frequency were used as model variables. Models were trained and internally validated with data from one hospital and externally validated using data from a different hospital. Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). RESULTS The training, internal validation, and external validation datasets comprised 150,699, 37,675, and 85,098 patients, respectively. The AUCs (95% CIs) for Models 1 (demographics + vital signs), 2 (demographics + vital signs + initial ED physician note), 3 (demographics + vital signs + laboratory tests), and 4 (demographics + vital signs + laboratory tests + initial ED physician note) in the internal validation dataset were 0.789 (0.782-0.796), 0.867 (0.862-0.872), 0.881 (0.876-0.887), and 0.911 (0.906-0.915), respectively. In the external validation dataset, the AUCs (95% CIs) of Models 1, 2, 3, and 4 were 0.824 (0.817-0.830), 0.895 (0.890-0.899), 0.879 (0.873-0.884), and 0.913 (0.909-0.917), respectively. Model 1 can be utilized immediately after ED triage, Model 2 can be utilized after the initial physician notes are recorded (median time from ED triage: 28 min), and Models 3 and 4 can be utilized after the initial laboratory tests are reported (median time from ED triage: 68 min). CONCLUSIONS We developed and validated models to identify suspected serious infection in the ED. Extracted information from initial ED physician notes using NLP contributed to increased model performance, permitting identification of suspected serious infection at early stages of ED visits.
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Affiliation(s)
- Dong Hyun Choi
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Republic of Korea; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sae Won Choi
- Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Ki Hong Kim
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Republic of Korea; Department of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea; Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yeongho Choi
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Republic of Korea; Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea; Disaster Medicine Research Center, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Yoonjic Kim
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Republic of Korea; Department of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea; Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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7
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Wang L, Tian W, Zhang W, Wen D, Yang S, Wang J, Han X, Wang J, Ding W, Wang L, Yu Y, Wu W. A Machine learning model for predicting sepsis based on an optimized assay for microbial cell-free DNA sequencing. Clin Chim Acta 2024; 559:119716. [PMID: 38710402 DOI: 10.1016/j.cca.2024.119716] [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: 03/22/2024] [Revised: 04/29/2024] [Accepted: 05/03/2024] [Indexed: 05/08/2024]
Abstract
OBJECTIVE To integrate an enhanced molecular diagnostic technique to develop and validate a machine-learning model for diagnosing sepsis. METHODS We prospectively enrolled patients suspected of sepsis from August 2021 to August 2023. Various feature selection algorithms and machine learning models were used to develop the model. The best classifier was selected using 5-fold cross validation set and then was applied to assess the performance of the model in the testing set. Additionally, we employed the Shapley Additive exPlanations (SHAP) method to illustrate the effects of the features. RESULTS We established an optimized mNGS assay and proposed using the copies of microbe-specific cell-free DNA per milliliter of plasma (CPM) as the detection signal to evaluate the real burden, with strong precision and high accuracy. In total, 237 patients were eligible for participation, which were randomly assigned to either the training set (70 %, n = 165) or the testing set (30 %, n = 72). The random forest classifier achieved accuracy, AUC and F1 scores of 0.830, 0.918 and 0.856, respectively, outperforming other machine learning models in the training set. Our model demonstrated clinical interpretability and achieved good prediction performance in differentiating between bacterial sepsis and non-sepsis, with an AUC value of 0.85 and an average precision of 0.91 in the testing set. Based on the SHAP value, the top nine features of the model were PCT, CPM, CRP, ALB, SBPmin, RRmax, CREA, PLT and HRmax. CONCLUSION We demonstrated the potential of machine-learning approaches for predicting bacterial sepsis based on optimized mcfDNA sequencing assay accurately.
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Affiliation(s)
- Lili Wang
- Department of Laboratory Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China; Department of Laboratory Medicine, Zhoushan Women and Children Hospital, Zhoushan, China
| | - Wenjie Tian
- Department of Laboratory Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Weijun Zhang
- Department of Critical Care Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Donghua Wen
- Department of Laboratory Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Simin Yang
- Department of Laboratory Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jichao Wang
- Matridx Biotechnology Co., Ltd, Hangzhou, China
| | - Xu Han
- Matridx Biotechnology Co., Ltd, Hangzhou, China
| | - Jun Wang
- Matridx Biotechnology Co., Ltd, Hangzhou, China
| | | | - Lihui Wang
- Department of Critical Care Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuetian Yu
- Department of Critical Care Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Key Laboratory of Multiple Organ Failure (Zhejiang University), Ministry of Education, Zhejiang, China; Key Laboratory of Intelligent Pharmacy and Individualized Therapy, Zhejiang, China; Guangxi Health Commission Key Laboratory of Diagnosis and Treatment of Acute Respiratory Distress Syndrome, Guangxi, China.
| | - Wenjuan Wu
- Department of Laboratory Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
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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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9
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Burton RJ, Raffray L, Moet LM, Cuff SM, White DA, Baker SE, Moser B, O’Donnell VB, Ghazal P, Morgan MP, Artemiou A, Eberl M. Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients. Clin Exp Immunol 2024; 216:293-306. [PMID: 38430552 PMCID: PMC11097916 DOI: 10.1093/cei/uxae019] [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: 09/08/2023] [Revised: 02/12/2024] [Accepted: 02/28/2024] [Indexed: 03/04/2024] Open
Abstract
Sepsis is characterized by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility of identifying integrative patterns from clinical parameters, plasma biomarkers, and extensive phenotyping of blood immune cells. While no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90-day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90-day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T-cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical, and clinical parameters.
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Affiliation(s)
- Ross J Burton
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
- Adult Critical Care, University Hospital of Wales, Cardiff and Vale University Health Board, Cardiff, UK
| | - Loïc Raffray
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
- Department of Internal Medicine, Félix Guyon University Hospital of La Réunion, Saint Denis, Réunion Island, France
| | - Linda M Moet
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Simone M Cuff
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Daniel A White
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Sarah E Baker
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Bernhard Moser
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
- Systems Immunity Research Institute, Cardiff University, Cardiff, UK
| | - Valerie B O’Donnell
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
- Systems Immunity Research Institute, Cardiff University, Cardiff, UK
| | - Peter Ghazal
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
- Systems Immunity Research Institute, Cardiff University, Cardiff, UK
| | - Matt P Morgan
- Adult Critical Care, University Hospital of Wales, Cardiff and Vale University Health Board, Cardiff, UK
| | - Andreas Artemiou
- School of Mathematics, Cardiff University, Cardiff, UK
- Department of Information Technologies, University of Limassol, 3025 Limassol, Cyprus
| | - Matthias Eberl
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
- Systems Immunity Research Institute, Cardiff University, Cardiff, UK
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10
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Wu M, Islam MM, Poly TN, Lin MC. Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis. Interact J Med Res 2024; 13:e54490. [PMID: 38621231 PMCID: PMC11058558 DOI: 10.2196/54490] [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: 11/11/2023] [Revised: 01/27/2024] [Accepted: 03/04/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. OBJECTIVE The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. METHODS We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. RESULTS A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). CONCLUSIONS This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.
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Affiliation(s)
- MeiJung Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Nursing, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Md Mohaimenul Islam
- Department of Outcomes and Translational Sciences, College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
- Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
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11
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Lin YH, Chang TC, Liu CF, Lai CC, Chen CM, Chou W. The intervention of artificial intelligence to improve the weaning outcomes of patients with mechanical ventilation: Practical applications in the medical intensive care unit and the COVID-19 intensive care unit: A retrospective study. Medicine (Baltimore) 2024; 103:e37500. [PMID: 38518051 PMCID: PMC10956977 DOI: 10.1097/md.0000000000037500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 02/14/2024] [Indexed: 03/24/2024] Open
Abstract
Patients admitted to intensive care units (ICU) and receiving mechanical ventilation (MV) may experience ventilator-associated adverse events and have prolonged ICU length of stay (LOS). We conducted a survey on adult patients in the medical ICU requiring MV. Utilizing big data and artificial intelligence (AI)/machine learning, we developed a predictive model to determine the optimal timing for weaning success, defined as no reintubation within 48 hours. An interdisciplinary team integrated AI into our MV weaning protocol. The study was divided into 2 parts. The first part compared outcomes before AI (May 1 to Nov 30, 2019) and after AI (May 1 to Nov 30, 2020) implementation in the medical ICU. The second part took place during the COVID-19 pandemic, where patients were divided into control (without AI assistance) and intervention (with AI assistance) groups from Aug 1, 2022, to Apr 30, 2023, and we compared their short-term outcomes. In the first part of the study, the intervention group (with AI, n = 1107) showed a shorter mean MV time (144.3 hours vs 158.7 hours, P = .077), ICU LOS (8.3 days vs 8.8 days, P = .194), and hospital LOS (22.2 days vs 25.7 days, P = .001) compared to the pre-intervention group (without AI, n = 1298). In the second part of the study, the intervention group (with AI, n = 88) exhibited a shorter mean MV time (244.2 hours vs 426.0 hours, P = .011), ICU LOS (11.0 days vs 18.7 days, P = .001), and hospital LOS (23.5 days vs 40.4 days, P < .001) compared to the control group (without AI, n = 43). The integration of AI into the weaning protocol led to improvements in the quality and outcomes of MV patients.
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Affiliation(s)
- Yang-Han Lin
- Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan City, Taiwan
| | - Ting-Chia Chang
- Division of Chest Medicine, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan, Yong-Kang District, Tainan City, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan City, Taiwan
| | - Chih-Cheng Lai
- Division of Hospital Medicine, Department of Internal Medicine, Chi Mei Medical Center, Yong-Kang District, Tainan City, Taiwan
| | - Chin-Ming Chen
- Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan City, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Jialixing Jiaxing Village, Jiali District, Tainan City, Taiwan
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12
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Mollura M, Chicco D, Paglialonga A, Barbieri R. Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records. PLOS DIGITAL HEALTH 2024; 3:e0000459. [PMID: 38489347 PMCID: PMC10942078 DOI: 10.1371/journal.pdig.0000459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/05/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND Systemic inflammatory response syndrome (SIRS) and sepsis are the most common causes of in-hospital death. However, the characteristics associated with the improvement in the patient conditions during the ICU stay were not fully elucidated for each population as well as the possible differences between the two. GOAL The aim of this study is to highlight the differences between the prognostic clinical features for the survival of patients diagnosed with SIRS and those of patients diagnosed with sepsis by using a multi-variable predictive modeling approach with a reduced set of easily available measurements collected at the admission to the intensive care unit (ICU). METHODS Data were collected from 1,257 patients (816 non-sepsis SIRS and 441 sepsis) admitted to the ICU. We compared the performance of five machine learning models in predicting patient survival. Matthews correlation coefficient (MCC) was used to evaluate model performances and feature importance, and by applying Monte Carlo stratified Cross-Validation. RESULTS Extreme Gradient Boosting (MCC = 0.489) and Logistic Regression (MCC = 0.533) achieved the highest results for SIRS and sepsis cohorts, respectively. In order of importance, APACHE II, mean platelet volume (MPV), eosinophil counts (EoC), and C-reactive protein (CRP) showed higher importance for predicting sepsis patient survival, whereas, SOFA, APACHE II, platelet counts (PLTC), and CRP obtained higher importance in the SIRS cohort. CONCLUSION By using complete blood count parameters as predictors of ICU patient survival, machine learning models can accurately predict the survival of SIRS and sepsis ICU patients. Interestingly, feature importance highlights the role of CRP and APACHE II in both SIRS and sepsis populations. In addition, MPV and EoC are shown to be important features for the sepsis population only, whereas SOFA and PLTC have higher importance for SIRS patients.
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Affiliation(s)
- Maximiliano Mollura
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Davide Chicco
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Italy
| | - Alessia Paglialonga
- CNR-Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni (CNR-IEIIT), Milan, Italy
| | - Riccardo Barbieri
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
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13
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Kijpaisalratana N, Saoraya J, Nhuboonkaew P, Vongkulbhisan K, Musikatavorn K. Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial. Intern Emerg Med 2024:10.1007/s11739-024-03535-5. [PMID: 38381351 DOI: 10.1007/s11739-024-03535-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 01/11/2024] [Indexed: 02/22/2024]
Abstract
Machine learning (ML) has been applied in sepsis recognition across different healthcare settings with outstanding diagnostic accuracy. However, the advantage of ML-assisted sepsis alert in expediting clinical decisions leading to enhanced quality for emergency department (ED) patients remains unclear. A cluster-randomized trial was conducted in a tertiary-care hospital. Adult patient data were subjected to an ML model for sepsis alert. Patient visits were assigned into one of two groups. In the intervention cluster, staff received alerts on a display screen if patients met the ML threshold for sepsis diagnosis, while patients in the control cluster followed the regular alert process. The study compared triage-to-antibiotic (TTA) time, length of stay, and mortality rate between the two groups. Additionally, the diagnostic performance of the ML model was assessed. A total of 256 (intervention) and 318 (control) sepsis patients were analyzed. The proportions of patients who received antibiotics within 1 and 3 h were higher in the intervention group than in the control group (in 1 h; 68.4 vs. 60.1%, respectively; P = 0.04, in 3 h; 94.5 vs. 89.0%, respectively; P = 0.02). The median TTA times were marginally shorter in the intervention group (46 vs. 50 min). The area under the receiver operating characteristic curve (AUROC) of ML in early sepsis identification was significantly higher than qSOFA, SIRS, and MEWS. The ML-assisted sepsis alert system may help sepsis ED patients receive antibiotics more rapidly than with the conventional, human-dedicated alert process. The diagnostic performance of ML in prompt sepsis detection was superior to that of the rule-based system.Trial registration Thai Clinical Trials Registry TCTR20230120001. Registered 16 January 2023-Retrospectively registered, https://www.thaiclinicaltrials.org/show/TCTR20230120001 .
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Affiliation(s)
- Norawit Kijpaisalratana
- Department of Emergency Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Jutamas Saoraya
- Department of Emergency Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
- Division of Academic Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Padcha Nhuboonkaew
- Department of Emergency Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Komsanti Vongkulbhisan
- Department of Emergency Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Khrongwong Musikatavorn
- Department of Emergency Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand.
- Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, 10330, Thailand.
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14
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Cohen SN, Foster J, Foster P, Lou H, Lyons T, Morley S, Morrill J, Ni H, Palmer E, Wang B, Wu Y, Yang L, Yang W. Subtle variation in sepsis-III definitions markedly influences predictive performance within and across methods. Sci Rep 2024; 14:1920. [PMID: 38253623 PMCID: PMC10803347 DOI: 10.1038/s41598-024-51989-6] [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: 04/04/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
Early detection of sepsis is key to ensure timely clinical intervention. Since very few end-to-end pipelines are publicly available, fair comparisons between methodologies are difficult if not impossible. Progress is further limited by discrepancies in the reconstruction of sepsis onset time. This retrospective cohort study highlights the variation in performance of predictive models under three subtly different interpretations of sepsis onset from the sepsis-III definition and compares this against inter-model differences. The models are chosen to cover tree-based, deep learning, and survival analysis methods. Using the MIMIC-III database, between 867 and 2178 intensive care unit admissions with sepsis were identified, depending on the onset definition. We show that model performance can be more sensitive to differences in the definition of sepsis onset than to the model itself. Given a fixed sepsis definition, the best performing method had a gain of 1-5% in the area under the receiver operating characteristic (AUROC). However, the choice of onset time can cause a greater effect, with variation of 0-6% in AUROC. We illustrate that misleading conclusions can be drawn if models are compared without consideration of the sepsis definition used which emphasizes the need for a standardized definition for sepsis onset.
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Affiliation(s)
- Samuel N Cohen
- Mathematical Institute, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - James Foster
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | | | - Hang Lou
- Department of Mathematics, University College London, Room 603, 25 Gordon St, London, WC1H 0AY, UK
| | - Terry Lyons
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Sam Morley
- Mathematical Institute, University of Oxford, Oxford, UK
| | - James Morrill
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Hao Ni
- Department of Mathematics, University College London, Room 603, 25 Gordon St, London, WC1H 0AY, UK.
| | - Edward Palmer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, UK
| | - Bo Wang
- The Alan Turing Institute, London, UK
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Yue Wu
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - Lingyi Yang
- Mathematical Institute, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Weixin Yang
- Mathematical Institute, University of Oxford, Oxford, UK
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15
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van der Vegt AH, Campbell V, Mitchell I, Malycha J, Simpson J, Flenady T, Flabouris A, Lane PJ, Mehta N, Kalke VR, Decoyna JA, Es’haghi N, Liu CH, Scott IA. Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain. J Am Med Inform Assoc 2024; 31:509-524. [PMID: 37964688 PMCID: PMC10797271 DOI: 10.1093/jamia/ocad220] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVE To identify factors influencing implementation of machine learning algorithms (MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a validated implementation framework. MATERIALS AND METHODS A systematic review of studies of implemented or trialed real-time clinical deterioration prediction MLAs was undertaken, which identified: how MLA implementation was measured; impact of MLAs on clinical processes and patient outcomes; and barriers, enablers and uncertainties within the implementation process. Review findings were then mapped to the SALIENT end-to-end implementation framework to identify the implementation stages at which these factors applied. RESULTS Thirty-seven articles relating to 14 groups of MLAs were identified, each trialing or implementing a bespoke algorithm. One hundred and seven distinct implementation evaluation metrics were identified. Four groups reported decreased hospital mortality, 1 significantly. We identified 24 barriers, 40 enablers, and 14 uncertainties and mapped these to the 5 stages of the SALIENT implementation framework. DISCUSSION Algorithm performance across implementation stages decreased between in silico and trial stages. Silent plus pilot trial inclusion was associated with decreased mortality, as was the use of logistic regression algorithms that used less than 39 variables. Mitigation of alert fatigue via alert suppression and threshold configuration was commonly employed across groups. CONCLUSIONS : There is evidence that real-world implementation of clinical deterioration prediction MLAs may improve clinical outcomes. Various factors identified as influencing success or failure of implementation can be mapped to different stages of implementation, thereby providing useful and practical guidance for implementers.
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Affiliation(s)
- Anton H van der Vegt
- Centre for Health Services Research, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Victoria Campbell
- Intensive Care Unit, Sunshine Coast Hospital and Health Service, Birtynia, QLD 4575, Australia
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Imogen Mitchell
- Office of Research and Education, Canberra Health Services, Canberra, ACT 2601, Australia
| | - James Malycha
- Department of Critical Care Medicine, The Queen Elizabeth Hospital, Woodville, SA 5011, Australia
| | - Joanna Simpson
- Eastern Health Intensive Care Services, Eastern Health, Box Hill, VIC 3128, Australia
| | - Tracy Flenady
- School of Nursing, Midwifery & Social Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia
| | - Arthas Flabouris
- Intensive Care Department, Royal Adelaide Hospital, Adelaide, SA 5000, Australia
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia
| | - Paul J Lane
- Safety Quality & Innovation, The Prince Charles Hospital, Chermside, QLD 4032, Australia
| | - Naitik Mehta
- Patient Safety and Quality, Clinical Excellence Queensland, Brisbane, QLD 4001, Australia
| | - Vikrant R Kalke
- Patient Safety and Quality, Clinical Excellence Queensland, Brisbane, QLD 4001, Australia
| | - Jovie A Decoyna
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Nicholas Es’haghi
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Chun-Huei Liu
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Ian A Scott
- Centre for Health Services Research, The University of Queensland, Brisbane, QLD 4102, Australia
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
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16
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Jadhav P, Sears T, Floan G, Joskowitz K, Nienow S, Cruz S, David M, de Cos V, Choi P, Ignacio RC. Application of a Machine Learning Algorithm in Prediction of Abusive Head Trauma in Children. J Pediatr Surg 2024; 59:80-85. [PMID: 37858394 DOI: 10.1016/j.jpedsurg.2023.09.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 09/07/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE We explored the application of a machine learning algorithm for the timely detection of potential abusive head trauma (AHT) using the first free-text note of an encounter and demographic information. METHODS First free-text physician notes and demographic information were collected for children under 5 years of age at a Level 1 Trauma Center. The control group, which included patients with head/neck injury, was compared to those with AHT diagnosed by the Child Protective Team. Differential scores accounted for words overrepresented in AHT patient vs. control notes. Sentiment scores were reflective of note positivity/negativity and subjectivity scores accounted for note subjectivity/objectivity. The composite scores reflected the patient's differential score modified by the subjectivity score. Composite, sentiment, and subjectivity scores combined with demographic information trained a Random Forest (RF) machine learning algorithm to predict AHT. RESULTS Final composite scores with demographic information were highly associated with AHT in a test dataset. The control group included 587 patients and the test group included 193 patients. Combining composite scores with demographic information into the RF model improved AHT classification area under the curve (AUC) from 0.68 to 0.78, with an overall accuracy of 84%. Feature importance analysis of our RF model revealed that composite score, sentiment, age, and subjectivity were the most impactful predictors of AHT. The sentiment was not significantly different between control and AHT notes (p = 0.87), while subjectivity trended higher for AHT notes (p = 0.081). CONCLUSION We conclude that a machine learning algorithm can recognize patterns within free-text notes and demographic information that aid in AHT detection in children. LEVEL OF EVIDENCE III.
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Affiliation(s)
- Priyanka Jadhav
- University of California San Diego School of Medicine, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Timothy Sears
- Department of Bioinformatics and Systems Biology Graduate Program, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA
| | - Gretchen Floan
- Department of General Surgery, Naval Medical Center San Diego, 34800 Bob Wilson Dr, San Diego, CA, 92134, USA
| | - Katie Joskowitz
- Rady Children's Hospital San Diego, 3020 Children's Way, San Diego, CA, 92123, USA
| | - Shalon Nienow
- Department of Pediatrics, Division of Child Abuse Pediatrics, University of California-San Diego School of Medicine, 9500 Gilman Dr, La Jolla, CA, 92093, USA; Chadwick Center for Children and Families at Rady Childrens Hospital, 3665 Kearny Villa Road, Suite 500, San Diego, CA, 92123, USA
| | - Sheena Cruz
- University of California San Diego School of Medicine, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Maya David
- Tulane University School of Medicine, 1430 Tulane Ave, New Orleans, LA, 70112, USA
| | - Víctor de Cos
- University of California San Diego School of Medicine, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Pam Choi
- Department of General Surgery, Naval Medical Center San Diego, 34800 Bob Wilson Dr, San Diego, CA, 92134, USA
| | - Romeo C Ignacio
- University of California San Diego School of Medicine, 9500 Gilman Dr, La Jolla, CA, 92093, USA; Division of Pediatric Surgery, Department of Surgery, University of California San Diego School of Medicine, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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17
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Rashid A, Al-Obeida F, Hafez W, Benakatti G, Malik RA, Koutentis C, Sharief J, Brierley J, Quraishi N, Malik ZA, Anwary A, Alkhzaimi H, Zaki SA, Khilnani P, Kadwa R, Phatak R, Schumacher M, Shaikh G, Al-Dubai A, Hussain A. ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES. Shock 2024; 61:4-18. [PMID: 37752080 DOI: 10.1097/shk.0000000000002227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
ABSTRACT Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.
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Affiliation(s)
| | | | | | | | | | | | | | - Joe Brierley
- Great Ormond Street Children's Hospital, London, UK
| | - Nasir Quraishi
- Centre for Spinal Studies & Surgery, Queen's Medical Centre. The University of Nottingham. Nottingham, UK
| | - Zainab A Malik
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences. Dubai, U.A.E
| | - Arif Anwary
- School of Computing, Edinburgh Napier University. Edinburgh, UK
| | | | | | | | | | - Rajesh Phatak
- Pediatric Intensive Care, Burjeel Hospital, Najda, Abu Dhabi
| | | | - Guftar Shaikh
- Endocrinology, Royal Hospital for Children. Glasgow, UK
| | - Ahmed Al-Dubai
- School of Computing, Edinburgh Napier University. Edinburgh, UK
| | - Amir Hussain
- School of Computing, Edinburgh Napier University. Edinburgh, UK
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18
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Litvinova O, Hammerle FP, Stoyanov J, Ksepka N, Matin M, Ławiński M, Atanasov AG, Willschke H. Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine. Healthcare (Basel) 2023; 11:3003. [PMID: 37998496 PMCID: PMC10671755 DOI: 10.3390/healthcare11223003] [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: 10/09/2023] [Revised: 11/02/2023] [Accepted: 11/11/2023] [Indexed: 11/25/2023] Open
Abstract
This study conducted a comprehensive patent and bibliometric analysis to elucidate the evolving scientific landscape surrounding the development and application of pulse oximeters, including in the field of digital medicine. Utilizing data from the Lens database for the period of 2000-2023, we identified the United States, China, the Republic of Korea, Japan, Canada, Australia, Taiwan, and the United Kingdom as the predominant countries in patent issuance for pulse oximeter technology. Our bibliometric analysis revealed a consistent temporal trend in both the volume of publications and citations, underscoring the growing importance of pulse oximeters in digitally-enabled medical practice. Using the VOSviewer software(version 1.6.18), we discerned six primary research clusters: (1) measurement accuracy; (2) integration with the Internet of Things; (3) applicability across diverse pathologies; (4) telemedicine and mobile applications; (5) artificial intelligence and deep learning; and (6) utilization in anesthesiology, resuscitation, and intensive care departments. The findings of this study indicate the prospects for leveraging digital technologies in the use of pulse oximetry in various fields of medicine, with implications for advancing the understanding, diagnosis, prevention, and treatment of cardio-respiratory pathologies. The conducted patent and bibliometric analysis allowed the identification of technical solutions to reduce the risks associated with pulse oximetry: improving precision and validity, technically improved clinical diagnostic use, and the use of machine learning.
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Affiliation(s)
- Olena Litvinova
- Department of Management and Quality Assurance in Pharmacy, National University of Pharmacy, Ministry of Health of Ukraine, 61002 Kharkiv, Ukraine
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, 1090 Vienna, Austria;
| | - Fabian Peter Hammerle
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, 1090 Vienna, Austria;
- Department of Anesthesia, General Intensiv Care and Pain Management, Medical University of Vienna, 1090 Vienna, Austria
| | | | - Natalia Ksepka
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, 05-552 Magdalenka, Poland; (N.K.); (M.M.); (M.Ł.)
| | - Maima Matin
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, 05-552 Magdalenka, Poland; (N.K.); (M.M.); (M.Ł.)
| | - Michał Ławiński
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, 05-552 Magdalenka, Poland; (N.K.); (M.M.); (M.Ł.)
- Department of General, Gastroenterologic and Oncologic Surgery, Medical University of Warsaw, 02-097 Warsaw, Poland
| | - Atanas G. Atanasov
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, 1090 Vienna, Austria;
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, 05-552 Magdalenka, Poland; (N.K.); (M.M.); (M.Ł.)
| | - Harald Willschke
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, 1090 Vienna, Austria;
- Department of Anesthesia, General Intensiv Care and Pain Management, Medical University of Vienna, 1090 Vienna, Austria
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Kim T, Tae Y, Yeo HJ, Jang JH, Cho K, Yoo D, Lee Y, Ahn SH, Kim Y, Lee N, Cho WH. Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data. J Clin Med 2023; 12:7156. [PMID: 38002768 PMCID: PMC10672000 DOI: 10.3390/jcm12227156] [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: 10/18/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning. METHODS Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment. RESULTS DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346). CONCLUSIONS The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation.
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Affiliation(s)
- Taehwa Kim
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, School of Medicine, Pusan National University and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea; (T.K.); (H.J.Y.); (J.H.J.)
| | - Yunwon Tae
- VUNO, Seoul 06541, Republic of Korea; (Y.T.); (K.C.); (D.Y.); (Y.L.)
| | - Hye Ju Yeo
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, School of Medicine, Pusan National University and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea; (T.K.); (H.J.Y.); (J.H.J.)
- Department of Internal Medicine, School of Medicine, Pusan National University, Busan 46241, Republic of Korea
| | - Jin Ho Jang
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, School of Medicine, Pusan National University and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea; (T.K.); (H.J.Y.); (J.H.J.)
| | - Kyungjae Cho
- VUNO, Seoul 06541, Republic of Korea; (Y.T.); (K.C.); (D.Y.); (Y.L.)
| | - Dongjoon Yoo
- VUNO, Seoul 06541, Republic of Korea; (Y.T.); (K.C.); (D.Y.); (Y.L.)
- Department of Critical Care Medicine and Emergency Medicine, Inha University College of Medicine, Incheon 22212, Republic of Korea
| | - Yeha Lee
- VUNO, Seoul 06541, Republic of Korea; (Y.T.); (K.C.); (D.Y.); (Y.L.)
| | - Sung-Ho Ahn
- Division of Biostatistics, Department of Neurology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea;
| | - Younga Kim
- Department of Pediatrics, School of Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (Y.K.); (N.L.)
| | - Narae Lee
- Department of Pediatrics, School of Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (Y.K.); (N.L.)
| | - Woo Hyun Cho
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, School of Medicine, Pusan National University and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea; (T.K.); (H.J.Y.); (J.H.J.)
- Department of Internal Medicine, School of Medicine, Pusan National University, Busan 46241, Republic of Korea
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Lashen H, St John TL, Almallah YZ, Sasidhar M, Shamout FE. Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates. JMIR AI 2023; 2:e45257. [PMID: 38875543 PMCID: PMC11041421 DOI: 10.2196/45257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/19/2023] [Accepted: 08/01/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could facilitate early intervention and consequently improve patient outcomes; for example, the National Early Warning Score (NEWS) system, which is recommended by the Royal College of Physicians in the United Kingdom, uses predefined alerting thresholds to assign scores to patients based on their vital signs. However, there is limited evidence of the reliability of such scores across patient cohorts in the United Arab Emirates. OBJECTIVE Our aim in this study was to propose a data-driven model that accurately predicts in-hospital deterioration in an inpatient cohort in the United Arab Emirates. METHODS We conducted a retrospective cohort study using a real-world data set that consisted of 16,901 unique patients associated with 26,073 inpatient emergency encounters and 951,591 observation sets collected between April 2015 and August 2021 at a large multispecialty hospital in Abu Dhabi, United Arab Emirates. The observation sets included routine measurements of heart rate, respiratory rate, systolic blood pressure, level of consciousness, temperature, and oxygen saturation, as well as whether the patient was receiving supplementary oxygen. We divided the data set of 16,901 unique patients into training, validation, and test sets consisting of 11,830 (70%; 18,319/26,073, 70.26% emergency encounters), 3397 (20.1%; 5206/26,073, 19.97% emergency encounters), and 1674 (9.9%; 2548/26,073, 9.77% emergency encounters) patients, respectively. We defined an adverse event as the occurrence of admission to the intensive care unit, mortality, or both if the patient was admitted to the intensive care unit first. On the basis of 7 routine vital signs measurements, we assessed the performance of the NEWS system in detecting deterioration within 24 hours using the area under the receiver operating characteristic curve (AUROC). We also developed and evaluated several machine learning models, including logistic regression, a gradient-boosting model, and a feed-forward neural network. RESULTS In a holdout test set of 2548 encounters with 95,755 observation sets, the NEWS system achieved an overall AUROC value of 0.682 (95% CI 0.673-0.690). In comparison, the best-performing machine learning models, which were the gradient-boosting model and the neural network, achieved AUROC values of 0.778 (95% CI 0.770-0.785) and 0.756 (95% CI 0.749-0.764), respectively. Our interpretability results highlight the importance of temperature and respiratory rate in predicting patient deterioration. CONCLUSIONS Although traditional early warning score systems are the dominant form of deterioration prediction models in clinical practice today, we strongly recommend the development and use of cohort-specific machine learning models as an alternative. This is especially important in external patient cohorts that were unseen during model development.
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Affiliation(s)
- Hazem Lashen
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | | | | | - Madhu Sasidhar
- Cleveland Clinic Tradition Hospital, Port St. Lucie, FL, United States
| | - Farah E Shamout
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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Cimino J, Braun C. Clinical Research in Prehospital Care: Current and Future Challenges. Clin Pract 2023; 13:1266-1285. [PMID: 37887090 PMCID: PMC10605888 DOI: 10.3390/clinpract13050114] [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: 08/21/2023] [Revised: 10/08/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
Prehospital care plays a critical role in improving patient outcomes, particularly in cases of time-sensitive emergencies such as trauma, cardiac failure, stroke, bleeding, breathing difficulties, systemic infections, etc. In recent years, there has been a growing interest in clinical research in prehospital care, and several challenges and opportunities have emerged. There is an urgent need to adapt clinical research methodology to a context of prehospital care. At the same time, there are many barriers in prehospital research due to the complex context, posing unique challenges for research, development, and evaluation. Among these, this review allows the highlighting of limited resources and infrastructure, ethical and regulatory considerations, time constraints, privacy, safety concerns, data collection and analysis, selection of a homogeneous study group, etc. The analysis of the literature also highlights solutions such as strong collaboration between emergency medical services (EMS) and hospital care, use of (mobile) health technologies and artificial intelligence, use of standardized protocols and guidelines, etc. Overall, the purpose of this narrative review is to examine the current state of clinical research in prehospital care and identify gaps in knowledge, including the challenges and opportunities for future research.
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Affiliation(s)
- Jonathan Cimino
- Clinical Research Unit, Fondation Hôpitaux Robert Schuman, 44 Rue d’Anvers, 1130 Luxembourg, Luxembourg
- Hôpitaux Robert Schuman, 9 Rue Edward Steichen, 2540 Luxembourg, Luxembourg
| | - Claude Braun
- Clinical Research Unit, Fondation Hôpitaux Robert Schuman, 44 Rue d’Anvers, 1130 Luxembourg, Luxembourg
- Hôpitaux Robert Schuman, 9 Rue Edward Steichen, 2540 Luxembourg, Luxembourg
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King AJ, Tang L, Davis BS, Preum SM, Bukowski LA, Zimmerman J, Kahn JM. Machine learning-based prediction of low-value care for hospitalized patients. INTELLIGENCE-BASED MEDICINE 2023; 8:100115. [PMID: 38130744 PMCID: PMC10735238 DOI: 10.1016/j.ibmed.2023.100115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Objective Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period; and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system.
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Affiliation(s)
- Andrew J. King
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lu Tang
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA
| | - Billie S. Davis
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sarah M. Preum
- Department of Computer Science, Dartmouth College, Hanover, NH, USA
| | - Leigh A. Bukowski
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - John Zimmerman
- Human-Computer Interaction Institute, Carnegie Mellon University School of Computer Science, Pittsburgh, PA, USA
| | - Jeremy M. Kahn
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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Cheng TY, Ho SYC, Chien TW, Chow JC, Chou W. A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis. Medicine (Baltimore) 2023; 102:e35156. [PMID: 37861508 PMCID: PMC10589539 DOI: 10.1097/md.0000000000035156] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/18/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND There are 3 issues in bibliometrics that need to be addressed: The lack of a clear definition for author collaborations in cluster analysis that takes into account collaborations with and without self-connections; The need to develop a simple yet effective clustering algorithm for use in coword analysis, and; The inadequacy of general bibliometrics in regard to comparing research achievements and identifying articles that are worth reading and recommended for readers. The study aimed to put forth a clustering algorithm for cluster analysis (called following leader clustering [FLCA], a follower-leading clustering algorithm), examine the dissimilarities in cluster outcomes when considering collaborations with and without self-connections in cluster analysis, and demonstrate the application of the clustering algorithm in bibliometrics. METHODS The study involved a search for articles and review articles published in JMIR Medical Informatics between 2016 and 2022, conducted using the Web of Science core collections. To identify author collaborations (ACs) and themes over the past 7 years, the study utilized the FLCA algorithm. With the 3 objectives of; Comparing the results obtained from scenarios with and without self-connections; Applying the FLCA algorithm in ACs and themes, and; Reporting the findings using traditional bibliometric approaches based on counts and citations, and all plots were created using R. RESULTS The study found a significant difference in cluster outcomes between the 2 scenarios with and without self-connections, with a 53.8% overlap (14 out of the top 20 countries in ACs). The top clusters were led by Yonsei University in South Korea, Grang Luo from the US, and model in institutes, authors, and themes over the past 7 years. The top entities with the most publications in JMIR Medical Informatics were the United States, Yonsei University in South Korea, Medical School, and Grang Luo from the US. CONCLUSION The FLCA algorithm proposed in this study offers researchers a comprehensive approach to exploring and comprehending the complex connections among authors or keywords. The study suggests that future research on ACs with cluster analysis should employ FLCA and R visualizations.
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Affiliation(s)
- Teng-Yun Cheng
- Department of Emergency Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
| | - Sam Yu-Chieh Ho
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Julie Chi Chow
- Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
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Patton MJ, Liu VX. Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges. Crit Care Clin 2023; 39:647-673. [PMID: 37704332 DOI: 10.1016/j.ccc.2023.02.001] [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: 09/15/2023]
Abstract
The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumption of health data feasible and a powerful engine for clinical innovation. In critical care research, the net convergence of these trends has resulted in an exponential increase in outcome prediction research. In the following article, we explore the history of outcome prediction in the intensive care unit (ICU), the growing use of EHR data, and the rise of artificial intelligence and ML (AI) in critical care.
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Affiliation(s)
- Michael J Patton
- Medical Scientist Training Program, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA; Hugh Kaul Precision Medicine Institute at the University of Alabama at Birmingham, 720 20th Street South, Suite 202, Birmingham, Alabama, 35233, USA.
| | - Vincent X Liu
- Kaiser Permanente Division of Research, Oakland, CA, USA.
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25
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Cheng TY, Yu-Chieh Ho S, Chien TW, Chou W. Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis. Medicine (Baltimore) 2023; 102:e35082. [PMID: 37746962 PMCID: PMC10519472 DOI: 10.1097/md.0000000000035082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND The field of critical care-related artificial intelligence (AI) research is rapidly gaining interest. However, there is still a lack of comprehensive bibliometric studies that measure and analyze scientific publications on a global scale. Network charts have traditionally been used to highlight author collaborations and coword phenomena (ACCP). It is necessary to determine whether chord network charts (CNCs) can provide a better understanding of ACCP, thus requiring clarification. This study aimed to achieve 2 objectives: evaluate global research trends in AI in intensive care medicine on publication outputs, coauthorships between nations, citations, and co-occurrences of keywords; and demonstrate the use of CNCs for ACCP in bibliometric analysis. METHODS The web of science database was searched for a total of 1992 documents published between 2013 and 2022. The document type was limited to articles and article reviews, and titles and abstracts were screened for eligibility. The characteristics of the publications, including preferred journals, leading research countries, international collaborations, top institutions, and major keywords, were analyzed using the category-journal rank-authorship-L-index score and trend analysis. The 100 most highly cited articles are also listed in detail. RESULTS Between 2018 and 2022, there was a sharp increase in publications, which accounted for 92.8% (1849/1992) of all papers included in the study. The United States and China were responsible for nearly 50% (936/1992) of the total publications. The leading countries, institutes, departments, authors, and journals in terms of publications were the US, Massachusetts Gen Hosp (US), Medical School, Zhongheng Zhang (China), and Science Reports. The top 3 primary keywords denoting research hotspots for AI in critically ill patients were mortality, model, and intensive care unit, with mortality having the highest burst strength (4.49). The keywords risk and system showed the highest growth trend (0.98) in counts over the past 4 years. CONCLUSIONS This study provides valuable insights into the potential for ACCP and future research opportunities. For AI-based clinical research to become widely accepted in critical care practice, collaborative research efforts are necessary to strengthen the maturity and robustness of AI-driven models using CNCs for display.
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Affiliation(s)
- Teng-Yun Cheng
- Department of Emergency Medicine, Chi-Mei Medical Center, Liouying, Tainan, Taiwan
| | - Sam Yu-Chieh Ho
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Jiali, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
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26
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Islam KR, Prithula J, Kumar J, Tan TL, Reaz MBI, Sumon MSI, Chowdhury MEH. Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review. J Clin Med 2023; 12:5658. [PMID: 37685724 PMCID: PMC10488449 DOI: 10.3390/jcm12175658] [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: 07/13/2023] [Revised: 08/13/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention. METHODS PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality. RESULTS This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding-article quality correlation. CONCLUSIONS This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data.
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Affiliation(s)
- Khandaker Reajul Islam
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Johayra Prithula
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh
| | - Jaya Kumar
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Toh Leong Tan
- Department of Emergency Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Mamun Bin Ibne Reaz
- Department of Electrical and Electronic Engineering, Independent University, Bangladesh Bashundhara, Dhaka 1229, Bangladesh
| | - Md. Shaheenur Islam Sumon
- Department of Biomedical Engineering, Military Institute of Science and Technology (MIST), Dhaka 1216, Bangladesh
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Yoon BR, Seol CH, Min IK, Park MS, Park JE, Chung KS. Biomarker-Based Assessment Model for Detecting Sepsis: A Retrospective Cohort Study. J Pers Med 2023; 13:1195. [PMID: 37623446 PMCID: PMC10455581 DOI: 10.3390/jpm13081195] [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: 06/05/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023] Open
Abstract
The concept of the quick sequential organ failure assessment (qSOFA) simplifies sepsis detection, and the next SOFA should be analyzed subsequently to diagnose sepsis. However, it does not include the concept of suspected infection. Thus, we simply developed a biomarker-based assessment model for detecting sepsis (BADS). We retrospectively reviewed the electronic health records of patients admitted to the intensive care unit (ICU) of a 2000-bed university tertiary referral hospital in South Korea. A total of 989 patients were enrolled, with 77.4% (n = 765) of them having sepsis. The patients were divided into a ratio of 8:2 and assigned to a training and a validation set. We used logistic regression analysis and the Hosmer-Lemeshow test to derive the BADS and assess the model. BADS was developed by analyzing the variables and then assigning weights to the selected variables: mean arterial pressure, shock index, lactate, and procalcitonin. The area under the curve was 0.754, 0.615, 0.763, and 0.668 for BADS, qSOFA, SOFA, and acute physiology and chronic health evaluation (APACHE) II, respectively, showing that BADS is not inferior in sepsis prediction compared with SOFA. BADS could be a simple scoring method to detect sepsis in critically ill patients quickly at the bedside.
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Affiliation(s)
- Bo Ra Yoon
- Department of Internal Medicine, New Korea Hospital, Gimpo-si 10086, Republic of Korea;
| | - Chang Hwan Seol
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin 16995, Republic of Korea;
| | - In Kyung Min
- Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Min Su Park
- Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Ji Eun Park
- Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea
| | - Kyung Soo Chung
- Division of Pulmonology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
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Valik JK, Ward L, Tanushi H, Johansson AF, Färnert A, Mogensen ML, Pickering BW, Herasevich V, Dalianis H, Henriksson A, Nauclér P. Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data. Sci Rep 2023; 13:11760. [PMID: 37474597 PMCID: PMC10359402 DOI: 10.1038/s41598-023-38858-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/16/2023] [Indexed: 07/22/2023] Open
Abstract
Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model-SepsisFinder-which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care.
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Affiliation(s)
- John Karlsson Valik
- Division of Infectious Diseases, Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden.
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.
| | - Logan Ward
- Treat Systems ApS, Aalborg, Denmark
- Department of Health Science and Technology, Center for Model-Based Medical Decision Support, Aalborg University, Aalborg, Denmark
| | - Hideyuki Tanushi
- Division of Infectious Diseases, Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Anders F Johansson
- Department of Clinical Microbiology and the Laboratory for Molecular Infection Medicine (MIMS), Umeå University, Umeå, Sweden
| | - Anna Färnert
- Division of Infectious Diseases, Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | | | - Brian W Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA
| | - Hercules Dalianis
- Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden
| | - Aron Henriksson
- Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden
| | - Pontus Nauclér
- Division of Infectious Diseases, Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
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Alanazi A, Aldakhil L, Aldhoayan M, Aldosari B. Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1276. [PMID: 37512087 PMCID: PMC10385427 DOI: 10.3390/medicina59071276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023]
Abstract
Background and Objectives: Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach. By analyzing the clinical laboratory results and vital signs of adult patients in the ICU, this approach can predict and detect the initial signs of sepsis. Materials and Methods: To examine survival rates and predict outcomes, the study utilized several models, including the proportional hazards model and data mining algorithms. We analyzed data from the BESTCare database at KAMC, with a focus on patients aged 14 and older who were admitted to the ICU between April and October 2018. We conducted a thorough analysis of the medical records of a total of 1182 patients who were diagnosed with sepsis. Results: We studied two approaches to predict sepsis in ICU patients. The regression model utilizing survival analysis showed moderate predictive ability, emphasizing the importance of only three factors-time (from sepsis to an outcome; discharge or death), lactic acid, and temperature-had a significant p-value (p = 0.000568, p = 0.01, p = 0.02, respectively). Other data mining algorithms may have limitations due to their assumptions of variable independence and linear classification nature. Conclusions: To achieve progress and accuracy in the field of sepsis prediction, it is important to continuously strive for improvement. By meticulously cleaning and selecting data attributes, we can create a strong foundation for future advancements in this area.
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Affiliation(s)
- Abdullah Alanazi
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Lujain Aldakhil
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Mohammed Aldhoayan
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Bakheet Aldosari
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
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van der Vegt AH, Scott IA, Dermawan K, Schnetler RJ, Kalke VR, Lane PJ. Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework. J Am Med Inform Assoc 2023:7161075. [PMID: 37172264 DOI: 10.1093/jamia/ocad075] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 04/04/2023] [Accepted: 04/23/2023] [Indexed: 05/14/2023] Open
Abstract
OBJECTIVE To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis prediction algorithms using systematic methods, identify implementation barriers, enablers, and key decisions and then map these to a novel end-to-end clinical AI implementation framework. MATERIALS AND METHODS Systematically review studies of clinically applied AI-based sepsis prediction algorithms in regard to methodological quality, deployment and evaluation methods, and outcomes. Identify contextual factors that influence implementation and map these factors to the SALIENT implementation framework. RESULTS The review identified 30 articles of algorithms applied in adult hospital settings, with 5 studies reporting significantly decreased mortality post-implementation. Eight groups of algorithms were identified, each sharing a common algorithm. We identified 14 barriers, 26 enablers, and 22 decision points which were able to be mapped to the 5 stages of the SALIENT implementation framework. DISCUSSION Empirical studies of deployed sepsis prediction algorithms demonstrate their potential for improving care and reducing mortality but reveal persisting gaps in existing implementation guidance. In the examined publications, key decision points reflecting real-word implementation experience could be mapped to the SALIENT framework and, as these decision points appear to be AI-task agnostic, this framework may also be applicable to non-sepsis algorithms. The mapping clarified where and when barriers, enablers, and key decisions arise within the end-to-end AI implementation process. CONCLUSIONS A systematic review of real-world implementation studies of sepsis prediction algorithms was used to validate an end-to-end staged implementation framework that has the ability to account for key factors that warrant attention in ensuring successful deployment, and which extends on previous AI implementation frameworks.
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Affiliation(s)
- Anton H van der Vegt
- Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Ian A Scott
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Australia
| | - Krishna Dermawan
- Centre for Information Resilience, The University of Queensland, St Lucia, Australia
| | - Rudolf J Schnetler
- School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Australia
| | - Vikrant R Kalke
- Patient Safety and Quality, Clinical Excellence Queensland, Queensland Health, Brisbane, Australia
| | - Paul J Lane
- Safety Quality & Innovation, The Prince Charles Hospital, Queensland Health, Brisbane, Australia
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Williams E, Kienast M, Medawar E, Reinelt J, Merola A, Klopfenstein SAI, Flint AR, Heeren P, Poncette AS, Balzer F, Beimes J, von Bünau P, Chromik J, Arnrich B, Scherf N, Niehaus S. A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study. JMIR Med Inform 2023; 11:e43847. [PMID: 36943344 PMCID: PMC10131740 DOI: 10.2196/43847] [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: 11/07/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited. OBJECTIVE In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard. METHODS We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database. RESULTS We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records. CONCLUSIONS Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.
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Affiliation(s)
| | | | | | | | | | | | - Anne Rike Flint
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Patrick Heeren
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Felix Balzer
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | | | - Jonas Chromik
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Bert Arnrich
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Nico Scherf
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis. NPJ Digit Med 2023; 6:15. [PMID: 36732666 PMCID: PMC9894526 DOI: 10.1038/s41746-023-00755-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting clinicians for real-time treatment of sepsis. While a value function quantifies the performance of policies in such decision-making processes, most value-based DRL algorithms cannot evaluate the target value function precisely and are not as safe as clinical experts. In this study, we propose a Weighted Dueling Double Deep Q-Network with embedded human Expertise (WD3QNE). A target Q value function with adaptive dynamic weight is designed to improve the estimate accuracy and human expertise in decision-making is leveraged. In addition, the random forest algorithm is employed for feature selection to improve model interpretability. We test our algorithm against state-of-the-art value function methods in terms of expected return, survival rate, action distribution and external validation. The results demonstrate that WD3QNE obtains the highest survival rate of 97.81% in MIMIC-III dataset. Our proposed method is capable of providing reliable treatment decisions with embedded clinician expertise.
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Mercurio L, Pou S, Duffy S, Eickhoff C. Risk Factors for Pediatric Sepsis in the Emergency Department: A Machine Learning Pilot Study. Pediatr Emerg Care 2023; 39:e48-e56. [PMID: 36648121 DOI: 10.1097/pec.0000000000002893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE To identify underappreciated sepsis risk factors among children presenting to a pediatric emergency department (ED). METHODS A retrospective observational study (2017-2019) of children aged 18 years and younger presenting to a pediatric ED at a tertiary care children's hospital with fever, hypotension, or an infectious disease International Classification of Diseases (ICD)-10 diagnosis. Structured patient data including demographics, problem list, and vital signs were extracted for 35,074 qualifying ED encounters. According to the Improving Pediatric Sepsis Outcomes Classification, confirmed by expert review, 191 patients met clinical sepsis criteria. Five machine learning models were trained to predict sepsis/nonsepsis outcomes. Top features enabling model performance (N = 20) were then extracted to identify patient risk factors. RESULTS Machine learning methods reached a performance of up to 93% sensitivity and 84% specificity in identifying patients who received a hospital diagnosis of sepsis. A random forest classifier performed the best, followed by a classification and regression tree. Maximum documented heart rate was the top feature in these models, with importance coefficients (ICs) of 0.09 and 0.21, which represent how much an individual feature contributes to the model. Maximum mean arterial pressure was the second most important feature (IC 0.05, 0.13). Immunization status (IC 0.02), age (IC 0.03), and patient zip code (IC 0.02) were also among the top features enabling models to predict sepsis from ED visit data. Stratified analysis revealed changes in the predictive importance of risk factors by race, ethnicity, oncologic history, and insurance status. CONCLUSIONS Machine learning models trained to identify pediatric sepsis using ED clinical and sociodemographic variables confirmed well-established predictors, including heart rate and mean arterial pressure, and identified underappreciated relationships between sepsis and patient age, immunization status, and demographics.
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Affiliation(s)
- Laura Mercurio
- From the Section of Pediatric Emergency Medicine, Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, RI
| | - Sovijja Pou
- Alpert Medical School of Brown University, Providence, RI
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Duan Y, Huo J, Chen M, Hou F, Yan G, Li S, Wang H. Early prediction of sepsis using double fusion of deep features and handcrafted features. APPL INTELL 2023; 53:1-17. [PMID: 36685641 PMCID: PMC9843111 DOI: 10.1007/s10489-022-04425-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2022] [Indexed: 01/19/2023]
Abstract
Sepsis is a life-threatening medical condition that is characterized by the dysregulated immune system response to infections, having both high morbidity and mortality rates. Early prediction of sepsis is critical to the decrease of mortality. This paper presents a novel early warning model called Double Fusion Sepsis Predictor (DFSP) for sepsis onset. DFSP is a double fusion framework that combines the benefits of early and late fusion strategies. First, a hybrid deep learning model that combines both the convolutional and recurrent neural networks to extract deep features is proposed. Second, deep features and handcrafted features, such as clinical scores, are concatenated to build the joint feature representation (early fusion). Third, several tree-based models based on joint feature representation are developed to generate the risk scores of sepsis onset that are combined with an End-to-End neural network for final sepsis detection (late fusion). To evaluate DFSP, a retrospective study was conducted, which included patients admitted to the ICUs of a hospital in Shanghai China. The results demonstrate that the DFSP outperforms state-of-the-art approaches in early sepsis prediction.
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Affiliation(s)
- Yongrui Duan
- School of Economics & Management, Tongji University, Shanghai, China
| | - Jiazhen Huo
- School of Economics & Management, Tongji University, Shanghai, China
| | - Mingzhou Chen
- School of Economics & Management, Tongji University, Shanghai, China
| | - Fenggang Hou
- Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Guoliang Yan
- Department of Geriatrics, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Shufang Li
- Emergency Department, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Haihui Wang
- Department of Geriatrics, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China
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Pankhurst T, Lucas L, Ryan S, Ragdale C, Gyves H, Denner L, Young I, Rathbone L, Shah A, McKee D, Coleman JJ, Evison F, Atia J, Rosser D, Garrick M, Baker R, Gallier S, Ball S. Benefits of electronic charts in intensive care and during a world health pandemic: advantages of the technology age. BMJ Open Qual 2023; 12:bmjoq-2021-001704. [PMID: 36649943 PMCID: PMC9853220 DOI: 10.1136/bmjoq-2021-001704] [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: 11/03/2021] [Accepted: 03/25/2022] [Indexed: 01/19/2023] Open
Abstract
AIMS AND OBJECTIVES This study sets out to describe benefits from the implementation of electronic observation charting in intensive care units (ICU). This was an extension to the existing hospital wide digital health system. We evaluated error reduction, time-savings and the costs associated with conversion from paper to digital records. The world health emergency of COVID-19 placed extraordinary strain on ICU and staff opinion was evaluated to test how well the electronic system performed. METHODS A clinically led project group working directly with programmers developed an electronic patient record for intensive care. Data error rates, time to add data and to make calculations were studied before and after the introduction of electronic charts. User feedback was sought pre and post go-live (during the COVID-19 pandemic) and financial implications were calculated by the hospital finance teams. RESULTS Error rates equating to 219 000/year were avoided by conversion to electronic charts. Time saved was the equivalent of a nursing shift each day. Recurrent cost savings per year were estimated to be £257k. Staff were overwhelmingly positive about electronic charts in ICU, even during a health pandemic and despite redeployment into intensive care where they were using the electronic charts for the first time. DISCUSSION Electronic ICU charts have been successfully introduced into our institution with benefits in terms of patient safety through error reduction and improved care through release of nursing time. Costs have been reduced. Staff feel supported by the digital system and report it to be helpful even during redeployment and in the unfamiliar environment of intensive care.
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Affiliation(s)
- Tanya Pankhurst
- Renal Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Laurie Lucas
- IT Services, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Steve Ryan
- IT Services, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Chris Ragdale
- IT Services, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Helen Gyves
- IT Services, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Louise Denner
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Ian Young
- IT Services, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Laura Rathbone
- IT Services, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Anwar Shah
- Anaesthetics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Deborah McKee
- IT Services, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Jamie J Coleman
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK,School of Medicine, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Felicity Evison
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Jolene Atia
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - David Rosser
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Mark Garrick
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Richard Baker
- Finance, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Suzy Gallier
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK,PIONEER: HDR-UK Health Data Research Hub in Acute care, University of Birmingham, Birmingham, UK
| | - Simon Ball
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK,Institute of Infection and Immunity, University of Birmingham, Birmingham, UK,Better Care, Health Data Research, London, UK
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36
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Tang M, Mu F, Cui C, Zhao JY, Lin R, Sun KX, Guan Y, Wang JW. Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis. Front Med (Lausanne) 2023; 9:1043589. [PMID: 36714139 PMCID: PMC9878129 DOI: 10.3389/fmed.2022.1043589] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 12/23/2022] [Indexed: 01/14/2023] Open
Abstract
Background With the increasing interest of academics in the application of artificial intelligence to sepsis, thousands of papers on this field had been published in the past few decades. It is difficult for researchers to understand the themes and latest research frontiers in this field from a multi-dimensional perspective. Consequently, the purpose of this study is to analyze the relevant literature in the application of artificial intelligence to sepsis through bibliometrics software, so as to better understand the development status, study the core hotspots and future development trends of this field. Methods We collected relevant publications in the application of artificial intelligence to sepsis from the Web of Science Core Collection in 2000 to 2021. The type of publication was limited to articles and reviews, and language was limited to English. Research cooperation network, journals, cited references, keywords in this field were visually analyzed by using CiteSpace, VOSviewer, and COOC software. Results A total of 8,481 publications in the application of artificial intelligence to sepsis between 2000 and 2021 were included, involving 8,132 articles and 349 reviews. Over the past 22 years, the annual number of publications had gradually increased exponentially. The USA was the most productive country, followed by China. Harvard University, Schuetz, Philipp, and Intensive Care Medicine were the most productive institution, author, and journal, respectively. Vincent, Jl and Critical Care Medicine were the most cited author and cited journal, respectively. Several conclusions can be drawn from the analysis of the cited references, including the following: screening and identification of sepsis biomarkers, treatment and related complications of sepsis, and precise treatment of sepsis. Moreover, there were a spike in searches relating to machine learning, antibiotic resistance and accuracy based on burst detection analysis. Conclusion This study conducted a comprehensive and objective analysis of the publications on the application of artificial intelligence in sepsis. It can be predicted that precise treatment of sepsis through machine learning technology is still research hotspot in this field.
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Chen Q, Li R, Lin C, Lai C, Huang Y, Lu W, Li L. SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis. Appl Clin Inform 2023; 14:65-75. [PMID: 36452980 PMCID: PMC9876660 DOI: 10.1055/a-1990-3037] [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: 07/20/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND The lack of information interoperability between different devices and systems in the intensive care unit (ICU) hinders further utilization of data, especially for early warning of specific diseases in the ICU. OBJECTIVES We aimed to establish a data integration system. Based on this system, the sepsis prediction module was added to compose the Sepsis PREdiction System (SEPRES), where real-time early warning of sepsis can be implemented at the bedside in the ICU. METHODS Data are collected from bedside devices through the integration hub and uploaded to the integration system through the local area network. The data integration system was designed to integrate vital signs data, laboratory data, ventilator data, demographic data, pharmacy data, nursing data, etc. from multiple medical devices and systems. It integrates, standardizes, and stores information, making the real-time inference of the early warning module possible. The built-in sepsis early warning module can detect the onset of sepsis within 5 hours preceding at most. RESULTS Our data integration system has already been deployed in Ruijin Hospital, confirming the feasibility of our system. CONCLUSION We highlight that SEPRES has the potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention.
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Affiliation(s)
- Qiyu Chen
- Division of Applied Mathematics, Fudan University, Shanghai, China
| | - Ranran Li
- Department of Critical Care Medicine, Shanghai Jiaotong University School of Medicine, Ruijin Hospital, Shanghai, China
| | - ChihChe Lin
- Department of Intelligent Medical Products, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China
| | - Chiming Lai
- Department of Intelligent Medical Products, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China
| | - Yaling Huang
- Department of Intelligent Medical Products, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China
| | - Wenlian Lu
- Division of Applied Mathematics, Fudan University, Shanghai, China
| | - Lei Li
- Department of Critical Care Medicine, Shanghai Jiaotong University School of Medicine, Ruijin Hospital, Shanghai, China
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Chen Q, Li R, Lin C, Lai C, Chen D, Qu H, Huang Y, Lu W, Tang Y, Li L. Transferability and interpretability of the sepsis prediction models in the intensive care unit. BMC Med Inform Decis Mak 2022; 22:343. [PMID: 36581881 PMCID: PMC9798724 DOI: 10.1186/s12911-022-02090-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/16/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND We aimed to develop an early warning system for real-time sepsis prediction in the ICU by machine learning methods, with tools for interpretative analysis of the predictions. In particular, we focus on the deployment of the system in a target medical center with small historical samples. METHODS Light Gradient Boosting Machine (LightGBM) and multilayer perceptron (MLP) were trained on Medical Information Mart for Intensive Care (MIMIC-III) dataset and then finetuned on the private Historical Database of local Ruijin Hospital (HDRJH) using transfer learning technique. The Shapley Additive Explanations (SHAP) analysis was employed to characterize the feature importance in the prediction inference. Ultimately, the performance of the sepsis prediction system was further evaluated in the real-world study in the ICU of the target Ruijin Hospital. RESULTS The datasets comprised 6891 patients from MIMIC-III, 453 from HDRJH, and 67 from Ruijin real-world data. The area under the receiver operating characteristic curves (AUCs) for LightGBM and MLP models derived from MIMIC-III were 0.98 - 0.98 and 0.95 - 0.96 respectively on MIMIC-III dataset, and, in comparison, 0.82 - 0.86 and 0.84 - 0.87 respectively on HDRJH, from 1 to 5 h preceding. After transfer learning and ensemble learning, the AUCs of the final ensemble model were enhanced to 0.94 - 0.94 on HDRJH and to 0.86 - 0.9 in the real-world study in the ICU of the target Ruijin Hospital. In addition, the SHAP analysis illustrated the importance of age, antibiotics, net balance, and ventilation for sepsis prediction, making the model interpretable. CONCLUSIONS Our machine learning model allows accurate real-time prediction of sepsis within 5-h preceding. Transfer learning can effectively improve the feasibility to deploy the prediction model in the target cohort, and ameliorate the model performance for external validation. SHAP analysis indicates that the role of antibiotic usage and fluid management needs further investigation. We argue that our system and methodology have the potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention. TRIAL REGISTRATION NCT05088850 (retrospectively registered).
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Affiliation(s)
- Qiyu Chen
- grid.8547.e0000 0001 0125 2443Department of Applied Mathematics, School of Mathematical Sciences, Fudan University, Shanghai, 200433 China
| | - Ranran Li
- grid.16821.3c0000 0004 0368 8293Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025 China
| | - ChihChe Lin
- grid.495525.a0000 0004 0552 4356Shanghai Electric Group Co., Ltd., Central Academe, Shanghai, China
| | - Chiming Lai
- grid.495525.a0000 0004 0552 4356Shanghai Electric Group Co., Ltd., Central Academe, Shanghai, China
| | - Dechang Chen
- grid.16821.3c0000 0004 0368 8293Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025 China
| | - Hongping Qu
- grid.16821.3c0000 0004 0368 8293Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025 China
| | - Yaling Huang
- grid.495525.a0000 0004 0552 4356Shanghai Electric Group Co., Ltd., Central Academe, Shanghai, China
| | - Wenlian Lu
- grid.8547.e0000 0001 0125 2443Department of Applied Mathematics, School of Mathematical Sciences, Fudan University, Shanghai, 200433 China
| | - Yaoqing Tang
- grid.16821.3c0000 0004 0368 8293Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025 China
| | - Lei Li
- grid.16821.3c0000 0004 0368 8293Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025 China
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Bennett N, Plečko D, Ukor IF, Meinshausen N, Bühlmann P. ricu: R's interface to intensive care data. Gigascience 2022; 12:giad041. [PMID: 37318234 PMCID: PMC10268223 DOI: 10.1093/gigascience/giad041] [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: 12/17/2022] [Revised: 03/07/2023] [Accepted: 05/18/2023] [Indexed: 06/16/2023] Open
Abstract
OBJECTIVE To develop a unified framework for analyzing data from 5 large publicly available intensive care unit (ICU) datasets. FINDINGS Using 3 American (Medical Information Mart for Intensive Care III, Medical Information Mart for Intensive Care IV, electronic ICU) and 2 European (Amsterdam University Medical Center Database, High Time Resolution ICU Dataset) databases, we constructed a mapping for each database to a set of clinically relevant concepts, which are grounded in the Observational Medical Outcomes Partnership Vocabulary wherever possible. Furthermore, we performed synchronization in the units of measurement and data type representation. On top of this, we built functionality, which allows the user to download, set up, and load data from all of the 5 databases, through a unified Application Programming Interface. The resulting ricu R-package represents the computational infrastructure for handling publicly available ICU datasets, and its latest release allows the user to load 119 existing clinical concepts from the 5 data sources. CONCLUSION The ricu R-package (available on GitHub and CRAN) is the first tool that enables users to analyze publicly available ICU datasets simultaneously (datasets are available upon request from respective owners). Such an interface saves researchers time when analyzing ICU data and helps reproducibility. We hope that ricu can become a community-wide effort, so that data harmonization is not repeated by each research group separately. One current limitation is that concepts were added on a case-to-case basis, and therefore the resulting dictionary of concepts is not comprehensive. Further work is needed to make the dictionary comprehensive.
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Affiliation(s)
- Nicolas Bennett
- Seminar for Statistics, ETH Zürich, 8092 Zürich (Rämistrasse 101), Switzerland
| | - Drago Plečko
- Seminar for Statistics, ETH Zürich, 8092 Zürich (Rämistrasse 101), Switzerland
| | - Ida-Fong Ukor
- Department of Anaesthesiology and Perioperative Medicine, Monash Health, Clayton VIC 3168, Australia
| | - Nicolai Meinshausen
- Seminar for Statistics, ETH Zürich, 8092 Zürich (Rämistrasse 101), Switzerland
| | - Peter Bühlmann
- Seminar for Statistics, ETH Zürich, 8092 Zürich (Rämistrasse 101), Switzerland
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Lombardi S, Partanen P, Francia P, Calamai I, Deodati R, Luchini M, Spina R, Bocchi L. Classifying sepsis from photoplethysmography. Health Inf Sci Syst 2022; 10:30. [PMID: 36330224 PMCID: PMC9622958 DOI: 10.1007/s13755-022-00199-3] [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: 02/21/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022] Open
Abstract
Sepsis is a life-threatening organ dysfunction. It is caused by a dysregulated immune response to an infection and is one of the leading causes of death in the intensive care unit (ICU). Early detection and treatment of sepsis can increase the survival rate of patients. The use of devices such as the photoplethysmograph could allow the early evaluation in addition to continuous monitoring of septic patients. The aim of this study was to verify the possibility of detecting sepsis in patients from whom the photoplethysmographic signal was acquired via a pulse oximeter. In this work, we developed a deep learning-based model for sepsis identification. The model takes a single input, the photoplethysmographic signal acquired by pulse oximeter, and performs a binary classification between septic and nonseptic samples. To develop the method, we used MIMIC-III database, which contains data from ICU patients. Specifically, the selected dataset includes 85 septic subjects and 101 control subjects. The PPG signals acquired from these patients were segmented, processed and used as input for the developed model with the aim of identifying sepsis. The proposed method achieved an accuracy of 76.37% with a sensitivity of 70.95% and a specificity of 81.04% on the test set. As regards the ROC curve, the Area Under Curve reached a value of 0.842. The results of this study indicate how the plethysmographic signal can be used as a warning sign for the early detection of sepsis with the aim of reducing the time for diagnosis and therapeutic intervention. Furthermore, the proposed method is suitable for integration in continuous patient monitoring.
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Affiliation(s)
- Sara Lombardi
- Department of Information Engineering, University of Florence, Via S. Marta, 3, 50139 Florence, Italy
| | - Petri Partanen
- Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland
| | - Piergiorgio Francia
- Department of Information Engineering, University of Florence, Via S. Marta, 3, 50139 Florence, Italy
| | - Italo Calamai
- S.O.C. Anestesia e Rianimazione, Ospedale S. Giuseppe, viale Giovanni Boccaccio, 16, 50053 Empoli, Italy
| | - Rossella Deodati
- S.O.C. Anestesia e Rianimazione, Ospedale S. Giuseppe, viale Giovanni Boccaccio, 16, 50053 Empoli, Italy
| | - Marco Luchini
- S.O.C. Anestesia e Rianimazione, Ospedale S. Giuseppe, viale Giovanni Boccaccio, 16, 50053 Empoli, Italy
| | - Rosario Spina
- S.O.C. Anestesia e Rianimazione, Ospedale S. Giuseppe, viale Giovanni Boccaccio, 16, 50053 Empoli, Italy
| | - Leonardo Bocchi
- Department of Information Engineering, University of Florence, Via S. Marta, 3, 50139 Florence, Italy
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Choi A, Chung K, Chung SP, Lee K, Hyun H, Kim JH. Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:7054. [PMID: 36146403 PMCID: PMC9504566 DOI: 10.3390/s22187054] [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: 07/25/2022] [Revised: 09/02/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (temperature >38 °C) and admitted to the isolation care unit of the ED. The AUROC of the fragmented model with device data was 0.858 (95% confidence interval [CI], 0.809−0.908), and that with manual data was 0.841 (95% CI, 0.789−0.893). The AUROC of the accumulated model with device data was 0.861 (95% CI, 0.811−0.910), and that with manual data was 0.853 (95% CI, 0.803−0.903). Fragmented and accumulated models with device data detected clinical deterioration in febrile patients at risk of septic shock 9 h and 5 h 30 min earlier, respectively, than those with manual data. Continuous vital sign monitoring using a wearable device could accurately predict clinical deterioration and reduce the time to recognise potential clinical deterioration in stable ED patients with fever.
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Affiliation(s)
- Arom Choi
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
| | - Kyungsoo Chung
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
| | - Sung Phil Chung
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
| | - Kwanhyung Lee
- AITRICS, 28 Hyoryeong-ro 77-gil, Seocho-gu, Seoul 06627, Korea
| | - Heejung Hyun
- AITRICS, 28 Hyoryeong-ro 77-gil, Seocho-gu, Seoul 06627, Korea
| | - Ji Hoon Kim
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
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Li M, Du S. Current status and trends in researches based on public intensive care databases: A scientometric investigation. Front Public Health 2022; 10:912151. [PMID: 36187634 PMCID: PMC9521614 DOI: 10.3389/fpubh.2022.912151] [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: 04/04/2022] [Accepted: 08/08/2022] [Indexed: 01/22/2023] Open
Abstract
Objective Public intensive care databases cover a wide range of data that are produced in intensive care units (ICUs). Public intensive care databases draw great attention from researchers since they were time-saving and money-saving in obtaining data. This study aimed to explore the current status and trends of publications based on public intensive care databases. Methods Articles and reviews based on public intensive care databases, published from 2001 to 2021, were retrieved from the Web of Science Core Collection (WoSCC) for investigation. Scientometric software (CiteSpace and VOSviewer) were used to generate network maps and reveal hot spots of studies based on public intensive care databases. Results A total of 456 studies were collected. Zhang Zhongheng from Zhejiang University (China) and Leo Anthony Celi from Massachusetts Institute of Technology (MIT, USA) occupied important positions in studies based on public intensive care databases. Closer cooperation was observed between institutions in the same country. Six Research Topics were concluded through keyword analysis. Result of citation burst indicated that this field was in the stage of rapid development, with more diseases and clinical problems being investigated. Machine learning is still the hot research method in this field. Conclusions This is the first time that scientometrics has been used in the investigation of studies based on public intensive databases. Although more and more studies based on public intensive care databases were published, public intensive care databases may not be fully explored. Moreover, it could also help researchers directly perceive the current status and trends in this field. Public intensive care databases could be fully explored with more researchers' knowledge of this field.
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Saab A, Abi Khalil C, Jammal M, Saikali M, Lamy JB. Early Prediction of All-Cause Clinical Deterioration in General Wards Patients: Development and Validation of a Biomarker-Based Machine Learning Model Derived From Rapid Response Team Activations. J Patient Saf 2022; 18:578-586. [PMID: 35985042 DOI: 10.1097/pts.0000000000001069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of the study is to evaluate the performance of a biomarker-based machine learning (ML) model (not including vital signs) derived from reviewed rapid response team (RRT) activations in predicting all-cause deterioration in general wards patients. DESIGN This is a retrospective single-institution study. All consecutive adult patients' cases on noncritical wards identified by RRT calls occurring at least 24 hours after patient admission, between April 2018 and June 2020, were included. The cases were reviewed and labeled for clinical deterioration by a multidisciplinary expert consensus panel. A supervised learning approach was adopted based on a set of biomarkers and demographic data available in the patient's electronic medical record (EMR). SETTING The setting is a 250-bed tertiary university hospital with a basic EMR, with adult (>18 y) patients on general wards. PATIENTS The study analyzed the cases of 514 patients for which the RRT was activated. Rapid response teams were extracted from the hospital telephone log data. Two hundred eighteen clinical deterioration cases were identified in these patients after expert chart review and complemented by 146 "nonevent" cases to build the training and validation data set. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS The best performance was achieved with the random forests algorithm, with a maximal area under the receiver operating curve of 0.90 and F1 score of 0.85 obtained at prediction time T0-6h, slightly decreasing but still acceptable (area under the receiver operating curve, >0.8; F1 score, >0.75) at T0-42h. The system outperformed most classical track-and-trigger systems both in terms of prediction performance and prediction horizon. CONCLUSIONS In hospitals with a basic EMR, a biomarker-based ML model could be used to predict clinical deterioration in general wards patients earlier than classical track-and-trigger systems, thus enabling appropriate clinical interventions for patient safety and improved outcomes.
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Affiliation(s)
| | | | - Mouin Jammal
- Department of Internal Medicine, Faculty of Medical Sciences, Saint Joseph University, Beirut, Lebanon
| | | | - Jean-Baptiste Lamy
- From the LIMICS, Université Sorbonne Paris Nord, INSERM, UMR 1142, Bobigny, France
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Kiser AC, Eilbeck K, Ferraro JP, Skarda DE, Samore MH, Bucher B. Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care-Associated Infection. JMIR Med Inform 2022; 10:e39057. [PMID: 36040784 PMCID: PMC9472055 DOI: 10.2196/39057] [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: 04/27/2022] [Revised: 08/09/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With the widespread adoption of electronic healthcare records (EHRs) by US hospitals, there is an opportunity to leverage this data for the development of predictive algorithms to improve clinical care. A key barrier in model development and implementation includes the external validation of model discrimination, which is rare and often results in worse performance. One reason why machine learning models are not externally generalizable is data heterogeneity. A potential solution to address the substantial data heterogeneity between health care systems is to use standard vocabularies to map EHR data elements. The advantage of these vocabularies is a hierarchical relationship between elements, which allows the aggregation of specific clinical features to more general grouped concepts. OBJECTIVE This study aimed to evaluate grouping EHR data using standard vocabularies to improve the transferability of machine learning models for the detection of postoperative health care-associated infections across institutions with different EHR systems. METHODS Patients who underwent surgery from the University of Utah Health and Intermountain Healthcare from July 2014 to August 2017 with complete follow-up data were included. The primary outcome was a health care-associated infection within 30 days of the procedure. EHR data from 0-30 days after the operation were mapped to standard vocabularies and grouped using the hierarchical relationships of the vocabularies. Model performance was measured using the area under the receiver operating characteristic curve (AUC) and F1-score in internal and external validations. To evaluate model transferability, a difference-in-difference metric was defined as the difference in performance drop between internal and external validations for the baseline and grouped models. RESULTS A total of 5775 patients from the University of Utah and 15,434 patients from Intermountain Healthcare were included. The prevalence of selected outcomes was from 4.9% (761/15,434) to 5% (291/5775) for surgical site infections, from 0.8% (44/5775) to 1.1% (171/15,434) for pneumonia, from 2.6% (400/15,434) to 3% (175/5775) for sepsis, and from 0.8% (125/15,434) to 0.9% (50/5775) for urinary tract infections. In all outcomes, the grouping of data using standard vocabularies resulted in a reduced drop in AUC and F1-score in external validation compared to baseline features (all P<.001, except urinary tract infection AUC: P=.002). The difference-in-difference metrics ranged from 0.005 to 0.248 for AUC and from 0.075 to 0.216 for F1-score. CONCLUSIONS We demonstrated that grouping machine learning model features based on standard vocabularies improved model transferability between data sets across 2 institutions. Improving model transferability using standard vocabularies has the potential to improve the generalization of clinical prediction models across the health care system.
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Affiliation(s)
- Amber C Kiser
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Karen Eilbeck
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Jeffrey P Ferraro
- Department of Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - David E Skarda
- Center for Value-Based Surgery, Intermountain Healthcare, Salt Lake City, UT, United States.,Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Matthew H Samore
- Department of Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States.,Informatics, Decision-Enhancement and Analytic Sciences Center 2.0, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, United States
| | - Brian Bucher
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States.,Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT, United States
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Albert K, Delano M. Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records. PATTERNS (NEW YORK, N.Y.) 2022; 3:100534. [PMID: 36033589 PMCID: PMC9403398 DOI: 10.1016/j.patter.2022.100534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
False assumptions that sex and gender are binary, static, and concordant are deeply embedded in the medical system. As machine learning researchers use medical data to build tools to solve novel problems, understanding how existing systems represent sex/gender incorrectly is necessary to avoid perpetuating harm. In this perspective, we identify and discuss three factors to consider when working with sex/gender in research: "sex/gender slippage," the frequent substitution of sex and sex-related terms for gender and vice versa; "sex confusion," the fact that any given sex variable holds many different potential meanings; and "sex obsession," the idea that the relevant variable for most inquiries related to sex/gender is sex assigned at birth. We then explore how these phenomena show up in medical machine learning research using electronic health records, with a specific focus on HIV risk prediction. Finally, we offer recommendations about how machine learning researchers can engage more carefully with questions of sex/gender.
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Affiliation(s)
- Kendra Albert
- Cyberlaw Clinic, Harvard Law School, Cambridge, MA 02138, USA
| | - Maggie Delano
- Engineering Department, Swarthmore College, Swarthmore, PA 19146, USA
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Rakhit S, Wang L, Lindsell CJ, Hosay MA, Stewart JW, Owen GD, Frutos-Vivar F, Pen Uelas O, Esteban AS, Anzueto AR, Raymondos K, Rios F, Thille AW, Gonza Lez M, Du B, Maggiore SM, Matamis D, Abroug F, Amin P, Zeggwagh AA, Ely EW, Vasilevskis EE, Patel MB. Multicenter International Cohort Validation of a Modified Sequential Organ Failure Assessment Score Using the Richmond Agitation-sedation Scale. Ann Surg 2022; 276:e114-e119. [PMID: 33201122 PMCID: PMC10573707 DOI: 10.1097/sla.0000000000004484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
OBJECTIVE In a multicenter, international cohort, we aimed to validate a modified Sequential Organ Failure Assessment (mSOFA) using the Richmond Agitation-Sedation Scale, hypothesized as comparable to the Glasgow Coma Scale (GCS)-based Sequential Organ Failure Assessment (SOFA). SUMMARY BACKGROUND DATA The SOFA score, whose neurologic component is based on the GCS, can predict intensive care unit (ICU) mortality. But, GCS is often missing in lieu of other assessments, such as the also reliable and validated Richmond Agitation Sedation Scale (RASS). Single-center data suggested an RASS-based SOFA (mSOFA) predicted ICU mortality. METHODS Our nested cohort within the prospective 2016 Fourth International Study of Mechanical Ventilation contains 4120 ventilated patients with daily RASS and GCS assessments (20,023 patient-days, 32 countries). We estimated GCS from RASS via a proportional odds model without adjustment. ICU mortality logistic regression models and c-statistics were constructed using SOFA (measured GCS) and mSOFA (measured RASS-estimated GCS), adjusted for age, sex, body-mass index, region (Europe, USA-Canada, Latin America, Africa, Asia, Australia-New Zealand), and postoperative status (medical/surgical). RESULTS Cohort-wide, the mean SOFA=9.4+/-2.8 and mean mSOFA = 10.0+/-2.3, with ICU mortality = 31%. Mean SOFA and mSOFA similarly predicted ICU mortality (SOFA: AUC = 0.784, 95% CI = 0.769-0.799; mSOFA: AUC = 0.778, 95% CI = 0.763-0.793, P = 0.139). Across models, other predictors of mortality included higher age, female sex, medical patient, and African region (all P < 0.001). CONCLUSIONS We present the first SOFA modification with RASS in a "real-world" international cohort. Estimating GCS from RASS preserves predictive validity of SOFA to predict ICU mortality. Alternative neurologic measurements like RASS can be viably integrated into severity of illness scoring systems like SOFA.
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Affiliation(s)
- Shayan Rakhit
- Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Nashville, TN
- Vanderbilt University School of Medicine, Nashville, TN
- Division of Trauma, Emergency General Surgery, and Surgical Critical Care, Department of Surgery, Section of Surgical Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Li Wang
- Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Nashville, TN
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | | | - Morgan A Hosay
- Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Nashville, TN
- Division of Trauma, Emergency General Surgery, and Surgical Critical Care, Department of Surgery, Section of Surgical Sciences, Vanderbilt University Medical Center, Nashville, TN
- Baylor University, Waco, TX
| | - James W Stewart
- Division of Trauma, Emergency General Surgery, and Surgical Critical Care, Department of Surgery, Section of Surgical Sciences, Vanderbilt University Medical Center, Nashville, TN
- Meharry Medical College, Nashville, TN
| | - Gary D Owen
- Department of Pharmacy, Vanderbilt University Medical Center, Nashville, TN
| | - Fernando Frutos-Vivar
- University Hospital of Getafe, Getafe, Community of Madrid, Spain
- Centro de Investigación Biomédica en red de Enfermedades Respiratorias, Getafe, Comunidad of Madrid, Madrid, Spain
| | - Oscar Pen Uelas
- University Hospital of Getafe, Getafe, Community of Madrid, Spain
- Centro de Investigación Biomédica en red de Enfermedades Respiratorias, Getafe, Comunidad of Madrid, Madrid, Spain
| | - Andre S Esteban
- University Hospital of Getafe, Getafe, Community of Madrid, Spain
- Centro de Investigación Biomédica en red de Enfermedades Respiratorias, Getafe, Comunidad of Madrid, Madrid, Spain
| | - Antonio R Anzueto
- Department of Pulmonary Diseases and Critical Care Medicine, University of Texas Health Science Center, San Antonio, TX
- Pulmonary Section, Audie L Murphy VA Hospital, South Texas Veterans Healthcare System, US Department of Veterans Affairs, San Antonio, TX
| | | | - Fernando Rios
- Alejandro Posadas National Hospital, El Palomar, Buenos Aires, Argentina
| | | | - Marco Gonza Lez
- Medellin Clinic and Pontifical Bolivaran University, Medellin, Colombia
| | - Bin Du
- Peking Union Medical College Hospital, Beijing, China
| | | | | | - Fekri Abroug
- Fattouma Bourguiba University Hospital, Monastir, Tunisia
| | - Pravin Amin
- Bombay Hospital Institute of Medical Sciences, Mumbai, India
| | | | - E Wesley Ely
- Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Nashville, TN
- Vanderbilt University School of Medicine, Nashville, TN
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Geriatric Research, Education, and Clinical Center (GRECC) Service, Nashville VA Medical Center, Tennessee Valley Healthcare System, US Department of Veterans Affairs, Nashville, TN
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN
| | - Eduard E Vasilevskis
- Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Nashville, TN
- Vanderbilt University School of Medicine, Nashville, TN
- Geriatric Research, Education, and Clinical Center (GRECC) Service, Nashville VA Medical Center, Tennessee Valley Healthcare System, US Department of Veterans Affairs, Nashville, TN
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN
- Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Ibn Sina University Hospital Center & Mohammed V University of Rabat, Rabat, Morocco
| | - Mayur B Patel
- Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Nashville, TN
- Vanderbilt University School of Medicine, Nashville, TN
- Division of Trauma, Emergency General Surgery, and Surgical Critical Care, Department of Surgery, Section of Surgical Sciences, Vanderbilt University Medical Center, Nashville, TN
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN
- Surgical Service, Nashville VA Medical Center, Tennessee Valley Healthcare System, US Department of Veterans Affairs Nashville, TN
- Departments of Neurosurgery and Hearing and Speech Sciences, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN
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Risk Management In Intensive Care Units With Artificial Intelligence Technologies: Systematic Review of Prediction Models Using Electronic Health Records. JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES 2022. [DOI: 10.30621/jbachs.993798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background and aim: Clinical risk assessments should be made to protect patients from negative outcomes, and the definition, frequency and severity of the risk should be determined. The information contained in the electronic health records (EHRs) can use in different areas such as risk prediction, estimation of treatment effect ect. Many prediction models using artificial intelligence (AI) technologies that can be used in risk assessment have been developed. The aim of this study is to bring together the researches on prediction models developed with AI technologies using the EHRs of patients hospitalized in the intensive care unit (ICU) and to evaluate them in terms of risk management in healthcare.
Methods: The study restricted the search to the Web of Science, Pubmed, Science Direct, and Medline databases to retrieve research articles published in English in 2010 and after. Studies with a prediction model using data obtained from EHRs in the ICU are included. The study focused solely on research conducted in ICU to predict a health condition that poses a significant risk to patient safety using artificial intellegence (AI) technologies.
Results: Recognized prediction subcategories were mortality (n=6), sepsis (n=4), pressure ulcer (n=4), acute kidney injury (n=3), and other areas (n=10). It has been found that EHR-based prediction models are good risk management and decision support tools and adoption of such models in ICUs may reduce the prevalence of adverse conditions.
Conclusions: The article results remarks that developed models was found to have higher performance and better selectivity than previously developed risk models, so they are better at predicting risks and serious adverse events in ICU. It is recommended to use AI based prediction models developed using EHRs in risk management studies. Future work is still needed to researches to predict different health conditions risks.
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Adams R, Henry KE, Sridharan A, Soleimani H, Zhan A, Rawat N, Johnson L, Hager DN, Cosgrove SE, Markowski A, Klein EY, Chen ES, Saheed MO, Henley M, Miranda S, Houston K, Linton RC, Ahluwalia AR, Wu AW, Saria S. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nat Med 2022; 28:1455-1460. [PMID: 35864252 DOI: 10.1038/s41591-022-01894-0] [Citation(s) in RCA: 82] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 06/08/2022] [Indexed: 12/20/2022]
Abstract
Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine learning-based early warning systems may reduce the time to recognition, but few systems have undergone clinical evaluation. In this prospective, multi-site cohort study, we examined the association between patient outcomes and provider interaction with a deployed sepsis alert system called the Targeted Real-time Early Warning System (TREWS). During the study, 590,736 patients were monitored by TREWS across five hospitals. We focused our analysis on 6,877 patients with sepsis who were identified by the alert before initiation of antibiotic therapy. Adjusting for patient presentation and severity, patients in this group whose alert was confirmed by a provider within 3 h of the alert had a reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction, and 18.7%, CI 9.4, 27.0%, adjusted relative reduction), organ failure and length of stay compared with patients whose alert was not confirmed by a provider within 3 h. Improvements in mortality rate (4.5%, CI 0.8, 8.3%, adjusted absolute reduction) and organ failure were larger among those patients who were additionally flagged as high risk. Our findings indicate that early warning systems have the potential to identify sepsis patients early and improve patient outcomes and that sepsis patients who would benefit the most from early treatment can be identified and prioritized at the time of the alert.
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Affiliation(s)
- Roy Adams
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.,Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Katharine E Henry
- Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | | | - Hossein Soleimani
- Health Informatics, University of California, San Francisco, CA, USA
| | - Andong Zhan
- Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Nishi Rawat
- Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lauren Johnson
- Department of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA
| | - David N Hager
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Sara E Cosgrove
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Eili Y Klein
- Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Edward S Chen
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Mustapha O Saheed
- Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Maureen Henley
- Department of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Sheila Miranda
- Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Katrina Houston
- Department of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | | | - Albert W Wu
- Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, USA. .,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. .,Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. .,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. .,Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Suchi Saria
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA. .,Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. .,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. .,Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. .,Bayesian Health, New York, NY, USA.
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Guo F, Zhu X, Wu Z, Zhu L, Wu J, Zhang F. Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter. J Transl Med 2022; 20:265. [PMID: 35690822 PMCID: PMC9187899 DOI: 10.1186/s12967-022-03469-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/30/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Sepsis is a life-threatening syndrome eliciting highly heterogeneous host responses. Current prognostic evaluation methods used in clinical practice are characterized by an inadequate effectiveness in predicting sepsis mortality. Rapid identification of patients with high mortality risk is urgently needed. The phenotyping of patients will assistant invaluably in tailoring treatments. METHODS Machine learning and deep learning technology are used to characterize the patients' phenotype and determine the sepsis severity. The database used in this study is MIMIC-III and MIMIC-IV ('Medical information Mart for intensive care') which is a large, public, and freely available database. The K-means clustering is used to classify the sepsis phenotype. Convolutional neural network (CNN) was used to predict the 28-day survival rate based on 35 blood test variables of the sepsis patients, whereas a double coefficient quadratic multivariate fitting function (DCQMFF) is utilized to predict the 28-day survival rate with only 11 features of sepsis patients. RESULTS The patients were grouped into four clusters with a clear survival nomogram. The first cluster (C_1) was characterized by low white blood cell count, low neutrophil, and the highest lymphocyte proportion. C_2 obtained the lowest Sequential Organ Failure Assessment (SOFA) score and the highest survival rate. C_3 was characterized by significantly prolonged PTT, high SIC, and a higher proportion of patients using heparin than the patients in other clusters. The early mortality rate of patients in C_3 was high but with a better long-term survival rate than that in C_4. C_4 contained septic coagulation patients with the worst prognosis, characterized by slightly prolonged partial thromboplastin time (PTT), significantly prolonged prothrombin time (PT), and high septic coagulation disease score (SIC). The survival rate prediction accuracy of CNN and DCQMFF models reached 92% and 82%, respectively. The models were tested on an external dataset (MIMIC-IV) and achieved good performance. A DCQMFF-based application platform was established for fast prediction of the 28-day survival rate. CONCLUSION CNN and DCQMFF accurately predicted the sepsis patients' survival, while K-means successfully identified the phenotype groups. The distinct phenotypes associated with survival, and significant features correlated with mortality were identified. The findings suggest that sepsis patients with abnormal coagulation had poor outcomes, abnormal coagulation increase mortality during sepsis. The anticoagulation effects of appropriate heparin sodium treatment may improve extensive micro thrombosis-caused organ failure.
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Affiliation(s)
- Fei Guo
- Ningbo Institute for Medicine & Biomedical Engineering Combined Innovation, Ningbo Medical Treatment Centre Lihuili Hospital, Ningbo University, Ningbo, 315040, Zhejiang, China
| | - Xishun Zhu
- School of Mechatronics Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China
| | - Zhiheng Wu
- School of Information Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China
| | - Li Zhu
- School of Information Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China
| | - Jianhua Wu
- School of Information Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China.
| | - Fan Zhang
- Department of Critical Care Medicine, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
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Martin B, DeWitt PE, Scott HF, Parker S, Bennett TD. Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission. Hosp Pediatr 2022; 12:590-603. [PMID: 35634885 DOI: 10.1542/hpeds.2021-005998] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND OBJECTIVES Serious bacterial infection (SBI) is common in the PICU. Antibiotics can mitigate associated morbidity and mortality but have associated adverse effects. Our objective is to develop machine learning models able to identify SBI-negative children and reduce unnecessary antibiotics. METHODS We developed models to predict SBI-negative status at PICU admission using vital sign, laboratory, and demographic variables. Children 3-months to 18-years-old admitted to our PICU, between 2011 and 2020, were included if evaluated for infection within 24-hours, stratified by documented antibiotic exposure in the 48-hours prior. Area under the receiver operating characteristic curve (AUROC) was the primary model accuracy measure; secondarily, we calculated the number of SBI-negative children subsequently provided antibiotics in the PICU identified as low-risk by each model. RESULTS A total of 15 074 children met inclusion criteria; 4788 (32%) received antibiotics before PICU admission. Of these antibiotic-exposed patients, 2325 of 4788 (49%) had an SBI. Of the 10 286 antibiotic-unexposed patients, 2356 of 10 286 (23%) had an SBI. In antibiotic-exposed children, a radial support vector machine model had the highest AUROC (0.80) for evaluating SBI, identifying 48 of 442 (11%) SBI-negative children provided antibiotics in the PICU who could have been spared a median 3.7 (interquartile range 0.9-9.0) antibiotic-days per patient. In antibiotic-unexposed children, a random forest model performed best, but was less accurate overall (AUROC 0.76), identifying 33 of 469 (7%) SBI-negative children provided antibiotics in the PICU who could have been spared 1.1 (interquartile range 0.9-3.7) antibiotic-days per patient. CONCLUSIONS Among children who received antibiotics before PICU admission, machine learning models can identify children at low risk of SBI and potentially reduce antibiotic exposure.
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Affiliation(s)
- Blake Martin
- Department of Pediatrics, Sections of Critical Care
- Children's Hospital Colorado, Aurora, Colorado
| | | | - Halden F Scott
- Emergency Medicine
- Children's Hospital Colorado, Aurora, Colorado
| | - Sarah Parker
- Infectious Diseases, University of Colorado School of Medicine, Aurora, Colorado
- Children's Hospital Colorado, Aurora, Colorado
| | - Tellen D Bennett
- Department of Pediatrics, Sections of Critical Care
- Informatics and Data Science
- Children's Hospital Colorado, Aurora, Colorado
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