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Tang S, Man Q, Zhu D, Yu X, Chen R, Wang S, Lu Y, Shi Q, Suo C, Xiong L. Risk factors for progression to severe infection and prolonged viral clearance time in hospitalized elderly patients infected with the Omicron variant of SARS-CoV-2: a retrospective study at Shanghai Fourth People's Hospital, School of Medicine, Tongji University. Front Microbiol 2024; 15:1361197. [PMID: 38686116 PMCID: PMC11056568 DOI: 10.3389/fmicb.2024.1361197] [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] [Received: 12/25/2023] [Accepted: 03/20/2024] [Indexed: 05/02/2024] Open
Abstract
Introduction In elderly patients infected with the Omicron variant, disease progression to severe infection can result in poor outcomes. This study aimed to identify risk and protective factors associated with disease progression to severe infection and viral clearance time in elderly Omicron-infected patients. Methods Shanghai Fourth People's Hospital, School of Medicine, Tongji University, was officially designated to provide treatment to patients with COVID-19. This study was conducted on confirmed Omicron cases admitted to the hospital between 10 April 2022 and 21 June 2022. In total, 1,568 patients aged 65 years or older were included. We conducted a retrospective, observational study using logistic regression to analyze risk and protective factors for the development of severe disease and Cox proportional hazards regression models to analyze factors influencing viral clearance time. Results Aged over 80 years, having 2 or more comorbidities, combined cerebrovascular disease, chronic neurological disease, and mental disorders were associated with the development of severe disease, and full vaccination was a protective factor. Furthermore, aged over 80 years, combined chronic respiratory disease, chronic renal disease, cerebrovascular disease, mental disorders, and high viral load were associated with prolonged viral clearance time, and full vaccination was a protective factor. Discussion This study analyzed risk factors for progression to severe infection and prolonged viral clearance time in hospitalized elderly Omicron-infected patients. Aged patients with comorbidities had a higher risk of developing severe infection and had longer viral clearance, while vaccination protected them against the Omicron infection.
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Affiliation(s)
- Siqi Tang
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
- Department of Epidemiology, Ministry of Education, Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Qiuhong Man
- Department of Clinical Laboratory, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Dongliang Zhu
- Department of Epidemiology, Ministry of Education, Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Xueying Yu
- Department of Clinical Laboratory, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ruilin Chen
- Department of Epidemiology, Ministry of Education, Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Shuo Wang
- Department of Epidemiology, Ministry of Education, Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Yihan Lu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
- Department of Epidemiology, Ministry of Education, Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Qiqing Shi
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chen Suo
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
- Department of Epidemiology, Ministry of Education, Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
| | - Lize Xiong
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China
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Pinsky MR, Bedoya A, Bihorac A, Celi L, Churpek M, Economou-Zavlanos NJ, Elbers P, Saria S, Liu V, Lyons PG, Shickel B, Toral P, Tscholl D, Clermont G. Use of artificial intelligence in critical care: opportunities and obstacles. Crit Care 2024; 28:113. [PMID: 38589940 PMCID: PMC11000355 DOI: 10.1186/s13054-024-04860-z] [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: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. MAIN BODY Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. CONCLUSIONS AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.
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Affiliation(s)
- Michael R Pinsky
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, 638 Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15261, USA.
| | - Armando Bedoya
- Algorithm-Based Clinical Decision Support (ABCDS) Oversight, Office of Vice Dean of Data Science, School of Medicine, Duke University, Durham, NC, 27705, USA
- Division of Pulmonary Critical Care Medicine, Duke University School of Medicine, Durham, NC, 27713, USA
| | - Azra Bihorac
- Department of Medicine, University of Florida College of Medicine Gainesville, Malachowsky Hall, 1889 Museum Road, Suite 2410, Gainesville, FL, 32611, USA
| | - Leo Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Matthew Churpek
- Department of Medicine, University of Wisconsin, 600 Highland Ave, Madison, WI, 53792, USA
| | - Nicoleta J Economou-Zavlanos
- Algorithm-Based Clinical Decision Support (ABCDS) Oversight, Office of Vice Dean of Data Science, School of Medicine, Duke University, Durham, NC, 27705, USA
| | - Paul Elbers
- Department of Intensive Care, Amsterdam UMC, Amsterdam, NL, USA
- Amsterdam UMC, ZH.7D.167, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Suchi Saria
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins Medical Institutions, Johns Hopkins University, 333 Malone Hall, 300 Wolfe Street, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, AI and Health Lab, Johns Hopkins University, Baltimore, MD, USA
- Bayesian Health, New york, NY, 10282, USA
| | - Vincent Liu
- Department of Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Mail Code UHN67, Portland, OR, 97239-3098, USA
- , 2000 Broadway, Oakland, CA, 94612, USA
| | - Patrick G Lyons
- Department of Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Mail Code UHN67, Portland, OR, 97239-3098, USA
| | - Benjamin Shickel
- Department of Medicine, University of Florida College of Medicine Gainesville, Malachowsky Hall, 1889 Museum Road, Suite 2410, Gainesville, FL, 32611, USA
- Amsterdam UMC, ZH.7D.167, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Patrick Toral
- Department of Intensive Care, Amsterdam UMC, Amsterdam, NL, USA
- Amsterdam UMC, ZH.7D.165, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - David Tscholl
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Gilles Clermont
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, 638 Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15261, USA
- VA Pittsburgh Health System, 131A Building 30, 4100 Allequippa St, Pittsburgh, PA, 15240, USA
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3
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Viderman D, Kotov A, Popov M, Abdildin Y. Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review. Int J Med Inform 2024; 182:105308. [PMID: 38091862 DOI: 10.1016/j.ijmedinf.2023.105308] [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: 09/15/2023] [Revised: 11/20/2023] [Accepted: 12/03/2023] [Indexed: 01/07/2024]
Abstract
INTRODUCTION Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of these methods and assess their practical utility for predicting various clinical outcomes. METHODS We searched PubMed, Scopus, and Cochrane Library, screened and selected the studies matching the inclusion criteria. The clinical analysis focused on the characteristics of the patient cohorts in the studies included in this review, the specific tasks in the context of the COVID-19 pandemic that machine and deep learning methods were used for, and their practical limitations. The technical analysis focused on the details of specific MDL methods and their performance. RESULTS Analysis of the 48 selected studies revealed that the majority (∼54 %) of them examined the application of MDL methods for the prediction of survival/mortality-related patient outcomes, while a smaller fraction (∼13 %) of studies also examined applications to the prediction of patients' physiological outcomes and hospital resource utilization. 21 % of the studies examined the application of MDL methods to multiple clinical tasks. Machine and deep learning methods have been shown to be effective at predicting several outcomes of COVID-19 patients, such as disease severity, complications, intensive care unit (ICU) transfer, and mortality. MDL methods also achieved high accuracy in predicting the required number of ICU beds and ventilators. CONCLUSION Machine and deep learning methods have been shown to be valuable tools for predicting disease severity, organ dysfunction and failure, patient outcomes, and hospital resource utilization during the COVID-19 pandemic. The discovered knowledge and our conclusions and recommendations can also be useful to healthcare professionals and artificial intelligence researchers in managing future pandemics.
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Affiliation(s)
- Dmitriy Viderman
- Department of Surgery, School of Medicine, Nazarbayev University, Astana, Kazakhstan; Department of Anesthesiology, Intensive Care, and Pain Medicine, National Research Oncology Center, Astana, Kazakhstan.
| | - Alexander Kotov
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, USA.
| | - Maxim Popov
- Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
| | - Yerkin Abdildin
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
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Lou SS, Liu Y, Cohen ME, Ko CY, Hall BL, Kannampallil T. National Multi-Institutional Validation of a Surgical Transfusion Risk Prediction Model. J Am Coll Surg 2024; 238:99-105. [PMID: 37737660 DOI: 10.1097/xcs.0000000000000874] [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: 09/23/2023]
Abstract
BACKGROUND Accurate estimation of surgical transfusion risk is important for many aspects of surgical planning, yet few methods for estimating are available for estimating such risk. There is a need for reliable validated methods for transfusion risk stratification to support effective perioperative planning and resource stewardship. STUDY DESIGN This study was conducted using the American College of Surgeons NSQIP datafile from 2019. S-PATH performance was evaluated at each contributing hospital, with and without hospital-specific model tuning. Linear regression was used to assess the relationship between hospital characteristics and area under the receiver operating characteristic (AUROC) curve. RESULTS A total of 1,000,927 surgical cases from 414 hospitals were evaluated. Aggregate AUROC was 0.910 (95% CI 0.904 to 0.916) without model tuning and 0.925 (95% CI 0.919 to 0.931) with model tuning. AUROC varied across individual hospitals (median 0.900, interquartile range 0.849 to 0.944), but no statistically significant relationships were found between hospital-level characteristics studied and model AUROC. CONCLUSIONS S-PATH demonstrated excellent discriminative performance, although there was variation across hospitals that was not well-explained by hospital-level characteristics. These results highlight the S-PATH's viability as a generalizable surgical transfusion risk prediction tool.
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Affiliation(s)
- Sunny S Lou
- From the Department of Anesthesiology, Washington University School of Medicine, St Louis, MO (Lou, Kannampallil)
| | - Yaoming Liu
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
| | - Mark E Cohen
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
| | - Clifford Y Ko
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
- Department of Surgery, David Geffen School of Medicine, University of California Los Angeles, and the VA Greater Los Angeles Health System, Los Angeles, CA (Ko)
| | - Bruce L Hall
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
- Department of Surgery, Washington University School of Medicine; Center for Health Policy and the Olin Business School at Washington University in St Louis; John Cochran Veterans Affairs Medical Center; and BJC Healthcare, St Louis, MO (Hall)
| | - Thomas Kannampallil
- From the Department of Anesthesiology, Washington University School of Medicine, St Louis, MO (Lou, Kannampallil)
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Zysman M, Asselineau J, Saut O, Frison E, Oranger M, Maurac A, Charriot J, Achkir R, Regueme S, Klein E, Bommart S, Bourdin A, Dournes G, Casteigt J, Blum A, Ferretti G, Degano B, Thiébaut R, Chabot F, Berger P, Laurent F, Benlala I. Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19. Eur Radiol 2023; 33:9262-9274. [PMID: 37405504 PMCID: PMC10667132 DOI: 10.1007/s00330-023-09759-x] [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: 12/01/2022] [Revised: 03/22/2023] [Accepted: 04/04/2023] [Indexed: 07/06/2023]
Abstract
OBJECTIVES COVID-19 pandemic seems to be under control. However, despite the vaccines, 5 to 10% of the patients with mild disease develop moderate to critical forms with potential lethal evolution. In addition to assess lung infection spread, chest CT helps to detect complications. Developing a prediction model to identify at-risk patients of worsening from mild COVID-19 combining simple clinical and biological parameters with qualitative or quantitative data using CT would be relevant to organizing optimal patient management. METHODS Four French hospitals were used for model training and internal validation. External validation was conducted in two independent hospitals. We used easy-to-obtain clinical (age, gender, smoking, symptoms' onset, cardiovascular comorbidities, diabetes, chronic respiratory diseases, immunosuppression) and biological parameters (lymphocytes, CRP) with qualitative or quantitative data (including radiomics) from the initial CT in mild COVID-19 patients. RESULTS Qualitative CT scan with clinical and biological parameters can predict which patients with an initial mild presentation would develop a moderate to critical form of COVID-19, with a c-index of 0.70 (95% CI 0.63; 0.77). CT scan quantification improved the performance of the prediction up to 0.73 (95% CI 0.67; 0.79) and radiomics up to 0.77 (95% CI 0.71; 0.83). Results were similar in both validation cohorts, considering CT scans with or without injection. CONCLUSION Adding CT scan quantification or radiomics to simple clinical and biological parameters can better predict which patients with an initial mild COVID-19 would worsen than qualitative analyses alone. This tool could help to the fair use of healthcare resources and to screen patients for potential new drugs to prevent a pejorative evolution of COVID-19. CLINICAL TRIAL REGISTRATION NCT04481620. CLINICAL RELEVANCE STATEMENT CT scan quantification or radiomics analysis is superior to qualitative analysis, when used with simple clinical and biological parameters, to determine which patients with an initial mild presentation of COVID-19 would worsen to a moderate to critical form. KEY POINTS • Qualitative CT scan analyses with simple clinical and biological parameters can predict which patients with an initial mild COVID-19 and respiratory symptoms would worsen with a c-index of 0.70. • Adding CT scan quantification improves the performance of the clinical prediction model to an AUC of 0.73. • Radiomics analyses slightly improve the performance of the model to a c-index of 0.77.
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Affiliation(s)
- Maéva Zysman
- CHU Bordeaux, 33600, Pessac, France.
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France.
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France.
| | | | - Olivier Saut
- "Institut de Mathématiques de Bordeaux" (IMB), UMR5251, CNRS, University of Bordeaux, 351 Cours Libération, 33400, Talence, France
- MONC Team & SISTM Team, INRIA Bordeaux Sud-Ouest, 200 Av Vieille Tour, 33400, Talence, France
| | | | - Mathilde Oranger
- Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d'Imagerie, Nancy, France
- Faculté de Médecine de Nancy, Université de Lorraine, Institut National de La Santé Et de La Recherche Médicale (INSERM) Unité Médicale de Recherche (UMR), S 1116, Vandœuvre-Lès-Nancy, France
| | - Arnaud Maurac
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
| | - Jeremy Charriot
- Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, 34295, Montpellier, France
- PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, 34295, Montpellier, France
| | | | | | | | - Sébastien Bommart
- Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, 34295, Montpellier, France
- PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, 34295, Montpellier, France
| | - Arnaud Bourdin
- Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, 34295, Montpellier, France
- PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, 34295, Montpellier, France
| | - Gael Dournes
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
| | | | - Alain Blum
- Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d'Imagerie, Nancy, France
| | - Gilbert Ferretti
- France Service de Radiologie Diagnostique Et Interventionnelle, Université Grenoble Alpes, CHU Grenoble-Alpes, Grenoble, France
| | - Bruno Degano
- France Service de Radiologie Diagnostique Et Interventionnelle, Université Grenoble Alpes, CHU Grenoble-Alpes, Grenoble, France
| | - Rodolphe Thiébaut
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
- MONC Team & SISTM Team, INRIA Bordeaux Sud-Ouest, 200 Av Vieille Tour, 33400, Talence, France
| | - Francois Chabot
- Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d'Imagerie, Nancy, France
- Faculté de Médecine de Nancy, Université de Lorraine, Institut National de La Santé Et de La Recherche Médicale (INSERM) Unité Médicale de Recherche (UMR), S 1116, Vandœuvre-Lès-Nancy, France
| | - Patrick Berger
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
| | - Francois Laurent
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
| | - Ilyes Benlala
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
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Matsumoto K, Nohara Y, Sakaguchi M, Takayama Y, Fukushige S, Soejima H, Nakashima N, Kamouchi M. Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study. JMIR Perioper Med 2023; 6:e50895. [PMID: 37883164 PMCID: PMC10636625 DOI: 10.2196/50895] [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: 07/16/2023] [Revised: 09/24/2023] [Accepted: 09/29/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications. OBJECTIVE The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model. METHODS The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method. RESULTS A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept -0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance. CONCLUSIONS The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.
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Affiliation(s)
| | - Yasunobu Nohara
- Big Data Science and Technology, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
| | - Mikako Sakaguchi
- Department of Nursing, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Yohei Takayama
- Department of Nursing, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Syota Fukushige
- Department of Inspection, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Hidehisa Soejima
- Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Naoki Nakashima
- Medical Information Center, Kyushu University Hospital, Fukuoka, Japan
| | - Masahiro Kamouchi
- Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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7
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Buttia C, Llanaj E, Raeisi-Dehkordi H, Kastrati L, Amiri M, Meçani R, Taneri PE, Ochoa SAG, Raguindin PF, Wehrli F, Khatami F, Espínola OP, Rojas LZ, de Mortanges AP, Macharia-Nimietz EF, Alijla F, Minder B, Leichtle AB, Lüthi N, Ehrhard S, Que YA, Fernandes LK, Hautz W, Muka T. Prognostic models in COVID-19 infection that predict severity: a systematic review. Eur J Epidemiol 2023; 38:355-372. [PMID: 36840867 PMCID: PMC9958330 DOI: 10.1007/s10654-023-00973-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 01/28/2023] [Indexed: 02/26/2023]
Abstract
Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.
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Affiliation(s)
- Chepkoech Buttia
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
- Epistudia, Bern, Switzerland
| | - Erand Llanaj
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
- ELKH-DE Public Health Research Group of the Hungarian Academy of Sciences, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Epistudia, Bern, Switzerland
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Hamidreza Raeisi-Dehkordi
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lum Kastrati
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mojgan Amiri
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Renald Meçani
- Department of Pediatrics, “Mother Teresa” University Hospital Center, Tirana, University of Medicine, Tirana, Albania
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Petek Eylul Taneri
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- HRB-Trials Methodology Research Network College of Medicine, Nursing and Health Sciences University of Galway, Galway, Ireland
| | | | - Peter Francis Raguindin
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Swiss Paraplegic Research, Nottwil, Switzerland
- Faculty of Health Sciences, University of Lucerne, Lucerne, Switzerland
| | - Faina Wehrli
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Farnaz Khatami
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Octavio Pano Espínola
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Department of Preventive Medicine and Public Health, University of Navarre, Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Spain
| | - Lyda Z. Rojas
- Research Group and Development of Nursing Knowledge (GIDCEN-FCV), Research Center, Cardiovascular Foundation of Colombia, Floridablanca, Santander, Colombia
| | | | | | - Fadi Alijla
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Beatrice Minder
- Public Health and Primary Care Library, University Library of Bern, University of Bern, Bern, Switzerland
| | - Alexander B. Leichtle
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, and Center for Artificial Intelligence in Medicine (CAIM), University of Bern, Bern, Switzerland
| | - Nora Lüthi
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Simone Ehrhard
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Yok-Ai Que
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laurenz Kopp Fernandes
- Deutsches Herzzentrum Berlin (DHZB), Berlin, Germany
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf Hautz
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Taulant Muka
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Epistudia, Bern, Switzerland
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8
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Branch-Elliman W, Sundermann AJ, Wiens J, Shenoy ES. The future of automated infection detection: Innovation to transform practice (Part III/III). ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2023; 3:e26. [PMID: 36865708 PMCID: PMC9972533 DOI: 10.1017/ash.2022.333] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 06/18/2023]
Abstract
Current methods of emergency-room-based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true "Learning Healthcare System" that will support near-real-time quality improvement efforts and advance the scientific basis for the practice of infection control.
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Affiliation(s)
- Westyn Branch-Elliman
- Section of Infectious Diseases, Department of Medicine, Veterans’ Affairs (VA) Boston Healthcare System, Boston, Massachusetts
- VA Boston Center for Healthcare Organization and Implementation Research (CHOIR), Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Alexander J. Sundermann
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jenna Wiens
- Division of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan
| | - Erica S. Shenoy
- Harvard Medical School, Boston, Massachusetts
- Infection Control Unit, Massachusetts General Hospital, Boston, Massachusetts
- Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
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9
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Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study. J Cardiovasc Dev Dis 2023; 10:jcdd10020039. [PMID: 36826535 PMCID: PMC9967447 DOI: 10.3390/jcdd10020039] [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: 11/28/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.
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Lyons PG, Chen V, Sekhar TC, McEvoy CA, Kollef MH, Govindan R, Westervelt P, Vranas KC, Maddox TM, Geng EH, Payne PRO, Politi MC. Clinician Perspectives on Barriers and Enablers to Implementing an Inpatient Oncology Early Warning System: A Mixed-Methods Study. JCO Clin Cancer Inform 2023; 7:e2200104. [PMID: 36706345 DOI: 10.1200/cci.22.00104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
PURPOSE To elicit end-user and stakeholder perceptions regarding design and implementation of an inpatient clinical deterioration early warning system (EWS) for oncology patients to better fit routine clinical practices and enhance clinical impact. METHODS In an explanatory-sequential mixed-methods study, we evaluated a stakeholder-informed oncology early warning system (OncEWS) using surveys and semistructured interviews. Stakeholders were physicians, advanced practice providers (APPs), and nurses. For qualitative data, we used grounded theory and thematic content analysis via the constant comparative method to identify determinants of OncEWS implementation. RESULTS Survey respondents generally agreed that an oncology-focused EWS could add value beyond clinical judgment, with nurses endorsing this notion significantly more strongly than other clinicians (nurse: median 5 on a 6-point scale [6 = strongly agree], interquartile range 4-5; doctors/advanced practice providers: 4 [4-5]; P = .005). However, some respondents would not trust an EWS to identify risk accurately (n = 36 [42%] somewhat or very concerned), while others were concerned that institutional culture would not embrace such an EWS (n = 17 [28%]).Interviews highlighted important aspects of the EWS and the local context that might facilitate implementation, including (1) a model tailored to the subtleties of oncology patients, (2) transparent model information, and (3) nursing-centric workflows. Interviewees raised the importance of sepsis as a common and high-risk deterioration syndrome. CONCLUSION Stakeholders prioritized maximizing the degree to which the OncEWS is understandable, informative, actionable, and workflow-complementary, and perceived these factors to be key for translation into clinical benefit.
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Affiliation(s)
- Patrick G Lyons
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, St Louis, MO.,Healthcare Innovation Lab, BJC HealthCare, St Louis, MO.,Siteman Cancer Center, St Louis, MO
| | - Vanessa Chen
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Tejas C Sekhar
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Colleen A McEvoy
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Marin H Kollef
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Ramaswamy Govindan
- Siteman Cancer Center, St Louis, MO.,Division of Hematology and Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Peter Westervelt
- Siteman Cancer Center, St Louis, MO.,Division of Hematology and Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Kelly C Vranas
- Division of Pulmonary and Critical Care Medicine, Oregon Health and Science University, Portland, OR.,Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, OR
| | - Thomas M Maddox
- Healthcare Innovation Lab, BJC HealthCare, St Louis, MO.,Division of Cardiology, Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Elvin H Geng
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, MO.,Center for Dissemination and Implementation in the Institute for Public Health, Washington University School of Medicine, St Louis, MO
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, St Louis, MO
| | - Mary C Politi
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO.,Center for Collaborative Care Decisions, Department of Surgery, Washington University School of Medicine, St Louis, MO
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Muto R, Fukuta S, Watanabe T, Shindo Y, Kanemitsu Y, Kajikawa S, Yonezawa T, Inoue T, Ichihashi T, Shiratori Y, Maruyama S. Predicting oxygen requirements in patients with coronavirus disease 2019 using an artificial intelligence-clinician model based on local non-image data. Front Med (Lausanne) 2022; 9:1042067. [PMID: 36530899 PMCID: PMC9748157 DOI: 10.3389/fmed.2022.1042067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/14/2022] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND When facing unprecedented emergencies such as the coronavirus disease 2019 (COVID-19) pandemic, a predictive artificial intelligence (AI) model with real-time customized designs can be helpful for clinical decision-making support in constantly changing environments. We created models and compared the performance of AI in collaboration with a clinician and that of AI alone to predict the need for supplemental oxygen based on local, non-image data of patients with COVID-19. MATERIALS AND METHODS We enrolled 30 patients with COVID-19 who were aged >60 years on admission and not treated with oxygen therapy between December 1, 2020 and January 4, 2021 in this 50-bed, single-center retrospective cohort study. The outcome was requirement for oxygen after admission. RESULTS The model performance to predict the need for oxygen by AI in collaboration with a clinician was better than that by AI alone. Sodium chloride difference >33.5 emerged as a novel indicator to predict the need for oxygen in patients with COVID-19. To prevent severe COVID-19 in older patients, dehydration compensation may be considered in pre-hospitalization care. CONCLUSION In clinical practice, our approach enables the building of a better predictive model with prompt clinician feedback even in new scenarios. These can be applied not only to current and future pandemic situations but also to other diseases within the healthcare system.
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Affiliation(s)
- Reiko Muto
- Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Molecular Medicine and Metabolism, Research Institute of Environmental Medicine, Nagoya University, Nagoya, Japan
| | - Shigeki Fukuta
- Artificial Intelligence Laboratory, Fujitsu Limited, Kawasaki, Japan
| | | | - Yuichiro Shindo
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoshihiro Kanemitsu
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Shigehisa Kajikawa
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine and Allergology, Aichi Medical University Hospital, Nagakute, Japan
| | - Toshiyuki Yonezawa
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine and Allergology, Aichi Medical University Hospital, Nagakute, Japan
| | - Takahiro Inoue
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Fujita Health University School of Medicine, Toyoake, Japan
| | - Takuji Ichihashi
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
| | - Yoshimune Shiratori
- Center for Healthcare Information Technology (C-HiT), Nagoya University, Nagoya, Japan
- Medical IT Center, Nagoya University Hospital, Nagoya, Japan
| | - Shoichi Maruyama
- Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, Japan
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12
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Wiegand M, Cowan SL, Waddington CS, Halsall DJ, Keevil VL, Tom BDM, Taylor V, Gkrania-Klotsas E, Preller J, Goudie RJB. Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study. BMJ Open 2022; 12:e060026. [PMID: 36691139 PMCID: PMC9445230 DOI: 10.1136/bmjopen-2021-060026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/13/2022] [Indexed: 02/02/2023] Open
Abstract
OBJECTIVES To develop a disease stratification model for COVID-19 that updates according to changes in a patient's condition while in hospital to facilitate patient management and resource allocation. DESIGN In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression. SETTING All data used in this study were obtained from a single UK teaching hospital. PARTICIPANTS We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021. PRIMARY AND SECONDARY OUTCOME MEASURES The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation. RESULTS Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88). CONCLUSIONS Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient's clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool. TRIAL REGISTRATION The study is registered as 'researchregistry5464' on the Research Registry (www.researchregistry.com).
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Affiliation(s)
- Martin Wiegand
- Faculty of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Sarah L Cowan
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | - David J Halsall
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Victoria L Keevil
- Department of Medicine, University of Cambridge, Cambridge, UK
- Department of Medicine for the Elderly, Addenbrooke's Hospital, Cambridge, UK
| | - Brian D M Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Vince Taylor
- Cancer Research UK, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | - Jacobus Preller
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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13
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Knaus WA. Prognostic Modeling and Major Dataset Shifts During the COVID-19 Pandemic. JAMA HEALTH FORUM 2022; 3:e221103. [DOI: 10.1001/jamahealthforum.2022.1103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- William A. Knaus
- Department of Public Health Sciences, The University of Virginia, Charlottesville
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14
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Fu Y, Zhong W, Liu T, Li J, Xiao K, Ma X, Xie L, Jiang J, Zhou H, Liu R, Zhang W. Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques. Front Public Health 2022; 10:880999. [PMID: 35677769 PMCID: PMC9168534 DOI: 10.3389/fpubh.2022.880999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/13/2022] [Indexed: 01/08/2023] Open
Abstract
Motivation Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical illness suddenly is a matter of great concern. Early identification and effective triaging of patients with a high risk of developing critical illness COVID-19 upon admission can aid in improving patient care, increasing the cure rate, and mitigating the burden on the medical care system. This study proposed and extended classical least absolute shrinkage and selection operator (LASSO) logistic regression to objectively identify clinical determination and risk factors for the early identification of patients at high risk of progression to critical illness at the time of hospital admission. Methods In this retrospective multicenter study, data of 1,929 patients with COVID-19 were assessed. The association between laboratory characteristics measured at admission and critical illness was screened with logistic regression. LASSO logistic regression was utilized to construct predictive models for estimating the risk that a patient with COVID-19 will develop a critical illness. Results The development cohort consisted of 1,363 patients with COVID-19 with 133 (9.7%) patients developing the critical illness. Univariate logistic regression analysis revealed 28 variables were prognosis factors for critical illness COVID-19 (p < 0.05). Elevated CK-MB, neutrophils, PCT, α-HBDH, D-dimer, LDH, glucose, PT, APTT, RDW (SD and CV), fibrinogen, and AST were predictors for the early identification of patients at high risk of progression to critical illness. Lymphopenia, a low rate of basophils, eosinophils, thrombopenia, red blood cell, hematocrit, hemoglobin concentration, blood platelet count, and decreased levels of K, Na, albumin, albumin to globulin ratio, and uric acid were clinical determinations associated with the development of critical illness at the time of hospital admission. The risk score accurately predicted critical illness in the development cohort [area under the curve (AUC) = 0.83, 95% CI: 0.78-0.86], also in the external validation cohort (n = 566, AUC = 0.84). Conclusion A risk prediction model based on laboratory findings of patients with COVID-19 was developed for the early identification of patients at high risk of progression to critical illness. This cohort study identified 28 indicators associated with critical illness of patients with COVID-19. The risk model might contribute to the treatment of critical illness disease as early as possible and allow for optimized use of medical resources.
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Affiliation(s)
- Yacheng Fu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Weijun Zhong
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Tao Liu
- Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Jianmin Li
- Department of Pulmonary and Critical Care Medicine, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Kui Xiao
- Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xinhua Ma
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lihua Xie
- B7 Department, Zhongfa District of Tongji Hospital, Tongji Medical, Huazhong University of Science and Technology, Wuhan, China
| | - Junyi Jiang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Honghao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Rong Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
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15
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Afnan M, Afnan MAM, Liu Y, Savulescu J, Mishra A, Conitzer V, Rudin C. Data solidarity for machine learning for embryo selection; a call for the creation of an open access repository of embryo data. Reprod Biomed Online 2022; 45:10-13. [DOI: 10.1016/j.rbmo.2022.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 10/18/2022]
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16
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Loder E. Progress against health inequities is slow and obstacles are numerous. BRITISH MEDICAL JOURNAL 2022. [DOI: 10.1136/bmj.o470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Affiliation(s)
- Anand R Habib
- Department of Medicine, University of California San Francisco, San Francisco, CA 94143-0119, USA
| | - Nathan C Lo
- Division of HIV, Infectious Diseases, and Global Medicine, University of California San Francisco, San Francisco, CA, USA
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18
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Dong GY, Jin FF, Huang Q, Wu CB, Zhu JH, Wang TB. Exploratory COVID-19 death risk score based on basic laboratory tests and physiological clinical measurements. World J Emerg Med 2022; 13:453-458. [PMID: 36636572 PMCID: PMC9807385 DOI: 10.5847/wjem.j.1920-8642.2022.103] [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: 03/09/2022] [Accepted: 06/10/2022] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND In the event of a sudden shortage of medical resources, a rapid, simple, and accurate prediction model is essential for the 30-day mortality rate of patients with COVID-19. METHODS This retrospective study compared the characteristics of the survivals and non-survivals of 278 patients with COVID-19. Logistic regression analysis was performed to obtain the "COVID-19 death risk score" (CDRS) model. Using the area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow goodness-of-fit test, discrimination and calibration were assessed. Internal validation was conducted using a regular bootstrap method. RESULTS A total of 63 (22.66%) of 278 included patients died. The logistic regression analysis revealed that high-sensitivity C-reactive protein (hsCRP; odds ratio [OR]=1.018), D-dimer (OR=1.101), and respiratory rate (RR; OR=1.185) were independently associated with 30-day mortality. CDRS was calculated as follows: CDRS=-10.245+(0.022×hsCRP)+(0.172×D-dimer)+(0.203×RR). CDRS had the same predictive effect as the sequential organ failure assessment (SOFA) and "confusion, uremia, respiratory rate, blood pressure, and age over 65 years" (CURB-65) scores, with AUROCs of 0.984 for CDRS, 0.975 for SOFA, and 0.971 for CURB-65, respectively. And CDRS showed good calibration. The AUROC through internal validations was 0.980 (95% confidence interval [CI]: 0.965-0.995). Regarding the clinical value, the decision curve analysis of CDRS showed a net value similar to that of CURB-65 in this cohort. CONCLUSION CDRS is a novel, efficient and accurate prediction model for the early identification of COVID-19 patients with poor outcomes. Although it is not as advanced as the other models, CDRS had a similar performance to that of SOFA and CURB-65.
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Affiliation(s)
- Gui-ying Dong
- Emergency Department, Peking University People’s Hospital, Beijing 100044, China
| | - Fei-fei Jin
- Trauma Center, Peking University People’s Hospital, Key Laboratory of Trauma and Neural Regeneration (Peking University), Ministry of Education, Beijing100044, China
| | - Qi Huang
- Department of Endocrinology and Metabolism, Peking University People’s Hospital, Beijing100044, China
| | - Chun-bo Wu
- Emergency Department, Peking University People’s Hospital, Beijing 100044, China
| | - Ji-hong Zhu
- Emergency Department, Peking University People’s Hospital, Beijing 100044, China,Corresponding Authors: Ji-hong Zhu, ;
| | - Tian-bing Wang
- Trauma Center, Peking University People’s Hospital, Key Laboratory of Trauma and Neural Regeneration (Peking University), Ministry of Education, Beijing100044, China,
Tian-bing Wang,
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