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Orsatti VN, Ribeiro VST, de Oliveira Montenegro C, Costa CJ, Raboni EA, Sampaio ER, Michielin F, Gasparetto J, Telles JP, Tuon FF. Sepsis death risk factor score based on systemic inflammatory response syndrome, quick sequential organ failure assessment, and comorbidities. Med Intensiva 2024; 48:263-271. [PMID: 38575400 DOI: 10.1016/j.medine.2024.03.005] [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: 04/06/2024]
Abstract
OBJECTIVE In this study, we aimed to evaluate the death risk factors of patients included in the sepsis protocol bundle, using clinical data from qSOFA, SIRS, and comorbidities, as well as development of a mortality risk score. DESIGN This retrospective cohort study was conducted between 2016 and 2021. SETTING Two university hospitals in Brazil. PARTICIPANTS Patients with sepsis. INTERVENTIONS Several clinical and laboratory data were collected focused on SIRS, qSOFA, and comorbidities. MAIN VARIABLE OF INTEREST In-hospital mortality was the primary outcome variable. A mortality risk score was developed after logistic regression analysis. RESULTS A total of 1,808 patients were included with a death rate of 36%. Ten variables remained independent factors related to death in multivariate analysis: temperature ≥38 °C (odds ratio [OR] = 0.65), previous sepsis (OR = 1.42), qSOFA ≥ 2 (OR = 1.43), leukocytes >12,000 or <4,000 cells/mm3 (OR = 1.61), encephalic vascular accident (OR = 1.88), age >60 years (OR = 1.93), cancer (OR = 2.2), length of hospital stay before sepsis >7 days (OR = 2.22,), dialysis (OR = 2.51), and cirrhosis (OR = 3.97). Considering the equation of the binary regression logistic analysis, the score presented an area under curve of 0.668, is not a potential model for death prediction. CONCLUSIONS Several risk factors are independently associated with mortality, allowing the development of a prediction score based on qSOFA, SIRS, and comorbidities data, however, the performance of this score is low.
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Affiliation(s)
- Vinicius Nakad Orsatti
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Victoria Stadler Tasca Ribeiro
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Carolina de Oliveira Montenegro
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Clarice Juski Costa
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Eduardo Albanske Raboni
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Eduardo Ramos Sampaio
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Fernando Michielin
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Juliano Gasparetto
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - João Paulo Telles
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Felipe Francisco Tuon
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil.
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Gallardo-Pizarro A, Peyrony O, Chumbita M, Monzo-Gallo P, Aiello TF, Teijon-Lumbreras C, Gras E, Mensa J, Soriano A, Garcia-Vidal C. Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning. Expert Rev Anti Infect Ther 2024; 22:179-187. [PMID: 38457198 DOI: 10.1080/14787210.2024.2322445] [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: 07/26/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine. AREAS COVERED In this review, we detail how the collection of a large number of high-quality data can be used to conduct precise mathematical studies with ML and AI. We explain the foundations of these techniques, covering the fundamentals of supervised and unsupervised learning, as well as the most important challenges, e.g. data quality, 'black box' model interpretation and overfitting. To conclude, we provide detailed examples of how AI and ML have been used to enhance predictions of chemotherapy-induced FN, detection of bloodstream infections (BSIs) and multidrug-resistant (MDR) bacteria, and anticipation of severe complications and mortality. EXPERT OPINION There is promising potential of implementing accurate AI and ML models whilst managing FN. However, their integration as viable clinical tools poses challenges, including technical and implementation barriers. Improving global accessibility, fostering interdisciplinary collaboration, and addressing ethical and security considerations are essential. By overcoming these challenges, we could transform personalized care for patients with FN.
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Affiliation(s)
| | - Olivier Peyrony
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Mariana Chumbita
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | | | | | | | - Emmanuelle Gras
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Josep Mensa
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Alex Soriano
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
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3
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Huang QS, Han TX, Fu HX, Meng H, Zhao P, Wu YJ, He Y, Zhu XL, Wang FR, Zhang YY, Mo XD, Han W, Yan CH, Wang JZ, Chen H, Chen YH, Han TT, Lv M, Chen Y, Wang Y, Xu LP, Liu KY, Huang XJ, Zhang XH. Prognostic Factors and Outcomes in Patients With Septic Shock After Allogeneic Hematopoietic Stem Cell Transplantation. Transplant Cell Ther 2024; 30:310.e1-310.e11. [PMID: 38151106 DOI: 10.1016/j.jtct.2023.12.013] [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: 10/07/2023] [Revised: 12/15/2023] [Accepted: 12/15/2023] [Indexed: 12/29/2023]
Abstract
Septic shock remains a potentially life-threatening complication among allogeneic hematopoietic stem cell transplant (allo-HSCT) recipients. There is a paucity of information on the clinical characteristics, outcome and prognostic factors of septic shock patients after allo-HSCT. We aimed to describe the clinical characteristics of septic shock after allo-HSCT and its associated health outcomes and to evaluate the role of patient demographics, transplantation-related laboratory and clinical variables associated with the short-term mortality of septic shock after allo-HSCT. We retrospectively studied 242 septic shock patients from 6105 consecutive patients allografted between 2007 and 2021. We assessed 29 risk factors as candidate predictors and used multivariable logistic regression to establish clinical model. The primary outcome was 28-day mortality. The median age of the subjects was 34 (IQR 24 to 45) years. A total of 148 patients (61.2%) had positive blood cultures. Gram-negative bacilli accounted for 61.5% of the positive isolates, gram-positive cocci accounted for 12.2%, and fungi accounted for 6.1%. Coinfections were found in 30 (20.3%) patients. Escherichia coli was the dominant isolated pathogen (31.1%), followed by Pseudomonas spp. (12.8%) and Klebsiella pneumoniae (10.1%). With a median follow-up of 34 (IQR: 2 to 528) days, a total of 142 (58.7%) patients died, of whom 118 (48.8%) died within the first 28 days after septic shock diagnosis, 131 (54.1%) died within 90 days, and 141 (58.3%) died within 1 year. A large majority of deaths (83.1% [118/142]) occurred within 28 days of septic shock diagnosis. Finally, 6 independent predictive variables of 28-day mortality were identified by multivariable logistic regression: time of septic shock, albumin, bilirubin, PaO2/FiO2, lactate, and sepsis-induced coagulopathy. Patients with late onset shock had higher 28-day mortality rates (64.6% versus 25.5%, P < .001) and more ICU admission (32.6% versus 7.1%, P < .001) than those with early onset shock. We highlight the poor survival outcomes in patients who develop septic shock, emphasizing the need for increasing awareness regarding septic shock after allo-HSCT. The information from the current study may help to assist clinicians in identifying high-risk patients.
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Affiliation(s)
- Qiu-Sha Huang
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Tian-Xiao Han
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Hai-Xia Fu
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Han Meng
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Peng Zhao
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Ye-Jun Wu
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Yun He
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Xiao-Lu Zhu
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Feng-Rong Wang
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Yuan-Yuan Zhang
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Xiao-Dong Mo
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Wei Han
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Chen-Hua Yan
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Jing-Zhi Wang
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Huan Chen
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Yu-Hong Chen
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Ting-Ting Han
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Meng Lv
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Yao Chen
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Yu Wang
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Lan-Ping Xu
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Kai-Yan Liu
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Xiao-Jun Huang
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Xiao-Hui Zhang
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, China; National Clinical Research Center for Hematologic Disease, Beijing, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China; Collaborative Innovation Center of Hematology, Peking University, Beijing, China.
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4
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Ferrara M, Bertozzi G, Di Fazio N, Aquila I, Di Fazio A, Maiese A, Volonnino G, Frati P, La Russa R. Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review. Healthcare (Basel) 2024; 12:549. [PMID: 38470660 PMCID: PMC10931321 DOI: 10.3390/healthcare12050549] [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: 01/29/2024] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need for hospitals to expand reactive and proactive clinical risk management programs is easily understood, and artificial intelligence fits well in this context. This systematic review aims to investigate the state of the art regarding the impact of AI on clinical risk management processes. To simplify the analysis of the review outcomes and to motivate future standardized comparisons with any subsequent studies, the findings of the present review will be grouped according to the possibility of applying AI in the prevention of the different incident type groups as defined by the ICPS. MATERIALS AND METHODS On 3 November 2023, a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was carried out using the SCOPUS and Medline (via PubMed) databases. A total of 297 articles were identified. After the selection process, 36 articles were included in the present systematic review. RESULTS AND DISCUSSION The studies included in this review allowed for the identification of three main "incident type" domains: clinical process, healthcare-associated infection, and medication. Another relevant application of AI in clinical risk management concerns the topic of incident reporting. CONCLUSIONS This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification.
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Affiliation(s)
- Michela Ferrara
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, Italy; (M.F.); (N.D.F.); (P.F.)
| | - Giuseppe Bertozzi
- Complex Intercompany Structure of Forensic Medicine, 85100 Potenza, Italy;
| | - Nicola Di Fazio
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, Italy; (M.F.); (N.D.F.); (P.F.)
| | - Isabella Aquila
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy;
| | - Aldo Di Fazio
- Regional Hospital “San Carlo”, 85100 Potenza, Italy;
| | - Aniello Maiese
- Department of Surgical Pathology, Medical, Molecular and Critical Area, Institute of Legal Medicine, University of Pisa, 56126 Pisa, Italy;
| | - Gianpietro Volonnino
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, Italy; (M.F.); (N.D.F.); (P.F.)
| | - Paola Frati
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, Italy; (M.F.); (N.D.F.); (P.F.)
| | - Raffaele La Russa
- Department of Clinical Medicine, Public Health, Life and Environment Science, University of L’Aquila, 67100 L’Aquila, Italy;
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Zhou Y, Smith J, Keerthi D, Li C, Sun Y, Mothi SS, Shyr DC, Spitzer B, Harris A, Chatterjee A, Chatterjee S, Shouval R, Naik S, Bertaina A, Boelens JJ, Triplett BM, Tang L, Sharma A. Longitudinal clinical data improve survival prediction after hematopoietic cell transplantation using machine learning. Blood Adv 2024; 8:686-698. [PMID: 37991991 PMCID: PMC10844815 DOI: 10.1182/bloodadvances.2023011752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/20/2023] [Accepted: 10/31/2023] [Indexed: 11/24/2023] Open
Abstract
ABSTRACT Serial prognostic evaluation after allogeneic hematopoietic cell transplantation (allo-HCT) might help identify patients at high risk of lethal organ dysfunction. Current prediction algorithms based on models that do not incorporate changes to patients' clinical condition after allo-HCT have limited predictive ability. We developed and validated a robust risk-prediction algorithm to predict short- and long-term survival after allo-HCT in pediatric patients that includes baseline biological variables and changes in the patients' clinical status after allo-HCT. The model was developed using clinical data from children and young adults treated at a single academic quaternary-care referral center. The model was created using a randomly split training data set (70% of the cohort), internally validated (remaining 30% of the cohort) and then externally validated on patient data from another tertiary-care referral center. Repeated clinical measurements performed from 30 days before allo-HCT to 30 days afterwards were extracted from the electronic medical record and incorporated into the model to predict survival at 100 days, 1 year, and 2 years after allo-HCT. Naïve-Bayes machine learning models incorporating longitudinal data were significantly better than models constructed from baseline variables alone at predicting whether patients would be alive or deceased at the given time points. This proof-of-concept study demonstrates that unlike traditional prognostic tools that use fixed variables for risk assessment, incorporating dynamic variability using clinical and laboratory data improves the prediction of mortality in patients undergoing allo-HCT.
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Affiliation(s)
- Yiwang Zhou
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Jesse Smith
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Dinesh Keerthi
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital, Memphis, TN
| | - Cai Li
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Yilun Sun
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Suraj Sarvode Mothi
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - David C. Shyr
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA
| | - Barbara Spitzer
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Andrew Harris
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Avijit Chatterjee
- Digital, Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Subrata Chatterjee
- Digital, Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Roni Shouval
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Swati Naik
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital, Memphis, TN
| | - Alice Bertaina
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA
| | - Jaap Jan Boelens
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Brandon M. Triplett
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital, Memphis, TN
| | - Li Tang
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Akshay Sharma
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital, Memphis, TN
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Padmanabhan R, Elomri A, Taha RY, El Omri H, Elsabah H, El Omri A. Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:526. [PMID: 36612856 PMCID: PMC9819091 DOI: 10.3390/ijerph20010526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/22/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
Reliable and rapid medical diagnosis is the cornerstone for improving the survival rate and quality of life of cancer patients. The problem of clinical decision-making pertaining to the management of patients with hematologic cancer is multifaceted and intricate due to the risk of therapy-induced myelosuppression, multiple infections, and febrile neutropenia (FN). Myelosuppression due to treatment increases the risk of sepsis and mortality in hematological cancer patients with febrile neutropenia. A high prevalence of multidrug-resistant organisms is also noted in such patients, which implies that these patients are left with limited or no-treatment options amidst severe health complications. Hence, early screening of patients for such organisms in their bodies is vital to enable hospital preparedness, curtail the spread to other weak patients in hospitals, and limit community outbreaks. Even though predictive models for sepsis and mortality exist, no model has been suggested for the prediction of multidrug-resistant organisms in hematological cancer patients with febrile neutropenia. Hence, for predicting three critical clinical complications, such as sepsis, the presence of multidrug-resistant organisms, and mortality, from the data available from medical records, we used 1166 febrile neutropenia episodes reported in 513 patients. The XGboost algorithm is suggested from 10-fold cross-validation on 6 candidate models. Other highlights are (1) a novel set of easily available features for the prediction of the aforementioned clinical complications and (2) the use of data augmentation methods and model-scoring-based hyperparameter tuning to address the problem of class disproportionality, a common challenge in medical datasets and often the reason behind poor event prediction rate of various predictive models reported so far. The proposed model depicts improved recall and AUC (area under the curve) for sepsis (recall = 98%, AUC = 0.85), multidrug-resistant organism (recall = 96%, AUC = 0.91), and mortality (recall = 86%, AUC = 0.88) prediction. Our results encourage the need to popularize artificial intelligence-based devices to support clinical decision-making.
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Affiliation(s)
- Regina Padmanabhan
- Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
| | - Adel Elomri
- Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
| | - Ruba Yasin Taha
- Department of Hematology and Bone Marrow Transplant, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, Qatar
| | - Halima El Omri
- Department of Hematology and Bone Marrow Transplant, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, Qatar
| | - Hesham Elsabah
- Department of Hematology and Bone Marrow Transplant, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, Qatar
| | - Abdelfatteh El Omri
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha 3050, Qatar
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Li M, Lu X, Yang H, Yuan R, Yang Y, Tong R, Wu X. Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics. Front Public Health 2022; 10:1000622. [PMID: 36466490 PMCID: PMC9714465 DOI: 10.3389/fpubh.2022.1000622] [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: 07/22/2022] [Accepted: 10/24/2022] [Indexed: 11/18/2022] Open
Abstract
Background Medication adherence is the main determinant of effective management of type 2 diabetes, yet there is no gold standard method available to screen patients with high-risk non-adherence. Developing machine learning models to predict high-risk non-adherence in patients with T2D could optimize management. Methods This cross-sectional study was carried out on patients with T2D at the Sichuan Provincial People's Hospital from April 2018 to December 2019 who were examined for HbA1c on the day of the survey. Demographic and clinical characteristics were extracted from the questionnaire and electronic medical records. The sample was randomly divided into a training dataset and a test dataset with a radio of 8:2 after data preprocessing. Four imputing methods, five sampling methods, three screening methods, and 18 machine learning algorithms were used to groom data and develop and validate models. Bootstrapping was performed to generate the validation set for external validation and univariate analysis. Models were compared on the basis of predictive performance metrics. Finally, we validated the sample size on the best model. Results This study included 980 patients with T2D, of whom 184 (18.8%) were defined as medication non-adherence. The results indicated that the model used modified random forest as the imputation method, random under sampler as the sampling method, Boruta as the feature screening method and the ensemble algorithms and had the best performance. The area under the receiver operating characteristic curve (AUC), F1 score, and area under the precision-recall curve (AUPRC) of the best model, among a total of 1,080 trained models, were 0.8369, 0.7912, and 0.9574, respectively. Age, present fasting blood glucose (FBG) values, present HbA1c values, present random blood glucose (RBG) values, and body mass index (BMI) were the most significant contributors associated with risks of medication adherence. Conclusion We found that machine learning methods could be used to predict the risk of non-adherence in patients with T2D. The proposed model was well performed to identify patients with T2D with non-adherence and could help improve individualized T2D management.
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Affiliation(s)
- Mengting Li
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China,Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
| | - Xiangyu Lu
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,The Second Department of Hepatobiliary Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - HengBo Yang
- School of Pharmacy, Chengdu Medical College, Chengdu, China
| | - Rong Yuan
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,Endocrine Department, Sichuan Provincial People's Hospital, Chengdu, China
| | - Yong Yang
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China,Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,*Correspondence: Yong Yang
| | - Rongsheng Tong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China,Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,Rongsheng Tong
| | - Xingwei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China,Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China,Xingwei Wu
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So M, Tsai H, Swaminathan N, Bartash R. Bring it on: Top five antimicrobial stewardship challenges in transplant infectious diseases and practical strategies to address them. ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2022; 2:e72. [PMID: 36483373 PMCID: PMC9726551 DOI: 10.1017/ash.2022.53] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 03/30/2022] [Indexed: 06/17/2023]
Abstract
Antimicrobial therapies are essential tools for transplant recipients who are at high risk for infectious complications. However, judicious use of antimicrobials is critical to preventing the development of antimicrobial resistance. Treatment of multidrug-resistant organisms is challenging and potentially leads to therapies with higher toxicities, intravenous access, and intensive drug monitoring for interactions. Antimicrobial stewardship programs are crucial in the prevention of antimicrobial resistance, though balancing these strategies with the need for early and frequent antibiotic therapy in these immunocompromised patients can be challenging. In this review, we summarize 5 frequently encountered transplant infectious disease stewardship challenges, and we suggest strategies to improve practices for each clinical syndrome. These 5 challenging areas are: asymptomatic bacteriuria in kidney transplant recipients, febrile neutropenia in hematopoietic stem cell transplantation, antifungal prophylaxis in liver and lung transplantation, treatment of left-ventricular assist device infections, and Clostridioides difficile infection in solid-organ and hematopoietic stem-cell transplant recipients. Common themes contributing to these challenges include limited data specific to transplant patients, shortcomings in diagnostic testing, and uncertainties in pharmacotherapy.
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Affiliation(s)
- Miranda So
- Sinai Health-University Health Network Antimicrobial Stewardship Program, University Health Network, Toronto, Ontario, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Helen Tsai
- Division of Infectious Diseases, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, United States
| | - Neeraja Swaminathan
- Division of Infectious Diseases, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, United States
| | - Rachel Bartash
- Division of Infectious Diseases, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, United States
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Na JY, Kim D, Kwon AM, Jeon JY, Kim H, Kim CR, Lee HJ, Lee J, Park HK. Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort. Sci Rep 2021; 11:22353. [PMID: 34785709 PMCID: PMC8595677 DOI: 10.1038/s41598-021-01640-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/01/2021] [Indexed: 12/14/2022] Open
Abstract
Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample populations. We aimed to compare the performance of artificial intelligence (AI) analysis with that of conventional analysis to identify risk factors associated with symptomatic PDA (sPDA) in very low birth weight infants. This nationwide cohort study included 8369 very low birth weight (VLBW) infants. The participants were divided into an sPDA group and an asymptomatic PDA or spontaneously close PDA (nPDA) group. The sPDA group was further divided into treated and untreated subgroups. A total of 47 perinatal risk factors were collected and analyzed. Multiple logistic regression was used as a standard analytic tool, and five AI algorithms were used to identify the factors associated with sPDA. Combining a large database of risk factors from nationwide registries and AI techniques achieved higher accuracy and better performance of the PDA prediction tasks, and the ensemble methods showed the best performances.
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Affiliation(s)
- Jae Yoon Na
- Department of Pediatrics, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea
| | - Dongkyun Kim
- Department of Electrical and Electronic Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan, 15588, Korea
| | - Amy M Kwon
- Artificial Intelligence Convergence Research Center, Hanyang University ERICA, Ansan, 15588, Korea
| | - Jin Yong Jeon
- Department of Medical and Digital Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea
| | - Hyuck Kim
- Department of Thoracic and Cardiovascular Surgery, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea
| | - Chang-Ryul Kim
- Department of Pediatrics, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea
| | - Joohyun Lee
- Department of Electrical and Electronic Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan, 15588, Korea.
| | - Hyun-Kyung Park
- Department of Pediatrics, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea.
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