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Aghajanian S, Jafarabady K, Abbasi M, Mohammadifard F, Bakhshali Bakhtiari M, Shokouhi N, Saleh Gargari S, Bakhtiyari M. Prediction of post-delivery hemoglobin levels with machine learning algorithms. Sci Rep 2024; 14:13953. [PMID: 38886458 PMCID: PMC11183065 DOI: 10.1038/s41598-024-64278-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care centers on patient delivery were incorporated into the current study. Patients with preexisting coagulopathy, traumatic cases, and allogenic blood transfusion were excluded from all analyses. The association of pre-delivery variables with 24-h post-delivery hemoglobin level was evaluated using feature selection with Elastic Net regression and Random Forest algorithms. A suite of ML algorithms was employed to predict post-delivery Hb levels. Out of 2051 pregnant women, 1974 were included in the final analysis. After data pre-processing and redundant variable removal, the top predictors selected via feature selection for predicting post-delivery Hb were parity (B: 0.09 [0.05-0.12]), gestational age, pre-delivery hemoglobin (B:0.83 [0.80-0.85]) and fibrinogen levels (B:0.01 [0.01-0.01]), and pre-labor platelet count (B*1000: 0.77 [0.30-1.23]). Among the trained algorithms, artificial neural network provided the most accurate model (Root mean squared error: 0.62), which was subsequently deployed as a web-based calculator: https://predictivecalculators.shinyapps.io/ANN-HB . The current study shows that ML models could be utilized as accurate predictors of indirect measures of PPH and can be readily incorporated into healthcare systems. Further studies with heterogenous population-based samples may further improve the generalizability of these models.
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Affiliation(s)
- Sepehr Aghajanian
- Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
- Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Kyana Jafarabady
- Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Mohammad Abbasi
- Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Fateme Mohammadifard
- Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Mina Bakhshali Bakhtiari
- Department of Obstetrics and Gynecology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nasim Shokouhi
- Yas University Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Soraya Saleh Gargari
- Department of Obstetrics and Gynecology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Men's health and Reproductive health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mahmood Bakhtiyari
- Department of Community Medicine, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
- Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran.
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Zhang JM, Wang Y, Mouton M, Zhang J, Shi M. Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis. J Med Internet Res 2024; 26:e53375. [PMID: 38568723 PMCID: PMC11024739 DOI: 10.2196/53375] [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: 10/04/2023] [Revised: 11/08/2023] [Accepted: 02/28/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND The initiation of clinical trials for messenger RNA (mRNA) HIV vaccines in early 2022 revived public discussion on HIV vaccines after 3 decades of unsuccessful research. These trials followed the success of mRNA technology in COVID-19 vaccines but unfolded amid intense vaccine debates during the COVID-19 pandemic. It is crucial to gain insights into public discourse and reactions about potential new vaccines, and social media platforms such as X (formerly known as Twitter) provide important channels. OBJECTIVE Drawing from infodemiology and infoveillance research, this study investigated the patterns of public discourse and message-level drivers of user reactions on X regarding HIV vaccines by analyzing posts using machine learning algorithms. We examined how users used different post types to contribute to topics and valence and how these topics and valence influenced like and repost counts. In addition, the study identified salient aspects of HIV vaccines related to COVID-19 and prominent anti-HIV vaccine conspiracy theories through manual coding. METHODS We collected 36,424 English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022. We used topic modeling and sentiment analysis to uncover latent topics and valence, which were subsequently analyzed across post types in cross-tabulation analyses and integrated into linear regression models to predict user reactions, specifically likes and reposts. Furthermore, we manually coded the 1000 most engaged posts about HIV and COVID-19 to uncover salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti-HIV vaccine conspiracy theories. RESULTS Topic modeling revealed 3 topics: HIV and COVID-19, mRNA HIV vaccine trials, and HIV vaccine and immunity. HIV and COVID-19 underscored the connections between HIV vaccines and COVID-19 vaccines, as evidenced by subtopics about their reciprocal impact on development and various comparisons. The overall valence of the posts was marginally positive. Compared to self-composed posts initiating new conversations, there was a higher proportion of HIV and COVID-19-related and negative posts among quote posts and replies, which contribute to existing conversations. The topic of mRNA HIV vaccine trials, most evident in self-composed posts, increased repost counts. Positive valence increased like and repost counts. Prominent anti-HIV vaccine conspiracy theories often falsely linked HIV vaccines to concurrent COVID-19 and other HIV-related events. CONCLUSIONS The results highlight COVID-19 as a significant context for public discourse and reactions regarding HIV vaccines from both positive and negative perspectives. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as observed in some anti-HIV vaccine conspiracy theories misleadingly connecting HIV vaccines with COVID-19. These findings have implications for public health communication strategies concerning HIV vaccines.
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Affiliation(s)
- Jueman M Zhang
- Harrington School of Communication and Media, University of Rhode Island, Kingston, RI, United States
| | - Yi Wang
- Department of Communication, University of Louisville, Louisville, KY, United States
| | - Magali Mouton
- School of Rehabilitation Sciences, University of Ottawa, Ottawa, ON, Canada
| | - Jixuan Zhang
- Polk School of Communications, Long Island University, Brooklyn, NY, United States
| | - Molu Shi
- College of Business, University of Louisville, Louisville, KY, United States
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Wu Q, Ye F, Gu Q, Shao F, Long X, Zhan Z, Zhang J, He J, Zhang Y, Xiao Q. A customised down-sampling machine learning approach for sepsis prediction. Int J Med Inform 2024; 184:105365. [PMID: 38350181 DOI: 10.1016/j.ijmedinf.2024.105365] [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: 09/25/2023] [Revised: 12/17/2023] [Accepted: 01/29/2024] [Indexed: 02/15/2024]
Abstract
OBJECTIVE Sepsis is a life-threatening condition in the ICU and requires treatment in time. Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing alarms could worsen alarm fatigue and desensitisation in ICUs, potentially compromising patient safety. In this retrospective study, we aim to develop an accurate, robust, and readily deployable method in ICUs, only based on the vital signs and laboratory tests. METHODS Our method consists of a customised down-sampling process and a specific dynamic sliding window and XGBoost to offer sepsis prediction. The down-sampling process was applied to the retrospective data for training the XGBoost model. During the testing stage, the dynamic sliding window and the trained XGBoost were used to predict sepsis on the retrospective datasets, PhysioNet and FHC. RESULTS With the filtered data from PhysioNet, our method achieved 80.74% accuracy (77.90% sensitivity and 84.42% specificity) and 83.95% (84.82% sensitivity and 82.00% specificity) on the test set of PhysioNet-A and PhysioNet-B, respectively. The AUC score was 0.89 for both datasets. On the FHC dataset, our method achieved 92.38% accuracy (88.37% sensitivity and 95.16% specificity) and 0.98 AUC score on the test set of FHC. CONCLUSION Our results indicate that the down-sampling process and the dynamic sliding window with XGBoost brought robust and accurate performance to give sepsis prediction under various hospital settings. The localisation and robustness of our method can assist in sepsis diagnosis in different ICU settings.
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Affiliation(s)
- Qinhao Wu
- Apriko Research, Eindhoven, the Netherlands; Department of Mathematics and Computer Science, Eindhoven University of Technology, De Zaale, Eindhoven, 5612 AZ, Noord Brabant, the Netherlands
| | - Fei Ye
- Apriko Research, Eindhoven, the Netherlands
| | - Qianqian Gu
- Digital, Data and Informatics, Natural History Museum, London, SW7 5BD, United Kingdom
| | - Feng Shao
- Apriko Research, Eindhoven, the Netherlands
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, Eindhoven, 5612 AZ, Noord Brabant, the Netherlands
| | - Zhuozhao Zhan
- Department of Mathematics and Computer Science, Eindhoven University of Technology, De Zaale, Eindhoven, 5612 AZ, Noord Brabant, the Netherlands
| | - Junjie Zhang
- E.N.T. Department, the First Hospital of Changsha, University of South China, Changsha, 410005, China
| | - Jun He
- Department of Critical Care Medicine, the First Hospital of Changsha, University of South China, Changsha, 410005, China
| | - Yangzhou Zhang
- Department of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, Changsha, 410008, China.
| | - Quan Xiao
- E.N.T. Department, the First Hospital of Changsha, University of South China, Changsha, 410005, China.
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Santacroce E, D'Angerio M, Ciobanu AL, Masini L, Lo Tartaro D, Coloretti I, Busani S, Rubio I, Meschiari M, Franceschini E, Mussini C, Girardis M, Gibellini L, Cossarizza A, De Biasi S. Advances and Challenges in Sepsis Management: Modern Tools and Future Directions. Cells 2024; 13:439. [PMID: 38474403 DOI: 10.3390/cells13050439] [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: 02/01/2024] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Sepsis, a critical condition marked by systemic inflammation, profoundly impacts both innate and adaptive immunity, often resulting in lymphopenia. This immune alteration can spare regulatory T cells (Tregs) but significantly affects other lymphocyte subsets, leading to diminished effector functions, altered cytokine profiles, and metabolic changes. The complexity of sepsis stems not only from its pathophysiology but also from the heterogeneity of patient responses, posing significant challenges in developing universally effective therapies. This review emphasizes the importance of phenotyping in sepsis to enhance patient-specific diagnostic and therapeutic strategies. Phenotyping immune cells, which categorizes patients based on clinical and immunological characteristics, is pivotal for tailoring treatment approaches. Flow cytometry emerges as a crucial tool in this endeavor, offering rapid, low cost and detailed analysis of immune cell populations and their functional states. Indeed, this technology facilitates the understanding of immune dysfunctions in sepsis and contributes to the identification of novel biomarkers. Our review underscores the potential of integrating flow cytometry with omics data, machine learning and clinical observations to refine sepsis management, highlighting the shift towards personalized medicine in critical care. This approach could lead to more precise interventions, improving outcomes in this heterogeneously affected patient population.
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Affiliation(s)
- Elena Santacroce
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Miriam D'Angerio
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Alin Liviu Ciobanu
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Linda Masini
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Domenico Lo Tartaro
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Irene Coloretti
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy
| | - Stefano Busani
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy
| | - Ignacio Rubio
- Department of Anesthesiology and Intensive Care Medicine, Center for Sepsis Control and Care, Jena University Hospital, 07747 Jena, Germany
| | - Marianna Meschiari
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy
| | - Erica Franceschini
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy
| | - Cristina Mussini
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy
| | - Massimo Girardis
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy
| | - Lara Gibellini
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Sara De Biasi
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy
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Grant RW, Schmittdiel JA, Liu VX, Estacio KR, Chen YI, Lieu TA. Training the next generation of delivery science researchers: 10-year experience of a post-doctoral research fellowship program within an integrated care system. Learn Health Syst 2024; 8:e10361. [PMID: 38249850 PMCID: PMC10797580 DOI: 10.1002/lrh2.10361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/25/2023] [Accepted: 01/31/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Learning health systems require a workforce of researchers trained in the methods of identifying and overcoming barriers to effective, evidence-based care. Most existing postdoctoral training programs, such as NIH-funded postdoctoral T32 awards, support basic and epidemiological science with very limited focus on rigorous delivery science methods for improving care. In this report, we present the 10-year experience of developing and implementing a Delivery Science postdoctoral fellowship embedded within an integrated health care delivery system. Methods In 2012, the Kaiser Permanente Northern California Division of Research designed and implemented a 2-year postdoctoral Delivery Science Fellowship research training program to foster research expertise in identifying and addressing barriers to evidence-based care within health care delivery systems. Results Since 2014, 20 fellows have completed the program. Ten fellows had PhD-level scientific training, and 10 fellows had clinical doctorates (eg, MD, RN/PhD, PharmD). Fellowship alumni have graduated to faculty research positions at academic institutions (9), and research or clinical organizations (4). Seven alumni now hold positions in Kaiser Permanente's clinical operations or medical group (7). Conclusions This delivery science fellowship program has succeeded in training graduates to address delivery science problems from both research and operational perspectives. In the next 10 years, additional goals of the program will be to expand its reach (eg, by developing joint research training models in collaboration with clinical fellowships) and strengthen mechanisms to support transition from fellowship to the workforce, especially for researchers from underrepresented groups.
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Affiliation(s)
- Richard W Grant
- Division of ResearchKaiser Permanente Northern CaliforniaOaklandCaliforniaUSA
- The Permanente Medical GroupOaklandCaliforniaUSA
| | - Julie A Schmittdiel
- Division of ResearchKaiser Permanente Northern CaliforniaOaklandCaliforniaUSA
| | - Vincent X Liu
- Division of ResearchKaiser Permanente Northern CaliforniaOaklandCaliforniaUSA
- The Permanente Medical GroupOaklandCaliforniaUSA
| | - Karen R Estacio
- Division of ResearchKaiser Permanente Northern CaliforniaOaklandCaliforniaUSA
| | | | - Tracy A Lieu
- Division of ResearchKaiser Permanente Northern CaliforniaOaklandCaliforniaUSA
- The Permanente Medical GroupOaklandCaliforniaUSA
- Department of Health Systems ScienceKaiser Permanente School of MedicinePasadenaCaliforniaUSA
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Seymour CW, Urbanek KL, Nakayama A, Kennedy JN, Powell R, Robinson RAS, Kapp KL, Billiar TR, Vodovotz Y, Gelhaus SL, Cooper VS, Tang L, Mayr F, Reitz KM, Horvat C, Meyer NJ, Dickson RP, Angus D, Palmer OP. A Prospective Cohort Protocol for the Remnant Investigation in Sepsis Study. Crit Care Explor 2023; 5:e0974. [PMID: 38304708 PMCID: PMC10833627 DOI: 10.1097/cce.0000000000000974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Sepsis is a common and deadly syndrome, accounting for more than 11 million deaths annually. To mature a deeper understanding of the host and pathogen mechanisms contributing to poor outcomes in sepsis, and thereby possibly inform new therapeutic targets, sophisticated, and expensive biorepositories are typically required. We propose that remnant biospecimens are an alternative for mechanistic sepsis research, although the viability and scientific value of such remnants are unknown. METHODS AND RESULTS The Remnant Biospecimen Investigation in Sepsis study is a prospective cohort study of 225 adults (age ≥ 18 yr) presenting to the emergency department with community sepsis, defined as sepsis-3 criteria within 6 hours of arrival. The primary objective was to determine the scientific value of a remnant biospecimen repository in sepsis linked to clinical phenotyping in the electronic health record. We will study candidate multiomic readouts of sepsis biology, governed by a conceptual model, and determine the precision, accuracy, integrity, and comparability of proteins, small molecules, lipids, and pathogen sequencing in remnant biospecimens compared with paired biospecimens obtained according to research protocols. Paired biospecimens will include plasma from sodium-heparin, EDTA, sodium fluoride, and citrate tubes. CONCLUSIONS The study has received approval from the University of Pittsburgh Human Research Protection Office (Study 21120013). Recruitment began on October 25, 2022, with planned release of primary results anticipated in 2024. Results will be made available to the public, the funders, critical care societies, laboratory medicine scientists, and other researchers.
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Affiliation(s)
- Christopher W Seymour
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Kelly Lynn Urbanek
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Anna Nakayama
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Jason N Kennedy
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Rachel Powell
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | | | - Kathryn L Kapp
- Department of Chemistry, Vanderbilt University, Nashville, TN
| | | | | | - Stacy L Gelhaus
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Vaughn S Cooper
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Lu Tang
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA
| | - Flo Mayr
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Katherine M Reitz
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Surgery, UPMC, Pittsburgh, PA
| | - Christopher Horvat
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Nuala J Meyer
- Pulmonary, Allergy, and Critical Care Medicine Division, Center for Translational Lung Biology University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Robert P Dickson
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
- Division of Pulmonary & Critical Care Medicine, Weil Institute for Critical Care Research and Innovation, Ann Arbor, MI
| | - Derek Angus
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Octavia Peck Palmer
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Critical Care Medicine, The CRISMA Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA
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Ferreira LD, McCants D, Velamuri S. Using machine learning for process improvement in sepsis management. J Healthc Qual Res 2023; 38:304-311. [PMID: 36319584 DOI: 10.1016/j.jhqr.2022.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/18/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
INTRODUCTION In the U.S., sepsis afflicts 1.7 million adults, causing 270,000 deaths each year. Early detection of sepsis could decrease the number of deaths by 92,000 annually and decrease hospital expenditures by 1.5 billion USD. Few prior studies and reviews have presented a holistic understanding of the relationship between machine learning and existing process improvement measures. This study, in addition to discussing machine learning and existing process improvements measures, elaborates on the disadvantages and the barriers to integrating machine learning into the clinic. This article synthesizes previous studies to educate healthcare professionals on effectively managing sepsis by leveraging the benefits of machine learning. METHODS This study used the PubMed database. Search terms include sepsis antibiotics, sepsis process improvement, sepsis machine learning. Our search criteria included previous studies published between January 1, 2017, and February 1, 2022. RESULTS/DISCUSSION Although machine learning algorithms have better predictive capabilities, their effectiveness in the clinical setting is limited as studies show mixed results because the medical staff often fails to intervene. To overcome poor interventional response, clinicians need to work with the facility's IT department to ensure integration into clinical workflow and minimize alert-fatigue. Algorithms should enhance the productivity of clinical teams, not attempt to replace them entirely. CONCLUSION Hospitals can employ process improvement measures that effectively utilize machine learning algorithms to ensure integration into clinical workflows. Healthcare professionals can utilize workflow tools in addition to the predictive capabilities of machine learning to enhance clinical decisions in sepsis.
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Affiliation(s)
- L D Ferreira
- Department of Student Affairs, Baylor College of Medicine, United States.
| | - D McCants
- Department of Internal Medicine, Baylor College of Medicine, United States
| | - S Velamuri
- Department of Internal Medicine, Baylor College of Medicine, United States; Luminare, Inc. United States
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Papathanakos G, Andrianopoulos I, Xenikakis M, Papathanasiou A, Koulenti D, Blot S, Koulouras V. Clinical Sepsis Phenotypes in Critically Ill Patients. Microorganisms 2023; 11:2165. [PMID: 37764009 PMCID: PMC10538192 DOI: 10.3390/microorganisms11092165] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/10/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.
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Affiliation(s)
- Georgios Papathanakos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Ioannis Andrianopoulos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Menelaos Xenikakis
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Athanasios Papathanasiou
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Despoina Koulenti
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QL 4029, Australia;
- Second Critical Care Department, Attikon University Hospital, Rimini Street, 12462 Athens, Greece
| | - Stijn Blot
- Department of Internal Medicine & Pediatrics, Ghent University, 9000 Ghent, Belgium;
| | - Vasilios Koulouras
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
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9
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Lörstad S, Shekarestan S, Jernberg T, Tehrani S, Åstrand P, Gille-Johnson P, Persson J. First Sampled High-Sensitive Cardiac Troponin T is Associated With One-Year Mortality in Sepsis Patients and 30- to 365-Day Mortality in Sepsis Survivors. Am J Med 2023; 136:814-823.e8. [PMID: 37156347 DOI: 10.1016/j.amjmed.2023.04.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/13/2023] [Accepted: 04/14/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Previous studies using cardiac troponin levels to investigate the relationship between myocardial injury and mortality in sepsis patients have been conflicting. Our aim was to investigate the relationship between plasma high-sensitive cardiac troponin T (hs-cTnT) level and 30-day and 1-year mortality in sepsis patients and 30- to 365-day mortality in sepsis survivors. METHODS Sepsis patients requiring vasopressor support and admitted to our institution between 2012 and 2021 (n = 586) were included in this retrospective cohort study. Elevated hs-cTnT values (≥15 ng/L) were divided into quartiles (Q): Q1 15-35 ng/L; Q2 36-61 ng/L; Q3 62-125 ng/L; Q4 126-8630 ng/L. Stratified Kaplan-Meier curves and multivariable Cox regression were used for survival analyses. RESULTS First sampled hs-cTnT was elevated in 529 (90%) patients. One-year mortality was 45% (n = 264). Increasing level of hs-cTnT was independently associated with higher adjusted hazard ratios (HR) for 1-year mortality compared with normal levels: Q1 HR 2.9 (95% confidence interval [CI], 1.03-8.1); Q2 HR 3.5 (95% CI, 1.2-9.8); Q3 HR 4.8 (95% CI, 1.7-13.4); Q4 HR 5.7 (95% CI, 2.1-16). In acute phase survivors, first sampled hs-cTnT was an independent predictor of 30- to 365-day mortality (HR 1.3; 95% CI, 1.1-1.6 per loge hs-cTnT). CONCLUSIONS First sampled plasma hs-cTnT in critically ill sepsis patients was independently associated with 30-day and 1-year mortality. Importantly, first sampled hs-cTnT was associated with mortality during the convalescence phase (30- to 365-day) and could be a feasible marker to identify acute phase survivors at high risk of death.
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Affiliation(s)
- Samantha Lörstad
- Division of Internal Medicine and Infectious Diseases, Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden.
| | - Shajan Shekarestan
- Division of Cardiovascular Medicine, Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden
| | - Tomas Jernberg
- Division of Cardiovascular Medicine, Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden
| | - Sara Tehrani
- Division of Internal Medicine and Infectious Diseases, Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden
| | - Per Åstrand
- Internal Medicine and Infectious Diseases Clinic, Danderyd University Hospital, Stockholm, Sweden
| | - Patrik Gille-Johnson
- Internal Medicine and Infectious Diseases Clinic, Danderyd University Hospital, Stockholm, Sweden
| | - Jonas Persson
- Division of Cardiovascular Medicine, Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden
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10
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Schertz AR, Lenoir KM, Bertoni AG, Levine BJ, Mongraw-Chaffin M, Thomas KW. Sepsis Prediction Model for Determining Sepsis vs SIRS, qSOFA, and SOFA. JAMA Netw Open 2023; 6:e2329729. [PMID: 37624600 PMCID: PMC10457723 DOI: 10.1001/jamanetworkopen.2023.29729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/07/2023] [Indexed: 08/26/2023] Open
Abstract
Importance The Sepsis Prediction Model (SPM) is a proprietary decision support tool created by Epic Systems; it generates a predicting sepsis score (PSS). The model has not undergone validation against existing sepsis prediction tools, such as Systemic Inflammatory Response Syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), or quick Sepsis-Related Organ Failure Asessement (qSOFA). Objective To assess the validity and timeliness of the SPM compared with SIRS, qSOFA, and SOFA. Design, Setting, and Participants This retrospective cohort study included all adults admitted to 5 acute care hospitals in a single US health system between June 5, 2019, and December 31, 2020. Data analysis was conducted from March 2021 to February 2023. Main Outcomes and Measures A sepsis event was defined as receipt of 4 or more days of antimicrobials, blood cultures collected within ±48 hours of initial antimicrobial, and at least 1 organ dysfunction as defined by the organ dysfunction criteria optimized for the electronic health record (eSOFA). Time zero was defined as 15 minutes prior to qualifying antimicrobial or blood culture order. Results Of 60 507 total admissions, 1663 (2.7%) met sepsis criteria, with 1324 electronic health record-confirmed sepsis (699 [52.8%] male patients; 298 [22.5%] Black patients; 46 [3.5%] Hispanic/Latinx patients; 945 [71.4%] White patients), 339 COVID-19 sepsis (183 [54.0%] male patients; 98 [28.9%] Black patients; 36 [10.6%] Hispanic/Latinx patients; and 189 [55.8%] White patients), and 58 844 (97.3%; 26 632 [45.2%] male patients; 12 698 [21.6%] Black patients; 3367 [5.7%] Hispanic/Latinx patients; 40 491 White patients) did not meet sepsis criteria. The median (IQR) age was 63 (51 to 73) years for electronic health record-confirmed sepsis, 69 (60 to 77) years for COVID-19 sepsis, and 60 (42 to 72) years for nonsepsis admissions. Within the vendor recommended threshold PSS range of 5 to 8, PSS of 8 or greater had the highest balanced accuracy for classifying a sepsis admission at 0.79 (95% CI, 0.78 to 0.80). Change in SOFA score of 2 or more had the highest sensitivity, at 0.97 (95% CI, 0.97 to 0.98). At a PSS of 8 or greater, median (IQR) time to score positivity from time zero was 68.00 (6.75 to 605.75) minutes. For SIRS, qSOFA, and SOFA, median (IQR) time to score positivity was 7.00 (-105.00 to 08.00) minutes, 74.00 (-22.25 to 599.25) minutes, and 28.00 (-108.50 to 134.00) minutes, respectively. Conclusions and Relevance In this cohort study of hospital admissions, balanced accuracy of the SPM outperformed other models at higher threshold PSS; however, application of the SPM in a clinical setting was limited by poor timeliness as a sepsis screening tool as compared to SIRS and SOFA.
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Affiliation(s)
- Adam R. Schertz
- Department of Internal Medicine, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina
- Section of Pulmonology, Critical Care, Allergy and Immunologic Diseases, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina
| | - Kristin M. Lenoir
- Department of Biostatistics and Data Science, Division of Public Health Science, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina
| | - Alain G. Bertoni
- Department of Internal Medicine, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina
- Department of Biostatistics and Data Science, Division of Public Health Science, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina
| | - Beverly J. Levine
- Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Morgana Mongraw-Chaffin
- Department of Epidemiology and Prevention, Atrium Health Wake Forest Baptist Winston-Salem, North Carolina
| | - Karl W. Thomas
- Department of Internal Medicine, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina
- Section of Pulmonology, Critical Care, Allergy and Immunologic Diseases, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina
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11
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Alanazi A, Aldakhil L, Aldhoayan M, Aldosari B. Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1276. [PMID: 37512087 PMCID: PMC10385427 DOI: 10.3390/medicina59071276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023]
Abstract
Background and Objectives: Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach. By analyzing the clinical laboratory results and vital signs of adult patients in the ICU, this approach can predict and detect the initial signs of sepsis. Materials and Methods: To examine survival rates and predict outcomes, the study utilized several models, including the proportional hazards model and data mining algorithms. We analyzed data from the BESTCare database at KAMC, with a focus on patients aged 14 and older who were admitted to the ICU between April and October 2018. We conducted a thorough analysis of the medical records of a total of 1182 patients who were diagnosed with sepsis. Results: We studied two approaches to predict sepsis in ICU patients. The regression model utilizing survival analysis showed moderate predictive ability, emphasizing the importance of only three factors-time (from sepsis to an outcome; discharge or death), lactic acid, and temperature-had a significant p-value (p = 0.000568, p = 0.01, p = 0.02, respectively). Other data mining algorithms may have limitations due to their assumptions of variable independence and linear classification nature. Conclusions: To achieve progress and accuracy in the field of sepsis prediction, it is important to continuously strive for improvement. By meticulously cleaning and selecting data attributes, we can create a strong foundation for future advancements in this area.
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Affiliation(s)
- Abdullah Alanazi
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Lujain Aldakhil
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Mohammed Aldhoayan
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Bakheet Aldosari
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
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12
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Zhao M, Ma J, Liu H, Luo Y, Deng H, Wang D, Wang F, Zhang P. The Gut Microbiota Contributes to Systemic Responses and Liver Injury in Gut-Derived Sepsis. Microorganisms 2023; 11:1741. [PMID: 37512913 PMCID: PMC10383566 DOI: 10.3390/microorganisms11071741] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 06/25/2023] [Accepted: 06/30/2023] [Indexed: 07/30/2023] Open
Abstract
The gut microbiota, as a major source of opportunistic pathogens, poses a great threat to systemic infection, whereas the role of the gut microbiota in sepsis is underestimated. Here, we aimed to explore the effects of different gut microbiota patterns (namely, enterotypes) in cecal ligation and puncture (CLP)-induced murine sepsis. To achieve this purpose, we built four kinds of enterotypes by exposing mice to different types of antibiotics (azithromycin, amoxicillin, metronidazole, and levofloxacin). The results showed that antibiotic exposure induced different enterotypes, which, in turn, led to varying levels of systemic inflammation in septic mice, with amoxicillin-associated enterotypes exhibiting the most severe inflammation, followed by metronidazole, azithromycin, and levofloxacin. Specifically, the amoxicillin-associated enterotype was characterized by an abundance of intestinal opportunistic pathogens, including Enterobacteriaceae, Sutterellaceae, and Morganellaceae. This enterotype played a significant role in promoting the pathogenic potential of the gut microbiota, ultimately contributing to the development of severe systemic inflammation. Furthermore, the amoxicillin-associated enterotype exaggerated the sepsis-related liver injury, as evidenced by higher levels of alanine aminotransferase, aspartate transaminase, and hepatic malondialdehyde. The results of the RNA sequencing and the fecal suspension intraperitoneal injection sepsis model indicated that the amoxicillin-associated enterotype provoked acute hepatic immune responses and led to more significant metabolic compensation in the event of sepsis. Collectively, we concluded that the gut microbiota was one crucial factor for heterogeneity in sepsis, where the modulated gut microbiota likely prevented or reduced the serious consequences of sepsis, at least in gut-derived sepsis.
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Affiliation(s)
- Meiqi Zhao
- School of Medicine, Nankai University, Tianjin 300071, China
- Department of Gastroenterology and Hepatology, Nankai University Affiliated Third Central Hospital, Tianjin 300072, China
| | - Jiajia Ma
- Department of Gastroenterology and Hepatology, Nankai University Affiliated Third Central Hospital, Tianjin 300072, China
- The Third Central Clinical College of Tianjin Medical University, Tianjin 300070, China
| | - Huiru Liu
- Department of Gastroenterology and Hepatology, Nankai University Affiliated Third Central Hospital, Tianjin 300072, China
- The Third Central Clinical College of Tianjin Medical University, Tianjin 300070, China
| | - Ying Luo
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Nankai University Affiliated Third Central Hospital, Tianjin 300072, China
| | - Huiting Deng
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Nankai University Affiliated Third Central Hospital, Tianjin 300072, China
| | - Dandan Wang
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Nankai University Affiliated Third Central Hospital, Tianjin 300072, China
| | - Fengmei Wang
- School of Medicine, Nankai University, Tianjin 300071, China
- Department of Gastroenterology and Hepatology, Nankai University Affiliated Third Central Hospital, Tianjin 300072, China
| | - Peng Zhang
- Life and Health Intelligent Research Institute, Tianjin University of Technology, Tianjin 300387, China
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13
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Valiente Fernández M, Delgado Moya FDP, Lesmes González de Aledo A, Martín Badía I. Machine-learning models for prediction of sepsis patients mortality: A needed consideration. Med Intensiva 2023:S2173-5727(23)00050-4. [PMID: 37211497 DOI: 10.1016/j.medine.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/10/2023] [Accepted: 04/16/2023] [Indexed: 05/23/2023]
Affiliation(s)
- Marcos Valiente Fernández
- Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Avenida de Córdoba, s/n, 28041, Madrid, Spain.
| | | | | | - Isaías Martín Badía
- Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Avenida de Córdoba, s/n, 28041, Madrid, Spain
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14
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Salehi M, Rezazade-Moayed F, Khalili H, Hemati H, Aghdami N, Dashtkoohi M, Dashtkoohi M, Beig-Mohammadi MT, Ramezani M, Hajiabdolbaghi M, Fattah-Ghazi S. Safety of megadose meropenem in the empirical treatment of nosocomial sepsis: a pilot randomized clinical trial. Future Microbiol 2023; 18:335-342. [PMID: 37140270 DOI: 10.2217/fmb-2022-0170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023] Open
Abstract
Objective: To evaluate the safety of megadose meropenem as empirical treatment of nosocomial sepsis. Materials & methods: Critically ill patients diagnosed with sepsis received either high-dose (2 g every 8 h) or megadose (4 g every 8 h) meropenem as an intravenous infusion over 3 h. Results: A total of 23 patients with nosocomial sepsis were eligible and included in the megadose (n = 11) or high-dose (n = 12) group. No treatment-related adverse events were observed during a 14-day follow-up. Clinical response was also comparable between the groups. Conclusion: Megadose meropenem may be considered for empirical treatment of nosocomial sepsis without serious concern regarding its safety.
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Affiliation(s)
- Mohammadreza Salehi
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Department of Infectious Diseases, Tehran University of Medical Sciences, Tehran, Iran
| | - Farah Rezazade-Moayed
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Department of Infectious Diseases, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Khalili
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Department of Clinical Pharmacy, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Homa Hemati
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Department of Clinical Pharmacy, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Nasser Aghdami
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Department of Infectious Diseases, Tehran University of Medical Sciences, Tehran, Iran
- Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology & Technology, Academic Center for Education, Culture & Research, Tehran, Iran
| | - Mohadese Dashtkoohi
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Dashtkoohi
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Masoud Ramezani
- Critical Care Department, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahboobeh Hajiabdolbaghi
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Department of Infectious Diseases, Tehran University of Medical Sciences, Tehran, Iran
| | - Samrand Fattah-Ghazi
- Critical Care Department, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
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15
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Bongers KS, Chanderraj R, Woods RJ, McDonald RA, Adame MD, Falkowski NR, Brown CA, Baker JM, Winner KM, Fergle DJ, Hinkle KJ, Standke AK, Vendrov KC, Young VB, Stringer KA, Sjoding MW, Dickson RP. The Gut Microbiome Modulates Body Temperature Both in Sepsis and Health. Am J Respir Crit Care Med 2023; 207:1030-1041. [PMID: 36378114 PMCID: PMC10112447 DOI: 10.1164/rccm.202201-0161oc] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 11/15/2022] [Indexed: 11/16/2022] Open
Abstract
Rationale: Among patients with sepsis, variation in temperature trajectories predicts clinical outcomes. In healthy individuals, normal body temperature is variable and has decreased consistently since the 1860s. The biologic underpinnings of this temperature variation in disease and health are unknown. Objectives: To establish and interrogate the role of the gut microbiome in calibrating body temperature. Methods: We performed a series of translational analyses and experiments to determine whether and how variation in gut microbiota explains variation in body temperature in sepsis and in health. We studied patient temperature trajectories using electronic medical record data. We characterized gut microbiota in hospitalized patients using 16S ribosomal RNA gene sequencing. We modeled sepsis using intraperitoneal LPS in mice and modulated the microbiome using antibiotics, germ-free, and gnotobiotic animals. Measurements and Main Results: Consistent with prior work, we identified four temperature trajectories in patients hospitalized with sepsis that predicted clinical outcomes. In a separate cohort of 116 hospitalized patients, we found that the composition of patients' gut microbiota at admission predicted their temperature trajectories. Compared with conventional mice, germ-free mice had reduced temperature loss during experimental sepsis. Among conventional mice, heterogeneity of temperature response in sepsis was strongly explained by variation in gut microbiota. Healthy germ-free and antibiotic-treated mice both had lower basal body temperatures compared with control animals. The Lachnospiraceae family was consistently associated with temperature trajectories in hospitalized patients, experimental sepsis, and antibiotic-treated mice. Conclusions: The gut microbiome is a key modulator of body temperature variation in both health and critical illness and is thus a major, understudied target for modulating physiologic heterogeneity in sepsis.
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Affiliation(s)
| | - Rishi Chanderraj
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Michigan
- Medicine Service, Infectious Diseases Section, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan
| | - Robert J. Woods
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Michigan
- Medicine Service, Infectious Diseases Section, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan
- Center for Computational Medicine and Bioinformatics and
| | | | - Mark D. Adame
- Division of Pulmonary and Critical Care Medicine and
| | | | - Christopher A. Brown
- Division of Pulmonary and Critical Care Medicine and
- Institute for Research on Innovation and Science, Institute for Social Research
| | - Jennifer M. Baker
- Division of Pulmonary and Critical Care Medicine and
- Department of Microbiology and Immunology, Medical School
| | - Katherine M. Winner
- Division of Pulmonary and Critical Care Medicine and
- Department of Microbiology and Immunology, Medical School
| | | | | | - Alexandra K. Standke
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Michigan
| | - Kimberly C. Vendrov
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Michigan
| | - Vincent B. Young
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Michigan
- Department of Microbiology and Immunology, Medical School
| | - Kathleen A. Stringer
- Division of Pulmonary and Critical Care Medicine and
- Department of Clinical Pharmacy, College of Pharmacy, and
- Weil Institute for Critical Care Research & Innovation, Ann Arbor, Michigan
| | - Michael W. Sjoding
- Division of Pulmonary and Critical Care Medicine and
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; and
- Weil Institute for Critical Care Research & Innovation, Ann Arbor, Michigan
| | - Robert P. Dickson
- Division of Pulmonary and Critical Care Medicine and
- Department of Microbiology and Immunology, Medical School
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; and
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16
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Inamdar S, Tylek T, Thumsi A, Suresh AP, Jaggarapu MMCS, Halim M, Mantri S, Esrafili A, Ng ND, Schmitzer E, Lintecum K, de Ávila C, Fryer JD, Xu Y, Spiller KL, Acharya AP. Biomaterial mediated simultaneous delivery of spermine and alpha ketoglutarate modulate metabolism and innate immune cell phenotype in sepsis mouse models. Biomaterials 2023; 293:121973. [PMID: 36549041 PMCID: PMC9868086 DOI: 10.1016/j.biomaterials.2022.121973] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/10/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022]
Abstract
Although different metabolic pathways have been associated with distinct macrophage phenotypes, the field of utilizing metabolites to modulate macrophage phenotype is in a nascent stage. In this report, we developed microparticles based on polymerization of alpha-ketoglutarate (a Krebs cycle metabolite), with or without encapsulation of spermine (a polyamine metabolite), to modulate cell phenotype that are critical for resolution of inflammation. Poly (alpha-ketoglutarate) microparticles encapsulated and released spermine (spermine (encap)paKG MPs) in vitro, which was accelerated in an acidic environment. When delivered to bone marrow-derived-macrophages, spermine (encap)paKG MPs induced a complex phenotypic profile outside of the typical M1/M2 paradigm, with distinct effects in the presence or absence of the pro-inflammatory stimulus lipopolysaccharide. Of particular interest was the increase in expression of CD163, which has been linked to anti-inflammatory responses in sepsis. Therefore, we systemically administered spermine (encap)paKG MPs to two different murine models of sepsis using acute or chronic injection of LPS. Macrophages and neutrophils in the liver and spleen of animals treated with spermine (encap)paKG MPs increased expression of CD163, concomitant with normalizing of glycolysis and oxidative phosphorylation, in both models. Overall, these results show that spermine (encap)paKG MPs modulate macrophage phenotype in vitro and in vivo, with potential applications in inflammation-associated diseases.
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Affiliation(s)
- Sahil Inamdar
- Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | - Tina Tylek
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Abhirami Thumsi
- Biological Design, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | - Abhirami P Suresh
- Biological Design, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | | | - Michelle Halim
- Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | - Shivani Mantri
- Biomedical Engineering, School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Arezoo Esrafili
- Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | - Nathan D Ng
- Molecular Biosciences and Biotechnology, The College of Liberal Arts and Sciences, Arizona State University, Tempe, AZ, 85281, USA
| | - Elizabeth Schmitzer
- Biomedical Engineering, School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Kelly Lintecum
- Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | - Camila de Ávila
- Department of Neuroscience, Mayo Clinic, Scottsdale, AZ, 85259, USA
| | - John D Fryer
- Department of Neuroscience, Mayo Clinic, Scottsdale, AZ, 85259, USA
| | - Ying Xu
- Department of Anesthesiology, Rutgers New Jersey Medical School, Newark, NJ, 07103, USA
| | - Kara L Spiller
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Abhinav P Acharya
- Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA; Biomedical Engineering, School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA; Materials Science and Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA; Biodesign Center for Immunotherapy, Vaccines and Virotherapy, Arizona State University, Tempe, AZ, 85281, USA.
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17
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Tang M, Mu F, Cui C, Zhao JY, Lin R, Sun KX, Guan Y, Wang JW. Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis. Front Med (Lausanne) 2023; 9:1043589. [PMID: 36714139 PMCID: PMC9878129 DOI: 10.3389/fmed.2022.1043589] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 12/23/2022] [Indexed: 01/14/2023] Open
Abstract
Background With the increasing interest of academics in the application of artificial intelligence to sepsis, thousands of papers on this field had been published in the past few decades. It is difficult for researchers to understand the themes and latest research frontiers in this field from a multi-dimensional perspective. Consequently, the purpose of this study is to analyze the relevant literature in the application of artificial intelligence to sepsis through bibliometrics software, so as to better understand the development status, study the core hotspots and future development trends of this field. Methods We collected relevant publications in the application of artificial intelligence to sepsis from the Web of Science Core Collection in 2000 to 2021. The type of publication was limited to articles and reviews, and language was limited to English. Research cooperation network, journals, cited references, keywords in this field were visually analyzed by using CiteSpace, VOSviewer, and COOC software. Results A total of 8,481 publications in the application of artificial intelligence to sepsis between 2000 and 2021 were included, involving 8,132 articles and 349 reviews. Over the past 22 years, the annual number of publications had gradually increased exponentially. The USA was the most productive country, followed by China. Harvard University, Schuetz, Philipp, and Intensive Care Medicine were the most productive institution, author, and journal, respectively. Vincent, Jl and Critical Care Medicine were the most cited author and cited journal, respectively. Several conclusions can be drawn from the analysis of the cited references, including the following: screening and identification of sepsis biomarkers, treatment and related complications of sepsis, and precise treatment of sepsis. Moreover, there were a spike in searches relating to machine learning, antibiotic resistance and accuracy based on burst detection analysis. Conclusion This study conducted a comprehensive and objective analysis of the publications on the application of artificial intelligence in sepsis. It can be predicted that precise treatment of sepsis through machine learning technology is still research hotspot in this field.
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18
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Sabharwal R, Miah SJ, Fosso Wamba S. Extending artificial intelligence research in the clinical domain: a theoretical perspective. ANNALS OF OPERATIONS RESEARCH 2022:1-32. [PMID: 36407943 PMCID: PMC9641309 DOI: 10.1007/s10479-022-05035-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.
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Affiliation(s)
- Renu Sabharwal
- Newcastle Business School, The University of Newcastle, Callaghan, NSW Australia
| | - Shah J. Miah
- Newcastle Business School, The University of Newcastle, Callaghan, NSW Australia
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19
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Coombes CE, Coombes KR, Fareed N. Sequences of Events from the Electronic Medical Record and the Onset of Infection. Chem Biodivers 2022; 19:e202200657. [PMID: 36216587 DOI: 10.1002/cbdv.202200657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/15/2022] [Indexed: 11/06/2022]
Abstract
We present a novel model of time-series analysis to learn from electronic health record (EHR) data when infection occurred in the intensive care unit (ICU) by translating methods from proteomics and Bayesian statistics. Using 48,536 patients hospitalized in an ICU, we describe each hospital course as an 'alphabet' of 23 physician actions ('events') in temporal order. We analyze these as k-mers of length 3-12 events and apply a Bayesian model of (cumulative) relative risk (RR). The log2-transformed RR (median=0.248, mean=0.226) supported the conclusion that the events selected were individually associated with increased risk of infection. Selecting from all possible cutoffs of maximum gain (MG), MG>0.0244 predicts administration of antibiotics with PPV 82.0 %, NPV 44.4 %, and AUC 0.706. Our approach holds value for retrospective analysis of other clinical syndromes for which time-of-onset is critical to analysis but poorly marked in EHRs, including delirium and decompensation.
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Affiliation(s)
- Caitlin E Coombes
- Department of Anesthesiology, Stanford University, 300 Pasteur Dr., Palo Alto, CA 94305, USA
| | - Kevin R Coombes
- Department of Population Health Sciences, Medical College of Georgia, 1420 Laney Walker Blvd, Augusta, GA 30912, USA
| | - Naleef Fareed
- Department of Biomedical Informatics, The Ohio State University College of Medicine, 370 W 9th Ave, Columbus, OH 43210, USA
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Beran A, Altorok N, Srour O, Malhas SE, Khokher W, Mhanna M, Ayesh H, Aladamat N, Abuhelwa Z, Srour K, Mahmood A, Altorok N, Taleb M, Assaly R. Balanced Crystalloids versus Normal Saline in Adults with Sepsis: A Comprehensive Systematic Review and Meta-Analysis. J Clin Med 2022; 11:jcm11071971. [PMID: 35407578 PMCID: PMC8999853 DOI: 10.3390/jcm11071971] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 02/08/2023] Open
Abstract
The crystalloid fluid of choice in sepsis remains debatable. We aimed to perform a comprehensive meta-analysis to compare the effect of balanced crystalloids (BC) vs. normal saline (NS) in adults with sepsis. A systematic search of PubMed, EMBASE, and Web of Sciences databases through 22 January 2022, was performed for studies that compared BC vs. NS in adults with sepsis. Our outcomes included mortality and acute kidney injury (AKI), need for renal replacement therapy (RRT), and ICU length of stay (LOS). Pooled risk ratio (RR) and mean difference (MD) with the corresponding 95% confidence intervals (CIs) were obtained using a random-effect model. Fifteen studies involving 20,329 patients were included. Overall, BC showed a significant reduction in the overall mortality (RR 0.88, 95% CI 0.81-0.96), 28/30-day mortality (RR 0.87, 95% CI 0.79-0.95), and AKI (RR 0.85, 95% CI 0.77-0.93) but similar 90-day mortality (RR 0.96, 95% CI 0.90-1.03), need for RRT (RR 0.91, 95% CI 0.76-1.08), and ICU LOS (MD -0.25 days, 95% CI -3.44, 2.95), were observed between the two groups. However, subgroup analysis of randomized controlled trials (RCTs) showed no statistically significant differences in overall mortality (RR 0.92, 95% CI 0.82-1.02), AKI (RR 0.71, 95% CI 0.47-1.06), and need for RRT (RR 0.71, 95% CI 0.36-1.41). Our meta-analysis demonstrates that overall BC was associated with reduced mortality and AKI in sepsis compared to NS among patients with sepsis. However, subgroup analysis of RCTs showed no significant differences in both overall mortality and AKI between the groups. There was no significant difference in the need for RRT or ICU LOS between BC and NS. Pending further data, our study supports using BC over NS for fluid resuscitation in adults with sepsis. Further large-scale RCTs are necessary to validate our findings.
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Affiliation(s)
- Azizullah Beran
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
- Correspondence: ; Tel.: +1-469-348-1347
| | - Nehaya Altorok
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
| | - Omar Srour
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
| | - Saif-Eddin Malhas
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
| | - Waleed Khokher
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
| | - Mohammed Mhanna
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
| | - Hazem Ayesh
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
| | - Nameer Aladamat
- Department of Neurology, University of Toledo, Toledo, OH 43606, USA;
| | - Ziad Abuhelwa
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
| | - Khaled Srour
- Department of Critical Care Medicine, Henry Ford Health System, Detroit, MI 48202, USA;
| | - Asif Mahmood
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
| | - Nezam Altorok
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
- Department of Rheumatology, University of Toledo, Toledo, OH 43606, USA
| | - Mohammad Taleb
- Department of Pulmonary and Critical Care Medicine, University of Toledo, Toledo, OH 43606, USA;
| | - Ragheb Assaly
- Department of Internal Medicine, University of Toledo, Toledo, OH 43606, USA; (N.A.); (O.S.); (S.-E.M.); (W.K.); (M.M.); (H.A.); (Z.A.); (A.M.); (N.A.); (R.A.)
- Department of Pulmonary and Critical Care Medicine, University of Toledo, Toledo, OH 43606, USA;
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Anesi GL, Liu VX, Chowdhury M, Small DS, Wang W, Delgado MK, Bayes B, Dress E, Escobar GJ, Halpern SD. Association of ICU Admission and Outcomes in Sepsis and Acute Respiratory Failure. Am J Respir Crit Care Med 2022; 205:520-528. [PMID: 34818130 PMCID: PMC8906481 DOI: 10.1164/rccm.202106-1350oc] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 ⩾ 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P = 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P = 0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.
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Affiliation(s)
- George L. Anesi
- Division of Pulmonary, Allergy, and Critical Care,,Palliative and Advanced Illness Research (PAIR) Center, and,Leonard Davis Institute of Health Economics
| | - Vincent X. Liu
- Division of Research, Kaiser Permanente, Oakland, California
| | | | - Dylan S. Small
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Wei Wang
- Palliative and Advanced Illness Research (PAIR) Center, and
| | - M. Kit Delgado
- Palliative and Advanced Illness Research (PAIR) Center, and,Center for Emergency Care Policy and Research, Department of Emergency Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;,Leonard Davis Institute of Health Economics
| | - Brian Bayes
- Palliative and Advanced Illness Research (PAIR) Center, and
| | - Erich Dress
- Palliative and Advanced Illness Research (PAIR) Center, and
| | | | - Scott D. Halpern
- Division of Pulmonary, Allergy, and Critical Care,,Palliative and Advanced Illness Research (PAIR) Center, and,Leonard Davis Institute of Health Economics
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22
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Dellinger RP, Levy MM, Schorr CA, Townsend SR. The authors reply. Crit Care Med 2022; 50:e335-e336. [PMID: 35191890 DOI: 10.1097/ccm.0000000000005406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
| | | | - Christa A Schorr
- Cooper Medical School of Rowan University and Cooper University Health, Camden, NJ
| | - Sean R Townsend
- University of California Pacific Medical Center, (Sutter Health), San Francisco, CA
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23
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Houri O, Gil Y, Chen R, Wiznitzer A, Hochberg A, Hadar E, Berezowsky A. Prediction of Type 2 Diabetes Mellitus According to Glucose Metabolism Patterns in Pregnancy Using a Novel Machine Learning Algorithm. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00685-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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24
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Evaluating machine learning models for sepsis prediction: A systematic review of methodologies. iScience 2022; 25:103651. [PMID: 35028534 PMCID: PMC8741489 DOI: 10.1016/j.isci.2021.103651] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 11/16/2021] [Accepted: 12/15/2021] [Indexed: 12/29/2022] Open
Abstract
Studies for sepsis prediction using machine learning are developing rapidly in medical science recently. In this review, we propose a set of new evaluation criteria and reporting standards to assess 21 qualified machine learning models for quality analysis based on PRISMA. Our assessment shows that (1.) the definition of sepsis is not consistent among the studies; (2.) data sources and data preprocessing methods, machine learning models, feature engineering, and inclusion types vary widely among the studies; (3.) the closer to the onset of sepsis, the higher the value of AUROC is; (4.) the improvement in AUROC is primarily due to using machine learning as a feature engineering tool; (5.) deep neural networks coupled with Sepsis-3 diagnostic criteria tend to yield better results on the time series data collected from patients with sepsis. The new evaluation criteria and reporting standards will facilitate the development of improved machine learning models for clinical applications. New evaluation/reporting standard for sepsis prediction machine learning models Major limitations in the current models for sepsis prediction have been identified We strongly suggest using machine learning as a feature engineering tool Recommending multilayer neural networks and Sepsis 3.0 for yield better result
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Evaluation of Incident 7-Day Infection and Sepsis Hospitalizations in an Integrated Health System. Ann Am Thorac Soc 2021; 19:781-789. [PMID: 34699730 PMCID: PMC9116341 DOI: 10.1513/annalsats.202104-451oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
RATIONALE Pre-hospital opportunities to predict infection and sepsis hospitalization may exist, but little is known about their incidence following common healthcare encounters. OBJECTIVES To evaluate the incidence and timing of infection and sepsis hospitalization within 7 days of living hospital discharge, emergency department discharge, and ambulatory visit settings. METHODS In each setting, we identified patients in clinical strata based on the presence of infection and severity of illness. We estimated number needed to evaluate values with hypothetical predictive model operating characteristics. RESULTS We identified 97,614,228 encounters including 1,117,702 (1.1 %) hospital discharges, 4,635,517 (4.7%) emergency department discharges, and 91,861,009 (94.1 %) ambulatory visits between 2012 and 2017. The incidence of 7-day infection hospitalization varied from 37,140 (3.3%) following inpatient discharge, 50,315 (1.1%) following emergency department discharge, and 277,034 (0.3%) following ambulatory visits. The incidence of 7-day infection hospitalization was increased for inpatient discharges with high readmission risk (10.0%), emergency department discharges with increased acute or chronic severity of illness (3.5% and 4.7%, respectively), and ambulatory visits with acute infection (0.7%). The timing of 7-day infection and sepsis hospitalizations differed across settings with an early rise following ambulatory visits, a later peak following emergency department discharges, and a delayed peak following inpatient discharge. Theoretical number needed to evaluate values varied by strata, but following hospital and emergency department discharge, were as low as 15 to 25. CONCLUSIONS Incident 7-day infection and sepsis hospitalizations following encounters in routine healthcare settings were surprisingly common and may be amenable to clinical predictive models.
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Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study. PLoS One 2021; 16:e0257005. [PMID: 34525098 PMCID: PMC8443081 DOI: 10.1371/journal.pone.0257005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/20/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Machine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous. METHODS Using simulated data, we use a ML algorithmic framework to evaluate data imputation performance and the resulting case fatality ratio (CFR) estimates, focusing on the scale and type of data missingness (i.e., missing completely at random-MCAR, missing at random-MAR, or missing not at random-MNAR). RESULTS Across ML methods, dataset sizes and proportions of training data used, the area under the receiver operating characteristic curve decreased by 7% (median, range: 1%-16%) when missingness was increased from 10% to 40%. Overall reduction in CFR bias for MAR across methods, proportion of missingness, outbreak size and proportion of training data was 0.5% (median, range: 0%-11%). CONCLUSION ML methods could reduce bias and increase the precision in CFR estimates at low levels of missingness. However, no method is robust to high percentages of missingness. Thus, a datacentric approach is recommended in outbreak settings-patient survival outcome data should be prioritised for collection and random-sample follow-ups should be implemented to ascertain missing outcomes.
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27
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DeMerle KM, Angus DC, Baillie JK, Brant E, Calfee CS, Carcillo J, Chang CCH, Dickson R, Evans I, Gordon AC, Kennedy J, Knight JC, Lindsell CJ, Liu V, Marshall JC, Randolph AG, Scicluna BP, Shankar-Hari M, Shapiro NI, Sweeney TE, Talisa VB, Tang B, Thompson BT, Tsalik EL, van der Poll T, van Vught LA, Wong HR, Yende S, Zhao H, Seymour CW. Sepsis Subclasses: A Framework for Development and Interpretation. Crit Care Med 2021; 49:748-759. [PMID: 33591001 PMCID: PMC8627188 DOI: 10.1097/ccm.0000000000004842] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Sepsis is defined as a dysregulated host response to infection that leads to life-threatening acute organ dysfunction. It afflicts approximately 50 million people worldwide annually and is often deadly, even when evidence-based guidelines are applied promptly. Many randomized trials tested therapies for sepsis over the past 2 decades, but most have not proven beneficial. This may be because sepsis is a heterogeneous syndrome, characterized by a vast set of clinical and biologic features. Combinations of these features, however, may identify previously unrecognized groups, or "subclasses" with different risks of outcome and response to a given treatment. As efforts to identify sepsis subclasses become more common, many unanswered questions and challenges arise. These include: 1) the semantic underpinning of sepsis subclasses, 2) the conceptual goal of subclasses, 3) considerations about study design, data sources, and statistical methods, 4) the role of emerging data types, and 5) how to determine whether subclasses represent "truth." We discuss these challenges and present a framework for the broader study of sepsis subclasses. This framework is intended to aid in the understanding and interpretation of sepsis subclasses, provide a mechanism for explaining subclasses generated by different methodologic approaches, and guide clinicians in how to consider subclasses in bedside care.
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Affiliation(s)
- Kimberley M DeMerle
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Derek C Angus
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - J Kenneth Baillie
- Anaesthesia, Critical Care, and Pain Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Emily Brant
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Carolyn S Calfee
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, CA
| | - Joseph Carcillo
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, Children's Hospital of Pittsburgh, Pittsburgh, PA
| | - Chung-Chou H Chang
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Robert Dickson
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI
| | - Idris Evans
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Anthony C Gordon
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Jason Kennedy
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Julian C Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | | | - Vincent Liu
- Kaiser Permanente Division of Research, Oakland, CA
| | - John C Marshall
- Keenan Research Centre for Biomedical Science, St Michael's Hospital, Toronto, ON, Canada
| | - Adrienne G Randolph
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA
| | - Brendon P Scicluna
- Center for Experimental and Molecular Medicine, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Manu Shankar-Hari
- Guy's and St Thomas' NHS Foundation Trust, ICU support Offices, St Thomas' Hospital, London, United Kingdom
- School of Immunology and Microbial Sciences, Kings College London, London, United Kingdom
| | - Nathan I Shapiro
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | | | - Victor B Talisa
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Benjamin Tang
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW, Australia
| | - B Taylor Thompson
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Ephraim L Tsalik
- Department of Medicine, Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC
| | - Tom van der Poll
- Center for Experimental and Molecular Medicine, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Lonneke A van Vught
- Center for Experimental and Molecular Medicine, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Hector R Wong
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, OH
| | - Sachin Yende
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Huiying Zhao
- Department of Critical Care Medicine, Peking University People's Hospital, Beijing, China
| | - Christopher W Seymour
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA
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Coombes CE, Liu X, Abrams ZB, Coombes KR, Brock G. Simulation-derived best practices for clustering clinical data. J Biomed Inform 2021; 118:103788. [PMID: 33862229 DOI: 10.1016/j.jbi.2021.103788] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 03/23/2021] [Accepted: 04/11/2021] [Indexed: 11/18/2022]
Abstract
INTRODUCTION Clustering analyses in clinical contexts hold promise to improve the understanding of patient phenotype and disease course in chronic and acute clinical medicine. However, work remains to ensure that solutions are rigorous, valid, and reproducible. In this paper, we evaluate best practices for dissimilarity matrix calculation and clustering on mixed-type, clinical data. METHODS We simulate clinical data to represent problems in clinical trials, cohort studies, and EHR data, including single-type datasets (binary, continuous, categorical) and 4 data mixtures. We test 5 single distance metrics (Jaccard, Hamming, Gower, Manhattan, Euclidean) and 3 mixed distance metrics (DAISY, Supersom, and Mercator) with 3 clustering algorithms (hierarchical (HC), k-medoids, self-organizing maps (SOM)). We quantitatively and visually validate by Adjusted Rand Index (ARI) and silhouette width (SW). We applied our best methods to two real-world data sets: (1) 21 features collected on 247 patients with chronic lymphocytic leukemia, and (2) 40 features collected on 6000 patients admitted to an intensive care unit. RESULTS HC outperformed k-medoids and SOM by ARI across data types. DAISY produced the highest mean ARI for mixed data types for all mixtures except unbalanced mixtures dominated by continuous data. Compared to other methods, DAISY with HC uncovered superior, separable clusters in both real-world data sets. DISCUSSION Selecting an appropriate mixed-type metric allows the investigator to obtain optimal separation of patient clusters and get maximum use of their data. Superior metrics for mixed-type data handle multiple data types using multiple, type-focused distances. Better subclassification of disease opens avenues for targeted treatments, precision medicine, clinical decision support, and improved patient outcomes.
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Affiliation(s)
- Caitlin E Coombes
- The Ohio State University College of Medicine, 370 W 9th Ave, Columbus, OH 43210, USA.
| | - Xin Liu
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr, Columbus, OH 43210, USA.
| | - Zachary B Abrams
- Institute for Informatics, Washington University in St. Louis, 444 Forest Park Ave., St. Louis, MO 63108, USA.
| | - Kevin R Coombes
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr, Columbus, OH 43210, USA.
| | - Guy Brock
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr, Columbus, OH 43210, USA.
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Coombes CE, Coombes KR, Fareed N. A novel model to label delirium in an intensive care unit from clinician actions. BMC Med Inform Decis Mak 2021; 21:97. [PMID: 33750375 PMCID: PMC7941123 DOI: 10.1186/s12911-021-01461-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 03/02/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance. METHODS EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation. RESULTS Our best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes. CONCLUSIONS Hurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models.
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Affiliation(s)
- Caitlin E Coombes
- College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Kevin R Coombes
- Department of Biomedical Informatics, The Ohio State University College of Medicine, 460 Medical Center Dr., 512 Institute of Behavioral Medicine Research, Columbus, OH, 43210, USA
| | - Naleef Fareed
- Department of Biomedical Informatics, The Ohio State University College of Medicine, 460 Medical Center Dr., 512 Institute of Behavioral Medicine Research, Columbus, OH, 43210, USA.
- Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
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The Presentation, Pace, and Profile of Infection and Sepsis Patients Hospitalized Through the Emergency Department: An Exploratory Analysis. Crit Care Explor 2021; 3:e0344. [PMID: 33655214 PMCID: PMC7909460 DOI: 10.1097/cce.0000000000000344] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
To characterize the signs and symptoms of sepsis, compare them with those from simple infection and other emergent conditions and evaluate their association with hospital outcomes. Design Setting Participants and INTERVENTION A multicenter, retrospective cohort study of 408,377 patients hospitalized through the emergency department from 2012 to 2017 with sepsis, suspected infection, heart failure, or stroke. Infected patients were identified based on Sepsis-3 criteria, whereas noninfected patients were identified through diagnosis codes. MEASUREMENTS AND MAIN RESULTS Signs and symptoms were identified within physician clinical documentation in the first 24 hours of hospitalization using natural language processing. The time of sign and symptom onset prior to presentation was quantified, and sign and symptom prevalence was assessed. Using multivariable logistic regression, the association of each sign and symptom with four outcomes was evaluated: sepsis versus suspected infection diagnosis, hospital mortality, ICU admission, and time of first antibiotics (> 3 vs ≤ 3 hr from presentation). A total of 10,825 signs and symptoms were identified in 6,148,348 clinical documentation fragments. The most common symptoms overall were as follows: dyspnea (35.2%), weakness (27.2%), altered mental status (24.3%), pain (23.9%), cough (19.7%), edema (17.8%), nausea (16.9%), hypertension (15.6%), fever (13.9%), and chest pain (12.1%). Compared with predominant signs and symptoms in heart failure and stroke, those present in infection were heterogeneous. Signs and symptoms indicative of neurologic dysfunction, significant respiratory conditions, and hypotension were strongly associated with sepsis diagnosis, hospital mortality, and intensive care. Fever, present in only a minority of patients, was associated with improved mortality (odds ratio, 0.67, 95% CI, 0.64-0.70; p < 0.001). For common symptoms, the peak time of symptom onset before sepsis was 2 days, except for altered mental status, which peaked at 1 day prior to presentation. Conclusions The clinical presentation of sepsis was heterogeneous and occurred with rapid onset prior to hospital presentation. These findings have important implications for improving public education, clinical treatment, and quality measures of sepsis care.
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31
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Tang G, Luo Y, Lu F, Li W, Liu X, Nan Y, Ren Y, Liao X, Wu S, Jin H, Zomaya AY, Sun Z. Prediction of Sepsis in COVID-19 Using Laboratory Indicators. Front Cell Infect Microbiol 2021; 10:586054. [PMID: 33747973 PMCID: PMC7966961 DOI: 10.3389/fcimb.2020.586054] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/14/2020] [Indexed: 01/08/2023] Open
Abstract
Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients’ condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19. Methods This study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors. Findings The model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94–94.31%), sensitivity 97.17% (95% CI, 94.97–98.46%), and specificity 82.05% (95% CI, 77.24–86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91–99.04%), 82.22% sensitivity (95% CI, 67.41–91.49%), and 84.00% specificity (95% CI, 63.08–94.75%). Interpretation We found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient’s prognosis and to reduce mortality.
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Affiliation(s)
- Guoxing Tang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Luo
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Lu
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Li
- The Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies, Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Xiongcheng Liu
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yucen Nan
- The Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies, Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Yufei Ren
- Department of Computer Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaofei Liao
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Song Wu
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Hai Jin
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Albert Y Zomaya
- The Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies, Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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32
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Meng R, Soper B, Lee HKH, Liu VX, Greene JD, Ray P. Nonstationary multivariate Gaussian processes for electronic health records. J Biomed Inform 2021; 117:103698. [PMID: 33617985 DOI: 10.1016/j.jbi.2021.103698] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 12/22/2020] [Accepted: 02/01/2021] [Indexed: 10/22/2022]
Abstract
Advances in the modeling and analysis of electronic health records (EHR) have the potential to improve patient risk stratification, leading to better patient outcomes. The modeling of complex temporal relations across the multiple clinical variables inherent in EHR data is largely unexplored. Existing approaches to modeling EHR data often lack the flexibility to handle time-varying correlations across multiple clinical variables, or they are too complex for clinical interpretation. Therefore, we propose a novel nonstationary multivariate Gaussian process model for EHR data to address the aforementioned drawbacks of existing methodologies. Our proposed model is able to capture time-varying scale, correlation and smoothness across multiple clinical variables. We also provide details on two inference approaches: Maximum a posteriori and Hamilton Monte Carlo. Our model is validated on synthetic data and then we demonstrate its effectiveness on EHR data from Kaiser Permanente Division of Research (KPDOR). Finally, we use the KPDOR EHR data to investigate the relationships between a clinical patient risk metric and the latent processes of our proposed model and demonstrate statistically significant correlations between these entities.
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Affiliation(s)
- Rui Meng
- Department of Statistics, University of California, Santa Cruz, CA, United States.
| | - Braden Soper
- Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Herbert K H Lee
- Department of Statistics, University of California, Santa Cruz, CA, United States
| | - Vincent X Liu
- Kaiser Permanente Division of Research, Oakland, CA, United States
| | - John D Greene
- Kaiser Permanente Division of Research, Oakland, CA, United States
| | - Priyadip Ray
- Lawrence Livermore National Laboratory, Livermore, CA, United States
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33
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Giacobbe DR, Signori A, Del Puente F, Mora S, Carmisciano L, Briano F, Vena A, Ball L, Robba C, Pelosi P, Giacomini M, Bassetti M. Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective. Front Med (Lausanne) 2021; 8:617486. [PMID: 33644097 PMCID: PMC7906970 DOI: 10.3389/fmed.2021.617486] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/19/2021] [Indexed: 12/15/2022] Open
Abstract
Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Alessio Signori
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Filippo Del Puente
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Sara Mora
- Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Luca Carmisciano
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Federica Briano
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Antonio Vena
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
| | - Lorenzo Ball
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Chiara Robba
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Paolo Pelosi
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
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Sharafoddini A, Dubin JA, Lee J. Identifying subpopulations of septic patients: A temporal data-driven approach. Comput Biol Med 2020; 130:104182. [PMID: 33370712 DOI: 10.1016/j.compbiomed.2020.104182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 12/14/2020] [Accepted: 12/14/2020] [Indexed: 01/31/2023]
Abstract
Sepsis is one of the deadliest diseases in North America and in spite of the vast amount of research on this topic there is still uncertainty in the outcome of sepsis treatments. This study aimed at investigating the informativeness of temporal electronic health records (EHR) in stratifying septic patients and identifying subpopulations of septic patients with similar trajectories and clinical needs. We performed hierarchical clustering and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analyses using data from septic patients in the MIMIC III intensive care unit database. The t-Distributed Stochastic Neighbor Embedding (t-SNE) method was utilized to map patients to a two-dimensional space. We utilized silhouette index and cluster-wise stability assessment by resampling to investigate the validity of the clusters. The hierarchical clustering with Euclidean metric identified twelve clinically recognizable subgroups that demonstrated different characteristics in spite of sharing common conditions. Our results demonstrated that data-driven approaches can help in customizing care platforms for septic patients by identifying similar clinically relevant groups.
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Affiliation(s)
- Anis Sharafoddini
- School of Public Health and Health Systems, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada.
| | - Joel A Dubin
- School of Public Health and Health Systems, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada; Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada.
| | - Joon Lee
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada; Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada.
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35
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Heming N, Azabou E, Cazaumayou X, Moine P, Annane D. Sepsis in the critically ill patient: current and emerging management strategies. Expert Rev Anti Infect Ther 2020; 19:635-647. [PMID: 33140679 DOI: 10.1080/14787210.2021.1846522] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Introduction: Sepsis, a dysregulated host response to infection, is a major cause of morbidity and mortality worldwide. Early identification and evidence-based treatment of sepsis are associated with improved outcomes.Areas covered: This narrative review was undertaken following a PubMed search for English language reports published before July 2020 using the terms 'sepsis,' 'septic shock,' 'fluids,' 'fluid therapy,' 'albumin,' 'corticosteroids,' 'vasopressor.' Emerging management strategies were identified following a search of the ClinicalTrails.gov database using the term 'sepsis.' Additional reports were identified by examining the reference lists of selected articles and based on personnel knowledge of the field of sepsis.Expert opinion: The core treatment of sepsis relies on source control, early antibiotics, and organ support. The main emerging strategies focus on immunomodulation, artificial intelligence, and on multi-omics approaches for a personalized therapy.
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Affiliation(s)
- Nicholas Heming
- Department of Intensive Care, Raymond Poincaré Hospital, GHU APHP Université Paris Saclay, Garches, France.,Laboratory Inflammation & Infection, U1173, School of Medicine Simone Veil, Université Paris Saclay-UVSQ and - INSERM 2 Avenue De La Source De La Bièvre, Montigny-le-Bretonneux, France.,FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for SEPSIS).,RHU RECORDS (Rapid rEcognition of CORticosteroiD Resistant or Sensitive Sepsis)
| | - Eric Azabou
- Laboratory Inflammation & Infection, U1173, School of Medicine Simone Veil, Université Paris Saclay-UVSQ and - INSERM 2 Avenue De La Source De La Bièvre, Montigny-le-Bretonneux, France.,FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for SEPSIS).,RHU RECORDS (Rapid rEcognition of CORticosteroiD Resistant or Sensitive Sepsis).,Clinical Neurophysiology and Neuromodulation Unit, Department of Physiology, Raymond Poincaré Hospital, GHU APHP Université Paris Saclay, Garches, France
| | - Xavier Cazaumayou
- Department of Intensive Care, Raymond Poincaré Hospital, GHU APHP Université Paris Saclay, Garches, France
| | - Pierre Moine
- Department of Intensive Care, Raymond Poincaré Hospital, GHU APHP Université Paris Saclay, Garches, France.,Laboratory Inflammation & Infection, U1173, School of Medicine Simone Veil, Université Paris Saclay-UVSQ and - INSERM 2 Avenue De La Source De La Bièvre, Montigny-le-Bretonneux, France.,FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for SEPSIS).,RHU RECORDS (Rapid rEcognition of CORticosteroiD Resistant or Sensitive Sepsis)
| | - Djillali Annane
- Department of Intensive Care, Raymond Poincaré Hospital, GHU APHP Université Paris Saclay, Garches, France.,Laboratory Inflammation & Infection, U1173, School of Medicine Simone Veil, Université Paris Saclay-UVSQ and - INSERM 2 Avenue De La Source De La Bièvre, Montigny-le-Bretonneux, France.,FHU SEPSIS (Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for SEPSIS).,RHU RECORDS (Rapid rEcognition of CORticosteroiD Resistant or Sensitive Sepsis)
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36
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Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features. Crit Care Med 2020; 48:e1343-e1349. [DOI: 10.1097/ccm.0000000000004691] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Abstract
OBJECTIVE To predict a woman's risk of postpartum hemorrhage at labor admission using machine learning and statistical models. METHODS Predictive models were constructed and compared using data from 10 of 12 sites in the U.S. Consortium for Safe Labor Study (2002-2008) that consistently reported estimated blood loss at delivery. The outcome was postpartum hemorrhage, defined as an estimated blood loss at least 1,000 mL. Fifty-five candidate risk factors routinely available on labor admission were considered. We used logistic regression with and without lasso regularization (lasso regression) as the two statistical models, and random forest and extreme gradient boosting as the two machine learning models to predict postpartum hemorrhage. Model performance was measured by C statistics (ie, concordance index), calibration, and decision curves. Models were constructed from the first phase (2002-2006) and externally validated (ie, temporally) in the second phase (2007-2008). Further validation was performed combining both temporal and site-specific validation. RESULTS Of the 152,279 assessed births, 7,279 (4.8%, 95% CI 4.7-4.9) had postpartum hemorrhage. All models had good-to-excellent discrimination. The extreme gradient boosting model had the best discriminative ability to predict postpartum hemorrhage (C statistic: 0.93; 95% CI 0.92-0.93), followed by random forest (C statistic: 0.92; 95% CI 0.91-0.92). The lasso regression model (C statistic: 0.87; 95% CI 0.86-0.88) and logistic regression (C statistic: 0.87; 95% CI 0.86-0.87) had lower-but-good discriminative ability. The above results held with validation across both time and sites. Decision curve analysis demonstrated that, although all models provided superior net benefit when clinical decision thresholds were between 0% and 80% predicted risk, the extreme gradient boosting model provided the greatest net benefit. CONCLUSION Postpartum hemorrhage on labor admission can be predicted with excellent discriminative ability using machine learning and statistical models. Further clinical application is needed, which may assist health care providers to be prepared and triage at-risk women.
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38
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Coombes CE, Abrams ZB, Li S, Abruzzo LV, Coombes KR. Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia. J Am Med Inform Assoc 2020; 27:1019-1027. [PMID: 32483590 PMCID: PMC7647286 DOI: 10.1093/jamia/ocaa060] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 04/08/2020] [Accepted: 04/24/2020] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological meaning, and visualization. We hypothesized that clustering could discover prognostic groups from patients with chronic lymphocytic leukemia, a disease that provides biological validation through well-understood outcomes. METHODS To address this challenge, we applied k-medoids clustering with 10 distance metrics to 2 experiments ("A" and "B") with mixed clinical features collapsed to binary vectors and visualized with both multidimensional scaling and t-stochastic neighbor embedding. To assess prognostic utility, we performed survival analysis using a Cox proportional hazard model, log-rank test, and Kaplan-Meier curves. RESULTS In both experiments, survival analysis revealed a statistically significant association between clusters and survival outcomes (A: overall survival, P = .0164; B: time from diagnosis to treatment, P = .0039). Multidimensional scaling separated clusters along a gradient mirroring the order of overall survival. Longer survival was associated with mutated immunoglobulin heavy-chain variable region gene (IGHV) status, absent Zap 70 expression, female sex, and younger age. CONCLUSIONS This approach to mixed-type data handling and selection of distance metric captured well-understood, binary, prognostic markers in chronic lymphocytic leukemia (sex, IGHV mutation status, ZAP70 expression status) with high fidelity.
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MESH Headings
- Adult
- Aged
- Aged, 80 and over
- Female
- Humans
- Immunoglobulin Heavy Chains/genetics
- Kaplan-Meier Estimate
- Leukemia, Lymphocytic, Chronic, B-Cell/immunology
- Leukemia, Lymphocytic, Chronic, B-Cell/metabolism
- Leukemia, Lymphocytic, Chronic, B-Cell/mortality
- Male
- Middle Aged
- Mutation
- Prognosis
- Proportional Hazards Models
- Unsupervised Machine Learning
- ZAP-70 Protein-Tyrosine Kinase/metabolism
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Affiliation(s)
- Caitlin E Coombes
- The Ohio State University College of Medicine, Columbus, Ohio, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA
| | - Zachary B Abrams
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA
| | - Suli Li
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA
| | - Lynne V Abruzzo
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | - Kevin R Coombes
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA
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Mathis MR, Engoren MC, Joo H, Maile MD, Aaronson KD, Burns ML, Sjoding MW, Douville NJ, Janda AM, Hu Y, Najarian K, Kheterpal S. Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach. Anesth Analg 2020; 130:1188-1200. [PMID: 32287126 DOI: 10.1213/ane.0000000000004630] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Heart failure with reduced ejection fraction (HFrEF) is a condition imposing significant health care burden. Given its syndromic nature and often insidious onset, the diagnosis may not be made until clinical manifestations prompt further evaluation. Detecting HFrEF in precursor stages could allow for early initiation of treatments to modify disease progression. Granular data collected during the perioperative period may represent an underutilized method for improving the diagnosis of HFrEF. We hypothesized that patients ultimately diagnosed with HFrEF following surgery can be identified via machine-learning approaches using pre- and intraoperative data. METHODS Perioperative data were reviewed from adult patients undergoing general anesthesia for major surgical procedures at an academic quaternary care center between 2010 and 2016. Patients with known HFrEF, heart failure with preserved ejection fraction, preoperative critical illness, or undergoing cardiac, cardiology, or electrophysiologic procedures were excluded. Patients were classified as healthy controls or undiagnosed HFrEF. Undiagnosed HFrEF was defined as lacking a HFrEF diagnosis preoperatively but establishing a diagnosis within 730 days postoperatively. Undiagnosed HFrEF patients were adjudicated by expert clinician review, excluding cases for which HFrEF was secondary to a perioperative triggering event, or any event not associated with HFrEF natural disease progression. Machine-learning models, including L1 regularized logistic regression, random forest, and extreme gradient boosting were developed to detect undiagnosed HFrEF, using perioperative data including 628 preoperative and 1195 intraoperative features. Training/validation and test datasets were used with parameter tuning. Test set model performance was evaluated using area under the receiver operating characteristic curve (AUROC), positive predictive value, and other standard metrics. RESULTS Among 67,697 cases analyzed, 279 (0.41%) patients had undiagnosed HFrEF. The AUROC for the logistic regression model was 0.869 (95% confidence interval, 0.829-0.911), 0.872 (0.836-0.909) for the random forest model, and 0.873 (0.833-0.913) for the extreme gradient boosting model. The corresponding positive predictive values were 1.69% (1.06%-2.32%), 1.42% (0.85%-1.98%), and 1.78% (1.15%-2.40%), respectively. CONCLUSIONS Machine-learning models leveraging perioperative data can detect undiagnosed HFrEF with good performance. However, the low prevalence of the disease results in a low positive predictive value, and for clinically meaningful sensitivity thresholds to be actionable, confirmatory testing with high specificity (eg, echocardiography or cardiac biomarkers) would be required following model detection. Future studies are necessary to externally validate algorithm performance at additional centers and explore the feasibility of embedding algorithms into the perioperative electronic health record for clinician use in real time.
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Affiliation(s)
- Michael R Mathis
- From the Department of Anesthesiology.,Center for Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan.,Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan
| | | | - Hyeon Joo
- From the Department of Anesthesiology
| | | | - Keith D Aaronson
- Department of Internal Medicine - Cardiovascular Medicine Division, University of Michigan Health System, Ann Arbor, Michigan
| | - Michael L Burns
- From the Department of Anesthesiology.,Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan
| | - Michael W Sjoding
- Center for Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan.,Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan.,Department of Internal Medicine - Pulmonary and Critical Care Division, University of Michigan Health System, Ann Arbor, Michigan
| | | | | | - Yaokun Hu
- From the Department of Anesthesiology
| | - Kayvan Najarian
- Center for Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan.,Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan
| | - Sachin Kheterpal
- From the Department of Anesthesiology.,Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan
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40
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Luz CF, Vollmer M, Decruyenaere J, Nijsten MW, Glasner C, Sinha B. Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies. Clin Microbiol Infect 2020; 26:1291-1299. [PMID: 32061798 DOI: 10.1016/j.cmi.2020.02.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/01/2020] [Accepted: 02/03/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. OBJECTIVES To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. SOURCES A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. CONTENT Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. IMPLICATIONS Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.
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Affiliation(s)
- C F Luz
- University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands.
| | - M Vollmer
- Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany
| | - J Decruyenaere
- Ghent University, Ghent University Hospital, Department of Intensive Care, Ghent, Belgium
| | - M W Nijsten
- University of Groningen, University Medical Center Groningen, Department of Critical Care, Groningen, the Netherlands
| | - C Glasner
- University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands
| | - B Sinha
- University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands
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