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Klüglich M, Santy B, Tanev M, Hristov K, Mincheva T. Patient feasibility as a novel approach for integrating IRT and LCA statistical models into patient-centric qualitative data-a pilot study. Front Digit Health 2024; 6:1378497. [PMID: 39416519 PMCID: PMC11479982 DOI: 10.3389/fdgth.2024.1378497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 09/02/2024] [Indexed: 10/19/2024] Open
Abstract
Introduction Clinical research increasingly recognizes the role and value of patient-centric data incorporation in trial design, aiming for more relevant, feasible, and engaging studies for participating patients. Despite recognition, research on analytical models regarding qualitative patient data analysis has been insufficient. Aim This pilot study aims to explore and demonstrate the analytical framework of the "patient feasibility" concept-a novel approach for integrating patient-centric data into clinical trial design using psychometric latent class analysis (LCA) and interval response theory (IRT) models. Methods A qualitative survey was designed to capture the diverse experiences and attitudes of patients in an oncological indication. Results were subjected to content analysis and categorization as a preparatory phase of the study. The analytical phase further employed LCA and hybrid IRT models to discern distinct patient subgroups and characteristics related to patient feasibility. Results LCA identified three latent classes each with distinct characteristics pertaining to a latent trait defined as patient feasibility. Covariate analyses further highlighted subgroup behaviors. In addition, IRT analyses using the two-parameter logistic model, generalized partial credit model, and nominal response model highlighted further distinct characteristics of the studied group. The results provided insights into perceived treatment challenges, logistic challenges, and limiting factors regarding the standard of care therapy and clinical trial attitudes.
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Affiliation(s)
- Matthias Klüglich
- Therapeutic Area Oncology Medicine, Boehringer Ingelheim International GmbH, Biberach, Germany
| | - Bert Santy
- Clinical Development and Operations, Boehringer Ingelheim SComm, Belgium, Germany
| | - Mihail Tanev
- Medical Advisory Department, FindMeCure Ltd., London, United Kingdom
| | - Kristian Hristov
- Medical Advisory Department, FindMeCure Ltd., London, United Kingdom
| | - Tsveta Mincheva
- Medical Advisory Department, FindMeCure Ltd., London, United Kingdom
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Bampa M, Miliou I, Jovanovic B, Papapetrou P. M-ClustEHR: A multimodal clustering approach for electronic health records. Artif Intell Med 2024; 154:102905. [PMID: 38908256 DOI: 10.1016/j.artmed.2024.102905] [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: 12/22/2023] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/24/2024]
Abstract
Sepsis refers to a potentially life-threatening situation where the immune system of the human body has an extreme response to an infection. In the presence of underlying comorbidities, the situation can become even worse and result in death. Employing unsupervised machine learning techniques, such as clustering, can assist in providing a better understanding of patient phenotypes by unveiling subgroups characterized by distinct sepsis progression and treatment patterns. More concretely, this study introduces M-ClustEHR, a clustering approach that utilizes medical data of multiple modalities by employing a multimodal autoencoder for learning comprehensive sepsis patient representations. M-ClustEHR consistently outperforms traditional clustering approaches in terms of several internal clustering performance metrics, as well as cluster stability in identifying phenotypes in the sepsis cohort. The unveiled patterns, supported by existing medical literature and clinicians, highlight the importance of multimodal clustering for advancing personalized sepsis care.
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Affiliation(s)
- Maria Bampa
- Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.
| | - Ioanna Miliou
- Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden
| | | | - Panagiotis Papapetrou
- Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden
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Soeorg H, Kalamees R, Lutsar I, Metsvaht T. Subgroup identification-based model selection to improve the predictive performance of individualized dosing. J Pharmacokinet Pharmacodyn 2024; 51:253-263. [PMID: 38400995 DOI: 10.1007/s10928-024-09909-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 02/13/2024] [Indexed: 02/26/2024]
Abstract
Currently, model-informed precision dosing uses one population pharmacokinetic model that best fits the target population. We aimed to develop a subgroup identification-based model selection approach to improve the predictive performance of individualized dosing, using vancomycin in neonates/infants as a test case. Data from neonates/infants with at least one vancomycin concentration was randomly divided into training and test dataset. Population predictions from published vancomycin population pharmacokinetic models were calculated. The single best-performing model based on various performance metrics, including median absolute percentage error (APE) and percentage of predictions within 20% (P20) or 60% (P60) of measurement, were determined. Clustering based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm was used to group neonates/infants according to their best-performing model. Subsequently, classification trees to predict the best-performing model using clinical and demographic characteristics were developed. A total of 208 vancomycin treatment episodes in training and 88 in test dataset was included. Of 30 identified models from the literature, the single best-performing model for training dataset had P20 26.2-42.6% in test dataset. The best-performing clustering approach based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm had P20 44.1-45.5% in test dataset, whereas P60 was comparable. Our proof-of-concept study shows that the prediction of the best-performing model for each patient according to the proposed model selection approaches has the potential to improve the predictive performance of model-informed precision dosing compared with the single best-performing model approach.
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Affiliation(s)
- Hiie Soeorg
- Department of Microbiology, University of Tartu, Ravila 19, Tartu, 50411, Estonia.
| | - Riste Kalamees
- Department of Microbiology, University of Tartu, Ravila 19, Tartu, 50411, Estonia
| | - Irja Lutsar
- Department of Microbiology, University of Tartu, Ravila 19, Tartu, 50411, Estonia
| | - Tuuli Metsvaht
- Department of Microbiology, University of Tartu, Ravila 19, Tartu, 50411, Estonia
- Pediatric Intensive Care Unit, Tartu University Hospital, Puusepa 8, Tartu, 50406, Estonia
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Velez T, Wang T, Garibaldi B, Singman E, Koutroulis I. Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records. JMIR Form Res 2023; 7:e46807. [PMID: 37642512 PMCID: PMC10589836 DOI: 10.2196/46807] [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: 02/26/2023] [Revised: 08/07/2023] [Accepted: 08/24/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to "hyperinflammation" associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes. OBJECTIVE The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management. METHODS This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission. RESULTS A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit-level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83. CONCLUSIONS Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.
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Affiliation(s)
- Tom Velez
- Computer Technology Associates, Cardiff, CA, United States
| | - Tony Wang
- Imedacs, Ann Arbor, MI, United States
| | - Brian Garibaldi
- Biocontainment Unit, Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Singman
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Ioannis Koutroulis
- Division of Emergency Medicine, Childrens National Hospital, Washington, DC, 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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Qin Y, Caldino Bohn RI, Sriram A, Kernan KF, Carcillo JA, Kim S, Park HJ. Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data. Front Pediatr 2023; 11:1035576. [PMID: 36793336 PMCID: PMC9923004 DOI: 10.3389/fped.2023.1035576] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/05/2023] [Indexed: 01/31/2023] Open
Abstract
Sepsis contributes to 1 of every 5 deaths globally with 3 million per year occurring in children. To improve clinical outcomes in pediatric sepsis, it is critical to avoid "one-size-fits-all" approaches and to employ a precision medicine approach. To advance a precision medicine approach to pediatric sepsis treatments, this review provides a summary of two phenotyping strategies, empiric and machine-learning-based phenotyping based on multifaceted data underlying the complex pediatric sepsis pathobiology. Although empiric and machine-learning-based phenotypes help clinicians accelerate the diagnosis and treatments, neither empiric nor machine-learning-based phenotypes fully encapsulate all aspects of pediatric sepsis heterogeneity. To facilitate accurate delineations of pediatric sepsis phenotypes for precision medicine approach, methodological steps and challenges are further highlighted.
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Affiliation(s)
- Yidi Qin
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Rebecca I. Caldino Bohn
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Aditya Sriram
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Kate F. Kernan
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, United States
| | - Joseph A. Carcillo
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, United States
| | - Soyeon Kim
- Division of Pediatric Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Hyun Jung Park
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
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Freda PJ, Kranzler HR, Moore JH. Novel digital approaches to the assessment of problematic opioid use. BioData Min 2022; 15:14. [PMID: 35840990 PMCID: PMC9284824 DOI: 10.1186/s13040-022-00301-1] [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] [Received: 08/26/2021] [Accepted: 06/30/2022] [Indexed: 11/16/2022] Open
Abstract
The opioid epidemic continues to contribute to loss of life through overdose and significant social and economic burdens. Many individuals who develop problematic opioid use (POU) do so after being exposed to prescribed opioid analgesics. Therefore, it is important to accurately identify and classify risk factors for POU. In this review, we discuss the etiology of POU and highlight novel approaches to identifying its risk factors. These approaches include the application of polygenic risk scores (PRS) and diverse machine learning (ML) algorithms used in tandem with data from electronic health records (EHR), clinical notes, patient demographics, and digital footprints. The implementation and synergy of these types of data and approaches can greatly assist in reducing the incidence of POU and opioid-related mortality by increasing the knowledge base of patient-related risk factors, which can help to improve prescribing practices for opioid analgesics.
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Affiliation(s)
- Philip J Freda
- Cedars-Sinai Medical Center, Department of Computational Biomedicine, 700 N. San Vicente Blvd., Pacific Design Center Suite G540, West Hollywood, CA, 90069, USA.
| | - Henry R Kranzler
- University of Pennsylvania, Center for Studies of Addiction, 3535 Market St., Suite 500 and Crescenz VAMC, 3800 Woodland Ave., Philadelphia, PA, 19104, USA
| | - Jason H Moore
- Cedars-Sinai Medical Center, Department of Computational Biomedicine, 700 N. San Vicente Blvd., Pacific Design Center Suite G540, West Hollywood, CA, 90069, USA
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