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Laffafchi S, Ebrahimi A, Kafan S. Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data. Health Inf Sci Syst 2024; 12:17. [PMID: 38464464 PMCID: PMC10917730 DOI: 10.1007/s13755-024-00276-9] [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: 12/15/2022] [Accepted: 01/17/2024] [Indexed: 03/12/2024] Open
Abstract
Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.
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Affiliation(s)
- Soroor Laffafchi
- Department of Business Administration and Entrepreneurship, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Ahmad Ebrahimi
- Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Samira Kafan
- Department of Pulmonary Medicine, Sina Hospital, International Relations Office, Medical School, Tehran University of Medical Sciences, PourSina St., Tehran, 1417613151 Iran
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Jin ZY, Li CM, Qu H, Yang WT, Wen JH, Ren HL. Validation of a pulmonary embolism risk assessment model in gynecological inpatients : Clinical trial: A single-center, retrospective study. Thromb J 2024; 22:47. [PMID: 38840142 PMCID: PMC11151723 DOI: 10.1186/s12959-024-00616-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/21/2024] [Indexed: 06/07/2024] Open
Abstract
OBJECTIVE To compare the predictive efficacy of the PADUA and Caprini models for pulmonary embolism (PE) in gynecological inpatients, analyze the risk factors for PE, and validate whether both models can effectively predict mortality rates. METHODS A total of 355 gynecological inpatients who underwent computed tomography pulmonary angiography (CTPA) were included in the retrospective analysis. The comparative assessment of the predictive capabilities for PE between the PADUA and Caprini was carried out using receiver operating characteristic (ROC) curves. Logistic regression analysis was used to identify risk factors associated with PE. Additionally, Kaplan-Meier survival analysis plots were generated to validate the predictive efficacy for mortality rates. RESULTS Among 355 patients, the PADUA and Caprini demonstrated the area under the curve (AUC) values of 0.757 and 0.756, respectively. There was no statistically significant difference in the AUC between the two models (P = 0.9542). Multivariate logistic analysis revealed immobility (P < 0.001), history of venous thromboembolism (VTE) (P = 0.002), thrombophilia (P < 0.001), hormonal treatment (P = 0.022), and obesity (P = 0.019) as independent risk factors for PE. Kaplan-Meier survival analysis demonstrated the reliable predictive efficacy of both the Caprini (P = 0.00051) and PADUA (P = 0.00031) for mortality. ROC for the three- and six-month follow-ups suggested that the Caprini model exhibited superior predictive efficacy for mortality. CONCLUSIONS The PADUA model can serve as a simple and effective tool for stratifying high-risk gynecological inpatients before undergoing CTPA. The Caprini model demonstrated superior predictive efficacy for mortality rates.
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Affiliation(s)
- Zhen-Yi Jin
- Department of Vascular Surgery, Beijing Chaoyang Hospital, Capital Medical University, No.8 Gongti South Road, Beijing, 100020, China
| | - Chun-Min Li
- Department of Vascular Surgery, Beijing Chaoyang Hospital, Capital Medical University, No.8 Gongti South Road, Beijing, 100020, China
| | - Hong Qu
- Department of Vascular Surgery, Beijing Chaoyang Hospital, Capital Medical University, No.8 Gongti South Road, Beijing, 100020, China
| | - Wen-Tao Yang
- Department of Vascular Surgery, Beijing Chaoyang Hospital, Capital Medical University, No.8 Gongti South Road, Beijing, 100020, China
| | - Jia-Hao Wen
- Department of Vascular Surgery, Beijing Chaoyang Hospital, Capital Medical University, No.8 Gongti South Road, Beijing, 100020, China
| | - Hua-Liang Ren
- Department of Vascular Surgery, Beijing Chaoyang Hospital, Capital Medical University, No.8 Gongti South Road, Beijing, 100020, China.
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Dicks AB, Moussallem E, Stanbro M, Walls J, Gandhi S, Gray BH. A Comprehensive Review of Risk Factors and Thrombophilia Evaluation in Venous Thromboembolism. J Clin Med 2024; 13:362. [PMID: 38256496 PMCID: PMC10816375 DOI: 10.3390/jcm13020362] [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: 12/15/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
Venous thromboembolism (VTE), which encompasses deep vein thrombosis (DVT) and pulmonary embolism (PE), is a significant cause of morbidity and mortality worldwide. There are many factors, both acquired and inherited, known to increase the risk of VTE. Most of these result in increased risk via several common mechanisms including circulatory stasis, endothelial damage, or increased hypercoagulability. Overall, a risk factor can be identified in the majority of patients with VTE; however, not all risk factors carry the same predictive value. It is important for clinicians to understand the potency of each individual risk factor when managing patients who have a VTE or are at risk of developing VTE. With this, many providers consider performing a thrombophilia evaluation to further define a patient's risk. However, guidance on who to test and when to test is controversial and not always clear. This comprehensive review attempts to address these aspects/concerns by providing an overview of the multifaceted risk factors associated with VTE as well as examining the role of performing a thrombophilia evaluation, including the indications and timing of performing such an evaluation.
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Affiliation(s)
- Andrew B. Dicks
- Department of Vascular Surgery, Prisma Health, University of South Carolina School of Medicine—Greenville, Greenville, SC 29601, USA; (E.M.); (M.S.); (S.G.); (B.H.G.)
| | - Elie Moussallem
- Department of Vascular Surgery, Prisma Health, University of South Carolina School of Medicine—Greenville, Greenville, SC 29601, USA; (E.M.); (M.S.); (S.G.); (B.H.G.)
| | - Marcus Stanbro
- Department of Vascular Surgery, Prisma Health, University of South Carolina School of Medicine—Greenville, Greenville, SC 29601, USA; (E.M.); (M.S.); (S.G.); (B.H.G.)
| | - Jay Walls
- Department of Hematology, Prisma Health, University of South Carolina School of Medicine—Greenville, Greenville, SC 29601, USA;
| | - Sagar Gandhi
- Department of Vascular Surgery, Prisma Health, University of South Carolina School of Medicine—Greenville, Greenville, SC 29601, USA; (E.M.); (M.S.); (S.G.); (B.H.G.)
| | - Bruce H. Gray
- Department of Vascular Surgery, Prisma Health, University of South Carolina School of Medicine—Greenville, Greenville, SC 29601, USA; (E.M.); (M.S.); (S.G.); (B.H.G.)
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Zu J, Yang T. Exploring Risk Factors for Lower Extremity Deep Vein Thrombosis Patients with Co-existing Pulmonary Embolism Based on Multiple Logistic Regression Model. Clin Appl Thromb Hemost 2024; 30:10760296241258230. [PMID: 38785063 PMCID: PMC11131404 DOI: 10.1177/10760296241258230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024] Open
Abstract
Valuable data on deep vein thrombosis (DVT) patients with coexisting pulmonary embolism (PE) is scarce. This study aimed to identify risk factors associated with these patients and develop logistic regression models to select high-risk DVT patients with coexisting PE. We retrospectively collected data on 150 DVT patients between July 15, 2022, and June 15, 2023, dividing them into groups based on the presence of coexisting PE. Univariate and multivariate logistic regression analyses were performed to identify significant risk factors and construct predictive models. Discrimination and calibration statistics evaluated the validation and accuracy of the developed models. Of the 130 patients analyzed, 40 (30.77%) had coexisting PE. Univariate analysis revealed four significant predictors of DVT patients with coexisting PE: sex (OR 3.83, 95% CI: [1.76; 8.59], P = 0.001), body mass index (BMI) (OR 1.50, 95% CI: [1.28; 1.75], P < 0.001), chronic disease (OR 5.15, 95% CI: [2.32; 11.8], P < 0.001), and high-density lipoprotein (HDL) (OR 0.03, 95% CI: [0.01; 0.20], P < 0.001). Additionally, BMI > 24 kg/m2 (OR 9.70, 95% CI: [2.70; 67.5], P < 0.001) and BMI > 28 kg/m2 (OR 4.80, 95% CI: [2.15; 11.0], P < 0.001) were associated with concurrent PE. Three multiple regression models were constructed, with areas under the receiver-operating characteristic curves of 0.925 (95% CI: [0.882; 0.968]), 0.908 (95% CI: [0.859; 0.957]), and 0.890 (95% CI: [0.836; 0.944]), respectively. Sex, BMI, chronic disease, and HDL levels are significant predictors of DVT patients with coexisting PE.
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Affiliation(s)
- Jiahong Zu
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Tao Yang
- General Surgery Department, Third Hospital of Shanxi Medical University, Taiyuan, China
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Kharawala A, Seo J, Barzallo D, Romero GH, Demirhan YE, Duarte GJ, Vegivinti CTR, Hache-Marliere M, Balasubramanian P, Santos HT, Nagraj S, Alhuarrat MAD, Karamanis D, Varrias D, Palaiodimos L. Assessment of the Utilization of Validated Diagnostic Predictive Tools and D-Dimer in the Evaluation of Pulmonary Embolism: A Single-Center Retrospective Cohort Study from a Public Hospital in New York City. J Clin Med 2023; 12:jcm12113629. [PMID: 37297824 DOI: 10.3390/jcm12113629] [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: 04/03/2023] [Revised: 05/17/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023] Open
Abstract
INTRODUCTION A significant increase in the use of computed tomography with pulmonary angiography (CTPA) for the diagnosis of pulmonary embolism (PE) has been observed in the past twenty years. We aimed to investigate whether the validated diagnostic predictive tools and D-dimers were adequately utilized in a large public hospital in New York City. METHODS We conducted a retrospective review of patients who underwent CTPA for the specific indication of ruling out PE over a period of one year. Two independent reviewers, blinded to each other and to the CTPA and D-dimer results, estimated the clinical probability (CP) of PE using Well's score, the YEARS algorithm, and the revised Geneva score. Patients were classified based on the presence or absence of PE in the CTPA. RESULTS A total of 917 patients were included in the analysis (median age: 57 years, female: 59%). The clinical probability of PE was considered low by both independent reviewers in 563 (61.4%), 487 (55%), and 184 (20.1%) patients based on Well's score, the YEARS algorithm, and the revised Geneva score, respectively. D-dimer testing was conducted in less than half of the patients who were deemed to have low CP for PE by both independent reviewers. Using a D-dimer cut-off of <500 ng/mL or the age-adjusted cut-off in patients with a low CP of PE would have missed only a small number of mainly subsegmental PE. All three tools, when combined with D-dimer < 500 ng/mL or <age-adjusted cut-off, yielded a NPV of > 95%. CONCLUSION All three validated diagnostic predictive tools were found to have significant diagnostic value in ruling out PE when combined with a D-dimer cut-off of <500 ng/mL or the age-adjusted cut-off. Excessive use of CTPA was likely secondary to suboptimal use of diagnostic predictive tools.
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Affiliation(s)
- Amrin Kharawala
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Jiyoung Seo
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Diego Barzallo
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Gabriel Hernandez Romero
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Yunus Emre Demirhan
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Gustavo J Duarte
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Charan Thej Reddy Vegivinti
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Manuel Hache-Marliere
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Prasanth Balasubramanian
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Heitor Tavares Santos
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Sanjana Nagraj
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Majd Al Deen Alhuarrat
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Dimitrios Karamanis
- Department of Economics, University of Piraeus, 18534 Attica, Greece
- Department of Health Informatics, Rutgers School of Health Professions, Newark, NJ 07107, USA
| | - Dimitrios Varrias
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Leonidas Palaiodimos
- Department of Medicine, New York City Health + Hospitals/Jacobi, Bronx, NY 10461, USA
- Albert Einstein College of Medicine, Bronx, NY 10461, USA
- School of Medicine, City University of New York, New York, NY 10031, USA
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Bortnick AE, Pllana E, Wolfe DS, Taub CC. Women’s Cardiovascular Health: Prioritizing the Majority Minority. J Cardiovasc Dev Dis 2023; 10:jcdd10030128. [PMID: 36975892 PMCID: PMC10057409 DOI: 10.3390/jcdd10030128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/06/2023] [Indexed: 03/19/2023] Open
Abstract
Women make up the majority of the global population, and [...]
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Affiliation(s)
- Anna E. Bortnick
- Department of Medicine, Division of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY 10467, USA
- Maternal Fetal Medicine-Cardiology Joint Program, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Edita Pllana
- Section of Cardiovascular Medicine, Heart and Vascular Center, Dartmouth-Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
- Department of Electrophysiology, Clinic of Cardiology, University Clinical Center of Kosovo, 10000 Prishina, Kosovo
| | - Diana S. Wolfe
- Department of Medicine, Division of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY 10467, USA
- Maternal Fetal Medicine-Cardiology Joint Program, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Obstetrics and Gynecology and Women’s Health, Division of Maternal Fetal Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY 10467, USA
| | - Cynthia C. Taub
- Section of Cardiovascular Medicine, Heart and Vascular Center, Dartmouth-Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
- Correspondence:
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