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Jawadi Z, He R, Srivastava PK, Fonarow GC, Khalil SO, Krishnan S, Eskin E, Chiang JN, Nsair A. Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach. ESC Heart Fail 2024. [PMID: 38637959 DOI: 10.1002/ehf2.14796] [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: 08/28/2023] [Revised: 01/31/2024] [Accepted: 03/25/2024] [Indexed: 04/20/2024] Open
Abstract
Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in-hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21 802 patients, of which 14 539 were in the training cohort and 7263 were in the validation cohort. The median age (25th-75th percentile) was 70 (58-82) for the entire cohort, 43.2% were female, and 6.7% experienced inpatient mortality. In the overall cohort, 7621 (35.0%) patients had heart failure with reduced ejection fraction (EF ≤ 40%), 1271 (5.8%) had heart failure with mildly reduced EF (EF 41-49%), and 12 910 (59.2%) had heart failure with preserved EF (EF ≥ 50%). Random forest models in the validation cohort demonstrated a c-statistic (95% confidence interval) of 0.96 (0.95-0.97), sensitivity (SN) of 87.3%, and specificity (SP) of 90.6% for the prediction of in-hospital mortality. Models for those with HFrEF demonstrated a c-statistic of 0.96 (0.94-0.98), SN 88.2%, and SP 91.0%, and those for patients with HFpEF showed a c-statistic of 0.95 (0.93-0.97), SN 87.4%, and SP 89.5% for predicting in-hospital mortality. The random forest model significantly outperformed logistic regression (c-statistic 0.87, SN 75.9%, and SP 86.9%), and current existing risk scores including the Acute Decompensated Heart Failure National Registry risk score (c-statistic of 0.70, SN 69%, and SP 62%), and the Get With the Guidelines-Heart Failure risk score (c-statistic 0.69, SN 67%, and SP 63%); P < 0.001 for comparison. Machine learning models built from commonly recorded patient information can accurately predict in-hospital mortality among patients hospitalized with a diagnosis of heart failure.
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Affiliation(s)
- Zina Jawadi
- UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Rosemary He
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Pratyaksh K Srivastava
- Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan-UCLA Medical Center, MRL 3-760, 675 C.E. Young Dr., Los Angeles, CA, 90095-1760, USA
| | - Gregg C Fonarow
- Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan-UCLA Medical Center, MRL 3-760, 675 C.E. Young Dr., Los Angeles, CA, 90095-1760, USA
| | - Suzan O Khalil
- Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan-UCLA Medical Center, MRL 3-760, 675 C.E. Young Dr., Los Angeles, CA, 90095-1760, USA
| | - Srikanth Krishnan
- Division of Cardiology, Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Los Angeles, CA, USA
| | - Eleazar Eskin
- Department of Computer Science, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Jeffrey N Chiang
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Neurosurgery, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Ali Nsair
- Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan-UCLA Medical Center, MRL 3-760, 675 C.E. Young Dr., Los Angeles, CA, 90095-1760, USA
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Singh H, Nim DK, Randhawa AS, Ahluwalia S. Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists. Expert Rev Clin Pharmacol 2024; 17:381-391. [PMID: 38340012 DOI: 10.1080/17512433.2024.2317963] [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: 10/27/2023] [Accepted: 02/08/2024] [Indexed: 02/12/2024]
Abstract
INTRODUCTION The integration of artificial intelligence (AI) into clinical pharmacology could be a potential approach for accelerating drug discovery and development, improving patient care, and streamlining medical research processes. AREAS COVERED We reviewed the current state of AI applications in clinical pharmacology, focusing on drug discovery and development, precision medicine, pharmacovigilance, and other ventures. Key AI applications in clinical pharmacology are examined, including machine learning, natural language processing, deep learning, and reinforcement learning etc. Additionally, the evolving role of clinical pharmacologists, ethical considerations, and challenges in implementing AI in clinical pharmacology are discussed. EXPERT OPINION The AI could be instrumental in accelerating drug discovery, predicting drug safety and efficacy, and optimizing clinical trial designs. It can play a vital role in precision medicine by helping in personalized drug dosing, treatment selection, and predicting drug response based on genetic, clinical, and environmental factors. The role of AI in pharmacovigilance, such as signal detection and adverse event prediction, is also promising. The collaboration between clinical pharmacologists and AI experts also poses certain ethical and practical challenges. Clinical pharmacologists can be instrumental in shaping the future of AI-driven clinical pharmacology and contribute to the improvement of healthcare systems.
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Affiliation(s)
- Harmanjit Singh
- Department of Pharmacology, Government Medical College & Hospital, Chandigarh, India
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Deqi Sensation to Predict Acupuncture Effect on Functional Dyspepsia: A Machine Learning Study. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:4824575. [PMID: 36159564 PMCID: PMC9492368 DOI: 10.1155/2022/4824575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 08/22/2022] [Accepted: 08/26/2022] [Indexed: 11/18/2022]
Abstract
Objectives The aim of the study was to predict the effect of acupuncture for treating functional dyspepsia (FD) using the support vector machine (SVM) techniques based on initial deqi sensations of patients. Methods This retrospective study involved 90 FD patients who had received four weeks of acupuncture treatment. The support vector classification model was used to distinguish higher responders (patients with Symptom Index of Dyspepsia improvement score ≥ 2) from lower responders (patients with Symptom Index of Dyspepsia improvement score < 2). A support vector regression model was used to predict the change in the Symptom Index of Dyspepsia at the end of acupuncture treatment. Deqi sensations of patients in the first acupuncture treatment of a 20-session acupuncture intervention were defined as features and used to train models. Models were validated by 10-fold cross-validation and evaluated by accuracy, specificity, sensitivity, the area under the receive-operating curve, the coefficient of determination (R2), and the mean squared error. Results The two models could predict the efficacy of acupuncture successfully. These models had an accuracy of 0.84 in predicting acupuncture response, and an R2 of 0.16 in the prediction of symptom improvements, respectively. The presence or absence of deqi sensation, the duration of deqi sensation, distention, and pain were finally selected as significant predicting features. Conclusion Based on the SVM algorithms and deqi sensation, the current study successfully predicted the acupuncture response as well as clinical symptom improvement in FD patients at the end of treatment. Our prediction models are expected to promote the clinical efficacy of acupuncture treatment for FD, reduce medical expenditures, and optimize the allocation of medical resources.
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Li B, de Mestral C, Mamdani M, Al-Omran M. Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning. J Vasc Surg Cases Innov Tech 2022; 8:466-472. [PMID: 36016703 PMCID: PMC9396444 DOI: 10.1016/j.jvscit.2022.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Background Artificial intelligence (AI) and machine learning (ML) are rapidly advancing fields with increasing utility in health care. We conducted a survey to determine the perceptions of Canadian vascular surgeons toward AI/ML. Methods An online questionnaire was distributed to 162 members of the Canadian Society for Vascular Surgery. Self-reported knowledge, attitudes, and perceptions with respect to potential applications, limitations, and facilitators of AI/ML were assessed. Results Overall, 50 of the 162 Canadian vascular surgeons (31%) responded to the survey. Most respondents were aged 30 to 59 years (72%), male (80%), and White (67%) and practiced in academic settings (72%). One half of the participants reported that their knowledge of AI/ML was poor or very poor. Most were excited or very excited about AI/ML (66%) and were interested or very interested in learning more about the field (83.7%). The respondents believed that AI/ML would be useful or very useful for diagnosis (62%), prognosis (72%), patient selection (56%), image analysis (64%), intraoperative guidance (52%), research (88%), and education (80%). The limitations that the participants were most concerned about were errors leading to patient harm (42%), bias based on patient demographics (42%), and lack of clinician knowledge and skills in AI/ML (40%). Most were not concerned or were mildly concerned about job replacement (86%). The factors that were most important to encouraging clinicians to use AI/ML models were improvements in efficiency (88%), accurate predictions (84%), and ease of use (84%). The comments from respondents focused on the pressing need for the implementation of AI/ML in vascular surgery owing to the potential to improve care delivery. Conclusions Canadian vascular surgeons have positive views on AI/ML and believe this technology can be applied to multiple aspects of the specialty to improve patient care, research, and education. Current self-reported knowledge is poor, although interest was expressed in learning more about the field. The facilitators and barriers to the effective use of AI/ML identified in the present study can guide future development of these tools in vascular surgery.
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Wang J, Xie S, Cheng Y, Li X, Chen J, Zhu M. Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy. Front Cardiovasc Med 2022; 9:972274. [PMID: 36082132 PMCID: PMC9445158 DOI: 10.3389/fcvm.2022.972274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveInflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.Materials and methodsThe microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).Results64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.ConclusionSERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.
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Affiliation(s)
- Jianru Wang
- Department of Cardiovascular, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Central Laboratory, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Shiyang Xie
- Department of Cardiovascular, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Central Laboratory, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Yanling Cheng
- Department of Cardiovascular, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Xiaohui Li
- Department of Cardiovascular, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Jian Chen
- Department of Vascular Disease, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Vascular Anomalies, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jian Chen,
| | - Mingjun Zhu
- Department of Cardiovascular, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Mingjun Zhu,
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Ravegnini G, Ferioli M, Morganti AG, Strigari L, Pantaleo MA, Nannini M, De Leo A, De Crescenzo E, Coe M, De Palma A, De Iaco P, Rizzo S, Perrone AM. Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review. J Pers Med 2021; 11:jpm11111179. [PMID: 34834531 PMCID: PMC8624692 DOI: 10.3390/jpm11111179] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/28/2021] [Accepted: 11/09/2021] [Indexed: 12/18/2022] Open
Abstract
Background: Recently, artificial intelligence (AI) with computerized imaging analysis is attracting the attention of clinicians, in particular for its potential applications in improving cancer diagnosis. This review aims to investigate the contribution of radiomics and AI on the radiological preoperative assessment of patients with uterine sarcomas (USs). Methods: Our literature review involved a systematic search conducted in the last ten years about diagnosis, staging and treatments with radiomics and AI in USs. The protocol was drafted according to the systematic review and meta-analysis preferred reporting project (PRISMA-P) and was registered in the PROSPERO database (CRD42021253535). Results: The initial search identified 754 articles; of these, six papers responded to the characteristics required for the revision and were included in the final analysis. The predominant technique tested was magnetic resonance imaging. The analyzed studies revealed that even though sometimes complex models included AI-related algorithms, they are still too complex for translation into clinical practice. Furthermore, since these results are extracted by retrospective series and do not include external validations, currently it is hard to predict the chances of their application in different study groups. Conclusion: To date, insufficient evidence supports the benefit of radiomics in USs. Nevertheless, this field is promising but the quality of studies should be a priority in these new technologies.
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Affiliation(s)
- Gloria Ravegnini
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy;
| | - Martina Ferioli
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.F.); (A.G.M.)
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy;
| | - Alessio Giuseppe Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.F.); (A.G.M.)
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy;
| | - Lidia Strigari
- Medical Physics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Maria Abbondanza Pantaleo
- Division of Oncology, IRCCS—Azienda Ospedaliero Universitaria di Bologna, 40138 Bologna, Italy; (M.A.P.); (M.N.)
| | - Margherita Nannini
- Division of Oncology, IRCCS—Azienda Ospedaliero Universitaria di Bologna, 40138 Bologna, Italy; (M.A.P.); (M.N.)
| | - Antonio De Leo
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy;
| | - Eugenia De Crescenzo
- Division of Oncologic Gynecology, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (E.D.C.); (P.D.I.)
- Department of Medical and Surgical Sciences (DIMEC)-Centro di Studio e Ricerca delle Neoplasie Ginecologiche (CSR), University of Bologna, 40138 Bologna, Italy
| | - Manuela Coe
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Alessandra De Palma
- Forensic Medicine and Integrated Risk Management Unit, Azienda Ospedaliero-Universitaria di Bologna, via Albertoni 15, 40138 Bologna, Italy;
| | - Pierandrea De Iaco
- Division of Oncologic Gynecology, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (E.D.C.); (P.D.I.)
- Department of Medical and Surgical Sciences (DIMEC)-Centro di Studio e Ricerca delle Neoplasie Ginecologiche (CSR), University of Bologna, 40138 Bologna, Italy
| | - Stefania Rizzo
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
- Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), via Buffi 13, 6900 Lugano, Switzerland
| | - Anna Myriam Perrone
- Division of Oncologic Gynecology, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (E.D.C.); (P.D.I.)
- Department of Medical and Surgical Sciences (DIMEC)-Centro di Studio e Ricerca delle Neoplasie Ginecologiche (CSR), University of Bologna, 40138 Bologna, Italy
- Correspondence:
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