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Yusuf H, Hillman A, Stegeman JA, Cameron A, Badger S. Expanding access to veterinary clinical decision support in resource-limited settings: a scoping review of clinical decision support tools in medicine and antimicrobial stewardship. Front Vet Sci 2024; 11:1349188. [PMID: 38895711 PMCID: PMC11184142 DOI: 10.3389/fvets.2024.1349188] [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: 12/04/2023] [Accepted: 05/13/2024] [Indexed: 06/21/2024] Open
Abstract
Introduction Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided. Methods The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature. Results A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist. Discussion We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.
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Affiliation(s)
| | | | - Jan Arend Stegeman
- Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
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Reina-Reina A, Barrera J, Maté A, Trujillo J, Valdivieso B, Gas ME. Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers. Heliyon 2023; 9:e22878. [PMID: 38125502 PMCID: PMC10731083 DOI: 10.1016/j.heliyon.2023.e22878] [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: 05/27/2023] [Revised: 11/15/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
Coronavirus disease (COVID-19) remains a significant global health challenge, prompting a transition from emergency response to comprehensive management strategies. Furthermore, the emergence of new variants of concern, such as BA.2.286, underscores the need for early detection and response to new variants, which continues to be a crucial strategy for mitigating the impact of COVID-19, especially among the vulnerable population. This study aims to anticipate patients requiring intensive care or facing elevated mortality risk throughout their COVID-19 infection while also identifying laboratory predictive markers for early diagnosis of patients. Therefore, haematological, biochemical, and demographic variables were retrospectively evaluated in 8,844 blood samples obtained from 2,935 patients before intensive care unit admission using an interpretable machine learning model. Feature selection techniques were applied using precision-recall measures to address data imbalance and evaluate the suitability of the different variables. The model was trained using stratified cross-validation with k=5 and internally validated, achieving an accuracy of 77.27%, sensitivity of 78.55%, and area under the receiver operating characteristic (AUC) of 0.85; successfully identifying patients at increased risk of severe progression. From a medical perspective, the most important features of the progression or severity of patients with COVID-19 were lactate dehydrogenase, age, red blood cell distribution standard deviation, neutrophils, and platelets, which align with findings from several prior investigations. In light of these insights, diagnostic processes can be significantly expedited through the use of laboratory tests, with a greater focus on key indicators. This strategic approach not only improves diagnostic efficiency but also extends its reach to a broader spectrum of patients. In addition, it allows healthcare professionals to take early preventive measures for those most at risk of adverse outcomes, thereby optimising patient care and prognosis.
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Affiliation(s)
- A. Reina-Reina
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - J.M. Barrera
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - A. Maté
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - J.C. Trujillo
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - B. Valdivieso
- The University and Polytechnic La Fe Hospital of Valencia, Avenida Fernando Abril Martorell, 106 Torre H 1st floor, 46026, Valencia, Spain
- The Medical Research Institute of Hospital La Fe, Avenida Fernando Abril Martorell, 106 Torre F 7th floor, 46026, Valencia, Spain
| | - María-Eugenia Gas
- The Medical Research Institute of Hospital La Fe, Avenida Fernando Abril Martorell, 106 Torre F 7th floor, 46026, Valencia, Spain
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Chimbunde E, Sigwadhi LN, Tamuzi JL, Okango EL, Daramola O, Ngah VD, Nyasulu PS. Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa. Front Artif Intell 2023; 6:1171256. [PMID: 37899965 PMCID: PMC10600470 DOI: 10.3389/frai.2023.1171256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/15/2023] [Indexed: 10/31/2023] Open
Abstract
Background COVID-19 has strained healthcare resources, necessitating efficient prognostication to triage patients effectively. This study quantified COVID-19 risk factors and predicted COVID-19 intensive care unit (ICU) mortality in South Africa based on machine learning algorithms. Methods Data for this study were obtained from 392 COVID-19 ICU patients enrolled between 26 March 2020 and 10 February 2021. We used an artificial neural network (ANN) and random forest (RF) to predict mortality among ICU patients and a semi-parametric logistic regression with nine covariates, including a grouping variable based on K-means clustering. Further evaluation of the algorithms was performed using sensitivity, accuracy, specificity, and Cohen's K statistics. Results From the semi-parametric logistic regression and ANN variable importance, age, gender, cluster, presence of severe symptoms, being on the ventilator, and comorbidities of asthma significantly contributed to ICU death. In particular, the odds of mortality were six times higher among asthmatic patients than non-asthmatic patients. In univariable and multivariate regression, advanced age, PF1 and 2, FiO2, severe symptoms, asthma, oxygen saturation, and cluster 4 were strongly predictive of mortality. The RF model revealed that intubation status, age, cluster, diabetes, and hypertension were the top five significant predictors of mortality. The ANN performed well with an accuracy of 71%, a precision of 83%, an F1 score of 100%, Matthew's correlation coefficient (MCC) score of 100%, and a recall of 88%. In addition, Cohen's k-value of 0.75 verified the most extreme discriminative power of the ANN. In comparison, the RF model provided a 76% recall, an 87% precision, and a 65% MCC. Conclusion Based on the findings, we can conclude that both ANN and RF can predict COVID-19 mortality in the ICU with accuracy. The proposed models accurately predict the prognosis of COVID-19 patients after diagnosis. The models can be used to prioritize COVID-19 patients with a high mortality risk in resource-constrained ICUs.
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Affiliation(s)
- Emmanuel Chimbunde
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Lovemore N. Sigwadhi
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jacques L. Tamuzi
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | | | - Olawande Daramola
- Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Veranyuy D. Ngah
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Peter S. Nyasulu
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Song K, Park H, Lee J, Kim A, Jung J. COVID-19 infection inference with graph neural networks. Sci Rep 2023; 13:11469. [PMID: 37454206 PMCID: PMC10349841 DOI: 10.1038/s41598-023-38314-3] [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: 08/01/2022] [Accepted: 07/06/2023] [Indexed: 07/18/2023] Open
Abstract
Infectious diseases spread rapidly, and epidemiological surveys are vital to detect high-risk transmitters and reduce transmission rates. To enhance efficiency and reduce the burden on epidemiologists, an automatic tool to assist with epidemiological surveys is necessary. This study aims to develop an automatic epidemiological survey to predict the influence of COVID-19-infected patients on future additional infections. To achieve this, the study utilized a dataset containing interaction information between confirmed cases, including contact order, contact times, and movement routes, as well as individual properties such as symptoms. Graph neural networks (GNNs) were used to incorporate interaction information and individual properties. Two variants of GNNs, graph convolutional and graph attention networks, were utilized, and the results showed that the graph-based models outperformed traditional machine learning models. For the area under the curve, the 2nd, 3rd, and 4th order spreading predictions showed higher performance by 0.200, 0.269, and 0.190, respectively. The results show that the contact information of an infected person is crucial data that can help predict whether that person will affect future infections. Our findings suggest that incorporating the relationships between an infected person and others can improve the effectiveness of an automatic epidemiological survey.
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Affiliation(s)
- Kyungwoo Song
- Department of Applied Statistics, Yonsei University, Seoul, 03722, Republic of Korea
| | - Hojun Park
- Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine and Science, Incheon, 21565, Republic of Korea
| | - Junggu Lee
- Department of E-Learning, Korea National Open University, 03087, Seoul, Republic of Korea
| | - Arim Kim
- Incheon Communicable Diseases Center, Incheon, 21554, Republic of Korea
| | - Jaehun Jung
- Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine and Science, Incheon, 21565, Republic of Korea.
- Department of Preventive Medicine, Gachon University College of Medicine, 38-13, Dokjeom-Ro 3 Beon-Gil, Incheon, 21565, Republic of Korea.
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Oliveira MC, Scharan KO, Thomés BI, Bernardelli RS, Reese FB, Kozesinski-Nakatani AC, Martins CC, Lobo SMA, Réa-Neto Á. Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard. BMC Pulm Med 2023; 23:81. [PMID: 36894945 PMCID: PMC9997428 DOI: 10.1186/s12890-023-02369-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND The gold-standard method for establishing a microbiological diagnosis of COVID-19 is reverse-transcriptase polymerase chain reaction (RT-PCR). This study aimed to evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard. METHODS Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong versus weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent). RESULTS With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5-99.5%) sensitivity, 70% (95% CI 65.8-74.2%) specificity, 85.5% (95% CI 83.4-87.7%) accuracy, PPV of 79.7% (95% CI 76.6-82.7%) and NPV of 97.6% (95% CI 95.9-99.2%). Similar performance was observed when evaluated in the subgroups of patients admitted with mild/moderate respiratory disfunction, and severe respiratory disfunction. CONCLUSION The proposed set of clinical-radiological criteria were accurate in identifying patients with strong versus weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.
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Affiliation(s)
- Mirella Cristine Oliveira
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,Complexo Hospitalar do Trabalhador (CHT), República Argentina Street, 4406, Curitiba, Paraná, 81050-000, Brazil
| | - Karoleen Oswald Scharan
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil
| | - Bruna Isadora Thomés
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil
| | - Rafaella Stradiotto Bernardelli
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,School of Medicine and Life Sciences, Pontifical Catholic University of Paraná, Imaculada Conceição Street, 1155, Curitiba, Paraná, 80215-901, Brazil
| | - Fernanda Baeumle Reese
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,Complexo Hospitalar do Trabalhador (CHT), República Argentina Street, 4406, Curitiba, Paraná, 81050-000, Brazil
| | - Amanda Christina Kozesinski-Nakatani
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,Hospital Santa Casa de Curitiba, Praça Rui Barbosa, 694, Curitiba, Paraná, 80010-030, Brazil
| | - Cintia Cristina Martins
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,Complexo Hospitalar do Trabalhador (CHT), República Argentina Street, 4406, Curitiba, Paraná, 81050-000, Brazil
| | - Suzana Margareth Ajeje Lobo
- Departament of Medicine, São José do Rio Preto Medical School, Brigadeiro Faria Lima avenue, 5416, São José do Rio Preto, São Paulo, 15090-000, Brazil
| | - Álvaro Réa-Neto
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil. .,Internal Medicine Department, Hospital de Clínicas, Federal University of Paraná, General Carneiro Street, 181, Curitiba, Paraná, 80060-900, Brazil.
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Authentication of Covid-19 Vaccines Using Synchronous Fluorescence Spectroscopy. J Fluoresc 2023; 33:1165-1174. [PMID: 36609659 PMCID: PMC9825072 DOI: 10.1007/s10895-022-03136-5] [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: 09/13/2022] [Accepted: 12/24/2022] [Indexed: 01/09/2023]
Abstract
The present study demonstrates the potential of synchronous fluorescence spectroscopy and multivariate data analysis for authentication of COVID-19 vaccines from various manufacturers. Synchronous scanning fluorescence spectra were recorded for DNA-based and mRNA-based vaccines obtained through the NHS Central Liverpool Primary Care Network. Fluorescence spectra of DNA and DNA-based vaccines as well as RNA and RNA-based vaccines were identical to one another. The application of principal component analysis (PCA), PCA-Gaussian Mixture Models (PCA-GMM)) and Self-Organising Maps (SOM) methods to the fluorescence spectra of vaccines is discussed. The PCA is applied to extract the characteristic variables of fluorescence spectra by analysing the major attributes. The results indicated that the first three principal components (PCs) can account for 99.5% of the total variance in the data. The PC scores plot showed two distinct clusters corresponding to the DNA-based vaccines and mRNA-based vaccines respectively. PCA-GMM clustering complemented the PCA clusters by further classifying the mRNA-based vaccines and the GMM clusters revealed three mRNA-based vaccines that were not clustered with the other vaccines. SOM complemented both PCA and PCA-GMM and proved effective with multivariate data without the need for dimensions reduction. The findings showed that fluorescence spectroscopy combined with machine learning algorithms (PCA, PCA-GMM and SOM) is a useful technique for vaccination verification and has the benefits of simplicity, speed and reliability.
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