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Hou H, Zhang R, Li J. Artificial intelligence in the clinical laboratory. Clin Chim Acta 2024; 559:119724. [PMID: 38734225 DOI: 10.1016/j.cca.2024.119724] [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: 04/17/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/13/2024]
Abstract
Laboratory medicine has become a highly automated medical discipline. Nowadays, artificial intelligence (AI) applied to laboratory medicine is also gaining more and more attention, which can optimize the entire laboratory workflow and even revolutionize laboratory medicine in the future. However, only a few commercially available AI models are currently approved for use in clinical laboratories and have drawbacks such as high cost, lack of accuracy, and the need for manual review of model results. Furthermore, there are a limited number of literature reviews that comprehensively address the research status, challenges, and future opportunities of AI applications in laboratory medicine. Our article begins with a brief introduction to AI and some of its subsets, then reviews some AI models that are currently being used in clinical laboratories or that have been described in emerging studies, and explains the existing challenges associated with their application and possible solutions, finally provides insights into the future opportunities of the field. We highlight the current status of implementation and potential applications of AI models in different stages of the clinical testing process.
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Affiliation(s)
- Hanjing Hou
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
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Agnello L, Vidali M, Padoan A, Lucis R, Mancini A, Guerranti R, Plebani M, Ciaccio M, Carobene A. Machine learning algorithms in sepsis. Clin Chim Acta 2024; 553:117738. [PMID: 38158005 DOI: 10.1016/j.cca.2023.117738] [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: 11/20/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]
Abstract
Sepsis remains a significant global health challenge due to its high mortality and morbidity, compounded by the difficulty of early detection given its variable clinical manifestations. The integration of machine learning (ML) into laboratory medicine for timely sepsis identification and outcome forecasting is an emerging field of interest. This comprehensive review assesses the current body of research on ML applications for sepsis within the realm of laboratory diagnostics, detailing both their strengths and shortcomings. An extensive literature search was performed by two independent investigators across PubMed and Scopus databases, employing the keywords "Sepsis," "Machine Learning," and "Laboratory" without publication date limitations, culminating in January 2023. Each selected study was meticulously evaluated for various aspects, including its design, intent (diagnostic or prognostic), clinical environment, demographics, sepsis criteria, data gathering period, and the scope and nature of features, in addition to the ML methodologies and their validation procedures. Out of 135 articles reviewed, 39 fulfilled the criteria for inclusion. Among these, the majority (30 studies) were focused on devising ML algorithms for diagnosis, fewer (8 studies) on prognosis, and one study addressed both aspects. The dissemination of these studies across an array of journals reflects the interdisciplinary engagement in the development of ML algorithms for sepsis. This analysis highlights the promising role of ML in the early diagnosis of sepsis while drawing attention to the need for uniformity in validating models and defining features, crucial steps for ensuring the reliability and practicality of ML in clinical setting.
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Affiliation(s)
- Luisa Agnello
- Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Matteo Vidali
- Clinical Pathology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Andrea Padoan
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University-Hospital of Padova, Padova, Italy; QI.LAB.MED., Spin-off of the University of Padova, Padova, Italy
| | - Riccardo Lucis
- Department of Medicine (DAME), University of Udine, 33100, Udine, Italy; Microbiology and Virology Unit, Department of Laboratory Medicine, Azienda Sanitaria Friuli Occidentale (ASFO), Santa Maria degli Angeli Hospital, 33170, Pordenone, Italy
| | - Alessio Mancini
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy; Operative Unit of Clinical Pathology, AST2 Ancona, Senigallia, Italy
| | - Roberto Guerranti
- Department of Medical Biotechnologies, University of Siena, Siena, Italy; Clinical Pathology Unit, Innovation, Experimentation and Clinical and Translational Research Department, University Hospital of Siena, Siena, Italy
| | - Mario Plebani
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University-Hospital of Padova, Padova, Italy; QI.LAB.MED., Spin-off of the University of Padova, Padova, Italy; Clinical Biochemistry and Clinical Molecular Biology, School of Medicine, University of Padova, Padova, Italy
| | - Marcello Ciaccio
- Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy; Department of Laboratory Medicine, University Hospital "P. Giaccone", Palermo, Italy.
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
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Alamoudi AA, Eldakhakhny S, Banjar H, Ajabnoor G, Aljohani SB, Basheer RR, Eldakhakhny B, Badawi M, Elsamanoudy A. Association between laboratory markers and Covid-19 disease severity and outcome: a retrospective cohort study in Saudi Arabia. Front Immunol 2023; 14:1198530. [PMID: 37497238 PMCID: PMC10366441 DOI: 10.3389/fimmu.2023.1198530] [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: 04/01/2023] [Accepted: 06/26/2023] [Indexed: 07/28/2023] Open
Abstract
Introduction In Saudi Arabia, limited studies have evaluated factors including epidemiologic, clinical, and laboratory findings that are associated with COVID-19 disease. The aim of this paper was to identify laboratory parameters used in King Abdulaziz University Hospital which show an association with disease severity and patient outcome in the form of mortality. Methods Age, gender, medical history, and laboratory parameters were all retrospectively assessed concerning disease severity and disease outcome in a total of 111 COVID-19 patients at King Abdulaziz University Hospital between July 2020 and August 2020. Patients were categorized into mild disease if they did not require ward admission, moderate if they met the Ministry of Health criteria for isolation ward admition, and severe if they were admitted to the ICU. Results Age but not gender was associated with the disease severity X2 (4, N = 110) = 27.2, p <0.001. Of all laboratory parameters on admission, only the levels of Albumin appeared to be significantly associated X2 (2, N =70) = 6.6, p <0.05 with disease severity. Age but not gender was also significantly associated with disease outcome X2 (2, N = 110) = 12.8, p < 0.01. Interestingly, RBC count also showed a significant relation with disease outcome X2 (2, N = 71) = 6.1, p <0.05. Discussion This study provides more understanding of the laboratory characteristics in our part of the world to efficiently manage the disease.
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Affiliation(s)
- Aliaa Amr Alamoudi
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Regenerative Medicine Unit, King Fahad Medical Research Center, King Abdulaziz Univeristy, Jeddah, Saudi Arabia
| | - Sahar Eldakhakhny
- Diagnostic Virology, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Banjar
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center for Artificial Intelligence in Precision Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ghada Ajabnoor
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sama Badr Aljohani
- King Abdulaziz and his Companions Foundation for Giftedness and Creativity “Mawhiba”, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rasha Ramadan Basheer
- Restorative Dentistry Department, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
- Conservative Dentistry Department, Faculty of Dentistry, October University for Modern Sciences and Arts University, Cairo, Egypt
| | - Basmah Eldakhakhny
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mazen Badawi
- Department of Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia
| | - Ayman Elsamanoudy
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Medical Biochemistry and Molecular Biology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
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Dobrijević D, Vilotijević-Dautović G, Katanić J, Horvat M, Horvat Z, Pastor K. Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms. Viruses 2023; 15:1522. [PMID: 37515208 PMCID: PMC10383367 DOI: 10.3390/v15071522] [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: 06/14/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility.
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Affiliation(s)
- Dejan Dobrijević
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Gordana Vilotijević-Dautović
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Jasmina Katanić
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Mirjana Horvat
- Faculty of Civil Engineering Subotica, University of Novi Sad, 24000 Subotica, Serbia
| | - Zoltan Horvat
- Faculty of Civil Engineering Subotica, University of Novi Sad, 24000 Subotica, Serbia
| | - Kristian Pastor
- Faculty of Technology, University of Novi Sad, 21000 Novi Sad, Serbia
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Cadamuro J, Cabitza F, Debeljak Z, De Bruyne S, Frans G, Perez SM, Ozdemir H, Tolios A, Carobene A, Padoan A. Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI). Clin Chem Lab Med 2023; 61:1158-1166. [PMID: 37083166 DOI: 10.1515/cclm-2023-0355] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 04/12/2023] [Indexed: 04/22/2023]
Abstract
OBJECTIVES ChatGPT, a tool based on natural language processing (NLP), is on everyone's mind, and several potential applications in healthcare have been already proposed. However, since the ability of this tool to interpret laboratory test results has not yet been tested, the EFLM Working group on Artificial Intelligence (WG-AI) has set itself the task of closing this gap with a systematic approach. METHODS WG-AI members generated 10 simulated laboratory reports of common parameters, which were then passed to ChatGPT for interpretation, according to reference intervals (RI) and units, using an optimized prompt. The results were subsequently evaluated independently by all WG-AI members with respect to relevance, correctness, helpfulness and safety. RESULTS ChatGPT recognized all laboratory tests, it could detect if they deviated from the RI and gave a test-by-test as well as an overall interpretation. The interpretations were rather superficial, not always correct, and, only in some cases, judged coherently. The magnitude of the deviation from the RI seldom plays a role in the interpretation of laboratory tests, and artificial intelligence (AI) did not make any meaningful suggestion regarding follow-up diagnostics or further procedures in general. CONCLUSIONS ChatGPT in its current form, being not specifically trained on medical data or laboratory data in particular, may only be considered a tool capable of interpreting a laboratory report on a test-by-test basis at best, but not on the interpretation of an overall diagnostic picture. Future generations of similar AIs with medical ground truth training data might surely revolutionize current processes in healthcare, despite this implementation is not ready yet.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Federico Cabitza
- DISCo, Università degli Studi di Milano-Bicocca, Milano, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Zeljko Debeljak
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
- Clinical Institute of Laboratory Diagnostics, University Hospital Center Osijek, Osijek, Croatia
| | - Sander De Bruyne
- Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Glynis Frans
- Department of Laboratory Medicine, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Salomon Martin Perez
- Unidad de Bioquímica Clínica, Hospital Universitario Virgen Macarena, Sevilla, Spain
| | - Habib Ozdemir
- Department of Medical Biochemistry, Faculty of Medicine, Manisa Celal Bayar University, Manisa, Türkiye
| | - Alexander Tolios
- Department of Transfusion Medicine and Cell Therapy, Medical University of Vienna, Vienna, Austria
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Andrea Padoan
- Department of Medicine (DIMED), University of Padova, Padova, Italy
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Galozzi P, Basso D, Plebani M, Padoan A. Artificial Intelligence and laboratory data in rheumatic diseases. Clin Chim Acta 2023; 546:117388. [PMID: 37187221 DOI: 10.1016/j.cca.2023.117388] [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: 02/14/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 05/17/2023]
Abstract
Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.
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Affiliation(s)
- Paola Galozzi
- Department of Medicine-DIMED, University of Padova, Padova, Italy.
| | - Daniela Basso
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Mario Plebani
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Andrea Padoan
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
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Banoei MM, Rafiepoor H, Zendehdel K, Seyyedsalehi MS, Nahvijou A, Allameh F, Amanpour S. Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study. Front Med (Lausanne) 2023; 10:1170331. [PMID: 37215714 PMCID: PMC10192907 DOI: 10.3389/fmed.2023.1170331] [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: 02/20/2023] [Accepted: 04/11/2023] [Indexed: 05/24/2023] Open
Abstract
Background At the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic increased the hospital burden of COVID-19 caused by the SARS-Cov-2 and became the most significant health challenge for nations worldwide. The severity and high mortality of COVID-19 have been correlated with various demographic characteristics and clinical manifestations. Prediction of mortality rate, identification of risk factors, and classification of patients played a crucial role in managing COVID-19 patients. Our purpose was to develop machine learning (ML)-based models for the prediction of mortality and severity among patients with COVID-19. Identifying the most important predictors and unraveling their relationships by classification of patients to the low-, moderate- and high-risk groups might guide prioritizing treatment decisions and a better understanding of interactions between factors. A detailed evaluation of patient data is believed to be important since COVID-19 resurgence is underway in many countries. Results The findings of this study revealed that the ML-based statistically inspired modification of the partial least square (SIMPLS) method could predict the in-hospital mortality among COVID-19 patients. The prediction model was developed using 19 predictors including clinical variables, comorbidities, and blood markers with moderate predictability (Q2 = 0.24) to separate survivors and non-survivors. Oxygen saturation level, loss of consciousness, and chronic kidney disease (CKD) were the top mortality predictors. Correlation analysis showed different correlation patterns among predictors for each non-survivor and survivor cohort separately. The main prediction model was verified using other ML-based analyses with a high area under the curve (AUC) (0.81-0.93) and specificity (0.94-0.99). The obtained data revealed that the mortality prediction model can be different for males and females with diverse predictors. Patients were classified into four clusters of mortality risk and identified the patients at the highest risk of mortality, which accentuated the most significant predictors correlating with mortality. Conclusion An ML model for predicting mortality among hospitalized COVID-19 patients was developed considering the interactions between factors that may reduce the complexity of clinical decision-making processes. The most predictive factors related to patient mortality were identified by assessing and classifying patients into different groups based on their sex and mortality risk (low-, moderate-, and high-risk groups).
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Affiliation(s)
| | - Haniyeh Rafiepoor
- Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Kazem Zendehdel
- Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
- Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Monireh Sadat Seyyedsalehi
- Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Azin Nahvijou
- Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Farshad Allameh
- Gastroenterology Ward, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences, Tehran, Iran
| | - Saeid Amanpour
- Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
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Bayraktar M, Tekin E, Kocak MN. How to diagnose COVID-19 in family practice? Usability of complete blood count as a COVID-19 diagnostic tool: a cross-sectional study in Turkey. BMJ Open 2023; 13:e069493. [PMID: 37068894 PMCID: PMC10111184 DOI: 10.1136/bmjopen-2022-069493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/19/2023] Open
Abstract
OBJECTIVE COVID-19 is currently diagnosed in hospital settings. An easy and practical diagnosis of COVID-19 is needed in primary care. For this purpose, the usability of complete blood count in the diagnosis of COVID-19 was investigated. DESIGN Retrospective, cross-sectional study. SETTING Single-centre study in a tertiary university hospital in Erzurum, Turkey. PARTICIPANTS Between March 2020 and February 2021, patients aged 18-70 years who applied to the hospital and underwent both complete blood count and reverse-transcription-PCR tests for COVID-19 were included and compared. Conditions affecting the test parameters (oncological-haematological conditions, chronic diseases, drug usage) were excluded. OUTCOME MEASURE The complete blood count and COVID-19 results of eligible patients identified using diagnostic codes [U07.3 (COVID-19) or Z03.8 (observation for other suspected diseases and conditions)] were investigated. RESULTS Of the 978 patients included, 39.4% (n=385) were positive for COVID-19 and 60.6% (n=593) were negative. The mean age was 41.5±14.5 years, and 53.9% (n=527) were male. COVID-19-positive patients were found to have significantly lower leucocyte, neutrophil, lymphocyte, monocyte, basophil, platelet and immature granulocyte (IG) values (p<0.001). Neutrophil/lymphocyte, neutrophil/monocyte and IG/lymphocyte ratios were also found to be significantly decreased (p<0.001). With logistic regression analysis, low lymphocyte count (OR 0.695; 95% CI 0.597 to 0.809) and low red cell distribution width-coefficient of variation (RDW-CV) (OR 0.887; 95% CI 0.818 to 0.962) were significantly associated with COVID-19 positivity. In receiver operating characteristic analysis, the cut-off values of lymphocyte and RDW-CV were 0.745 and 12.35, respectively. CONCLUSION Although our study was designed retrospectively and reflects regional data, it is important to determine that low lymphocyte count and RDW-CV can be used in the diagnosis of COVID-19 in primary care.
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Affiliation(s)
| | - Erdal Tekin
- Emergency Medicine, Ataturk University, Erzurum, Turkey
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Cadamuro J. Disruption vs. evolution in laboratory medicine. Current challenges and possible strategies, making laboratories and the laboratory specialist profession fit for the future. Clin Chem Lab Med 2023; 61:558-566. [PMID: 36038391 DOI: 10.1515/cclm-2022-0620] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/01/2022] [Indexed: 01/06/2023]
Abstract
Since beginning of medical diagnostics, laboratory specialists have done an amazing job, continuously improving quality, spectrum and speed of laboratory tests, currently contributing to the majority of medical decision making. These improvements are mostly of an incremental evolutionary fashion, meaning improvements of current processes. Sometimes these evolutionary innovations are of a radical fashion, such as the invention of automated analyzers replacing manual testing or the implementation of mass spectrometry, leading to one big performance leap instead of several small ones. In few cases innovations may be of disruptive nature. In laboratory medicine this would be applicable to digitalization of medicine or the decoding of the human genetic material. Currently, laboratory medicine is again facing disruptive innovations or technologies, which need to be adapted to as soon as possible. One of the major disruptive technologies is the increasing availability and medical use of artificial intelligence. It is necessary to rethink the position of the laboratory specialist within healthcare settings and the added value he or she can provide to patient care. The future of the laboratory specialist profession is bright, as it the only medical profession comprising such vast experience in patient diagnostics. However, laboratory specialists need to develop strategies to provide this expertise, by adopting to the quickly evolving technologies and demands. This opinion paper summarizes some of the disruptive technologies as well as strategies to secure and/or improve the quality of diagnostic patient care and the laboratory specialist profession.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
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10
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Lennerz JK, Salgado R, Kim GE, Sirintrapun SJ, Thierauf JC, Singh A, Indave I, Bard A, Weissinger SE, Heher YK, de Baca ME, Cree IA, Bennett S, Carobene A, Ozben T, Ritterhouse LL. Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML. Clin Chem Lab Med 2023; 61:544-557. [PMID: 36696602 DOI: 10.1515/cclm-2022-1151] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.
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Affiliation(s)
- Jochen K Lennerz
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia
| | - Grace E Kim
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | | | - Julia C Thierauf
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
- Department of Otorhinolaryngology, Head and Neck Surgery, German Cancer Research Center (DKFZ), Heidelberg University Hospital and Research Group Molecular Mechanisms of Head and Neck Tumors, Heidelberg, Germany
| | - Ankit Singh
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Iciar Indave
- European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), Lisbon, Portugal
| | - Adam Bard
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Yael K Heher
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Ian A Cree
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France
| | - Shannon Bennett
- Department of Laboratory Medicine and Pathology (DLMP), Mayo Clinic, Rochester, MN, USA
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Tomris Ozben
- Medical Faculty, Dept. of Clinical Biochemistry, Akdeniz University, Antalya, Türkiye
- Medical Faculty, Clinical and Experimental Medicine, Ph.D. Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Lauren L Ritterhouse
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
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11
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Carobene A, Cabitza F, Bernardini S, Gopalan R, Lennerz JK, Weir C, Cadamuro J. Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories. Clin Chem Lab Med 2023; 61:535-543. [PMID: 36327445 DOI: 10.1515/cclm-2022-1030] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. METHODS We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. RESULTS The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. CONCLUSIONS In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.
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Affiliation(s)
- Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federico Cabitza
- IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy.,DISCo, Università Degli Studi di Milano-Bicocca, Milan, Italy
| | - Sergio Bernardini
- Unit of Laboratory Medicine, Tor Vergata University Hospital, Rome, Italy.,Department of Experimental Medicine, University of Tor Vergata, Rome, Italy
| | - Raj Gopalan
- Siemens Healthcare Diagnostics, Siemens Healthineers, Malvern, PA, USA
| | - Jochen K Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | | | - Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
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12
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Santaniello A, Perruolo G, Cristiano S, Agognon AL, Cabaro S, Amato A, Dipineto L, Borrelli L, Formisano P, Fioretti A, Oriente F. SARS-CoV-2 Affects Both Humans and Animals: What Is the Potential Transmission Risk? A Literature Review. Microorganisms 2023; 11:microorganisms11020514. [PMID: 36838479 PMCID: PMC9959838 DOI: 10.3390/microorganisms11020514] [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: 11/30/2022] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
In March 2020, the World Health Organization Department declared the coronavirus (COVID-19) outbreak a global pandemic, as a consequence of its rapid spread on all continents. The COVID-19 pandemic has been not only a health emergency but also a serious general problem as fear of contagion and severe restrictions put economic and social activity on hold in many countries. Considering the close link between human and animal health, COVID-19 might infect wild and companion animals, and spawn dangerous viral mutants that could jump back and pose an ulterior threat to us. The purpose of this review is to provide an overview of the pandemic, with a particular focus on the clinical manifestations in humans and animals, the different diagnosis methods, the potential transmission risks, and their potential direct impact on the human-animal relationship.
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Affiliation(s)
- Antonio Santaniello
- Department of Veterinary Medicine and Animal Production, Federico II University of Naples, 80134 Naples, Italy
- Correspondence: (A.S.); (S.C.); Tel.: +39-081-253-6134 (A.S.)
| | - Giuseppe Perruolo
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy
| | - Serena Cristiano
- Department of Veterinary Medicine and Animal Production, Federico II University of Naples, 80134 Naples, Italy
- Correspondence: (A.S.); (S.C.); Tel.: +39-081-253-6134 (A.S.)
| | - Ayewa Lawoe Agognon
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy
| | - Serena Cabaro
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy
| | - Alessia Amato
- Department of Veterinary Medicine and Animal Production, Federico II University of Naples, 80134 Naples, Italy
| | - Ludovico Dipineto
- Department of Veterinary Medicine and Animal Production, Federico II University of Naples, 80134 Naples, Italy
| | - Luca Borrelli
- Department of Veterinary Medicine and Animal Production, Federico II University of Naples, 80134 Naples, Italy
| | - Pietro Formisano
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy
| | - Alessandro Fioretti
- Department of Veterinary Medicine and Animal Production, Federico II University of Naples, 80134 Naples, Italy
| | - Francesco Oriente
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy
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13
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Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort. Int J Med Inform 2023; 170:104932. [PMID: 36459836 DOI: 10.1016/j.ijmedinf.2022.104932] [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: 08/23/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population. MATERIALS AND METHODS Starting from a public database (National Health and Nutrition Examination Survey, NHANES), representative of the American population with 7265 eligible subjects (control population n = 6828, with Fibroscan values E < 9.7 KPa; target population n = 437 with Fibroscan values E ≥ 9.7 KPa), we set up an SVM algorithm able to discriminate for individuals with liver fibrosis among the general US population. The algorithm set up involved the removal of missing data and a sampling optimization step to managing the data imbalance (only ∼ 5 % of the dataset is the target population). RESULTS For the feature selection, we performed an unbiased analysis, starting from 33 clinical, anthropometric, and biochemical parameters regardless of their previous application as biomarkers of liver diseases. Through PCA analysis, we identified the 26 more significant features and then used them to set up a sampling method on an SVM algorithm. The best sampling technique to manage the data imbalance was found to be oversampling through the SMOTE-NC. For final model validation, we utilized a subset of 300 individuals (150 with liver fibrosis and 150 controls), subtracted from the main dataset prior to sampling. Performances were evaluated on multiple independent runs. CONCLUSIONS We provide proof of concept of an ML clinical decision support tool for liver fibrosis diagnosis in the general US population. Though the presented ML model represents at this stage only a prototype, in the future, it might be implemented and potentially applied to program broad screenings for liver fibrosis.
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14
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Padoan A, Plebani M. Artificial intelligence: is it the right time for clinical laboratories? Clin Chem Lab Med 2022; 60:1859-1861. [DOI: 10.1515/cclm-2022-1015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Andrea Padoan
- Department of Laboratory Medicine , University-Hospital of Padova , Padova , Italy
- Department of Medicine-DIMED , University of Padova , Padova , Italy
| | - Mario Plebani
- Department of Laboratory Medicine , University-Hospital of Padova , Padova , Italy
- Department of Medicine-DIMED , University of Padova , Padova , Italy
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15
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Cadamuro J, Simundic AM. The preanalytical phase – from an instrument-centred to a patient-centred laboratory medicine. Clin Chem Lab Med 2022; 61:732-740. [PMID: 36330758 DOI: 10.1515/cclm-2022-1036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022]
Abstract
Abstract
In order to guarantee patient safety, medical laboratories around the world strive to provide highest quality in the shortest amount of time. A major leap in quality improvement was achieved by aiming to avoid preanalytical errors within the total testing process. Although these errors were first described in the 1970s, it took additional years/decades for large-scale efforts, aiming to improve preanalytical quality by standardisation and/or harmonisation. Initially these initiatives were mostly on the local or national level. Aiming to fill this void, in 2011 the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) working group “Preanalytical Phase” (WG-PRE) was founded. In the 11 years of its existence this group was able to provide several recommendations on various preanalytical topics. One major achievement of the WG-PRE was the development of an European consensus guideline on venous blood collection. In recent years the definition of the preanalytical phase has been extended, including laboratory test selection, thereby opening a huge field for improvement, by implementing strategies to overcome misuse of laboratory testing, ideally with the support of artificial intelligence models. In this narrative review, we discuss important aspects and milestones in the endeavour of preanalytical process improvement, which would not have been possible without the support of the Clinical Chemistry and Laboratory Medicine (CCLM) journal, which was one of the first scientific journals recognising the importance of the preanalytical phase and its impact on laboratory testing quality and ultimately patient safety.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine , Paracelsus Medical University Salzburg , Salzburg , Austria
| | - Ana-Maria Simundic
- Department of Medical Laboratory Diagnostics , University Hospital “Sveti Duh”, University of Zagreb, Faculty of Pharmacy and Biochemistry , Zagreb , Croatia
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16
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Wen X, Leng P, Wang J, Yang G, Zu R, Jia X, Zhang K, Mengesha BA, Huang J, Wang D, Luo H. Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC Bioinformatics 2022; 23:387. [PMID: 36153474 PMCID: PMC9509545 DOI: 10.1186/s12859-022-04926-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/13/2022] [Indexed: 11/29/2022] Open
Abstract
The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.
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17
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Bellini C, Padoan A, Carobene A, Guerranti R. A survey on Artificial Intelligence and Big Data utilisation in Italian clinical laboratories. Clin Chem Lab Med 2022; 60:2017-2026. [PMID: 36067004 DOI: 10.1515/cclm-2022-0680] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/25/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES The Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC) Big Data and Artificial Intelligence (BAI) Working Group promoted a survey to frame the knowledge, skills and technological predisposition in clinical laboratories. METHODS A questionnaire, focussing on digitization, information technology (IT) infrastructures, data accessibility, and BAI projects underway was sent to 1,351 SIBioC participants. The responses were evaluated using SurveyMonkey software and Google Sheets. RESULTS The 227 respondents (17%) from all over Italy (47% of 484 labs), mainly biologists, laboratory physicians and managers, mostly from laboratories of public hospitals, revealed lack of hardware, software and corporate Wi-Fi, and dearth of PCs. Only 25% work daily on clouds, while 65%-including Laboratory Directors-cannot acquire health data from sources other than laboratories. Only 50% of those with access can review a clinical patient's health record, while the other access only to laboratory information. The integration of laboratory data with other health data is mostly incomplete, which limits BAI-type analysis. Many are unaware of integration platforms. Over 90% report pulling data from the Laboratory Information System, with varying degrees of autonomy. Very few have already undertaken BAI projects, frequently relying on IT partnerships. The majority consider BAI as crucial in helping professional judgements, indicating a growing interest. CONCLUSIONS The questionnaire received relevant feedback from SIBioC participants. It highlighted the level of expertise and interest in BAI applications. None of the obstacles stands out more than the others, emphasising the need to all-around work: IT infrastructures, data warehouses, BAI analysis software acquisition, data accessibility and training.
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Affiliation(s)
- Claudia Bellini
- Clinical Chemistry Laboratory Analysis Unit, M isericordia Hospital Grosseto, South East Tuscany USL, Grosseto, Italy
| | - Andrea Padoan
- Department of Medicine-DIMED, University of Padova, Padova, Italy.,Department of Laboratory Medicine, University-Hospital of Padova, Padova, Italy
| | - Anna Carobene
- Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberto Guerranti
- Department of Medical Biotechnologies, University of Siena, Siena, Italy.,Clinical Pathology Unit, Innovation, Experimentation and Clinical and Translational Research Department, University Hospital of Siena, Siena, Italy
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18
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Araújo DC, Veloso AA, Borges KBG, Carvalho MDG. Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil. Int J Med Inform 2022; 165:104835. [PMID: 35908372 PMCID: PMC9327247 DOI: 10.1016/j.ijmedinf.2022.104835] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/17/2022] [Accepted: 07/19/2022] [Indexed: 01/08/2023]
Abstract
Background: Despite an extensive network of primary care availability, Brazil has suffered profoundly during the COVID-19 pandemic, experiencing the greatest sanitary collapse in its history. Thus, it is important to understand phenotype risk factors for SARS-CoV-2 infection severity in the Brazilian population in order to provide novel insights into the pathogenesis of the disease. Objective: This study proposes to predict the risk of COVID-19 death through machine learning, using blood biomarkers data from patients admitted to two large hospitals in Brazil. Methods: We retrospectively collected blood biomarkers data in a 24-h time window from 6,979 patients with COVID-19 confirmed by positive RT-PCR admitted to two large hospitals in Brazil, of whom 291 (4.2%) died and 6,688 (95.8%) were discharged. We then developed a large-scale exploration of risk models to predict the probability of COVID-19 severity, finally choosing the best performing model regarding the average AUROC. To improve generalizability, for each model five different testing scenarios were conducted, including two external validations. Results: We developed a machine learning-based panel composed of parameters extracted from the complete blood count (lymphocytes, MCV, platelets and RDW), in addition to C-Reactive Protein, which yielded an average AUROC of 0.91 ± 0.01 to predict death by COVID-19 confirmed by positive RT-PCR within a 24-h window. Conclusion: Our study suggests that routine laboratory variables could be useful to identify COVID-19 patients under higher risk of death using machine learning. Further studies are needed for validating the model in other populations and contexts, since the natural history of SARS-CoV-2 infection and its consequences on the hematopoietic system and other organs is still quite recent.
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Affiliation(s)
- Daniella Castro Araújo
- Huna, São Paulo, SP, Brazil; Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
| | - Adriano Alonso Veloso
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
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San-Cristobal R, Martín-Hernández R, Ramos-Lopez O, Martinez-Urbistondo D, Micó V, Colmenarejo G, Villares Fernandez P, Daimiel L, Martínez JA. Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort. J Clin Med 2022; 11:jcm11123327. [PMID: 35743398 PMCID: PMC9224935 DOI: 10.3390/jcm11123327] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 01/27/2023] Open
Abstract
The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11−30.54, and Cluster C 14.29 CI: 6.66−34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64−3.01, and Cluster-C 1.71 CI: 1.08−2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.
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Affiliation(s)
- Rodrigo San-Cristobal
- Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, Spain; (V.M.); (J.A.M.)
- Correspondence:
| | - Roberto Martín-Hernández
- Biostatistics & Bioinformatics Unit, Madrid Institute for Advanced Studies (IMDEA) Food, CEI UAM + CSIS, 28049 Madrid, Spain; (R.M.-H.); (G.C.)
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico;
| | - Diego Martinez-Urbistondo
- Internal Medicine Department, Hospital Universitario HM Sanchinarro, 28050 Madrid, Spain; (D.M.-U.); (P.V.F.)
| | - Víctor Micó
- Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, Spain; (V.M.); (J.A.M.)
| | - Gonzalo Colmenarejo
- Biostatistics & Bioinformatics Unit, Madrid Institute for Advanced Studies (IMDEA) Food, CEI UAM + CSIS, 28049 Madrid, Spain; (R.M.-H.); (G.C.)
| | - Paula Villares Fernandez
- Internal Medicine Department, Hospital Universitario HM Sanchinarro, 28050 Madrid, Spain; (D.M.-U.); (P.V.F.)
| | - Lidia Daimiel
- Nutritional Control of the Epigenome Group, IMDEA Food Institute, CEI UAM + CSIC, 28049 Madrid, Spain;
| | - Jose Alfredo Martínez
- Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, Spain; (V.M.); (J.A.M.)
- CIBERobn Physiopathology of Obesity and Nutrition, Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
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