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Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices.
Asteris, Panagiotis G; Kokoris, Styliani; Gavriilaki, Eleni; Tsoukalas, Markos Z; Houpas, Panagiotis; Paneta, Maria; Koutzas, Andreas; Argyropoulos, Theodoros; Alkayem, Nizar Faisal; Armaghani, Danial J; Bardhan, Abidhan; Cavaleri, Liborio; Cao, Maosen; Mansouri, Iman; Mohammed, Ahmed Salih; Samui, Pijush; Gerber, Gloria; Boumpas, Dimitrios T; Tsantes, Argyrios; Terpos, Evangelos; Dimopoulos, Meletios A.
  • Asteris PG; Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
  • Kokoris S; Laboratory of Hematology and Hospital Blood Transfusion Department, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece. Electronic address: skokori@med.uoa.gr.
  • Gavriilaki E; Hematology Department - BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece.
  • Tsoukalas MZ; Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
  • Houpas P; Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
  • Paneta M; Fourth Department of Internal Medicine, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece.
  • Koutzas A; Abbott Laboratories (Hellas) SA, Athens, Greece.
  • Argyropoulos T; Gastroenterology Department, Red Cross General Hospital of Athens, Greece.
  • Alkayem NF; Jiangxi Province Key Laboratory of Environmental Geotechnical Engineering and Hazards Control, Jiangxi University of Science and Technology, Ganzhou 341000, China.
  • Armaghani DJ; Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, Chelyabinsk 454080, Russian Federation.
  • Bardhan A; Civil Engineering Department, National Institute of Technology Patna, Bihar, India.
  • Cavaleri L; Department of Civil, Environmental, Aerospace and Materials Engineering, University of Palermo, Palermo, Italy.
  • Cao M; Jiangxi Province Key Laboratory of Environmental Geotechnical Engineering and Hazards Control, Jiangxi University of Science and Technology, Ganzhou 341000, China.
  • Mansouri I; Department of Civil and Environmental Engineering, Princeton University Princeton, Princeton, NJ 08544, USA.
  • Mohammed AS; Engineering Department, American University of Iraq, Sulaimani, Kurdistan-Region, Iraq.
  • Samui P; Civil Engineering Department, National Institute of Technology Patna, Bihar, India.
  • Gerber G; Hematology Division, Johns Hopkins University, Baltimore, USA.
  • Boumpas DT; "Attikon" University Hospital of Athens, Rheumatology and Clinical Immunology, Medical School, National and Kapodistrian University of Athens, Athens, Attica, Greece.
  • Tsantes A; Laboratory of Hematology and Hospital Blood Transfusion Department, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece.
  • Terpos E; Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece.
  • Dimopoulos MA; Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece.
Clin Immunol ; 246: 109218, 2023 01.
Article in English | MEDLINE | ID: covidwho-2165169
ABSTRACT
We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study Limits: Adult / Humans Language: English Journal: Clin Immunol Journal subject: Allergy and Immunology Year: 2023 Document Type: Article Affiliation country: J.clim.2022.109218

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study Limits: Adult / Humans Language: English Journal: Clin Immunol Journal subject: Allergy and Immunology Year: 2023 Document Type: Article Affiliation country: J.clim.2022.109218