{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:35:32Z","timestamp":1772120132022,"version":"3.50.1"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Oper. Res. Forum"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3\u2009\u00b1\u200918.7\u00a0years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation\u2009&lt;\u200993%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings.<\/jats:p>","DOI":"10.1007\/s43069-022-00191-3","type":"journal-article","created":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T03:02:36Z","timestamp":1677898956000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Identification of Clinical Features Associated with Mortality in COVID-19 Patients"],"prefix":"10.1007","volume":"4","author":[{"given":"Rahimeh","family":"Eskandarian","sequence":"first","affiliation":[]},{"given":"Roohallah","family":"Alizadehsani","sequence":"additional","affiliation":[]},{"given":"Mohaddeseh","family":"Behjati","sequence":"additional","affiliation":[]},{"given":"Mehrdad","family":"Zahmatkesh","sequence":"additional","affiliation":[]},{"given":"Zahra Alizadeh","family":"Sani","sequence":"additional","affiliation":[]},{"given":"Azadeh","family":"Haddadi","sequence":"additional","affiliation":[]},{"given":"Kourosh","family":"Kakhi","sequence":"additional","affiliation":[]},{"given":"Mohamad","family":"Roshanzamir","sequence":"additional","affiliation":[]},{"given":"Afshin","family":"Shoeibi","sequence":"additional","affiliation":[]},{"given":"Sadiq","family":"Hussain","sequence":"additional","affiliation":[]},{"given":"Fahime","family":"Khozeimeh","sequence":"additional","affiliation":[]},{"given":"Mohammad Tayarani","family":"Darbandy","sequence":"additional","affiliation":[]},{"given":"Javad Hassannataj","family":"Joloudari","sequence":"additional","affiliation":[]},{"given":"Reza","family":"Lashgari","sequence":"additional","affiliation":[]},{"given":"Abbas","family":"Khosravi","sequence":"additional","affiliation":[]},{"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[]},{"given":"Sheikh Mohammed Shariful","family":"Islam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"issue":"7798","key":"191_CR1","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1038\/s41586-020-2012-7","volume":"579","author":"P Zhou","year":"2020","unstructured":"Zhou P, Yang X-L, Wang X-G, Hu B, Zhang L, Zhang W et al (2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579(7798):270\u2013273","journal-title":"Nature"},{"issue":"1","key":"191_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3109\/10408360903507283","volume":"47","author":"D Goldberg","year":"2010","unstructured":"Goldberg D (2010) Critical reviews in clinical laboratory sciences. Crit Rev Clin Lab Sci 47(1):1\u20134","journal-title":"Crit Rev Clin Lab Sci"},{"key":"191_CR3","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102622","volume":"68","author":"D Sharifrazi","year":"2021","unstructured":"Sharifrazi D, Alizadehsani R, Roshanzamir M, Joloudari JH, Shoeibi A, Jafari M et al (2021) Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomed Signal Process Control 68:102622","journal-title":"Biomed Signal Process Control"},{"key":"191_CR4","doi-asserted-by":"crossref","unstructured":"Alizadehsani R, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Gorriz JM et al (2021) Uncertainty-aware semi-supervised method using large unlabeled and limited labeled COVID-19 data. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 17(3s):1\u201324","DOI":"10.1145\/3462635"},{"key":"191_CR5","unstructured":"Joloudari JH, Azizi F, Nodehi I, Nematollahi MA, Kamrannejhad F, Mosavi A et al (2021) DNN-GFE: a deep neural network model combined with global feature extractor for COVID-19 diagnosis based on CT scan images. EasyChair; Report No.: 2516\u20132314"},{"key":"191_CR6","doi-asserted-by":"crossref","DOI":"10.1016\/j.rinp.2021.104495","volume":"27","author":"N Ayoobi","year":"2021","unstructured":"Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H et al (2021) Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results in Physics 27:104495","journal-title":"Results in Physics"},{"issue":"1","key":"191_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-93543-8","volume":"11","author":"F Khozeimeh","year":"2021","unstructured":"Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R et al (2021) Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci Rep 11(1):1\u201318","journal-title":"Sci Rep"},{"key":"191_CR8","unstructured":"Shoeibi A, Khodatars M, Alizadehsani R, Ghassemi N, Jafari M, Moridian P et al (2020) Automated detection and forecasting of COVID-19 using deep learning techniques: a review. Preprint at\u00a0arXiv:200710785"},{"key":"191_CR9","unstructured":"Nasab RZ, Ghamsari MRE, Argha A, Macphillamy C, Beheshti A, Alizadehsani R et al (2022) Deep learning in spatially resolved transcriptomics: a comprehensive technical view. Preprint at arXiv:221004453"},{"issue":"11","key":"191_CR10","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1001\/jama.2020.1585","volume":"323","author":"D Wang","year":"2020","unstructured":"Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J et al (2020) Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus\u2013infected pneumonia in Wuhan. China Jama 323(11):1061\u20131069","journal-title":"China Jama"},{"issue":"7","key":"191_CR11","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1515\/cclm-2020-0240","volume":"58","author":"G Lippi","year":"2020","unstructured":"Lippi G, Plebani M (2020) The critical role of laboratory medicine during coronavirus disease 2019 (COVID-19) and other viral outbreaks. Clin Chem Lab Med (CCLM) 58(7):1063\u20131069","journal-title":"Clin Chem Lab Med (CCLM)"},{"issue":"8","key":"191_CR12","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1093\/cid\/ciaa674","volume":"71","author":"A Bhargava","year":"2020","unstructured":"Bhargava A, Fukushima EA, Levine M, Zhao W, Tanveer F, Szpunar SM et al (2020) Predictors for severe COVID-19 infection. Clin Infect Dis 71(8):1962\u20131968","journal-title":"Clin Infect Dis"},{"key":"191_CR13","doi-asserted-by":"crossref","unstructured":"Hamming I, Timens W, Bulthuis M, Lely A, Navis G, van Goor H (2004) Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J Pathol 203(2):631\u20137","DOI":"10.1002\/path.1570"},{"key":"191_CR14","doi-asserted-by":"crossref","DOI":"10.1016\/j.lfs.2020.117839","volume":"255","author":"K Renu","year":"2020","unstructured":"Renu K, Prasanna PL, Valsala GA (2020) Coronaviruses pathogenesis, comorbidities and multi-organ damage \u2013 a review. Life Sci 255:117839","journal-title":"Life Sci"},{"issue":"5","key":"191_CR15","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1055\/s-0040-1709650","volume":"120","author":"G Lippi","year":"2020","unstructured":"Lippi G, Favaloro EJ (2020) D-dimer is associated with severity of coronavirus disease 2019: a pooled analysis. Thromb Haemost 120(5):876\u2013878","journal-title":"Thromb Haemost"},{"issue":"8","key":"191_CR16","doi-asserted-by":"crossref","first-page":"e435","DOI":"10.1016\/S2589-7500(20)30142-4","volume":"2","author":"S Whitelaw","year":"2020","unstructured":"Whitelaw S, Mamas MA, Topol E, Van Spall HGC (2020) Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digit Health 2(8):e435\u2013e440","journal-title":"Lancet Digit Health"},{"key":"191_CR17","doi-asserted-by":"crossref","unstructured":"Alizadehsani R, Eskandarian R, Behjati M, Zahmatkesh M, Roshanzamir M, Izadi NH et al (2022) Factors associated with mortality in hospitalized cardiovascular disease patients infected with COVID-19. Immun Inflamm Dis 10(3):e561","DOI":"10.1002\/iid3.561"},{"issue":"1","key":"191_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-022-05052-x","volume":"12","author":"H Asgharnezhad","year":"2022","unstructured":"Asgharnezhad H, Shamsi A, Alizadehsani R, Khosravi A, Nahavandi S, Sani ZA et al (2022) Objective evaluation of deep uncertainty predictions for COVID-19 detection. Sci Rep 12(1):1\u201311","journal-title":"Sci Rep"},{"issue":"1","key":"191_CR19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.4018\/jkdb.2012010104","volume":"3","author":"R Alizadehsani","year":"2012","unstructured":"Alizadehsani R, Hosseini MJ, Boghrati R, Ghandeharioun A, Khozeimeh F, Sani ZA (2012) Exerting cost-sensitive and feature creation algorithms for coronary artery disease diagnosis. Int J Knowl Discov Bioinform (IJKDB) 3(1):59\u201379","journal-title":"Int J Knowl Discov Bioinform (IJKDB)"},{"key":"191_CR20","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106541","volume":"213","author":"D Nahavandi","year":"2022","unstructured":"Nahavandi D, Alizadehsani R, Khosravi A, Acharya UR (2022) Application of artificial intelligence in wearable devices: Opportunities and challenges. Comput Methods Programs Biomed 213:106541","journal-title":"Comput Methods Programs Biomed"},{"key":"191_CR21","doi-asserted-by":"crossref","unstructured":"Sharifrazi D, Alizadehsani R, Joloudari JH, Shamshirband S, Hussain S, Sani ZA et al (2020) CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering. Math Biosci Eng 19(3):2381\u20132402","DOI":"10.20944\/preprints202007.0650.v1"},{"key":"191_CR22","unstructured":"Joloudari JH, Alizadehsani R, Nodehi I, Mojrian S, Fazl F, Shirkharkolaie SK et al (2022) Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review. Preprint at arXiv:220312315"},{"key":"191_CR23","doi-asserted-by":"crossref","unstructured":"Joloudari JH, Mojrian S, Nodehi I, Mashmool A, Zadegan ZK, Shirkharkolaie SK et al (2022) Application of artificial intelligence techniques for automated detection of myocardial infarction: a review.\u00a0Physiol Meas 43(8):08TR01","DOI":"10.1088\/1361-6579\/ac7fd9"},{"key":"191_CR24","doi-asserted-by":"crossref","unstructured":"Joloudari JH, Saadatfar H, GhasemiGol M, Alizadehsani R, Sani ZA, Hasanzadeh F et al (2022) FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model. Preprint at arXiv:220204645","DOI":"10.3934\/mbe.2022167"},{"key":"191_CR25","unstructured":"Halpern NA, Tan KS. Society of critical care medicine. US ICU availability for COVID-19. https:\/\/sccm.org\/getattachment\/Blog\/March-2020\/United-States-Resource-Availability-for-COVID-19\/United-States-Resource-Availability-for-COVID-19.pdf?lang=en-U. Accessed 22 Dec\u00a02020"},{"key":"191_CR26","doi-asserted-by":"crossref","unstructured":"Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R et al (2022) An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: methods, challenges, and future works. Comput Biol Med 106053","DOI":"10.1016\/j.compbiomed.2022.106053"},{"key":"191_CR27","unstructured":"Dick S. News National. Singapore\u2019s coronavirus temperature screening and tracking are leading the way. https:\/\/thenewdaily.com.au\/news\/national\/2020\/03\/19\/singapore-coronavirus-temperature-scans\/. Accessed 22 Dec\u00a02020"},{"issue":"4","key":"191_CR28","doi-asserted-by":"crossref","first-page":"e166","DOI":"10.1016\/S2589-7500(20)30054-6","volume":"2","author":"B McCall","year":"2020","unstructured":"McCall B (2020) COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health 2(4):e166\u2013e167","journal-title":"Lancet Digit Health"},{"key":"191_CR29","unstructured":"Jafari M, Shoeibi A, Ghassemi N, Heras J, Khosravi A, Ling SH et al (2022) Automatic diagnosis of myocarditis disease in cardiac MRI modality using deep transformers and explainable artificial intelligence. Preprint at arXiv:221014611"},{"key":"191_CR30","doi-asserted-by":"crossref","unstructured":"Moridian P, Shoeibi A, Khodatars M, Jafari M, Pachori RB, Khadem A et al (2022) Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: methods, challenges, and future works. Wiley Interdiscip Rev: Data Min Knowl Discov e1478","DOI":"10.1002\/widm.1478"},{"issue":"1","key":"191_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-022-15374-5","volume":"12","author":"F Khozeimeh","year":"2022","unstructured":"Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R et al (2022) RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance. Sci Rep 12(1):1\u201312","journal-title":"Sci Rep"},{"key":"191_CR32","doi-asserted-by":"crossref","unstructured":"Kakhi K, Alizadehsani R, Kabir HD, Khosravi A, Nahavandi S, Acharya UR (2022) The internet of medical things and artificial intelligence: trends, challenges, and opportunities. Biocybern Biomed Eng\u00a042(3):749\u2013771","DOI":"10.1016\/j.bbe.2022.05.008"},{"issue":"7","key":"191_CR33","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.2337\/dc20-0598","volume":"43","author":"Q Shi","year":"2020","unstructured":"Shi Q, Zhang X, Jiang F, Zhang X, Hu N, Bimu C et al (2020) Clinical characteristics and risk factors for mortality of COVID-19 patients with diabetes in Wuhan, China: a two-center, retrospective study. Diabetes Care 43(7):1382\u20131391","journal-title":"Diabetes Care"},{"key":"191_CR34","doi-asserted-by":"crossref","unstructured":"Yadaw AS, Li Y-c, Bose S, Iyengar R, Bunyavanich S, Pandey G (2020) Clinical features of COVID-19 mortality: development and validation of a clinical prediction model. Lancet Digit Health 2(10):e516-e25","DOI":"10.1016\/S2589-7500(20)30217-X"},{"key":"191_CR35","unstructured":"Joloudari JH, Hussain S, Nematollahi MA, Bagheri R, Fazl F, Alizadehsani R et al (2022) BERT-deep CNN: state-of-the-art for sentiment analysis of COVID-19 tweets. Preprint at arXiv:221109733"},{"key":"191_CR36","unstructured":"Roshanzamir M, Alizadehsani R, Roshanzamir M, Shoeibi A, Gorriz JM, Khosrave A et al (2021) What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors. Preprint at\u00a0arXiv:210905457"},{"key":"191_CR37","doi-asserted-by":"publisher","unstructured":"Sharifrazi D, Alizadehsani R, Hoseini Izadi N, Roshanzamir M, Shoeibi A, Khozeimeh F et al (2021) Hypertrophic cardiomyopathy diagnosis based on cardiovascular magnetic resonance using deep learning techniques.\u00a0Lancet. https:\/\/doi.org\/10.2139\/ssrn.3855445","DOI":"10.2139\/ssrn.3855445"},{"key":"191_CR38","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579\u20132605"},{"issue":"2","key":"191_CR39","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1080\/00401706.1960.10489894","volume":"2","author":"RG Steel","year":"1960","unstructured":"Steel RG (1960) A rank sum test for comparing all pairs of treatments. Technometrics 2(2):197\u2013207","journal-title":"Technometrics"},{"issue":"1","key":"191_CR40","first-page":"58","volume":"25","author":"LM Connelly","year":"2016","unstructured":"Connelly LM (2016) Fisher\u2019s exact test. Medsurg Nurs 25(1):58\u201360","journal-title":"Medsurg Nurs"},{"key":"191_CR41","doi-asserted-by":"crossref","unstructured":"Iqbal MS, Ahmad W, Alizadehsani R, Hussain S, Rehman R (eds) (2022) Breast Cancer Dataset, Classification and Detection Using Deep Learning. Healthcare: MDPI\u00a0Healthcare: MDPI 10(12):2395","DOI":"10.3390\/healthcare10122395"},{"key":"191_CR42","doi-asserted-by":"crossref","unstructured":"Khalili H, Rismani M, Nematollahi MA, Masoudi MS, Asadollahi A, Taheri R et al (2022) Survival prediction in traumatic brain injury patients using machine learning algorithms.\u00a0Sci Rep 13:960","DOI":"10.21203\/rs.3.rs-1916615\/v1"},{"key":"191_CR43","doi-asserted-by":"crossref","unstructured":"Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R et al (2022) An overview of artificial intelligence techniques for diagnosis of schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 105554","DOI":"10.1016\/j.compbiomed.2022.105554"},{"issue":"5","key":"191_CR44","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1016\/j.clnu.2022.03.024","volume":"41","author":"N Kiss","year":"2022","unstructured":"Kiss N, Steer B, de van der Schueren M, Loeliger J, Alizadehsani R, Edbrooke L et al (2022) Comparison of the prevalence of 21 GLIM phenotypic and etiologic criteria combinations and association with 30-day outcomes in people with cancer: a retrospective observational study. Clin Nutr 41(5):1102\u201311","journal-title":"Clin Nutr"},{"key":"191_CR45","doi-asserted-by":"crossref","unstructured":"Shoeibi A, Sadeghi D, Moridian P, Ghassemi N, Heras J, Alizadehsani R et al (2021) Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models. Front Neuroinform 15:777977","DOI":"10.3389\/fninf.2021.777977"},{"issue":"10229","key":"191_CR46","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/S0140-6736(20)30566-3","volume":"395","author":"F Zhou","year":"2020","unstructured":"Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z et al (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395(10229):1054\u20131062","journal-title":"Lancet"},{"issue":"10","key":"191_CR47","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1002\/oby.22941","volume":"28","author":"NN Pettit","year":"2020","unstructured":"Pettit NN, MacKenzie EL, Ridgway JP, Pursell K, Ash D, Patel B et al (2020) Obesity is associated with increased risk for mortality among hospitalized patients with COVID-19. Obesity 28(10):1806\u20131810","journal-title":"Obesity"},{"issue":"1","key":"191_CR48","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.chest.2020.04.010","volume":"158","author":"R Chen","year":"2020","unstructured":"Chen R, Liang W, Jiang M, Guan W, Zhan C, Wang T et al (2020) Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019 from a nationwide analysis in China. Chest 158(1):97\u2013105","journal-title":"Chest"},{"key":"191_CR49","doi-asserted-by":"crossref","unstructured":"Iftimie S, L\u00f3pez-Azcona AF, Vicente-Miralles M, Descarrega-Reina R, Hern\u00e1ndez-Aguilera A, Riu F et al (2020) Risk factors associated with mortality in hospitalized patients with SARS-CoV-2 infection. A prospective, longitudinal, unicenter study in Reus, Spain. PloS one 15(9):e0234452","DOI":"10.1371\/journal.pone.0234452"},{"issue":"7","key":"191_CR50","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.jamda.2020.06.008","volume":"21","author":"R De Smet","year":"2020","unstructured":"De Smet R, Mellaerts B, Vandewinckele H, Lybeert P, Frans E, Ombelet S et al (2020) Frailty and mortality in hospitalized older adults with COVID-19: retrospective observational study. J Am Med Dir Assoc 21(7):928\u201332.e1","journal-title":"J Am Med Dir Assoc"},{"issue":"6","key":"191_CR51","first-page":"E19","volume":"68","author":"H Sun","year":"2020","unstructured":"Sun H, Ning R, Tao Y, Yu C, Deng X, Zhao C et al (2020) Risk factors for mortality in 244 older adults with COVID-19 in Wuhan, China: a retrospective study. J Am Geriatr Soc 68(6):E19\u2013E23","journal-title":"J Am Geriatr Soc"},{"issue":"1","key":"191_CR52","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.jaci.2020.04.006","volume":"146","author":"X Li","year":"2020","unstructured":"Li X, Xu S, Yu M, Wang K, Tao Y, Zhou Y et al (2020) Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol 146(1):110\u2013118","journal-title":"J Allergy Clin Immunol"},{"key":"191_CR53","doi-asserted-by":"publisher","unstructured":"Nasirzadeh A, Jafarzadeh Maivan MH., Bazeli J, Hajavi J, Yavarmanesh N, Zahedi M, Abounoori M, Razavi A, Maddah MM, Mortazavi P, Moradi M, Heidarzadeh S, Mardaneh J, Shoeibi A, Alizadehsani R, Islam SMS (2021) Inhibiting IL-6 during cytokine storm in COVID-19: Potential role of natural products. Preprints, pp 1\u201321, 2021060131. https:\/\/doi.org\/10.20944\/preprints202106.0131.v1","DOI":"10.20944\/preprints202106.0131.v1"},{"key":"191_CR54","doi-asserted-by":"publisher","unstructured":"Mardaneh J, Nasirzadeh A, Bazeli J, Hajavi J, Zahedi M, Abounoori M, Razavi A, Maddah MM, Mortazavi P, Moradi M, Salehi F, Heidarzadeh S, Jafarzadeh Maivan H, Shoeibi A, Alizadehsani R, Islam SMS (2021) Inhibiting NF-?B during cytokine storm in COVID-19: Potential role of natural products as a promising therapeutic approach. Preprints pp, 1\u201335, 2021060130. https:\/\/doi.org\/10.20944\/preprints202106.0130.v1","DOI":"10.20944\/preprints202106.0130.v1"},{"key":"191_CR55","doi-asserted-by":"crossref","unstructured":"Chen R, Liang W, Jiang M, Guan W, Zhan C, Wang T et al (2020) Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019\u00a0from a nationwide analysis in China. Chest\u00a0158(1):97\u2013105","DOI":"10.1016\/j.chest.2020.04.010"},{"issue":"3","key":"191_CR56","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.4269\/ajtmh.20-0483","volume":"103","author":"RdCM Soares","year":"2020","unstructured":"Soares RdCM, Mattos LR, Raposo LM (2020) Risk factors for hospitalization and mortality due to COVID-19 in Esp\u00edrito Santo State\u00a0Brazil. Am J Trop Med Hyg 103(3):1184\u201390","journal-title":"Am J Trop Med Hyg"},{"issue":"5","key":"191_CR57","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1007\/s00134-020-05991-x","volume":"46","author":"Q Ruan","year":"2020","unstructured":"Ruan Q, Yang K, Wang W, Jiang L, Song J (2020) Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan. China Intensive Care Medicine 46(5):846\u2013848","journal-title":"China Intensive Care Medicine"},{"key":"191_CR58","doi-asserted-by":"crossref","unstructured":"Iftime S, L\u00f3pez-Azcona AF, Vicente-Miralles M, Descarrega-Reina R, Hern\u00e1ndez-Aguilera A, Riu F et al (2020) Risk factors associated with mortality in hospitalized patients with SARS-CoV-2 infection. A prospective, longitudinal, unicenter study in Reus, Spain. bioRxiv","DOI":"10.1101\/2020.05.29.122986"},{"issue":"12","key":"191_CR59","doi-asserted-by":"crossref","first-page":"3188","DOI":"10.1093\/cid\/ciaa920","volume":"71","author":"J Chen","year":"2020","unstructured":"Chen J, Bai H, Liu J, Chen G, Liao Q, Yang J et al (2020) Distinct clinical characteristics and risk factors for mortality in female inpatients with coronavirus disease 2019 (COVID-19): a sex-stratified, large-scale cohort study in Wuhan. China Clinical Infectious Diseases 71(12):3188\u20133195","journal-title":"China Clinical Infectious Diseases"},{"key":"191_CR60","doi-asserted-by":"crossref","unstructured":"Ciardullo S, Zerbini F, Perra S, Muraca E, Cannistraci R, Lauriola M et al (2020) Impact of diabetes on COVID-19-related in-hospital mortality: a retrospective study from Northern Italy.\u00a0J Endocrinol 44:843\u2013885","DOI":"10.1007\/s40618-020-01382-7"},{"issue":"4","key":"191_CR61","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1002\/agm2.12126","volume":"3","author":"MJ Rawle","year":"2020","unstructured":"Rawle MJ, Bertfield DL, Brill SE (2020) Atypical presentations of COVID-19 in care home residents presenting to secondary care: a UK single centre study. Aging Medicine 3(4):237\u2013244","journal-title":"Aging Medicine"},{"issue":"6","key":"191_CR62","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1681\/ASN.2020030276","volume":"31","author":"G Pei","year":"2020","unstructured":"Pei G, Zhang Z, Peng J, Liu L, Zhang C, Yu C et al (2020) Renal involvement and early prognosis in patients with COVID-19 pneumonia. J Am Soc Nephrol 31(6):1157","journal-title":"J Am Soc Nephrol"},{"issue":"4","key":"191_CR63","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1002\/jmv.26699","volume":"93","author":"R Alizadehsani","year":"2021","unstructured":"Alizadehsani R, Alizadeh Sani Z, Behjati M, Roshanzamir Z, Hussain S, Abedini N et al (2021) Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients. J Med Virol 93(4):2307\u20132320","journal-title":"J Med Virol"},{"issue":"10","key":"191_CR64","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/S1470-2045(20)30442-3","volume":"21","author":"LYW Lee","year":"2020","unstructured":"Lee LYW, Cazier J-B, Starkey T, Briggs SEW, Arnold R, Bisht V et al (2020) COVID-19 prevalence and mortality in patients with cancer and the effect of primary tumour subtype and patient demographics: a prospective cohort study. Lancet Oncol 21(10):1309\u20131316","journal-title":"Lancet Oncol"},{"issue":"7","key":"191_CR65","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1158\/2159-8290.CD-20-0516","volume":"10","author":"V Mehta","year":"2020","unstructured":"Mehta V, Goel S, Kabarriti R, Cole D, Goldfinger M, Acuna-Villaorduna A et al (2020) Case fatality rate of cancer patients with COVID-19 in a New York Hospital System. Cancer Discov 10(7):935\u2013941","journal-title":"Cancer Discov"},{"issue":"6","key":"191_CR66","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1158\/2159-8290.CD-20-0422","volume":"10","author":"M Dai","year":"2020","unstructured":"Dai M, Liu D, Liu M, Zhou F, Li G, Chen Z et al (2020) Patients with cancer appear more vulnerable to SARS-CoV-2: a multicenter study during the COVID-19 outbreak. Cancer Discov 10(6):783\u2013791","journal-title":"Cancer Discov"},{"issue":"5","key":"191_CR67","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.ccell.2020.09.007","volume":"38","author":"LF Westblade","year":"2020","unstructured":"Westblade LF, Brar G, Pinheiro LC, Paidoussis D, Rajan M, Martin P et al (2020) SARS-CoV-2 viral load predicts mortality in patients with and without cancer who are hospitalized with COVID-19. Cancer Cell 38(5):661\u201371.e2","journal-title":"Cancer Cell"},{"key":"191_CR68","doi-asserted-by":"crossref","unstructured":"de Melo AC, Thuler LC, da Silva JL, de Albuquerque LZ, Pecego AC, Rodrigues LdO et al (2020) Cancer inpatients with COVID-19: a report from the Brazilian National Cancer Institute. PloS one 15(10):e0241261","DOI":"10.1371\/journal.pone.0241261"},{"issue":"2","key":"191_CR69","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s00277-020-04328-4","volume":"100","author":"MM R\u00fcthrich","year":"2021","unstructured":"R\u00fcthrich MM, Giessen-Jung C, Borgmann S, Classen AY, Dolff S, Gr\u00fcner B et al (2021) COVID-19 in cancer patients: clinical characteristics and outcome\u2014an analysis of the LEOSS registry. Ann Hematol 100(2):383\u2013393","journal-title":"Ann Hematol"},{"key":"191_CR70","doi-asserted-by":"crossref","unstructured":"Alizadehsani R, Khosravi A, Roshanzamir M, Abdar M, Sarrafzadegan N, Shafie D et al (2020) Coronary artery disease detection using artificial intelligence techniques: a survey of trends, geographical differences and diagnostic features 1991\u20132020.\u00a0Comput Biol Med\u00a0104095","DOI":"10.1016\/j.compbiomed.2020.104095"},{"issue":"2","key":"191_CR71","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1002\/jmv.26389","volume":"93","author":"RK Reddy","year":"2021","unstructured":"Reddy RK, Charles WN, Sklavounos A, Dutt A, Seed PT, Khajuria A (2021) The effect of smoking on COVID-19 severity: a systematic review and meta-analysis. J Med Virol 93(2):1045\u20131056","journal-title":"J Med Virol"},{"key":"191_CR72","doi-asserted-by":"crossref","unstructured":"Magfira N, Helda H (2020) Correlation between adult tobacco smoking prevalence and mortality of Coronavirus Disease-19 across the world. Comput Biol Med 128:104095","DOI":"10.1101\/2020.12.01.20241596"},{"key":"191_CR73","doi-asserted-by":"crossref","unstructured":"Mendy A, Apewokin S, Wells AA, Morrow AL (2020) Factors associated with hospitalization and disease severity in a racially and ethnically diverse population of COVID-19 patients. medRxiv. 2020.06.25.20137323","DOI":"10.1101\/2020.06.25.20137323"},{"key":"191_CR74","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2020.100490","volume":"26","author":"TE Poloni","year":"2020","unstructured":"Poloni TE, Carlos AF, Cairati M, Cutaia C, Medici V, Marelli E et al (2020) Prevalence and prognostic value of delirium as the initial presentation of COVID-19 in the elderly with dementia: an Italian retrospective study. EClinicalMedicine 26:100490","journal-title":"EClinicalMedicine"},{"issue":"11","key":"191_CR75","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1164\/rccm.202005-1885OC","volume":"202","author":"S Hue","year":"2020","unstructured":"Hue S, Beldi-Ferchiou A, Bendib I, Surenaud M, Fourati S, Frapard T et al (2020) Uncontrolled innate and impaired adaptive immune responses in patients with COVID-19 acute respiratory distress syndrome. Am J Respir Crit Care Med 202(11):1509\u20131519","journal-title":"Am J Respir Crit Care Med"},{"key":"191_CR76","doi-asserted-by":"crossref","unstructured":"Chen Q, Zheng Z, Zhang C (2020) Clinical characteristics of 145 patients with corona virus disease 2019 (COVID-19) in Taizhou. Infection 48(4):543\u2013551","DOI":"10.1007\/s15010-020-01432-5"},{"key":"191_CR77","doi-asserted-by":"crossref","unstructured":"Zhang JJ, Dong X, Cao YY, Yuan YD, Yang YB, Yan YQ et al (2020) Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy 75(7):1730\u201341","DOI":"10.1111\/all.14238"},{"issue":"9","key":"191_CR78","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0239519","volume":"15","author":"F Homayounieh","year":"2020","unstructured":"Homayounieh F, Zhang EW, Babaei R, Karimi Mobin H, Sharifian M, Mohseni I et al (2020) Clinical and imaging features predict mortality in COVID-19 infection in Iran. PLoS ONE 15(9):e0239519","journal-title":"PLoS ONE"},{"issue":"1","key":"191_CR79","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/s13027-020-00339-y","volume":"15","author":"M Sorouri","year":"2020","unstructured":"Sorouri M, Kasaeian A, Mojtabavi H, Radmard AR, Kolahdoozan S, Anushiravani A et al (2020) Clinical characteristics, outcomes, and risk factors for mortality in hospitalized patients with COVID-19 and cancer history: a propensity score-matched study. Infect. Agents Cancer 15(1):74","journal-title":"Infect. Agents Cancer"}],"container-title":["Operations Research Forum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43069-022-00191-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43069-022-00191-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43069-022-00191-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T03:11:55Z","timestamp":1682392315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43069-022-00191-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,4]]},"references-count":79,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["191"],"URL":"https:\/\/doi.org\/10.1007\/s43069-022-00191-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.04.19.21255715","asserted-by":"object"}]},"ISSN":["2662-2556"],"issn-type":[{"value":"2662-2556","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,4]]},"assertion":[{"value":"13 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Semnan Hospital Ethics Committee.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"All the patients completed written consent forms before their enrolment in the data collection procedure.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The signed consent to publish\u00a0gives the publisher the permission of the author to publish the work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"16"}}