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Bohn M, Lippi G, Horvath A, et al. Molecular, serological, and biochemical diagnosis and monitoring of COVID-19: IFCC taskforce evaluation of the latest evidence. Clin Chem and Lab Med (CCLM). 2020;58(7):1037–1052.BohnMLippiGHorvathAMolecular, serological, and biochemical diagnosis and monitoring of COVID-19: IFCC taskforce evaluation of the latest evidence202058710371052Search in Google Scholar
U.S. Food and Drug Administration. Policy for coronavirus disease-2019 tests during the public health emergency (revised). https://www.fda.gov/regulatory-information/search-fda-guidance-documents. Accessed: 6 May 2023.U.S. Food and Drug Administrationhttps://www.fda.gov/regulatory-information/search-fda-guidance-documents. Accessed: 6 May 2023.Search in Google Scholar
World Health Organization WHO coronavirus 2019 (COVID-19) pandemic. (2021). Accessed: 24/06/2023: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.World Health Organization2021Accessed: 24/06/2023: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.Search in Google Scholar
Peng Y, Xu B, Sun B, Han G, Zhou YH. Importance of timely management of patients in reducing fatality rate of coronavirus disease 2019. J Infect Public Health. 2020;13:890–2.PengYXuBSunBHanGZhouYHImportance of timely management of patients in reducing fatality rate of coronavirus disease 20192020138902Search in Google Scholar
Siow WT, Liew MF, Shrestha BR, Muchtar F, See KC. Managing COVID-19 in resource-limited settings: critical care considerations. Crit Care. 2020;24(1):167–9.SiowWTLiewMFShresthaBRMuchtarFSeeKCManaging COVID-19 in resource-limited settings: critical care considerations20202411679Search in Google Scholar
Barbas CSV, Mazza BF. Is It Possible to Predict Respiratory Evolution in COVID-19 Patients? Respiration. 2022;101(7):621–623.BarbasCSVMazzaBFIs It Possible to Predict Respiratory Evolution in COVID-19 Patients?20221017621623Search in Google Scholar
Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020; 369:m1328.WynantsLVan CalsterBCollinsGSPrediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal2020369m1328Search in Google Scholar
Aziz AB, Raqib R, Khan WA, Rahman M, Haque R, Alam M, Zaman K, Ross AG. Integrated control of COVID-19 in resource-poor countries. Int J Infect Dis. 2020;101:98–101.AzizABRaqibRKhanWARahmanMHaqueRAlamMZamanKRossAGIntegrated control of COVID-19 in resource-poor countries202010198101Search in Google Scholar
Zhang K, Jiang X, Madadi M, Chen L, Savitz S, Shams S, DB Net. A novel deep learning framework for mechanical ventilation prediction using electronic health records. In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Gainesville, FL, USA, 1–4 August 2021.ZhangKJiangXMadadiMChenLSavitzSShamsSDB NetInProceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health InformaticsGainesville, FL, USA1–4 August 2021Search in Google Scholar
Pitamberwale A, Mahmood T, Ansari A, et al. Biochemical Parameters as Prognostic Markers in Severely Ill COVID-19 Patients. Cureus. 2022;14(8): e28594.PitamberwaleAMahmoodTAnsariABiochemical Parameters as Prognostic Markers in Severely Ill COVID-19 Patients2022148e28594Search in Google Scholar
Bendavid I, Statlender L, Shvartser L, Teppler S, Azullay R, Sapir R, Singer P. A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19. Sci Rep. 2022 Jun 22;12(1):10573.BendavidIStatlenderLShvartserLTepplerSAzullayRSapirRSingerPA novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-192022Jun2212110573Search in Google Scholar
Gupta M, Agrawal N, Sharma SK, Ansari AK, Mahmood T, Singh L: Study of utility of basic arterial blood gas parameters and lactate as prognostic markers in patients with severe dengue. Cureus. 2022;14:e24682.GuptaMAgrawalNSharmaSKAnsariAKMahmoodTSinghLStudy of utility of basic arterial blood gas parameters and lactate as prognostic markers in patients with severe dengue202214e24682Search in Google Scholar
Booth A, Reed AB, Ponzo S, Yassaee A, Aral M, Plans D, Labrique A, Mohan D. Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis. PLoS One. 2021 Mar 4;16(3):e0247461.BoothAReedABPonzoSYassaeeAAralMPlansDLabriqueAMohanDPopulation risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis2021Mar4163e0247461Search in Google Scholar
Somers VK, Kara T, Xie J. Progressive Hypoxia: A Pivotal Pathophysiologic Mechanism of COVID-19 Pneumonia. Mayo Clin Proc. 2020 Nov;95(11):2339–2342.SomersVKKaraTXieJProgressive Hypoxia: A Pivotal Pathophysiologic Mechanism of COVID-19 Pneumonia2020Nov951123392342Search in Google Scholar
Ferro A, Kotecha S, Auzinger G, Yeung E, Fan K. Systematic review and meta-analysis of tracheostomy outcomes in COVID-19 patients. Br J Oral Maxillofac Surg. 2021 Nov;59(9):1013–1023.FerroAKotechaSAuzingerGYeungEFanKSystematic review and meta-analysis of tracheostomy outcomes in COVID-19 patients2021Nov59910131023Search in Google Scholar
Mat Nor MB, Md Ralib A: Procalcitonin clearance for early prediction of survival in critically ill patients with severe sepsis. Crit Care Res Pract. 2014;2014:819034.Mat NorMBMd RalibAProcalcitonin clearance for early prediction of survival in critically ill patients with severe sepsis20142014819034Search in Google Scholar
Ahmed S, Jafri L, Hoodbhoy Z, Siddiqui I. Prognostic Value of Serum Procalcitonin in COVID-19 Patients: A Systematic Review. Indian J Crit Care Med. 2021 Jan;25(1):77–84AhmedSJafriLHoodbhoyZSiddiquiIPrognostic Value of Serum Procalcitonin in COVID-19 Patients: A Systematic Review2021Jan2517784Search in Google Scholar
Twe CW, Khoo DKY, Law KB, et al. The role of procalcitonin in predicting risk of mechanical ventilation and mortality among moderate to severe COVID-19 patients. BMC Infect Dis. 2022 Apr 15;22(1):378.TweCWKhooDKYLawKBThe role of procalcitonin in predicting risk of mechanical ventilation and mortality among moderate to severe COVID-19 patients2022Apr15221378Search in Google Scholar
Xiang HX, Fei J, Xiang Y, et al.: Renal dysfunction and prognosis of COVID-19 patients: a hospital-based retrospective cohort study. BMC Infect Dis. 2021, 21:158.XiangHXFeiJXiangYRenal dysfunction and prognosis of COVID-19 patients: a hospital-based retrospective cohort study202121158Search in Google Scholar
Tzoulis P, Waung JA, Bagkeris E, et al.. Dysnatremia is a Predictor for Morbidity and Mortality in Hospitalized Patients with COVID-19. J Clin Endocrinol Metab. 2021 May 13;106(6):1637–1648.TzoulisPWaungJABagkerisEDysnatremia is a Predictor for Morbidity and Mortality in Hospitalized Patients with COVID-192021May13106616371648Search in Google Scholar
Tzoulis P, Grossman AB, Baldeweg SE, Bouloux P, Kaltsas G. MANAGEMENT OF ENDOCRINE DISEASE: Dysnatraemia in COVID-19: prevalence, prognostic impact, pathophysiology, and management. Eur J Endocrinol. 2021 Sep 6;185(4):R103–R111.TzoulisPGrossmanABBaldewegSEBoulouxPKaltsasGMANAGEMENT OF ENDOCRINE DISEASE: Dysnatraemia in COVID-19: prevalence, prognostic impact, pathophysiology, and management2021Sep61854R103R111Search in Google Scholar
Li W, Lin F, Dai M, Chen L, Han D, Cui Y, Pan P. Early predictors for mechanical ventilation in COVID-19 patients. Ther Adv Respir Dis. 2020 Jan–Dec;14:1753466620963017.LiWLinFDaiMChenLHanDCuiYPanPEarly predictors for mechanical ventilation in COVID-19 patients2020Jan–Dec141753466620963017Search in Google Scholar
Vidali S, Morosetti D, Cossu E, Luisi MLE, Pancani S, Semeraro V, Consales G. D-dimer as an indicator of prognosis in SARSCoV-2 infection: a systematic review. ERJ Open Res. 2020 Jul 13;6(2):00260–2020.VidaliSMorosettiDCossuELuisiMLEPancaniSSemeraroVConsalesGD-dimer as an indicator of prognosis in SARSCoV-2 infection: a systematic review2020Jul1362002602020Search in Google Scholar
Yao Y, Cao J, Wang Q, et al. D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: a case control study. J Intensive Care. 2020 Jul 10;8:49.YaoYCaoJWangQD-dimer as a biomarker for disease severity and mortality in COVID-19 patients: a case control study2020Jul10849Search in Google Scholar
Varikasuvu SR, Varshney S, Dutt N, Munikumar M, Asfahan S, Kulkarni PP, Gupta P. D-dimer, disease severity, and deaths (3D-study) in patients with COVID-19: a systematic review and meta-analysis of 100 studies. Sci Rep. 2021;11(1):21888.VarikasuvuSRVarshneySDuttNMunikumarMAsfahanSKulkarniPPGuptaPD-dimer, disease severity, and deaths (3D-study) in patients with COVID-19: a systematic review and meta-analysis of 100 studies202111121888Search in Google Scholar
Kulkarni, AR, Athavale A.M, Sahni A, et al. Deep learning model to predict the need for mechanical ventilation using chest X ray images in hospitalised patients with COVID-19. BMJ Innov. 2021;7:261–270.KulkarniARAthavaleA.MSahniADeep learning model to predict the need for mechanical ventilation using chest X ray images in hospitalised patients with COVID-1920217261270Search in Google Scholar
Aljouie AF, Almazroa A, Bokhari Y, et al. Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning. J. Multidiscip. Healthc. 2021;14:2017–2033.AljouieAFAlmazroaABokhariYEarly Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning20211420172033Search in Google Scholar
Mat Nor MB, Md Ralib A. Procalcitonin clearance for early prediction of survival in critically ill patients with severe sepsis. Crit Care Res Pract. 2014;2014:819034.Mat NorMBMd RalibAProcalcitonin clearance for early prediction of survival in critically ill patients with severe sepsis20142014819034Search in Google Scholar
Burke H, Freeman A, O’Regan P, et al. Biomarker identification using dynamic time warping analysis: a longitudinal cohort study of patients with COVID-19 in a UK tertiary hospital. BMJ 2022 15;12(2):e050331.BurkeHFreemanAO’ReganPBiomarker identification using dynamic time warping analysis: a longitudinal cohort study of patients with COVID-19 in a UK tertiary hospital202215122e050331Search in Google Scholar