Login Register Cart 0
Generic placeholder image

Current Respiratory Medicine Reviews

Editor-in-Chief

ISSN (Print): 1573-398X
ISSN (Online): 1875-6387

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

A Machine Learning Approach to Predict In-Hospital Mortality in COVID-19 Patients with Underlying Cardiovascular Disease using Artificial Neural Network

Author(s): Samaneh Sabouri, Mohammad Hossein Khademian, Mehrdad Sharifi, Razieh Sadat Mousavi-Roknabadi and Vahid Ebrahimi*

Volume 18, Issue 4, 2022

Published on: 27 August, 2022

Page: [289 - 296] Pages: 8

DOI: 10.2174/1573398X18666220810093416

Price: $65

conference banner
Abstract

Background: Machine learning algorithms, such as artificial neural networks (ANN), provide more accurate predictions by discovering complex patterns within data. Since COVID-19 disease is prevalent, using advanced statistical tools can upgrade clinical decision making by identifying high risk patients at the time of admission.

Objective: This study aims to predict in-hospital mortality in COVID-19 patients with underlying cardiovascular disease (CVD) using the ANN model.

Methods: In the current retrospective cohort study, 880 COVID-19 patients with underlying CVD were enrolled from 26 health centers affiliated with Shiraz University of Medical Sciences and followed up from 10 June to 26 December 2020. The five-fold cross-validation method was utilized to build the optimal ANN model for predicting in-hospital death. Moreover, the predictive power of the ANN model was assessed with concordance indices and the area under the ROC curve (AUC).

Results: The median (95% CI) survival time of hospitalization was 16.7 (15.2-18.2) days and the empirical death rate was calculated to be 17.5%. About 81.5% of intubated COVID-19 patients were dead and the majority of the patients were admitted to the hospital with triage level two (54%). According to the ANN model, intubation, blood urea nitrogen, C-reactive protein, lactate dehydrogenase, and serum calcium were the most important prognostic indicators associated with patients’ in-hospital mortality. In addition, the accuracy of the ANN model was obtained to be 83.4%, with a sensitivity and specificity of 72.7% and 85.6%, respectively (AUC=0.861).

Conclusion: In this study, the ANN model demonstrated a good performance in the prediction of in-hospital mortality in COVID-19 patients with a history of CVD.

Keywords: Artificial neural network, cardiovascular disease, COVID-19, machine learning, cohort study, ROC curve.

« Previous Next »
Graphical Abstract

[1]
Noor, F.M.; Islam, M.M. Prevalence and associated risk factors of mortality among COVID-19 patients: A meta-analysis. J. Community Health, 2020, 45(6), 1270-1282.
[http://dx.doi.org/10.1007/s10900-020-00920-x] [PMID: 32918645]
[2]
Sun, K.; Chen, J.; Viboud, C. Early epidemiological analysis of the 2019-nCoV outbreak based on a crowdsourced data. MedRxiv, 2020.
[http://dx.doi.org/10.1101/2020.01.31.20019935]
[3]
Rastad, H.; Karim, H.; Ejtahed, H.S. Risk and predictors of in-hospital mortality from COVID-19 in patients with diabetes and cardiovascular disease. Diabetol. Metab. Syndr., 2020, 12, 57.
[http://dx.doi.org/10.1186/s13098-020-00565-9] [PMID: 32641974]
[4]
van Gerwen, M.; Alsen, M.; Little, C. Risk factors and outcomes of COVID-19 in New York City; A retrospective cohort study. J. Med. Virol., 2021, 93(2), 907-915.
[http://dx.doi.org/10.1002/jmv.26337] [PMID: 32706392]
[5]
Inciardi, R.M.; Lupi, L.; Zaccone, G. Cardiac involvement in a patient with coronavirus disease 2019 (COVID-19). JAMA Cardiol., 2020, 5(7), 819-824.
[http://dx.doi.org/10.1001/jamacardio.2020.1096] [PMID: 32219357]
[6]
Momtazmanesh, S.; Shobeiri, P.; Hanaei, S.; Mahmoud-Elsayed, H.; Dalvi, B.; Malakan Rad, E. Cardiovascular disease in COVID-19: A systematic review and meta-analysis of 10,898 patients and proposal of a triage risk stratification tool. Egypt. Heart J., 2020, 72(1), 41.
[7]
Sarrafzadegan, N.; Mohammmadifard, N. Cardiovascular disease in Iran in the last 40 years: Prevalence, mortality, morbidity, challenges and strategies for cardiovascular prevention. Arch. Iran Med., 2019, 22(4), 204-210.
[PMID: 31126179]
[8]
Liu, J.; Liu, Y.; Xiang, P. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J. Transl. Med., 2020, 18(1), 206.
[http://dx.doi.org/10.1186/s12967-020-02374-0] [PMID: 32434518]
[9]
Hu, L.; Chen, S.; Fu, Y. Risk factors associated with clinical outcomes in 323 Coronavirus disease 2019 (COVID-19) hospitalized patients in Wuhan, China. Clin. Infect. Dis., 2020, 71(16), 2089-2098.
[http://dx.doi.org/10.1093/cid/ciaa539] [PMID: 32361738]
[10]
Cheng, A.; Hu, L.; Wang, Y. Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients. Int. J. Antimicrob. Agents, 2020, 56(3), 106110.
[http://dx.doi.org/10.1016/j.ijantimicag.2020.106110] [PMID: 32712332]
[11]
Di Castelnuovo, A.; Bonaccio, M.; Costanzo, S. Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: Survival analysis and machine learning-based findings from the multicentre Italian CORIST Study. Nutr. Metab. Cardiovasc. Dis., 2020, 30(11), 1899-1913.
[http://dx.doi.org/10.1016/j.numecd.2020.07.031] [PMID: 32912793]
[12]
Han, Y.; Zhang, H.; Mu, S. Lactate dehydrogenase, an independent risk factor of severe COVID-19 patients: A retrospective and observational study. Aging, 2020, 12(12), 11245-11258.
[http://dx.doi.org/10.18632/aging.103372] [PMID: 32633729]
[13]
Pirnia, B.; Pirnia, K.; Malekanmehr, P.; Zahiroddin, A. Challenges of differential diagnosis, symptoms of coronavirus disease 2019 (COVID-19) or Cannabinoid hyperemesis syndrome (CHS)? A rare case report. Iran. J. Public Health, 2020, 49(Suppl. 1), 109-111.
[http://dx.doi.org/10.18502/ijph.v49iS1.3677] [PMID: 34268213]
[14]
Achterberg, M.A.; Prasse, B.; Ma, L.; Trajanovski, S.; Kitsak, M.; Van Mieghem, P. Comparing the accuracy of several network-based COVID-19 prediction algorithms. Int. J. Forecast., 2022, 38(2), 489-504.
[http://dx.doi.org/10.1016/j.ijforecast.2020.10.001] [PMID: 33071402]
[15]
Alazab, M.; Awajan, A.; Mesleh, A.; Abraham, A.; Jatana, V.; Alhyari, S. COVID-19 prediction and detection using deep learning. Int J Comp Inform Syst Ind Manag Appl, 2020, 12, 168-181.
[16]
Deniz, A.; Kiziloz, H.E.; Sevinc, E.; Dokeroglu, T. Predicting the severity of COVID‐19 patients using a multi‐threaded evolutionary feature selection algorithm. Expert Syst., 2022, e12949.
[http://dx.doi.org/10.1111/exsy.12949]
[17]
Santosh, K.C. COVID-19 Prediction models and unexploited data. J. Med. Syst., 2020, 44(9), 170.
[http://dx.doi.org/10.1007/s10916-020-01645-z] [PMID: 32794042]
[18]
Panch, T.; Szolovits, P.; Atun, R. Artificial intelligence, machine learning and health systems. J. Glob. Health, 2018, 8(2), 020303.
[http://dx.doi.org/10.7189/jogh.08.020303] [PMID: 30405904]
[19]
Friedman, J.; Hastie, T.; Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw., 2010, 33(1), 1-22.
[http://dx.doi.org/10.18637/jss.v033.i01] [PMID: 20808728]
[20]
Yao, Y.; Cao, J.; Wang, Q. D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: A case control study. J. Intensive Care, 2020, 8, 49.
[http://dx.doi.org/10.1186/s40560-020-00466-z] [PMID: 32665858]
[21]
Kazemnejad, A.; Batvandi, Z.; Faradmal, J. Comparison of artificial neural network and binary logistic regression for determination of impaired glucose tolerance/diabetes. EMHJ-. East. Mediterr. Health J., 2010, 16(6), 615-620.
[22]
Sharifi, M.; Khademian, M.H.; Mousavi-Roknabadi, R.S.; Ebrahimi, V.; Sadegh, R. A new rapid approach for predicting death in coronavirus patients: The development and validation of the COVID-19 Risk-Score in Fars Province (CRSF). Iran. J. Public Health, 2022, 51(1), 178-187.
[http://dx.doi.org/10.18502/ijph.v51i1.8310] [PMID: 35223639]
[23]
Ebrahimi, V.; Sharifi, M.; Mousavi-Roknabadi, R.S. Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: A machine-learning algorithm. BMC Public Health, 2022, 22(1), 10.
[http://dx.doi.org/10.1186/s12889-021-12383-3] [PMID: 34986818]
[24]
Manning, T.; Sleator, R.D.; Walsh, P. Biologically inspired intelligent decision making: A commentary on the use of artificial neural networks in bioinformatics. Bioengineered, 2014, 5(2), 80-95.
[http://dx.doi.org/10.4161/bioe.26997] [PMID: 24335433]
[25]
Parsaeian, M.; Mohammad, K.; Mahmoudi, M.; Zeraati, H. Comparison of logistic regression and artificial neural network in low back pain prediction: Second national health survey. Iran. J. Public Health, 2012, 41(6), 86-92.
[PMID: 23113198]
[26]
James, G.; Witten, D.; Hastie, T.; Tibshirani, R. An introduction to statistical learning. Springer Nature, 2013, 103, 236.
[http://dx.doi.org/10.1007/978-1-4614-7138-7]
[27]
Garson, D.G. Interpreting neural network connection weights. AI Expert, 1991, 6, 47-51.
[28]
Aggarwal, G.; Cheruiyot, I.; Aggarwal, S. Association of Cardiovascular disease with Coronavirus disease 2019 (COVID-19) severity: A meta-analysis. Curr. Probl. Cardiol., 2020, 45(8), 100617.
[http://dx.doi.org/10.1016/j.cpcardiol.2020.100617] [PMID: 32402515]
[29]
Mehra, M.R.; Desai, S.S.; Kuy, S.; Henry, T.D.; Patel, A.N. Retraction: Cardiovascular disease, drug therapy, and mortality in COVID-19. N. Engl. J. Med., 2020, 382(26), 2582.
[http://dx.doi.org/10.1056/NEJMoa2007621]
[30]
Salinas-Escudero, G.; Carrillo-Vega, M.F.; Granados-García, V.; Martínez-Valverde, S.; Toledano-Toledano, F.; Garduño-Espinosa, J. A survival analysis of COVID-19 in the Mexican population. BMC Public Health, 2020, 20(1), 1616.
[http://dx.doi.org/10.1186/s12889-020-09721-2] [PMID: 33109136]
[31]
Pranata, R.; Huang, I.; Lim, M.A.; Wahjoepramono, E.J.; July, J. Impact of cerebrovascular and cardiovascular diseases on mortality and severity of COVID-19–systematic review, meta-analysis, and meta-regression. J. Stroke Cerebrovasc. Dis., 2020, 29(8), 104949.
[http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2020.104949]
[32]
Kutner, M.H.; Nachtsheim, C.J.; Neter, J.; Li, W. Applied linear statistical models; McGraw-Hill Irwin Boston, 2005.
[33]
Gill, M.K.; Asefa, T.; Kaheil, Y.; McKee, M. Effect of missing data on performance of learning algorithms for hydrologic predictions: Implications to an imputation technique. Water Resour. Res., 2007, 43(7), W07416.
[http://dx.doi.org/10.1029/2006WR005298]
[34]
Eftekhar, B.; Mohammad, K.; Ardebili, H.E.; Ghodsi, M.; Ketabchi, E. Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Med. Inform. Decis. Mak., 2005, 5(1), 3.
[http://dx.doi.org/10.1186/1472-6947-5-3] [PMID: 15713231]
[35]
Rezaeian, A.; Rezaeian, M.; Khatami, S.F.; Khorashadizadeh, F.; Moghaddam, F.P. Prediction of mortality of premature neonates using neural network and logistic regression. J. Ambient Intell. Humaniz. Comput., 2022, 13, 1269-1277.
[36]
Zhu, L.; Luo, W.; Su, M. Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients. Biomed. Rep., 2013, 1(5), 757-760.
[http://dx.doi.org/10.3892/br.2013.140] [PMID: 24649024]
[37]
Sabouri, S.; Esmaily, H.; Shahidsales, S.; Emadi, M. Survival prediction in patients with colorectal cancer using artificial neural network and cox regression. Int. J. Cancer Manag., 2020, 13(1), 1-6.
[http://dx.doi.org/10.5812/ijcm.81161]
[38]
Gromov, M.S.; Rogacheva, S.M.; Barulina, M.A.; Reshetnikov, A.A.; Prokhozhev, D.A.; Fomina, A.Y. Analysis of some physiological and biochemical indices in patients with Covid-19 pneumonia using mathematical methods. J. Evol. Biochem. Physiol., 2021, 57(6), 1394-1407.
[http://dx.doi.org/10.1134/S0022093021060181] [PMID: 34955552]
[39]
Knight, S.R.; Ho, A.; Pius, R. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO clinical characterisation protocol: Development and validation of the 4C mortality score. BMJ, 2020, 370, m3339.
[http://dx.doi.org/10.1136/bmj.m3339] [PMID: 32907855]
[40]
Wynants, L.; Van Calster, B.; Collins, G.S. Prediction models for diagnosis and prognosis of COVID-19: Systematic review and critical appraisal. BMJ, 2020, 369, m1328.
[http://dx.doi.org/10.1136/bmj.m1328] [PMID: 32265220]
[41]
Bansal, M. Cardiovascular disease and COVID-19. Diabetes Metab. Syndr., 2020, 14(3), 247-250.
[http://dx.doi.org/10.1016/j.dsx.2020.03.013] [PMID: 32247212]
[42]
Zareifopoulos, N.; Lagadinou, M.; Karela, A.; Platanaki, C.; Karantzogiannis, G.; Velissaris, D. Management of COVID-19: The risks associated with treatment are clear, but the benefits remain uncertain. Monaldi Arch. Chest Dis., 2020, 90(2), 242-245.
[43]
Liu, Q.; Song, N.C.; Zheng, Z.K.; Li, J.S.; Li, S.K. Laboratory findings and a combined multifactorial approach to predict death in critically ill patients with COVID-19: A retrospective study. Epidemiol. Infect., 2020, 148, e129.
[http://dx.doi.org/10.1017/S0950268820001442] [PMID: 32600484]
[44]
Smilowitz, N.R.; Kunichoff, D.; Garshick, M. C-reactive protein and clinical outcomes in patients with COVID-19. Eur. Heart J., 2021, 42(23), 2270-2279.
[http://dx.doi.org/10.1093/eurheartj/ehaa1103] [PMID: 33448289]
[45]
Xu, H.; Hou, K.; Xu, R. Clinical characteristics and risk factors of cardiac involvement in COVID-19. J. Am. Heart Assoc., 2020, 9(18), e016807.
[http://dx.doi.org/10.1161/JAHA.120.016807] [PMID: 32806998]
[46]
Li, C.; Ye, J.; Chen, Q. Elevated Lactate Dehydrogenase (LDH) level as an independent risk factor for the severity and mortality of COVID-19. Aging, 2020, 12(15), 15670-15681.
[http://dx.doi.org/10.18632/aging.103770] [PMID: 32805722]
[47]
Pourfathi, M.; Cereda, M.; Chatterjee, S. Lung metabolism and inflammation during mechanical ventilation; An imaging approach. Sci. Rep., 2018, 8(1), 3525.
[http://dx.doi.org/10.1038/s41598-018-21901-0] [PMID: 29476083]
[48]
Raesi, A.; Saedi Dezaki, E.; Moosapour, H. Hypocalcemia in COVID-19: A prognostic marker for severe disease. Iran. J. Pathol., 2021, 16(2), 144-153.
[http://dx.doi.org/10.30699/ijp.2020.130491.2442] [PMID: 33936225]
[49]
Bennouar, S.; Cherif, A.B.; Kessira, A.; Bennouar, D-E.; Abdi, S.; Vitamin, D. Deficiency and low serum calcium as predictors of poor prognosis in patients with severe COVID-19. J. Am. Coll. Nutr., 2020, 40, 104-110.
[PMID: 33434117]
[50]
Zhou, X.; Chen, D.; Wang, L. Low serum calcium: A new, important indicator of COVID-19 patients from mild/moderate to severe/critical. Biosci. Rep., 2020, 40(12), BSR20202690.
[http://dx.doi.org/10.1042/BSR20202690] [PMID: 33252122]

Rights & Permissions Print Cite
Article Metrics
27
2
Wayfinder Image
© 2024 Bentham Science Publishers | Privacy Policy