A Machine Learning-Based Predictive Model for Outcome of Covid-19 in Kidney Transplant Recipients

  1. Ignacio Revuelta
  2. Francisco Javier Santos Arteaga 1
  3. Enrique Montagud-Marrahi 2
  4. Pedro Ventura Aguiar 2
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  2. 2 Hospital Clinic Barcelona
    info

    Hospital Clinic Barcelona

    Barcelona, España

    ROR https://ror.org/02a2kzf50

Aktak:
American Transplant Congress

Argitaletxea: https://publons.com/wos-op/publon/51111900/

ISSN: 1600-6135

Argitalpen urtea: 2021

Mota: Biltzar ekarpena

DOI: 10.1007/S10462-021-10008-0 GOOGLE SCHOLAR

Laburpena

In an overwhelming demand scenario, such as the SARS-CoV-2 pandemic, pressure over health systems may outburst their predicted capacity to deal with such extreme situations. Therefore, in order to successfully face a health emergency, scientific evidence and validated models are needed to provide real-time information that could be applied by any health center, especially for high-risk populations, such as transplant recipients. We have developed a hybrid prediction model whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Using hospital admission data from a cohort of hospitalized transplant patients, our hybrid Data Envelopment Analysis (DEA)-Artificial Neural Network (ANN) model extrapolates the progression towards severe COVID-19 disease with an accuracy of 96.3%, outperforming any competing model, such as logistic regression (65.5%) and random forest (44.8%). In this regard, DEA-ANN allows us to categorize the evolution of patients through the values of the analyses performed at hospital admission. Our prediction model may help guiding COVID-19 management through the identification of key predictors that permit a sustainable management of resources in a patient-centered model. Supplementary information: The online version contains supplementary material available at 10.1007/s10462-021-10008-0.

Erreferentzia bibliografikoak

  • Ahmadvand S, Pishvaee MS (2018) An efficient method for kidney allocation problem: a credibility-based fuzzy common weights data envelopment analysis approach. Health Care Manag Sci. https:// doi. org/ 10. 1007/ s10729- 017- 9414-6
  • Akalin E et al (2020) Covid-19 and kidney transplantation. N Engl J Med. https:// doi. org/ 10. 1056/ NEJMc 20111 17
  • Albahri AS, Hamid RA, Albahri OS, Zaidan AA (2021) Detection-based prioritisation: framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods. Artif Intell Med. https:// doi. org/ 10. 1016/j. artmed. 2020. 101983
  • Alberici F et al (2020a) A single center observational study ofthe clinical characteristics and short-term outcome of 20 kidney transplant patients admitted for SARS-CoV2 pneumonia. Kidney Int
  • Alberici F et al (2020b) Management of patients on dialysis and with kidney transplantation during the SARS-CoV-2 (COVID-19) Pandemic in Brescia, Italy. Kidney Int Rep. https:// doi. org/ 10. 1016/J. EKIR. 2020. 04. 001
  • Alzubaidi MA, Otoom M, Otoum N, Etoom Y, Banihani R (2021) A novel computational method for assigning weights of importance to symptoms of COVID-19 patients. Artif Intell Med. https:// doi. org/ 10. 1016/j. artmed. 2021. 102018
  • Angelico R etal (2020) The COVID-19 outbreak in Italy: initial implications for organ transplantation pro-grams. Am J Transpl. https:// doi. org/ 10. 1111/ ajt. 15904
  • Arora N, Banerjee AK, Narasu ML (2020) The role of artificial intelligence in tackling COVID-19. Future Virol. https:// doi. org/ 10. 2217/ fvl- 2020- 0130
  • Aubert O etal (2019) Archetype analysis identifies distinct profiles in renal transplant recipients with trans-plant glomerulopathy associated with allograft survival. J Am Soc Nephrol. https:// doi. org/ 10. 1681/ ASN. 20180 70777
  • Bae S etal (2020) Machine learning to predict transplant outcomes: Helpful or hype? A national cohort study. Transpl Int. https:// doi. org/ 10. 1111/ tri. 13695
  • Bagley SC, White H, Golomb BA (2001) Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. J Clin Epidemiol. https:// doi. org/ 10. 1016/ S0895- 4356(01) 00372-9
  • Bishop CM (2006) Pattern recoginiton and machine learning. Information science and statistics
  • Boyarsky BJ et al (2020) Early impact of COVID-19 on transplant center practices and policies in the United States. Am J Transpl. https:// doi. org/ 10. 1111/ ajt. 15915
  • Cafri G, Li L, Paxton EW, Fan J (2018) Predicting risk for adverse health events using random forest. J Appl Stat. https:// doi. org/ 10. 1080/ 02664 763. 2017. 14141 66
  • Cao B etal (2020) A trial of lopinavir-ritonavir in adults hospitalized with severe Covid-19. N Engl J Med. https:// doi. org/ 10. 1056/ nejmo a2001 282
  • Cortegiani A, Ingoglia G, Ippolito M, Giarratano A, Einav S (2020) A systematic review on the efficacy and safety of chloroquine for the treatment of COVID-19. J Crit Care. https:// doi. org/ 10. 1016/j. jcrc. 2020. 03. 005
  • Editorial (2021) Artificial intelligence for COVID-19: Saviour or saboteur? The Lancet Digital Health. https:// doi. org/ 10. 1016/ S2589- 7500(20) 30295-8
  • Emanuel EJ etal (2020) Fair allocation of scarce medical resources in the time of Covid-19. N Engl J Med. https:// doi. org/ 10. 1056/ NEJMs b2005 114
  • Fernández-Ruiz M et al (2020) COVID-19 in solid organ transplant recipients: a single-center case series from Spain. Am J Transp. https:// doi. org/ 10. 1111/ ajt. 15929
  • Fontana F etal (2020) Covid-19 pneumonia in a kidney transplant recipient successfully treated with Tocilizumab and Hydroxychloroquine. Am J Transpl. https:// doi. org/ 10. 1111/ ajt. 15935
  • Gautret P etal (2020) Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial. Int J Antimicrob Agents. https:// doi. org/ 10. 1016/j. ijant imicag. 2020. 105949
  • Giordano G et al (2020) Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med. https:// doi. org/ 10. 1038/ s41591- 020- 0883-7
  • Guan W etal (2020) Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. https:// doi. org/ 10. 1056/ nejmo a2002 032
  • Huang C et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395:497–506
  • Ison MG, Hirsch HH (2019) Community-acquired respiratory viruses in transplant patients: diversity, impact, unmet clinical needs. Clin Microbiol Rev 32
  • Kumar R, Ison MG (2019) Opportunistic infections in transplant patients. Infect Dis Clin North Am 33:1143–1157
  • Loupy A etal (2019) Prediction system for risk of allograft loss in patients receiving kidney transplants: International derivation and validation study. BMJ. https:// doi. org/ 10. 1136/ bmj. l4923
  • Luo P etal (2020) Tocilizumab treatment in COVID-19: a single center experience. J Med Virol. https:// doi. org/ 10. 1002/ jmv. 25801
  • Martino F, Plebani M, Ronco C (2020) Kidney transplant programmes during the COVID-19 pandemic. Lancet Respir Med. https:// doi. org/ 10. 1016/ s2213- 2600(20) 30182-x
  • Massie AB etal (2020) Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: a stochastic simulation and machine learning study. Am J Transpl. https:// doi. org/ 10. 1111/ ajt. 16117
  • Misiunas N, Oztekin A, Chen Y, Chandra K (2016) DEANN: a healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status. Omega (United Kingdom). https:// doi. org/ 10. 1016/j. omega. 2015. 03. 010
  • Onder G, Rezza G, Brusaferro S (2020) Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA. https:// doi. org/ 10. 1001/ jama. 2020. 4683
  • Pencina MJ, Ph D, Goldstein BA, Ph D, Ralph B, D’Agostino P (2020) Prediction models: development, evaluation, and clinical application. N Engl J Med 382:1583–1586
  • Pereira MR etal (2020) COVID-19 in solid organ transplant recipients: initial report from the US epi-center. Am J Transp. https:// doi. org/ 10. 1111/ ajt. 15941
  • Rao C, Lin H, Liu M (2020a) Design of comprehensive evaluation index system for P2P credit risk of “three rural” borrowers. Soft Comput. https:// doi. org/ 10. 1007/ s00500- 019- 04613-z
  • Rao C, Liu M, Goh M, Wen J (2020b) 2-stage modified random forest model for credit risk assessment of P2P network lending to “Three Rurals” borrowers. Appl Soft Comput. https:// doi. org/ 10. 1016/j. asoc. 2020. 106570
  • Rasheed J etal (2020) A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. Chaos Soliton Fract. https:// doi. org/ 10. 1016/j. chaos. 2020. 110337
  • Rashid MT, Wang D (2021) CovidSens: a vision on reliable social sensing for COVID-19. Artif Intell Rev. https:// doi. org/ 10. 1007/ s10462- 020- 09852-3
  • Santos Arteaga FJ, Tavana M, Di Caprio D, Toloo M (2019) A dynamic multi-stage slacks-based meas-ure Data Envelopment Analysis model with knowledge accumulation and technological evolution. Eur J Oper Res. https:// doi. org/ 10. 1016/j. ejor. 2018. 09. 008
  • Santos Arteaga FJ, Di Caprio D, Cucchiari D, Campistol JM, Oppenheimer F, Diekmann F, Revuelta I (2020) Modeling patients as decision making units: evaluating the efficiency of kidney trans-plantation through Data Envelopment Analysis. Health Care Manag Sci. https:// doi. org/ 10. 1007/ s10729- 020- 09516-2
  • Sato Y, Yanagita M (2019) Immunology of the ageing kidney. Nat Rev Nephrol 15:625–640
  • Siga MM etal (2020) Prediction of all-cause mortality in haemodialysis patients using a Bayesian net-work. Nephrol Dial Transpl. https:// doi. org/ 10. 1093/ ndt/ gfz295
  • Silva JT, Fernández-Ruiz M, Aguado JM (2020) Prevention and therapy of viral infections in patients with solid organ transplantation. Enferm Infecc Microbiol Clin. https:// doi. org/ 10. 1016/j. eimc. 2020. 01. 021
  • The CUKTP (2020) Early description of coronavirus 2019 disease in kidney transplant recipients in New York. J Am Soc Nephrol. https:// doi. org/ 10. 1681/ ASN. 20200 30375
  • Toloo M, Zandi A, Emrouznejad A (2015) Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach. J Supercomput. https:// doi. org/ 10. 1007/ s11227- 015- 1387-y
  • Tsolas IE, Charles V, Gherman T (2020) Supporting better practice benchmarking: a DEA-ANN approach to bank branch performance assessment. Expert Syst Appl. https:// doi. org/ 10. 1016/j. eswa. 2020. 113599
  • Vaishya R, Javaid M, Khan IH, Haleem A (2020) Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. https:// doi. org/ 10. 1016/j. dsx. 2020. 04. 012
  • Weitz JS, Beckett SJ, Coenen AR, Demory D, Dominguez-Mirazo M, Dushoff J, Leung CY, Li G, Măgălie A, Park SW, Rodriguez-Gonzalez R, Shivam S (2020) Modeling shield immunity to reduce COVID-19 epidemic spread. Nat Med
  • World Health Organization (2019) Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases.Wynants L etal (2020a) Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ 369:m1328
  • Wynants L etal (2020b) Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. https:// doi. org/ 10. 1136/ bmj. m1328
  • Zhou F et al (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395:1054–1062
  • Zhu J (2014) Quantitative models for performance evaluation and benchmarking: DEA with spreadsheets. Internat Ser Oper Res Manag Sci. https:// doi. org/ 10. 1007/ s13398- 014- 0173-7.2