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Prediction models for prognosis of influenza: a systematic review and critical appraisal

  • Yao Sun1,2,†
  • Yiwu Zhou1,†
  • Shu Zhang1

1Department of Emergency Medicine, Emergency Medical Laboratory, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, China

2West China Medical School, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, China

DOI: 10.22514/sv.2021.148 Vol.17,Issue 5,September 2021 pp.18-29

Submitted: 18 July 2021 Accepted: 23 August 2021

Published: 08 September 2021

*Corresponding Author(s): Shu Zhang E-mail: zhangs@wchscu.cn

† These authors contributed equally.

Abstract

The influenza epidemic has become an important public health issue throughout the world. Early recognition of potentially terrible outcomes is important in the emergency department (ED). Efficient prognosis of the disease is conducive to reducing the financial burden and providing appropriate care for patients. Prediction models containing several features to estimate the risk of patients with confirmed infection could help clinicians give appropriate treatment when health care resources are limited. We conducted a literature review of studies about influenza published until June 2021 and updated the literature during the creation process. We researched PubMed, Web of Science, and Google Scholar databases to collect articles in English relevant to influenza between Jan 1, 1900, and Dec 30, 2020. The terms used for the search were “influenza”, “diagnostic”, “prognostic”, “prediction”, “score”, “artificial intelligence”, and so on. If the study involved animals, children, pregnant women or the study type was pragmatic and explanatory clinical trial, guideline, protocol, letter, a case report was also excluded. The GRACE checklist in our study was used to assess the 34 studies for quality. Thirty-four articles were included in the review, and relevant data were extracted from the risk prognosis model. Cardiovascular disease and central nervous symptoms play an important role in prognostic models of influenza. In addition, some commonly used scoring systems can also play a certain role in evaluation. This systematic review compared different types of models for predicting the prognosis of influenza infection, informing us of risk factors for the predictive model in predicting the prognosis of influenza in the early stage. The articles were limited to retrospective observational studies, sample size, time limitation, incomplete data, imbalanced prognosis treatment, and so on.


Keywords

Prediction models; Prognosis; Influenza; Review; Critical appraisal


Cite and Share

Yao Sun,Yiwu Zhou,Shu Zhang. Prediction models for prognosis of influenza: a systematic review and critical appraisal. Signa Vitae. 2021. 17(5);18-29.

References

[1] Chu S, Seak C, Su T, Chaou C, Tseng H, Li C. Prognostic accuracy of SIRS criteria and qSOFA score for in-hospital mortality among influenza patients in the emergency department. BMC Infectious Diseases. 2020; 20: 385.

[2] Nishiura H. Case fatality ratio of pandemic influenza. The Lancet Infectious Diseases. 2010; 10: 443–444.

[3] Chang S, Yeh C, Chen Y, Hsu C, Chen J, Chen W, et al. Quick-SOFA score to predict mortality among geriatric patients with influenza in the emergency department. Medicine. 2019; 98: e15966.

[4] Nguyen JL, Yang W, Ito K, Matte TD, Shaman J, Kinney PL. Seasonal Influenza Infections and Cardiovascular Disease Mortality. JAMA Cardiology. 2016; 1: 274–281.

[5] Cox NJ, Subbarao K. Global epidemiology of influenza: past and present. Annual Review of Medicine. 2000; 51: 407–421.

[6] Cvetanovska M, Milenkovik Z, Uroshevik VK, Demiri I, Cvetanovski V. Factors Associated with Lethal Outcome in Patients with Severe Form of Influenza. Prilozi (Makedonska Akademija Na Naukite i Umetnostite. Oddelenie Za Medicinski Nauki). 2016; 37: 63–72.

[7] Wong JY, Kelly H, Ip DKM, Wu JT, Leung GM, Cowling BJ. Case fatality risk of influenza a (H1N1pdm09): a systematic review. Epidemiology. 2013; 24: 830–841.

[8] Ergönül Ö, Alan S, Ak Ö, Sargın F, Kantürk A, Gündüz A, et al. Predictors of fatality in pandemic influenza a (H1N1) virus infection among adults. BMC Infectious Diseases. 2014; 14: 317.

[9] Tai HC, Yeh C, Chen Y, Hsu C, Chen J, Chen W, et al. Utilization of systemic inflammatory response syndrome criteria in predicting mortality among geriatric patients with influenza in the emergency department. BMC Infectious Diseases. 2019; 19: 639.

[10] Rothberg MB, Haessler SD, Brown RB. Complications of viral influenza. The American Journal of Medicine. 2008; 121: 258–264.

[11] Schoen K, Horvat N, Guerreiro NFC, de Castro I, de Giassi KS. Spectrum of clinical and radiographic findings in patients with diagnosis of H1N1 and correlation with clinical severity. BMC Infectious Diseases. 2019; 19: 964.

[12] Shah NS, Greenberg JA, McNulty MC, Gregg KS, Riddell J, Mangino JE, et al. Severe Influenza in 33 us Hospitals, 2013-2014: Complications and Risk Factors for Death in 507 Patients. Infection Control and Hospital Epidemiology. 2015; 36: 1251–1260.

[13] Rezkalla SH, Kloner RA. Influenza-related viral myocarditis. Wisconsin Medical Journal. 2010; 109: 209–213.

[14] Simonsen L, Fukuda K, Schonberger LB, Cox NJ. The impact of influenza epidemics on hospitalizations. The Journal of Infectious Diseases. 2000; 181: 831–837.

[15] Park M, Wu P, Goldstein E, Joo Kim W, Cowling BJ. Influenza-Associated Excess Mortality in South Korea. American Journal of Preventive Medicine. 2016; 50: e111–e119.

[16] Zheng J, Huo X, Huai Y, Xiao L, Jiang H, Klena J, et al. Epidemiology, Seasonality and Treatment of Hospitalized Adults and Adolescents with Influenza in Jingzhou, China, 2010-2012. PLoS ONE. 2016; 11: e0150713.

[17] Kalil AC, Thomas PG. Influenza virus-related critical illness: pathophys-iology and epidemiology. Critical Care. 2019; 23: 258.

[18] Hsieh Y, Tsao K, Huang C, Chang K, Huang Y, Gong Y. Clinical charac-teristics of patients with laboratory-confirmed influenza a(H1N1)pdm09 during the 2013/2014 and 2015/2016 clade 6B/6B.1/6B.2-predominant outbreaks. Scientific Reports. 2018; 8: 15636.

[19] Yin R, Luusua E, Dabrowski J, Zhang Y, Kwoh CK. Tempel: time-series mutation prediction of influenza a viruses via attention-based recurrent neural networks. Bioinformatics. 2020; 36: 2697–2704.

[20] Yin R, Zhou X, Rashid S, Kwoh CK. HopPER: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC Medical Genomics. 2020; 13: 9.

[21] Shlomai A, Nutman A, Kotlovsky T, Schechner V, Carmeli Y, Guzner-Gur H. Predictors of pandemic (H1N1) 2009 virus positivity and adverse outcomes among hospitalized patients with a compatible syndrome. The Israel Medical Association Journal. 2010; 12: 622–627.

[22] Cunha BA, Syed U, Mickail N, Strollo S. Rapid clinical diagnosis in fatal swine influenza (H1N1) pneumonia in an adult with negative rapid influenza diagnostic tests (RIDTs): diagnostic swine influenza triad. Heart & Lung. 2010; 39: 78–86.

[23] Teng F, Wan T, Guo S, Liu X, Cai J, Qi X, et al. Outcome prediction using the Mortality in Emergency Department Sepsis score combined with procalcitonin for influenza patients. Medicina ClíNica. 2019; 153: 411–417.

[24] Ho Y, Wang J, Wang J, Wu U, Chang C, Wu H, et al. Prognostic factors for fatal adult influenza pneumonia. The Journal of Infection. 2009; 58: 439–445.

[25] Morton B, Nweze K, O’Connor J, Turton P, Joekes E, Blakey JD, et al. Oxygen exchange and C-reactive protein predict safe discharge in patients with H1N1 influenza. QJM: Monthly Journal of the Association of Physicians. 2017; 110: 227–232.

[26] Cho WH, Kim YS, Jeon DS, Kim JE, Kim KI, Seol HY, et al. Outcome of pandemic H1N1 pneumonia: clinical and radiological findings for severity assessment. The Korean Journal of Internal Medicine. 2011; 26: 160–167.

[27] Khan Z, Hulme J, Sherwood N. An assessment of the validity of SOFA score based triage in H1N1 critically ill patients during an influenza pandemic. Anaesthesia. 2009; 64: 1283–1288.

[28] Chung J, Hsu C, Chen J, Chen W, Lin H, Guo H, et al. Geriatric influenza death (GID) score: a new tool for predicting mortality in older people with influenza in the emergency department. Scientific Reports. 2018; 8: 9312.

[29] Adeniji KA, Cusack R. The Simple Triage Scoring System (STSS) successfully predicts mortality and critical care resource utilization in H1N1 pandemic flu: a retrospective analysis. Critical Care. 2011; 15: R39.

[30] Chung J, Hsu C, Chen J, Chen W, Lin H, Guo H, et al. Shock index predicted mortality in geriatric patients with influenza in the emergency department. The American Journal of Emergency Medicine. 2019; 37: 391–394.

[31] Oh WS, Lee S, Lee C, Hur J, Hur A, Park YS, et al. A prediction rule to identify severe cases among adult patients hospitalized with pandemic influenza a (H1N1) 2009. Journal of Korean Medical Science. 2011; 26: 499–506.

[32] Yin R, Tran VH, Zhou X, Zheng J, Kwoh CK. Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model. PLoS ONE. 2018; 13: e0207777.

[33] Dreyer NA, Bryant A, Velentgas P. The GRACE Checklist: a Validated Assessment Tool for High Quality Observational Studies of Comparative Effectiveness. Journal of Managed Care & Specialty Pharmacy. 2016; 22: 1107–1113.

[34] Wong CM, Yang L, Chan KP, Chan WM, Song L, Lai HK, et al. Cigarette smoking as a risk factor for influenza-associated mortality: evidence from an elderly cohort. Influenza and other Respiratory Viruses. 2013; 7: 531–539.

[35] Louie JK, Acosta M, Samuel MC, Schechter R, Vugia DJ, Harriman K, et al. A novel risk factor for a novel virus: obesity and 2009 pandemic influenza a (H1N1). Clinical Infectious Diseases. 2011; 52: 301–312.

[36] Lopez-Delgado JC, Rovira A, Esteve F, Rico N, Mañez Mendiluce R, Ballús Noguera J, et al. Thrombocytopenia as a mortality risk factor in acute respiratory failure in H1N1 influenza. Swiss Medical Weekly. 2013; 143: w13788.

[37] Demirjian SG, Raina R, Bhimraj A, Navaneethan SD, Gordon SM, Schreiber MJ, et al. 2009 influenza a infection and acute kidney injury: incidence, risk factors, and complications. American Journal of Nephrology. 2011; 34: 1–8.

[38] Bijani B, Pahlevan AA, Qasemi-Barqi R, Jahanihashemi H. Metabolic syndrome as an independent risk factor of hypoxaemia in influenza A (H1N1) 2009 pandemic. Le Infezioni in Medicina. 2016; 24: 123–130.

[39] Atamna A, Daskal R, Babich T, Ayada G, Ben-Zvi H, Elis A, et al. The impact of obesity on seasonal influenza: a single-center, retrospective study conducted in Israel. European Journal of Clinical Microbiology & Infectious Diseases. 2021; 40: 1471–1476.

[40] Fujikura Y, Kawano S, Kouzaki Y, Shinoda M, Hara Y, Shinkai M, et al. Mortality and severity evaluation by routine pneumonia prediction models among Japanese patients with 2009 pandemic influenza a (H1N1) pneumonia. Respiratory Investigation. 2014; 52: 280–287.

[41] Capelastegui A, Quintana JM, Bilbao A, España PP, Garin O, Alonso J, et al. Score to identify the severity of adult patients with influenza a (H1N1) 2009 virus infection at hospital admission. European Journal of Clinical Microbiology & Infectious Diseases. 2012; 31: 2693–2701.

[42] Choi WI, Yim JJ, Park J, Kim SC, Na MJ, Lee WY, et al. Clinical characteristics and outcomes of H1N1-associated pneumonia among adults in South Korea. International Journal of Tuberculosis and Lung Disease. 2011; 15: 270–275, i.

[43] Cheung W, Myburgh J, Seppelt IM, Parr MJ, Blackwell N, Demonte S, et al. Development and evaluation of an influenza pandemic intensive care unit triage protocol. Critical Care and Resuscitation. 2012; 14: 185–190.

[44] Rodriguez-Noriega E, Gonzalez-Diaz E, Morfin-Otero R, Gomez-Abundis GF, Briseño-Ramirez J, Perez-Gomez HR, et al. Hospital triage system for adult patients using an influenza-like illness scoring system during the 2009 pandemic–Mexico. PLoS ONE. 2010; 5: e10658.

[45] Zhang P, Cao B, Li X, Liang L, Yang S, Gu L, et al. Risk factors for adult death due to 2009 pandemic influenza a (H1N1) virus infection: a 2151 severe and critical cases analysis. Chinese Medical Journal. 2013; 126: 2222–2228.

[46] Riquelme R, Jiménez P, Videla AJ, Lopez H, Chalmers J, Singanayagam A, et al. Predicting mortality in hospitalized patients with 2009 H1N1 influenza pneumonia. The International Journal of Tuberculosis and Lung Disease. 2011; 15: 542–546.

[47] Pereira JM, Moreno RP, Matos R, Rhodes A, Martin-Loeches I, Cecconi M, et al. Severity assessment tools in ICU patients with 2009 influenza a (H1N1) pneumonia. Clinical Microbiology and Infection. 2012; 18: 1040–1048.

[48] Commons RJ, Denholm J. Triaging pandemic flu: pneumonia severity scores are not the answer. The International Journal of Tuberculosis and Lung Disease. 2012; 16: 670–673.

[49] Kiliç H, Kanbay A, Karalezli A, Hasanoğlu HC, Ateş C. Clinical characteristics of 75 pandemic H1N1 influenza patientsfrom Turkey; risk factors for fatality. Turkish Journal of Medical Sciences. 2015; 45: 562–567.

[50] Brandão-Neto RA, Goulart AC, Santana ANC, Martins HS, Ribeiro SCC, Ho LY, et al. The role of pneumonia scores in the emergency room in patients infected by 2009 H1N1 infection. European Journal of Emergency Medicine. 2012; 19: 200–202.

[51] Muller MP, McGeer AJ, Hassan K, Marshall J, Christian M. Evaluation of pneumonia severity and acute physiology scores to predict ICU admission and mortality in patients hospitalized for influenza. PLoS ONE. 2010; 5: e9563.

[52] Challen K, Bright J, Bentley A, Walter D. Physiological-social score (PMEWS) vs. CURB-65 to triage pandemic influenza: a comparative validation study using community-acquired pneumonia as a proxy. BMC Health Services Research. 2007; 7: 33.

[53] Rowan KM, Harrison DA, Walsh TS, McAuley DF, Perkins GD, Taylor BL, et al. The Swine Flu Triage (SwiFT) study: development and ongoing refinement of a triage tool to provide regular information to guide immediate policy and practice for the use of critical care services during the H1N1 swine influenza pandemic. Health Technol Assess. 2010; 14: 335–492.

[54] Bjarnason A, Thorleifsdottir G, Löve A, Gudnason JF, Asgeirsson H, Hallgrimsson KL, et al. Severity of influenza a 2009 (H1N1) pneumonia is underestimated by routine prediction rules. Results from a prospective, population-based study. PLoS ONE. 2012; 7: e46816.

[55] Shi SJ, Li H, Liu M, Liu YM, Zhou F, Liu B, et al. Mortality prediction to hospitalized patients with influenza pneumonia: PO2 /FiO2 combined lymphocyte count is the answer. The Clinical Respiratory Journal. 2017; 11: 352–360.

[56] Pawelka E, Karolyi M, Daller S, Kaczmarek C, Laferl H, Niculescu I, et al. Influenza virus infection: an approach to identify predictors for in-hospital and 90-day mortality from patients in Vienna during the season 2017/18. Infection. 2020; 48: 51–56.

[57] Franchini M, Veneri D, Lippi G. Thrombocytopenia and infections. Expert Review of Hematology. 2017; 10: 99–106.

[58] Siontis GCM, Tzoulaki I, Castaldi PJ, Ioannidis JPA. External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. Journal of Clinical Epidemiology. 2015; 68: 25–34.

[59] Debray TPA, Riley RD, Rovers MM, Reitsma JB, Moons KGM. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use. PLoS Medicine. 2015; 12: e1001886.

[60] Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. Journal of Clinical Epidemiol-ogy. 2016; 69: 245–247.

[61] Riley RD, Ensor J, Snell KIE, Debray TPA, Altman DG, Moons KGM, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. British Medical Journal. 2016; 353: i3140.

[62] Collins GS, Moons KGM. Comparing risk prediction models. British Medical Journal. 2012; 344: e3186.

[63] Wynants L, Kent DM, Timmerman D, Lundquist CM, Van Calster B. Untapped potential of multicenter studies: a review of cardiovascular risk prediction models revealed inappropriate analyses and wide variation in reporting. Diagnostic and Prognostic Research. 2019; 3: 6.

[64] Wynants L, Riley RD, Timmerman D, Van Calster B. Random-effects meta-analysis of the clinical utility of tests and prediction models. Statistics in Medicine. 2019; 37: 2034–2052.

[65] Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. British Medical Journal. 2020; 368: m441.

[66] Guo L, Wei D, Zhang X, Wu Y, Li Q, Zhou M, et al. Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score. Frontiers in Microbiology. 2019; 10: 2752.

[67] Kuba K, Imai Y, Rao S, Gao H, Guo F, Guan B, et al. A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus-induced lung injury. Nature Medicine. 2005; 11: 875–879.

[68] Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, et al. Pathological find-ings of COVID-19 associated with acute respiratory distress syndrome. The Lancet Respiratory Medicine. 2020; 8: 420–422.

[69] Damen JAAG, Hooft L, Schuit E, Debray TPA, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. British Medical Journal. 2016; 353: i2416.



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