Sage Journals: Discover world-class research

Abstract

Introduction

Having reliable predictive models of prognosis/the risk of infection in systemic lupus erythematosus (SLE) patients would allow this problem to be addressed on an individual basis to study and implement possible preventive or therapeutic interventions.

Objective

To identify and analyze all predictive models of prognosis/the risk of infection in patients with SLE that exist in medical literature.

Methods

A structured search in PubMed, Embase, and LILACS databases was carried out until May 9, 2020. In addition, a search for abstracts in the American Congress of Rheumatology (ACR) and European League Against Rheumatism (EULAR) annual meetings’ archives published over the past eight years was also conducted. Studies on developing, validating or updating predictive prognostic models carried out in patients with SLE, in which the outcome to be predicted is some type of infection, that were generated in any clinical context and with any time horizon were included. There were no restrictions on language, date, or status of the publication. To carry out the systematic review, the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline recommendations were followed. The PROBAST tool (A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies) was used to assess the risk of bias and the applicability of each model.

Results

We identified four models of infection prognosis in patients with SLE. Mostly, there were very few events per candidate predictor. In addition, to construct the models, an initial selection was made based on univariate analyses with no contraction of the estimated coefficients being carried out. This suggests that the proposed models have a high probability of overfitting and being optimistic.

Conclusions

To date, very few prognostic models have been published on the infection of SLE patients. These models are very heterogeneous and are rated as having a high risk of bias and methodological weaknesses. Despite the widespread recognition of the frequency and severity of infections in SLE patients, there is no reliable predictive prognostic model that facilitates the study and implementation of personalized preventive or therapeutic measures.

Protocol registration number: PROSPERO CRD42020171638.

Keywords

Systemic lupus erythematosus infection clinical prediction models prognosis systematic literature review

Get full access to this article

View all access options for this article.

References

Yurkovich

Vostretsova

Chen

and Aviña-Zubieta JA. Overall and cause-specific mortality in patients with systemic lupus erythematosus: a meta-analysis of observational studies. Arthritis Care Res (Hoboken) 2014; 66: 608–616.

Barber

Gold

Fortin

PR.

Infections in the lupus patient: perspectives on prevention. Curr Opin Rheumatol 2011; 23: 358–365.

Yee

C-S

Toescu

, et al. Birmingham SLE cohort: outcomes of a large inception cohort followed for up to 21 years. Rheumatology (Oxford) 2015; 54: 836–843.

Wongchinsri

Tantawichien

Osiri

Akkasilpa S and Deesomchok U. Infection in Thai patients with systemic lupus erythematosus: a review of hospitalized patients. J Med Assoc Thai 2002; 85: S34–S39.

Lee

Dhillon

Pope

All-cause hospitalizations in systemic lupus erythematosus from a large Canadian referral Centre. Rheumatology 2013; 52: 905–909.

Muñoz-Grajales

Pinto Peñaranda

Velásquez Franco

Márquez Hernández JD and Restrepo-Escobar M. Complicaciones infecciosas en lupus eritematoso sistémico. Rev Colomb Reumatol 2013; 20: 141–147.

Fanouriakis

Kostopoulou

Alunno

, et al. 2019 Update of the EULAR recommendations for the management of systemic lupus erythematosus. Ann Rheum Dis 2019; 78: 736–745.

Danza

Ruiz-Irastorza

Infection risk in systemic lupus erythematosus patients: susceptibility factors and preventive strategies. Lupus 2013; 22: 1286–1294.

Zhan

Lao

Chen D, Liang L and Yang X. Hospital-acquired infection in patients with systemic lupus erythematosus: a case-control study in a Southern Chinese population. Clin Rheumatol 2018; 37: 709–717.

10.

Faco

MMM

Leone

Campos

LMA

Febrônio MV, Marques HHS and Silva CA. Risk factors associated with the death of patients hospitalized for juvenile systemic lupus erythematosus. Braz J Med Biol Res 2007; 40: 993–1002.

11.

Rúa-Figueroa

López-Longo

Campo

, et al. Bacteremia in systemic lupus erythematosus in patients from a Spanish registry: risk factors, clinical and microbiological characteristics, and outcomes. J Rheumatol 2020; 47: 234–240.

12.

Ramírez Gómez

Velásquez

Granda

Builes CA and Jaimes F. Risk factors for nosocomial infections in patients with systemic lupus erythematosus at a teaching hospital in Medellin, Colombia. Prospective study: 2001-2004. Rev Colomb Reumatol 2007; 14: 177–186.

13.

Fessler

BJ.

Infectious diseases in systemic lupus erythematosus: risk factors, management and prophylaxis. Best Pract Res Clin Rheumatol 2002; 16: 281–291.

14.

Yuhara

Takemura

Akama

Suzuki H, Yamane K and Kashiwagi H. Predicting infection in hospitalized patients with systemic lupus erythematosus. Intern Med 1996; 35: 629–636.

15.

Tejera Segura B Rúa-Figueroa

Pego-Reigosa

del Campo Pérez V, Isenberg D and Rahman A. THU0256 development and validation of a score to predict the risk of severe infection in sle. In: Poster Presentations. BMJ Publishing Group Ltd and European League Against Rheumatism, pp. 300.3–301.

16.

Tejera Segura B Rúa-Figueroa

Pego-Reigosa

Del Campo V, Isenberg D and Rahman A. S2A:5 Development and validation of a score to predict the risk of severe infection in SLE. Lupus Sci Med 2018; 5: A6.

17.

Tejera Segura

Rua-Figueroa

Pego-Reigosa

, et al. Can we validate a clinical score to predict the risk of severe infection in patients with systemic lupus erythematosus? A longitudinal retrospective study in a British cohort. BMJ Open 2019; 9: e028697.

18.

Torres-Ruiz

Mejía-Domínguez

Zentella-Dehesa

, et al. The systemic lupus erythematosus infection predictive index (LIPI): a clinical-immunological tool to predict infections in lupus patients. Front Immunol 2018; 9: 3144.

19.

Feldman

Hiraki

Winkelmayer

, et al. Serious infections among adult Medicaid beneficiaries with systemic lupus erythematosus and lupus nephritis. Arthritis Rheumatol 2015; 67: 1577–1585.

20.

Teh

Wan

Ling

GR.

Severe infections in systemic lupus erythematosus: disease pattern and predictors of infection-related mortality. Clin Rheumatol 2018; 37: 2081–2086.

21.

Jung

J-Y

Yoon

Choi

Kim H-A and Suh C-H. Associated clinical factors for serious infections in patients with systemic lupus erythematosus. Sci Rep 2019; 9: 9704.

22.

Steyerberg

Moons

KGM

van der Windt

, PROGRESS Groupet al. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013; 10: e1001381.

23.

Wolff

Moons

KGM

Riley

, PROBAST Groupet al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 2019; 170: 51–58.

24.

NIHR. Predictive models of infection in patients with systemic lupus erythematosus. Systematic review of the literature. Available at: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=171638 (accessed 15 May 2020).

25.

Moons

KGM

de Groot

JAH

Bouwmeester

, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 2014; 11: e1001744.

26.

Geersing

G-J

Bouwmeester

Zuithoff

Spijker R, Leeflang M and Moons KGM. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One 2012; 7: e32844.

27.

Ingui

Rogers

MA.

Searching for clinical prediction rules in MEDLINE. J Am Med Inform Assoc 2001; 8: 391–397.

28.

Wong

SS-L

Wilczynski

Haynes

and Ramkissoonsingh R. Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE. AMIA Annu Symp Proc 2003; 2003: 728–732.

29.

Keogh

Wallace

O'Brien

, et al. Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish a web-based register. J Clin Epidemiol 2011; 64: 848–860.

30.

Moons

KGM

Wolff

Riley

, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med 2019; 170: W1–W33.

31.

Moons

KGM

Altman

Reitsma

, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162: W1–W73.

32.

Liberati

Altman

Tetzlaff

, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med 2009; 151: W65–W94.

33.

Castaño-Gonzalez

Restrepo-Escobar

Morales-Maya

, et al. Prediction of hospital-acquired bacterial infections in patients with SLE - ACR Meeting Abstracts. Available at: https://acrabstracts.org/abstract/prediction-of-hospital-acquired-bacterial-infections-in-patients-with-sle/ (accessed 4 October 2019).

34.

Gómez-Martín

Torres-Ruíz

Mejía-Domínguez

, et al. A Predictive Clinical-Immunological Index for infections unveils novel innate and adaptive immunity abnormalities as key risk factors for infections in a cohort of patients with systemic lupus erythematosus - ACR Meeting Abstracts. Available at: https://acrabstracts.org/abstract/a-predictive-clinical-immunological-index-for-infections-unveils-novel-innate-and-adaptive-immunity-abnormalities-as-key-risk-factors-for-infections-in-a-cohort-of-patients-with-systemic-lupus-erythem/ (accessed 15 May 2020).

35.

Rúa-Figueroa

López-Longo

Galindo-Izquierdo

, et al. Incidence, associated factors and clinical impact of severe infections in a large, multicentric cohort of patients with systemic lupus erythematosus. Semin Arthritis Rheum 2017; 47: 38–45.

36.

van Smeden

Moons

KGM

de Groot

JAH

, et al. Sample size for binary logistic prediction models: beyond events per variable criteria. Stat Methods Med Res 2019; 28: 2455–2474.

37.

Royston

Moons

KGM

Altman

and Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. BMJ 2009; 338: b604.

38.

Steyerberg

EW.

Clinical prediction models. A practical approach to development, validation, and updating. 2nd ed. Switzerland: Springer International Publishing.

39.

Moons

KGM

Harrell

Steyerberg

EW.

Should scoring rules be based on odds ratios or regression coefficients?

J Clin Epidemiol 2002; 55: 1054–1055.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB

0.05 MB

Predictive models of infection in patients with systemic lupus erythematosus: A systematic literature review

Abstract

Introduction

Objective

Methods

Results

Conclusions

Keywords

Get full access to this article

References

Supplementary Material