A simple score can identify kidney transplant recipients at high risk of severe infection over the following 2 years

Abstract

Background The aim of this study was to determine whether a composite score of simple immune biomarkers and clinical characteristics could predict severe infections in kidney transplant recipients. Methods We conducted a prospective study of 168 stable kidney transplant recipients who underwent measurement of lymphocyte subsets, immunoglobulins, and renal function at baseline and were followed up for 2 years for the development of any severe infections, defined as infection requiring hospitalization. A point score was developed to predict severe infection based on logistic regression analysis of factors in baseline testing. Results Fifty‐nine (35%) patients developed severe infection, 36 (21%) had two or more severe infections, and 3 (2%) died of infection. A group of 19 (11%) patients had the highest predicted infectious risk (>60%), as predicted by the score. Predictive variables were mycophenolate use, graft function, CD4+, and natural killer cell number. The level of immunosuppression score had an area under the receiver operating curve of 0.75 (95% CI: 0.67‐0.83). Conclusion Our level of immunosuppression score for predicting the development of severe infection over 2 years has sufficient prognostic accuracy for identification of high‐risk patients. This data can inform research that examines strategies to reduce the risks of infection.

Derivation and external validation of the SIMPLICITY score as a simple immune-based risk score to predict infection in kidney transplant recipients

“…However, no information on score performance was provided, and we were not able to externally validate the discriminative capacity of this immune risk phenotype. 25 Dendle et al 16 have recently proposed a "level of immunosuppression score" (based on CD4 þ T-cell and natural killer-cell counts, graft function, and use of mycophenolate mofetil) for predicting the occurrence of severe infection over the next 2 years. The resulting auROC was 0.750, and the cumulative incidence of infection in patients classified within the highest risk category reached 84%.…”

“…The resulting auROC was 0.750, and the cumulative incidence of infection in patients classified within the highest risk category reached 84%. 16 Nevertheless, this score still lacks external validation, and inclusion criteria were restricted to patients at least at their third post-transplant month, which implies that the earlier period (with the highest incidence of infection) was not accounted for. Regarding other SOT populations, Sarmiento et al 14 constructed an infection risk score for heart transplant recipients that included serum IgG levels < 600 mg/dl at baseline or post-transplant day 7, serum C3 levels < 80 mg/dl at day 7, and D/R CMV mismatch.…”

“…Nevertheless, previous attempts to develop an immune-based score aimed at assessing the risk of infection after SOT have been of limited applicability because of the single-center design or small sizes of studies. [9][10][11][12][13][14][15][16] Moreover, some of them incorporate complex assay procedures (such as T-cell proliferative responses) not always accessible to clinical laboratories. 10,14 More importantly, none of these scores has been externally validated, and because the same datasets were typically used to derive prediction rules and to calculate diagnostic proprieties, overestimation of predictive capacity cannot be ruled out.…”

Immune monitoring of prevalent kidney transplant recipients using Torque Teno Virus: Protocol for a single-centre prospective cohort study

“…Predictive scores comprising of several demographic and laboratory factors have also been tested but have not been shown to be effective across multiple time points and may be cumbersome to monitor. Moreover, most factors included, such as age and kidney function, may not be modifiable in clinical practice 13 14. Importantly, most strategies previously investigated may only account for an isolated aspect of the immune system.…”

Eudaimonia: An Aristotelian approach to transplantation

“…However, the complex, polygenic, multidimensional properties of the data available for a given patient have exceeded the capacity for human intuitive integration. Currently used models are limited in their predictive ability, and there is no model that formally organizes and integrates the incomprehensible amount of data available on each patient into an aggregate, individual risk assessment 1‐5 . The fundamental problem is not a lack of awareness that risks have relationships with one another, but an inability to integrate the data describing social determinants of health and patient satisfaction with mechanistic and point‐of ‐care data such that they can inform clinical practice.…”