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

Abstract

Existing approaches for infection risk stratification in kidney transplant recipients are suboptimal. Here, we aimed to develop and validate a weighted score integrating nonpathogen-specific immune parameters and clinical variables to predict the occurrence of post-transplant infectious complications. To this end, we retrospectively analyzed a single-center derivation cohort of 410 patients undergoing kidney transplantation in 2008-2013 in Madrid. Peripheral blood lymphocyte subpopulations, serum immunoglobulin and complement levels were measured at one-month posttransplant. The primary and secondary outcomes were overall and bacterial infection through month six. A point score was derived from a logistic regression model and prospectively applied on a validation cohort of 522 patients undergoing kidney transplantation at 16 centers throughout Spain in 2014-2015. The SIMPLICITY score consisted of the following variables measured at month one after transplantation: C3 level, CD4 D T-cell count, CD8 D T-cell count, IgG level, glomerular filtration rate, recipient age, and infection within the first month. The discrimination capacity in the derivation and validation cohorts was good for overall (areas under the receiver operating curve of 0.774 and 0.730) and bacterial infection (0.767 and 0.734, respectively). The cumulative incidence of overall infection significantly increased across risk categories in the derivation (low-risk 13.7%; intermediate-risk, 35.9%; high-risk 77.6%) and validation datasets (10.2%, 28.9% and 50.4%, respectively). Thus, the SIMPLICITY score, based on easily available immune parameters, allows for stratification of kidney transplant recipients at month one according to their expected risk of subsequent infection.

When performance is not enough—A multidisciplinary view on clinical decision support

“…This differs considerably from other domains, where image recognition plays an important role (radiology, pathology). Most work which is related to the prediction of infectious complications in kidney transplantation does not use machine learning methods, but classical statistical methods [ 14 – 18 ]. A summary of the relevant studies in the field of kidney transplantation is provided in Table 1 .…”

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.…”

“…The prognostic performance of TTV DNA levels will also be compared against other clinical markers such as absolute lymphocyte counts, lymphocyte subpopulation counts (CD4, CD8, CD19, CD56), IgG and C3 levels. The study also aims to compare the prognostic value of TTV against other common biomarkers and two predictive scoring systems by Fernandez-Ruiz et al and Dendle et al 13 14…”