Nomogram for Predicting Early Mortality after Umbilical Cord Blood Transplantation in Children with Inborn Errors of Immunity

Abstract

Purpose Pediatric patients with inborn errors of immunity (IEI) undergoing umbilical cord blood transplantation (UCBT) are at risk of early mortality. Our aim was to develop and validate a prediction model for early mortality after UCBT in pediatric IEI patients based on pretransplant factors. Methods Data from 230 pediatric IEI patients who received their first UCBT between 2014 and 2021 at a single center were analyzed retrospectively. Data from 2014–2019 and 2020–2021 were used as training and validation sets, respectively. The primary outcome of interest was early mortality. Machine learning algorithms were used to identify risk factors associated with early mortality and to build predictive models. The model with the best performance was visualized using a nomogram. Discriminative ability was measured using the area under the curve (AUC) and decision curve analysis. Results Fifty days was determined as the cutoff for distinguishing early mortality in pediatric IEI patients undergoing UCBT. Of the 230 patients, 43 (18.7%) suffered early mortality. Multivariate logistic regression with pretransplant albumin, CD4 (absolute count), elevated C-reactive protein, and medical history of sepsis showed good discriminant AUC values of 0.7385 (95% CI, 0.5824–0.8945) and 0.827 (95% CI, 0.7409–0.9132) in predicting early mortality in the validation and training sets, respectively. The sensitivity and specificity were 0.5385 and 0.8154 for validation and 0.7667 and 0.7705 for training, respectively. The final model yielded net benefits across a reasonable range of risk thresholds. Conclusion The developed nomogram can predict early mortality in pediatric IEI patients undergoing UCBT.

A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital

“…The validation strategy, described in our previous study, was as follows: 'By applying the nal bestperforming model to make predictions, patients in the holdout validation cohort were classi ed into two prognostic groups (i.e., high-risk group vs. low-risk group) based on their predicted probability of DKD and the selected cut-off probability. Their survival curves were compared using the Kaplan-Meier method' [27].…”

A SuperLearner approach to predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital

“…The validation strategy was described in our previous study as below "By applying the nal bestperforming model to make predictions, patients in the holdout validation cohort were classi ed into two prognostic groups (i.e., high-risk group vs. low-risk group) based on their predicted probability of DKD and the selected cutoff probability. Their survival curves were compared using the Kaplan-Meier method" [27] .…”