Dynamic forecasting of severe acute graft-versus-host disease after transplantation

Abstract

Forecasting of severe acute graft-versus-host disease (aGVHD) after transplantation is a challenging ‘large p, small n’ problem that suffers from nonuniform data sampling. We propose a dynamic probabilistic algorithm, daGOAT, that accommodates sampling heterogeneity, integrates multidimensional clinical data and continuously updates the daily risk score for severe aGVHD onset within a two-week moving window. In the studied cohorts, the cross-validated area under the receiver operator characteristic curve (AUROC) of daGOAT rose steadily after transplantation and peaked at ≥0.78 in both the adult and pediatric cohorts, outperforming the two-biomarker MAGIC score, three-biomarker Ann Arbor score, peri-transplantation features-based models and XGBoost. Simulation experiments indicated that the daGOAT algorithm is well suited for short time-series scenarios where the underlying process for event generation is smooth, multidimensional and where there are frequent and irregular data missing. daGOAT’s broader utility was demonstrated by performance testing on a remotely different task, that is, prediction of imminent human postural change based on smartphone inertial sensor time-series data.

‘Dynamic zero-COVID’ policy and viral clearance during an omicron wave in Tianjin, China: a city-wide retrospective observational study

“…Like previous reports,25 26 we affirmed that younger age and higher lymphocyte count were associated with earlier viral clearance. The multidimensional approach in this study echoes recent advances in phenotypic analysis in other diseases 27–29…”

“…The multidimensional approach in this study echoes recent advances in phenotypic analysis in other diseases. [27][28][29] It has been well documented that prolonged isolation-either in residential compounds or at a medical facility-may lead to mental distress. 1 30 During the study period, several measures were undertaken to minimise mental distress and financial burden for the PCR-positive cases: First, family members were treated in the same room whenever possible.…”

The Composite Immune Risk Score predicts overall survival after allogeneic hematopoietic stem cell transplantation: A retrospective analysis of 1838 cases

“…In recent years, there has been growing interest in devising new computational methods for analyzing multidimensional phenotypes in diseases (for instance, acute respiratory distress syndrome, 30 type 2 diabetes, 31 aGVHD, 32 and sepsis 33 ). For post‐transplant immune reconstitution, Toor et al have developed the methodology of using logistic dynamics to classify the temporal profiles of the total lymphocyte count post‐transplant into three distinct growth patterns, 21 Koenig et al have explored the application of principal component analysis to measure the distance between an HSCT patient's immune status and the immune status of non‐hematological patients and the use of this distance for predicting overall survival, 10 and Mellgren et al have proposed to use reflected discriminant analysis to decompose post‐transplant immune reconstitution into two independent axes (one for cell normalization and the other for functional maturation).…”

“…For 420 (21.6% of 1945) of the patients in SKIRT, the multivariate time‐series of ≥159 clinical features during the initial 100 days post‐transplant and their peri‐transplant characteristics such as patient age, patient sex, primary diagnosis, transplant type, and stem cell source have been published elsewhere. 32 The computational code used in this study is available in a public GitHub repository ( https://github.com/chenjunren-ihcams/SKIRT ).…”

Machine learning applications and challenges in graft-versus-host disease: a scoping review

“…Liu et al [66 ▪▪ ] devised a multidimensional probabilistic model called daGOAT that integrates multidimensional time-series data – including vital signs, CBCs, routine serum chemistries, electrolytes, cytokines, flow cytometric data, and antibodies – to calculate the risk for severe acute GVHD. The accuracy of daGOAT was compared to two landmark-specific plasma biomarker-based models (the two-biomarker MAGIC score and the three-biomarker Ann arbor score), two peri-transplantation features-based models (‘PeriHSCT-Naïve Bayes or ‘PeriHSCT-Random Forest), and XGBoost (a gradient-boosting tree algorithm).…”