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Background Graft-versus-host disease (GVHD) remains a major determinant of morbidity and mortality following allogeneic hematopoietic stem cell transplantation (allo-HSCT). Existing GVHD prediction models demonstrate modest discrimination and limited generalizability, and calibration drift across external populations is rarely characterized despite its essential role in the clinical interpretability of predicted probabilities. Objectives To develop and externally validate an explainable machine learning framework for predicting acute and chronic GVHD and associated overall survival in patients with acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), and myelodysplastic syndromes (MDS) undergoing allo-HSCT, and to systematically characterize calibration across heterogeneous external validation cohorts to inform deployment requirements. Study Design The model was developed on three publicly available registry-derived datasets (N = 2,509) and externally validated across six independent cohorts (N = 14,788) comprising adult and pediatric allo-HSCT recipients, including a regional Middle Eastern cohort (UAE and Jordan). A standardized preprocessing pipeline harmonized heterogeneous datasets. Gradient boosting models (CatBoost) were used for binary GVHD prediction; exploratory overall survival analysis used a Cox proportional hazards model with predicted acute GVHD risk as a covariate. Discrimination (AUROC with bootstrap 95% CI), calibration (logistic recalibration intercept and slope with analytical 95% CI), and feature importance (SHapley Additive exPlanations, SHAP) were assessed in training out-of-fold and all external cohorts. Results In internal validation, AUROC was 0.63 (95% CI 0.61–0.65) for acute GVHD and 0.72 (95% CI 0.70–0.74) for chronic GVHD. External validation demonstrated AUROC ranges of 0.51–0.57 (acute) and 0.54–0.64 (chronic), with consistent performance across disease subgroups despite substantial heterogeneity in transplant practices and feature availability. In exploratory survival analysis, the acute-GVHD-informed Cox model achieved a training-cohort C-index of 0.679 (95% CI 0.658–0.697); external C-indices ranged from 0.47–0.53. Calibration analysis identified systematic external risk overestimation (negative calibration intercept in 10 of 11 evaluable external cohort-target combinations) with heterogeneous slope drift requiring cohort-specific recalibration. Key predictors included recipient age, graft source, conditioning intensity, GVHD prophylaxis, and HLA match ratio. Conclusions An explainable, externally validated GVHD prediction framework was developed using heterogeneous registry-derived datasets, with systematic characterization of calibration drift across multiple external cohorts, an analysis rarely reported in prior GVHD prediction literature. Predictive performance was modest for acute GVHD and moderate for chronic GVHD, constrained by missing immunobiological variables and incomplete HLA characterization. Per-cohort recalibration is required before clinical deployment, with prospective validation and benchmarking against established GVHD risk scores identified as priority next steps.

Abstract Background: Graft-versus-host disease (GVHD) remains the leading cause of non-relapse morbidity and mortality in allogeneic hematopoietic-stem-cell transplantation (allo-HSCT) recipients. Clinical scoring systems (e.g., EBMT, HCT-CI, DRI, etc.) can stratify risk but show modest predictive performance (C-statistics 0.49–0.67) when it comes to GVHD. To address this gap, we developed GVHD-Intel 1.0, a real-time, scalable, and interpretable machine learning (ML) framework from an international study to predict both acute (aGVHD) and chronic GVHD (cGVHD), providing individual and bulk predictions using common input variables and HLA alleles. Methods: This retrospective global study, approved by respective institutional review boards, included electronic health records from three tertiary care centers in Abu Dhabi, UAE, and King Hussein Cancer Center (KHCC), Jordan, from 2009 to 2023. The model was developed in phases. The UAE399 cohort (399 HSCT recipients) served as the baseline dataset. To enhance model generalizability, 55 adult allo-HSCT recipients from KHCC were added to the training set. Two distinct test cohorts were used: UAE46 (46 allogeneic HSCT recipients from UAE) and KHCC150 (150 adult allogeneic HSCT recipients from KHCC), collected later and independently of training data. Variables included donor type, sex mismatch, disease status, time from diagnosis to transplant, and agents used in conditioning and GVHD prophylaxis (e.g., TBI, ATG, alemtuzumab use etc.). HLA alleles (A, B, C, DQ, DR) were symbolically encoded and transformed via one-hot encoding. GVHD events were classified as aGVHD (<100 days) or cGVHD (≥100 days). Missing values were excluded, and class imbalance was handled via class weighting. GVHD-Intel 1.0, co-developed with MBZ University of Artificial Intelligence, features modular ensemble architecture, drug-level feature engineering, novel HLA encoding for low-resolution compatibility scoring, and built-in interpretability using SHAP and LIME. The framework is cloud-deployable for both real-time and batch predictions. Results: The GVHD-Intel 1.0 was externally tested on two independent, temporally and institutionally distinct cohorts (UAE46 and KHCC150), demonstrating strong and consistent predictive performance for both acute and chronic GVHD. For cGVHD prediction, the model achieved an AUC (area under the curve or C-statistic for binary outcomes) of 0.832, F1 score of 0.896, accuracy of 82.7%, PPV of 0.930, sensitivity of 0.864, Brier score of 0.178, and log loss of 0.543. For aGVHD, performance metrics included an AUC of 0.802, F1 score of 0.860, accuracy of 77.6%, PPV of 0.930, sensitivity of 0.799, Brier score of 0.196, and log loss of 0.581. Model calibration remained strong across both tasks, with Brier scores <0.20. Conclusions: GVHD-Intel 1.0 is a robust, interpretable machine learning framework developed from real-world, multicenter data. By integrating widely available clinical variables and HLA allele data, it outperforms conventional clinical risk scores in predicting both acute and chronic GVHD in allo-HSCT recipients. Slightly reduced performance in aGVHD prediction is likely due to the lack of relevant variables. Next steps include prospective validation across multiple transplant centers globally and continued refinement through expanded data integration. GVHD-Intel 1.0 holds strong potential as a clinically valuable, scalable decision-support tool that can be enacted widely and easily by practicing transplant physicians worldwide in all types of settings.

ABSTRACT Background Autosomal dominant polycystic kidney disease (ADPKD) is the most common renal monogenic disease, characterized by bilateral accumulation of renal fluid-filled cysts leading to progressive renal volume enlargement and gradual impairment of kidney function, often resulting in end-stage renal disease. Kuwait could provide valuable genetic insights about ADPKD, including intrafamilial phenotypic variation, given its large household size. This study aims to provide a comprehensive description of the pathogenic variants linked to ADPKD in the Kuwaiti population using multiple genetic analysis modalities and to describe and analyse the ADPKD phenotypic spectrum in terms of kidney function, kidney volume and renal survival. Methods A total of 126 ADPKD patients from 11 multiplex families and 25 singletons were recruited into the study. A combination of targeted next-generation sequencing (tNGS), long-range polymerase chain reaction, Sanger sequencing and multiplex ligation-dependent probe amplification were utilized for genetic diagnosis. Clinical evaluation was conducted through renal function testing and ultrasonographic kidney volume analysis. Results We identified 29 ADPKD pathogenic mutations from 36 families achieving an overall molecular genetic diagnostic rate of 112/126 (88.9%), including 29/36 (80.6%) in families. A total of 28/36 (77.8%) families had pathogenic mutations in PKD1, of which 17/28 (60.7%) were truncating, and 1/36 (2.8%) had a pathogenic variant in the IFT140 gene. A total of 20/29 (69%) of the identified ADPKD mutations were novel and described for the first time, including a TSC2-PKD1 contiguous syndrome. Clinical analysis indicated that genetically unresolved ADPKD cases had no apparent association between kidney volume and age. Conclusion We describe for the first time the genetic landscape of ADPKD in Kuwait. The observed genetic heterogeneity underlining ADPKD along with the wide phenotypic spectrum reveal the level of complexity in disease pathophysiology. ADPKD genetic testing could improve the care of patients through improved disease prognostication, guided treatment and genetic counselling. However, to fulfil the potential of genetic testing, it is important to overcome the hurdle of genetically unresolved ADPKD cases.