Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy.

Background: Immunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy. Methods: A baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (T pre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T-score (base model plus T bio), and clinical variables and T pre (base model plus T pre) were developed separately in 1,168 patients with regular follow-up to evaluate whether T pre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using T pre. Results: The features selected by AUCRF for the T pre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the T pre was 0.82 (95% CI: 0.80-0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75-0.97). When the T bio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75-0.97) to 0.92 (95% CI: 0.85-0.98); P = 0.03]. There was no difference in AUC between the base model plus T pre and the base model plus T bio [0.90 (95% CI: 0.82-0.99) vs. 0.92 (95% CI: 0.85-0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using T pre was 0.93 (95% CI: 0.87-0.99) in the external validation set. Conclusion: A pathology T-score prediction (T pre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.

Machine learning algorithm as a prognostic tool for Epstein-Barr virus reactivation after haploidentical hematopoietic stem cell transplantation.

Epstein-Barr virus (EBV) reactivation is one of the most important infections after hematopoietic stem cell transplantation (HSCT) using haplo-identical related donors (HID). We aimed to establish a comprehensive model with machine learning, which could predict EBV reactivation after HID HSCT with anti-thymocyte globulin (ATG) for graft-versus-host disease (GVHD) prophylaxis. We enrolled 470 consecutive acute leukemia patients, 60% of them (n = 282) randomly selected as a training cohort, the remaining 40% (n = 188) as a validation cohort. The equation was as follows: Probability (EBV reactivation) =   1 1       +       e x p ( - Y ) , where Y = 0.0250 × (age) - 0.3614 × (gender) + 0.0668 × (underlying disease) - 0.6297 × (disease status before HSCT) - 0.0726 × (disease risk index) - 0.0118 × (hematopoietic cell transplantation-specific comorbidity index [HCT-CI] score) + 1.2037 × (human leukocyte antigen disparity) + 0.5347 × (EBV serostatus) + 0.1605 × (conditioning regimen) - 0.2270 × (donor/recipient gender matched) + 0.2304 × (donor/recipient relation) - 0.0170 × (mononuclear cell counts in graft) + 0.0395 × (CD34+ cell count in graft) - 2.4510. The threshold of probability was 0.4623, which separated patients into low- and high-risk groups. The 1-year cumulative incidence of EBV reactivation in the low- and high-risk groups was 11.0% versus 24.5% (P < .001), 10.7% versus 19.3% (P = .046), and 11.4% versus 31.6% (P = .001), respectively, in total, training and validation cohorts. The model could also predict relapse and survival after HID HSCT. We established a comprehensive model that could predict EBV reactivation in HID HSCT recipients using ATG for GVHD prophylaxis.

A comprehensive model to predict severe acute graft-versus-host disease in acute leukemia patients after haploidentical hematopoietic stem cell transplantation.

BACKGROUND: Acute graft-versus-host disease (aGVHD) remains the major cause of early mortality after haploidentical related donor (HID) hematopoietic stem cell transplantation (HSCT). We aimed to establish a comprehensive model which could predict severe aGVHD after HID HSCT. METHODS: Consecutive 470 acute leukemia patients receiving HID HSCT according to the protocol registered at https://clinicaltrials.gov (NCT03756675) were enrolled, 70% of them (n = 335) were randomly selected as training cohort and the remains 30% (n = 135) were used as validation cohort. RESULTS: The equation was as follows: Probability (grade III-IV aGVHD) = [Formula: see text], where Y = -0.0288 × (age) + 0.7965 × (gender) + 0.8371 × (CD3 + /CD14 + cells ratio in graft) + 0.5829 × (donor/recipient relation) - 0.0089 × (CD8 + cell counts in graft) - 2.9046. The threshold of probability was 0.057392 which helped separate patients into high- and low-risk groups. The 100-day cumulative incidence of grade III-IV aGVHD in the low- and high-risk groups was 4.1% (95% CI 1.9-6.3%) versus 12.8% (95% CI 7.4-18.2%) (P = 0.001), 3.2% (95% CI 1.2-5.1%) versus 10.6% (95% CI 4.7-16.5%) (P = 0.006), and 6.1% (95% CI 1.3-10.9%) versus 19.4% (95% CI 6.3-32.5%) (P = 0.017), respectively, in total, training, and validation cohort. The rates of grade III-IV skin and gut aGVHD in high-risk group were both significantly higher than those of low-risk group. This model could also predict grade II-IV and grade I-IV aGVHD. CONCLUSIONS: We established a model which could predict the development of severe aGVHD in HID HSCT recipients.

A Predicted Model for Refractory/Recurrent Cytomegalovirus Infection in Acute Leukemia Patients After Haploidentical Hematopoietic Stem Cell Transplantation.

Objective: We aimed to establish a model that can predict refractory/recurrent cytomegalovirus (CMV) infection after haploidentical donor (HID) hematopoietic stem cell transplantation (HSCT). Methods: Consecutive acute leukemia patients receiving HID HSCT were enrolled (n = 289). We randomly selected 60% of the entire population (n = 170) as the training cohort, and the remaining 40% comprised the validation cohort (n = 119). Patients were treated according to the protocol registered at https://clinicaltrials.gov (NCT03756675). Results: The model was as follows: Y = 0.0322 × (age) - 0.0696 × (gender) + 0.5492 × (underlying disease) + 0.0963 × (the cumulative dose of prednisone during pre-engraftment phase) - 0.0771 × (CD34+ cell counts in graft) - 1.2926. The threshold of probability was 0.5243, which helped to separate patients into high- and low-risk groups. In the low- and high-risk groups, the 100-day cumulative incidence of refractory/recurrent CMV was 42.0% [95% confidence interval (CI), 34.7%-49.4%] vs. 63.7% (95% CI, 54.8%-72.6%) (P < 0.001) for total patients and was 50.5% (95% confidence interval (CI), 40.9%-60.1%) vs. 71.0% (95% CI, 59.5%-82.4%) (P = 0.024) for those with acute graft-versus-host disease. It could also predict posttransplant mortality and survival. Conclusion: We established a comprehensive model that could predict the refractory/recurrent CMV infection after HID HSCT. Clinical Trial Registration: https://clinicaltrials.gov, identifier NCT03756675.

Basiliximab for steroid-refractory acute graft-versus-host disease: A real-world analysis.

Steroid-refractory (SR) acute graft-versus-host disease (aGVHD) is one of the leading causes of early mortality after allogeneic hematopoietic stem cell transplantation (allo-HSCT). We investigated the efficacy, safety, prognostic factors, and optimal therapeutic protocol for SR-aGVHD patients treated with basiliximab in a real-world setting. Nine hundred and forty SR-aGVHD patients were recruited from 36 hospitals in China, and 3683 doses of basiliximab were administered. Basiliximab was used as monotherapy (n = 642) or in combination with other second-line treatments (n = 298). The cumulative incidence of overall response rate (ORR) at day 28 after basiliximab treatment was 79.4% (95% confidence interval [CI] 76.5%-82.3%). The probabilities of nonrelapse mortality and overall survival at 3 years after basiliximab treatment were 26.8% (95% CI 24.0%-29.6%) and 64.3% (95% CI 61.2%-67.4%), respectively. A 1:1 propensity score matching was performed to compare the efficacy and safety between the monotherapy and combined therapy groups. Combined therapy did not increase the ORR; conversely, it increased the infection rates compared with monotherapy. The multivariate analysis showed that combined therapy, grade III-IV aGVHD, and high-risk refined Minnesota aGVHD risk score before basiliximab treatment were independently associated with the therapeutic response. Hence, we created a prognostic scoring system that could predict the risk of having a decreased likelihood of response after basiliximab treatment. Machine learning was used to develop a protocol that maximized the efficacy of basiliximab while maintaining acceptable levels of infection risk. Thus, real-world data suggest that basiliximab is safe and effective for treating SR-aGVHD.

Meta-Analysis of Interleukin-2 Receptor Antagonists as the Treatment for Steroid-Refractory Acute Graft-Versus-Host Disease.

Acute graft-versus-host disease (aGVHD) is a major complication after allogeneic hematopoietic stem cell transplantation (HSCT). Corticosteroid is the first-line treatment for aGVHD, but its response rate is only approximately 50%. At present, no uniformly accepted treatment for steroid-refractory aGVHD (SR-aGVHD) is available. Blocking interleukin-2 receptors (IL-2Rs) on donor T cells using pharmaceutical antagonists alleviates SR-aGVHD. This meta-analysis aimed to compare the efficacy and safety of four commercially available IL-2R antagonists (IL-2RAs) in SR-aGVHD treatment. A total of 31 studies met the following inclusion criteria (1): patients of any race, any sex, and all ages (2); those diagnosed with SR-aGVHD after HSCT; and (3) those using IL-2RA-based therapy as the treatment for SR-aGVHD. The overall response rate (ORR) at any time after treatment with basiliximab and daclizumab was 0.81 [95% confidence interval (CI): 0.74-0.87)] and 0.71 (95% CI: 0.56-0.82), respectively, which was better than that of inolimomab 0.54 (95% CI: 0.39-0.68) and denileukin diftitox 0.56 (95% CI: 0.35-0.76). The complete response rate (CRR) at any time after treatment with basiliximab and daclizumab was 0.55 (95% CI: 0.42-0.68) and 0.42 (95%CI: 0.29-0.56), respectively, which was better than that of inolimomab 0.30 (95% CI: 0.16-0.51) and denileukin diftitox 0.37 (95% CI: 0.24-0.52). The ORR and CRR were better after 1-month treatment with basiliximab and daclizumab than after treatment with inolimomab and denileukin diftitox. The incidence of the infection was higher after inolimomab treatment than after treatment with the other IL-2RAs. In conclusion, the efficacy and safety of different IL-2RAs varied. The response rate of basiliximab was the highest, followed by that of daclizumab. Prospective, randomized controlled trials are needed to compare the efficacy and safety of different IL-2RAs.