Validation of the molecular international prognostic scoring system in patients with myelodysplastic syndromes defined by international consensus classification.

Myelodysplastic syndromes (MDS) have varied prognoses and require a risk-adapted treatment strategy for treatment optimization. Recently, a molecular prognostic model (Molecular International Prognostic Scoring System [IPSS-M]) that combines clinical parameters, cytogenetic abnormalities, and mutation topography was proposed. This study validated the IPSS-M in 649 patients with primary MDS (based on the 2022 International Consensus Classification [ICC]) and compared its prognostic power to those of the IPSS and revised IPSS (IPSS-R). Overall, 42.5% of the patients were reclassified and 29.3% were up-staged from the IPSS-R. After the reclassification, 16.9% of the patients may receive different treatment strategies. The IPSS-M had greater discriminative potential than the IPSS-R and IPSS. Patients with high, or very high-risk IPSS-M might benefit from allogeneic hematopoietic stem cell transplantation. IPSS-M, age, ferritin level, and the 2022 ICC categorization predicted outcomes independently. After analyzing demographic and genetic features, complementary genetic analyses, including KMT2A-PTD, were suggested for accurate IPSS-M categorization of patients with ASXL1, TET2, STAG2, RUNX1, SF3B1, SRSF2, DNMT3A, U2AF1, and BCOR mutations and those classified as MDS, not otherwise specified with single lineage dysplasia/multi-lineage dysplasia based on the 2022 ICC. This study confirmed that the IPSS-M can better risk-stratified MDS patients for optimized therapeutic decision-making.

Early prediction of delirium upon intensive care unit admission: Model development, validation, and deployment.

STUDY OBJECTIVE: To develop, validate, and deploy models for predicting delirium in critically ill adult patients as early as upon intensive care unit (ICU) admission. DESIGN: Retrospective cohort study. SETTING: Single university teaching hospital in Taipei, Taiwan. PATIENTS: 6238 critically ill patients from August 2020 to August 2021. MEASUREMENTS: Data were extracted, pre-processed, and split into training and testing datasets based on the time period. Eligible variables included demographic characteristics, Glasgow Coma Scale, vital signs parameters, treatments, and laboratory data. The predicted outcome was delirium, defined as any positive result (a score ≥ 4) of the Intensive Care Delirium Screening Checklist that was assessed by primary care nurses in each 8-h shift within 48 h after ICU admission. We trained models to predict delirium upon ICU admission (ADM) and at 24 h (24H) after ICU admission by using logistic regression (LR), gradient boosted trees (GBT), and deep learning (DL) algorithms and compared the models' performance. MAIN RESULTS: Eight features were extracted from the eligible features to train the ADM models, including age, body mass index, medical history of dementia, postoperative intensive monitoring, elective surgery, pre-ICU hospital stays, and GCS score and initial respiratory rate upon ICU admission. In the ADM testing dataset, the incidence of ICU delirium occurred within 24 h and 48 h was 32.9% and 36.2%, respectively. The area under the receiver operating characteristic curve (AUROC) (0.858, 95% CI 0.835-0.879) and area under the precision-recall curve (AUPRC) (0.814, 95% CI 0.780-0.844) for the ADM GBT model were the highest. The Brier scores of the ADM LR, GBT, and DL models were 0.149, 0.140, and 0.145, respectively. The AUROC (0.931, 95% CI 0.911-0.949) was the highest for the 24H DL model and the AUPRC (0.842, 95% CI 0.792-0.886) was the highest for the 24H LR model. CONCLUSION: Our early prediction models based on data obtained upon ICU admission could achieve good performance in predicting delirium occurred within 48 h after ICU admission. Our 24-h models can improve delirium prediction for patients discharged >1 day after ICU admission.