Drugs; Cancer; Artificial intelligence

mplify the Pediatric Index of Mortality 3 scoring system in order to decrease time and labor required for data collection and entry for Pediatric Index of Mortality 3. DESIGN: Single-center, retrospective cohort study. Data from the Virtual Pediatric Systems for patients admitted to Cleveland Clinic Children`s PICU between January 2008 and December 2019 was obtained. Light Gradient Boosting Machine Regressor (a gradient boosting decision tree algorithm) was used for building the machine learning models. Variable importance was analyzed by SHapley Additive exPlanations. All of the 11 Pediatric Index of Mortality 3 variables were used as input variables in the machine learning models to predict Pediatric Index of Mortality 3 risk of mortality as the outcome variable. Mean absolute error, root mean squared error, and R-squared were calculated for each of the 11 machine learning models as model performance parameters. SETTING: Quaternary children’s hospital. PATIENTS: PICU patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Five-thousand sixty-eight patients were analyzed. The machine learning models were able to maintain similar predictive error until the number of input variables decreased to four. The machine learning model with five input variables (mechanical ventilation in the first hour of PICU admission, very-high-risk diagnosis, surgical recovery from a noncardiac procedure, low-risk diagnosis, and base excess) produced lowest mean root mean squared error of 1.49 (95% CI, 1.05–1.93) and highest R-squared of 0.73 (95% CI, 0.6–0.86) with mean absolute error of 0.43 (95% CI, 0.35–0.5) among all the 11 machine learning models. CONCLUSIONS: Explainable machine learning methods were feasible in simplifying the Pediatric Index of Mortality 3 scoring system with similar risk of mortality predictions compared to the original Pediatric Index of Mortality 3 model tested in a single-center dataset....

Myelodysplastic syndromes (MDSs) are potentially devastating monoclonal deviations of hematopoiesis that lead to bone marrow dysplasia and variable cytopenias. Predicting severity of disease progression and likelihood to undergo acute myeloid leukemia transformation is the basis of treatment strategy. Some patients belong to a low-risk cohort best managed...

Heterogeneity of treatment related myeloid neoplasms (tMN) might be partially related to inadvertent inclusion of coincidental second disease and neoplasia due to genetic predisposition. Clinical features or traditional morphology and cytogenetics are not sufficiently distinctive for possible subtypes of tMN and thus we investigated whether a therapy-related somatic molecular signature can be found. Using controls, including patients who developed myeloid neoplasms (MN) as a second cancer after surgical therapy of a primary tumor, we identified somatic mutations in TP53 and EZH2 likely related to cytotoxic therapy and radiation and compared them to those in primary cases. We further divided tMN types based on the derivation of founder mutations, into cases derived from antecedent or treatment-induced clonal hematopoiesis of indeterminate potential (CHIP) versus those to de novo driver mutations. Treatment-associated myeloid neoplasia are serious complications of cytotoxic therapies of primary cancers.31