Radakovich N, Sallman DA, Buckstein R, Brunner A, Dezern A, Mukerjee S, Komrokji R, Al-Ali N, Shreve J, Rouphail Y, Parmentier A, Mamedov A, Siddiqui M, Guan Y, Kuzmanovic T, Hasipek M, Jha B, Maciejewski JP, Sekeres MA, Nazha A. A machine learning model of response to hypomethylating agents in myelodysplastic syndromes.
iScience 2022;
25:104931. [PMID:
36157589 PMCID:
PMC9490588 DOI:
10.1016/j.isci.2022.104931]
[Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/10/2022] [Accepted: 08/09/2022] [Indexed: 11/30/2022] Open
Abstract
Hypomethylating agents (HMA) prolong survival and improve cytopenias in individuals with higher-risk myelodysplastic syndrome (MDS). Only 30-40% of patients, however, respond to HMAs, and responses may not occur for more than 6 months after HMA initiation. We developed a model to more rapidly assess HMA response by analyzing early changes in patients’ blood counts. Three institutions’ data were used to develop a model that assessed patients’ response to therapy 90 days after the initiation using serial blood counts. The model was developed with a training cohort of 424 patients from 2 institutions and validated on an independent cohort of 90 patients. The final model achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 in the train/test group and 0.84 in the validation group. The model provides cohort-wide and individual-level explanations for model predictions, and model certainty can be interrogated to gauge the reliability of a given prediction.
We developed a model to more rapidly assess patients’ response to hypomethylating agents
The model’s predictions use exclusively routinely collected blood count data
The model confirmed prior findings and identified potential new prognostic factors
Model predictions are interpretable on both the individual and cohort-wide level
Collapse