Improved risk prediction of chemotherapy-induced neutropenia-model development and validation with real-world data.

ABSTRACT:

Background

The existing risk prediction models for chemotherapy-induced febrile neutropenia (FN) do not necessarily apply to real-life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning-based risk prediction model could outperform the previously introduced models, especially when validated against real-world patient data from another institution not used for model training.

Methods

Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non-hematological cancer between the years 2010 and 2017 (N = 5879). An experimental surrogate endpoint was first-cycle neutropenic infection (NI), defined as grade IV neutropenia wit

SUBMITTER: Venalainen MS 

PROVIDER: S-EPMC8817096 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature