Improved risk prediction of chemotherapy‐induced neutropenia—model development and validation with real‐world data

Venäläinen, Mikko S; Heervä, Eetu; Hirvonen, Outi; Saraei, Sohrab; Suomi, Tomi; Mikkola, Toni; Bärlund, Maarit; Jyrkkiö, Sirkku; Laitinen, Tarja; Elo, Laura L.

doi:10.1002/cam4.4465

Cited by 8 publications

(2 citation statements)

References 29 publications

(112 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To develop computational models for predicting the probability of surviving 180 days of csDMARD therapy without laboratory abnormalities, we applied Lasso penalized Cox proportional hazards regression together with stable iterative variable selection (SIVS) to the data in the training cohort to identify the most influential variables on the outcome and to develop a simple-to-use computational model for clinical use 13 . Previously, penalized regression with SIVS has been reported to be an effective method for developing accurate, well-generalizable models with minimum number of variables 14 , 15 . In SIVS, model training with fivefold cross-validation was repeated 100 times.…”

Section: Methodsmentioning

confidence: 99%

Development of prediction model for alanine transaminase elevations during the first 6 months of conventional synthetic DMARD treatment

Kuusalo

Venäläinen

Kirjala

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Frequent laboratory monitoring is recommended for early identification of toxicity when initiating conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). We aimed at developing a risk prediction model to individualize laboratory testing at csDMARD initiation. We identified inflammatory joint disease patients (N = 1196) initiating a csDMARD in Turku University Hospital 2013–2019. Baseline and follow-up safety monitoring results were drawn from electronic health records. For rheumatoid arthritis patients, diagnoses and csDMARD initiation/cessation dates were manually confirmed. Primary endpoint was alanine transaminase (ALT) elevation of more than twice the upper limit of normal (ULN) within 6 months after treatment initiation. Computational models for predicting incident ALT elevations were developed using Lasso Cox proportional hazards regression with stable iterative variable selection (SIVS) and were internally validated against a randomly selected test cohort (1/3 of the data) that was not used for training the models. Primary endpoint was reached in 82 patients (6.9%). Among baseline variables, Lasso model with SIVS predicted subsequent ALT elevations of > 2 × ULN using higher ALT, csDMARD other than methotrexate or sulfasalazine and psoriatic arthritis diagnosis as important predictors, with a concordance index of 0.71 in the test cohort. Respectively, at first follow-up, in addition to baseline ALT and psoriatic arthritis diagnosis, also ALT change from baseline was identified as an important predictor resulting in a test concordance index of 0.72. Our computational model predicts ALT elevations after the first follow-up test with good accuracy and can help in optimizing individual testing frequency.

show abstract

Section: Methodsmentioning

confidence: 99%

Development of prediction model for alanine transaminase elevations during the first 6 months of conventional synthetic DMARD treatment

Kuusalo

Venäläinen

Kirjala

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Patients with neutropenia are generally more susceptible to infections and sepsis (Nesher and Rolston, 2013;Kochanek et al, 2019). There are many studies which have identified risk factors for neutropenia through the use of machine learning (Cho et al, 2020;Venäläinen et al, 2021;Wiberg et al, 2021). Machine learning uses algorithms in order to uncover possible relationships between variables in a dataset.…”

Section: Introductionmentioning

confidence: 99%

Identification of small cell lung cancer patients who are at risk of developing common serious adverse event groups with machine learning

Wanika,

Evans,

Chappell

2023

Front. Drug Saf. Regul.

View full text Add to dashboard Cite

Introduction: Across multiple studies, the most common serious adverse event groups that Small Cell Lung Cancer (SCLC) patients experience, whilst undergoing chemotherapy treatment, are: Blood and Lymphatic Disorders, Infections and Infestations together with Metabolism and Nutrition Disorders. The majority of the research that investigates the relationship between adverse events and SCLC patients, focuses on specific adverse events such as neutropenia and thrombocytopenia.Aim: This study aims to utilise machine learning in order to identify those patients who are at risk of developing common serious adverse event groups, as well as their specific adverse event classification grade.Methods: Data from five clinical trial studies were analysed and 12 analysis groups were formed based on the serious adverse event group and grade.Results: The best test runs for each of the models were able to produce an area under the curve (AUC) score of at least 0.714. The best model was the Blood and Lymphatic Disorder group, SAE grade 0 vs. grade 3 (best AUC = 1, sensitivity rate = 0.84, specificity rate = 0.96).Conclusion: The top features that contributed to this prediction were total bilirubin, alkaline phosphatase, and age. Future work should investigate the relationship between these features and common SAE groups.

show abstract