Predicting risk of chemotherapy-induced severe neutropenia: A pooled analysis in individual patients data with advanced lung cancer

Cao, Xiao-Wen; Ganti, Apar Kishor; Stinchcombe, Thomas E.; Wong, Melisa L.; Ho, James Chung Man; Shen, Chen; Liu, Yingzhou; Crawford, Jennifer L.; Pang, Herbert; Wang, Xiaofei

doi:10.1016/j.lungcan.2020.01.004

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…[ 26 ] Moreover, some studies revealed that the Qol score (KPS or ECOG) can predict the risk of myelosuppression, with a lower Qol score indicating a higher possibility of CIM. [ 27 , 28 ] Therefore, before the beginning of chemotherapeutic treatment, the functional status should not be neglected exceptionally for these patients with low KPS scores.…”

Section: Discussionmentioning

confidence: 99%

Predictive value of TCM tongue characteristics for chemotherapy-induced myelosuppression in patients with lung cancer

Sun,

Lou,

Huang

et al. 2024

Medicine

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This study aimed to investigate the clinical predictors, including traditional Chinese medicine tongue characteristics and other clinical parameters for chemotherapy-induced myelosuppression (CIM), and then to develop a clinical prediction model and construct a nomogram. A total of 103 patients with lung cancer were prospectively enrolled in this study. All of them were scheduled to receive first-line chemotherapy regimens. Participants were randomly assigned to either the training group (n = 52) or the test group (n = 51). Tongue characteristics and clinical parameters were collected before the start of chemotherapy, and then the incidence of myelosuppression was assessed after treatment. We used univariate logistic regression analysis to identify the risk predictors for assessing the incidence of CIM. Moreover, we developed a predictive model and a nomogram using multivariate logistic regression analysis. Finally, we evaluated the predictive performance of the model by examining the area under the curve value of the receiver operating characteristic, calibration curve, and decision curve analysis. As a result, a total of 3 independent predictors were found to be associated with the CIM in multivariate regression analysis: the fat tongue (OR = 3.67), Karnofsky performance status score (OR = 0.11), and the number of high-toxic drugs in chemotherapy regimens (OR = 4.78). Then a model was constructed using these 3 predictors and it exhibited a robust predictive performance with an area under the curve of 0.82 and the consistent calibration curves. Besides, the decision curve analysis results suggested that applying this predictive model can result in more net clinical benefit for patients. We established a traditional Chinese medicine prediction model based on the tongue characteristics and clinical parameters, which could serve as a useful tool for assessing the risk of CIM.

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Section: Discussionmentioning

confidence: 99%

Predictive value of TCM tongue characteristics for chemotherapy-induced myelosuppression in patients with lung cancer

Sun,

Lou,

Huang

et al. 2024

Medicine

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“…Patients were excluded from analysis if they were treated for more than one of these cancers during this period, or if they received only immunotherapy or bone modifying agents. A pragmatic literature review was conducted to identify commonly available patient characteristics and laboratory values with possible power in predicting neutropenia 6,8,9,[15][16][17][18][19][20][21][22][23][24][25][26] (Table S1), and these data were included in addition to medication administration records describing chemotherapeutic agents, G-CSF, and immunotherapy. ANCs were excluded from analysis if their values were greater than 15 k/μL, or showed variation greater than 4 k/μL per 24 h period in the absence of G-CSF administered within the preceding 72 h 27 because these values were deemed physiologically unlikely and adversely impacted model estimation.…”

Section: Data Sourcementioning

confidence: 99%

Clinical decision support for chemotherapy‐induced neutropenia using a hybrid pharmacodynamic/machine learning model

Hughes,

Tong,

Burns

et al. 2023

CPT Pharmacom & Syst Pharma

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Consensus guidelines recommend use of granulocyte colony stimulating factor in patients deemed at risk of chemotherapy‐induced neutropenia, however, these risk models are limited in the factors they consider and miss some cases of neutropenia. Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic/pharmacodynamic (PKPD)/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data. We demonstrate this approach using models developed on a large real‐world data set of 9121 patients treated for lymphoma, breast, or thoracic cancer. We also investigate the benefits of augmenting the training data using synthetic data simulated with the PKPD model. We find that PKPD‐enrichment of ML models improves prediction of grade 3–4 neutropenia, as measured by higher precision (61%) and recall (39%) compared to PKPD model predictions (47%, 33%) or base ML model predictions (51%, 31%). PKPD augmentation of ML models showed minor improvements in recall (44%) but not precision (56%), and data augmentation required careful tuning to control overfitting its predictions to the PKPD model. PKPD enrichment of ML shows promise for leveraging both the physiology‐informed predictions of PKPD and the ability of ML to learn predictor‐outcome relationships from large data sets to predict patient response to drugs in a clinical precision dosing context.

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“…Neutropenia is a common complication of systemic chemotherapy due to direct toxicity to neutrophil precursors in the BM. The severity of neutropenia is dependent on treatment intensity and regimen 30 . In patients with a poor marrow reserve, there is a high risk of infection.…”

Section: Secondary Causes Of Neutropenia With Decreased Bm Reservementioning

confidence: 99%

How I investigate neutropenia

Frater

2020

Int J Lab Hematology

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Neutropenia is a common laboratory finding in adults and children. Its underlying causes are extremely heterogeneous and include benign conditions, autoimmune disorders, infections, and malignancies. The clinical laboratory plays a central role in the diagnosis of these disorders, including data derived from hematology, microbiology, molecular biology/cytogenetics, and clinical chemistry. The purpose of this review is to (a) highlight the clinical, hematologic, and molecular genetic features of the major entities resulting in neutropenia and (b) outline an algorithm-based approach to permit the classification of neutropenias. K E Y W O R D S neutropenia, neutrophils

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Predicting risk of chemotherapy-induced severe neutropenia: A pooled analysis in individual patients data with advanced lung cancer

Cited by 14 publications

References 13 publications

Predictive value of TCM tongue characteristics for chemotherapy-induced myelosuppression in patients with lung cancer

Predictive value of TCM tongue characteristics for chemotherapy-induced myelosuppression in patients with lung cancer

Clinical decision support for chemotherapy‐induced neutropenia using a hybrid pharmacodynamic/machine learning model

How I investigate neutropenia

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