Development of a simplified multivariable model to predict neutropenic complications in cancer patients undergoing chemotherapy

Razzaghdoust, Abolfazl; Mofid, Bahram; Hamzeloo-Moghadam, Maryam

doi:10.1007/s00520-018-4224-z

Cited by 22 publications

(27 citation statements)

References 39 publications

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“…This cohort study was conducted using medical records from 305 cancer patients initiating a new chemotherapy line for the treatment of solid tumors or lymphoma between February 2015 and March 2016 at a tertiary referral medical center. 17 All patients were followed during all chemotherapy cycles. All procedures performed in study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.…”

Section: Study Design and Patientsmentioning

confidence: 99%

“…Regimens used and their related thrombocytopenia risk category are listed in the online supplementary Table. A detailed report regarding the material and methods used was presented in the supplementary of our previous study. 17…”

Section: Study Design and Patientsmentioning

confidence: 99%

“…neutropenia and anemia) have been identified in a cohort of 305 cancer patients receiving chemotherapy for solid tumors or lymphoma. 17,18 The objective of the present study was to develop a predictive model for CIT in the cohort of cancer patients.…”

Section: Introductionmentioning

confidence: 99%

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Predicting chemotherapy-induced thrombocytopenia in cancer patients with solid tumors or lymphoma

Razzaghdoust

Mofid

Zangeneh

2019

J Oncol Pharm Pract

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Purpose Chemotherapy-induced thrombocytopenia is a serious complication in chemotherapy-treated patients. Identification of patients at risk for chemotherapy-induced thrombocytopenia could have clinical value in personalized management of patients and optimized administration of prophylactic thrombopoietic agents. The aim of this study was to develop a predictive model for chemotherapy-induced thrombocytopenia (platelet count < 100,000/µl) in cancer patients undergoing chemotherapy. Methods A total of 14 covariates were prospectively assessed as explanatory variables in a cohort of consecutive patients with solid tumors or lymphoma. A multivariable logistic regression model was developed after univariable analysis. A bootstrapping technique was applied for internal validation. Results Data from 305 patients during 1732 chemotherapy cycles were considered for analysis. Forty-eight patients (15.73%) developed chemotherapy-induced thrombocytopenia during their treatment course. The multivariable model exhibited three final predictors for chemotherapy-induced thrombocytopenia, including high ferritin (odds ratio, 4.41; bootstrap P = 0.001), estimated glomerular filtration rate <60 ml/min/1.73 m2 (odds ratio, 3.08; bootstrap P = 0.005), and body mass index <23 kg/m2 (odds ratio, 2.23; bootstrap P = 0.044). The main characteristics of the model include sensitivity 75%, specificity 65.4%, positive likelihood ratio 2.16, and negative likelihood ratio 0.382. Moreover, the model was well calibrated (Hosmer–Lemeshow P = 0.713) and the area under the receiver operating characteristic curve was 0.735 (95% confidence interval, 0.654–0.816; P < 0.001). Conclusions We developed a predictive model for chemotherapy-induced thrombocytopenia based on readily available and easily assessable clinical and laboratory factors. This study may provide a valuable insight to guide optimized treatment of cancer patients. Further studies with larger sample size are warranted.

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Section: Study Design and Patientsmentioning

confidence: 99%

Section: Study Design and Patientsmentioning

confidence: 99%

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Predicting chemotherapy-induced thrombocytopenia in cancer patients with solid tumors or lymphoma

Razzaghdoust

Mofid

Zangeneh

2019

J Oncol Pharm Pract

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“…We previously presented the FEbrile Neutropenia after ChEmotherapy (FENCE) score for identifying patients at high risk of developing FN in the first cycle based on pretreatment risk factors . Others have published similar methods . These methods assume that the risk of FN in the first cycle can be extrapolated to the remainder of the chemotherapy course.…”

Section: Introductionmentioning

confidence: 99%

“…8 Others have published similar methods. [9][10][11] These methods assume that the risk of FN in the first cycle can be extrapolated to the remainder of the chemotherapy course. However, guidelines recommend assessing the risk of FN at the start of each cycle to initiate preventive measures for FN in high-risk patients, such as prophylaxis with granulocyte colony-stimulating factors (G-CSF).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a cycle‐specific risk score for febrile neutropenia during chemotherapy cycles 2–6 in patients with solid cancers: The ^CSRFENCE score

Aagaard

Reekie

Roen

et al. 2019

Intl Journal of Cancer

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The absolute risk reduction by prophylaxis in chemotherapy‐induced febrile neutropenia (FN) is largest in patients at highest underlying risk. Therefore, reliable predictive models are needed. Here, we develop and validate such a model for risk of FN during chemotherapy cycles 2–6. A prediction score for risk of FN during the first cycle has recently been published. Patients with solid cancers initiating first‐line chemotherapy in 2010–2016 were included. Cycle‐specific risk factors were assessed by Poisson regression using generalized estimating equations and random split sampling. The derivation cohort included 4,590 patients treated with 15,419 cycles, wherein 326 (2.1%) FN events occurred. Predictors of FN in multivariable analyses were: higher predicted risk of FN in the first cycle, platinum‐ or taxane‐containing therapies, concurrent radiotherapy, treatment in cycle 2 compared to later cycles, previous FN or neutropenia and not receiving granulocyte colony‐stimulating factors. Each predictor added between −2 and 8 points to each patient's score (median score 4; interquartile range, 1–6). The incidence rate ratios for developing FN in the intermediate (score 1–4), high (score 5–6) and very high risk groups (score ≥7) were 7.8 (95% CI, 2.4–24.9), 18.6 (95% CI, 5.9–58.8) and 51.7 (95% CI, 16.5–162.3) compared to the low risk group (score ≤0), respectively. The score had good discriminatory ability with a Harrell's C‐statistic of 0.78 (95% CI, 0.76–0.80) in the derivation and 0.75 (95% CI, 0.72–0.78) in the validation cohort (patient n = 2,295, cycle n = 7,670). The Cycle‐Specific Risk of FEbrile Neutropenia after ChEmotherapy score is the first published method to estimate cycle‐specific risk of FN.

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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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Development of a simplified multivariable model to predict neutropenic complications in cancer patients undergoing chemotherapy

Abstract: We developed and internally validated a user-friendly prediction model to guide personalized decision-making using available clinical data and few cost-effective laboratory tests. External validation in other centers with different patients is required.

Cited by 22 publications

References 39 publications

Predicting chemotherapy-induced thrombocytopenia in cancer patients with solid tumors or lymphoma

Predicting chemotherapy-induced thrombocytopenia in cancer patients with solid tumors or lymphoma

Development and validation of a cycle‐specific risk score for febrile neutropenia during chemotherapy cycles 2–6 in patients with solid cancers: The ^CSRFENCE score

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

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Development of a simplified multivariable model to predict neutropenic complications in cancer patients undergoing chemotherapy

Abstract: We developed and internally validated a user-friendly prediction model to guide personalized decision-making using available clinical data and few cost-effective laboratory tests. External validation in other centers with different patients is required.

Cited by 22 publications

References 39 publications

Predicting chemotherapy-induced thrombocytopenia in cancer patients with solid tumors or lymphoma

Predicting chemotherapy-induced thrombocytopenia in cancer patients with solid tumors or lymphoma

Development and validation of a cycle‐specific risk score for febrile neutropenia during chemotherapy cycles 2–6 in patients with solid cancers: The CSRFENCE score

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

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Development and validation of a cycle‐specific risk score for febrile neutropenia during chemotherapy cycles 2–6 in patients with solid cancers: The ^CSRFENCE score