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

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

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.

A contrast-enhanced CT-based whole-spleen radiomics signature for early prediction of oxaliplatin-related thrombocytopenia in patients with gastrointestinal malignancies: a retrospective study

Background

Thrombocytopenia is a common adverse event of oxaliplatin-based chemotherapy. Grade 2 or higher oxaliplatin-related thrombocytopenia may result in dose reduction, discontinuation or delay initiation of chemotherapy and may adversely affect the therapeutic efficacy and even overall survival of patients. Early recognition of patients at risk of developing grade 2 or higher thrombocytopenia is critical. However, to date there is no well-established method to early identify patients at high risk. The aims of this study were to develop and validate a contrast-enhanced CT-based whole-spleen radiomics signature for early prediction of grade 2 or higher thrombocytopenia in patients with gastrointestinal malignancies treated with oxaliplatin-based chemotherapy and to explore the incremental value of combining the radiomics signature and conventional clinical factors for risk prediction.

Methods

A total of 119 patients with gastrointestinal malignancies receiving oxaliplatin-based chemotherapy from March 2017 to December 2020 were retrospectively included and randomly divided into a training cohort (n = 85) and a validation cohort (n = 34). Grade 2 or higher thrombocytopenia occurred in 26.1% of patients (22 and nine patients in the training and validation cohort, respectively) with a median time interval of 101 days from the start of chemotherapy. The whole-spleen radiomics features were extracted on the portal venous phase of the first follow-up CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to select radiomics features and to build the radiomics signature for the prediction of grade 2 or higher thrombocytopenia. A clinical model that included clinical factors only and a clinical-radiomics model that incorporated clinical factors and radiomics signature were constructed. The performances of both models were evaluated and compared in the training, validation and the whole cohorts.

Results

The radiomics signature yielded favorable performance in predicting grade 2 or higher thrombocytopenia, with the area under the curve (AUC), sensitivity and specificity being 0.865, 81.8% and 84.1% in the training cohort and 0.747, 77.8% and 80.0% in the validation cohort. The AUCs of the clinical-radiomics model in the training and validation cohorts reached 0.913 (95% CI [0.720-0.935]) and 0.867 (95% CI [0.727-1.000]), greater than the AUCs of the clinical model. Integrated discrimination improvement (IDI) index showed that incorporating radiomic signature into conventional clinical factors significantly improved the predictive accuracy by 17.0% (95% CI [4.9%-29.1%], p = 0.006) in the whole cohort.

Conclusions

Contrast-enhanced CT-based whole-spleen radiomics signature might serve as an early predictor for grade 2 or higher thrombocytopenia during oxaliplatin-based chemotherapy in patients with gastrointestinal malignancies and provide incremental value over conventional clinical factors.

Development of a Machine Learning-Based Prediction Model for Chemotherapy-Induced Myelosuppression in Children with Wilms' Tumor

Purpose: Develop and validate an accessible prediction model using machine learning (ML) to predict the risk of chemotherapy-induced myelosuppression (CIM) in children with Wilms' tumor (WT) before chemotherapy is administered, enabling early preventive management. Methods: A total of 1433 chemotherapy cycles in 437 children with WT who received chemotherapy in our hospital from January 2009 to March 2022 were retrospectively analyzed. Demographic data, clinicopathological characteristics, hematology and blood biochemistry baseline results, and medication information were collected. Six ML algorithms were used to construct prediction models, and the predictive efficacy of these models was evaluated to select the best model to predict the risk of grade ≥ 2 CIM in children with WT. A series of methods, such as the area under the receiver operating characteristic curve (AUROC), the calibration curve, and the decision curve analysis (DCA) were used to test the model's accuracy, discrimination, and clinical practicability. Results: Grade ≥ 2 CIM occurred in 58.5% (839/1433) of chemotherapy cycles. Based on the results of the training and validation cohorts, we finally identified that the extreme gradient boosting (XGB) model has the best predictive efficiency and stability, with an AUROC of up to 0.981 in the training set and up to 0.896 in the test set. In addition, the calibration curve and the DCA showed that the XGB model had the best discrimination and clinical practicability. The variables were ranked according to the feature importance, and the five variables contributing the most to the model were hemoglobin (Hgb), white blood cell count (WBC), alkaline phosphatase, coadministration of highly toxic chemotherapy drugs, and albumin. Conclusions: The incidence of grade ≥ 2 CIM was not low in children with WT, which needs attention. The XGB model was developed to predict the risk of grade ≥ 2 CIM in children with WT for the first time. The model has good predictive performance and stability and has the potential to be translated into clinical applications. Based on this modeling and application approach, the extension of CIM prediction models to other pediatric malignancies could be expected.

Afebrile chemotherapy-induced neutropenia: an international survey spots oncologists’ routine clinical practice versus the standard of care and the impact of COVID-19

Introduction

Afebrile chemotherapy-induced neutropenia represents a frequent clinical situation where chemotherapy protocol, patient’s comorbidities, and disease status determine the risk of infection hence the management plan. Internationally distributed, this questionnaire aims to evaluate the routine practice and the impact of the COVID-19 pandemic on afebrile chemotherapy-induced neutropenia management.

Material and methods

Coordinators from Egypt, Morocco, Azerbaijan, and Russia developed a 12-item questionnaire using Google forms to explore how oncologists deal with afebrile chemotherapy-induced neutropenia. The link to the survey was available internationally through social media and to their local societies over the period from July to September 2021.

Results

We received 151 responses from 4 world regions: 58.9, 9.9, 11.3, and 15.2% from the Mena area, Russia, Europe, and Asia. The responses deviated from the guideline-driven practice as G-CSF was the most chosen option for intermediate risk that was statistically different based on the academic background of the treating physician. Half of the responders ignored patients and disease risk factors in the intermediate-risk cases that trend was statistically different based on the geographical distribution. The steroid was a valid option for intermediate and low-risk as per oncologists practicing in Russia. COVID-19 pandemic positively affected the rate of prescription of G-CSF as expected.

Conclusion

The disparities in the routine practice of oncologists based on their geographical and academic backgrounds highlight the need to analyze the underlying obstacles that hinder guideline-based practice like workload or lack of the proper knowledge.

Milk and Egg Are Risk Factors for Adverse Effects of Capecitabine-Based Chemotherapy in Chinese Colorectal Cancer Patients

Background:

Chemotherapy-induced adverse effects (CIAEs) remain a challenging problem due to their high incidences and negative impacts on treatment in Chinese colorectal cancer (CRC) patients. We aimed to identify risk factors and predictive markers for CIAEs using food/nutrition data in CRC patients receiving post-operative capecitabine-based chemotherapy.

Methods:

Food/nutrition data from 130 Chinese CRC patients were analyzed. Univariate and multivariate analyses were used to identify CIAE-related food/nutrition factors. Prediction models were constructed based on the combination of these factors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the discrimination ability of models.

Results:

A total of 20 food/nutrition factors associated with CIAEs were identified in the univariate analysis after adjustments for total energy and potential confounding factors. Based on multivariate analysis, we found that, among these factors, dessert, eggs, poultry, and milk were associated with several CIAEs. Most importantly, poultry was an overall protective factor; milk and egg were risk factors for hand-foot syndrome (HFS) and bone marrow suppression (BMS), respectively. Developed multivariate models in predicting grade 1 to 3 CIAEs and grade 2/3 CIAEs both had good discrimination (AUROC values from 0.671 to 0.778, 0.750 to 0.946 respectively), which had potential clinical application value in the early prediction of CIAEs, especially for more severe CIAEs.

Conclusions:

Our findings suggest that patients with high milk and egg intakes should be clinically instructed to control their corresponding dietary intake to reduce the likelihood of developing HFS and BMS during capecitabine-based chemotherapy, respectively.

Trial registration:

ClinicalTrials.gov Identifier: NCT03030508.

OUP accepted manuscript

Background

The FEbrile Neutropenia after ChEmotherapy (FENCE) score was developed to estimate the risk of febrile neutropenia (FN) at first cycle of chemotherapy but has not been externally validated. We aimed to validate the FENCE score based on its risk groups in patients treated at a comprehensive cancer center.

Methods

We conducted a retrospective study of treatment-naïve adult patients with solid tumors and diffuse large B-cell lymphoma who received first-cycle chemotherapy between January and November 2019. Patients were followed until the second cycle of chemotherapy to identify any FN events (neutrophil count <0.5 × 10⁹/L with fever ≥38.2°C). The FENCE score was determined and patients classified as low, intermediate, high, and very high risk. The discriminatory ability of classifying patients into FENCE risk groups was calculated as the area under the receiver operating characteristics curve and incidence rate ratios within each FENCE risk group.

Results

FN was documented during the first cycle of chemotherapy in 45 of the 918 patients included (5%). The area under the receiver operating characteristics curve was 0.66 (95% confidence interval [CI] = 0.58 to 0.73). Compared with the low-risk group (n = 285), the incidence rate ratio of developing FN was 1.58 (95% CI = 0.54 to 5.21), 3.16 (95% CI = 1.09 to 10.25), and 3.93 (95% CI = 1.46 to 12.27) in the intermediate (n = 293), high (n = 162), and very high (n = 178) risk groups, respectively.

Conclusions

In this study, classifying patients into FENCE risk groups demonstrated moderate discriminatory ability for predicting FN. Further validation in multicenter studies is necessary to determine its generalizability.

Risk Factors for Infections, Antibiotic Therapy, and Its Impact on Cancer Therapy Outcomes for Patients with Solid Tumors

Infections represent a significant cause of morbidity and mortality in cancer patients. Multiple factors related to the patient, tumor, and cancer therapy can affect the risk of infection in patients with solid tumors. A thorough understanding of such factors can aid in the identification of patients with substantial risk of infection, allowing medical practitioners to tailor therapy and apply prophylactic measures to avoid serious complications. The use of novel treatment modalities, including targeted therapy and immunotherapy, brings diagnostic and therapeutic challenges into the management of infections in cancer patients. A growing body of evidence suggests that antibiotic therapy can modulate both toxicity and antitumor response induced by chemotherapy, radiotherapy, and especially immunotherapy. This article provides a comprehensive review of potential risk factors for infections and therapeutic approaches for the most prevalent infections in patients with solid tumors, and discusses the potential effect of antibiotic therapy on toxicity and efficacy of cancer therapy.

A Risk-Prediction Nomogram for Neutropenia or Febrile Neutropenia after Etoposide-Based Chemotherapy in Cancer Patients: A Retrospective Cohort Study

INTRODUCTION

This study was conducted to develop and validate a nomogram for predicting the risk of neutropenia or febrile neutropenia (FN) in tumor patients in the first cycle of etoposide-based chemotherapy.

METHODS

This retrospective cohort study used an information system to monitor patients with non-Hodgkin's lymphoma or solid tumors receiving an etoposide regimen in the first chemotherapy cycle in our hospital from 2009 to 2020. Binary logistic regression analysis was used to identify the influencing factors of patients with neutropenia or FN. Those factors were then used to develop a nomogram.

RESULTS

A total of 1,554 patients were divided into the development group (n = 1,072) and validation group (n = 482). Variables used to predict neutropenia or FN were Karnofsky performance status (odds ratio [OR] = 0.85, 95% confidence interval [CI] = 0.81-0.89, p < 0.01), metastatic sites ≥3 (OR = 6.33, 95% CI = 2.66-15.11, p < 0.01), comorbidity of heart disease (OR = 4.88, 95% CI = 1.74-13.67, p < 0.01), recent surgery (OR = 7.96, 95% CI = 1.96-32.36, p < 0.01), administration of alkylating agents (OR = 4.50, 95% CI = 1.10-18.48, p < 0.01), total bilirubin ≥25 μmol/L (OR = 11.42, 95% CI = 4.00-32.61, p < 0.01), and lymphocyte count <0.7 × 109/L (OR = 4.22, 95% CI = 2.00-9.75, p < 0.01).

CONCLUSION

This model can aid the early identification and screening of the potential risk of neutropenia or FN in the first cycle of treatment for patients using etoposide-based chemotherapy.

Risk factors for severe neutropenia in pancreatic cancer patients treated with gemcitabine/nab-paclitaxel combination therapy

AIM

Combination therapy with gemcitabine and nanoparticle albumin-bound paclitaxel (nab-paclitaxel), known as GnP therapy, significantly prolongs the survival of pancreatic cancer patients compared with gemcitabine monotherapy. However, it may cause severe neutropenia, requiring discontinuation of treatment. This study aimed to clarify the risk factors for Grade 3/4 neutropenia during GnP therapy.

METHODS

Clinical data of pancreatic cancer patients who underwent GnP therapy at the Cancer Institute Hospital of the Japanese Foundation for Cancer Research from December 2014 to December 2016 were retrospectively collected. The relationship of Grade 3/4 neutropenia onset to laboratory values and patient background factors was investigated by multivariate logistic regression analysis.

RESULTS

Clinical data of 222 patients were analyzed. Grade 3/4 neutropenia occurred in 118 patients (53.2%) in the first cycle of GnP therapy. Multivariate analysis identified low absolute neutrophil count (ANC), high total bilirubin (T-Bil), and low C-reactive protein (CRP) as risk factors for Grade 3/4 neutropenia. Age was not a risk factor. The incidence of neutropenia was 85.7% in patients with all three risk factors, but only 27.7% in patients with none of them.

CONCLUSION

Low ANC, high T-Bil, and low CRP may be risk factors for Grade 3/4 neutropenia in patients receiving GnP therapy, even if these laboratory values are within normal reference ranges. Patients with these risk factors should be carefully monitored for adverse events.

Risk prediction models based on hematological/body parameters for chemotherapy-induced adverse effects in Chinese colorectal cancer patients

PURPOSE

To determine risk factors and develop novel prediction models for chemotherapy-induced adverse effects (CIAEs) in Chinese colorectal cancer (CRC) patients receiving capecitabine.

METHODS

A total of 233 Chinese CRC patients receiving post-operative chemotherapy with capecitabine were randomly divided into a training set (70%) and a validation set (30%). CIAE-related hematological/body parameters were screened by univariate logistic regression. Based on a set of factors selected from LASSO (least absolute shrinkage and selection operator) logistic regression, stepwise multivariate logistic regression was applied to develop prediction models. Area under the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow (HL) test were used to evaluate the discriminatory ability and the goodness of fit of each model.

RESULTS

In total, 35 variables were identified to be associated with CIAEs in univariate analysis. Developed multivariable models had AUCs (area under curve) ranging from 0.625 to 0.888 and 0.428 to 0.760 in the training and validation set, respectively. The grade ≥ 1 anemia multivariable model achieved the best discriminatory ability with AUC of 0.760 (95%CI: 0.609-0.912) and good calibration with HL P value of 0.450. Then, a nomogram was constructed to predict grade ≥ 1 anemia, which included variables of age, pre-operative hemoglobin count, and pre-operative albumin count, with C-indexes of 0.775 and 0.806 in the training and validation set, respectively.

CONCLUSIONS

This study identified valuable hematological/body parameters related to CIAEs. A nomogram based on the multivariable model including three hematological/body predictors can accurately predict grade ≥ 1 anemia, facilitating clinicians to implement personalized medicine early for Chinese CRC patients receiving post-operative chemotherapy for better safety treatment. Trial registration This study was registered as a clinical trial at www.clinicaltrials.gov (NCT03030508).

Prevention of docetaxel-associated febrile neutropenia with primary granulocyte colony-stimulating factor in Chinese metastatic hormone-sensitive and castration-resistant prostate cancer patients

INTRODUCTION

Asian prostate cancer (PC) patients are particularly susceptible to docetaxel-related febrile neutropenia (FN). We evaluated primary granulocyte colony-stimulating factor (GCSF) for preventing FN in Chinese patients with metastatic hormone-sensitive PC (mHSPC) and castration-resistant PC (mCRPC).

PATIENTS AND METHODS

Data from two cohorts of 377 Chinese patients with mHSPC (100; 26.5%) and mCRPC (277; 73.5%) treated with docetaxel at six public oncology centres were analysed with multivariate regression. Primary GCSF prophylaxis was defined as administration within 5 days of starting docetaxel. The primary outcome was FN within 21 days of the first docetaxel cycle (1st FN).

RESULTS

Primary GCSF was given to 71 (18.8%) patients. FN occurred in 61 patients (16.2%) including 37 (9.8%) during the first cycle. Among patients who developed 1st cycle FN (n = 37) or not (n = 340), 2 and 69 received primary GCSF (5.4 vs. 20.3%, P = .03). Primary GCSF was associated with an overall reduced risk of 1st cycle FN (odds ratio [OR] = 0.22; 95% confidence interval [CI]: 0.05-0.96, P = .04), and similar trends were observed in the mHSPC (OR = 0.36, P = .35) and mCRPC (OR = 0.16, P = .08) subgroups. Poor Eastern Cooperative Oncology Group performance status (>1) was associated with an increased risk of 1st FN (OR = 3.90; 95% CI: 1.66-9.13, P = .002).

CONCLUSIONS

To alleviate the risk of docetaxel-related FN, primary GCSF prophylaxis is suggested for Asian mCRPC and mHSPC patients, particularly those with poor performance status.

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

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.

The vertebral 3'-deoxy-3'-18F-fluorothymidine uptake predicts the hematological toxicity after systemic chemotherapy in patients with lung cancer

OBJECTIVES

Although hematological toxicities (HT) are the leading adverse events of systemic chemotherapy, the estimation of severe HT is challenging. Recently, 3'-deoxy-3'-[¹⁸F]-fluorothymidine (¹⁸F-FLT) accumulation with PET has been considered a biomarker of the cell proliferation. This study aims to elucidate whether the vertebral accumulation of ¹⁸F-FLT could estimate severe HT during platinum-doublet chemotherapy.

METHODS

In this Institutional Review Board-approved retrospective study, 50 patients with primary lung cancer underwent ¹⁸F-FLT PET scan before platinum-doublet chemotherapy. We evaluated the standardized uptake value, total vertebral proliferation (TVP), and TVP/body surface area (TVP/BSA) of the vertebral body (Th4, Th8, Th12, and L4), and then the associations between those parameters and frequency of severe HT during platinum-doublet chemotherapy were assessed.

RESULTS

Severe HT (grade 3/4) was observed in 40.0% of patients during the first cycle. The ROC curve analyses revealed that the TVP/BSA of L4 was the most discriminative parameter among PET parameters for the prediction of severe HT. The multivariate logistic regression analysis revealed the TVP/BSA of L4 (odds ratio [OR], 0.94; p = 0.0036) and the frequency of the grade 3/4 hematological toxicity in previous clinical trials (OR, 1.03; p = 0.023) were independent predictors. Furthermore, the sensitivity, specificity, and accuracy of the TVP/BSA of L4 cut-off of 68.7 to predict grade 3/4 HT were 80.0%, 86.7%, and 84.0%, respectively. A low TVP/BSA of L4 (< 68.7) as a binary variable was a significant indicator of severe HT (OR, 26.0; p = 0.000026).

CONCLUSIONS

The low ¹⁸F-FLT uptake in the lower vertebral body is a predictor of severe HT in patients with lung cancer who receive platinum-doublet chemotherapy.

TRIAL REGISTRATION

Trial registration: UMIN000027540 KEY POINTS: • The vertebral ¹⁸ F-FLT uptake with PET is an independent predictor of the severe hematological toxicity during the first cycle of platinum-doublet chemotherapy. • The ¹⁸ F-FLT uptake in L4 vertebral body estimated hematological toxicities better than that in the upper vertebra (Th4, Th8, and Th12). • The evaluation of the amount and activity of hematopoietic cells in the bone marrow cavity using ¹⁸ F-FLT PET imaging could provide predictive data of severe hematological toxicities and help determine an appropriate drug combination or dose intensity in patients with advanced malignant diseases.

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

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.

Development and Validation of a Risk Score for Febrile Neutropenia After Chemotherapy in Patients With Cancer: The FENCE Score

Background

Febrile neutropenia (FN) after chemotherapy causes a high burden of morbidity and mortality. We aimed to develop and validate a risk score to predict FN in the first cycle of chemotherapy.

Methods

We included patients with solid cancers and diffuse large B-cell lymphomas at Rigshospitalet, University of Copenhagen, 2010-2016. Predictors of FN were analyzed using Poisson regression and random split-sampling.

Results

Among 6294 patients in the derivation cohort, 360 developed FN. Female sex, older age, cancer type, disease stage, low albumin, elevated bilirubin, low creatinine clearance, infection before chemotherapy, and number of and type of chemotherapy drugs predicted FN. Compared with those at low risk (n = 2520, 40.0%), the incidence rate ratio of developing FN was 4.8 (95% confidence interval [CI] = 2.9 to 8.1), 8.7 (95% CI = 5.3 to 14.1) and 24.0 (95% CI = 15.2 to 38.0) in the intermediate (n = 1294, 20.6%), high (n = 1249, 19.8%) and very high (n = 1231, 19.6%) risk groups, respectively, corresponding to a number needed to treat with granulocyte colony-stimulating factors to avoid one FN event in the first cycle of 284, 60, 34 and 14. The discriminatory ability (Harrell’s C-statistic = 0.80, 95% CI = 0.78 to 0.82) was similar in the validation cohort (n = 3163) (0.79, 95% CI = 0.75 to 0.82).

Conclusion

We developed and internally validated a risk score for FN in the first cycle of chemotherapy. The FENCE score is available online and provides good differentiation of risk groups.

Body composition as a predictor of toxicity after treatment with eribulin for advanced soft tissue sarcoma

BACKGROUND

Despite the clinical benefits of eribulin on overall survival of advanced soft tissue sarcoma (STS) patients, treatment-related toxicity reduces their QOL. Body composition metrics (BCMs) are associated with poor outcome and drug toxicities in several cancers. This study investigated whether BCMs could predict drug toxicity occurrence in advanced STS patients treated with eribulin.

METHODS

This study included 23 advanced STS patients treated with eribulin between March 2016 and April 2018. BCMs were evaluated using a CT scan obtained within 1 month before or after treatment initiation. The relationship of BCMs and other clinical factors was evaluated and CART analysis used to develop classification models for risk groups of drug toxicity.

RESULTS

Sixteen patients (69.6%) experienced any grade 3/4 toxicity. Eleven patients (47.8%) developed G4 hematologic toxicity, which was significantly higher in those with low skeletal muscle gauge (SMG) (P = 0.02) and low pretreatment neutrophil count (P = 0.0002). Six patients (26.1%) had grade 3/4 non-hematologic toxicity, and was higher in those with low SMG (P = 0.004), and low serum albumin level (P = 0.02). Five patients with high BMI (P = 0.03) experienced febrile neutropenia (FN) and low pretreatment neutrophil count (P = 0.02). CART analysis classified three risk groups, and area under the receiver operating characteristic curve (AUROCC) was 0.92, 0.88, 0.92 in G4 hematologic AE, G3/4 non-hematologic AE, FN, respectively.

CONCLUSIONS

SMG is a significant predictive factor of eribulin drug toxicity in advanced STS patients. Risk classification of drug toxicity through combining predictive factors, could improve the therapeutic strategy used in chemotherapy.