A Patient Risk Model of Chemotherapy-Induced Febrile Neutropenia: Lessons Learned From the ANC Study Group

Authors:
Gary H. Lyman From Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center, and University of Washington, Seattle, Washington.
From Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center, and University of Washington, Seattle, Washington.

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 MD, MPH, FRCP(Edin)
and
Marek S. Poniewierski From Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center, and University of Washington, Seattle, Washington.

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 MD, MS
Volume/Issue:
Volume 15: Issue 12
Online Publication Date:
Dec 2017
DOI:
https://doi.org/10.6004/jnccn.2017.7038
Full access

Neutropenia and its complications, including febrile neutropenia (FN), represent major toxicities associated with cancer chemotherapy, resulting in considerable morbidity, mortality, and costs. The myeloid growth factors such as granulocyte colony-stimulating factor (G-CSF) have been shown to reduce the risk of neutropenia complications while enabling safe and effective chemotherapy dose intensity. Concerns about the high costs of these agents along with limited physician adherence to clinical practice guidelines, resulting in both overuse and underuse, has stimulated interest in models for individual patient risk assessment to guide appropriate use of G-CSF. In a model developed and validated by the ANC Study Group, half of patients were classified as high risk and half as low risk based on patient-, disease-, and treatment-related factors. This model has been further validated in an independent patient population. Physician-assessed risk of FN, as well as the decision to use prophylactic CSF, has been shown to correlate poorly with the FN risk estimated by the model. Additional modeling efforts in both adults and children receiving cancer treatment have been reported. Identification of patients at a high individual risk for FN and its consequences may offer the potential for optimal chemotherapy delivery and patient outcomes. Likewise, identification of patients at low risk for neutropenic events may reduce costs when such supportive care is not warranted. This article reviews and summarizes FN modeling studies and the opportunities for personalizing supportive care in patients receiving chemotherapy.

Neutropenia and its complications represent the major dose-limiting toxicities associated with systemic cancer chemotherapy and is associated with considerable morbidity, mortality, and cost.1 Neutropenic events may result in dose reductions or treatment delays, and subsequently compromise disease control and overall survival.2–6 The myeloid growth factors (MGFs), including granulocyte colony-stimulating factor (G-CSF), have been shown to decrease the risk of neutropenic complications, facilitating the safe delivery of planned chemotherapy dose intensity on schedule. Guidelines from NCCN and ASCO on the use of MGFs have recently been updated.7,8 When selecting patients for primary prophylaxis with G-CSF, current guidelines recognize the need to consider a range of risk factors for the occurrence and consequences of febrile neutropenia (FN), defined as body temperature >38.5°C or 2 consecutive measurements >38°C with an absolute neutrophil count <0.5 × 109/L.

Risk Factors for FN and Its Consequences

The risk of FN in patients receiving systemic chemotherapy has generally been based on the rates of FN in patients eligible for randomized controlled clinical trials (RCTs).9 A risk of FN of >20% has been established as a threshold for the routine use of primary G-CSF prophylaxis in guidelines from NCCN, ASCO, and EORTC based on results from RCTs.7,8,10,11 However, the rates of chemotherapy-induced FN reported in RCTs vary considerably for commonly used chemotherapy regimens.12,13 The risk of FN found in observational or “real-world” patient populations is often greater than that reported in patients eligible for RCTs.14 Variation in chemotherapy treatment intensity and intent as well as additional risk factors, including patient characteristics and comorbidities, have been identified for chemotherapy-induced FN.15–18 Current guidelines distinguish between risk factors that increase the likelihood of FN and those that increase the risk of serious medical consequences or death in those who develop FN, which may be important even among patients considered at low or intermediate risk for developing FN (Table 1).7,8,11,19

Myeloid Growth Factors

Potential Benefits

MGFs have been developed and approved for reducing the risk of FN and subsequent neutropenic complications.20,21 FN can be a potentially life-threatening complication of chemotherapy either directly,

Table 1.

Patient Risk Factors for FN and Its Consequences
Table 1.
leading to infectious complications and death, or indirectly, prompting a reduction in chemotherapy intensity or duration and thereby resulting in greater risk of disease recurrence and cancer-related mortality. A recent analysis of patients receiving systemic chemotherapy in a large US health claims database estimated a 35% increase in early mortality and overall a 15% increased risk of all-cause mortality among patients experiencing FN.22 Although no RCT has been powered to address the issue of disease-free or overall survival,23 a systematic review and meta-analysis of 17 RCTs of primary prophylaxis with GCSF, including approximately 3,500 patients with solid tumors or lymphoma, confirmed a significant reduction in the risk of FN, as seen in each individual trial.24 Importantly, in this pooled analysis, signifi-cant reductions in the risk of infection-related mortality and early all-cause mortality were observed. In a more recent systematic review of RCTs in patients receiving systemic chemotherapy and randomized to G-CSF support with an average follow-up of 5 years, nearly a 10% reduction in the relative risk for all-cause mortality with G-CSF support was observed.25,26 A significant association was also observed between reduced mortality and increases in relative chemotherapy dose intensity in patients treated with G-CSF. As noted earlier, G-CSF is routinely administered in support of dose-dense chemotherapy regimens as well as to enable full-dose chemotherapy on schedule in the curative setting. Greater reductions in mortality were observed among trials with longer follow-up, when treatment was clearly for curative intent, and in which survival was the primary study outcome.26

Potential Harms

At the same time, MGFs are associated with established and hypothetical risks as well as considerable costs.27 Transient bone pain, occasionally severe, represents the most common symptom reported by patients receiving G-CSF. Of greater concern has been the potential for an increased risk for developing acute myeloid leukemia (AML) or myelodysplastic syndromes (MDS) in patients treated with MGFs. In an analysis of data from the SEER-Medicare linked files, Hershman et al28 observed a doubling of the risk of subsequent AML or MDS from 0.7% to 1.8% with CSF support among older women diagnosed with early-stage breast cancer from 1991 to 1999 who received MGFs with their adjuvant chemotherapy. In the previously mentioned meta-analysis of comparative studies with at least 2 years of follow-up, approximately 25 RCTs involving >12,000 patients reported on rates of AML/MDS or all secondary malignancies.25 AML or MDS was observed in 0.36% of controls and 0.79% with G-CSF, with nearly a doubling of the relative risk. Due to the enhancement of delivered dose intensity with known leukemogenic chemotherapies with G-CSF support, a direct cause-and-effect relationship with G-CSF could not be established. In addition, the observed overall reduction in all-cause mortality was nearly 10-fold greater than the estimated potential risk of AML/MDS across these studies.

Costs

Despite the lack of definitive evidence for serious long-term toxicities associated with G-CSF support of chemotherapy, the associated cost remains of considerable concern and may contribute significantly to the large financial burden associated with modern cancer therapy, resulting in great financial hardship and even bankruptcy and representing a barrier to access to optimal treatment.29,30 Despite the recent introduction of biosimilars, G-CSF remains one of the most expensive agents used in cancer management.31,32 Numerous cost-effectiveness studies have been undertaken during the past 20 years as the costs of G-CSF and of hospitalization for treating FN have increased dramatically.33 Most studies have demonstrated reasonable cost-effectiveness for G-CSF prophylaxis when used according to guideline recommendations in patients at high risk for FN.

Appropriate Use of MGFs

Several studies have shown that the risk of an initial episode of FN is greatest during the first cycle of treatment when patients are generally receiving the full dose intensity.34–36 The appropriate balance depends largely on the ability of G-CSF to reduce the risk of hospitalization for FN and resulting complications, and the length of hospitalization.37,38 However, the ability of G-CSF to decrease the need for dose reductions and treatment delays, potentially improving overall relative dose intensity, treatment effect, and disease control, must also be considered.26,39–41 Nevertheless, concerns about underuse and overuse of MGFs has led to risk-based recommendations from clinical practice guidelines and initiatives such as Choosing Wisely to foster more appropriate, evidence-based, and cost-effective use of these agents.7,8,10,11 Although these recommendations suggest that oncologists consider patient-specific characteristics when prescribing G-CSF, a mechanism to quantify individual patient risk is lacking. As a result, the decision whether to use G-CSF is based on the chemotherapy regimen. However, many investigators have demonstrated that the risk of FN in selected patients in RCTs may differ significantly from patients in a real-world setting where age, socioeconomic conditions, or major medical comorbidities may significantly increase a patient's risk of developing FN or experiencing serious adverse consequences, including death.

Personalized Supportive Care

Personalization of supportive care strategies, such as the appropriate and targeted use of the MGFs in a fashion similar to the personalization of targeted cancer therapies, offers considerable potential for more effective, safe, and cost-effective cancer care along with improved survival and quality of life.19 Due to the number of identified risk factors for FN and its consequences that have been identified, uncertainty remains about their relative importance and contribution to the overall FN risk. As a result, there has been an increasing interest in the development and validation of multivariable risk models for chemotherapy-associated neutropenia and its consequences. A number of modeling efforts have been designed to predict the risk of serious complications, including death, in patients with established FN.42–44 Model performance has often been limited, challenging broad use of these models. Clinical practice guidelines uniformly discuss the importance of identifying patient- and treatment-specific risk factors and the need for further research on the risk of FN in the general cancer population.7,8

Risk Models for FN and Its Consequences

The Lyman Risk Model

Attention has recently shifted to creating risk models for FN development in ambulatory patients receiving chemotherapy to guide the appropriate use of MGFs.45,46 The ANC Study Group conducted a prospective cohort study of >3,000 patients treated at oncology practices throughout the United States to explicitly evaluate the incidence of and risk factors for neutropenic events in patients receiving systemic chemotherapy.45 Consecutive eligible patients with solid tumors or malignant lymphoma initiating a new chemotherapy regimen were enrolled. Risk factors considered included age, sex, ethnicity, employment and educational status, performance status, body surface area, cancer type, disease stage, history of prior cancer and treatment, concomitant medications, baseline hematology and chemistry results, and planned chemotherapy drugs, dose, and schedule. The primary outcome of the study was severe neutropenia or FN in cycle 1 due to the dominant risk of events in the first cycle and their major impact on subsequent risk and treatment decisions.2,3,36 Secondary outcomes included the cumulative risk of neutropenic events including FN, and dose reductions, treatment delays, and delivered chemotherapy dose intensity during the period of observation. Factors significantly associated with neutropenic complications in multivariable analysis included a history of previous chemotherapy as well as baseline leukopenia, hepatic or renal dysfunction, planned chemotherapy relative dose intensity, and the use of prophylactic MGF (see supplemental eAppendix 1, available with this article at JNCCN.org). Using the median predicted risk of neutropenic events, 34% of high-risk patients experienced cycle 1 events compared with 4% in low-risk patients. Figure 1 displays the cumulative risk of FN over repeated cycles of chemotherapy for high- and low-risk patients. Kaplan-Meier estimates of the cumulative FN risk was approximately 20% in high-risk patients compared with 5% in low-risk patients.

External Validation Studies

An external validation of the Lyman risk model was recently reported based on automated retrospective extraction of electronic health record (EHR) data on a cohort of adult patients with newly diagnosed breast, colorectal, lung, lymphoid, or ovarian cancer who received the first cycle of a cytotoxic chemotherapy regimen from 2008 to 2013 at a single cancer clinic.47 After chart review validation of EHR treatment data, neutropenia risk stratification was conducted and risk model performance was assessed (Table 2).

Figure 1.
Figure 1.

Kaplan-Meier plot displaying the cumulative proportion of patients experiencing ≥1 episodes of FN over time in the days following chemotherapy initiation for both high-risk and low-risk patients based on the risk model.

Abbreviations: FN, febrile neutropenia; HR, hazard ratio.

From Lyman GH, Kuderer NM, Crawford J, et al. Predicting individual risk of neutropenic complications in patients receiving cancer chemotherapy. Cancer 2011;117:1925; with permission.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 15, 12; 10.6004/jnccn.2017.7038

The risk prediction tool classified 126 patients (57%) as being low risk for FN, 44 (20%) as intermediate risk, and 51 (23%) as high risk. Model discrimination was considered adequate and comparable to the original model (Figure 2). In a secondary analysis, actual G-CSF prophylaxis was examined according to level of hypothetical risk based on the Lyman risk model but assuming no G-CSF prophylaxis was administered (Figure 3). The authors concluded that the individualized neutropenia risk prediction model performed well in this retrospective external cohort. In this secondary analysis, it was observed that most patients given G-CSF prophylaxis were high risk, based on the model. However, 37% of those predicted to be high risk did not receive G-CSF prophylaxis (Figure 3). The authors estimate that if the model risk-based recommendations had been followed, the overall FN risk of 13% would have been reduced to 8% or even less. Additional external validation of the risk model is currently being pursued in different settings and patient populations. Additional validation has recently been demonstrated using data from a large integrated health system,48 and further efforts are underway based on prospective clinical trials.49

Comparison With Physician Risk Assessment

A prospective cohort study recently evaluated the correlation between the FN risk in patients with

Table 2.

Neutropenia Eventa Rates Among Patients Receiving Cycle One Chemotherapy Treatment With Any Regimen
Table 2.
nonmyeloid malignancies receiving chemotherapy estimated by physicians versus the risk predicted by the Lyman validated multivariate risk model.50 Before patient enrollment, physician and site characteristics were recorded, and 124 community oncologists self-reported the FN risk at which they typically consider primary G-CSF prophylaxis. For each of 944 eligible patients, the treating physician electronically recorded their estimated FN risk, orders for G-CSF primary prophylaxis, and patient characteristics for model predictions. Primary outcomes were the correlations between physician-assessed and model-predicted FN risks and between physician-assessed
Figure 2.
Figure 2.

Area under the receiver operating characteristic curve demonstrating model fit for the neutropenia risk prediction model (area under the curve, 0.7475).

From Pawloski PA, Thomas AJ, Kane S, et al. Predicting neutropenia risk in patients with cancer using electronic data. J Am Med Inform Assoc 2017;24:e133; with permission.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 15, 12; 10.6004/jnccn.2017.7038

FN risk and G-CSF orders. The median physician-assessed and model-assessed FN risks were 20%, and 18%, respectively, with a weak correlation of 0.249 (Figure 4). A moderate correlation between physician-assessed FN risk and subsequent orders for primary G-CSF prophylaxis was observed (0.313). This limited correlation is explained by the classification of different patients as either high risk or not by the physician and the risk model. Among patients with a physician-assessed FN risk ≥20%, 14% did not receive G-CSF orders. At the same time, prophylactic
Figure 3.
Figure 3.

A secondary analysis examined actual G-CSF prophylaxis by level of hypothetical risk estimated using the model and assuming no G-CSF prophylaxis was administered.

Abbreviations: G-CSF, granulocyte colony-stimulating factor; med, medium.

From Pawloski PA, Thomas AJ, Kane S, et al. Predicting neutropenia risk in patients with cancer using electronic data. J Am Med Inform Assoc 2017;24:e133; with permission.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 15, 12; 10.6004/jnccn.2017.7038

Figure 4.
Figure 4.

Correlation between physician-assessed and model-predicted febrile neutropenia (FN) risk over all chemotherapy cycles. The line indicates the estimated mean function, and the shaded area indicates the 95% pointwise confidence interval. Physician-assessed FN risk estimates varied considerably around the model-predicted risk estimates and did not increase linearly with increasing model-predicted estimates >30%.

From Lyman GH, Dale DC, Legg JC, et al. Assessing patients' risk of febrile neutropenia: is there a correlation between physician-assessed risk and model-predicted risk? Cancer Med 2015;4:1158; with permission.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 15, 12; 10.6004/jnccn.2017.7038

G-CSF was ordered in 21% of patients considered not high risk by the treating physician. The authors concluded that further research and education on FN risk factors and appropriate G-CSF use are needed.50

Additional Modeling Efforts

A number of additional modeling efforts have been reported for the risk of FN as well as the consequences of FN in patients receiving chemotherapy. Several studies have developed models tailored to specific cancer types in adults, such as breast and ovarian cancer and lymphoma, or to children receiving cancer chemotherapy.3,15,35,51–58 Although most models consider a range of agents, others are tailored to specific chemotherapeutic agents or regimens.34,35,53 Because the risk of FN is greatest in the first cycle of chemotherapy when most patients are receiving planned dose intensity, most models are based on commonly available clinical, treatment, or laboratory parameters available at the start of treatment. Nevertheless, some investigators have developed risk models incorporating genetic risk factors,55,59 whereas others have developed risk models for future FN risk conditional on outcomes during the first cycle of treatment.60 Unfortunately, none of these were able to consider the delivered chemotherapy dose intensity, which appears to have a substantial effect on the risk and consequences of FN. Finally, as noted previously, several early modeling efforts in both children and adults were developed to predict serious adverse outcomes, including death, in patients with established FN.43 One of the earliest and most broadly studied risk models was developed under the Multinational Association of Supportive Care in Cancer (MASCC).42,61–63 Despite early popularity and continued interest, the limitations of the MASCC model have been highlighted elsewhere.44

Conclusions

Primary G-CSF prophylaxis starting in the first cycle of chemotherapy has been shown to reduce the risk of serious and potentially life-threatening complications of cancer treatment while allowing for the safe and adequate delivery of effective chemotherapy dose intensity. However, concerns about overtreatment and undertreatment, limited physician adherence to guidelines, and the high costs associated with MGFs have encouraged the development and validation of risk models for more individual risk assessment to guide the use of G-CSF support. In the model presented by Lyman et al,50 approximately one-half of patients considered to be intermediate risk were classified as high risk based on the risk model that incorporated other patient-, disease-, and treatment-related factors, whereas the remainder experienced an average risk of 5%. This model has been retrospectively validated both internally and externally in an independent patient population. In addition, a priori physician-assessed risk of FN and the decision to use prophylactic CSF correlated poorly with the FN risk estimated by the risk model.52 Additional modeling efforts in adults and children receiving cancer treatment to determine the risk of FN and serious adverse consequences in those with FN have been reported and deserve further study. Clearly, the identification of patients at a personal high risk for FN and its complications offers the potential for optimal chemotherapy delivery and patient outcomes. Alternatively, identification of patients at low risk for neutropenic complications can potentially offer cost savings when more aggressive supportive care is not warranted. Nevertheless, further research is needed to establish and validate optimal FN risk prediction tools as well as provide for model integration into electronic chemotherapy order entry systems.

Dr. Lyman has disclosed that he is the principal investigator on a research grant to the Fred Hutchinson Cancer Research Center from Amgen. Dr. Poniewierski has disclosed that he has no financial interests, arrangements, affiliations, or commercial interests with the manufacturers of any products discussed in this article or their competitors.

See JNCCN.org for supplemental online content.

References

  • 1.↑

    Kuderer NM, Dale DC, Crawford J et al.. Mortality, morbidity, and cost associated with febrile neutropenia in adult cancer patients. Cancer 2006;106:2258–2266.

  • 2.↑

    Lyman GH, Dale DC, Crawford J. Incidence and predictors of low dose-intensity in adjuvant breast cancer chemotherapy: a nationwide study of community practices. J Clin Oncol 2003;21:4524–4531.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.↑

    Lyman GH, Dale DC, Friedberg J et al.. Incidence and predictors of low chemotherapy dose-intensity in aggressive non-Hodgkin's lymphoma: a nationwide study. J Clin Oncol 2004;22:4302–4311.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Lyman GH. Impact of chemotherapy dose intensity on cancer patient outcomes. J Natl Compr Canc Netw 2009;7:99–108.

  • 5.

    Shayne M, Culakova E, Wolff D et al.. Dose intensity and hematologic toxicity in older breast cancer patients receiving systemic chemotherapy. Cancer 2009;115:5319–5328.

  • 6.↑

    Shayne M, Culakova E, Poniewierski MS et al.. Dose intensity and hematologic toxicity in older cancer patients receiving systemic chemotherapy. Cancer 2007;110:1611–1620.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.↑

    Crawford J, Becker PS, Armitage JO et al.. NCCN Clinical Practice Guidelines in Oncology: Myeloid Growth Factors. Version 2.2017. Accessed November 5, 2017. To view the most recent version of these guidelines, visit NCCN.org.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.↑

    Smith TJ, Bohlke K, Lyman GH et al.. Recommendations for the use of WBC growth factors: American Society of Clinical Oncology clinical practice guideline update. J Clin Oncol 2015;33:3199–3212.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.↑

    Lyman GH. Risk assessment in oncology clinical practice. From risk factors to risk models. Oncology (Williston Park) 2003;17:8–13.

  • 10.↑

    Schnipper LE, Smith TJ, Raghavan D et al.. American Society of Clinical Oncology identifies five key opportunities to improve care and reduce costs: the top five list for oncology. J Clin Oncol 2012;30:1715–1724.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.↑

    Aapro MS, Bohlius J, Cameron DA et al.. 2010 update of EORTC guidelines for the use of granulocyte-colony stimulating factor to reduce the incidence of chemotherapy-induced febrile neutropenia in adult patients with lymphoproliferative disorders and solid tumours. Eur J Cancer 2011;47:8–32.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.↑

    Dale D, Crawford J, Lyman GH. Myelotoxicity and dose intensity of chemotherapy: reporting practices from randomized clinical trials. J Natl Compr Canc Netw 2003;1:440–454.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.↑

    Kuderer NM, Wolff AC. Enhancing therapeutic decision making when options abound: toxicities matter. J Clin Oncol 2014;32:1990–1993.

  • 14.↑

    Truong J, Lee EK, Trudeau ME et al.. Interpreting febrile neutropenia rates from randomized controlled trials for consideration of primary prophylaxis in the real world: a systematic review and meta-analysis. Ann Oncol 2016;27:608–618.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.↑

    Laskey RA, Poniewierski MS, Lopez MA et al.. Predictors of severe and febrile neutropenia during primary chemotherapy for ovarian cancer. Gynecol Oncol 2012;125:625–630.

  • 16.

    Lyman GH, Abella E, Pettengell R. Risk factors for febrile neutropenia among patients with cancer receiving chemotherapy: a systematic review. Crit Rev Oncol Hematol 2014;90:190–199.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Weycker D, Li X, Barron R et al.. Importance of risk factors for febrile neutropenia among patients receiving chemotherapy regimens not classified as high-risk in guidelines for myeloid growth factor use. J Natl Compr Canc Netw 2015;13:979–986.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.↑

    Weycker D, Li X, Edelsberg J et al.. Risk of febrile neutropenia in patients receiving emerging chemotherapy regimens. Support Care Cancer 2014;22:3275–3285.

  • 19.↑

    Kuderer NM, Lyman GH. Personalized medicine and cancer supportive care: appropriate use of colony-stimulating factor support of chemotherapy. J Natl Cancer Inst 2011;103:910–913.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.↑

    Rajan SS, Lyman GH, Stearns SC et al.. Effect of primary prophylactic granulocyte-colony stimulating factor use on incidence of neutropenia hospitalizations for elderly early-stage breast cancer patients receiving chemotherapy. Med Care 2011;49:649–657.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.↑

    Rajan SS, Stearns SC, Lyman GH et al.. Effect of primary prophylactic G-CSF use on systemic therapy administration for elderly breast cancer patients. Breast Cancer Res Treat 2011;130:255–266.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.↑

    Lyman GH, Michels SL, Reynolds MW et al.. Risk of mortality in patients with cancer who experience febrile neutropenia. Cancer 2010;116:5555–5563.

  • 23.↑

    Lyman GH, Reiner M, Morrow PK et al.. The effect of filgrastim or pegfilgrastim on survival outcomes of patients with cancer receiving myelosuppressive chemotherapy. Ann Oncol 2015;26:1452–1458.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.↑

    Kuderer NM, Dale DC, Crawford J et al.. Impact of primary prophylaxis with granulocyte colony-stimulating factor on febrile neutropenia and mortality in adult cancer patients receiving chemotherapy: a systematic review. J Clin Oncol 2007;25:3158–3167.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.↑

    Lyman GH, Dale DC, Wolff DA et al.. Acute myeloid leukemia or myelodysplastic syndrome in randomized controlled clinical trials of cancer chemotherapy with granulocyte colony-stimulating factor: a systematic review. J Clin Oncol 2010;28:2914–2924.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.↑

    Lyman GH, Dale DC, Culakova E et al.. The impact of the granulocyte colony-stimulating factor on chemotherapy dose intensity and cancer survival: a systematic review and meta-analysis of randomized controlled trials. Ann Oncol 2013;24:2475–2484.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.↑

    Rajan SS, Carpenter WR, Stearns SC et al.. Short-term costs associated with primary prophylactic G-CSF use during chemotherapy. Am J Manag Care 2013;19:150–159.

  • 28.↑

    Hershman D, Neugut AI, Jacobson JS et al.. Acute myeloid leukemia or myelodysplastic syndrome following use of granulocyte colony-stimulating factors during breast cancer adjuvant chemotherapy. J Natl Cancer Inst 2007;99:196–205.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.↑

    Tefferi A, Kantarjian H, Rajkumar SV et al.. In support of a patient-driven initiative and petition to lower the high price of cancer drugs. Mayo Clin Proc 2015;90:996–1000.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.↑

    Ramsey SD, Lyman GH, Bangs R. Addressing skyrocketing cancer drug prices comes with tradeoffs: pick your poison. JAMA Oncol 2016;2:425–426.

  • 31.↑

    Hirsch BR, Lyman GH. Will biosimilars gain momentum? J Natl Compr Canc Netw 2013;11:1291–1297.

  • 32.↑

    Hirsch BR, Lyman GH. Biosimilars: a cure to the U.S. health care cost conundrum? Blood Rev 2014;28:263–268.

  • 33.↑

    Fust K, Parthan A, Maschio M et al.. Granulocyte colony-stimulating factors in the prevention of febrile neutropenia: review of cost-effectiveness models. Expert Rev Pharmacoecon Outcomes Res 2017;17:39–52.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.↑

    Lyman GH, Morrison VA, Dale DC et al.. Risk of febrile neutropenia among patients with intermediate-grade non-Hodgkin's lymphoma receiving CHOP chemotherapy. Leuk Lymphoma 2003;44:2069–2076.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.↑

    Lyman GH, Delgado DJ. Risk and timing of hospitalization for febrile neutropenia in patients receiving CHOP, CHOP-R, or CNOP chemotherapy for intermediate-grade non-Hodgkin lymphoma. Cancer 2003;98:2402–2409.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.↑

    Crawford J, Dale DC, Kuderer NM et al.. Risk and timing of neutropenic events in adult cancer patients receiving chemotherapy: the results of a prospective nationwide study of oncology practice. J Natl Compr Canc Netw 2008;6:109–118.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.↑

    Agiro A, Ma Q, Acheson AK et al.. Risk of neutropenia-related hospitalization in patients who received colony-stimulating factors with chemotherapy for breast cancer [published online ahead of print September 19, 2016]. J Clin Oncol, pii: JCO672899.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.↑

    Weycker D, Li X, Tzivelekis S et al.. Burden of chemotherapy-induced febrile neutropenia hospitalizations in US clinical practice, by use and patterns of prophylaxis with colony-stimulating factor. Support Care Cancer 2017;25:439–447.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.↑

    Denduluri N, Patt DA, Wang Y et al.. Dose delays, dose reductions, and relative dose intensity in patients with cancer who received adjuvant or neoadjuvant chemotherapy in community oncology practices. J Natl Compr Canc Netw 2015;13:1383–1393.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Dinan MA, Hirsch BR, Lyman GH. Management of chemotherapy-induced neutropenia: measuring quality, cost, and value. J Natl Compr Canc Netw 2015;13:e1–7.

  • 41.↑

    Lyman GH, Crawford J, Tomita D et al.. Changing patterns of chemotherapy relative dose intensity and supportive care for aggressive B-cell non-Hodgkin lymphoma. Leuk Lymphoma 2015:1–8.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.↑

    Klastersky J, Paesmans M. The Multinational Association for Supportive Care in Cancer (MASCC) risk index score: 10 years of use for identifying low-risk febrile neutropenic cancer patients. Support Care Cancer 2013;21:1487–1495.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43.↑

    Talcott JA, Siegel RD, Finberg R et al.. Risk assessment in cancer patients with fever and neutropenia: a prospective, two-center validation of a prediction rule. J Clin Oncol 1992;10:316–322.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.↑

    Carmona-Bayonas A, Jimenez-Fonseca P, Virizuela Echaburu J et al.. Prediction of serious complications in patients with seemingly stable febrile neutropenia: validation of the Clinical Index of Stable Febrile Neutropenia in a prospective cohort of patients from the FINITE study. J Clin Oncol 2015;33:465–471.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.↑

    Lyman GH, Kuderer NM, Crawford J et al.. Predicting individual risk of neutropenic complications in patients receiving cancer chemotherapy. Cancer 2011;117:1917–1927.

  • 46.↑

    Jenkins P, Scaife J, Freeman S. Validation of a predictive model that identifies patients at high risk of developing febrile neutropaenia following chemotherapy for breast cancer. Ann Oncol 2012;23:1766–1771.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47.↑

    Pawloski PA, Thomas AJ, Kane S et al.. Predicting neutropenia risk in patients with cancer using electronic data. J Am Med Inform Assoc 2017;24:e129–135.

  • 48.↑

    Li Y, Family L, Chen LH et al.. Value of incorporating newly identified risk factors into risk prediction for chemotherapy-induced febrile neutropenia (FN). Presented at the 58th Annual Meeting & Exposition of the American Society of Hematology; December 5, 2016; San Diego, California.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.↑

    Bansal A, Sullivan SD, Hershman DL et al.. A stakeholder-informed randomized, controlled comparative effectiveness study of an order prescribing intervention to improve colony stimulating factor use for cancer patients receiving myelosuppressive chemotherapy: the TrACER study. J Comp Eff Res 2017;6:461–470.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50.↑

    Lyman GH, Dale DC, Legg JC et al.. Assessing patients' risk of febrile neutropenia: is there a correlation between physician-assessed risk and model-predicted risk? Cancer Med 2015;4:1153–1160.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 51.↑

    Chen K, Zhang X, Deng H et al.. Clinical predictive models for chemotherapy-induced febrile neutropenia in breast cancer patients: a validation study. PLoS One 2014;9:e96413.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 52.↑

    Chia VM, Page JH, Rodriguez R et al.. Chronic comorbid conditions associated with risk of febrile neutropenia in breast cancer patients treated with chemotherapy. Breast Cancer Res Treat 2013;138:621–631.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 53.↑

    Jenkins P, Freeman S. Pretreatment haematological laboratory values predict for excessive myelosuppression in patients receiving adjuvant FEC chemotherapy for breast cancer. Ann Oncol 2009;20:34–40.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 54.

    Pettengell R, Bosly A, Szucs TD et al.. Multivariate analysis of febrile neutropenia occurrence in patients with non-Hodgkin lymphoma: data from the INC-EU Prospective Observational European Neutropenia Study. Br J Haematol 2009;144:677–685.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 55.↑

    Pfeil AM, Vulsteke C, Paridaens R et al.. Multivariable regression analysis of febrile neutropenia occurrence in early breast cancer patients receiving chemotherapy assessing patient-related, chemotherapy-related and genetic risk factors. BMC Cancer 2014;14:201.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 56.

    Schwenkglenks M, Pettengell R, Szucs TD et al.. Hodgkin lymphoma treatment with ABVD in the US and the EU: neutropenia occurrence and impaired chemotherapy delivery. J Hematol Oncol 2010;3:27.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 57.

    Moreau M, Klastersky J, Schwarzbold A et al.. A general chemotherapy myelotoxicity score to predict febrile neutropenia in hematological malignancies. Ann Oncol 2009;20:513–519.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 58.↑

    Wicki S, Keisker A, Aebi C et al.. Risk prediction of fever in neutropenia in children with cancer: a step towards individually tailored supportive therapy? Pediatr Blood Cancer 2008;51:778–783.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 59.↑

    Tsuji D, Ikeda M, Yamamoto K et al.. Drug-related genetic polymorphisms affecting severe chemotherapy-induced neutropenia in breast cancer patients: a hospital-based observational study. Medicine (Baltimore) 2016;95:e5151.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 60.↑

    Ray-Coquard I, Borg C, Bachelot T et al.. Baseline and early lymphopenia predict for the risk of febrile neutropenia after chemotherapy. Br J Cancer 2003;88:181–186.

  • 61.↑

    Klastersky J, Paesmans M, Rubenstein EB et al.. The Multinational Association for Supportive Care in Cancer risk index: a multinational scoring system for identifying low-risk febrile neutropenic cancer patients. J Clin Oncol 2000;18:3038–3051.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 62.

    de Souza Viana L, Serufo JC, da Costa Rocha MO et al.. Performance of a modified MASCC index score for identifying low-risk febrile neutropenic cancer patients. Support Care Cancer 2008;6:841–846.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 63.↑

    Hui EP, Leung LK, Poon TC et al.. Prediction of outcome in cancer patients with febrile neutropenia: a prospective validation of the Multinational Association for Supportive Care in Cancer risk index in a Chinese population and comparison with the Talcott model and artificial neural network. Support Care Cancer 2001;19:1625–1635.

    • PubMed
    • Search Google Scholar
    • Export Citation

Correspondence: Gary H. Lyman, MD, MPH, FRCP(Edin), Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024. E-mail: glyman@fredhutch.org

Supplementary Materials

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    Kaplan-Meier plot displaying the cumulative proportion of patients experiencing ≥1 episodes of FN over time in the days following chemotherapy initiation for both high-risk and low-risk patients based on the risk model.

    Abbreviations: FN, febrile neutropenia; HR, hazard ratio.

    From Lyman GH, Kuderer NM, Crawford J, et al. Predicting individual risk of neutropenic complications in patients receiving cancer chemotherapy. Cancer 2011;117:1925; with permission.

  • View in gallery

    Area under the receiver operating characteristic curve demonstrating model fit for the neutropenia risk prediction model (area under the curve, 0.7475).

    From Pawloski PA, Thomas AJ, Kane S, et al. Predicting neutropenia risk in patients with cancer using electronic data. J Am Med Inform Assoc 2017;24:e133; with permission.

  • View in gallery

    A secondary analysis examined actual G-CSF prophylaxis by level of hypothetical risk estimated using the model and assuming no G-CSF prophylaxis was administered.

    Abbreviations: G-CSF, granulocyte colony-stimulating factor; med, medium.

    From Pawloski PA, Thomas AJ, Kane S, et al. Predicting neutropenia risk in patients with cancer using electronic data. J Am Med Inform Assoc 2017;24:e133; with permission.

  • View in gallery

    Correlation between physician-assessed and model-predicted febrile neutropenia (FN) risk over all chemotherapy cycles. The line indicates the estimated mean function, and the shaded area indicates the 95% pointwise confidence interval. Physician-assessed FN risk estimates varied considerably around the model-predicted risk estimates and did not increase linearly with increasing model-predicted estimates >30%.

    From Lyman GH, Dale DC, Legg JC, et al. Assessing patients' risk of febrile neutropenia: is there a correlation between physician-assessed risk and model-predicted risk? Cancer Med 2015;4:1158; with permission.

Kaplan-Meier plot displaying the cumulative proportion of patients experiencing ≥1 episodes of FN over time in the days following chemotherapy initiation for both high-risk and low-risk patients based on the risk model.

Abbreviations: FN, febrile neutropenia; HR, hazard ratio.

From Lyman GH, Kuderer NM, Crawford J, et al. Predicting individual risk of neutropenic complications in patients receiving cancer chemotherapy. Cancer 2011;117:1925; with permission.

Area under the receiver operating characteristic curve demonstrating model fit for the neutropenia risk prediction model (area under the curve, 0.7475).

From Pawloski PA, Thomas AJ, Kane S, et al. Predicting neutropenia risk in patients with cancer using electronic data. J Am Med Inform Assoc 2017;24:e133; with permission.

A secondary analysis examined actual G-CSF prophylaxis by level of hypothetical risk estimated using the model and assuming no G-CSF prophylaxis was administered.

Abbreviations: G-CSF, granulocyte colony-stimulating factor; med, medium.

From Pawloski PA, Thomas AJ, Kane S, et al. Predicting neutropenia risk in patients with cancer using electronic data. J Am Med Inform Assoc 2017;24:e133; with permission.

Correlation between physician-assessed and model-predicted febrile neutropenia (FN) risk over all chemotherapy cycles. The line indicates the estimated mean function, and the shaded area indicates the 95% pointwise confidence interval. Physician-assessed FN risk estimates varied considerably around the model-predicted risk estimates and did not increase linearly with increasing model-predicted estimates >30%.

From Lyman GH, Dale DC, Legg JC, et al. Assessing patients' risk of febrile neutropenia: is there a correlation between physician-assessed risk and model-predicted risk? Cancer Med 2015;4:1158; with permission.
Copyright:
Copyright © 2017 by the National Comprehensive Cancer Network 2017
Page Numbers:
8
Article Category:
Review Article
Received:
08 Aug 2017
Accepted:
18 Sep 2017
Print Publication Date:
01 Dec 2017
Online Publication Date:
Dec 2017
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