Talcott and colleagues were the first to propose risk-based subsets in patients with chemotherapy-induced FN. Patients were classified into four groups (Table 8.2). Groups 1, 2, and 3 (all hospitalized patients) were not considered to be low-risk. However, patients in group IV (with controlled cancer and without medical comorbidity, who developed their febrile episode outside the hospital) were considered to be at low-risk [44]. The construction of the groups was done using clinical arguments and expertise and was initially tested in a retrospective series of 261 patients from a single institution. It was then validated in a prospective series of 444 episodes of FN at two institutions [45]. The model was constructed without distinguishing patients with solid tumors or hematologic malignancy although the definition of controlled cancer was different for patients with leukemia (complete response on the last examination) than for patients with solid tumor (initiation of treatment or absence of documentation of progression). The validation series included 24 % of patients with non-Hodgkin’s lymphoma and 17 % of patients with acute myeloid leukemia. The diagnostic characteristics of the model were not stratified by underlying disease. The ultimate goal of the model was to identify low-risk patients, and groups I to III were never defined in order to further refine risk stratification. The model was further applied in a randomized trial [46] that aimed to assess whether outpatient management of predicted low-risk patients increases the risk of medical event. Patients with fever and neutropenia persisting after 24 h inpatient observation were randomized between continued inpatient care and early discharge without changing the antibiotic regimen unless medically required. The study was initially designed to detect an increase from 4 to 8 % in medical complication rate and then revised to detect an increase from 4 to 10 % with a planned sample size of 448 episodes. Stopped early due to poor accrual in 2000, the study was published with 66 episodes randomized in the hospital care arm and 47 episodes in the early discharge arm [46]. Although the study is underpowered, the authors concluded to no evidence of adverse medical consequences of the home arm (9 % complications rate versus 8 % and a 95 % confidence interval for the difference from −10 to 13 %). Having included only predicted low-risk patients, the study cannot be viewed as a full validation one, and we also can wonder why the study hypothesis was the inferiority of the experimental arm.

The second model was developed as the result of an international prospective study conducted by the Multinational Association for Supportive Care in Cancer (MASCC) [24]. The original design of the study included a validation part. Before carrying out any data analysis, study subjects were split into a derivation set (n = 756 episodes) and a validation set (n = 383). The score derived from the first set was obtained after multivariate logistic regression. A numeric risk index score, the so-called MASCC score, was constructed by attributing weights to seven independent factors shown to be associated with a high probability of favorable outcome. This score is presented in Table 8.3. It ranges from 0 to 26, with a score of 21 or more defined as being predictive of low-risk for the development of complications. This threshold was chosen from the derivation set, using a complication rate of 5 %, as a compromise between positive predictive value and sensitivity of the prediction rule. Similar to the Talcott model, the intended purpose of this model was to identify patients at sufficiently low-risk for the development of serious complications. The targeted positive predictive value of the score (i.e., the rate of patients without serious medical complication predicted by the rule) decreased, as expected, from 95 to 93 %, on the validation set. The characteristics of both models, based on the validation set are shown in Table 8.4. The MASCC study provides further validation of the Talcott classification in a multicentric setting. Comparing the characteristics of the prediction rules, the MASCC score did improve upon the sensitivity and the overall misclassification rate of the Talcott scheme. On the other hand, the positive predictive value might be considered suboptimal, at least when the threshold of 21 is used. Increasing the threshold might increase the positive predictive value but will also reduce the sensitivity of the model. In the Talcott model, the underlying disease particularly the presence of a solid tumor or hematologic malignancy impacted on the degree of risk only in the form of an interaction with the existence of a previous fungal infection or suspected fungal infection. The underlying disease was a predictive factor on univariate analysis, but was not subsequently identified as an independent risk predictive factor.

Due to its immediate validation as planned in the study protocol, its increased sensitivity compared to the Talcott scheme, and its acceptable positive predictive value, the MASCC score has been proposed as a useful tool for predicting low-risk febrile neutropenia in the IDSA guidelines since 2002 [13, 18].

It has also been the subject of several independent validation studies. The primary objective of one of these studies was to attempt to improve the MASCC score through the estimation of the further duration of neutropenia. Indeed, expected further neutropenia duration, if correlated with the underlying tumor, could be the true factor underlying a higher risk for patients with hematologic malignancies than for patients with solid tumors. However, it is difficult to assess at presentation. A multicentric study was therefore conducted with detailed data collection about chemotherapy. This study [35] included 1,003 febrile episodes selected in 1,003 patients from 10 participating institutions. Among them, 546 had hematologic malignancy including 246 with acute leukemia. A model predicting further neutropenia duration as a binary status (long versus short duration) was developed. Almost all leukemic patients were predicted to have a long duration, and all patients with solid tumors were predicted to have a short duration of neutropenia, but the model was unable to split the patients with hematologic malignancies other than leukemia into subgroups with short or long predicted duration. Unfortunately, the addition of this covariate did not result in a risk prediction model more satisfactory than the one obtained with the MASCC risk index.

Table 8.5 summarizes the results of the independent series attempting to validate the MASCC score for identifying low-risk patients. Although some of the series are small, they all show positive predictive values that are above 85 %, except one study [8] not reported in the table. This study used a very different definition of complications which included a change in the empiric antibiotic regimen. Consequently, the reported rate of complications is huge (62 %), and this paper cannot be considered to be a true validation of the MASCC score. Looking at the data summarized in Table 8.5, one can observe that when the proportion of patients with hematologic malignancies increases, the positive predictive value decreases, suggesting that the score should be used with greater caution in patients with hematologic malignancies. One could also consider increasing the threshold for defining low-risk in order to increase the positive predictive value, albeit at the price of decreased sensitivity. Table 8.6 shows how the diagnostic characteristics may evolve with changes in threshold Table 8.7.