Intensive multiagent chemotherapy has been a cornerstone of curative-intent treatment for adults with acute myeloid leukemia (AML) for several decades.1,2 More recently, the market introduction of several new drugs has expanded the therapeutic options for such patients.3-5 With increasingly diverse treatments available, there is growing interest in instruments that assess medical fitness to aid the selection of the most suitable therapeutic strategy for individual patients. Now widely used for this purpose are criteria proposed by investigators from the Italian Society of Hematology (SIE), the Italian Society of Experimental Hematology (SIES), and the Italian Group for Bone Marrow Transplantation (GITMO).6 We recently reported in 703 adults with AML or other high-grade myeloid neoplasm that these “Ferrara” criteria are indeed useful for patient risk-stratification and have a good to very good accuracy for the prediction of shorter-term mortality following intensive AML chemotherapy.7
Our data indicated the importance of pulmonary assessments to categorize patients as fit or unfit for intensive AML chemotherapy as pulmonary abnormalities were the single most common reason for medical unfitness based on Ferrara criteria.7 However, this fitness evaluation requires lung function testing, which is not routinely performed in many institutions and may be perceived as an unnecessary burden for asymptomatic patients. For example, in our cohort, only 159 of the 703 patients underwent pulmonary function testing before chemotherapy initiation. Some of our analyses suggested that the absence of known pulmonary comorbidities combined with the lack of respiratory symptoms could serve as a surrogate for normal pulmonary function, thereby avoiding the need for formal testing.7 In order to test this idea, we analyzed a cohort of adults ≥18 years of age with previously untreated AML (2016 World Health Organization criteria8) who were admitted to the Hematology Unit of the Policlinico Tor Vergata (Rome, Italy) between 01/2009 and 12/2020 and underwent pulmonary function testing as routine part of the pre-treatment assessment. This retrospective analysis was approved by the Fred Hutchinson Cancer Center and Policlinico Tor Vergata Institutional Review Board.
Information on pulmonary function testing, chest radiographs, smoking status, and prior/concurrent pulmonary comorbidities was collected from medical records. Patients were classified as pulmonary unfit according to
Table 1.Characteristics of study population.
Table 2.Results from pulmonary function testing across different subsets of patients who had no pulmonary symptoms and lacked Ferrara criteria unfitness-defining pulmonary comorbidities (196 patients).
Ferrara criteria (F-unfit) in case of abnormal lung function testing with diffusing capacity for carbon monoxide (DLCO) ≤65% predicted (relative to accepted reference values) or forced expiratory volume in the first second (FEV1) ≤65% predicted, dyspnea at rest, need for supplemental oxygen, or history of any pleural neoplasm or uncontrolled lung neoplasm.6 Chest imaging reports were reviewed by one of the authors (RP) and categorized as “normal” (no radiological abnormalities reported), “new” (previously unknown abnormalities reported), “stable” (previously known abnormalities reported to be unchanged), “changed” (previously known abnormalities reported to be changed), and “increased” or “decreased” (worsening or improvement of previously known abnormalities).
Two hundred forty-three patients met our study inclusion criteria. As summarized in Table 1, current and former smokers accounted for a minority of patients in these cohorts. Pulmonary comorbidities were identified in 14 of 243 (6%) of the patients. Chest imaging studies were available in all cases and were normal in 120 of 243 (49%) of the patients. Forty-five of the 243 patients (19%) had dyspnea at rest requiring oxygen. Less than half (103/243 [42%]) had normal pulmonary function testing results (i.e., DLCO and FEV1 ≥81%). In contrast, in 47 (19%) of the patients, pulmonary function was severely abnormal (i.e., FEV1 and/or DLCO ≤65%), establishing pulmonary unfitness according to the criteria proposed by Ferrara and colleagues.6
Of the 243 patients, 59 (24%) met criteria for pulmonary Ferrara F-unfitness. Isolated pulmonary function test abnormalities were the reason for F-unfitness in only 12 of 59 (20%) of the patients, with isolated dyspnea (n=11 [19%]), active lung cancer (n=1 [2%]), and the combination of abnormal lung function tests together with dyspnea at rest (n=34 [58%]) or active lung cancer (n=1 [2%]) accounting for the remaining cases.
Because of our recent data suggesting the absence of F-unfitness-defining pulmonary comorbidities and lack of respiratory symptoms (i.e., dyspnea at rest or requiring supplemental oxygen, or any pleural neoplasm or uncontrolled lung neoplasm) could potentially serve as a surrogate for normal pulmonary function,7 we were particularly interested in the patients without pulmonary symptoms and no history of F-unfitness-defining pulmonary disease. Among the 243 patients, 196 (81%) met these characteristics (Online Supplementary Table S1). Of these 196 individuals, pulmonary function testing was severely abnormal (i.e., denoting F-unfitness) in 12 (6%) patients. In this small subset of patients with severely abnormal pulmonary function tests, seven (58%) had abnormal radiographic findings, the majority of which (6/7 [85%]) were increased or new. Furthermore, five of 12 (42%) of these patients had lung comorbidities that were documented but did not qualify for F-unfitness on their own, including obstructive pulmonary diseases (n=4) and prior lobectomy (n=1). Finally, current/prior smokers accounted for over half of these patients (8/12 [67%]). As one might expect, the likelihood of severely abnormal lung function differed across individual patient subsets depending on past/current smoking status, presence of pulmonary comorbidities, and chest imaging findings (Table 2). Among life-long non-smokers without known pulmonary comorbidities and normal chest imaging studies – the largest subset of patients, overall accounting for 78 of 196 (40%) of the patients – lung function studies were severely abnormal in only two (1%) of the patients. On the other hand, among the seven past/current smokers with known pulmonary comorbidities and abnormal chest imaging findings, lung function studies were severely abnormal in five (71%) of the patients (Online Supplementary Figure S1).
Together, in the cohort of adults with previously untreated AML we studied, the vast majority of patients neither had respiratory symptoms nor pulmonary comorbidities that would qualify for unfitness based on Ferrara criteria.6 Thus, the question of whether the absence of pulmonary symptoms and lack of pulmonary comorbidities could serve as a surrogate for normal pulmonary function for the purpose of Ferrara fitness assessment is clinically relevant as pulmonary function testing is not routinely performed in many centers treating AML. Our data show that the lack of respiratory symptoms together with lack of pulmonary comorbidities is not indicative of having normal pulmonary function tests. Mildly or moderately abnormal test results were common. Moreover, across all patients, severely abnormal pulmonary function tests were found in almost 20% of cases. However, our findings indicate that among non-smokers with normal chest imaging studies and no history of pulmonary comorbidities, severely abnormal pulmonary function tests were uncommon. In our cohort, only 1% of such patients had a severely abnormal lung function. In other words, for patients meeting these characteristics, about 100 pulmonary function testing studies would need to be done to identify one patient with findings abnormal enough to qualify for Ferrara unfitness. Thus, in such patients, it would appear reasonable to forgo pulmonary function testing if only done for the purpose of Ferrara fitness assessment using current criteria.
Footnotes
- Received May 21, 2022
- Accepted July 25, 2022
Correspondence
Disclosures
No conflicts of interest to disclose.
Contributions
RP contributed to the collection and assembly of data, data interpretation, and drafting of the manuscript. MO and GSC participated in data interpretation and drafting of the manuscript. FB, GP, LM, MIDP, GS and AV contributed to the provision of study material, patient recruitment, and acquisition of data. RBW conceptualized and designed this study and participated in data analysis and interpretation and drafting of the manuscript. All authors revised the manuscript critically and gave final approval to submit for publication.
Data-sharing statement
For original, de-identified data, please contact the corresponding author.
References
- Döhner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia. N Engl J Med. 2015; 373(12):1136-1152. https://doi.org/10.1056/NEJMra1406184PubMedGoogle Scholar
- Döhner H, Estey E, Grimwade D. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017; 129(4):424-447. https://doi.org/10.1182/blood-2016-08-733196PubMedPubMed CentralGoogle Scholar
- Short NJ, Konopleva M, Kadia TM. Advances in the treatment of acute myeloid leukemia: new drugs and new challenges. Cancer Discov. 2020; 10(4):506-525. https://doi.org/10.1158/2159-8290.CD-19-1011PubMedGoogle Scholar
- Bazinet A, Assouline S. A review of FDA-approved acute myeloid leukemia therapies beyond '7 + 3'. Expert Rev Hematol. 2021; 14(2):185-197. https://doi.org/10.1080/17474086.2021.1875814PubMedGoogle Scholar
- Döhner H, Wei AH, Löwenberg B. Towards precision medicine for AML. Nat Rev Clin Oncol. 2021; 18(9):577-590. https://doi.org/10.1038/s41571-021-00509-wPubMedGoogle Scholar
- Ferrara F, Barosi G, Venditti A. Consensus-based definition of unfitness to intensive and non-intensive chemotherapy in acute myeloid leukemia: a project of SIE, SIES and GITMO group on a new tool for therapy decision making. Leukemia. 2013; 27(5):997-999. https://doi.org/10.1038/leu.2012.303PubMedGoogle Scholar
- Palmieri R, Othus M, Halpern AB. Accuracy of SIE/SIES/GITMO consensus criteria for unfitness to predict early mortality after intensive chemotherapy in adults with AML or other high-grade myeloid neoplasm. J Clin Oncol. 2020; 38(35):4163-4174. https://doi.org/10.1200/JCO.20.01392PubMedPubMed CentralGoogle Scholar
- Arber DA, Orazi A, Hasserjian R. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016; 127(20):2391-2405. https://doi.org/10.1182/blood-2016-03-643544PubMedGoogle Scholar
Data Supplements
Figures & Tables
Article Information
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.