Characteristics and Outcomes of Patients With Hematologic Malignancies Hospitalized With Respiratory Viral Infections.

Respiratory infections cause substantial morbidity and mortality among patients with hematologic malignancies, including hematopoietic cell transplant (HCT) recipients [ 1–5 ]. Even prior to the Coronavirus Disease 2019 pandemic, viral pathogens were increasingly recognized as a substantial contributor to the burden of acute respiratory failure in this population [ 2 , 5 , 6 ]. Moreover, the rising incidence of hematologic malignancies is likely to increase the number of patients encountering these infections [ 7 , 8 ].

However, existing knowledge about the clinical course and outcomes of respiratory viral infections among patients with hematologic malignancies has been extrapolated from specific populations or drawn from studies at a single center or focusing on a single pathogen [ 9–11 ]. In unselected populations, trajectories and outcomes for viral pathogens differ based on pathogen-level factors as well as host factors such as age and chronic conditions [ 12 ]. Although patients with hematologic malignancies may at first glance appear homogenous relative to the general population, within this group there remains substantial variation across a range of clonal cell populations, genetics, and therapy (ie, immunocompromised may be related to cytopenias, immune cell dysfunction, or both).

Therefore, we characterized pathogen-specific hospital courses and outcomes of respiratory viral infections in patients with hematologic malignancies. We hypothesized that these patients would display pathogen-level differences in terms of presentation, clinical host response, and outcome.

METHODS

Setting and Patients

We conducted a retrospective cohort study of adult patients with a hematologic malignancy and/or prior HCT who were hospitalized at 2 academic hospitals with National Cancer Institute-designated Comprehensive Cancer Centers: Oregon Health & Science University (OHSU; 01/01/2019 through 06/30/2023) and Barnes Jewish Hospital (BJH, the teaching hospital of Washington University School of Medicine; 01/01/2020 through 06/30/2022). We determined eligibility for inclusion through encounter-level International Classification of Diseases (10th Revision)—Clinical Modification codes (ICD-10-CM; Supplementary eTable 1 ) and used these codes to categorize patients into 4 discrete groups: leukemia, lymphoma, myeloma, and HCT recipients [ 13 , 14 ]. Of these hospitalizations, we included encounters associated with a newly-positive polymerase chain reaction or antigen test for a respiratory virus +/-7 days (regardless of source) from the date of admission [ 12 ]. This window balanced sensitivity with attribution to the index hospitalization while reducing misclassification of remote or hospital-acquired infection. For each patient, we included only the first positive test per year (July–June) with each virus.

To capture physiologically significant respiratory tract infections (as opposed to mild infections or asymptomatic carriage) [ 15 ], we included only hospitalizations in which patients had a recorded respiratory rate > 20 breaths per minute, peripheral oxygen saturation < 93%, or new use of supplemental oxygen or advanced respiratory support (supplemental oxygen without a concurrent ICD-10-CM code for chronic supplemental oxygen use, or any use of humidified high-flow nasal oxygen [HFNO], noninvasive ventilation [NIV], or invasive mechanical ventilation [IMV]) within the first 5 days of admission. The institutional review boards at OHSU (#0026331) and Washington University (#202008041) approved this study, and we prepared these results according to relevant guidelines for reporting observational studies ( Supplementary Appendix ) [ 16 ]. Some of these results have previously been presented as an abstract [ 17 , 18 ].

Data Preparation

From each hospital's research data warehouse, we extracted electronic health record (EHR; Epic, Verona, WI) data including demographics, billing codes, admission/discharge/transfer information, vital signs and respiratory flowsheets, laboratory values and microbiology, and medication orders and administrations [ 12 , 19 ].

At OHSU, 2 independent reviewers (Z. A. L. and P. G. L., resolving discrepancies by discussion) performed chart review to determine granular clinical details (eg, type of HCT), performance status, and current oncologic treatment. Because of inconsistent EHR documentation, we imputed Eastern Cooperative Oncology Group (ECOG) scores based on Karnofsky Performance Status values when the former were unavailable [ 20 , 21 ]. Based on documented antineoplastic therapies within 90 days of admission, we categorized each patient's treatment status as immunotherapy (chimeric antigen receptor T cell, PD-(L)1 and/or cytotoxic T-lymphocyte associated protein-4 (CTLA-4) inhibitors, or bispecific T-cell engagers), nonimmunotherapy systemic treatment, or no treatment while also identifying all B-cell targeting treatments within the prior 6 months (regardless of the most recent treatment regimen) [ 22 , 23 ].

Care Processes and Outcomes

The primary outcome was the composite of all-cause hospital mortality or hospice discharge. Full details on secondary outcomes are in Supplementary eTable 1 , but in brief these included hospital length of stay, ICU admission, receipt of respiratory support and vasoactive medications (and days alive and free of these interventions) [ 24 ], concurrent bloodstream and respiratory infections (within 72 hours of admission), and receipt of nonprophylactic antimicrobials and systemic corticosteroids ( Supplementary eTable 2 ).

Per prior methods, we categorized each patient's respiratory support into 5 mutually-exclusive levels based on their maximal support each hour: room air, nasal cannula equivalent (NC), HFNO, NIV, and IMV [ 12 , 25 ]. We also calculated each patient's lowest SpO 2 :FiO 2 (S:F) ratio within 72 hours of presentation. We determined concurrent infections based on blood and respiratory cultures obtained within the first 7 days of hospitalization.

Additional Data

From laboratory data, we determined each encounter's first absolute lymphocyte count (ALC) and neutrophil count (ANC) within 72 hours of admission. We categorized lymphopenia as any ALC <1000/μL and neutropenia as (when present) as mild (1000–1500/μL), moderate (500–1000/μL), or severe (<500/μL) [ 26 , 27 ]. Finally, because respiratory viral infections can manifest as sepsis [ 28 ], we calculated Systemic inflammatory response syndrome (SIRS) and Quick Sequential Organ Failure Assessment (qSOFA) scores through hour 72 of admission, reasoning that positive screening on 1 or both of these tools (ie, scores ≥ 2) might prompt additional clinical attention [ 29 , 30 ].

Chest Radiography

We identified all patients at OHSU who had at least 1 chest radiograph obtained by hour 72 of hospitalization. From these patients, we calculated the radiographic assessment of lung edema (RALE) score on each available radiograph during the hospitalization. Radiographic Assessment of Lung Edema is a validated measure of pulmonary edema severity in ARDS based on the extent and density of airspace opacities on imaging (range: 0 [none]—48 [dense consolidation in >75% of the air space]) [ 12 ]. Two investigators (Z. A. L., R. W.) independently reviewed the first 10 images to ensure adequate interrater reliability. Based on an intraclass correlation coefficient of .95, we divided the remaining radiographs to be read by a single rater.

Statistical Analysis

We summarized data using frequencies with proportions or medians with interquartile ranges (IQR) as appropriate. For unadjusted comparative outcomes analysis across pathogens, we used chi-squared, Fisher's exact, and Kruskal–Wallis tests as appropriate. To quantify co-infection appropriately (ie, to prevent inappropriate statistical inference from double-counting), we estimated the Jeffreys interval around each frequency [ 31 ].

To determine the independent association between infecting virus and the primary outcome, we fit a hierarchical multivariable logistic regression model adjusting for age (continuous), malignancy (categorical), and qSOFA (continuous), with hospital as a random effect. In this model, we a priori categorized the viruses as SARS-CoV-2, influenza, respiratory syncytial virus (RSV), and “other” (to preserve statistical power and because we expected the first 3 of these to be associated with the most severe outcomes), adding a dichotomous variable to adjust for the presence of more than 1 virus. In the OHSU dataset, we used multivariable logistic regression to determine the independent relationship between RALE scores and the primary outcome, adjusting for virus (categorized as above) and initial S:F ratio. Post hoc, we fit analogous multivariable logistic regression models to determine the relationship between RALE score and receipt of (1) advanced respiratory support (composite of IMV, NIV, and HFNO) and (2) antimicrobials. No encounter records were missing the aforementioned variables.

We considered P values less than or equal to .05 statistically significant. We analyzed data using R 4.3.1 (R Foundation for Statistical Computing; Vienna, Austria) with the tidyverse, ratesci, and lme4 packages [ 32–35 ].

RESULTS

Patient Characteristics

We evaluated 385 hospitalizations from 346 unique patients—182 from OHSU and 203 from BJH ( Supplementary eFigure 1 ). SARS-CoV-2 was the most common pathogen (n = 162, 42%), followed by rhino-enterovirus (RVEV, n = 92, 24%) and influenza (n = 33, 8.6%). Patient characteristics were similar across all pathogens ( Table 1 ); the overall median age was 64 years (interquartile range [IQR] 51–72), and 152 patients (39%) were female. Patients at OHSU were younger (median age 60 [39–71] vs 66 [58–73]) than patients at BJH, and infections with RSV (9.3% [n = 17] vs 5.4% [n = 11]) and RVEV (30% [n = 55] vs 18% [n = 37]) were more common at OHSU, while the opposite was true for SARS-CoV-2 (34% [n = 62] vs 49% [n = 100] ( Supplementary eTable 3 ).

Leukemia was the most common category of malignancy (n = 144, 37%), followed by HCT status (n = 103, 27%). SARS-CoV-2 was the most common pathogen among each of the active malignancy categories (leukemia, n = 73 [51%]; lymphoma, n = 44 [51%]; multiple myeloma [n = 24, 46%]) but not among HCT recipients (n = 21 [20%] vs RVEV, n = 25 [24%]). RSV infections were most likely to be detected in HCT recipients (n = 12, 43%).

Within the OHSU cohort, 95 patients had performance status available; the median ECOG score was 1 (IQR 1–2). ECOG scores did not differ by virus ( Supplementary eTable 3 ) but were nonsignificantly higher among patients who experienced the composite outcome (median 2 [IQR 1–3], 13 missing vs 1 [1–2], 74 missing; P = .11). Among the OHSU cohort, a plurality of recent treatments were non-immunotherapy (n = 82, 45%); only 10 patients (5%) had received immunotherapy within 3 months of hospitalization ( Supplementary eTable 4 ). B-cell depleting agents had been given to 40 patients (22%) in this time frame. Outcome rates did not differ significantly by treatment category or B-cell depletion status ( Supplementary eTable 5 ).

We did not observe pathogen-level differences in terms of initial clinical presentation ( Figure 1 ). Specifically, patients were similar with regard to their maximal temperature, minimum SpO 2 , ALC, or ANC within the initial 72 hours of presentation. Most patients were SIRS-positive (n = 328, 85%), whereas only 36% (n = 138) were qSOFA-positive.

Care Processes and Outcomes

Overall, 54 patients (14%) died in the hospital or were discharged to hospice ( Table 2 , Supplementary eTable 6 , Supplementary eTable 7 ). Patients with SARS-CoV-2 (n = 29, 18%), adenovirus (n = 1, 17%), influenza (n = 5, 15%), and human metapneumovirus (HMPV, n = 3, 15%) infections had particularly high rates of this composite outcome, although pathogen-level differences were not statistically significant. Mixed-effects logistic regression modeling revealed nonsignificantly increased adjusted odds for the composite outcome by virus (with “other” as reference, SARS-CoV-2 adjusted odds ratio [aOR] 1.98, 95% confidence interval [CI] .95–4.12, P = .067; influenza aOR 2.21, 0.68–7.22, P = .19; RSV aOR 0.92, 0.23–3.78, P = .869).

Several care processes did differ across pathogens. First, directed antivirals were more frequently used for influenza (n = 28 receiving oseltamivir, 85%) than SARS-CoV 2 (n = 86 receiving remdesivir, 53%) or RSV (n = 10 receiving ribavirin, 36%). Second, although rates of concurrent infection were similarly low for all viruses except adenovirus ( Supplementary eTable 8 ), intravenous antibiotics were less frequently used for patients with SARS-CoV-2 (n = 98, 60%), influenza (n = 22, 67%), RSV (n = 17, 61%), and RVEV (n = 59, 64%) than for other viruses (parainfluenza n = 22, 73%; coronavirus n = 25, 83%; HMPV n = 16, 80%; adenovirus n = 6, 100%). In contrast, corticosteroids were used more frequently among patients with SARS-CoV-2 infections than other viruses (n = 99, 61% vs 17%–57%, P < .001).

Despite these differences in pharmacologic therapies, we did not observe statistical differences in rates of ICU admission, receipt of vasoactive medications, or maximal respiratory support requirements. We did, however, observe differential trajectories in respiratory support ( Figure 2 ). Patients with RV/EV were slightly less likely to receive high levels of respiratory support (ie, IMV, NIV, or high-flow oxygen) during the early course of hospitalization, while patients with SARS-CoV-2 had longer courses of high respiratory support.

Imaging

Patients with SARS-CoV-2 had higher RALE scores than other viruses on initial and subsequent chest radiographs ( Figure 3 , Supplementary eTable 9 ). More patients with SARS-CoV-2 had at least 1 radiographic quadrant with a RALE subscore ≥ 2 (n = 13, 100%) than patients with other viral pathogens (range 50%–67%, P = .049). Initial RALE scores did not correlate with S:F ratio (Spearman's coefficient −0.06, P = .66). Moreover, whereas S:F values improved over the initial week of hospitalization, RALE values remained stable over this period ( Figure 4 ). After adjustment for virus and nadir S:F ratio, the maximal RALE score was associated with significantly higher adjusted odds for the composite outcome (aOR 1.08, 95% CI 1.01–1.16, P = .033). The initial RALE score was not significantly associated with the composite outcome (aOR 1.06, 95% CI .99–1.15, P = .097), nor was it associated with advanced respiratory support (aOR 1.19, 95% CI .97–2.62, P = .44) or antibiotic use (aOR 0.96, 95% CI .88–1.04, P = .3).

DISCUSSION

Across 2 comprehensive cancer centers, we found that hospitalized patients with hematologic malignancies and respiratory viral infections presented with similar clinical physiology—measurable parameters from vital signs, respiratory support, and laboratory data—regardless of pathogen. Despite these similar presentations, we observed statistical differences in early radiography and certain care processes as well as nonsignificant differences in clinical courses and patient outcomes. Taken together, our findings suggest that in this high-risk patient population, neither the initial clinical presentation (eg, observable vital signs and other physiologic signals) nor the early hospital trajectory (eg, respiratory support) are sufficient to distinguish among the respiratory viruses.

Several findings stand out from this analysis. First, clinical presentations were similar between different viral pathogens, despite different radiography and known differences in host-pathogen interaction profiles (eg, previously-described pathogen-specific immune responses and predictors of outcomes) [ 5 , 12 , 36 , 37 ]. These findings suggest that clinical features alone (ie, vital signs and laboratory data that would be immediately available at the bedside) may be insufficient for profiling pathogen-specific host responses in this population. Our radiographic findings, on the other hand, raise the hypothesis that lower respiratory tract infection might be a differentiating factor (with the caveat that we did not assess for baseline imaging abnormalities in our cohort). In unselected patients with community-acquired pneumonia, progression of radiographic abnormalities has been associated with worse clinical outcomes [ 38 ]. Pathogen-specific radiographic differences, however, remain under-explored [ 12 ]. In addition to radiology, alternative approaches, such as integrating the status of other organ failures, biomarkers, and/or longitudinal (rather than cross-sectional) patterns may warrant further investigation [ 12 , 39–42 ]. At present, however, this issue highlights the clinical importance of prompt multiplex testing to enable appropriate use of therapeutics and infection prevention practices.

Prior work in immunocompromised groups, including lung transplant and HCT recipients [ 43–46 ], has also shown comparable risks of hospitalization and death across diverse respiratory viruses. In broader hospitalized populations, morbidity and mortality may likewise vary less by pathogen than previously assumed [ 47–49 ]. It may be that more aggressive treatment (eg, lower threshold to initiate ribavirin or particular supportive care measures) could be mitigating severe outcomes in patients with more virulent pathogens. Finally, it is also possible that our findings represent a “failure mode” of some patients reaching the end of life, which has been described in solid tumor patients with sepsis [ 50 , 51 ]. However, most available performance status values were favorable, which might suggest that these patients were not imminently dying. Finally, these findings may be influenced by collider bias (ie, conditioning on hospitalization); if only the frailest patients with eg, RVEV are hospitalized, this would worsen their apparent outcomes and misleadingly suggest a false equivalence among pathogens. Future work should strive to overcome this limitation by including systematically captured nonhospitalized patients.

Third, we did observe some differences across pathogens, malignancies, and institutions. For instance, patients with “other” viral pneumonias received more antimicrobials than did other patients despite having no worse imaging than other groups. We speculate that this pattern may be due to early diagnostic uncertainty in patients for whom SARS-CoV-2, influenza, and RSV have been ruled out by rapid testing. Concern for bacterial co-infection (even once a virus has been identified) is another possibility, but rates of co-infection were low in our cohort. In contrast, patients with SARS-CoV-2 received corticosteroids more frequently than other patients, in keeping with strong evidence of mortality benefits for patients with true pneumonia due to this pathogen [ 52 ]. In terms of differences across other groups, HCT patients more frequently had RSV infections. While this finding could be in keeping with the known relationship between cell-mediated immunity and RSV infection [ 53 ], it could, alternatively, be due to differential testing practices in this cohort. Additionally, we found that care processes and outcomes differed between institutions. A larger percentage of patients were given IV antibiotics and corticosteroids at BJH compared with OHSU. Among patients with RSV, those at OHSU had worse outcomes, with more requiring invasive mechanical ventilation or dying/discharge to hospice during their admission. These latter findings should be interpreted with particular caution, as they were not the focus of our analysis; however, they suggest that variation in care practices for these patients exists and should be further studied—ideally through multicenter collaborations designed to enable reproducible research across diverse health systems [ 54 ].

In addition, our findings raise several important hypotheses that could have broader implications. In our study, 85% of all patients met SIRS criteria, with no differences among pathogens. These findings contribute to growing evidence that SIRS demonstrates low discrimination among patients who are hospitalized with hematologic malignancies and suspected infections and that more accurate tools are needed in this population [ 55–57 ]. While SIRS is no longer part of the formal definition of sepsis, it remains an active criterion for many hospitals’ sepsis clinical decision support and quality programs. Furthermore, we found that initial RALE scores did not correlate with the S:F ratio, indicating that the radiographic evidence of respiratory compromise and functional lung compromise are independent of one another. However, we did observe that while S:F ratios improved in patients, RALE scores did not concurrently change. This aligns with literature in community acquired pneumonia, suggesting that radiologic resolution of pneumonia lags behind clinical resolution of symptoms of pneumonia [ 58 ].

This work validates and extends the work of others in this area. Prior studies of critically ill patients with hematologic malignancies have demonstrated that viral pneumonias are a common complication in this population of patients with poor prognosis and mortality [ 59 , 60 ], but few investigations have described the presentation and course of patients outside the ICU [ 61 , 62 ]. Furthermore, our study adds an analysis of pathogen-specific clinical course and radiographic findings that have not been thoroughly examined in previous works.

Strengths of this study include its inclusion of patients from 2 geographically distinct hospitals and its use of readily available EHR data whenever possible to increase generalizability. Additionally, augmenting 1 site's data collection via chart review allowed us to capture information not available within the EHR. Accordingly, our work represents one of the first descriptions of RALE scores in viral pneumonia, specifically pathogen-level differences in RALE scores and change in RALE with concurrent respiratory physiology over the course of hospitalization. An additional strength is our consideration of a wide range of care processes and clinical outcomes, which provide granular insight into the treatment process of these viral pneumonias in this population.

This work should be interpreted in light of its limitations. First, statistically nonsignificant findings could represent Type 2 error in a relatively small sample. Second, we conducted this work within the selected populations at 2 major cancer centers within a discrete time frame, and so our results may not generalize to other settings or time periods. Moreover, viral variant predominance, vaccine availability and uptake, prior immunity, and antiviral availability have evolved over time for all of these pathogens (most prominently during the COVID-19 pandemic); in a small sample, this variation constrains the inferences that can be drawn. Additionally, heterogeneity in institutional practices, many of which were not directly measured in this study, may affect the observed outcomes and limit broader applicability. Third, we were not able to ascertain vaccination status reliably, which might impact the generalizability of our data to populations with higher vaccination rates; higher rates of influenza and SARS-CoV-2 vaccination have been shown to reduce hospitalization and severity in breakthrough infections [ 5 , 63 , 64 ]. Fourth, patients were not uniformly evaluated for secondary infections. Thus, while co-infection estimates may reflect real-world practice (ie, clinically indicated testing and documentation) the lack of standardized evaluation in our study means that rates could be biased in either direction. Last, these findings may be influenced by misclassification bias, as prolonged viral shedding has been associated with HCT and B-cell depletion [ 65 ]. However, we took efforts to pre-empt this possibility through our inclusion criteria, and relatively few patients had received B-cell depleting agents in our cohort. Further, disentangling persistent or recurrent infection in the HCT population is clinically challenging at the bedside and in terms of screening (ie, broader respiratory panel testing could increase viral detection in immunocompromised hosts, but interpretation and clinical utility remain uncertain), and our approach is likely reflective of real-world practice.

In conclusion, among patients with hematologic malignancies or HCT who are hospitalized with viral respiratory infections, we found similar clinical presentations but radiographic and care process differences between pathogens. Our findings may have implications for triage and hospital care practices for patients with these infections.

Supplementary Material

Notes

Financial support. National Institute of Health (KL2TR002370, K08CA270383 to P.G.L. and K24HL141526 to C.L.H.).

Contributor Information

Zulfiqar A Lokhandwala, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA; School of Public Health, Oregon Health & Science University-Portland State University, Portland, Oregon, USA.

Brenna Park-Egan, Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA.

Ravneet Waraich, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA.

Colleen A McEvoy, Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA; Siteman Cancer Center, Washington University School of Medicine, Saint Louis, Missouri, USA.

Andrew P Michelson, Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA; Data Science, and Biostatistics, Institute for Informatics, Washington University School of Medicine, Saint Louis, Missouri, USA.

Alice F Bewley, Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA; Division of Infectious Diseases, Washington University School of Medicine, Saint Louis, Missouri, USA.

Lynne Strasfeld, Division of Infectious Diseases, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA.

Rachel Cook, Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA.

Brandon Hayes-Lattin, Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA.

Catherine L Hough, Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA.

Patrick G Lyons, Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA.

Supplementary Data

Supplementary materials are available at Open Forum Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.

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