Abstract
Background: Lymphopenia has been associated with in-hospital, early, and late mortality. We aimed to elucidate differences in baseline characteristics in patients with lymphopenia with and without acute respiratory distress syndrome (ARDS) and determine predictors of in-hospital mortality in this patient population. Methods: Patients ≥ 18 years of age with lymphopenia were identified in the National Inpatient Sample (2017–2021) and stratified according to ARDS diagnosis. Predictors of in-hospital mortality were determined using multivariate analyses with a logistic regression model. Results: From 183,185 patients with lymphopenia, 10,420 (5.7%) had ARDS, of which 92.8% had coronavirus disease 2019. The patients with ARDS suffered from more in-hospital mortality (47% versus 6.7%, p < 0.001). ARDS increased the odds of in-hospital mortality by eight-fold (odds ratio [OR]: 7.91 [7.06–8.86], p < 0.001). Age ≥ 65 years (OR: 4.88 [3.98–5.99]), moderate/severe liver disease (OR: 2.53 [1.87–3.42]), and metastatic cancer (OR: 2.18 [1.68–2.82]) were among the strongest positive predictors of in-hospital mortality (all p < 0.001). Conclusions: Patients with lymphopenia who have ARDS have higher in-hospital mortality, likely due to the condition’s clinical course. Lymphopenia may be a marker of immune dysregulation and systemic involvement in ARDS.
Keywords: acute respiratory distress syndrome, coronavirus disease 2019, lymphopenia, in-hospital mortality
1. Introduction
Immune dysregulation can occur among critically ill intensive care unit (ICU) patients, including higher neutrophil/lymphocyte ratios; reduced natural killer (NK) cells; and reduced T cells, particularly CD4+ T cells [1]. Lymphopenia, characterized by low lymphocyte counts with varying cutoffs, has been shown to have prevalences of 38% and 34% in all-cause hospitalizations and ICU admissions, respectively [2]. Although one study found no association between lymphopenia and infections [2], another large prospective study found 26% to 126% increases in the risks of different infections, including pneumonia, urinary tract infections, and diarrheal disease [3]. Additionally, lymphopenia, especially persistent lymphopenia, can predispose individuals to secondary infections [4] with subsequent worse outcomes, including longer lengths of stay and mortality [5]. Lymphopenia has been associated with higher mortality across multiple conditions [2,6,7,8,9].
Acute respiratory distress syndrome (ARDS) is a severe, life-threatening condition with several inciting etiologies such as pneumonia, pulmonary contusion, sepsis from a non-pulmonary source, and pancreatitis [10]. Pneumocyte and alveolar injuries prompt a cascade of events leading to an inflammatory response and vascular leakage, resulting in hypoxemia and the need for invasive mechanical ventilation [10,11]. Immune alterations have been studied in patients with ARDS. While one study found that lymphopenia was not a predictor of ARDS in severe acute respiratory syndrome (SARS) (not coronavirus disease 2019 [COVID-19]) [12], other studies have analyzed the prognostic value of immune cell count ratios in ARDS, including the lymphocyte/neutrophil ratio [13] and the neutrophil to lymphocyte and platelet ratio [14]. However, studies regarding clinical features of lymphopenia and ARDS are limited.
Given the pervasiveness of lymphopenia in hospitalized patients, the morbidity and mortality of patients with ARDS, and the paucity of research describing associations between lymphopenia and ARDS in terms of clinical characteristics, we aimed to (1) determine differences in baseline characteristics between those with and without ARDS in a cohort of patients with lymphopenia and (2) identify predictors of in-hospital mortality, including ARDS, in the entire cohort. We used the National Inpatient Sample (NIS), where lymphopenia was captured in patients with ARDS.
2. Methods
2.1. Dataset and Patient Population
The NIS is a publicly available all-payer inpatient database provided by the Agency for Healthcare Research and Quality through the Healthcare Cost and Utilization Project (HCUP) [15]. It has information on demographic, clinical, economic, and hospital characteristics.
We identified patients who were at least 18 years of age with a primary (main reason for hospitalization) or secondary diagnosis (coded during hospitalization) of lymphopenia using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) [16] code D72.810 during the years 2017 to 2021 (D72.810 reflects a clinical diagnosis of lymphopenia as documented by the treating provider). The patients were further stratified based on whether they had ARDS using the ICD-10-CM code J80 (Figure 1). Patients with missing data on in-hospital mortality were excluded. We conducted all analyses on weighted estimates, given the stratified sampling design of the NIS, using the DISCWT variable provided by the HCUP.
Figure 1.
Survival of patients with lymphopenia according to acute respiratory distress syndrome (ARDS) diagnosis. Patients with lymphopenia were stratified according to whether they had coexisting acute respiratory distress syndrome. In-hospital mortality in each group is also shown.
This study was conducted in accordance with the principles outlined by the Declaration of Helsinki (1975, revised in 2013). Institutional Review Board approval was waived, given that the NIS is de-identified and publicly available [17].
2.2. Data Collection and Outcome Measurements
Baseline demographic, economic, and healthcare utilization measures and hospital, and clinical characteristics were obtained. Several chronic comorbidities were evaluated, as was COVID-19, a common cause of ARDS. A Charlson Comorbidity Index (CCI) of ≥4 indicated multimorbidity. The primary outcome of interest was in-hospital mortality.
2.3. Statistical Analysis
We assessed the normality of the data using histograms, box plots, and QQ plots. The baseline characteristics of those with and without ARDS were compared using the Pearson chi-square test for categorical variables and the two-sample t-test for continuous variables. Categorical variables are presented as frequencies (percentages), while continuous variables are presented as means (standard deviations). Multivariate analysis with a logistic regression model adjusted for demographics and confounders was used to identify predictors of in-hospital mortality. The aforementioned variables of interest were assessed as predictors, except for total charges, total charges adjusted for yearly inflation, total cost, and calendar year, which were not assessed as predictors. ARDS was assessed as a predictor. Adjusted odds ratios (ORs) with corresponding 95% confidence intervals and p-values are presented. A p-value <0.05 was regarded as statistically significant. Stata 18 (StataCorp, College Station, TX, USA) was used to perform all analyses.
3. Results
3.1. Baseline Characteristics
Among 183,185 hospitalized patients with lymphopenia, 10,420 (5.7%) had coexisting ARDS (Table 1). The patients with ARDS were more likely to be male (63.5% versus 55.4%, p < 0.001) and younger (61.13 [14.71] years versus 61.97 [17.51] years, p < 0.001). Myocardial infarction (11.6% versus 9.1%, p < 0.001), uncomplicated diabetes (27.6% versus 17.2%, p < 0.001), and hemiplegia or paraplegia (2.3% versus 1.5%, p = 0.005) were more prevalent among the patients with ARDS. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19 was found in 92.8% and 45.5% of the patients with and without ARDS, respectively (p < 0.001). The patients with ARDS (47.0%) suffered more from in-hospital mortality than those without ARDS (6.7%) (p < 0.001).
Table 1.
Baseline characteristics of patients with lymphopenia with and without acute respiratory distress syndrome.
| ARDS | No ARDS | Total | p-Value | |
|---|---|---|---|---|
| N | 10,420 (5.7%) | 172,765 (94.3%) | 183,185 (100.0%) | |
| Sex | <0.001 | |||
| Male | 6620 (63.5%) | 95,675 (55.4%) | 102,295 (55.8%) | |
| Female | 3800 (36.5%) | 77,090 (44.6%) | 80,890 (44.2%) | |
| Age in years at admission (y) | 61.13 (14.71) | 61.97 (17.51) | 61.92 (17.36) | 0.024 |
| Age group | <0.001 | |||
| 18 to less than 45 years | 1455 (14.0%) | 30,335 (17.6%) | 31,790 (17.4%) | |
| 45 to less than 65 years | 4445 (42.7%) | 59,070 (34.2%) | 63,515 (34.7%) | |
| ≥65 years | 4520 (43.4%) | 83,360 (48.3%) | 87,880 (48.0%) | |
| Race | <0.001 | |||
| White | 4755 (47.3%) | 96,950 (57.6%) | 101,705 (57.0%) | |
| Black | 1555 (15.5%) | 29,090 (17.3%) | 30,645 (17.2%) | |
| Hispanic | 2615 (26.0%) | 28,430 (16.9%) | 31,045 (17.4%) | |
| Asian or Pacific Islander | 435 (4.3%) | 5940 (3.5%) | 6375 (3.6%) | |
| Native American | 100 (1.0%) | 1035 (0.6%) | 1135 (0.6%) | |
| Other | 600 (6.0%) | 6990 (4.1%) | 7590 (4.3%) | |
| Length of stay (days) | 18.98 (13.66) | 7.32 (8.07) | 7.99 (8.91) | <0.001 |
| Length of stay, grouped | <0.001 | |||
| <7 days | 1235 (11.9%) | 107,280 (62.1%) | 108,515 (59.2%) | |
| ≥7 days | 9185 (88.1%) | 65,485 (37.9%) | 74,670 (40.8%) | |
| Primary expected payer/insurance | <0.001 | |||
| Medicare | 4320 (41.5%) | 85,620 (49.6%) | 89,940 (49.1%) | |
| Medicaid | 1820 (17.5%) | 27,530 (16.0%) | 29,350 (16.0%) | |
| Private insurance | 3395 (32.6%) | 46,350 (26.9%) | 49,745 (27.2%) | |
| Self-pay | 440 (4.2%) | 6955 (4.0%) | 7395 (4.0%) | |
| No charge | 45 (0.4%) | 590 (0.3%) | 635 (0.3%) | |
| Other | 390 (3.7%) | 5545 (3.2%) | 5935 (3.2%) | |
| Region of hospital | 0.051 | |||
| Northeast | 2050 (19.7%) | 41,510 (24.0%) | 43,560 (23.8%) | |
| Midwest | 3055 (29.3%) | 47,585 (27.5%) | 50,640 (27.6%) | |
| South | 2875 (27.6%) | 50,505 (29.2%) | 53,380 (29.1%) | |
| West | 2440 (23.4%) | 33,165 (19.2%) | 35,605 (19.4%) | |
| Bed size of hospital | 0.845 | |||
| Small | 2265 (21.7%) | 36,475 (21.1%) | 38,740 (21.1%) | |
| Medium | 2545 (24.4%) | 43,920 (25.4%) | 46,465 (25.4%) | |
| Large | 5610 (53.8%) | 92,370 (53.5%) | 97,980 (53.5%) | |
| Location/teaching status of hospital | 0.140 | |||
| Rural | 400 (3.8%) | 9935 (5.8%) | 10,335 (5.6%) | |
| Urban non-teaching | 1505 (14.4%) | 24,655 (14.3%) | 26,160 (14.3%) | |
| Urban teaching | 8515 (81.7%) | 138,175 (80.0%) | 146,690 (80.1%) | |
| Total charges (USD) | 298,132.96 (312,065.67) | 90,182.45 (138,502.68) | 101,968.03 (161,010.78) | <0.001 |
| Total charges (USD) adjusted for yearly inflation | 305,667.25 (321,230.59) | 93,061.34 (143,324.11) | 105,110.72 (166,252.04) | <0.001 |
| Total cost (USD) | 71,275.78 (71,430.22) | 20,814.30 (28,874.74) | 23,674.19 (34,809.09) | <0.001 |
| Calendar year | <0.001 | |||
| 2017 | 15 (0.1%) | 6690 (3.9%) | 6705 (3.7%) | |
| 2018 | 45 (0.4%) | 8095 (4.7%) | 8140 (4.4%) | |
| 2019 | 55 (0.5%) | 8475 (4.9%) | 8530 (4.7%) | |
| 2020 | 5510 (52.9%) | 86,360 (50.0%) | 91,870 (50.2%) | |
| 2021 | 4795 (46.0%) | 63,145 (36.5%) | 67,940 (37.1%) | |
| Comorbidities | ||||
| Myocardial infarction | 1210 (11.6%) | 15,705 (9.1%) | 16,915 (9.2%) | <0.001 |
| Congestive heart failure | 1830 (17.6%) | 35,210 (20.4%) | 37,040 (20.2%) | 0.004 |
| Peripheral vascular disease | 460 (4.4%) | 10,835 (6.3%) | 11,295 (6.2%) | <0.001 |
| Cerebrovascular disease | 455 (4.4%) | 8305 (4.8%) | 8760 (4.8%) | 0.383 |
| Dementia | 510 (4.9%) | 13,950 (8.1%) | 14,460 (7.9%) | <0.001 |
| Chronic obstructive pulmonary disease | 2410 (23.1%) | 43,710 (25.3%) | 46,120 (25.2%) | 0.035 |
| Rheumatoid disease | 300 (2.9%) | 7525 (4.4%) | 7825 (4.3%) | 0.002 |
| Peptic ulcer disease | 130 (1.2%) | 1990 (1.2%) | 2120 (1.2%) | 0.696 |
| Mild liver disease | 535 (5.1%) | 11,270 (6.5%) | 11,805 (6.4%) | 0.016 |
| Moderate/severe liver disease | 90 (0.9%) | 3105 (1.8%) | 3195 (1.7%) | 0.001 |
| Uncomplicated diabetes | 2875 (27.6%) | 29,665 (17.2%) | 32,540 (17.8%) | <0.001 |
| Diabetes with chronic complications | 1510 (14.5%) | 25,145 (14.6%) | 26,655 (14.6%) | 0.940 |
| Hemiplegia or paraplegia | 240 (2.3%) | 2590 (1.5%) | 2830 (1.5%) | 0.005 |
| Renal disease | 1830 (17.6%) | 37,600 (21.8%) | 39,430 (21.5%) | <0.001 |
| Cancer (metastatic) | 65 (0.6%) | 6125 (3.5%) | 6190 (3.4%) | <0.001 |
| Cancer (other) | 240 (2.3%) | 9745 (5.6%) | 9985 (5.5%) | <0.001 |
| Acquired immunodeficiency syndrome | 65 (0.6%) | 2680 (1.6%) | 2745 (1.5%) | <0.001 |
| COVID-19 | 9670 (92.8%) | 78,645 (45.5%) | 88,315 (48.2%) | <0.001 |
| Charlson Comorbidity Index | <0.001 | |||
| <4 | 8500 (81.6%) | 128,755 (74.5%) | 137,255 (74.9%) | |
| ≥4 | 1920 (18.4%) | 44,010 (25.5%) | 45,930 (25.1%) | |
| Died during hospitalization | 4895 (47.0%) | 11,640 (6.7%) | 16,535 (9.0%) | <0.001 |
The baseline characteristics of the entire cohort of patients with lymphopenia, stratified by the presence of coexisting acute respiratory distress syndrome. ARDS: acute respiratory distress syndrome; COVID-19: coronavirus disease 2019.
3.2. Predictors of In-Hospital Mortality and Survival
ARDS was the strongest predictor of in-hospital mortality (OR: 7.91 [7.06–8.86], p < 0.001), followed by age ≥ 65 years compared to age 18 to less than 45 years (OR: 4.88 [3.98–5.99], p < 0.001) (Table 2). Moderate/severe liver disease (OR: 2.53 [1.87–3.42], p < 0.001) and metastatic cancer (OR: 2.18 [1.68–2.82], p < 0.001) were associated with in-hospital mortality. Acquired immunodeficiency syndrome (OR: 1.55 [0.97–2.49], p = 0.069) and COVID-19 (OR: 0.88 [0.74–1.04], p = 0.133) showed no significant associations. Determinants of survival included age less than 45 years; female sex (OR: 0.77 [0.70–0.83], p < 0.001); length of stay < 7 days; hospitals in the Midwest region compared to the Northeast region (OR: 0.85 [0.76–0.96], p = 0.009); and the absence of comorbidities such as moderate/severe liver disease, metastatic cancer, congestive heart failure, and ARDS.
Table 2.
Predictors of in-hospital mortality in patients with lymphopenia.
| Predictor | Odds Ratio (95% Confidence Interval) | p-Value |
|---|---|---|
| Female | 0.77 (0.70–0.83) | <0.001 |
| Age group | ||
| 18 to less than 45 years | reference | — |
| 45 to less than 65 years | 2.18 (1.81–2.63) | <0.001 |
| ≥65 years | 4.88 (3.98–5.99) | <0.001 |
| Race/Ethnicity | ||
| White | reference | — |
| Black | 1.22 (1.09–1.37) | 0.001 |
| Hispanic | 1.22 (1.09–1.37) | 0.001 |
| Asian or Pacific Islander | 0.90 (0.71–1.13) | 0.344 |
| Native American | 1.41 (0.87–2.29) | 0.161 |
| Other | 1.40 (1.16–1.70) | <0.001 |
| Length of stay ≥ 7 days | 1.96 (1.80–2.15) | <0.001 |
| Primary expected payer | ||
| Medicare | reference | — |
| Medicaid | 1.04 (0.88–1.22) | 0.655 |
| Private insurance | 0.90 (0.79–1.03) | 0.109 |
| Self-pay | 1.27 (0.99–1.64) | 0.063 |
| No charge | 1.20 (0.55–2.62) | 0.652 |
| Other | 1.14 (0.90–1.45) | 0.264 |
| Region of hospital | ||
| Northeast | reference | — |
| Midwest | 0.85 (0.76–0.96) | 0.009 |
| South | 0.90 (0.80–1.01) | 0.075 |
| West | 1.00 (0.88–1.14) | 0.986 |
| Bed size of hospital | ||
| Small | reference | — |
| Medium | 1.19 (1.06–1.34) | 0.004 |
| Large | 1.10 (0.99–1.22) | 0.083 |
| Location/teaching status of hospital | ||
| Rural | reference | — |
| Urban non-teaching | 1.28 (1.02–1.60) | 0.034 |
| Urban teaching | 1.17 (0.95–1.44) | 0.146 |
| Acute respiratory distress syndrome | 7.91 (7.06–8.86) | <0.001 |
| Comorbidities | ||
| Myocardial infarction | 1.56 (1.38–1.76) | <0.001 |
| Congestive heart failure | 1.51 (1.36–1.68) | <0.001 |
| Peripheral vascular disease | 1.05 (0.90–1.24) | 0.526 |
| Cerebrovascular disease | 1.27 (1.07–1.50) | 0.007 |
| Dementia | 1.52 (1.34–1.72) | <0.001 |
| Chronic obstructive pulmonary disease | 1.11 (1.01–1.22) | 0.029 |
| Rheumatoid disease | 0.93 (0.73–1.17) | 0.529 |
| Peptic ulcer disease | 0.90 (0.60–1.35) | 0.601 |
| Mild liver disease | 1.07 (0.89–1.28) | 0.469 |
| Moderate/severe liver disease | 2.53 (1.87–3.42) | <0.001 |
| Uncomplicated diabetes | 1.15 (1.03–1.28) | 0.01 |
| Diabetes with chronic complications | 1.29 (1.12–1.48) | <0.001 |
| Hemiplegia or paraplegia | 1.60 (1.19–2.14) | 0.002 |
| Renal disease | 1.31 (1.14–1.49) | <0.001 |
| Cancer (metastatic) | 2.18 (1.68–2.82) | <0.001 |
| Cancer (other) | 1.21 (0.98–1.48) | 0.072 |
| Acquired immunodeficiency syndrome | 1.55 (0.97–2.49) | 0.069 |
| COVID-19 | 0.88 (0.74–1.04) | 0.133 |
Predictors of in-hospital mortality in patients with lymphopenia. Determined via multivariate analysis using a logistic regression model. COVID-19: coronavirus disease 2019.
4. Discussion
We found that among patients with lymphopenia, those with ARDS suffered more frequently from in-hospital mortality; ARDS served as a major predictor of in-hospital mortality. Despite its high prevalence among patients with ARDS, COVID-19 was not associated with in-hospital mortality. Moderate/severe liver disease and metastatic cancer joined ARDS in predicting in-hospital mortality in the entire cohort.
The existing literature describes associations between lymphopenia and adverse outcomes, particularly with sepsis/septic shock, where lymphopenia may be driven by decreased lymphocyte and lymphocyte precursor production in the bone marrow and thymus, migration of lymphocytes to sites of infection, or destruction via cell death or apoptosis [18]. Clinically, lymphopenia was bidirectionally associated with septic shock [2,6]. Among hospitalized patients with pneumococcal community-acquired pneumonia (CAP), those with an absolute lymphocyte count <500/mm3 were more likely to be admitted to the ICU and develop septic shock [9]. Patients with CAP, sepsis, and concomitant lymphopenia had longer hospital stays and higher in-hospital mortality and 30-day mortality [7].
Sepsis is often an antecedent to ARDS [10]. ARDS is characterized by an exudative phase with interstitial and alveolar edema, a restorative or proliferative phase, and a possible fibrotic stage; this underlying pathophysiology can lead to the need for mechanical ventilation if there is refractory hypoxemia. The high mortality rate and increased odds of in-hospital mortality in our study may be due to the inherent nature of ARDS itself, where the mortality rate can be as high as 45% in severe cases [10], or the antecedent condition, such as sepsis. Furthermore, baseline immunosuppression may be playing a role, as immunocompromised patients with ARDS have higher in-hospital mortality than immunocompetent patients despite comparable ARDS severity [19]. While we cannot determine the prognoses of patients with ARDS and lymphopenia compared to those with only ARDS, a question that warrants further study, we show that ARDS is a multisystem condition or one step in the process of multiorgan failure [11]. The immune dysregulation exemplified in this condition may be a prognostic indicator of weaning failure [20] and mortality [13,14].
Nearly all patients with ARDS had a coded diagnosis of COVID-19 in our study. The SARS-CoV-2 virus induces lymphopenia through the processes of cell destruction and suppression of lymphopoiesis [21], and changes in lymphocyte count and function and cytokine/chemokine release occur. Patients with severe COVID-19 displayed lower lymphocyte counts, a greater neutrophil/lymphocyte ratio, decreased T-lymphocyte counts, and signs of T-cell exhaustion [21,22]. Monocytic and granulocytic myeloid-derived suppressor cells (MDSCs) were inversely correlated with the T-cell count, suggesting a role in T-cell dysfunction [22]. Patients with COVID-19, severe pneumonia, or ARDS had elevated levels of interleukin (IL)-6 [22,23], IL-10 [22,23], and granulocyte colony-stimulating factor (G-CSF) [22,24,25]. In fact, one study focusing on the immunopathology of COVID-19 versus non-COVID-19 ARDS found that C-X-C motif chemokine ligand-10 (CXCL10), granulocyte–macrophage colony-stimulating factor (GM-CSF), and IL-10 were related to COVID-19 ARDS, while a second cluster that included IL-6 was related to the Sequential Organ Failure Assessment (SOFA) score [24].
The high prevalence of COVID-19 suggests this is the most likely cause of ARDS, although other etiologies and incidental COVID-19 infections are not excluded. Lymphopenia in the setting of COVID-19 is associated with worse outcomes. Lymphopenia was associated with severe COVID-19 [26] and an increased risk [27] or odds [28] of ARDS. Lower lymphocyte counts were noted among those who had severe COVID-19 [26], suffered from ARDS, required ICU care [26,29], or died [26,29]. Lymphopenia was associated with invasive mechanical ventilation; dialysis; and in-hospital mortality [29], including in-hospital mortality in immunosuppressed individuals [28]. Our study differs in that we found no association between COVID-19 and in-hospital mortality among patients with lymphopenia. This could be related to the severity of the infection. Additionally, if lymphopenia is appropriately coded in all COVID-19 patients, statistically significant associations may emerge. Together these findings highlight the intersection between COVID-19, lymphopenia, and ARDS, potentially identifying a group of patients who would benefit from interventions to restore immunological balance and reduce hyperinflammation [30] or to re-establish immune competency [30,31].
Moderate/severe liver disease and metastatic cancer significantly predicted in-hospital mortality, joining ARDS as two of the strongest predictors based on comorbidities. Liver dysfunction impairs proinflammatory cytokine clearance and synthesis of essential proteins such as clotting factors and acute-phase reactants, predisposing patients to coagulopathy and infections [32]. Critically ill patients with lymphopenia and liver cirrhosis exhibited significantly higher mortality rates compared to those without cirrhosis [33]. Similarly, patients with cancer and concomitant lymphopenia experienced worse outcomes [34], possibly due to increased vulnerability to infections, a decreased capacity to recover from critical illnesses [35], poor nutritional status, reduced functional reserve, and increased disease burden.
We used a nationally representative dataset to examine the associations between lymphopenia, ARDS, and in-hospital mortality. However, there are limitations to this study. An administrative database is subject to potential errors in coding, including undercoding of lymphopenia. We did not have clinical data (e.g., vital signs and organ failure assessments) to determine the baseline conditions (e.g., the SOFA scores) of the patients with ARDS. A threshold for lymphopenia could not be established, given the use of the NIS, which may have led to variability in clinical interpretation and underreporting of lymphopenia. Details regarding lymphopenia, such as the timing of onset, severity, and persistence, could not be determined. The patients may have had lymphopenia due to a pre-existing immunologic vulnerability (patients with a baseline immunosuppressed state were not excluded), as an acquired feature of a critical illness, or as a marker of disease severity. Although the etiology of ARDS was not obtained, it was likely secondary to COVID-19; thus, the findings may not be generalizable to those with non-COVID-19 ARDS. Further studies should divide patients with lymphopenia into those who have non-COVID-19 ARDS and those who have COVID-19 ARDS. Given the cross-sectional, discharge-level National Inpatient Sample (NIS) database, the absence of longitudinal follow-up data precludes survival curve estimation. There are likely unmeasured confounders due to the usage of administrative codes within this retrospective study. Given the administrative nature of the dataset, future prospective, granular data can be applied to potentially highlight more robust causal inference methods and stratification.
5. Conclusions
Patients with lymphopenia who develop ARDS suffer more from in-hospital mortality, with ARDS serving as an independent predictor of in-hospital mortality. While it is likely secondary to the inherent nature of ARDS, the immune dysregulation caused by inciting conditions, and possibly ARDS itself, cannot be understated, highlighting the systemic disease process. Lymphopenia may be a marker of this impaired immune response and could serve as a prognostic marker. Moderate/severe liver disease and metastatic cancer, both known to be associated with lymphopenia, worsened outcomes in patients with lymphopenia. Future studies should focus on the efficacy of therapies targeting the immune response to improve outcomes in patients with ARDS, developing cost-effective ARDS management techniques, and implementing interventions to optimize major and minor comorbidities.
Abbreviations
The following abbreviations are used in this manuscript:
| ARDS | acute respiratory distress syndrome |
| CAP | community-acquired pneumonia |
| CCI | Charlson Comorbidity Index |
| COVID-19 | coronavirus disease 2019 |
| CXCL | C-X-C motif chemokine ligand |
| G-CSF | granulocyte colony-stimulating factor |
| GM-CSF | granulocyte–macrophage colony-stimulating factor |
| HCUP | Healthcare Cost and Utilization Project |
| ICD-10-CM | International Classification of Diseases, Tenth Revision, Clinical Modification |
| ICU | intensive care unit |
| IL | interleukin |
| MDSC | myeloid-derived suppressor cell |
| NIS | National Inpatient Sample |
| NK | natural killer |
| OR | odds ratio |
| SARS | severe acute respiratory syndrome |
| SARS-CoV-2 | severe acute respiratory syndrome coronavirus-2 |
| SOFA | Sequential Organ Failure Assessment |
Author Contributions
Conceptualization: A.G., I.G. and B.A.; Methodology: I.G., A.R. and A.A.; Validation: A.R. and A.A.; Formal analysis: A.R. and A.A.; Investigation: I.G., A.R. and A.A.; Writing—original draft preparation: A.G., D.K., S.A. and R.P. (Rishi Panjala).; Writing—review and editing: A.G., T.P., R.P. (Rohan Patil), N.S., S.P., A.P. and B.A.; Visualization: A.G. and T.P.; Supervision: B.A.; Funding acquisition: B.A. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Ethical review and approval were waived for this study due to the NIS being de-identified and publicly available.
Informed Consent Statement
Patient consent was waived due to the NIS from the Healthcare Cost and Utilization Project being completely de-identified and publicly available. Written informed consent to publish this paper was waived.
Data Availability Statement
The datasets used to generate the results of this study are not available upon request due to mandates from the Healthcare Cost and Utilization Project.
Conflicts of Interest
Bindu Akkanti, MD, discloses speaker/advisory board work with Johnson and Johnson in the last 24 months. The rest of the authors declare no conflicts of interest.
Funding Statement
The authors acknowledge the Graham Foundation for funding the publication of the manuscript through the Graham Distinguished Endowed Professorship in Pulmonary Medicine.
Footnotes
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used to generate the results of this study are not available upon request due to mandates from the Healthcare Cost and Utilization Project.