Lymphopenia as a Predictor for Adverse Clinical Outcomes in Hospitalized Patients with COVID-19: A Single Center Retrospective Study of 4485 Cases

Abstract

Lymphopenia is commonly present in patients with COVID-19. We sought to determine if lymphopenia on admission predicts COVID-19 clinical outcomes. A retrospective chart review was performed on 4485 patients with laboratory-confirmed COVID-19, who were admitted to the hospital. Of those, 2409 (57.3%) patients presented with lymphopenia (absolute lymphocyte count < 1.1 × 10⁹/L) on admission, and had higher incidences of ICU admission (17.9% versus 9.5%, p < 0.001), invasive mechanical ventilation (14.4% versus 6.5%, p < 0.001), dialysis (3.4% versus 1.8%, p < 0.001) and in-hospital mortality (16.6% versus 6.6%, p < 0.001), with multivariable-adjusted odds ratios of 1.86 (95% confidence interval [CI], 1.55-2.25), 2.09 (95% CI, 1.69-2.59), 1.77 (95% CI, 1.19-2.68), and 2.19 (95% CI 1.76-2.72) for the corresponding outcomes, respectively, compared to those without lymphopenia. The restricted cubic spline models showed a non-linear relationship between lymphocyte count and adverse outcomes, with an increase in the risk of adverse outcomes for lower lymphocyte counts in patients with lymphopenia. The predictive powers of lymphopenia, expressed as areas under the receiver operating characteristic curves, were 0.68, 0.69, 0.78, and 0.79 for the corresponding adverse outcomes, respectively, after incorporating age, gender, race, and comorbidities. In conclusion, lymphopenia is a useful metric in prognosticating outcomes in hospitalized COVID-19 patients.

Keywords: COVID-19; adverse clinical outcomes; lymphocyte count; lymphopenia; receiver operating characteristic curves; restricted cubic splines.

Figure 1

Flow chart of study population and clinical course assessment. Lymphocytes were determined using enrollment complete blood counts. ICU, intensive care unit; AKI, acute kidney failure.

Figure 2

Relationships of ALCs on hospital admission with adverse clinical outcomes in patients with COVID-19. Violin plots of absolute lymphocyte counts (ALCs) showing significantly lower lymphocyte counts in patients who died (A) and those who required ICU admission (B), invasive mechanical ventilation (C), and dialysis (D) than those who were not (p < 0.001, Mann–Whitney U-test). Solid black lines indicate medians, and dashed black lines represent quartile ranges.

Figure 3

Comparison of the adverse outcomes in patients with or without lymphopenia upon hospital admission. Bar graphs show the incidences of in-hospital death (A), ICU admission (B), invasive mechanical ventilation (C), and dialysis due to AKI (D) in the study population classified into two groups according to their absolute lymphocyte counts at admission. Patients with lymphopenia had higher incidences of adverse outcomes (all p < 0.001).

Figure 4

Forest plots showing association of lymphopenia with risks of adverse clinical outcomes in patients with COVID-19. Independent effects of lymphopenia (ALC < 1.1 × 10⁹/L) on the risks of ICU admission (A), invasive mechanical ventilation (B), dialysis due to AKI (C), and in-hospital mortality (D) during hospitalization were analyzed using logistic regression models and compared to individuals with ALC ≥ 1.1 × 10⁹/L. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs), adjusted for age-, sex-, and multi-variables including age, sex, race, diabetes, hypertension, obesity, smoking, COPD, CKD, CHD, and malignancy. ICU, intensive care unit; AKI, acute kidney injury.

Figure 5

Dose–response association of absolute lymphocyte count and risk of the outcomes of interest in patients with COVID-19. Restricted cubic splines were generated using logistic regression models, and the lymphocyte value of 1.1 × 10⁹/L was set as reference (denoted by red vertical lines) for the continuous model adjusted to age, gender, race, and history of hypertension, diabetes, chronic obstructive pulmonary disease, chronic kidney disease, coronary artery disease, malignancy, obesity, and smoking. Solid red curves are ORs for the outcomes of interest (A–D), and dashed black lines indicate 95% confidence intervals based on fitting of cubic splines to risk estimates obtained using logistic regression.

Figure 6

ROC curves of lymphopenia for predicting risks of the outcomes of interest. The areas under the ROC curves (AUCs) are values for estimating incidences of the outcomes of interest (A–D) through the use of logistic regression function calculated from unadjusted lymphopenia, model A, and model B, respectively. Model A, adjusted for age and gender; Model B, adjusted for age, gender, race, hypertension, diabetes, COPD, chronic kidney disease, coronary artery disease, malignancy, obesity, and smoking. Lymphopenia have the largest AUCs (areas under the green curves) to predict individual risk of adverse clinical outcomes with the incorporation of the known clinical risk factors. AKI, acute kidney injury; CI, confidence interval.