Longitudinal trajectories of pneumonia lesions and lymphocyte counts associated with disease severity among convalescent COVID-19 patients: a group-based multi-trajectory analysis

Temporal patterns of organ dysfunction in COVID-19 patients hospitalized in the intensive care unit: A group-based multitrajectory modeling analysis

BACKGROUND

The course of organ dysfunction (OD) in Corona Virus Disease 2019 (COVID-19) patients is unknown. Herein, we analyze the temporal patterns of OD in intensive care unit-admitted COVID-19 patients.

METHODS

Sequential organ failure assessment scores were evaluated daily within 2 weeks of admission to determine the temporal trajectory of OD using group-based multitrajectory modeling (GBMTM).

RESULTS

A total of 392 patients were enrolled with a 28-day mortality rate of 53.6%. GBMTM identified four distinct trajectories. Group 1 (mild OD, n = 64), with a median APACHE II score of 13 (IQR 9-21), had an early resolution of OD and a low mortality rate. Group 2 (moderate OD, n = 140), with a median APACHE II score of 18 (IQR 13-22), had a 28-day mortality rate of 30.0%. Group 3 (severe OD, n = 117), with a median APACHR II score of 20 (IQR 13-27), had a deterioration trend of respiratory dysfunction and a 28-day mortality rate of 69.2%. Group 4 (extremely severe OD, n = 71), with a median APACHE II score of 20 (IQR 17-27), had a significant and sustained OD affecting all organ systems and a 28-day mortality rate of 97.2%.

CONCLUSIONS

Four distinct trajectories of OD were identified, and respiratory dysfunction trajectory could predict nonpulmonary OD trajectories and patient prognosis.

Effect of Thymalfasin (Thymosin-α-1) on Reversing Lymphocytopenia among Patients with COVID-19

Introduction

Thymosin-α-1 (Tα1) elevates lymphocyte counts among patients with COVID-19, but its effect on reversing lymphocytopenia is unknown.

Methods

24 patients treated with Tα1 and 100 patients in the control arm were included in this analysis. The incidence rate of reversing lymphocytopenia, overall and stratified by baseline oxygen support, above the threshold for classification of lymphocytopenia (i.e., Total Lymphocyte Count (TLC) < 1.5 x 10⁹/L) and severe lymphocytopenia (i.e., TLC < 1.0 x 10⁹/L) within 3, 5, and 7 days of treatment initiation was calculated, along with incidence rate ratios (IRRs) and 95% confidence intervals (CIs).

Results

Compared with the standard of care, the rate of reversing lymphocytopenia (IRR: 2.38, 95% CI: 0.92 – 5.81) and severe lymphocytopenia (IRR: 1.57, 95% CI: 0.59 – 3.72), especially among patients with severe lymphocytopenia on high flow oxygen support (IRR: 3.64, 95% CI: 0.71 – 23.44), was greater for patients treated with Tα1 within 3 days of treatment initiation, although analyses were not significant.

Conclusion

Among patients with hypoxemia and lymphocytopenia, Tα1 may reverse lymphocytopenia and severe lymphocytopenia, particularly within 3 days of treatment initiation, faster than the standard of care.

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.

Lymphocyte trajectories are associated with prognosis in critically ill patients: A convenient way to monitor immune status

Background

Immunosuppression is a risk factor for poor prognosis of critically ill patients, but current monitoring of the immune status in clinical practice is still inadequate. Absolute lymphocyte count (ALC) is not only a convenient biomarker for immune status monitoring but is also suitable for clinical application. In this study, we aimed to explore different trajectories of ALC, and evaluate their relationship with prognosis in critically ill patients.

Methods

We retrospectively enrolled 10,619 critically ill patients admitted to a general intensive care unit (ICU) with 56 beds from February 2016 to May 2020. Dynamic ALC was defined as continuous ALC from before ICU admission to 5 days after ICU admission. Initial ALC was defined as the minimum ALC within 48 h after ICU admission. Group-based trajectory modeling (GBTM) was used to group critically ill patients according to dynamic ALC. Multivariate cox regression model was used to determine the independent association of trajectory endotypes with death and persistent inflammation, immunosuppression, catabolism syndrome (PICS).

Results

A total of 2022 critically ill patients were unsupervisedly divided into four endotypes based on dynamic ALC, including persistent lymphopenia endotype (n = 1,211; 58.5%), slowly rising endotype (n = 443; 22.6%), rapidly decreasing endotype (n = 281; 14.5%) and normal fluctuation endotype (n = 87; 4.4%). Among the four trajectory endotypes, the persistent lymphopenia endotype had the highest incidence of PICS (24.9%), hospital mortality (14.5%) and 28-day mortality (10.8%). In multivariate cox regression model, persistent lymphopenia was associated with increased risk of 28-day mortality (HR: 1.54; 95% CI: 1.06–2.23), hospital mortality (HR: 1.66; 95% CI: 1.20–2.29) and PICS (HR: 1.79; 95% CI: 1.09–2.94), respectively. Sensitivity analysis further confirmed that the ALC trajectory model of non-infected patients and non-elderly patients can accurately distinguished 91 and 90% of critically ill patients into the same endotypes as the original model, respectively.

Conclusion

The ALC trajectory model is helpful for grouping critically ill patients, and early persistent lymphopenia is associated with poor prognosis. Notably, persistent lymphopenia may be a robust signal of immunosuppression in critically ill patients.