Monocyte-to-lymphocyte ratio is associated with 28-day mortality in patients with acute respiratory distress syndrome: a retrospective study

Evaluation of the Effectiveness of Systemic Inflammatory Indices in the Diagnosis of Respiratory Distress Syndrome in Preterm with Gestational Age of ≤32 Weeks

OBJECTIVE

 It is not yet known whether systemic inflammatory indices affect the development of respiratory distress syndrome (RDS) in premature infants. We aimed to evaluate the relationship between systemic inflammatory indices obtained on the first day of life and the development of RDS in premature infants.

STUDY DESIGN

 Premature infants with gestational age of ≤32 weeks were included in the study. Six systemic inflammatory indices involving neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), pan-immune-inflammation value (PIV), and systemic inflammation response index (SIRI) were measured in the first 1 hour after birth and compared in premature infants with and without RDS.

RESULTS

 A total of 931 premature infants, 579 infants in the RDS group and 352 infants in the non-RDS group, were included in the study. MLR, PLR, and SIRI values were similar between the groups (p > 0.05 for all parameters). NLR, PIV, and SII values in the RDS group were significantly higher than those in the non-RDS group (p = 0.005, p = 0.011, and p < 0.001, respectively). In the predictivity of RDS, the AUC value of SII was 0.842 and the cut-off value was ≥78.200. Multiple logistic analysis showed that a higher level of SII (≥78.2) was independently associated with RDS (odds ratio: 3.03, 95% confidence interval: 1.761-5.301).

CONCLUSION

 Our results demonstrated that a higher SII level (≥78.2) may be a predictor for the development of RDS in premature infants with gestational age of ≤32 weeks.

KEY POINTS

· It is not yet known whether systemic inflammatory indices affect the development of RDS.. · Our results demonstrated high SII levels may be a predictor for the development of RDS.. · SII may provide an advantage as a low-cost, easy-to-detect, useful and powerful parameter in RDS..

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data

Background

Acute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage.

Objective

We aim to develop a machine-learning model for predicting ARDS in patients with sepsis in the intensive care unit (ICU).

Methods

The initial clinical data of patients with sepsis admitted to the hospital (including population characteristics, clinical diagnosis, complications, and laboratory tests) were used to predict ARDS, and screen out the crucial variables. After comparing eight different algorithms, namely, XG boost, logistic regression, light GBM, random forest, GaussianNB, complement NB, support vector machine (SVM), and K nearest neighbors (KNN), rebuilding a prediction model with the best one. When remodeling with the best algorithm, 10% was randomly selected to test, and the remaining was trained for cross-validation. Using the area under the curve (AUC), sensitivity, accuracy, specificity, positive and negative predictive value, F1 score, kappa value, and clinical decision curve to evaluate the model's performance. Eventually, the application in the model illustrated by the SHAP package.

Results

Ten critical features were screened utilizing the lasso method, namely, PaO2/PAO2, A-aDO2, PO2(T), CRP, gender, PO2, RDW, MCH, SG, and chlorine. The prior ranking of variables demonstrated that PaO2/PAO2 was the most significant variable. Among the eight algorithms, the performance of the Gaussian NB algorithm was significantly better than that of the others. After remodeling with the best algorithm, the AUC in the training and validation sets were 0.777 and 0.770, respectively, and the algorithm performed well in the test set (AUC = 0.781, accuracy = 78.6%, sensitivity = 82.4%, F1 score = 0.824). A comparison of the overlap factors with those of previous models revealed that the model we developed performs better.

Conclusion

Sepsis-associated ARDS can be accurately predicted early via a machine learning model based on existing clinical data. These findings are helpful for accurate identification and improvement of the prognosis in patients with sepsis-associated ARDS.

The associations between dietary fibers intake and systemic immune and inflammatory biomarkers, a multi-cycle study of NHANES 2015-2020

Background

In recent years, there has been considerable growth in abnormal inflammatory reactions and immune system dysfunction, which are implicated in chronic inflammatory illnesses and a variety of other conditions. Dietary fibers have emerged as potential regulators of the human immune and inflammatory response. Therefore, this study aims to investigate the associations between dietary fibers intake and systemic immune and inflammatory biomarkers.

Methods

This cross-sectional study used data from the National Health and Nutrition Examination Survey (2015-2020). Dietary fibers intake was defined as the mean of two 24-h dietary recall interviews. The systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), neutrophil-to-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), red blood cell distribution width-to-albumin ratio (RA), ferritin, high-sensitivity C-reactive protein (hs-CRP), and white blood cell (WBC) count were measured to evaluate systemic immune and inflammatory states of the body. The statistical software packages R and EmpowerStats were used to examine the associations between dietary fibers intake and systemic immune and inflammatory biomarkers.

Results

Overall, 14,392 participants were included in this study. After adjusting for age, gender, race, family monthly poverty level index, alcohol consumption, smoking status, vigorous recreational activity, body mass index, hyperlipidemia, hypertension, diabetes, and dietary inflammatory index, dietary fibers intake was inversely associated with SII (β = -2.19885, 95% CI: -3.21476 to -1.18294, p = 0.000248), SIRI (β = -0.00642, 95% CI: -0.01021 to -0.00263, p = 0.001738), NLR (β = -0.00803, 95% CI: -0.01179 to -0.00427, p = 0.000284), RA (β = -0.00266, 95% CI: -0.00401 to -0.00131, p = 0.000644), ferritin (β = -0.73086, 95% CI: -1.31385 to -0.14787, p = 0.020716), hs-CRP (β = -0.04629, 95% CI: -0.0743 to -0.01829, p = 0.002119), WBC (β = -0.01624, 95% CI: -0.02685 to -0.00563, p = 0.004066), neutrophils (β = -0.01346, 95% CI: -0.01929 to -0.00764, p = 0.000064). An inverse association between dietary fibers and PLR was observed in the middle (β = -3.11979, 95% CI: -5.74119 to -0.4984, p = 0.028014) and the highest tertile (β = -4.48801, 95% CI: -7.92369 to -1.05234, p = 0.016881) and the trend test (βtrend = -2.2626, 95% CI: -3.9648 to -0.5604, Ptrend = 0.0150). The observed associations between dietary fibers intake and SII, SIRI, NLR, RA, ferritin, hs-CRP, WBC, and neutrophils remained robust and consistent in the sensitivity analysis. No significant interaction by race was found.

Conclusion

Dietary fibers intake is associated with the improvement of the parameters of the immune response and inflammatory biomarkers, supporting recommendations to increase dietary fibers intake for enhanced immune health.

Korean Real-World Data on Patients With Unresectable Stage III NSCLC Treated With Durvalumab After Chemoradiotherapy: PACIFIC-KR

INTRODUCTION

This study aimed to investigate real-world evidence for efficacy and safety of durvalumab consolidation (DC) after chemoradiotherapy (CRT) in patients with unresectable stage III NSCLC.

METHODS

Patients with stage III NSCLC who started DC after CRT between September 2018 and December 2020 and were treated at five tertiary hospitals in the Republic of Korea were included. The primary end point was real-world progression-free survival (rwPFS). Secondary end points were overall survival, objective response rate, and adverse events including radiation pneumonitis (RP) and immune-related adverse events (irAEs).

RESULTS

A total of 157 patients were enrolled. At the median follow-up of 19.1 months, median rwPFS of DC was 25.9 months (95% confidence interval: 16.5-35.4) and the 1-, 2-, and 3-year rwPFS rates were 59.4%, 51.8%, and 43.5%, respectively. The median overall survival was not mature, and objective response rate of DC was 51.0%. High programmed death-ligand 1 expression (≥50%) and development of RP requiring steroid treatment were significantly associated with longer (p = 0.043) and shorter rwPFS (p = 0.036), respectively. RP, RP requiring steroid treatment, and irAEs developed in 57 (36.3%), 42 (26.8%), and 53 (33.8%) patients, respectively. Among peripheral blood cell counts at the initiation of DC, a high derived monocyte-to-lymphocyte ratio was the most significant risk factor for the development of RP requiring steroid treatment (OR 44.76, 95% CI: 8.89-225.43, p < 0.001) and irAEs (OR 2.85, 95% CI: 1.27-6.41, p = 0.011).

CONCLUSIONS

Compared with the outcome of the PACIFIC trial, these real-world data revealed favorable survival benefits of DC after CRT in patients with unresectable stage III NSCLC. Blood-based biomarkers could predict higher-grade RP and irAEs before the initiation of DC.

Association of preoperative monocyte/lymphocyte ratio with postoperative oxygenation impairment in patients with acute aortic syndrome

BACKGROUND

Postoperative oxygenation impairment represents a common complication in patients with the acute aortic syndrome (AAS). The study aimed to explore the relationship between inflammatory indicators and AAS patients with oxygenation impairment after operation.

METHODS

In this study, 330 AAS patients who underwent surgery were enrolled and divided into 2 groups based on postoperative oxygenation impairment (non-oxygenation impairment group and oxygenation impairment group). Regression analysis was performed to assess the relationship between inflammatory indicators and postoperative oxygenation impairment. A smooth curve and interaction analysis were further conducted. Stratified analysis was used according to preoperative monocyte/lymphocyte ratio (MLR) (Tertiles).

RESULTS

Multivariate analysis showed that preoperative MLR was independently related to oxygenation impairment after surgery in AAS patients (OR, 95% CI, P: 2.77, 1.10-7.00, 0.031). The smooth curve indicated the risk of postoperative oxygenation impairment was higher with the elevated preoperative MLR. Interaction analysis revealed that patients with AAS with high preoperative MLR who had coronary artery disease (CAD) had a higher risk of oxygenation impairment after operation. Moreover, stratified analysis was performed according to baseline MLR (Tertiles), and a higher baseline MLR level in AAS patients was correlated with a lower arterial oxygen tension (P_aO2) / inspiratory oxygen fraction (F_iO2) ratio perioperatively.

CONCLUSIONS

In AAS patients, preoperative MLR level was independently related to postoperative oxygenation impairment.

A potential novel inflammation biomarker for predicting the prognosis of decompensated liver cirrhosis

OBJECTIVE

This study aimed to explore the prognostic value of the lymphocyte (LYM)-to-white blood cell (WBC) ratio (LWR) in patients with decompensated liver cirrhosis (DLC).

METHODS

This study was conducted by recruiting 214 patients with DLC with different aetiologies (development cohort). Receiver operating characteristic (ROC) curve analyses were used to assess the predictive accuracy of the LWR, and Youden's index was used to determine the optimal cut-off values of the LWR based on the ROC curve. Next, patients were divided into high- and low-LWR groups according to the cut-off values. Multivariate logistic analyses were performed to determine the independent predictors for the 1-, 3- and 6-month mortality. Restricted cubic spline (RCS) was used to determine and visualize the association between LWR and the risk of death. We verified the predictive ability of LWR in the validation cohort of 139 patients.

RESULTS

In the development cohort, there were 16 (7.5%), 22 (10.3%) and 30 patients (14.0%) who died at 1, 3 and 6 months, respectively. The LWR was significantly lower in non-survivors than in survivors and was an independent predictor of poor outcomes. The ROC analyses with the Delong test showed that the LWR had comparable predictive power with the Model for End-Stage Liver Disease (MELD) score, neutrophil-to-LYM ratio (NLR) and Chronic Liver Failure consortium score for acute decompensated (CLIF-C ADs). RCS showed a non-linear relationship between the LWR and the risk of death at 1 and 3 months, whereas a linear relationship was observed between the LWR and the risk of death at 6 months. We verified that the decreased LWR was an independent predictor of adverse outcomes at 3-, and 6-month follow-up endpoints in the validation cohort.

CONCLUSIONS

Our findings indicate that a lower LWR is an independent factor for unfavourable outcomes and may serve as a potential novel prognostic predictor in patients with DLC.KEY MESSAGESThis study is the first report on the prognostic value of the lymphocyte (LYM)-to-white blood cell (WBC) ratio (LWR) in patients with decompensated liver cirrhosis (DLC).Decreased LWR is an independent factor for adverse outcomes in patients with DLC.

Tumor Progression Locus 2 Protects against Acute Respiratory Distress Syndrome in Influenza A Virus-Infected Mice

Excessive inflammation in patients with severe influenza disease may lead to acute lung injury that results in acute respiratory distress syndrome (ARDS). ARDS is associated with alveolar damage and pulmonary edema that severely impair gas exchange, leading to hypoxia. With no existing FDA-approved treatment for ARDS, it is important to understand the factors that lead to virus-induced ARDS development to improve prevention, diagnosis, and treatment. We have previously shown that mice deficient in the serine-threonine mitogen-activated protein kinase, Tpl2 (MAP3K8 or COT), succumb to infection with a typically low-pathogenicity strain of influenza A virus (IAV; HKX31, H3N2 [x31]). The goal of the current study was to evaluate influenza A virus-infected Tpl2-/- mice clinically and histopathologically to gain insight into the disease mechanism. We hypothesized that Tpl2-/- mice succumb to IAV infection due to development of ARDS-like disease and pulmonary dysfunction. We observed prominent signs of alveolar septal necrosis, hyaline membranes, pleuritis, edema, and higher lactate dehydrogenase (LDH) levels in the lungs of IAV-infected Tpl2-/- mice compared to wild-type (WT) mice from 7 to 9 days postinfection (dpi). Notably, WT mice showed signs of regenerating epithelium, indicative of repair and recovery, that were reduced in Tpl2-/- mice. Furthermore, biomarkers associated with human ARDS cases were upregulated in Tpl2-/- mice at 7 dpi, demonstrating an ARDS-like phenotype in Tpl2-/- mice in response to IAV infection. IMPORTANCE This study demonstrates the protective role of the serine-threonine mitogen-activated protein kinase, Tpl2, in influenza virus pathogenesis and reveals that host Tpl2 deficiency is sufficient to convert a low-pathogenicity influenza A virus infection into severe influenza disease that resembles ARDS, both histopathologically and transcriptionally. The IAV-infected Tpl2-/- mouse thereby represents a novel murine model for studying ARDS-like disease that could improve our understanding of this aggressive disease and assist in the design of better diagnostics and treatments.

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio Can Predict the Severity in COVID-19 Patients from Ethiopia: A Retrospective Study

Background

Coronaviruses are a broad family of pathogens that can cause mild to severe respiratory illnesses. Due to a strong inflammatory response and a weak immunological response, viral pneumonia inflammation, like Coronavirus Disease 2019 (COVID-19), displays an unbalanced immune response. Therefore, circulating biomarkers of inflammation and the immune system can serve as reliable predictors of a patient's prognosis for COVID-19. Hematological ratios are reliable markers of inflammation that are frequently utilized in pneumonia, primarily in viral infections with low cost in developing countries.

Purpose

To examine the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) in predicting the severity of COVID-19 patients.

Methods

An institutional-based retrospective study was done on 105 hospitalized COVID-19 patients at the University of Gondar comprehensive specialized referral hospital, Northwest Ethiopia. The laboratory evaluations that were gathered, evaluated, and reported on included the total leucocyte count (TLC), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute monocyte count (AMC), NLR, LMR, and PLR. The Kruskal-Wallis test and Wilcoxon matched-pairs signed test were used to see whether there were any differences between the continuous variables. Receiver operating curve (ROC) analysis was used to determine the appropriate cut-off values for NLR, PLR, and LMR. P-value <0.05 was considered a statistically significant association.

Results

ANC, NLR, and PLR were highest in the critical group (p = 0.001), while this group had the least ALC and LMR (p = 0.001). We calculated the optimal cut-off values of the hematological ratios; NLR (8.4), LMR (1.4), and PLR (18.0). NLR had the highest specificity and sensitivity, at 83.8% and 80.4%, respectively.

Conclusion

Our research showed that NLR and PLR were good indicators of severity in COVID-19. However, our findings indicate that MLR is not a reliable predictor.

Prognostic value of neutrophils to lymphocytes and platelets ratio for 28-day mortality in patients with acute respiratory distress syndrome: a retrospective study

Background

Acute respiratory distress syndrome (ARDS) is a rapidly progressive and fatal respiratory failure disease that often occurs in critically ill patients. Since ARDS is associated with immune dysregulation and coagulation abnormalities, it is necessary to identify an appropriate predictor that can accurately predict ARDS mortality based on its pathophysiology. Therefore, this study aimed to evaluate the clinical value of neutrophils to lymphocytes and platelets ratio (N/LPR) in predicting 28-day mortality in ARDS patients.

Methods

From July 2018 to October 2021, the medical records of ARDS patients were retrospective reviewed. Neutrophil count, lymphocyte count, and platelet count were collected, and the neutrophil-to-lymphocyte ratio (NLR) and N/LPR were calculated. Multivariate logistic regression analyses were performed to identify independent predictors of 28-day mortality in ARDS. Receiver operating characteristic (ROC) curve with the area under curve (AUC) was used to evaluate optimal cut-off values for 28-day mortality in ARDS. Kaplan–Meier analysis was used to estimate the 28-day survival probabilities stratified by optimal cut-off values of N/LPR and NLR.

Results

A total of 136 ARDS patients were included in this study and were further divided into survivors (n = 69) and non-survivors (n = 67) groups according to their survival status on day 28. There were no significant differences between the two groups in age, sex, history of smoking and drinking, comorbidities, and reasons of admission (P > 0.05). Non-survivors had significantly higher neutrophil counts, NLR and N/LPR and had significantly lower platelet counts than survivors (P < 0.05). Multivariate regression analysis revealed that N/LPR, NLR and platelet counts were independent predictors for 28-day mortality in ARDS (P < 0.05). The ROC analyses showed that N/LPR with optimal cut-off value of 10.57 (sensitivity: 74.6%; specificity: 72.5%) is a more reliable predictor for 28-day mortality in ARDS than NLR and platelet count (AUC: 0.785 vs. 0.679 vs. 0.326). Further subgroup analysis confirmed that ARDS patients with N/LPR < 10.57 had significantly lower 28-day mortality than patients with N/LPR ≥ 10.57 (P < 0.001). Kaplan–Meier analysis also confirmed that ARDS patients with N/LPR < 10.57 had significantly longer survival.

Conclusion

N/LPR is an independent risk factor associated with 28-day mortality in ARDS patients and shows better performance in predicting mortality rate than NLR.

Monocyte-to-Lymphocyte Ratio Was an Independent Factor of the Severity of Spinal Tuberculosis

Purpose

The purpose was to explore the relationship between monocyte-to-lymphocyte ratio (MLR) and the severity of spinal tuberculosis.

Methods

A total of 1,000 clinical cases were collected, including 496 cases of spinal tuberculosis and 504 cases of nonspinal tuberculosis. Laboratory blood results were collected, including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), white blood cells (WBC), hemoglobin (HGB), platelets (PLT), neutrophil count, percentage of neutrophils, lymphocyte count, percentage of lymphocytes, monocyte count, percentage of monocytes, MLR, platelets -to- monocyte ratio (PMR), platelets -to- lymphocyte ratio (PLR), neutrophil -to- lymphocyte ratio (NLR), and platelets -to- neutrophil ratio (PNR). The statistical parameters analyzed by the Least Absolute Shrinkage and Selection Operator (LASSO) and receiver-operating characteristic (ROC) curves were used to construct the nomogram. The nomogram was assessed by C-index, calibration curve, ROC curve, and decision curve analysis (DCA) curve.

Results

The C-index of the nomogram in the training set and external validation set was 0.801 and 0.861, respectively. Similarly, AUC was 0.801 in the former and 0.861 in the latter. The net benefit of the former nomogram ranged from 0.1 to 0.95 and 0.02 to 0.99 in the latter nomogram. Furthermore, there was a correlation between MLR and the severity of spinal tuberculosis.

Conclusion

MLR was an independent factor in the diagnosis of spinal tuberculosis and was associated with the severity of spinal tuberculosis. Additionally, MLR may be a predictor of active spinal tuberculosis.