The association between Monocyte-to-Lymphocyte ratio and postoperative delirium in ICU patients in cardiac surgery

Comparative analysis of inflammatory markers as predictive markers for postoperative delirium in cardiac surgery patients: an observational study

Background

Postoperative delirium (POD) is a common complication following cardiac surgery that significantly affects patient outcomes. Among inflammatory markers, the monocyte-to-lymphocyte ratio (MLR) has shown potential in predicting POD. However, studies on the relationship between neutrophil-to-lymphocyte ratio (NLR) or platelet-to-lymphocyte ratio (PLR) and POD are still lacking. Moreover, a direct comparison of the predictive capabilities of these three inflammatory markers (NLR, MLR, and PLR) for POD remains unexplored.

Methods

This observational study utilized the MIMIC database. We included 2,095 patients who underwent cardiac surgery. Multivariable logistic regression analysis, restricted cubic spline (RCS) analysis, and receiver operating characteristic (ROC) curve analysis were employed to assess the relationship between NLR, MLR, PLR, and POD.

Results

POD occurred in 415 patients (19.8%). Multivariable logistic regression identified NLR (OR 1.05, 95% CI 1.03-1.08), MLR (OR 1.39, 95% CI 1.01-1.92), and PLR (OR 1.00, 95% CI 1.00-1.00) as independent risk factors for POD, all with P-values < 0.05. ROC curve analysis revealed NLR had the strongest predictive ability (AUC = 0.610, 95% CI: 0.589-0.631), outperforming MLR (AUC = 0.575, 95% CI: 0.553-0.596) and PLR (AUC = 0.553, 95% CI: 0.531-0.574). RCS analysis indicated linear or near-linear relationships between these markers and POD risk.

Conclusion

NLR, MLR, and PLR independently predicted postoperative delirium following cardiac surgery, with NLR demonstrating the strongest predictive capacity. These findings provided new tools for preoperative risk assessment and may improve postoperative management strategies for cardiac surgery patients.

A nomogram for predicting postoperative delirium in pediatric patients following cardiopulmonary bypass: A prospective observational study

OBJECTIVES

To create a nomogram for early delirium detection in pediatric patients following cardiopulmonary bypass.

RESEARCH METHODOLOGY/DESIGN

This prospective, observational study was conducted in the Cardiac Intensive Care Unit at a Children's Hospital, enrolling 501 pediatric patients from February 2022 to January 2023. Perioperative data were systematically collected through the hospital information system. Postoperative delirium was assessed using the Cornell Assessment of Pediatric Delirium (CAPD). For model development, Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify the most relevant predictors. These selected predictors were then incorporated into a multivariable logistic regression model to construct the predictive nomogram. The performance of the model was evaluated by Harrell's concordance index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. External validity of the model was confirmed through the C-index and calibration plots.

RESULTS

Five independent predictors were identified: age, SpO2 levels, lymphocyte count, diuretic use, and midazolam administration, integrated into a predictive nomogram. This nomogram demonstrated strong predictive capacity (AUC 0.816, concordance index 0.815) with good model fit (Hosmer-Lemeshow test p = 0.826) and high accuracy. Decision curve analysis showed a significant net benefit, and external validation confirmed the nomogram's reliability.

CONCLUSIONS

The study successfully developed a precise and effective nomogram for identifying pediatric patients at high risk of post-cardiopulmonary bypass delirium, incorporating age, SpO2 levels, lymphocyte counts, diuretic use, and midazolam medication.

IMPLICATIONS FOR CLINICAL PRACTICE

This nomogram aids early delirium detection and prevention in critically ill children, improving clinical decisions and treatment optimization. It enables precise monitoring and tailored medication strategies, significantly contributes to reducing the incidence of delirium, thereby enhancing the overall quality of patient care.

Predictive value of the Naples prognostic score on postoperative delirium in the elderly with gastrointestinal tumors: a retrospective cohort study

BACKGROUND

Postoperative delirium (POD) is a common complication among elderly patients after surgery. The Naples Prognostic Score (NPS), a novel prognostic marker based on immune-inflammatory and nutritional status, was widely used in the assessment of the prognosis of surgical patients. However, no study has evaluated the relationship between NPS and POD. The aim of this article was to investigate the association between NPS and POD and test the predictive efficacy of preoperative NPS for POD in elderly patients with gastrointestinal tumors.

MATERIALS AND METHODS

In the present study, we retrospectively collected perioperative data of 176 patients (≥ 60 years) who underwent elective gastrointestinal tumor surgery from June 2022 to September 2023. POD was defined according to the chart-based method and the NPS was calculated for each patient. We compared all the demographics and laboratory data between POD and non-POD groups. Univariate and multivariate logistic regression analysis was used to explore risk factors of POD. Moreover, the accuracy of NPS in predicting POD was further assessed by utilizing receiver operating characteristic (ROC) curves.

RESULTS

20 had POD (11.4%) in a total of 176 patients, with a median age of 71 (65-76). The outcomes by univariate analysis pointed out that age, ASA status ≥ 3, creatinine, white blood cell count, fasting blood glucose (FBG), and NPS were associated with the risk of POD. Multivariate logistic regression analysis further showed that age, ASA grade ≥ 3, FBG and NPS were independent risk factors of POD. Additionally, the ROC curves revealed that NPS allowed better prognostic capacity for POD than other variables with the largest area under the curve (AUC) of 0.798, sensitivity of 0.800 and specificity of 0.667, respectively.

CONCLUSION

Age, ASA grade ≥ 3, and FBG were independent risk factors for POD in the elderly underwent gastrointestinal tumor surgery. Notably, the preoperative NPS was a more effective tool in predicting the incidence of POD, but prospective trials were still needed to further validate our conclusion.

TRIAL REGISTRATION

The registration information for the experiment was shown below. (date: 3rd January 2024; number: ChiCTR2400079459).

Promising Effects of Montelukast for Critically Ill Asthma Patients via a Reduction in Delirium

Background: Montelukast (MTK), a potent antagonist of cysteinyl leukotriene receptor 1, has shown therapeutic promise for the treatment of neuropsychiatric disorders. Delirium, a common complication in critically ill patients, lacks effective treatment. This study aims to explore the impact of pre-intensive care unit (ICU) MTK use on in-hospital delirium incidence and, subsequent, prognosis in critically ill patients. Methods: A retrospective cohort study (n = 6344) was conducted using the MIMIC-IV database. After propensity score matching, logistic/Cox regression, E-value sensitivity analysis, and causal mediation analysis were performed to assess associations between pre-ICU MTK exposure and delirium and prognosis in critically ill patients. Results: Pre-ICU MTK use was significantly associated with reduced in-hospital delirium (OR: 0.705; 95% CI 0.497-0.999; p = 0.049) and 90-day mortality (OR: 0.554; 95% CI 0.366-0.840; p = 0.005). The association was more significant in patients without myocardial infarction (OR: 0.856; 95% CI 0.383-0.896; p = 0.014) and could be increased by extending the duration of use. Causal mediation analysis showed that the reduction in delirium partially mediated the association between MTK and 90-day mortality (ACME: -0.053; 95% CI -0.0142 to 0.0002; p = 0.020). Conclusions: In critically ill patients, MTK has shown promising therapeutic benefits by reducing the incidence of delirium and 90-day mortality. This study highlights the potential of MTK, beyond its traditional use in respiratory disease, and may contribute to the development of novel therapeutic strategies for delirium.

A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records

BACKGROUND

Previous models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients.

METHODS

The electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (Random Forest Classifier, Logistic Regression, Support Vector Machine Classifier, K-nearest Neighbors Classifier, Gaussian Naive Bayes, Gradient Boosting Decision Tree, and Perceptron.) were used to develop delirium prediction models under full (q = 31) and selected (q = 19) feature sets, respectively.

RESULT

The Random Forest classifier performs exceptionally well in both feature datasets, with an Area Under the Curve (AUC) of 0.92 for the full feature dataset and an AUC of 0.86 for the selected feature dataset. Additionally, it achieves a relatively lower Expected Calibration Error (ECE) and the highest Average Precision (AP), with an AP of 0.80 for the full feature dataset and an AP of 0.73 for the selected feature dataset. To further evaluate the best-performing Random Forest classifier, SHAP (Shapley Additive Explanations) was used, and the importance matrix plot, scatter plots, and summary plots were generated.

CONCLUSIONS

We established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.

Thoracic delirium index for predicting postoperative delirium in elderly patients following thoracic surgery: A retrospective case-control study

BACKGROUND

Postoperative delirium (POD) is an acute neurological complication in the elderly undergoing thoracic surgery and can result in serious adverse consequences.

AIMS

This study aimed to identify the related risk factors for POD following thoracic surgery, primarily focusing on preoperative serum biomarkers, and further to establish a novel delirium index to better predict POD.

METHODS

A total of 279 patients aged ≥60 years who underwent elective thoracic surgery from August 2021 to August 2022 were enrolled in this observational study. The platelet-to-white blood cell ratio (PWR) was calculated as number the of platelets divided by the number of white blood cells. POD was defined by the confusion assessment method twice daily during the postoperative first 3 days. Multivariate regression analysis was performed to identify all potential variables for POD. Moreover, a novel thoracic delirium index (TDI) was developed based on the related risk factors. The accuracy of TDI and its component factors in predicting POD was determined by the curve of receiver operating characteristic (ROC).

RESULTS

In total, 25 of 279 patients developed POD (8.96%). Age, PWR, and average pain scores within the first 3 days after surgery were regarded as the independent risk factors for POD. Moreover, the ROC analysis showed the TDI, including age, PWR, and average pain scores within the first 3 days after surgery, can more accurately predict POD with the largest area under the curve of 0.790 and the optimal cutoff value of 9.072, respectively.

CONCLUSION

The TDI can scientifically and effectively predict POD to provide optimal clinical guidance for older patients after thoracic surgery.

Prognostic Value of the Baseline and Early Changes in Monocyte-to-Lymphocyte Ratio for Short-Term Mortality among Critically Ill Patients with Acute Kidney Injury

(1) Background: Inflammation plays an important role in the onset and progression of acute kidney injury (AKI). Despite this, evidence regarding the prognostic effect of the monocyte-to-lymphocyte ratio (MLR), a novel systemic inflammation marker, among patients with AKI is scarce. This study sets out to investigate the prognostic potential of both baseline and early changes in MLR for short-term mortality among critically ill patients with AKI. (2) Method: Eligible patients with AKI from the Medical Information Mart for Intensive Care IV database were retrospectively analyzed. MLR cutoff values were determined using maximally selected rank statistics and tertiles. The clinical outcomes were 30-day and 90-day mortality in the intensive care unit. A restricted cubic splines model and Cox proportional hazards models were utilized to evaluate the association between the baseline MLR and short-term mortality. Then, the trends in MLR over time were compared between the 30-day survivors and non-survivors using a generalized additive mixed model (GAMM). (3) Result: A total of 15,986 patients were enrolled. Multivariable Cox regression analysis identified baseline MLR ≥ 0.48 as an independent risk factor predicting 30-day mortality (HR 1.33, 95%CI 1.24, 1.45, p < 0.001) and 90-day mortality (HR 1.34, 95%CI 1.23, 1.52, p < 0.001) after adjusting for potential confounders. Similar trends were observed for 30-day and 90-day mortality when tertiles were used to group patients. The restricted cubic splines model revealed a non-linear association between MLR and 30-day and 90-day mortality (both p for non-linear < 0.001, both p for overall < 0.001). The area under the curve of 0.64 for MLR was higher than that of monocytes (0.55) and lymphocytes (0.61). In the subgroup analyses, despite the noted significant interactions, the direction of the observed association between MLR and 30-day mortality was consistent across most prespecified subgroups, except for shock and black ethnicity. The GAMM results highlighted that, as time went on, MLR in the 30-day survival group consistently declined, whereas MLR in the non-survival group rose within 15 days post-ICU admission. The difference between the two groups persisted significantly even after adjusting for confounders (p = 0.006). (4) Conclusion: A higher baseline MLR was identified as an independent risk factor predicting 30-day and 90-day mortality. The early increase in MLR was associated with high 30-day mortality, suggesting that dynamic monitoring of MLR could potentially better predict survival in critically ill patients with AKI.