Monocyte-to-lymphocyte ratio as a predictor of stroke-associated pneumonia: A retrospective study-based investigation

Utility of the Systemic Inflammation Response Index as a Predictor of Pneumonia After Spontaneous Intracerebral Hemorrhage

OBJECTIVE

We sought to determine whether the initial Systemic Inflammatory Response Index (SIRI) was associated with pneumonia after spontaneous intracerebral hemorrhage (SICH) in hospitalized patients.

PATIENTS AND METHODS

Patients with SICH admitted to Taizhou People's Hospital between January 2019 and December 2021 were retrospectively analyzed. Baseline variables were compared between stroke-associated pneumonia (SAP) and non-SAP groups. Multivariable logistic regression analyses were utilized to calculate the relationship between SIRI and SAP risk.

RESULTS

Of 495 patients included in this research, 192 (38.79%) developed SAP ultimately. The SIRI values exhibited the highest area under the curve value for SAP incidence (area under the curve = 0.736, 95% CI: 0.692-0.781), with respective sensitivity and specificity values of 0.646 and 0.749 at the optimal cutoff threshold of 2.53. In multivariate analysis, high SIRI (≥2.53) was a significant independent predictor of post-SICH SAP even after controlling for other possible confounding variables (odds ratio: 5.11, 95% CI: 2.89-9.04, P < 0.001). According to the restricted cubic splines model, SAP risk increases as SIRI increases.

CONCLUSIONS

We observed that SIRI values may offer high diagnostic utility as a predictor of SAP risk among patients with SICH during the early stages of the disease.

A Novel Machine Learning Model for Predicting Stroke-Associated Pneumonia After Spontaneous Intracerebral Hemorrhage

BACKGROUND

Pneumonia is one of the most common complications after spontaneous intracerebral hemorrhage (sICH), i.e., stroke-associated pneumonia (SAP). Timely identification of targeted patients is beneficial to reduce poor prognosis. So far, there is no consensus on SAP prediction, and application of existing predictors is limited. The aim of this study was to develop a machine learning model to predict SAP after sICH.

METHODS

We retrospectively reviewed 748 patients diagnosed with sICH and collected data from 4 dimensions-demographic features, clinical features, medical history, and laboratory tests. Five machine learning algorithms-logistic regression, gradient boosting decision tree, random forest, extreme gradient boosting, and category boosting-were used to build and validate the predictive model. We also applied recursive feature elimination with cross-validation to obtain the best feature combination for each model. Predictive performance was evaluated by area under the receiver operating characteristic curve.

RESULTS

SAP was diagnosed in 237 patients. The model developed by category boosting yielded the most satisfactory outcomes overall with area under the receiver operating characteristic curves in the training set and test set of 0.8307 and 0.8178, respectively.

CONCLUSIONS

The incidence of SAP after sICH in our center was 31.68%. Machine learning could potentially provide assistance in the prediction of SAP after sICH.

The systemic inflammation indexes after admission predict in-hospital mortality in patients with extensive burns

PURPOSE

To explore the clinical value of various complete blood count (CBC)-derived inflammation indicators to predict in-hospital mortality in patients with extensive burns.

METHODS

Systemic inflammation indexes, including lymphocyte-platelet ratio (LPR), neutrophil-lymphocyte ratio (NLR), neutrophil-monocyte ratio (NMR), monocyte-lymphocyte ratio (MLR), neutrophil-to-lymphocyte * platelet (NLPR), systemic inflammation index (SII), and systemic inflammation response index (SIRI) on days 1, 3, and 7 after admission were calculated in 135 patients with extensive burns.

RESULTS

We included 135 patients with extensive burns, including 97 survivors and 38 non-survivors. After adjusting for confounders, only the LPR on day 1, NLPR on days 3 and 7 were significantly associated with survival (OR= 1.237, 1.097, 1.104; 95 % CI: 1.055-1.451, 1.002-1.202, 1.005-1.212; respectively) in the analysis of multivariate logistic regression. The optimum cutoff values of the LPR on day 1 and NLPR on day 3 were 6.37 and 8.06, and the area under the curves (AUC) were 0.695 and 0.794, respectively. The AUC of NLPR on day 7 had the highest value, 0.814, and the optimum cut-off value was 3.84. The efficacy of LPR on day 1, NLPR on days 3 and 7 combined with the burn prognostic score index in predicting the prognosis of patients was higher than that of the burn index alone, and the three composite inflammatory indexes combined with PBI had the highest efficacy in predicting the prognosis (AUC = 0.994). Kaplan-Meier survival analysis showed poor prognosis in patients with higher LPR on day 1 and higher NLPR on days 3 and 7 (log-rank χ2 =9.623,31.564, 20.771, respectively; P < 0.01).

CONCLUSIONS

LPR on day 1 and NLPR on days 3 and 7 after admission are reliable predictors of prognosis in patients with severe extensive burns. The combination of the burn prognostic score index, LPR on day 1, and NLPR on days 3 and 7 was superior to the burn indexes alone in predicting a patient's prognosis.

Laboratory markers to identify acute histological chorioamnionitis in febrile parturients undergoing epidural analgesia: a retrospective study

BACKGROUND

This study aimed to investigate the effect of the pathological staging of acute histological chorioamnionitis (HCA) on laboratory indicators and to conduct further studies to reassess the threshold values used by clinicians to identify acute HCA in febrile parturients undergoing epidural analgesia.

METHODS

A retrospective study of febrile mothers receiving epidural analgesia at Nanjing Maternal and Child Health Care Hospital from January 1, 2018 to December 31, 2018. The participants were grouped by the progression of acute HCA, and the laboratory parameters were compared between groups. The ability of C-reactive protein (CRP), neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), and monocyte-leukocyte ratio (M%), alone or in combination, to identify acute HCA in febrile parturients undergoing epidural analgesia was assessed using logistic regression and ROC curves.

RESULTS

The area under the curve (AUC) of the best logistic regression model predicting HCA climbed to 0.706 (CRP + MLR). Maternal CRP, NLR, and MLR significantly and progressively increased with the progression of acute HCA (p < 0.0001). Based on the ROC curves, the following thresholds were selected to define increased laboratory indicators for identifying acute HCA: CRP ≥ 6.90 mg/L, NLR ≥ 11.93, and MLR ≥ 0.57. In addition, the AUC of the best logistic regression model predicting HCA ≥ stage 2 was 0.710, so these inflammatory markers were more precise in predicting HCA ≥ stage 2.

CONCLUSION

Increased CRP (≥ 6.90 mg/L), NLR (≥ 11.93), and MLR (≥ 0.57) may help clinicians to identify early potential acute HCA in febrile parturients receiving epidural analgesia and to monitor progression to optimize clinical treatment options.

TRIAL REGISTRATION

The study was registered in the Chinese Clinical Trial Registry on November 24, 2021 ( http://www.chictr.org.cn , ChiCTR2100053554).

Comparison of the Predictive Value of Inflammatory Biomarkers for the Risk of Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke

Purpose

To investigate the predictive value of various inflammatory biomarkers in patients with acute ischemic stroke (AIS) and evaluate the relationship between stroke-associated pneumonia (SAP) and the best predictive index.

Patients and Methods

We calculated the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), prognostic nutritional index (PNI), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), Glasgow prognostic score (GPS), modified Glasgow prognostic score (mGPS), and prognostic index (PI). Variables were selectively included in the logistic regression analysis to explore the associations of NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI with SAP. We assessed the predictive performance of biomarkers by analyzing receiver operating characteristic (ROC) curves. We further used restricted cubic splines (RCS) to investigate the association. Next, we conducted subgroup analyses to investigate whether specific populations were more susceptible to NLR.

Results

NLR, PLR, MLR, SIRI, SII, GPS, mGPS, and PI increased significantly in SAP patients, and PNI was significantly decreased. After adjustment for potential confounders, the association of inflammatory biomarkers with SAP persisted. NLR showed the most favorable discriminative performance and was an independent risk factor predicting SAP. The RCS showed an increasing nonlinear trend of SAP risk with increasing NLR. The AUC of the combined indicator of NLR and C-reactive protein (CRP) was significantly higher than those of NLR and CRP alone (DeLong test, P<0.001). Subgroup analyses suggested good generalizability of the predictive effect.

Conclusion

NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI can predict the occurrence of SAP. Among the indices, the NLR was the best predictor of SAP occurrence. It can therefore be used for the early identification of SAP.

Predictors of stroke-associated pneumonia and the predictive value of neutrophil percentage-to-albumin ratio

BACKGROUND

Early recognition of stroke-associated pneumonia (SAP) is critical to reducing morbidity and mortality associated with SAP. This study investigated the predictors of SAP, and the predictive value of the neutrophil percentage-to-albumin ratio (NPAR) for SAP.

METHODS

This retrospective cohort study was conducted among stroke patients admitted to Jordan University Hospital from January 2015 to May 2021. Multivariable logistic regression was used to identify independent predictors for SAP. The predictive performance was assessed using C-statistics, described as the area under the receiver-operating characteristic curve (AUC, ROC) with a 95% confidence interval.

RESULTS

Four hundred and six patients were included in the analysis, and the prevalence of SAP was 19.7%. Multivariable logistic analysis showed that males (Adjusted Odds Ratio (AOR): 5.74; 95% Confidence Interval (95%CI): 2.04-1 6.1)], dysphagia (AOR: 5.29; 95% CI: 1.80-15.5), hemiparesis (AOR: 3.27; 95% CI: 1.13-9.47), lower GCS score (AOR: 0.73; 95% CI: 0.58-0.91), higher levels of neutrophil-lymphocyte ratio (NLR) (AOR: 1.15; 95% CI: 1.07-1.24), monocyte-lymphocyte ratio (MLR) (AOR: 1.49; 95% CI: 1.13-1.96), and neutrophil percentage to albumin ratio (NPAR) (AOR: 1.53; 95% CI: 1.33-1.76) were independent predictors of SAP. The NPAR demonstrated a significantly higher AUC than both the NLR (0.939 versus 0.865, Z = 3.169, p = 0.002) and MLR (0.939 versus 0.842, Z = 3.940, p < 0.001). The AUCs of the NLR and MLR were comparable (0.865 versus 0.842, Z = 1.274, p = 0.203).

CONCLUSION

Male gender, dysphagia and hemiparesis were the strongest predictors of SAP, and NPAR has an excellent performance in predicting SAP which was better than high NLR and MLR.

Prognostic value of monocyte-to-lymphocyte ratio for 90-day all-cause mortality in type 2 diabetes mellitus patients with chronic kidney disease

The role of inflammation and the correlation between inflammatory markers and type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) have been studied. In clinical work, a large number of T2DM patients complicated with CKD, but the cause of CKD was not clear. Our study aimed to evaluate the relationship between monocyte-to-lymphocyte ratio (MLR) and mortality in T2DM patients with CKD. The data from Medical Information Mart for Intensive Care III was analyzed. The primary outcome was 90-day all-cause mortality; the secondary outcomes were the length of ICU stay, hospital mortality and 30-day all-cause mortality. Cox regression was used to evaluate the association between MLR and 90-day mortality. We performed subgroup analyses to determine the consistency of this association, and used Kaplan-Meier survival curve to analysis the survival of different levels of MLR. A total of 1830 patients were included in study retrospectively. The length of ICU stay, 30-day all-cause mortality, and 90-day all-cause mortality in the MLR > 0.71 group were significantly higher than those in the MLR < 0.28 and 0.28 ≤ MLR ≤ 0.71 group. In Cox regression analysis, high MLR level was significantly associated with increased greater risk of 90-day all-cause mortality. The adjusted HR (95%CIs) for the model 1, model 2, and model 3 were 2.429 (1.905-3.098), 2.070 (1.619-2.647), and 1.898 (1.478-2.437), respectively. Subgroup analyses also showed the consistency of association between MLR and 90-day all-cause mortality. The Kaplan-Meier survival curve analysis revealed that MLR > 0.71 had worst prognosis. In T2DM patients with CKD in the intensive care unit, high MLR was significantly associated with increased risk 90-day all-cause mortality.

Evaluation of Inflammation and Oxidative Stress Markers in Patients with Obstructive Sleep Apnea (OSA)

Background: The identification of circulating markers of oxidative stress and systemic inflammation might enhance risk stratification in obstructive sleep apnea (OSA). We investigated the association between specific haematological parameters, as easily measurable markers of oxidative stress and inflammation, and the degree of hypoxia during polysomnography using the apnea hypopnea index (AHI), oxygen desaturation index (ODI), and oxygen saturation (SpO₂), in OSA patients. Methods: Associations between polysomnographic parameters and demographic, clinical, and laboratory characteristics were assessed in a consecutive series of patients with OSA attending the Respiratory Disease Unit of the University Hospital of Sassari, north Sardinia (Italy), between 2015 and 2019. Results: In 259 OSA patients (195 males and 64 females), the body mass index (BMI) was significantly and positively associated with the AHI and ODI, and negatively associated with the mean SpO₂. No haematological parameter was independently associated with the AHI or ODI. By contrast, albumin, neutrophil, and monocyte counts, and the systemic inflammatory response index (SIRI) were independently associated with a lower SpO₂. Conclusions: Our results suggest that albumin and specific haematological parameters are promising markers of reduced oxygen saturation in OSA.

Neutrophil-lymphocyte ratio, monocyte-lymphocyte ratio, and platelet-lymphocyte ratio in stroke-associated pneumonia: a systematic review and meta-analysis

BACKGROUND

Predicting stroke-associated pneumonia (SAP) is crucial for intensifying preventive measures and decreasing morbidity and mortality. This meta-analysis aims to evaluate the association between baseline neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) with SAP and to determine the strength of the association.

METHODS

The Web of Science, SCOPUS, and PUBMED databases were searched to find eligible studies. The standardized mean difference (SMD) and 95% confidence interval (CI) were used to evaluate the differences in NLR, MLR, and PLR levels between SAP and non-SAP patients. The meta-analysis was conducted using the software "Review Manager" (RevMan, version 5.4.1, September 2020). The random-effect model was used for the pooling analysis if there was substantial heterogeneity. Otherwise, the fixed-effect model was adopted.

RESULTS

Twelve studies comprising 6302 stroke patients were included. The pooled analyses revealed that patients with SAP had significantly higher levels of NLR, MLR, and PLR than the non-SAP group. The SMD, 95% CI, p-value, and I² for them were respectively reported as (0.88, 0.70-1.07, .00001, 77%); (0.94, 0.43-1.46, .0003, 93%); and (0.61, 0.47-0.75, .001, 0%). Subgroup analysis of NLR studies showed no significant differences in the effect size index between the severity of the stroke, the sample size, and the period between the stroke onset and the blood sampling.

CONCLUSION

This systematic review and meta-analysis suggest that an elevated NLR, MLR, and PLR were associated with SAP, indicating that they could be promising blood-based biomarkers for predicting SAP. Large-scale prospective studies from various ethnicities are recommended to validate this association before they can be applied in clinical practice.

Systemic inflammation and immune index predicting outcomes in patients with intracerebral hemorrhage

OBJECT

Recent evidence has suggested that systemic inflammatory and immune index (SIRI) and systematic inflammation index (SII) could predict prognosis in stroke patients. This study aimed to determine the effects of SIRI and SII on predicting in-hospital infections and unfavorable outcomes in patients with acute intracerebral hemorrhage (ICH).

METHODS

We used the data from a prospective and registry-based study recruiting ICH patients between January 2014 and September 2016 in a single comprehensive stroke center. All patients were stratified by quartiles of SIRI or SII. Logistic regression analysis was used to estimate the associations with follow-up prognosis. The receiver operating characteristics (ROC) curves were performed to examine the predictive utility of these indexes for infections and prognosis.

RESULTS

Six hundred and forty spontaneous ICH patients were enrolled in this study. Compared with the lowest quartile (Q1), SIRI or SII values both showed positive correlations with increased risks for poor 1-month outcomes (adjusted ORs in Q4 was 2.162 [95% CI: 1.240-3.772] for SIRI, 1.797 [95% CI: 1.052-3.070] for SII). Additionally, a higher level of SIRI, but not SII, was independently associated with a higher risk of infections and an unfavorable 3-month prognosis. The C-statistic for the combined SIRI and ICH score was higher than SIRI or ICH score alone for predicting in-hospital infections and poor outcomes.

CONCLUSION

Elevated SIRI values were associated with in-hospital infections and poor functional outcomes. It may provide a new biomarker for ICH prognosis prediction, especially in the acute stage.

Inflammatory response biomarkers nomogram for predicting pneumonia in patients with spontaneous intracerebral hemorrhage

Objectives

Inflammatory response biomarkers are promising prognostic factors to improve the prognosis of stroke-associated pneumonia (SAP) after ischemic stroke. This study aimed to investigate the prognostic significance of inflammatory response biomarkers on admission in SAP after spontaneous intracerebral hemorrhage (SICH) and establish a corresponding nomogram.

Methods

The data of 378 patients with SICH receiving conservative treatment from January 2019 to December 2021 at Taizhou People's Hospital were selected. All eligible patients were randomized into the training (70%, 265) and validation cohorts (30%, 113). In the training cohort, multivariate logistic regression analysis was used to establish an optimal nomogram, including inflammatory response biomarkers and clinical risk factors. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram's discrimination, calibration, and performance, respectively. Moreover, this model was further validated in a validation cohort.

Results

A logistic regression analysis showed that intraventricular hemorrhage (IVH), hypertension, dysphagia, Glasgow Coma Scale (GCS), National Institute of Health Stroke Scale (NIHSS), systemic inflammation response index (SIRI), and platelet/lymphocyte ratio (PLR) were correlated with SAP after SICH (P < 0.05). The nomogram was composed of all these statistically significant factors. The inflammatory marker-based nomogram showed strong prognostic power compared with the conventional factors, with an AUC of 0.886 (95% CI: 0.841-0.921) and 0.848 (95% CI: 0.799-0.899). The calibration curves demonstrated good homogeneity between the predicted risks and the observed outcomes. In addition, the model has a significant net benefit for SAP, according to DCA. Also, internal validation demonstrated the reliability of the prediction nomogram. The length of hospital stay was shorter in the non-SAP group than in the SAP group. At the 3-month follow-up, clinical outcomes were worse in the SAP group (P < 0.001).

Conclusion

SIRI and PLR at admission can be utilized as prognostic inflammatory biomarkers in patients with SICH in the upper brain treated with SAP. A nomogram covering SIRI and PLR can more accurately predict SAP in patients' supratentorial SICH. SAP can influence the length of hospital stay and the clinical outcome.

Slightly Elevated Lymphocyte to Monocyte Ratio Predicting Favorable Outcomes in Patients with Spontaneous Intracerebral Hemorrhage

Objective

This study was designed to determine the association between admission lymphocyte to monocyte ratio (LMR) values and clinical outcomes in patients with spontaneous intracerebral hemorrhage (ICH).

Methods

We used a prospective and registry-based database, and ICH patients were consecutively recruited in Beijing Tiantan Hospital between January 2014 and September 2016. All participants were stratified by quartiles of the LMR. Univariable and multivariable logistic regression analyses were plotted to evaluate the association between LMR levels and functional outcomes. Kaplan-Meier survival curves and Cox regression analysis were also performed to examine the relevance between different LMR quartiles and case fatality at follow-up.

Results

Six hundred and forty patients with spontaneous ICH were finally included in this study. Compared with the patients with LMR values in quartile 1 (Q1), slightly elevated LMR values showed a negative correlation with risks of poor short-term outcomes (adjusted ORs in Q2 were 0.572 [95% CI: 0.338-0.968] at 1 month, 0.515 [95% CI: 0.305-0.871] at 3 months). Patients with LMR values in Q1 had the highest cumulative death rate. A slightly elevated LMR was also independently relevant to a deduced mortality rate compared to that in Q1 (adjusted HRs in Q2 were 0.471 [95% CI: 0.274-0.809] at 1 month, 0.474 [95% CI: 0.283-0.793] at 3 months, 0.575 [95% CI: 0.361-0.917] at 1 year). Additionally, a higher LMR value was associated with a lower risk of in-hospital infections.

Conclusion

This study suggests that a lower LMR value is associated with higher risks of in-hospital infections, poor functional outcomes, and follow-up mortality in patients with ICH. However, a slightly elevated LMR value, especially in Q2, relates to a favorable prognosis, which may reflect an inner balance between inflammation and immunodepression and thus provides a promising marker for predicting ICH prognosis.

Monocyte-to-lymphocyte ratio as a predictor of stroke-associated pneumonia: A retrospective study-based investigation

BACKGROUND AND PURPOSE

Early prediction of stroke-associated pneumonia (SAP) is significant in clinical practice, as it is frequently challenging due to delays in typical clinical manifestations and radiological changes. The monocyte-to-lymphocyte ratio (MLR) has been proposed as an indicator of systemic inflammation and infection. However, none of these studies have focused on the predictive value of the MLR for SAP. We investigated the predictive value of MLR for SAP and investigated its relationship with disease severity.

METHODS

In this retrospective study, we assessed 399 consecutive patients with acute stroke. SAP was defined according to the modified Centers for Disease Control and Prevention criteria. The severity of pneumonia was rated using the pneumonia severity index (PSI). MLR was calculated by dividing absolute monocyte counts by absolute lymphocyte counts.

RESULTS

Among all the patients, SAP occurred in 116 patients (29.1%). White blood cell (WBC), neutrophil, monocyte, and MLR levels in the SAP group were higher than those in the non-SAP group, while lymphocyte levels were lower (p < .05). Multivariable regression analysis revealed that the MLR (OR = 7.177; 95% CI = 1.190-43.292, p = .032) remained significant after adjusting for confounders. The ROC curve showed that the AUC value of MLR for SAP was 0.779, the optimal cutoff value of MLR for SAP was 0.388, with a specificity of 64.7% and sensitivity of 81.3%. The MLR levels were significantly higher in the severe pneumonia group when assessed by PSI (p = .024) than in the mild group. The AUC value of MLR was 0.622 (95% CI = 0.520-0.724, p = .024) in the severe pneumonia group. The optimal cutoff value of MLR was 0.750, with a specificity of 91.0% and a sensitivity of 33.0%.

CONCLUSIONS

Our study shows that a high MLR is an independent risk factor for SAP and has a predictive value for severe pneumonia in patients with SAP.