Prognostic role of the preoperative neutrophil-to-lymphocyte ratio and albumin for 30-day mortality in patients with postoperative acute pulmonary embolism

The prognostic value of blood cellular indices in pulmonary embolism

Prognostication in acute pulmonary embolism (PE) requires reliable markers. While cellular indices such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) appear promising, their utility in PE prognostication needs further exploration. We utilized data from the RIETE registry and the Loyola University Medical Center (LUMC) to assess the prognostic value of NLR, PLR, and SII in acute PE, using logistic regression models. The primary outcome was 30-day all-cause mortality. We compared their prognostic value versus the simplified Pulmonary Embolism Severity Index (sPESI) alone. We included 10 085 patients from RIETE and 700 from the LUMC. Thirty-day mortality rates were 4.6% and 8.3%, respectively. On multivariable analysis, an elevated NLR (>7.0) was associated with increased mortality (adjusted odds ratio [aOR]: 3.46; 95% CI: 2.60-4.60), outperforming the PLR > 220 (aOR: 2.36; 95% CI: 1.77-3.13), and SII > 1600 (aOR: 2.52; 95% CI: 1.90-3.33). The c-statistic for NLR in patients with low-risk PE was 0.78 (95% CI: 0.69-0.86). Respective numbers were 0.66 (95% CI: 0.63-0.69) and 0.68 (95% CI: 0.59-0.76) for intermediate-risk and high-risk patients. These findings were mirrored in the LUMC cohort. Among 9810 normotensive patients in RIETE, those scoring 0 points in sPESI and with an NLR ≤ 7.0 (35% of the population) displayed superior sensitivity (97.1%; 95% CI: 95.5-98.7) and negative predictive value (99.7%; 95% CI: 99.5-99.8) than sPESI alone (87.1%; 95% CI: 83.9-90.3, and 98.7%; 95% CI: 98.4-99.1, respectively) for 30-day mortality. The NLR is a significant prognostic marker for 30-day mortality in PE patients, especially useful to identify patients with very low-risk PE.

Prognostic Value of Neutrophil/Lymphocyte Ratio for Pulmonary Embolism: A Meta-Analysis and External Validation

BACKGROUND

Prognostic value of neutrophil/lymphocyte ratio (NLR) for pulmonary embolism (PE) has been reported in several retrospective studies. The purpose of this investigation was to perform a pooled analysis and external validation of predictive value of NLR.

METHODS

PubMed, Embase, and Cochrane databases were searched from inception to November 5, 2022. A random effects model was used. Grade was used to evaluate the certainty of evidence. External validation was conducted in clinical cohorts before and after a propensity scoring matching (PSM). Covariates include basic clinical characteristics, such as age, gender, etc. The value of NLR in prediction model was also evaluated.

RESULTS

A total of 15 studies comprising 5,874 patients were included. Pooled risk ratio of NLR was 2.33 (95% confidence interval [CI]: 1.97-2.75), with an area under the curve of 0.78 (95% CI: 0.74-0.81), a sensitivity of 0.75 (95% CI: 0.71-0.79), a specificity of 0.67 (95% CI: 0.61-0.73), and a median cut-off value of 5.7. Grade of Recommendations Assessment Development and Evaluation (GRADE) certainty analysis showed the quality of the evidence was moderate. Before (n = 336) and after (n = 152) propensity scoring matching, risk ratio of NLR was 2.69 (95% CI: 1.04-6.97) and 6.58 (95% CI: 1.99-17.75). A prediction model consisting of NLR, age, D-dimer, and simplified PE severity index had an area under the curve of 0.809 (95% CI: 0.738-0.88), a sensitivity of 0.638 (95% CI: 0.511-0.745), and a specificity of 0.851 (95% CI: 0.709-0.917). Net reclassification index (12%, P = 0.035) and integrated discrimination improvement (17%, P = 0.022) indicated an improvement caused by NLR.

CONCLUSIONS

Prognostic value of NLR for PE was confirmed by meta-analysis and validated in an independent cohort, deserving further clinical application.

Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as predictors of mortality in acute pulmonary embolism: A systematic review and meta-analysis

Objective

The purpose of this review was to examine the association between neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) and mortality rates in patients with acute pulmonary embolism (PE).

Methods

PubMed Central, Scopus, Web of Science, and Embase were searched for studies reporting the association between NLR and PLR with mortality up to March 17th 2023. Adjusted ratios were sourced from studies and combined to generate pooled outcomes as odds ratio (OR) in a random-effects model. Risk of bias was assessed using the Newcastle Ottawa Scale.

Results

Fifteen studies were included. Meta-analysis showed that NLR was a significant predictor of mortality in patients with PE (OR: 1.42 95% CI: 1.26, 1.61 I2=92%). Results were unchanged on sensitivity analysis and subgroup analysis based on study location, method of diagnosis, sample size, overall mortality rates, cut-offs, and follow-up. Pooled analysis failed to demonstrate PLR as a predictor of mortality in patients with PE (OR: 1.00 95% CI: 1.00, 1.01 I2=57%). Results were unchanged on sensitivity analysis and subgroup analysis based on study location, diagnosis of PE, overall mortality rates, and cut-off.

Conclusion

Current evidence from retrospective studies shows that NLR can independently predict mortality in acute PE. Data on PLR was limited and failed to indicate an independent role in the prognosis of PE patients. Registration No. PROSPERO (CRD42023407573).

A novel prognostic prediction indicator in patients with acute pulmonary embolism: Naples prognostic score

Acute pulmonary embolism (APE) is a potentially fatal disease. Early risk stratification is essential to determining appropriate treatment. We aimed to investigate the predictive value of the Naples Prognostic Score (NPS) for 30-day all-cause mortality in patients with APE. In this retrospective analysis, 325 hospitalized patients with APE were divided into Groups 0 (n = 131), 1 (n = 153), and 2 (n = 41) according to the NPS. The primary outcome event was all-cause mortality during 30 days of follow-up from the day of admission. The correlation between NPS, clinical features, and outcomes in each group was evaluated. The patients were divided into two groups, survivor (n = 294) and nonsurvivor (n = 31), according to their prognosis. The results of the comparison between the three NPS groups revealed that patients with older age, faster heart rate, lower systolic blood pressure, low albumin and total cholesterol levels, high neutrophil to lymphocyte ratio (NLR), low lymphocyte-to-monocyte ratio (LMR), right heart dilatation, heart failure, malignancy, and lower extremity venous thrombosis had significantly higher 30-day all-cause mortality (P < 0.05). Area under the receiver operating characteristic curve (AUC) for NPS to predict all-cause death within 30 days in patients with APE was 0.780 (95% confidence interval [CI] = 0.678-0.855), with sensitivity being 80.6% (95% CI = 0.667-0.946) and specificity being 72.1% (95% CI = 0.670-0.772). Kaplan-Meier (KM) curves showed that Group 2 APE patients had the highest risk of all-cause mortality compared with the other two groups (log-rank test, P = 0.0004). Forest plot visualization using the Cox proportional hazard model showed a significant increase in the risk of 30-day all-cause mortality by 239% (hazard ratio [HR] = 3.385 [1.115-10.273], P = 0.031) and 338% (HR = 4.377 [1.228-15.598], P = 0.023), and the trend test showed a statistical difference (P = 0.042). The study concluded that NPS is a novel, reliable, and multidimensional prognostic scoring system with good prediction of 30-day all-cause mortality in patients with APE.

Platelet/Lymphocyte Ratio Predicted Long-Term Prognosis for Acute Upper Extremity Deep Vein Thrombosis from a Retrospective Study

Background

In this study, we aimed to determine the mortality risk factors and whether placement of a vena cava filter improves the prognosis of acute upper extremity deep vein thrombosis (UEDVT).

Methods

Clinical data and follow-up results were retrospectively analyzed. Cox regression analysis was conducted to identify the risk factors associated with all-cause mortality in all patients and subgroups of patients. Results are expressed as hazard ratio (HR) with 95% confidence intervals (95% CI). Receiver operating characteristic curves (ROC) were used to determine the optimal cut-off value. Kaplan-Meier survival curves were constructed and compared by the Log rank test.

Results

The study cohort comprised 109 patients of median age 56 years (47.5, 64.5). The median follow-up time was 25 months (8, 47): 39 patients (35.8%) had died by 12 months, 55 (50.5%) by 36 months, and 60 (55%) by the end of follow-up. Presence of malignancy (HR: 5.882, 95% CI: 2.128-16.667), D-dimer (HR: 1.56, 95% CI: 1.09-1.94), platelet/lymphocyte ratio (PLR; HR: 2.02, 95% CI: 1.15-3.54), and the systemic immune/inflammatory index (SII; HR: 1.471, 95% CI: 1.062-1.991) were identified as independent risk factors for mortality. Subgroup analysis of patients with malignancy determined gender (HR: 2.936, 95% CI: 1.599-5.393) and PLR (HR: 1.427,95% CI: 1.023-1.989) as independent risk factors. Kaplan-Meier analysis showed that the mortality rate was much higher in patients with malignancy, high D-dimer (≥ 0.92ug/mL), high PLR (≥ 291) and high SII (≥ 1487). However, there was no significant difference between patients with and without vena cava filters.

Conclusion

In this study, we identified PLR as an new independent predictor of mortality in patients with acute UEDVT. Emergency placement of a vena cava filter did not improve long-term prognosis.

A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

Introduction

Pulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific.

Methods

Using the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient's basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier.

Results

In the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches.

Discussion

The experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE.

The Association of Neutrophil-Lymphocyte Ratio with Venous Thromboembolism: A Systematic Review and Meta-Analysis

The neutrophil–lymphocyte ratio(NLR) has been used for diagnosing venous thromboembolism (VTE). We aimed to assess the accuracy of NLR to diagnose VTE by meta-analysis. Systematic electronic searches were conducted June 2, 2021 in PubMed, Embase(Ovid), and Cochrane Library. The search did not have any language or time restriction applied. Our search strategy was based on keywords in combination with both medical subject headings (MeSH) terms and text words. The diagnostic odds ratio, summary receiver operating characteristics, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were estimated. 10 articles with 1513 VTE participants and 2593 control participants were included for quantitative synthesis. The pooled values were as follows: sensitivity = 0.68(95% CI 0.45-0.84), specificity = 0.73(95% CI 0.6-0.83), positive likelihood ratio = 2.5(95% CI 1.8-3.4), negative likelihood ratio = 0.44(95% CI 0.26-0.75), diagnostic odds ratio = 6(95% CI 3-11), and SROC = 0.76(95% CI: 0.73-0.8). NLR could be diagnostic factor for the detection of potential VTE, the accuracy thereof in the current meta-analysis exhibited moderate accuracy for diagnosing VTE. Furthermore, further large cohort studies are needed to determine optimal cut-off values of NLR.

Diagnostic accuracy of adropin as a preliminary test to exclude acute pulmonary embolism: a prospective study

Background

This study aims to investigate the diagnostic accuracy of adropin as a biomarker to exclude the diagnosis of acute pulmonary embolism (PE).

Methods

Patients admitted to the emergency department of a tertiary health centre between August 2019 and August 2020 and diagnosed with PE were included in this prospective cohort study. The amount of serum adropin was determined in patients with (PE) and compared with that of healthy volunteers. Receiver operating characteristic analysis was performed with the obtained data, and the area under the curve (AUC) with a 95% confidence interval was determined. The parameters of diagnostic accuracy for PE were determined.

Results

A total of 57 participants were included in the study (28 controls and 29 PE patients). The mean adropin level in the PE group was 187.33 ± 62.40 pg/ml, which was significantly lower than that in the control group (524.06 ± 421.68 pg/ml) (p < 0.001). When the optimal adropin cut-off value was 213.78 pg/ml, the likelihood ratio of the adropin test was 3.4, and the sensitivity of the adropin test at this value was 82% with specificity of 75% (95% CI; AUC: 0.821).

Conclusion

Our results suggest that adropin may be considered for further study as a candidate marker for the exclusion of the diagnosis of PE. However, more research is required to verify and support the generalizability of our study results.

A Prediction Model Based on Blood Biomarker for Mortality Risk in Patients with Acute Venous Thromboembolism

Background

Most studies to date have focused on predicting the risk of venous thromboembolism (VTE), but prediction models about mortality risk in VTE are rarely reported. We sought to develop and validate a multivariable model to predict the all-cause mortality risk in patients with acute VTE in emergency settings.

Methods

A total of 700 patients were included from Qilu Hospital of Shandong University and were randomly assigned into training set (n=490) and validation set (n=210) in an 7:3 ratio. Multivariate logistics regression analysis was performed to identify independent variables and develop a prediction model, which was validated internally using bootstrap method. The discrimination, calibration and clinical utility were evaluated by receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow (HL) test, Kaplan-meier (KM) analysis and decision curve analysis (DCA).

Results

There were 52 patients (10.6%) dying and 437 (89.4%) surviving in training set. Age (odds ratio [OR]: 4.158, 95% confidence interval [CI]: 2.426–7.127), pulmonary embolism (OR: 1.779, 95% CI: 1.124–2.814), platelet count (OR: 0.507, 95% CI: 0.310–0.830), D-dimer (OR: 1.826, 95% CI: 1.133–2.942) and platelet/lymphocyte ratio (OR: 2.166, 95% CI: 1.259–3.727) were independent risk variables associated with all-cause mortality. The model had good predictive capability with an AUC of 0.746 (95% CI: 0.668,0.825), a sensitivity of 0.769 (95% CI: 0.607,0.889), a specificity of 0.672 (95% CI: 0.634,0.707). The validation model had an AUC of 0.739 (95% CI: 0.685,0.793), a sensitivity of 0.690 (95% CI: 0.580,0.787), a specificity of 0.693 (95% CI: 0.655,0.729). The model is well calibrated and the HL test showed a good fit (χ²=5.291, p=0.726, Nagelkerke R²=0.137). KM analysis and DCA showed a good clinical utility of the nomogram.

Conclusion

This study identified independent variables affecting all-cause mortality in patients with acute VTE, and developed a prediction model and provided a nomogram with good prediction capability and clinical utility.

The role of the multi-inflammatory index as a novel inflammation-related index in the differential diagnosis of massive and non-massive pulmonary embolism

OBJECTIVE

Systemic inflammatory response and pro-coagulator factors are critical in acute pulmonary embolism. Recently, there is increasing evidence that the multi-inflammatory index (MII) may be prognostic in many clinical situations. The study investigated whether MII-1 and MII-2 can distinguish massive (MPE) and non-massive pulmonary embolism (NMPE).

METHODS

A total of 146 patients (73 MPE and 73 NMPE) were included in the study that was designed as a retrospective cross-sectional analysis. Following haemogram analysis, MII-1 (platelet-to-lymphocyte x CRP) and MII-2 (neutrophil-to-lymphocyte x CRP) were calculated manually.

RESULTS

MII-1 and 2 showed a positive significant correlation with PLR and NLR, moderate positive correlation with CPR, and weak positive correlation with PAP (P < .001). CRP, MII-1, MII-2, PAP and systolic BP variables showed strong diagnostic value in distinguishing MPE and NMPE [AUC (SE): 0.816 (0.037), 0.811 (0.038), 0.810 (0.037), 0.870 (0.032) and 0.864 (0.032); respectively] (P < .001). MPR, MPV, RDW, and PLR in distinguishing MPE and NMPE groups did not show significance (P > .05).

CONCLUSION

Although MII-1 and MII-2 are simple, cheap, and easy indexes to calculate, they showed a strong ability to distinguish MPE and NMPE compared with the previously studied classical inflammatory indexes.

Diagnostic and Prognostic Value of Immune/Inflammation Biomarkers for Venous Thromboembolism: Is It Reliable for Clinical Practice?

Venous thromboembolism (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), has been an important cause of sudden in-hospital death. Studies have shown that the immune/inflammatory response plays an important role in the pathogenesis of vascular disease, with representative markers in the blood including the neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), monocyte/lymphocyte ratio (MLR), systemic immune/inflammatory index (SII), etc. However, there is a variety of immune/inflammatory indicators. Moreover, most previous studies have been single-center investigations involving one or two indicators, with varying nature of cases, number of cases and study objectives, thereby making it difficult to reach consensus conclusions with good clinical guidelines. This article reviews the clinical value of immunoinflammatory indicators for VTE based on previous studies, including the diagnostic and prognostic capabilities. In conclusion, NLR provides promising predictive capability for the onset and prognosis of VTE and deserves extensive application in clinical practice. PLR also has certain diagnostic and prognostic value, but further studies are warranted to identify its reliability and stability. Monocytes, eosinophils and platelet-related indicators show some clinical association with VTE, although the predictive capabilities are mediocre. SII is of promising potential value for VTE and deserves further investigations. This review will provide new clues and valuable clinical guidance for the diagnosis and therapy of VTE.

Development and validation of a novel risk assessment model to estimate the probability of pulmonary embolism in postoperative patients

Pulmonary embolism (PE) is a leading cause of mortality in postoperative patients. Numerous PE prevention clinical practice guidelines are available but not consistently implemented. This study aimed to develop and validate a novel risk assessment model to assess the risk of PE in postoperative patients. Patients who underwent Grade IV surgery between September 2012 and January 2020 (n = 26,536) at the Affiliated Dongyang Hospital of Wenzhou Medical University were enrolled in our study. PE was confirmed by an identified filling defect in the pulmonary artery system in CT pulmonary angiography. The PE incidence was evaluated before discharge. All preoperative data containing clinical and laboratory variables were extracted for each participant. A novel risk assessment model (RAM) for PE was developed with multivariate regression analysis. The discrimination ability of the RAM was evaluated by the area under the receiver operating characteristic curve, and model calibration was assessed by the Hosmer–Lemeshow statistic. We included 53 clinical and laboratory variables in this study. Among them, 296 postoperative patients developed PE before discharge, and the incidence rate was 1.04%. The distribution of variables between the training group and the validation group was balanced. After using multivariate stepwise regression, only variable age (OR 1.070 [1.054–1.087], P < 0.001), drinking (OR 0.477 [0.304–0.749], P = 0.001), malignant tumor (OR 2.552 [1.745–3.731], P < 0.001), anticoagulant (OR 3.719 [2.281–6.062], P < 0.001), lymphocyte percentage (OR 2.773 [2.342–3.285], P < 0.001), neutrophil percentage (OR 10.703 [8.337–13.739], P < 0.001), red blood cell (OR 1.872 [1.384–2.532], P < 0.001), total bilirubin (OR 1.038 [1.012–1.064], P < 0.001), direct bilirubin (OR 0.850 [0.779–0.928], P < 0.001), prothrombin time (OR 0.768 [0.636–0.926], P < 0.001) and fibrinogen (OR 0.772 [0.651–0.915], P < 0.001) were selected and significantly associated with PE. The final model included four variables: neutrophil percentage, age, malignant tumor and lymphocyte percentage. The AUC of the model was 0.949 (95% CI 0.932–0.966). The risk prediction model still showed good calibration, with reasonable agreement between the observed and predicted PE outcomes in the validation set (AUC 0.958). The information on sensitivity, specificity and predictive values according to cutoff points of the score in the training set suggested a threshold of 0.012 as the optimal cutoff value to define high-risk individuals. We developed a new approach to select hazard factors for PE in postoperative patients. This tool provided a consistent, accurate, and effective method for risk assessment. This finding may help decision-makers weigh the risk of PE and appropriately select PE prevention strategies.