Red Cell Distribution Width to Platelet Count Ratio: A Promising Routinely Available Indicator of Mortality for Acute Traumatic Brain Injury

Predictive value of admission red cell distribution width-to-platelet ratio for 30-day death in patients with spontaneous intracerebral hemorrhage: an analysis of the MIMIC database

Aim

Prognostic assessment plays an important role in the effective management of patients with spontaneous intracerebral hemorrhage (ICH). The study aimed to investigate whether elevated red cell distribution width-to-platelet ratio (RPR) at admission was related to 30-day death in patients with spontaneous intracerebral hemorrhage (ICH).

Methods

This retrospective cohort study included 2,823 adult patients with ICH from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) III and IV databases between 2001 and 2019. The Cox proportional hazard model was utilized to evaluate the relationship between RPR levels and 30-day death risk. The area under receiver-operating characteristic curve (AUC) was used to assess the predictive ability of RPR for 30-day death in patients with ICH.

Results

At the end of the 30-day follow-up, 799 (28.30%) patients died, and the median RPR level was 0.066 (0.053, 0.087). After adjusting for confounders, the tertile 3 of RPR levels [hazard ratio (HR) = 1.37, 95% confidence interval (CI): 1.15-1.64] were associated with a higher risk of 30-day death in patients with ICH compared with tertile 1. In the stratified analyses, elevated RPR levels were found to be associated with an increased risk of 30-day death in patients aged <65 years (HR = 1.77, 95%CI: 1.29-2.43), aged ≥65 years (HR = 1.30, 95%CI: 1.05-1.61), with Glasgow Coma Score (GCS) <14 (HR = 1.65, 95%CI: 1.27-2.14), with Charlson comorbidity index (CCI) ≥4 (HR = 1.45, 95%CI: 1.17-1.80), with (HR = 1.66, 95%CI: 1.13-2.43) or without sepsis (HR = 1.32, 95%CI: 1.08-1.61), and female patients (HR = 1.75, 95%CI: 1.35-2.26) but not in male patients (P = 0.139) and patients with GCS ≥14 (P = 0.058) or CCI <4 (P = 0.188). The AUC for RPR to predict 30-day death in patients with ICH was 0.795 (95%CI: 0.763-0.828) in the testing set, indicating a good predictive ability.

Conclusion

Elevated RPR levels were correlated with an increased risk of 30-day death in patients with ICH, and RPP levels showed good predictive ability for 30-day death.

Red blood cell distribution width and Charlson comorbidity index help to identify frail polytraumatized patients : Experiences from a level I trauma center

INTRODUCTION

Little is known about the potential impact of the red blood cell distribution width (RDW) and pre-existing comorbidities on the late-phase survival of polytraumatized patients.

METHODS

A total of 173 polytraumatized patients were included retrospectively in this cohort study in a level I trauma center from January 2012 to December 2015. The Charlson comorbidity index (CCI) scores and RDW values were evaluated.

RESULTS

Out of all polytraumatized patients (n = 173), 72.8% (n = 126) were male, the mean ISS was 31.7 points (range 17-75) and the mean age was 45.1 years (range 18-93 years). Significantly higher RDW values (13.90 vs. 13.37; p = 0.006) and higher CCI scores (3.38 vs. 0.49; p < 0.001) were seen in elderly polytraumatized patients (age > 55 years). RDW values > 13.75% (p = 0.033) and CCI scores > 2 points (p = 0.001) were found to have a significant influence on the late-phase survival of polytraumatized patients. Age > 55 years (p = 0.009, HR 0.312; 95% confidence interval (CI) 0.130-0.749) and the presence of severe traumatic brain injury (TBI) (p = 0.007; HR 0.185; 95% CI 0.054-0.635) remained as independent prognostic factors on the late-phase survival after multivariate analysis.

CONCLUSION

Even younger elderly polytraumatized patients (> 55 years of age) showed significant higher RDW values and higher CCI scores. In addition to the presence of severe TBI and age > 55 years, RDW value > 13.75% on admission and CCI score > 2 might help to identify the "younger" frail polytraumatized patient at risk.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Effect of Red Cell Distribution Width on the Prognosis of Patients with Traumatic Brain Injury: A Retrospective Cohort Study

BACKGROUND

The link between red cell distribution width (RDW) and prognosis of traumatic brain injury (TBI) is controversial. Whether RDW can increase the prognostic value of established predictors remains unknown. This study aimed to provide supportive evidence for the prognostic value of RDW.

METHODS

Clinical data of 1488 patients with TBI were extracted from the Multiparameter Intelligent Monitoring in Intensive Care III database and classified into 2 groups: 1) one with RDW <14.5% (n = 1061) and 2) the other with RDW ≥14.5% (n = 427). Multivariable logistic regression models were used to estimate the relationship between RDW and outcomes. Stratified analyses and interactions were also performed. We compared the area under the receiver operating characteristic curve of the International Mission for Prognoses and Clinical Trial Design in TBI (IMPACT) core and extended models with and without RDW.

RESULTS

After adjusting for confounding factors, RDW was an independent risk consideration for TBI prognoses; the odds ratios were 1.62 (95% confidence interval (CI): 1.05, 2.50) and 1.89 (95% CI: 1.35, 2.64) for hospital mortality and 6-month mortality, respectively. This association was crucial for patients with a Glasgow Coma Score of 3-12 (odds ratio, 2.79; 95% CI: 1.33, 5.87). For 6-month mortality, when RDW was added to the core and extended IMPACT models, the area under the receiver operating characteristic curve increased from 0.833 to 0.851 (P = 0.001) and from 0.842 to 0.855 (P = 0.002), respectively.

CONCLUSIONS

Elevated RDW is an independent risk consideration for hospital and 6-month mortality rates. When RDW was added to the IMPACT core and extended models, it improved its predictive ability for 6-month mortality in patients with TBI.

Prediction of Mortality in Geriatric Traumatic Brain Injury Patients Using Machine Learning Algorithms

Background: The number of geriatric traumatic brain injury (TBI) patients is increasing every year due to the population’s aging in most of the developed countries. Unfortunately, there is no widely recognized tool for specifically evaluating the prognosis of geriatric TBI patients. We designed this study to compare the prognostic value of different machine learning algorithm-based predictive models for geriatric TBI. Methods: TBI patients aged ≥65 from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. To develop and validate machine learning algorithm-based prognostic models, included patients were divided into a training set and a testing set, with a ratio of 7:3. The predictive value of different machine learning based models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy and F score. Results: A total of 1123 geriatric TBI patients were included, with a mortality of 24.8%. Non-survivors had higher age (82.2 vs. 80.7, p = 0.010) and lower Glasgow Coma Scale (14 vs. 7, p < 0.001) than survivors. The rate of mechanical ventilation was significantly higher (67.6% vs. 25.9%, p < 0.001) in non-survivors while the rate of neurosurgical operation did not differ between survivors and non-survivors (24.3% vs. 23.0%, p = 0.735). Among different machine learning algorithms, Adaboost (AUC: 0.799) and Random Forest (AUC: 0.795) performed slightly better than the logistic regression (AUC: 0.792) on predicting mortality in geriatric TBI patients in the testing set. Conclusion: Adaboost, Random Forest and logistic regression all performed well in predicting mortality of geriatric TBI patients. Prognostication tools utilizing these algorithms are helpful for physicians to evaluate the risk of poor outcomes in geriatric TBI patients and adopt personalized therapeutic options for them.

A Novel Blood Inflammatory Indicator for Predicting Deterioration Risk of Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) has a relatively higher incidence in aging people due to walking problems. Cranial computed tomography and magnetic resonance imaging provide the standard diagnostic tool to identify intracranial complications in patients with mTBI. However, it is still necessary to further explore blood biomarkers for evaluating the deterioration risk at the early stage of mTBI to improve medical decision-making in the emergency department. The activation of the inflammatory response is one of the main pathological mechanisms leading to unfavorable outcomes of mTBI. As complete blood count (CBC) analysis is the most extensively used laboratory test in practice, we extracted clinical data of 994 patients with mTBI from two large clinical cohorts (MIMIC-IV and eICU-CRD) and selected inflammation-related indicators from CBC analysis to investigate their relationship with the deterioration after mTBI. The combinatorial indices neutrophil-to-lymphocyte ratio (NLR), red cell distribution width-to-platelet ratio (RPR), and NLR times RPR (NLTRP) were supposed to be potential risk predictors, and the data from the above cohorts were integratively analyzed using our previously reported method named MeDICS. We found that NLR, RPR, and NLTRP levels were higher among deteriorated patients than non-deteriorated patients with mTBI. Besides, high NLTRP was associated with increased deterioration risk, with the odds ratio increasing from NLTRP of 1–2 (2.69, 1.48–4.89) to &gt; 2 (4.44, 1.51–13.08), using NLTRP of 0–1 as the reference. NLTRP had a moderately good prognostic performance with an area under the ROC curve of 0.7554 and a higher prediction value than both NLR and RPR, indicated by the integrated discrimination improvement index. The decision curve analysis also showed greater clinical benefits of NLTRP than NLR and RPR in a large range of threshold probabilities. Subgroup analysis further suggested that NLTRP is an independent risk factor for the deterioration after mTBI. In addition, in vivo experiments confirmed the association between NLTRP and neural/systemic inflammatory response after mTBI, which emphasized the importance of controlling inflammation in clinical treatment. Consequently, NLTRP is a promising biomarker for the deterioration risk of mTBI. It can be used in resource-limited settings, thus being proposed as a routinely available tool at all levels of the medical system.

A Prognostic Model Incorporating Red Cell Distribution Width to Platelet Ratio for Patients with Traumatic Brain Injury

BACKGROUND

As an inflammation-based marker, red cell distribution width to platelet ratio (RPR) has been verified to be associated with disease severity and outcome in many clinical settings. We designed this study to evaluate the prognostic value of RPR in patients with traumatic brain injury (TBI).

METHODS

A total of 420 patients admitted with TBI were included in this study. Laboratory and clinical data were collected from an electronic medical record system. Univariate and multivariate logistic regression analyses were sequentially performed to discover risk factors of in-hospital mortality. Receiver operating characteristic (ROC) curves were drawn to confirm the predictive value of different markers including RPR in training set and testing set.

RESULTS

Non-survivors had higher level of RPR than survivors (P<0.001). Logistic regression analysis showed that RPR was significantly associated with mortality even after adjusting for confounding factors (P<0.001). The area under the ROC curve (AUC) value of Glasgow Coma Scale (GCS) for predicting mortality was 0.761 and 0775 in training set and testing set, respectively. And the constructed predictive model incorporating RPR had the highest AUC value of 0.858 and 0.884 in training set and testing set.

CONCLUSION

RPR is significantly associated with mortality in TBI patients. Utilizing RPR to construct a predictive model is valuable to evaluate prognosis of TBI patients.