The Predictor Potential Role of the Glucose to Potassium Ratio in the Diagnostic Differentiation of Massive and Non-Massive Pulmonary Embolism

Serum glucose-potassium ratio predicts inhospital mortality in patients admitted to coronary care unit

OBJECTIVE

The aim of our study was to determine the role of serum glucose-potassium ratio in predicting inhospital mortality in coronary care unit patients.

METHODS

This study used data from the MORtality in CORonary Care Units in Turkey study, a national, observational, multicenter study that included all patients admitted to coronary care units between September 1, 2022, and September 30, 2022. Statistical analyses assessed the independent predictors of mortality. Two models were created. Model 1 included age, history of heart failure, chronic kidney disease, hypertension, diabetes mellitus, and coronary artery disease. Model 2 included glucose-potassium ratio in addition to these variables. Multivariate regression and receiver operating characteristic analysis were performed to compare Model 1 and Model 2 to identify if the glucose-potassium ratio is an independent predictor of inhospital mortality.

RESULTS

In a study of 3,157 patients, the mortality rate was 4.3% (n=137). Age (p=0.002), female gender (p=0.004), mean blood pressure (p<0.001), serum creatinine (p<0.001), C-reactive protein (p=0.002), white blood cell (p=0.002), and glucose-potassium ratio (p<0.001) were identified as independent predictors of mortality through multivariate regression analysis. The receiver operating characteristic analysis indicated that Model 2 had a statistically higher area under the curve than Model 1 (area under the curve 0.842 vs area under the curve 0.835; p<0.001). A statistically significant correlation was found between the inhospital mortality and glucose-potassium ratio (OR 1.015, 95%CI 1.006-1.024, p<0.001).

CONCLUSION

Our study showed that the glucose-potassium ratio may be a significant predictor of inhospital mortality in coronary care unit patients.

Relationship between serum glucose-potassium ratio and 90-day outcomes of patients with acute ischemic stroke

Background

Recent studies have shown that the serum glucose-potassium ratio (GPR) upon admission is correlated with the prognosis of cerebrovascular disorders. Herein, we investigated the relationship between GPR and 90-day functional outcomes in patients with acute ischaemic stroke (AIS).

Methods

Clinical data were collected from patients with AIS registered at the Stroke Center of Jiangsu Provincial Hospital of Chinese Medicine. The relationship between the GPR and 90-day outcomes was analysed using univariate and multivariate logistic regression analyses, linear regression analyses, and subgroup analyses.

Results

A total of 1826 patients met the enrolment requirements. The number of patients with a glucose-to-potassium ratio greater than the median value increased proportionally with increases in the NIHSS at admission and the 90-day modified Rankin scale (mRS). Univariate logistic regression analysis revealed a significant relationship between GPR and 90-day negative prognosis (OR 1.34 [95%Cl, 1.17-1.54], P < 0.001). After adjusting for all confounding variables, the relationship between GPR and 90-day adverse prognosis was shown to be nonlinearly U-shaped, with an inflection point of the curve for GPR of 1.347. Two linear regression analyses were performed on the basis of the inflection points of the curves. The results of this analysis revealed a negative correlation between GPR and 90-day adverse outcomes at GPR<1.347 (OR 0.86 [95%CI,0.09-7.86], P = 0.897), as well as a positive correlation between GPR and 90-day adverse outcomes at GPR≥1.347 (OR1.52 [95%CI, 1.19-1.93], P = 0.001). Subgroup analyses verified that the association between GPR and 90-day poor prognosis still existed, regardless of whether the patient had a history of diabetes mellitus (DM). (with DM: OR 1.39 [ 95%Cl, 1.05-1.83], P = 0.001); without DM: OR 0.93 [ 95%Cl,0.56-1.55], P = 0.016).

Conclusions

GPR significantly correlated with poor prognosis at 90-days in patients with AIS. Early intervention and control of GPR are expected to enhance functional outcomes in patients with AIS.

Evaluating the prognostic value of the stress index in trauma patients

Background

The stress index (SI), defined as the serum glucose to potassium ratio, has emerged as a potential prognostic indicator in some patient populations. This study aims to evaluate the predictive value of SI on the trauma patients sustained by all trauma causes.

Methods

A retrospective analysis was conducted on 20,040 adult trauma patients admitted to a single trauma center from January 1, 2009, to December 31, 2022. The SI was calculated according to the serum levels of glucose (mg/dL) and potassium (mEq/L) upon patients' arrival to emergency room. The enrolled patients were stratified into two groups based on an optimal SI cutoff value determined by receiver operating characteristic (ROC) curve analysis. The association between SI and in-hospital mortality, as well as other clinical outcomes, was assessed using multivariate logistic regression, adjusting for potential confounders.

Results

The mortality patients had a significantly higher SI (59.7 ± 30.6 vs. 39.5 ± 17.5, p < 0.001) than those who survived. The SI was identified as a significant independent predictor of mortality (odds ratio [OR] 4.65, 95 % confidence interval [CI]: 2.61-8.27, p < 0.001) in the multivariate analysis. In addition, patients in the high SI group (≥42.7) demonstrated significantly worse outcomes, including higher in-hospital mortality (7.5 % vs. 1.4 %, p < 0.001), longer hospital stays compared to the low SI group (<42.7).

Conclusion

The SI serves as a simple and valuable prognostic tool in risk stratification of the trauma patients.

Role of the Stress Index in Predicting Mortality among Patients with Traumatic Femoral Fractures

BACKGROUND

Traumatic femoral fractures, often resulting from high-energy impacts such as traffic accidents, necessitate immediate management to avoid severe complications. The Stress Index (SI), defined as the glucose-to-potassium ratio, serves as a predictor of mortality and adverse outcomes in various trauma contexts. This study aims to evaluate the prognostic value of the SI in patients with traumatic femoral fractures.

METHODS

This retrospective cohort study included adult trauma patients aged 20 or above with traumatic femoral fractures from the Trauma Registry System at a level 1 trauma center in southern Taiwan between 1 January 2009 and 31 December 2022. At the emergency room, serum electrolyte levels were assessed using baseline laboratory testing. By dividing blood glucose (mg/dL) by potassium (mEq/L), the SI was calculated. The best cut-off value of the SI for predicting mortality was determined using the Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC).

RESULTS

A total of 3717 patients made up the final group, of which 3653 survived and 64 died. In comparison to survivors, deceased patients had substantially higher blood glucose levels (199.3 vs. 159.0 mg/dL, p < 0.001) and SIs (53.1 vs. 41.6, p < 0.001). The optimal SI cut-off value for predicting mortality was 49.7, with a sensitivity of 53.1% and a specificity of 78.7% (AUC = 0.609). High SI was associated with increased mortality (4.2% vs. 1.0%, p < 0.001) and longer hospital stays (12.8 vs. 9.5 days, p < 0.001). The adjusted odds ratios of mortality, controlled by comorbidities, the Glasgow Coma Scale, and the Injury Severity Score, were significantly higher in patients with a higher SI (AOR 2.05, p = 0.016) than those with a lower SI.

CONCLUSIONS

Elevated SI upon admission correlates with higher mortality and extended hospital stay in patients with traumatic femoral fractures. Although the SI has a moderate predictive value, it remains a useful early risk assessment tool, necessitating further prospective, multi-center studies for validation and standardization.

The Stress Index as a Predictor of Mortality in Patients with Isolated Moderate to Severe Traumatic Brain Injury

BACKGROUND

The Stress Index (SI), calculated as the ratio of blood glucose to serum potassium levels, is a promising prognostic marker in various acute care settings. This study aimed to evaluate the utility of the SI for predicting mortality in patients with isolated moderate-to-severe traumatic brain injury (TBI).

METHODS

This retrospective cohort study included adult trauma patients (aged ≥ 20 years) with isolated moderate to severe TBI (Abbreviated Injury Scale ≥ 3 for only head region) treated from 2009-2022. The SI was computed from the initial glucose and potassium levels upon arrival at the emergency department. Logistic regression models were used to assess the association between the SI and mortality after adjusting for relevant covariates. The most effective threshold value of the SI for predicting mortality was identified using receiver operating characteristic (ROC) analysis.

RESULTS

Among the 4357 patients with isolated moderate and severe TBI, 463 (10.6%) died. Deceased patients had a significantly higher SI (61.7 vs. 44.1, p < 0.001). In multivariate analysis, higher SI independently predicted greater mortality risk (odds ratio (OR) 6.70, 95% confidence interval (CI) 1.66-26.99, p = 0.007). The optimal SI cutoff for predicting mortality was 48.50 (sensitivity 62.0%, specificity 71.4%, area under the curve 0.724). Patients with SI ≥ 48.5 had nearly two-fold higher adjusted mortality odds compared to those below the threshold (adjusted OR 1.94, 95% CI 1.51-2.50, p < 0.001).

CONCLUSIONS

SI is a useful predictor of mortality in patients with isolated moderate-to-severe TBI. Incorporating SI with standard clinical assessments could enhance risk stratification and management approaches for this patient population.

The utility of serum glucose potassium ratio as a predictive factor for haemorrhagic transformation, stroke recurrence, and mortality among ischemic stroke patients

Background and Objective

Glucose-Potassium Ratio (GPR) has emerged as a biomarker in several pathophysiological conditions. However, the association between GPR and long-term outcomes in stroke patients has not been investigated. Our study evaluated the applicability of baseline GPR as a predictive prognostic tool for clinical outcomes in ischemic stroke patients.

Methods

The multicenter retrospective cohort study included acute-subacute adult ischemic stroke patients who had their baseline serum GPR levels measured. Eligible patients were categorized into two sub-cohorts based on the baseline GPR levels (<1.67 vs. ≥ 1.67). The primary outcome was the incidence of 30-day hemorrhagic transformation, while stroke recurrence, and all-cause mortality within twelve months, were considered secondary.

Results

Among 4083 patients screened, 1047 were included in the current study. In comparison with GPR < 1.67 group, patients with ≥ 1.67 GPR had a significantly higher ratio of all-cause mortality within twelve months (aHR 2.07 [95 % CI 1.21-3.75] p = 0.01), and higher ratio of 30-day hemorrhagic transformation but failed to reach the statistical significance (aHR 1.60 [95 % CI 0.95-2.79], p = 0.08).

Conclusion

Overall, baseline GPR serum is an independent predictor of all-cause mortality within twelve months in patients with acute and subacute ischemic stroke. Further clinical studies are necessary to validate these findings.

Establishment of machine learning-based tool for early detection of pulmonary embolism

BACKGROUND AND OBJECTIVES

Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex pathology, which easily leads to misdiagnosis and missed diagnosis by inexperienced doctors. Especially, CT pulmonary angiography, the gold standard method, is not widely available. In this study, we aim to establish a rapid and accurate screening model for pulmonary embolism using machine learning technology. Importantly, data required for disease prediction are easily accessed, including routine laboratory data and medical record information of patients.

METHODS

We extracted features from patients' routine laboratory results and medical records, including blood routine, biochemical group, blood coagulation routine and other test results, as well as symptoms and medical history information. Samples with a feature loss rate greater than 0.8 were deleted from the original database. Data from 4723 cases were retained, 231 of which were positive for pulmonary embolism. 50 features were retained through the positive and negative statistical hypothesis testing which was used to build the predictive model. In order to avoid identification as majority-class samples caused by the imbalance of sample proportion, we used the method of Synthetic Minority Oversampling Technique (SMOTE) to increase the amount of information on minority samples. Five typical machine learning algorithms were used to model the screening of pulmonary embolism, including Support Vector Machines, Logistic Regression, Random Forest, XGBoost, and Back Propagation Neural Networks. To evaluate model performance, sensitivity, specificity and AUC curve were analyzed as the main evaluation indicators. Furthermore, a baseline model was established using the characteristics of the pulmonary embolism guidelines as a comparison model.

RESULTS

We found that XGBoost showed better performance compared to other models, with the highest sensitivity and specificity (0.99 and 0.99, respectively). Moreover, it showed significant improvement in performance compared to the baseline model (sensitivity and specificity were 0.76 and 0.76 respectively). More important, our model showed low missed diagnosis rate (0.46) and high AUC value (0.992). Finally, the calculation time of our model is only about 0.05 s to obtain the possibility of pulmonary embolism.

CONCLUSIONS

In this study, five machine learning classification models were established to assess the likelihood of patients suffering from pulmonary embolism, and the XGBoost model most significantly improved the precision, sensitivity, and AUC for pulmonary embolism screening. Collectively, we have established an AI-based model to accurately predict pulmonary embolism at early stage.

The association between serum glucose to potassium ratio on admission and short-term mortality in ischemic stroke patients

High serum glucose to potassium ratio (GPR) at admission is implicated for a poor outcome in acute brain injury, acute intracranial hemorrhage, and aneurysmal subarachnoid hemorrhage. However, the relationship between GPR and the outcome of ischemic stroke (IS) remains unknown. In all, 784 IS patients from a large emergency Norwegian cohort were included for secondary analysis. The exposure and outcome were GPR at baseline and all-cause mortality within 30 days after the first admission. Multivariable logistic regression analysis was performed to estimate the risk of 30-day mortality based on GPR levels. In addition, we examined whether there was a nonlinear relationship between admission GPR and 30-day mortality using two-piecewise linear regression with a smoothing function and threshold level analysis. The results of multivariable regression analysis showed that GPR at baseline was positively associated with the 30-day mortality (OR 2.01, 95% CI 1.12, 3.61) after adjusting for potential confounders (age, gender, department, serum sodium, serum albumin, serum-magnesium, hypertension, heart failure, chronic renal failure, and pneumonia). When GPR was translated to a categorical variable, the ORs and 95% CIs in the tertiles 2 to 3 versus the tertile 1 were 1.24 (0.60, 2.56) and 2.15 (1.09, 4.24), respectively (P for trend = 0.0188). Moreover, the results of the two-piecewise linear regression and curve fitting revealed a linear relationship between GPR and 30-day mortality. In IS patients, GPR is positively correlated with 30-day mortality, and the relationship between them is linear. The GPR at admission may be a promising predictor for the short-term outcome in IS patients.