Blood urea nitrogen (BUN) independently predicts mortality in critically ill patients admitted to ICU: A multicenter study

Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study

BACKGROUND

Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes.

OBJECTIVE

We aimed to establish 2 machine learning models to predict the risk and prognosis of AKD in the elderly and to deploy the models as online apps.

METHODS

Data on elderly patients with AKI (n=3542) and AKD (n=2661) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used to develop 2 models for predicting the AKD risk and in-hospital mortality, respectively. Data collected from Xiangya Hospital of Central South University were for external validation. A bootstrap method was used for internal validation to obtain relatively stable results. We extracted the indicators within 24 hours of the first diagnosis of AKI and the fluctuation range of some indicators, namely delta (day 3 after AKI minus day 1), as features. Six machine learning algorithms were used for modeling; the area under the receiver operating characteristic curve (AUROC), decision curve analysis, and calibration curve for evaluating; Shapley additive explanation (SHAP) analysis for visually interpreting; and the Heroku platform for deploying the best-performing models as web-based apps.

RESULTS

For the model of predicting the risk of AKD in elderly patients with AKI during hospitalization, the Light Gradient Boosting Machine (LightGBM) showed the best overall performance in the training (AUROC=0.844, 95% CI 0.831-0.857), internal validation (AUROC=0.853, 95% CI 0.841-0.865), and external (AUROC=0.755, 95% CI 0.699-0.811) cohorts. In addition, LightGBM performed well for the AKD prognostic prediction in the training (AUROC=0.861, 95% CI 0.843-0.878), internal validation (AUROC=0.868, 95% CI 0.851-0.885), and external (AUROC=0.746, 95% CI 0.673-0.820) cohorts. The models deployed as online prediction apps allowed users to predict and provide feedback to submit new data for model iteration. In the importance ranking and correlation visualization of the model's top 10 influencing factors conducted based on the SHAP value, partial dependence plots revealed the optimal cutoff of some interventionable indicators. The top 5 factors predicting the risk of AKD were creatinine on day 3, sepsis, delta blood urea nitrogen (BUN), diastolic blood pressure (DBP), and heart rate, while the top 5 factors determining in-hospital mortality were age, BUN on day 1, vasopressor use, BUN on day 3, and partial pressure of carbon dioxide (PaCO2).

CONCLUSIONS

We developed and validated 2 online apps for predicting the risk of AKD and its prognostic mortality in elderly patients, respectively. The top 10 factors that influenced the AKD risk and mortality during hospitalization were identified and explained visually, which might provide useful applications for intelligent management and suggestions for future prospective research.

Multicomponent prediction of 2-year mortality and amputation in patients with diabetic foot using a random survival forest model: Uric acid, alanine transaminase, urine protein and platelet as important predictors

The current methods for the prediction of mortality and amputation for inpatients with diabetic foot (DF) use only conventional, simple variables, which limits their performance. Here, we used a random survival forest (RSF) model and multicomponent variables to improve the prediction of mortality and amputation for these patients. We performed a retrospective cohort study of 175 inpatients with DF who were recruited between 2014 and 2021. Thirty-one predictors in six categories were considered as potential covariates. Seventy percent (n = 122) of the participants were randomly selected to constitute a training set, and 30% (n = 53) were assigned to a testing set. The RSF model was used to screen appropriate variables for their value as predictors of 2-year all-cause mortality and amputation, and a multicomponent prediction model was established. Model performance was evaluated using the area under the curve (AUC) and the Hosmer-Lemeshow test. The AUCs were compared using the Delong test. Seventeen variables were selected to predict mortality and 23 were selected to predict amputation. Uric acid and alanine transaminase were the top two most useful variables for the prediction of mortality, whereas urine protein and platelet were the top variables for the prediction of amputation. The AUCs were 0.913 and 0.851 for the prediction of mortality for the training and testing sets, respectively; and the equivalent AUCs were 0.963 and 0.893 for the prediction of amputation. There were no significant differences between the AUCs for the training and testing sets for both the mortality and amputation models. These models showed a good degree of fit. Thus, the RSF model can predict mortality and amputation in inpatients with DF. This multicomponent prediction model could help clinicians consider predictors of different dimensions to effectively prevent DF from clinical outcomes .

Development of a nomogram for predicting 90-day mortality in patients with sepsis-associated liver injury

The high mortality rate in sepsis patients is related to sepsis-associated liver injury (SALI). We sought to develop an accurate forecasting nomogram to estimate individual 90-day mortality in SALI patients. Data from 34,329 patients were extracted from the public Medical Information Mart for Intensive Care (MIMIC-IV) database. SALI was defined by total bilirubin (TBIL) > 2 mg/dL and the occurrence of an international normalized ratio (INR) > 1.5 in the presence of sepsis. Logistic regression analysis was performed to establish a prediction model called the nomogram based on the training set (n = 727), which was subsequently subjected to internal validation. Multivariate logistic regression analysis showed that SALI was an independent risk factor for mortality in patients with sepsis. The Kaplan‒Meier curves for 90-day survival were different between the SALI and non-SALI groups after propensity score matching (PSM) (log rank: P < 0.001 versus P = 0.038), regardless of PSM balance. The nomogram demonstrated better discrimination than the sequential organ failure assessment (SOFA) score, logistic organ dysfunction system (LODS) score, simplified acute physiology II (SAPS II) score, and Albumin-Bilirubin (ALBI) score in the training and validation sets, with areas under the receiver operating characteristic curve (AUROC) of 0.778 (95% CI 0.730-0.799, P < 0.001) and 0.804 (95% CI 0.713-0.820, P < 0.001), respectively. The calibration plot showed that the nomogram was sufficiently successful to predict the probability of 90-day mortality in both groups. The DCA of the nomogram demonstrated a higher net benefit regarding clinical usefulness than SOFA, LODS, SAPSII, and ALBI scores in the two groups. The nomogram performs exceptionally well in predicting the 90-day mortality rate in SALI patients, which can be used to assess the prognosis of patients with SALI and may assist in guiding clinical practice to enhance patient outcomes.

The National Early Warning Score 2(NEWS2) to Predict Early Progression to Severe Community-Acquired Pneumonia

This study aimed to assess the predictive performance of the National Early Warning Score 2 (NEWS2) to identify the early progression to severe disease in patients with community-acquired pneumonia (CAP). A prospective-cohort study was conducted among patients with CAP admitted to a university hospital between October 2020 and December 2021. The endpoint of interest was the progression to severe CAP, defined as the requirement for a mechanical ventilator, a vasopressor, or death within 72 h after hospital admission. Among 260 patients, 53 (25.6%) had early progression to severe CAP. The median NEWS2 of the early progression group was higher than that of the non-progression group [8 (6-9) vs. 7 (5-8), p = 0.015, respectively]. The AUROC of NEWS2 to predict early progression to severe CAP was 0.61 (95% CI: 0.52-0.70), while IDSA/ATS minor criteria ≥ 3 had AUROC 0.56 (95% CI 0.48-0.65). The combination of NEWS2 ≥ 8, albumin level < 3 g/dL and BUN ≥ 30 mg/dL improved AUROC from 0.61 to 0.71 (p = 0.015). NEWS2 and IDSA/ATS minor criteria showed fair predictive-accuracy in predicting progression to severe CAP. The NEWS2 cut-off ≥ 8 in combination with low albumin and uremia improved predictive-accuracy, and could be easily used in general practice.

The correlation study between blood urea nitrogen to serum albumin ratio and prognosis of patients with sepsis during hospitalization

BACKGROUND

Sepsis is a common critical illness in intensive care unit (ICU) and seriously threatens the life of patients. Therefore, to identify a simple and effective clinical indicator to determine prognosis is essential for the management of sepsis patients. This study was mainly based on blood urea nitrogen to albumin ratio (B/A), a comprehensive index, to explore its correlation with the prognosis of sepsis patients during hospitalization.

METHODS

Totally, adult patients in ICU who were diagnosed with sepsis in Medical Information Mart for Intensive Care IV(MIMIC-IV) database from 2008 to 2019 were involved in this study. The study population were divided into survivors group and non-survivors group based on the prognosis during hospitalization. Restricted cubic spline (RCS) was utilized to analyze the association between B/A level and the risk of ICU all-cause mortality in patients with sepsis and determine the optimal cut-off value of B/A. The study population was divided into low B/A group and high B/A group based on the optimal cut-off value. The survival curve of ICU cumulative survival rate was draw through Kaplan-Meier method. The correlation between B/A and the prognosis of patients was conducted by multivariate Cox regression analysis. Furthermore, we performed sensitivity analyses to assess the robustness of the results.

RESULTS

A total of 10,578 patients with sepsis were enrolled, and the ICU all-cause mortality was 15.89%. The patients in the non-survivors group had higher B/A values and more comorbidities than those in the survivors group. RCS showed that the risk of ICU all-cause mortality increased with the B/A level, showing a non-linear trend (χ2 = 66.82, p < 0.001). The mortality rate in the high B/A group was significantly higher than that in the low B/A group (p < 0.001). Kaplan-Meier curves revealed that compared with the low B/A group, the ICU cumulative survival rate of patients with sepsis was significantly lower in the high B/A group (log-rank test, χ2 = 148.620, p < 0.001). Further analysis of multivariate Cox proportional hazards regression showed that an elevated B/A (≥ 7.93) was an independent factor associated with ICU mortality among patients with sepsis.

CONCLUSIONS

An elevated B/A might be a useful prognostic indicator in patients with sepsis. This study could offer a deeper insight into treating sepsis.

Comparison of Prognostic Value of 10 Biochemical Indices at Admission for Prediction Postoperative Myocardial Injury and Hospital Mortality in Patients with Osteoporotic Hip Fracture

Aim: To evaluate the prognostic impact at admission of 10 biochemical indices for prediction postoperative myocardial injury (PMI) and/or hospital death in hip fracture (HF) patients. Methods: In 1273 consecutive patients with HF (mean age 82.9 ± 8.7 years, 73.5% women), clinical and laboratory parameters were collected prospectively, and outcomes were recorded. Multiple logistic regression and receiver-operating characteristic analyses (the area under the curve, AUC) were preformed, the number needed to predict (NNP) outcome was calculated. Results: Age ≥ 80 years and IHD were the most prominent clinical factors associated with both PMI (with cardiac troponin I rise) and in-hospital death. PMI occurred in 555 (43.6%) patients and contributed to 80.3% (49/61) of all deaths (mortality rate 8.8% vs. 1.9% in non-PMI patients). The most accurate biochemical predictive markers were parathyroid hormone > 6.8 pmol/L, urea > 7.5 mmol/L, 25(OH)vitamin D < 25 nmol/L, albumin < 33 g/L, and ratios gamma-glutamyl transferase (GGT) to alanine aminotransferase > 2.5, urea/albumin ≥ 2.0 and GGT/albumin ≥ 7.0; the AUC for developing PMI ranged between 0.782 and 0.742 (NNP: 1.84−2.13), the AUC for fatal outcome ranged from 0.803 to 0.722, (NNP: 3.77−9.52). Conclusions: In HF patients, easily accessible biochemical indices at admission substantially improve prediction of hospital outcomes, especially in the aged >80 years with IHD.

A Prognostic Model for In-Hospital Mortality in Critically Ill Patients with Pneumonia

Purpose

To determine the utility of a novel serum biomarker for the outcome prediction of critically ill patients with pneumonia.

Patients and Methods

A retrospective analysis of critically ill patients was performed at an emergency department. The expression and prediction value of parameters were assessed. Binary logistic regression analysis was utilized to determine the indicators associated with in-hospital mortality of pneumonia patients. The Last Absolute Shrinkage and Selection Operator was used to further determine the independent predictors, which were validated by multiple logistic regression. The receiver operator characteristic curve was performed to assess their prediction values. A prognostic nomogram model was finally established for the outcome prediction for critically ill patients with pneumonia.

Results

Retinol-binding protein (RBP) was significantly reduced in non-survived and pneumonia patients. CURB-65 score, levels of RBP, and blood urea nitrogen (BUN) were associated with in-hospital mortality of critically ill patients with pneumonia. Their combination was determined to be an ideal prognostic predictor (area under the curve of 0.762) and further developed into a nomogram prediction model (c-index 0.764).

Conclusion

RBP is a novel in-hospital mortality predictor, which well supplements the CURB-65 score for critical pneumonia patients.

Accuracy of SCORTEN in predicting mortality in toxic epidermal necrolysis

BACKGROUND

Toxic epidermal necrolysis (TEN) patients require multi-directional and multi-disciplinary treatment. In most cases, they are hospitalised at intensive care units and require multi-directional, burn-complication preventive care. Choosing the most appropriate treatment option might be troublesome even when predicting scores are used. SCORTEN is the most renowned prognostic score for TEN patients, however, there are some data indicating that the accuracy of this test may be limited. The credibility of not just the predicted mortality risk, but also componential laboratory results and clinical features subject to debate. The aim of this study was to evaluate the efficacy and credibility of SCORTEN in clinical practice, on proprietary material.

METHODS

A retrospective analysis of 35 patients with diagnosed in histopathology TEN was performed. The inclusion criteria were as follows: day of submission before 5th day from the onset of the symptoms, full protocol of plasmaphereses and IVIGs according to our scheme. Our protocol includes cycle of plasmapheresis with frozen fresh plasma twice daily for the first 2 days following admission, and once daily for the subsequent 5 to 7 days. IVIGs were administered after the first two sessions of plasmapheresis, for 4 to 7 days. The dosage was calculated according to body weight, at 0.4 to 0.5 g/kg per dose.

RESULTS

The sensitivity of SCORTEN for the analysed cohort was 100%, with a specificity of 24%. The estimated death was 41,9%, while the actual death rates were 12,5%. Our protocol improved the survival, OR = 26,57, RR = 6,34, p = 0,022. Decrease in mortality was caused by a combined treatment protocol we use- plasmaphereses with IVIGs. No independent risk factor was significant in death evaluation.

CONCLUSION

Our data suggest that the scoring system for predicting death among TEN patients are reliable when they are high. New prognostic factors should be found to improve the evaluation of patients with low SCORTEN.

Estimation of biochemical factors affecting survival in intensive care COVID-19 patients undergoing chest CT scoring: A retrospective cross-sectional study

Coronavirus disease 2019 (COVID-19) is a rapidly spreading deadly respiratory disease that emerged in the city of Wuhan in December 2019. As a result of its rapid and widespread transmission, the WHO declared a pandemic on March 11, 2020 and studies evaluating mortality and prognosis in COVID-19 gained importance. The aim of this study was to determine the factors affecting the survival of COVID-19 patients followed up in a tertiary intensive care unit (ICU) and undergoing chest computed tomography (CT) scoring. This retrospective cross-sectional study was conducted with the approval of Uşak University Medical Faculty Ethics Committee between July and September 2020. It included 187 symptomatic patients (67 females, 120 males) with suspected COVID-19 who underwent chest CT scans in the ICU. Demographics, acute physiology and chronic health evaluation (APACHE II), chest CT scores, COVID-19 real-time polymerase chain reaction (RT PCR) results, and laboratory parameters were recorded. SPSS 15.0 for Windows was used for the data analysis. The ages of the patients ranged from 18 to 94 and the mean age was 68.0 ± 13.9 years. The COVID-19 RT PCR test was positive in 86 (46.0%) patients and 110 patients (58.8%) died during the follow-up. ICU stay (P = .024) and total invasive mechanical ventilation time (P < .001) were longer and blood urea nitrogen (BUN) was higher (P < .001) in the nonsurvivors. Patients with an APACHE II score of 23 and above had a 1.12-fold higher mortality rate (95% CI 0.061-0.263). There was no significant difference in total chest CT score between the survivors and nonsurvivors (P = .210). Chest CT score was not significantly associated with mortality in COVID-19 patients. Our idea that COVID-19 will cause greater mortality in patients with severe chest CT findings has changed. More studies on COVID-19 are needed to reveal the markers that affect prognosis and mortality in this period when new variants are affecting the world.

E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database

Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients' survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality prediction are identified, and the effects of changing each feature on the prediction are studied. We used supervised machine learning models and illness severity scoring systems to benchmark the mortality prediction. We also implemented a combination of SHAP, LIME, partial dependence, and individual conditional expectation plots to explain the predictions made by the best-performing model (CatBoost). We proposed E-CatBoost, an optimized and efficient patient mortality prediction model, which can accurately predict the patients' discharge status using only ten input features. We used eICU-CRD v2.0 to train and validate the models; the dataset contains information on over 200,000 ICU admissions. The patients were divided into twelve disease groups, and models were fitted and tuned for each group. The models' predictive performance was evaluated using the area under a receiver operating curve (AUROC). The AUROC scores were 0.86 [std:0.02] to 0.92 [std:0.02] for CatBoost and 0.83 [std:0.02] to 0.91 [std:0.03] for E-CatBoost models across the defined disease groups; if measured over the entire patient population, their AUROC scores were 7 to 18 and 2 to 12 percent higher than the baseline models, respectively. Based on SHAP explanations, we found age, heart rate, respiratory rate, blood urine nitrogen, and creatinine level as the most critical cross-disease features in mortality predictions.

Blood Urea Nitrogen/Albumin Ratio and Mortality Risk in Patients with COVID-19

Introduction

We researched blood urea nitrogen (BUN), albumin and their ratio (BAR), and compared them with C-reactive protein (CRP), D-dimer, and computed tomography severity scores (CT-SS), to predict in-hospital mortality.

Methods

One-hundred and thirty-one coronavirus disease-2019 (COVID-19) confirmed patients brought to the emergency department (ED) were dispensed to the survivor or non-survivor group, in light of in-hospital mortality. Information on age, gender, complaints, comorbidities, laboratory parameters, and outcome were gathered from the patient's record files.

Results

The median BUN, mean total protein, mean albumin, median BAR, median creatinine, median CRP, and median D-dimer were recorded. CT-SS were utilized in categorizing the patient as mild, moderate, and severe. In-hospital mortality occurred in 42 (32.06%) patients (non-survivor group) and did not occur in 89 (67.94%) patients (survivor group). The median BUN (mg/dL) and BAR (mg/gm) values were significantly raised in the non-survivor group than in the survivor group [BUN: 23.48 (7.51–62.75) and 20.66 (4.07–74.67), respectively (p = 0.009); BAR: 8.33 mg/g (2.07–21.86) and 6.11 mg/g (1.26–23.33); (p = 0.0003)]. The mean albumin levels (gm/dL) in the non-survivor group were significantly lower than in the survivor group [2.96 ± 0.35 and 3.27 ± 0.35, respectively (p <0.0001)]. Albumin with an odd's ratio of 6.14 performed the best in predicting in-hospital mortality, followed by D-dimer (4.98). BAR and CRP had similar outcome of 3.75; BUN showed an OR of 3.13 at the selected cutoff value.

Conclusion

The BUN, albumin, and BAR were found to be dependable predictors of in-hospital mortality in COVID-19 patients, with albumin (hypoalbuminemia) performing even better.

How to cite this article

Singh S, Singh K. Blood Urea Nitrogen/Albumin Ratio and Mortality Risk in Patients with COVID-19. Indian J Crit Care Med 2022;26(5):626–631.

Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning

Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power.

Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death.

Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients.

Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.

Blood Urea Nitrogen to Serum Albumin Ratio in the Prediction of Acute Kidney Injury of Patients with Rib Fracture in Intensive Care Unit

Background

We hypothesized that the blood urea nitrogen (BUN) to serum albumin ratio (BAR) could serve as an independent predictor for incident acute kidney injury (AKI) in intensive care unit (ICU) patients with rib fracture.

Methods

Rib fracture patients in ICU were extracted from Medical Information Mart for Intensive Care IV (MIMIC-IV v1.0) database. The primary outcome in this study was the incidence of AKI. Univariate and multivariate logistic regression analyses were used to determine the relationship between BAR and AKI and propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were also applied to assure the robustness of our results.

Results

The optimal cut-off value for BAR was 5.26 based on receiver operator characteristic curve. Among the 953 patients who diagnosed with rib fracture, 197 high-BAR group (≥5.26) patients and 197 low-BAR group (<5.26) patients who had similar propensity scores were finally included in the matched cohort. High-BAR group patients had a significantly higher incidence of AKI (odds ratio, OR, 3.85, 95% confidence index, 95% CI, 2.58–5.79, P<0.001) in the original cohort, in the matched cohort (OR, 4.47, 95% CI 2.71–7.53, P<0.001), and in the weighted cohort (OR, 4.28, 95% CI 2.80–6.53, P<0.001). Furthermore, BAR was superior to that of acute physiology score III for predicting AKI and could add more net benefit for incident AKI in critical care patients with rib fracture.

Conclusion

As an easily access and cost-effective parameter, BAR could serve as a good diagnostic predictor for AKI in ICU patients with rib fracture.

Blood Urea Nitrogen for Short-Term Prognosis in Patients with Cardiogenic Shock Complicating Acute Myocardial Infarction

PURPOSE

Cardiogenic shock (CS) is the leading cause of death in patients with acute myocardial infarction (AMI). Our study aimed to evaluate the short-term prognostic value of admission blood urea nitrogen (BUN) in patients with CS complicating AMI.

MATERIALS AND METHODS

218 consecutive patients with CS after AMI were enrolled. The primary endpoint was 30-day mortality. The association of admission BUN and 30-day mortality and major adverse cardiovascular event (MACE) was investigated by Cox regression. The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) further examined the predictive value of BUN.

RESULTS

During a period of 30-day follow-up, 105 deaths occurred. Compared to survivors, nonsurvivors had significantly higher admission BUN (p < 0.001), creatinine (p < 0.001), BUN/creatinine (p = 0.03), and a lower glomerular filtration rate (p < 0.001). The area under the curve (AUC) of the 4 indices for predicting 30-day mortality was 0.781, 0.734, 0.588, and 0.773, respectively. When compared to traditional markers associated with CS, the AUC for predicting 30-day mortality of BUN, lactate, and left ventricular ejection fraction were 0.781, 0.776, and 0.701, respectively. The optimal cut-off value of BUN for predicting 30-day mortality was 8.95 mmol/L with Youden-Index analysis. Multivariate Cox analysis indicated BUN >8.95 mmol/L was an important independent predictor for 30-day mortality (HR 2.08, 95%CI 1.28-3.36, p = 0.003) and 30-day MACE (HR 1.85, 95%CI 1.29-2.66, p = 0.001). IDI (0.053, p = 0.005) and NRI (0.135, p = 0.010) showed an improvement in the accuracy for mortality prediction of the new model when BUN was included compared with the standard model of predictors in previous scores.

CONCLUSION

An admission BUN >8.95 mmol/L was robustly associated with increased short-term mortality and MACE in patients with CS after AMI. The prognostic value of BUN was superior to other renal markers and comparable to traditional markers. This easily accessible index might be promising for early risk stratification in CS patients following AMI.

The association of blood urea nitrogen levels upon emergency admission with mortality in acute exacerbation of chronic obstructive pulmonary disease

Background and purpose

High blood urea nitrogen (BUN) is associated with an elevated risk of mortality in various diseases, such as heart failure and pneumonia. Heart failure and pneumonia are common comorbidities of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). However, data on the relationship of BUN levels with mortality in patients with AECOPD are sparse. The purpose of this study was to evaluate the correlation between BUN level and in-hospital mortality in a cohort of patients with AECOPD who presented at the emergency department (ED).

Methods

A total of 842 patients with AECOPD were enrolled in the retrospective observational study from January 2018 to September 2020. The outcome was all-cause in-hospital mortality. Receiver operating characteristic (ROC) curve analysis and logistic regression models were performed to evaluate the association of BUN levels with in-hospital mortality in patients with AECOPD. Propensity score matching was used to assemble a cohort of patients with similar baseline characteristics, and logistic regression models were also performed in the propensity score matching cohort.

Results

During hospitalization, 26 patients (3.09%) died from all causes, 142 patients (16.86%) needed invasive ventilation, and 190 patients (22.57%) were admitted to the ICU. The mean level of blood urea nitrogen was 7.5 ± 4.5 mmol/L. Patients in the hospital non-survivor group had higher BUN levels (13.48 ± 9.62 mmol/L vs. 7.35 ± 4.14 mmol/L, p < 0.001) than those in the survivor group. The area under the curve (AUC) was 0.76 (95% CI 0.73–0.79, p < 0.001), and the optimal BUN level cutoff was 7.63 mmol/L for hospital mortality. As a continuous variable, BUN level was associated with hospital mortality after adjusting respiratory rate, level of consciousness, pH, PCO_2, lactic acid, albumin, glucose, CRP, hemoglobin, platelet distribution width, D-dimer, and pro-B-type natriuretic peptide (OR 1.10, 95% CI 1.03–1.17, p=0.005). The OR of hospital mortality was significantly higher in the BUN level ≥7.63 mmol/L group than in the BUN level <7.63 mmol/L group in adjusted model (OR 3.29, 95% CI 1.05–10.29, p=0.041). Similar results were found after multiple imputation and in the propensity score matching cohort.

Conclusions

Increased BUN level at ED admission is associated with hospital mortality in patients with AECOPD who present at the ED. The level of 7.63 mmol/L can be used as a cutoff value for critical stratification.

A descriptive and validation study of a predictive model of severity of SARS-COV-2 infection

Objectives

The strain the SARS-COV-2 pandemic is putting on hospitals requires that predictive values are identified for a rapid triage and management of patients at a higher risk of developing severe COVID-19. We developed and validated a prognostic model of COVID-19 severity.

Methods

A descriptive, comparative study of patients with positive vs. negative PCR-RT for SARS-COV-2 and of patients who developed moderate vs. severe COVID-19 was conducted. The model was built based on analytical and demographic data and comorbidities of patients seen in an Emergency Department with symptoms consistent with COVID-19. A logistic regression model was designed from data of the COVID-19-positive cohort.

Results

The sample was composed of 410 COVID-positive patients (303 with moderate disease and 107 with severe disease) and 81 COVID-negative patients. The predictive variables identified included lactate dehydrogenase, C-reactive protein, total proteins, urea, and platelets. Internal calibration showed an area under the ROC curve (AUC) of 0.88 (CI 95%: 0.85-0.92), with a rate of correct classifications of 85.2% for a cut-off value of 0.5. External validation (100 patients) yielded an AUC of 0.79 (95% CI: 0.71-0.89), with a rate of correct classifications of 73%.

Conclusions

The predictive model identifies patients at a higher risk of developing severe COVID-19 at Emergency Department, with a first blood test and common parameters used in a clinical laboratory. This model may be a valuable tool for clinical planning and decision-making.

Identifying a Clinical Risk Triage Score for Adult Emergency Department

Emergency triage is crucial for the treatment and prognosis of emergency patients, but its validity needs further improvement. The purpose of this study was to identify a risk score for adult triage. We conducted a regression analysis of physiological and biochemical data from 1,522 adult patients. A 60-point triage scoring model included temperature, pulse, systolic blood pressure, oxygen saturation, consciousness, dyspnea, admission mode, syncope history, chest pain or chest tightness, complexion, hematochezia or hematemesis, hemoptysis, white blood count, creatinine, bicarbonate, platelets, and creatine kinase. The area under curve in predicting ICU admission was 0.929 (95% CI [0.913-0.944]) for the derivation cohort and 0.911 (95% CI [0.884-0.938]) for the validation cohort. Four categories: critical level (≥13 points), severe level (6-12 points), urgency level (1-5 points), and sub-acute level (0 points) were divided, which significantly distinguished the severity of emergency patients.

Urea to albumin ratio is a predictor of mortality in patients with septic shock

BACKGROUND

The purpose of this study was to evaluate urea to albumin ratio (UAR) as predictor of mortality in patients with septic shock.

METHODS

We included all individuals aged ≥ 18 years, with the diagnosis of septic shock at Intensive Care Unit (ICU) admission. Laboratorial and clinical data was recorded within the first 24 h of the patient's admission. Serum urea and albumin concentration were used for UAR calculation. All patients were followed during their ICU stay and the mortality rate was recorded.

RESULTS

222 patients were included in the analysis; the mean age was 62.3 ± 15.1 years and 66% were male. Mortality rate during the ICU stay was 59.9% and the median UAR was 40.7 (24.5-66.1). The UAR was also higher in patients who died in the ICU and was positively correlated with APACHE II, SOFA score and CRP. The ROC ICU mortality development (AUC: 0.617; CI 95%: 0.541-0.693; p: 0.003) at the cutoff of ≥47.25. Furthermore, UAR values were associated with ICU mortality when adjusted by age, sex and APACHE II (OR: 1.011; CI95%:1.000-1.022; p = 0.043) and when adjusted by lactate (OR: 1.014; CI95%:1.003-1.024; p = 0.009).

CONCLUSIONS

Our data suggest that UAR could play a role as predictor of ICU mortality in patients with septic shock.

Prediction of 72-hour mortality in patients with extremely high serum C-reactive protein levels using a novel weighted average of risk scores

The risk factors associated with mortality in patients with extremely high serum C-reactive protein (CRP) levels are controversial. In this retrospective single-center cross-sectional study, the clinical and laboratory data of patients with CRP levels ≥40 mg/dL treated in Saitama Medical Center, Japan from 2004 to 2017 were retrieved from medical records. The primary outcome was defined as 72-hour mortality after the final CRP test. Forty-four mortal cases were identified from the 275 enrolled cases. Multivariate logistic regression analysis (MLRA) was performed to explore the parameters relevant for predicting mortality. As an alternative method of prediction, we devised a novel risk predictor, “weighted average of risk scores” (WARS). WARS features the following: (1) selection of candidate risk variables for 72-hour mortality by univariate analyses, (2) determination of C-statistics and cutoff value for each variable in predicting mortality, (3) 0–1 scoring of each risk variable at the cutoff value, and (4) calculation of WARS by weighted addition of the scores with weights assigned according to the C-statistic of each variable. MLRA revealed four risk variables associated with 72-hour mortality—age, albumin, inorganic phosphate, and cardiovascular disease—with a predictability of 0.829 in C-statistics. However, validation by repeated resampling of the 275 records showed that a set of predictive variables selected by MLRA fluctuated occasionally because of the presence of closely associated risk variables and missing data regarding some variables. WARS attained a comparable level of predictability (0.837) by combining the scores for 10 risk variables, including age, albumin, electrolytes, urea, lactate dehydrogenase, and fibrinogen. Several mutually related risk variables are relevant in predicting 72-hour mortality in patients with extremely high CRP levels. Compared to conventional MLRA, WARS exhibited a favorable performance with flexible coverage of many risk variables while allowing for missing data.

Evaluation of a shorter algorithm in an automated analysis of sublingual microcirculation

OBJECTIVE

Diagnostic and risk stratification in intensive and emergency medicine must be fast, accurate, and reliable. The assessment of sublingual microcirculation is a promising tool for this purpose. However, its value is limited because the measurement is time-consuming in unstable patients. This proof-of-concept validation study examines the non-inferiority of a reduced frame rate in image acquisition regarding quality, measurement results, and time.

METHODS

This prospective observational study included healthy volunteers. Sublingual measurement of microcirculation was performed using a sidestream dark field camera (SDF, MicroVision Medical®). Video-quality was evaluated with a modified MIQS (microcirculation image quality score). AVA 4.3C software calculated microcirculatory parameters.

RESULTS

Thirty-one volunteers were included. There was no impact of the frame rate on the time needed by the software algorithm to measure one video (4.5 ± 0.5 minutes) for AVA 4.3C. 86 frames per video provided non inferior video quality (MIQS 1.8 ± 0.7 for 86 frames versus MIQS 2.2 ± 0.6 for 215 frames, p < 0.05), equal results for all microcirculatory parameters, but did not result in an advantage in terms of speed. No complications occurred.

CONCLUSION

Video captures with 86 frames offer equal video quality and results for consensus parameters compared to 215 frames. However, there was no advantage regarding the time needed for the overall measurement procedure.

Efficacy of blood urea nitrogen and the neutrophil-to-lymphocyte ratio as predictors of mortality among elderly patients with genitourinary tract infections: A retrospective multicentre study

OBJECTIVES

To investigate whether initial blood urea nitrogen (BUN) and the neutrophil-to-lymphocyte ratio (NLR) in the emergency department (ED) are associated with mortality in elderly patients with genitourinary tract infections.

METHODS

A total of 541 patients with genitourinary tract infections in 5 EDs between November 2016 and February 2017 were included and retrospectively reviewed. We assessed age, sex, comorbidities, vital signs, and initial laboratory results, including BUN, NLR and the SOFA criteria. The primary outcome was all-cause in-hospital mortality.

RESULTS

The nonsurvivor group included 32 (5.9%) elderly patients, and the mean arterial pressure (MAP), NLR and BUN were significantly higher in this group than in the survivor group (p < 0.001, p = 0.003, p < 0.001). In multivariate analysis, MAP <70 mmHg, NLR ≥23.8 and BUN >28 mg/dl were shown to be independent risk factors for in-hospital mortality (OR 3.62, OR 2.51, OR 2.76: p = 0.002, p = 0.033, p = 0.038, respectively). Additionally, NLR ≥23.8 and BUN >28 were shown to be independent risk factors for mortality in admitted elderly with complicated UTI (p = 0.030, p = 0.035). When BUN and NLR were combined with MAP, the area under the ROC curve (AUROC) value was 0.807 (0.771-0.839) for the prediction of mortality, the sensitivity was 87.5% (95% CI 71.0-96.5), and the specificity was 61.3% (95% CI 56.9-65.5%).

CONCLUSION

The initial BUN and NLR values with the MAP were good predictors associated with all-cause in-hospital mortality among elderly genitourinary tract infections visiting the ED.

Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients

The crude mortality rate in critical pneumonia cases with coronavirus disease 2019 (COVID-19) reaches 49%. This study aimed to test whether levels of blood urea nitrogen (BUN) in combination with D-dimer were predictors of in-hospital mortality in COVID-19 patients. The clinical characteristics of 305 COVID-19 patients were analysed and were compared between the survivor and non-survivor groups. Of the 305 patients, 85 (27.9%) died and 220 (72.1%) were discharged from hospital. Compared with discharged cases, non-survivor cases were older and their BUN and D-dimer levels were significantly higher (P < 0.0001). Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses identified BUN and D-dimer levels as independent risk factors for poor prognosis. Kaplan-Meier analysis showed that elevated levels of BUN and D-dimer were associated with increased mortality (log-rank, P < 0.0001). The area under the curve for BUN combined with D-dimer was 0.94 (95% CI 0.90-0.97), with a sensitivity of 85% and specificity of 91%. Based on BUN and D-dimer levels on admission, a nomogram model was developed that showed good discrimination, with a concordance index of 0.94. Together, initial BUN and D-dimer levels were associated with mortality in COVID-19 patients. The combination of BUN ≥ 4.6 mmol/L and D-dimer ≥ 0.845 μg/mL appears to identify patients at high risk of in-hospital mortality, therefore it may prove to be a powerful risk assessment tool for severe COVID-19 patients.

Clinician perception of long-term survival at the point of critical care discharge: a prospective cohort study

Critical care survivors suffer persistent morbidity and increased risk of mortality as compared with the general population. Nevertheless, there are no standardised tools to identify at-risk patients. Our aim was to establish whether the Sabadell score, a simple tool applied by the treating clinician upon critical care discharge, was independently associated with 5-year mortality through a prospective observational cohort study of adults admitted to a general critical care unit. The Sabadell score, which is a measure of clinician-assigned survival perception, was applied to all patients from September 2011 to December 2017. The primary outcome was 5-year mortality, assessed using a multivariable flexible parametric survival analysis adjusted for baseline characteristics and clinically relevant covariates. We studied 5954 patients with a minimum of 18 months follow-up. Mean (SD) age was 59.5 (17.0) years and 3397 (57.1%) patients were men. We categorised 2287 (38.4%) patients as Sadabell 0; 2854 (47.9%) as Sadabell 1; 629 (10.5%) as Sadabell 2; and 183 (3.1%) as Sadabell 3. Adjusted hazard ratios for mortality were 2.1 (95%CI 1.9-2.4); 4.0 (95%CI 3.4-4.6); and 21.0 (95%CI 17.2-25.7), respectively. Sabadell 3 patients had 99.9%, 99.5%, 98.5% and 87.4% mortality at 5 years for patients in the age brackets ≥ 80, 60-79, 40-59 and 16-39 years, respectively. Sabadell 2 patients had 71.0%, 52.7%, 44.8% and 23.7% 5-year mortality for these same age categories. The Sabadell score was independently associated with 5-year survival after critical care discharge. These findings can be used to guide provision of increased support for patients after critical care discharge and/or informed discussions with patients and relatives about dying to ascertain their future wishes.

Determination of frail state and association of frailty with inflammatory markers among cardiac surgery patients in a Central European patient population

INTRODUCTION

With the aging of the population, the screening of frail patients, especially before high-risk surgery, come to the fore. The background of the frail state is not totally clear, most likely inflammatory processes are involved in the development.

METHODS

Our survey of patients over age of 65 who were on cardiac surgery were performed with Edmonton Frail Scale (EFS). Patients' demographic, perioperative data, incidence of complications and correlations of inflammatory laboratory parameters were studied with the severity of the frail state.

RESULTS

On the basis of EFS, 313 patients were divided into non-frail (NF,163,52%), pre-frail (PF,89,28.5%) and frail (F,61,19.5%) groups. Number of complications in the three groups were different (NF:0.67/patient, PF:0.76/patient, F:1.08/patient). We showed significant difference between NF and F in both intensive care and hospital stay, but there was no statistical difference between the groups in hospital deaths (NF:5/163, PF:3/89, F:5/61). We also found a significant difference between NF and F patients in preoperative fibrinogen-, CRP- and white blood cell count levels.

CONCLUSIONS

We first present the incidence of frailty in patients with heart surgery in a Central-European population. According to our results, inflammatory processes are likely to play a role in the development of the frail state.