Blood Urea Nitrogen Is Associated with In-Hospital Mortality in Critically Ill Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Propensity Score Matching Analysis

A salivary urea sensor based on a microsieve disposable gate AlGaN/GaN high electron mobility transistor

The abundant bio-markers in saliva provide a new option for non-invasive testing. However, due to the presence of impurities in the saliva background, most of the existing saliva testing methods rely on pre-processing, which limits the application of saliva testing as a convenient means of testing in daily life. Herein, a disposable-gate AlGaN/GaN high electron mobility transistor (HEMT) biosensor integrated with a micro-sieve was introduced to solve the problem of signal interference caused by charged impurities in saliva for HEMT based biosensors, where the micro-sieve was utilized as a pre-treatment unit to remove large particles of impurities from saliva through the size effect and thus greatly improving the accuracy of detection. The experimental results showed that the HEMT based biosensor has excellent linearity (R2 = 0.9977) and a high sensitivity of 6.552 μA dec-1 for urea sensing from 1 fM to 100 mM in 0.1× PBS solution. When it comes to artificial saliva detection, compared to the HEMT sensor without the micro-sieve (sensitivity = 3.07432 μA dec-1), the sensitivity of the HEMT sensor integrated with the micro-sieve showed almost no change. Moreover, to verify that urea can be detected in actual saliva, urea is sensed directly in human saliva. The addition of the microsieve module provides a new way for biosensors to detect specific markers in saliva in real time, and the designed HEMT biosensor with the microsieve function has a wide range of application potential in rapid saliva detection.

Blood urea nitrogen to serum albumin ratio as a new prognostic indicator in type 2 diabetes mellitus patients with chronic kidney disease

Chronic kidney disease (CKD) is often a common comorbidity in critically ill patients with type 2 diabetes mellitus (T2DM). This study explored the relationship between blood urea nitrogen to serum albumin ratio (BAR) and mortality in T2DM patients with CKD in intensive care unit (ICU). Patients were recruited from the Medical Information Mart database, retrospectively. The primary and secondary outcomes were 90-day mortality, the length of ICU stay, hospital mortality and 30-day mortality, respectively. Cox regression model and Kaplan-Meier survival curve were performed to explore the association between BAR and 90-day mortality. Subgroup analyses were performed to determine the consistency of this association. A total of 1920 patients were enrolled and divided into the three groups (BAR < 9.2, 9.2 ≤ BAR ≤ 21.3 and BAR > 21.3). The length of ICU stay, 30-day mortality, and 90-day mortality in the BAR > 21.3 group were significantly higher than other groups. In Cox regression analysis showed that high BAR level was significantly associated with increased greater risk of 90-day mortality. The adjusted HR (95%CIs) for the model 1, model 2, and model 3 were 1.768 (1.409-2.218), 1.934, (1.489-2.511), and 1.864, (1.399-2.487), respectively. Subgroup analysis also showed the consistency of results. The Kaplan-Meier survival curve analysis revealed similar results as well that BAR > 21.3 had lower 90-day survival rate. High BAR was significantly associated with increased risk of 90-day mortality. BAR could be a simple and useful prognostic tool in T2DM patients with CKD in ICU.

A simple clinical risk score (ABCDMP) for predicting mortality in patients with AECOPD and cardiovascular diseases

BACKGROUND

The morbidity and mortality among hospital inpatients with AECOPD and CVDs remains unacceptably high. Currently, no risk score for predicting mortality has been specifically developed in patients with AECOPD and CVDs. We therefore aimed to derive and validate a simple clinical risk score to assess individuals' risk of poor prognosis.

STUDY DESIGN AND METHODS

We evaluated inpatients with AECOPD and CVDs in a prospective, noninterventional, multicenter cohort study. We used multivariable logistic regression analysis to identify the independent prognostic risk factors and created a risk score model according to patients' data from a derivation cohort. Discrimination was evaluated by the area under the receiver-operating characteristic curve (AUC), and calibration was assessed by the Hosmer-Lemeshow goodness-of-fit test. The model was validated and compared with the BAP-65, CURB-65, DECAF and NIVO models in a validation cohort.

RESULTS

We derived a combined risk score, the ABCDMP score, that included the following variables: age > 75 years, BUN > 7 mmol/L, consolidation, diastolic blood pressure ≤ 60 mmHg, mental status altered, and pulse > 109 beats/min. Discrimination (AUC 0.847, 95% CI, 0.805-0.890) and calibration (Hosmer‒Lemeshow statistic, P = 0.142) were good in the derivation cohort and similar in the validation cohort (AUC 0.811, 95% CI, 0.755-0.868). The ABCDMP score had significantly better predictivity for in-hospital mortality than the BAP-65, CURB-65, DECAF, and NIVO scores (all P < 0.001). Additionally, the new score also had moderate predictive performance for 3-year mortality and can be used to stratify patients into different management groups.

CONCLUSIONS

The ABCDMP risk score could help predict mortality in AECOPD and CVDs patients and guide further clinical research on risk-based treatment.

CLINICAL TRIAL REGISTRATION

Chinese Clinical Trail Registry NO.:ChiCTR2100044625; URL: http://www.chictr.org.cn/showproj.aspx?proj=121626 .

Risk factors of in-hospital mortality and discriminating capacity of NIVO score in exacerbations of COPD requiring noninvasive ventilation

BACKGROUND

Noninvasive mechanical ventilation (NIV) is recommended as the initial mode of ventilation to treat acute respiratory failure in patients with AECOPD. The Noninvasive Ventilation Outcomes (NIVO) score has been proposed to evaluate the prognosis in patients with AECOPD requiring assisted NIV. However, it is not validated in Chinese patients.

METHODS

We used data from the MAGNET AECOPD Registry study, which is a prospective, noninterventional, multicenter, real-world study conducted between September 2017 and July 2021 in China. Data for the potential risk factors of mortality were collected and the NIVO score was calculated, and the in-hospital mortality was evaluated using the NIVO risk score.

RESULTS

A total of 1164 patients were included in the study, and 57 patients (4.9%) died during their hospital stay. Multiple logistic regression analysis revealed that age ≥75 years, DBP <60 mmHg, Glasgow Coma Scale ≤14, anemia and BUN >7 mmol/L were independent predictors of in-hospital mortality. The in-hospital mortality was associated with an increase in the risk level of NIVO score and the difference was statistically significant (p < .001). The NIVO risk score showed an acceptable accuracy for predicting the in-hospital mortality in AECOPD requiring assisted NIV (AUC: 0.657, 95% CI: 0.584-0.729, p < .001).

CONCLUSION

Our findings identified predictors of mortality in patients with AECOPD receiving NIV, providing useful information to identify severe patients and guide the management of AECOPD. The NIVO score showed an acceptable predictive value for AECOPD receiving NIV in Chinese patients, and additional studies are needed to develop and validate predictive scores based on specific populations.

Development and validation of a nomogram for predicting in-hospital mortality in patients with nonhip femoral fractures

BACKGROUND

The incidence of nonhip femoral fractures is gradually increasing, but few studies have explored the risk factors for in-hospital death in patients with nonhip femoral fractures in the ICU or developed mortality prediction models. Therefore, we chose to study this specific patient group, hoping to help clinicians improve the prognosis of patients.

METHODS

This is a retrospective study based on the data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to screen risk factors. The receiver operating characteristic (ROC) curve was drawn, and the areas under the curve (AUC), net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. The consistency between the actual probability and the predicted probability was assessed by the calibration curve and Hosmer-Lemeshow goodness of fit test (HL test). Decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.

RESULTS

The LASSO regression analysis showed that heart rate, temperature, red blood cell distribution width, blood urea nitrogen, Glasgow Coma Scale (GCS), Simplified Acute Physiology Score II (SAPSII), Charlson comorbidity index and cerebrovascular disease were independent risk factors for in-hospital death in patients with nonhip femoral fractures. The AUC, IDI and NRI of our model in the training set and validation set were better than those of the GCS and SAPSII scoring systems. The calibration curve and HL test results showed that our model prediction results were in good agreement with the actual results (P = 0.833 for the HL test of the training set and P = 0.767 for the HL test of the validation set). DCA showed that our model had a better clinical net benefit than the GCS and SAPSII scoring systems.

CONCLUSION

In this study, the independent risk factors for in-hospital death in patients with nonhip femoral fractures were determined, and a prediction model was constructed. The results of this study may help to improve the clinical prognosis of patients with nonhip femoral fractures.

COPD Exacerbation: Why It Is Important to Avoid ICU Admission

Chronic obstructive pulmonary disease (COPD) is one of the major causes of morbidity and mortality worldwide. Hospitalization due to acute exacerbations of COPD (AECOPD) is a relevant health problem both for its impact on disease outcomes and on health system resources. Severe AECOPD causing acute respiratory failure (ARF) often requires admission to an intensive care unit (ICU) with endotracheal intubation and invasive mechanical ventilation. AECOPD also acts as comorbidity in critically ill patients; this condition is associated with poorer prognoses. The prevalence reported in the literature on ICU admission rates ranges from 2 to 19% for AECOPD requiring hospitalization, with an in-hospital mortality rate of 20-40% and a re-hospitalization rate for a new severe event being 18% of the AECOPD cases admitted to ICUs. The prevalence of AECOPD in ICUs is not properly known due to an underestimation of COPD diagnoses and COPD misclassifications in administrative data. Non-invasive ventilation in acute and chronic respiratory failure may prevent AECOPD, reducing ICU admissions and disease mortality, especially when associated with a life-threating episode of hypercapnic ARF. In this review, we report on up to date evidence from the literature, showing how improving the knowledge and management of AECOPD is still a current research issue and clinical need.