Predictive value of arterial blood lactate to serum albumin ratio for in-hospital mortality of patients with community-acquired pneumonia admitted to the Intensive Care Unit

OBJECTIVE

To investigate the predictive value of the arterial blood lactate to serum albumin ratio (LAR) on in-hospital mortality of patients with community-acquired pneumonia (CAP) admitted to the Intensive Care Unit (ICU).

METHODS

Clinical datasets of 1720 CAP patients admitted to ICU from MIMIC-IV database were retrospectively analyzed. Patients were randomly assigned to the training cohort (n=1204) and the validation cohort (n=516) in a ratio of 7:3. X-tile software was used to find the optimal cut-off value for LAR. The receiver operating curve (ROC) analysis was conducted to compare the performance between LAR and other indicators. Univariate and multivariate Cox regression analyses were applied to select prognostic factors associated with in-hospital mortality. Based on the observed prognostic factors, a nomogram model was created in training cohort, and the validation cohort was utilized to further validate the nomogram.

RESULTS

The optimal cut-off value for LAR in CAP patients admitted to ICU was 1.6 (the units of lactate and albumin were, respectively, 'mmol/L' and 'g/dL'). The ROC analysis showed that the discrimination abilities of LAR were superior to other indicators except Sequential Organ Failure Assessment score and Simplified acute physiology score (SAPSII), which had the same abilities. Age, mean arterial pressure, SpO2, heart rate, SAPSII score, neutrophil-to-lymphocyte ratio, and LAR were found to be independent predictors of poor overall survival in the training cohort by multivariate Cox regression analysis and were incorporated into the nomogram for in-hospital mortality as independent factors. The nomogram model, exhibiting medium discrimination, had a C-index of 0.746 (95% CI = 0.715-0.777) in the training cohort and 0.716 (95% CI = 0.667-0.765) in the validation cohort.

CONCLUSION

LAR could predict in-hospital mortality of patients with CAP admitted to ICU independently as a readily accessible biomarker. The nomogram that included LAR with other independent factors performed well in predicting in-hospital mortality.

Analysis of Risk Factors for Pneumonia Death in ICU Environment Based on Logistic Regression

Objective

To explore the risk and protect factors for death of pneumonia patients in intensive care unit (ICU), we conducted this logistic regression research.

Methods

We collected demographic and nursing care data for 80 patients form Wuhan fourth hospital, in which 40 patients were dead and the other 40 patients were alive. Difference analysis, Pearson's correlation matrix, and logistic regression were conducted to explore the risk and protective factors for living status of pneumonia patients in ICU.

Results

A total of 40 individuals were dead from pneumonia in ICU. The demographic and nursing information had no difference between death and living groups except age. After that, correlation analysis showed that there were correlations between living status, age, and marriage. Logistic regression showed that age (odds ratio (OR) = 0.94, P < 0.05) and no education (OR = 0.21, P < 0.05) may be harmful for pneumonia patients living status while high-quality nursing (OR = 2.72, P < 0.05) may be helpful for pneumonia patients living status.

Conclusion

High-quality care plays an important role in protecting the survival of patients with pneumonia, and old age and uneducated may be the risk factors for the death of patients with pneumonia.

Admission lysophosphatidylethanolamine acyltransferase level predicts the severity and prognosis of community-acquired pneumonia

PURPOSE

Early diagnosis and prognosis of patients with community-acquired pneumonia (CAP) are still difficult clinical challenges. This study aimed to investigate the role of lysophosphatidylethanolamine acyltransferase (LPEAT) in CAP and to evaluate the effectiveness of this enzyme as an indicator of disease severity and risk of death in CAP.

METHODS

This retrospective, multi-center study was conducted in 2017. A total of 267 patients with CAP were included. Of these 267 patients, 175 patients had non-severe CAP (non-SCAP) and 92 patients had severe CAP (SCAP). In addition, we recruited 15 healthy volunteers and 42 hospitalized disease controls in our study. The demographic and clinical characteristics were recorded for all participants. Admission levels of LPEAT were determined by quantitative enzyme-linked immunosorbent assay.

RESULTS

Admission levels of LPEAT in patients with SCAP were significantly higher, particularly in non-survivors and were not affected by the causative etiology. Furthermore, when the patients were stratified according to PSI and CURB-65 scores, the patients with high severity scores had higher LPEAT levels upon admission than patients with low severity scores. LPEAT also performed well in predicting SCAP in patients with CAP. Moreover, LPEAT could predict the 30-day mortality rate of patients with CAP, and combining LPEAT with the clinical severity score further improved the accuracy of mortality prediction.

CONCLUSION

Elevated LPEAT levels can reliably predict the severity of illness in patients with CAP at the time of admission. Adding LPEAT to clinical scoring methods could improve prognostic accuracy. Trial registration ClinicalTrials.gov, NCT03093220. Registered on March 28th, 2017.

Hospital Admission Location Prediction via Deep Interpretable Networks for the Year-Round Improvement of Emergency Patient Care

OBJECTIVE

This paper presents a deep learning method of predicting where in a hospital emergency patients will be admitted after being triaged in the Emergency Department (ED). Such a prediction will allow for the preparation of bed space in the hospital for timely care and admission of the patient as well as allocation of resource to the relevant departments, including during periods of increased demand arising from seasonal peaks in infections.

METHODS

The problem is posed as a multi-class classification into seven separate ward types. A novel deep learning training strategy was created that combines learning via curriculum and a multi-armed bandit to exploit this curriculum post-initial training.

RESULTS

We successfully predict the initial hospital admission location with area-under-receiver-operating-curve (AUROC) ranging between 0.60 to 0.78 for the individual wards and an overall maximum accuracy of 52% where chance corresponds to 14% for this seven-class setting. Our proposed network was able to interpret which features drove the predictions using a 'network saliency' term added to the network loss function.

CONCLUSION

We have proven that prediction of location of admission in hospital for emergency patients is possible using information from triage in ED. We have also shown that there are certain tell-tale tests which indicate what space of the hospital a patient will use.

SIGNIFICANCE

It is hoped that this predictor will be of value to healthcare institutions by allowing for the planning of resource and bed space ahead of the need for it. This in turn should speed up the provision of care for the patient and allow flow of patients out of the ED thereby improving patient flow and the quality of care for the remaining patients within the ED.

Predicting Bacterial Versus Viral Infection, or None of the Above: Current and Future Prospects of Biomarkers

Sepsis and pneumonia cause significant morbidity and mortality worldwide. Despite improvements in diagnostic methodologies for organism identification, the early recognition and further risk stratification of these infections can be challenging. Although traditional clinical scoring systems are beneficial for the management of sepsis and pneumonia, biomarkers supporting the diagnosis and management of these infectious diseases are needed. Many biomarkers have been identified and there is no lack of studies and meta-analyses assessing the utility of biomarkers. Focusing primarily on sepsis and pneumonia, this article discusses the most commonly used biomarkers for which clinical laboratory testing methods are available.

Inflammatory biomarkers are associated with aetiology and predict outcomes in community-acquired pneumonia: results of a 5-year follow-up cohort study

Background

Biomarkers may facilitate clinical decisions in order to guide antimicrobial treatment and prediction of prognosis in community-acquired pneumonia (CAP). We measured serum C-reactive protein, procalcitonin (PCT) and calprotectin levels, and plasma pentraxin 3 (PTX3) and presepsin levels, along with whole-blood white cell counts, at three time-points, and examined their association with microbial aetiology and adverse clinical outcomes in CAP.

Methods

Blood samples were obtained at hospital admission, clinical stabilisation and 6-week follow-up from 267 hospitalised adults with CAP. Adverse short-term outcome was defined as intensive care unit admission and 30-day mortality. Long-term outcome was evaluated as 5-year all-cause mortality.

Results

Peak levels of all biomarkers were seen at hospital admission. Increased admission levels of C-reactive protein, PCT and calprotectin were associated with bacterial aetiology of CAP, while increased admission levels of PCT, PTX3 and presepsin were associated with adverse short-term outcome. In univariate and multivariate regression models, white blood cells and calprotectin at 6-week follow-up were predictors of 5-year all-cause mortality.

Conclusions

Calprotectin emerges as both a potential early marker of bacterial aetiology and a predictor for 5-year all-cause mortality in CAP, whereas PCT, PTX3 and presepsin may predict short-term outcome.

In 267 adults with community-acquired pneumonia, systemic calprotectin emerges as an early marker of bacterial aetiology and a predictor of 5-year mortality, whereas systemic procalcitonin, pentraxin 3 and presepsin are predictors of short-term outcomehttp://ow.ly/dz6S30nAFvn

The moderate predictive value of serial serum CRP and PCT levels for the prognosis of hospitalized community-acquired pneumonia

BACKGROUND

To predict the prognosis by observing the dynamic change of C-reactive protein (CRP) and procalcitonin (PCT) for hospitalized community-acquired pneumonia (CAP).

METHODS

The data were collected from January to December 2017 from the first affiliated Hospital of Zhengzhou University. Demographic and clinical patient information including age, length of hospital stay and Charlson Comorbidity Index (CCI) were recorded. Blood samples were taken for CRP, PCT, and white blood cell count (WBC). Receiver Operating Characteristic (ROC) curve was used to verify each biomarker's association with the prognosis of pneumonia.

RESULTS

A total of 350 patients were enrolled in the study. The 30-day mortality was 10.86%. Serial serum CRP3, CRP5, PCT3, PCT5 and PCT5c levels were statistically lower in CAP survivors than non-survivors. CRP3c < 0, CRP5c < 0 and PCT5c < 0 were observed with a statistically lower frequency in patients with 30-day mortality and initial treatment failure. The AUC for 30-day mortality for serial CRP levels combined with CRP clearances was 0.85 (95% CI 0.77-0.92), as compared to an AUC of 0.81 (95% CI 0.73-0.9) for serial PCT levels combined with PCT clearances.

CONCLUSIONS

Serum serial CRP and PCT levels had moderate predictive value for hospitalized CAP prognosis. The dynamic CRP and PCT changes may potentially be used in the future to predict hospitalized CAP prognosis.

A small pericardial effusion is a marker of complicated hospitalization in patients with community-acquired pneumonia

OBJECTIVE

Although often asymptomatic, presence of small pericardial effusion (SPE) is shown to be associated with adverse events and increased mortality in various conditions. This study aimed to evaluate the frequency and prognostic importance of SPE in a cohort of patients hospitalized for community-acquired pneumonia (CAP).

METHODS

We prospectively followed 154 consecutive adult patients hospitalized with CAP. The severity of CAP was evaluated with the pneumonia severity index (PSI) and the CURB-65 (confusion, urea, respiratory rate, arterial blood pressure and age) score. All patients underwent transthoracic echocardiography within the first 48h of admission. Patients were followed-up until hospital discharge or death. The outcomes of interest were length of stay in hospital and complicated hospitalization (CH) which is defined as intensive care unit admission, need for mechanical ventilation or in-hospital mortality. This study was registered with ClinicalTrials.gov, number NCT02441855.

RESULTS

A total 34 episodes of CHs occurred in 21 (13.6%) patients. Older patients and those with more co-morbid conditions such as diabetes, coronary artery diseases, cerebrovascular diseases, and chronic obstructive pulmonary diseases tended to have a higher rate of CH. Patients with CH had higher N-terminal pro-brain natriuretic peptide, troponin and creatinine levels on admission compared to patients without CH. Patients with CH had also higher CURB-65 and PSI scores and had longer durations of stay compared to patients with uncomplicated course. SPE was noted in 24 (15.6%) of the patients in our study cohort. Incidence of CH was greater for patients with a SPE (26 CHs occurred in 14 of the 24 patients) compared to those without an effusion (8 CHs occurred in 7 of the 130 patients, p<0.001). Logistic multivariate analysis revealed that the presence of SPE was an independent predictor of CH (OR: 3.26; 95% CI: 2.19-8.71; p=0.008).

CONCLUSION

This study is the first to demonstrate that the presence of SPE is associated with increased adverse events in patients with CAP.

A new method to predict hospital mortality in severe community acquired pneumonia

BACKGROUND & AIMS

The aim of this study is to develop a new method that is able to accurately predict the 28day hospital mortality in patients with severe community acquired pneumonia (SCAP) at an early stage.

METHODS

We selected 37,348 SCAP patients in ICU from 173 hospitals during 2011.1-2013.12. The predictive factors for 28day hospital mortality were evaluated retrospectively. All cases underwent intensive care, blood routine, blood biochemical tests and arterial blood gas analysis. Under the Classification and Regression Tree (CART) analysis, a new clinical scoring system was developed for early prediction in SCAP patients. The receiver-operating characteristic (ROC) curve was plotted to calculate the area under the receiver operating characteristic curve (AUC).

RESULTS

A novel clinical model named CLCGH scoring system, including Serum creatinine (Cr) >259.5μmol/L, leukocyte (WBC)>17.35×10⁹/L, C-reactive protein (CRP)>189.4μg/mL, GCS≤9 and serum HCO₃^-≤17.65mmol/L, was carried out and each index was an independent factor for hospital mortality in SCAP. In validation cohort, the AUC of the new scoring system was 0.889 for prediction of hospital mortality, which was similar to SOFA score 0.877, APACHEII score 0.864, and was better than the PSI score 0.761 and CURB-65 score 0.767.

CONCLUSIONS

The new scoring system CLCGH is an efficient, accurate and objective method to predicate the early hospital mortality among SCAP patients.

Prognostic value of transthoracic echocardiography and biomarkers of cardiac dysfunction in community-acquired pneumonia

The aim of this study was to determine the prognostic role of echocardiography and compare with admission N-terminal proB-type natriuretic peptide (NT-proBNP) levels in adult patients with community-acquired pneumonia (CAP). Consecutive adult patients hospitalized with CAP were prospectively enrolled and followed-up until hospital discharge or death. Echocardiography was performed within the first 48 hours. Complicated hospitalization (CH) was defined as intensive care unit admission, need for mechanical ventilation or in-hospital mortality. This study was registered with ClinicalTrials.gov, number NCT02441855. A total of 15 CH (13.5%) occurred among 111 patients with CAP. CAP patients with a CH compared with those without CH had significantly higher NT-proBNP values (1267.4±1146.1 vs. 305.6±545.7 pg/mL, p <0.001) and troponin I (23.8±24.3 vs. 10.3±6.3 ng/mL, p 0.02) but had lower left ventricle ejection fraction (52.7±8.7 vs. 60.5±6.7%, p <0.001) and tricuspid annular plane systolic excursion (TAPSE), which is a measure of right ventricular systolic function (17.1±4.4 vs. 21.8±4 mm; p 0.001). Patients with elevation of NT-proBNP and decreased TAPSE at presentation had a significantly higher probability of CH (60%) than patients with either elevated NT-proBNP or decreased TAPSE (40%). Patients with neither elevated NT-proBNP nor decreased TAPSE had a 0% probability of CH. This is the first study to demonstrate that decreased right ventricular systolic function is associated with increased rates of adverse events in patients with CAP.