Correlation of inflammatory and cardiovascular biomarkers with pneumonia severity scores

Role of the inflammatory response in community-acquired pneumonia: clinical implications

INTRODUCTION

Despite adequate antibiotic coverage, community-acquired pneumonia (CAP) remains a leading cause of hospitalization and mortality worldwide. It induces both a local pulmonary and a systemic inflammatory response, particularly significant in severe cases. The intensity of the dysregulated host response varies from patient to patient and has a negative impact on survival and other outcomes.

AREAS COVERED

This comprehensive review summarizes the pathophysiological aspects of the inflammatory response in CAP, briefly discusses the usefulness of biomarkers, and assesses the clinical evidence for modulating the inflammatory pathways. We searched PubMed for the most relevant studies, reviews, and meta-analysis until August 2020.

EXPERT OPINION

Notable efforts have been made to identify biomarkers that can accurately differentiate between viral and bacterial etiology, and indeed, to enhance risk stratification in CAP. However, none has proven ideal and no recommended biomarker-guided algorithms exist. Biomarker signatures from proteomic and metabolomic studies could be more useful for such assessments. To date, most studies have produced contradictory results concerning the role of immunomodulatory agents (e.g. corticosteroids, macrolides, and statins) in CAP. Adequately identifying the population who may benefit most from effective modulation of the inflammatory response remains a challenge.

COVID-19 mortality risk assessment: An international multi-center study

Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.

Higher Serum C Reactive Protein Determined C Reactive Protein Single-Nucleotide Polymorphisms Are Involved in Inherited Depression

OBJECTIVE

The pathogenesis of depression is not fully understood yet, but studies have suggested higher circulating C reactive protein (CRP) level might relate to depression occurrence. However, due to high variability of patients' individual condition, the results to date are inconsistent. Considering CRP single-nucleotide polymorphisms (SNPs) could also regulate plasma CRP levels, in the present study, we hypothesized that inherited CRP allelic variations may co-vary with depressive symptomatology.

METHODS

We recruited 60 depression patients with family depression history and 60 healthy control volunteers into this project. We detected circulation CRP level as well as genome CRP SNPs from participants of this project.

RESULTS

We have found a significantly higher circulating CRP level in patients with a positive family history. Furthermore, we also identified some certain inherited CRP SNPs (A allele in rs1417938 and C allele in rs1205) could up regulate serum CRP level and distributed more in depression patients with family history.

CONCLUSION

Our finding may raise new evidence that genetically increased serum CRP level through SNPs variation is likely to induce family inherited depression.

Depression is Associated with CRP SNPs in Patients with Family History

Objective

The pathogenesis of depression is not fully understood, but studies have suggested that higher circulating levels of C reactive protein (CRP) might relate to depression occurrence. However, due to the highly variability of individual patients’ conditions, the results to date are inconsistent. Considering Single nucleotide polymorphisms (SNPs) of CRP gene have also been suggested to predict plasma CRP levels. In the present study, we hypothesize that inherited CRP allelic variations may co-vary with depressive symptomatology.

Methods

We recruited patients with a diagnosis of depression, with or without family depression history. We then detected serum CRP levels, as well as genome CRP SNPs from participants of this project.

Results

We found a significantly higher circulating CRP levels in patients with a positive family history. Furthermore, we also identified certain inherited CRP SNPs (A allele in rs1417938 and C allele in rs1205) which could up-regulate serum CRP levels and thus be associated with depression occurrence.

Conclusion

Our findings raise new evidence for the relationship between circulating CRP level and depression occurrence.

Prognostic value of mid-regional pro-adrenomedullin (MR-proADM) in patients with community-acquired pneumonia: a systematic review and meta-analysis

Background

The early identification of patients at risk of dying from community-acquired pneumonia (CAP) is critical for their treatment and for defining hospital resource consumption. Mid-regional pro-adrenomedullin (MR-proADM) has been extensively investigated for its prognostic value in CAP. However, the results are conflicting. The purpose of the present meta-analysis was to explore the diagnostic accuracy of MR-proADM for predicting mortality in patients suffering from CAP, particularly emergency department (ED) patients.

Method

We systematically searched the PubMed, Embase, Web of Knowledge and Cochrane databases. Studies were included if a 2 × 2 contingency table could be constructed based on both the MR-proADM level and the complications or mortality of patients diagnosed with CAP. The prognostic accuracy of MR-proADM in CAP was assessed using the bivariate meta-analysis model. We used the Q-test and I² index to evaluate heterogeneity.

Results

MR-proADM displayed moderate diagnostic accuracy for predicting complications in CAP, with an overall area under the SROC curve (AUC) of 0.74 (95 % CI: 0.70–0.78). Eight studies with a total of 4119 patients in the emergency department (ED) were included. An elevated MR-proADM level was associated with increased risk of death from CAP (RR 6.16, 95 % CI 4.71–8.06); the I² value was 0.0 %, and a fixed-effects model was used to pool RR. The pooled sensitivity and specificity were 0.74 (95 % CI: 0.67–0.79) and 0.73 (95 % CI: 0.70–0.77), respectively. The positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 2.8 (95 % CI, 2.3–3.3) and 0.36 (95 % CI, 0.29–0.45), respectively. In addition, the diagnostic odds ratio (DOR) was 8 (95 % CI, 5–11), and the overall area under the SROC curve was 0.76 (95 % CI, 0.72–0.80).

Conclusions

Our study has demonstrated that MR-proADM is predictive of increased complications and higher mortality rates in patients suffering from CAP. Future studies are warranted to determine the prognostic accuracy of MR-proADM in conjunction with severity scores or other biomarkers and to determine an optimal cut-off level.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1566-3) contains supplementary material, which is available to authorized users.