Plasma-induced nanoparticle aggregation for stratifying COVID-19 patients according to disease severity

Stratifying patients according to disease severity has been a major hurdle during the COVID-19 pandemic. This usually requires evaluating the levels of several biomarkers, which may be cumbersome when rapid decisions are required. In this manuscript we show that a single nanoparticle aggregation test can be used to distinguish patients that require intensive care from those that have already been discharged from the intensive care unit (ICU). It consists of diluting a platelet-free plasma sample and then adding gold nanoparticles. The nanoparticles aggregate to a larger extent when the samples are obtained from a patient in the ICU. This changes the color of the colloidal suspension, which can be evaluated by measuring the pixel intensity of a photograph. Although the exact factor or combination of factors behind the different aggregation behavior is unknown, control experiments demonstrate that the presence of proteins in the samples is crucial for the test to work. Principal component analysis demonstrates that the test result is highly correlated to biomarkers of prognosis and inflammation that are commonly used to evaluate the severity of COVID-19 patients. The results shown here pave the way to develop nanoparticle aggregation assays that classify COVID-19 patients according to disease severity, which could be useful to de-escalate care safely and make a better use of hospital resources.

Absolute lymphocyte count as a predictor of mortality and readmission in heart failure hospitalization

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Lymphopenia (<1500/mm³) was associated with 82% higher mortality in heart failure patients irrespective of ejection fraction.

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Lymphopenia was a good predictor of all-cause readmission in heart failure patients with reduced ejection fraction.

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Due to cost-effectiveness, easy availability, and ability to predict outcomes in the short-term and medium-term, lymphopenia can be a valuable tool in the mortality, readmission prediction model of heart failure.

Background

There is renewed interest in pursuing frugal and readily available laboratory markers to predict mortality and readmission in heart failure. We aim to determine the relationship between absolute lymphocyte count (ALC) and clinical outcomes in patients with heart failure hospitalization.

Methods

This was a retrospective cohort study of patients with heart failure. Patients were divided into two groups based on ALC, less than or equal to 1500 cells/mm3 and > 1500 cells/ mm3. The primary outcome was all-cause mortality. We did subgroup analysis based on ejection fraction and studied the association between ALC categories and clinical outcomes. Both ALC groups are matched by propensity score, outcomes were analyzed by Cox regression, and estimates are presented in hazard ratios (HR) and 95% confidence intervals (CI).

Results

We included 1029 patients in the pre-matched cohort and 766 patients in the propensity-score matched cohort. The median age was 64 years (IQR, 54–75), and 60.78% were male. In the matched cohort, ALC less than or equal to 1500 cells/mm3 had a higher risk of mortality compared with ALC > 1500 cells/mm3 (HR 1.51, 95% CI: 1.17–1.95; P = 0.002). These results were reproducible in subgroups of heart failure. When ALC was divided into four groups based on their levels, the lowest group of ALC had the highest risk of mortality.

Conclusions

In patients with heart failure and both subgroups, ALC less than or equal to 1500 cells/mm3 had a higher risk of mortality. Patients in lower groups of the ALC categories had a higher risk of mortality.

Predictive Value of the Platelet-to-Lymphocyte Ratio in Cancer Patients Undergoing Transcatheter Aortic Valve Replacement

Objectives

The purpose of this study is to investigate the predictive value of the platelet-to-lymphocyte ratio (PLR)in cancer patients undergoing transcatheter aortic valve replacement (TAVR).

Background

The PLR is a promising marker to predict clinical outcomes in various cancer types as well as in cardiovascular disease.

Methods

Consecutive TAVR patients were enrolled in the study. We stratified patients into 2 groups: cancer and noncancer. Baseline complete blood counts with a differential hemogram were collected before TAVR. The primary outcome was all-cause death within a 3-year follow-up.

Results

In total, 240 of 1,204 patients (19.9%) had a cancer history. Cancer patients had a significantly higher baseline PLR than noncancer patients (median [interquartile range], 159.8 [109.6 to 244.6] vs. 150.3 [108.7 to 209.0]; p = 0.024). Kaplan-Meier analysis revealed that cancer patients had worse outcomes than noncancer patients (log-rank p < 0.001). Patients who died had a significantly higher baseline PLR than those who survived both in the cancer (p = 0.009) and noncancer (p = 0.027) groups. Multivariable analyses showed that the PLR (by 100 increase) was an independent predictor of adverse outcomes in both cancer (hazard ratio: 1.07; 95% confidence interval: 1.02 to 1.13; p = 0.006) and noncancer (hazard ratio: 1.20; 95% confidence interval: 1.06 to 1.36; p = 0.004). The highest mortality was observed for patients with cancer and increased PLR (above the median) (log-rank p < 0.001).

Conclusions

Cancer patients undergoing TAVR had a significantly higher PLR than those without cancer. Higher PLR was associated with a worse outcome following TAVR.

Pre-operative lymphocyte-to-monocyte ratio as a predictor of overall survival in patients suffering from osteosarcoma. FEBS Open Bio. 2015;5:682-687. [PMID: 26380812 PMCID: PMC4556728 DOI: 10.1016/j.fob.2015.08.002]

Inflammatory markers have been proposed to predict clinical outcomes in many types of cancers. The purpose of this study was to explore the influence of the lymphocyte-to-monocyte ratio (LMR) on clinical prognosis of patients with osteosarcoma. This study collected 327 patients who underwent surgical treatment for osteosarcoma during the period 2006-2010. LMR was calculated from pre-operative peripheral blood cells counts. The optimal cut-off value of LMR was determined based on receiver operating characteristic curve analysis. Overall survival (OS) and event free survival (EFS) was plotted using the Kaplan-Meier method and evaluated by the log-rank test. A predictive model was established to predict clinical prognosis for OS, and the predictive accuracy of this model was determined by concordance index (c-index). Our results showed that young age, elevated alkaline phosphatase, metastasis at diagnosis, chemotherapy, lymphocyte and monocyte counts were significantly associated with LMR. Low LMR was associated with shorter OS and EFS (P < 0.001), and was an independent predictor of both OS and EFS (HR = 1.72, 95% CI = 1.14-2.60, P = 0.010; HR = 1.89, 95% CI = 1.32-2.57, P = 0.009). The nomogram performed well in the prediction of overall survival in patients with osteosarcoma (c-index 0.630). In conclusion, low pre-operative LMR is associated with a poor prognosis in patients suffering from osteosarcoma. A prospective study is warranted for further validation of our results.