Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients.

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.

Longitudinal nucleocapsid antibody testing reveals undocumented SARS-CoV-2 infections in patients with lung cancer.

Patients diagnosed with lung cancer (LC) exhibit increased susceptibility to SARS-CoV-2 infection. Rodilla et al. monitor the levels of plasma anti-nucleocapsid antibodies within a cohort of fully vaccinated LC patients and reveal that the actual infection rate is nearly twice the documented rate, indicating a significant prevalence of unreported cases.

A Combination of Inflammatory and Hematological Markers is Strongly Associated with the Risk of Death in Both Mild and Severe Initial Disease in Unvaccinated Individuals with COVID-19 Infection.

Background: Inflammatory and hematological markers are used extensively for early prognostication and monitoring in COVID-19.We aimed to determine whether routinely prescribed laboratory markers can predict adverse outcome at presentation in COVID-19. Methods: This retrospective observational study was performed on 401 samples collected between July to December 2020 from COVID-19 positive subjects, admitted at All India Institute of Medical Sciences, Delhi, India. Clinical details and laboratory investigations within 3 days of COVID-19 positivity were obtained. Clinical outcomes were noted from patient medical records, till discharge or death. Laboratory parameters, with individually defined cut-offs, were used, either singly or in combination to distinguish survival and death for those having severe and non-severe disease at initial presentation. Findings: Total Leukocyte count, Absolute neutrophil count, Neutrophil to Lymphocyte ratio, C-Reactive Protein (CRP), Interleukin-6 (IL-6), Lactate Dehydrogenase, Ferritin and Lymphocyte to CRP ratio (LCR) were significantly altered at presentation in severe COVID-19 as compared to non-severe cases; and, also in those who died due to COVID-19 compared to those who survived. A combination of four markers, CRP (≥3.9mg/dL); IL-6 (≥45.37pg/ml); Ferritin (≥373ng/mL); 1/LCR ≥0.405 was found to strongly predict mortality in cases with non-severe presentation as also in severe cases. Conclusion and Interpretation: The combination of routinely used markers, CRP, IL-6, Ferritin and 1/LCR can be used to predict adverse outcomes, even in those presenting with mild to moderate disease. This would identify subset of patients who would benefit from closer monitoring than usual for non-severe disease.

Serological Response to SARS-CoV-2 after COVID-19 Vaccination in Lung Cancer Patients: Short Review.

In comparison to the general population, lung cancer patients are more likely to suffer from severe Coronavirus disease (COVID-19) and associated mortality. Considering this increased risk, and in order to prevent symptoms and severe disease, patients with lung cancer have been prioritized for COVID-19 vaccination primary and booster doses. Despite this, the pivotal clinical trials did not include these patients, which leaves open questions regarding vaccine efficacy and humoral immune response. This review outlines the findings of recent investigations into the humoral responses of lung cancer patients to COVID-19 vaccination, particularly the primary doses and first boost.

Predictive Capability of PD-L1 Protein Expression for Patients With Advanced NSCLC: Any Differences Based on Histology?

Lung cancer is responsible for 1.8 million annual deaths. Non-small cell lung cancers (NSCLC) represent 85% of lung cancer tumors. While surgery is an effective early-stage treatment, the majority of newly identified US lung cancer cases are stage III/IV. Immunotherapy, using programmed death-ligand 1 (PD-L1) or programmed death 1 (PD-1) receptor antibody therapeutics, has increased survival for patients with NSCLC. PD-L1 protein expression is widely used as a predictive biomarker informing treatment decisions. However, only a minority of patients (27%-39%) respond to PD-L1/PD-1 treatment. PD-L1 protein expression by immunohistochemistry assay has deficiencies in identifying responding and refractory patients. Given the different characteristics of squamous and nonsquamous NSCLC, the predictability of PD-L1 levels in determining which patients would benefit from immunotherapy could vary between the 2 histologies. We analyzed 17 phase-III clinical studies and a retrospective study to determine if the predictive capability of PD-L1 expression varies between squamous and nonsquamous NSCLC. For patients with NSCLC treated with mono or dual-immune checkpoint inhibitors (ICI), PD-L1 expression was more predictive of benefit for patients with nonsquamous NSCLC than squamous NSCLC. Patients with nonsquamous histology and PD-L1 high tumor proportion scores (TPS) survived 2.0x longer compared to those with low TPS, when treated with monotherapy ICI. Among patients with squamous NSCLC, that difference was 1.2 to 1.3x. For patients treated with ICIs and chemotherapy, there was no clear difference in the predictive value of PD-L1 levels between histologies. We encourage future researchers to analyze the predictability of PD-L1 biomarker expression separately for squamous and nonsquamous NSCLC.