In-Hospital Mortality and Morbidity in Cancer Patients with COVID-19: A Nationwide Analysis from the United States

Background: Coronavirus disease 2019 (COVID-19) caused significant mortality and mortality worldwide. There is limited information describing the outcomes of COVID-19 in cancer patients. Methods: We utilized the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (NIS) 2020 database to collect information on cancer patients hospitalized for COVID-19 in the United States. Using the International Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) coding system, adult (≥18 years) patients with COVID-19 were identified. Adjusted analyses were performed to assess for mortality, morbidity, and resource utilization among cancer patients. Results: A total of 1,050,045 patients were included. Of them, 27,760 had underlying cancer. Cancer patients were older and had more comorbidities. The all-cause in-hospital mortality rate in cancer patients was 17.58% vs. 11% in non-cancer. After adjusted logistic regression, cancer patients had a 21% increase in the odds of all-cause in-hospital mortality compared with those without cancer (adjusted odds ratio (aOR) 1.21, 95%CI 1.12–1.31, p-value < 0.001). Additionally, an increased odds in acute respiratory failure rate was found (aOR 1.14, 95%CI 1.06–1.22, p-value < 0.001). However, no significant differences were found in the odds of septic shock, acute respiratory distress syndrome, and mechanical ventilation between the two groups. Additionally, no significant differences in the mean length of hospital stay and the total hospitalization charges between cancer and non-cancer patients. Conclusion: Cancer patients hospitalized for COVID-19 had increased odds of all-cause in hospital mortality and acute respiratory failure compared with non-cancer patients

In-Hospital Mortality and Morbidity in Cancer Patients with COVID-19: A Nationwide Analysis from the United States

Background: Coronavirus disease 2019 (COVID-19) caused significant mortality and mortality worldwide. There is limited information describing the outcomes of COVID-19 in cancer patients. Methods: We utilized the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (NIS) 2020 database to collect information on cancer patients hospitalized for COVID-19 in the United States. Using the International Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) coding system, adult (≥18 years) patients with COVID-19 were identified. Adjusted analyses were performed to assess for mortality, morbidity, and resource utilization among cancer patients. Results: A total of 1,050,045 patients were included. Of them, 27,760 had underlying cancer. Cancer patients were older and had more comorbidities. The all-cause in-hospital mortality rate in cancer patients was 17.58% vs. 11% in non-cancer. After adjusted logistic regression, cancer patients had a 21% increase in the odds of all-cause in-hospital mortality compared with those without cancer (adjusted odds ratio (aOR) 1.21, 95%CI 1.12–1.31, p-value p-value < 0.001). However, no significant differences were found in the odds of septic shock, acute respiratory distress syndrome, and mechanical ventilation between the two groups. Additionally, no significant differences in the mean length of hospital stay and the total hospitalization charges between cancer and non-cancer patients. Conclusion: Cancer patients hospitalized for COVID-19 had increased odds of all-cause in hospital mortality and acute respiratory failure compared with non-cancer patients

Machine Learning for Prediction of Survival Outcomes with Immune-Checkpoint Inhibitors in Urothelial Cancer

Machine learning (ML) may enhance the efficiency of developing accurate prediction models for survival, which is critical in informing disease prognosis and care planning. This study aimed to develop an ML prediction model for survival outcomes in patients with urothelial cancer-initiating atezolizumab and to compare model performances when built using an expert-selected (curated) versus an all-in list (uncurated) of variables. Gradient-boosted machine (GBM), random forest, Cox-boosted, and penalised, generalised linear models (GLM) were evaluated for predicting overall survival (OS) and progression-free survival (PFS) outcomes. C-statistic (c) was utilised to evaluate model performance. The atezolizumab cohort in IMvigor210 was used for model training, and IMvigor211 was used for external model validation. The curated list consisted of 23 pretreatment factors, while the all-in list consisted of 75. Using the best-performing model, patients were stratified into risk tertiles. Kaplan–Meier analysis was used to estimate survival probabilities. On external validation, the curated list GBM model provided slightly higher OS discrimination (c = 0.71) than that of the random forest (c = 0.70), CoxBoost (c = 0.70), and GLM (c = 0.69) models. All models were equivalent in predicting PFS (c = 0.62). Expansion to the uncurated list was associated with worse OS discrimination (GBM c = 0.70; random forest c = 0.69; CoxBoost c = 0.69, and GLM c = 0.69). In the atezolizumab IMvigor211 cohort, the curated list GBM model discriminated 1-year OS probabilities for the low-, intermediate-, and high-risk groups at 66%, 40%, and 12%, respectively. The ML model discriminated urothelial-cancer patients with distinctly different survival risks, with the GBM applied to a curated list attaining the highest performance. Expansion to an all-in approach may harm model performance

The association of depression and anxiety with treatment outcomes in patients with rheumatoid arthritis – a pooled analysis of five randomised controlled trials

Background: Rheumatoid arthritis (RA) is an inflammatory autoimmune condition associated with an increased risk of developing depression and anxiety. Depression and anxiety are associated with worse outcomes in RA, but the magnitude of the effect of each condition on RA outcomes is unclear. It is also unknown how pharmacological treatment of depression affects RA outcomes. Objective: The primary aim of this study was to investigate the association of comorbid depression and anxiety with remission in patients with RA. Secondary aims were to determine the association between comorbid depression and anxiety on patient-reported outcomes and the relationship between concomitant use of antidepressants and remission in patients with depression. Design: Data from patients with moderate to severe RA were pooled from five randomised controlled trials investigating tocilizumab and conventional synthetic disease-modifying agents. Methods: Remission was defined as a clinical disease activity index (CDAI) of ⩽2.8 and simple disease activity index (SDAI) of ⩽3.3. The association between the time to reach remission and depression and anxiety was analysed using Cox proportional hazard analysis. Results: Individual patient data were available from 5502 subjects, of whom 511 had depression, 236 had anxiety and 387 were using antidepressants. Depression was significantly associated with reduced remission [adjusted HR (95% CI): 0.62 (0.48–0.80), p  < 0.001 and adjusted HR (95% CI): 0.59 (0.44–0.79), p  < 0.001] using CDAI and SDAI, respectively. Depression was associated with a lower likelihood of achieving more subjective outcomes (⩽1 physician global assessment, ⩽1 patient global assessment) and ⩽1 28-swollen joint count, but not ⩽1 28-tender joint count or C-reactive protein measurement. Treatment with antidepressants did not improve outcomes for patients with depression. Anxiety was not significantly associated with RA remission. Conclusion: Comorbid depression, but not anxiety, was associated with less frequent remission. Concomitant antidepressant use was not associated with improvements in RA outcomes in patients with depression

Prediction of severe neutropenia and diarrhoea in breast cancer patients treated with abemaciclib

Introduction: Neutropenia and diarrhoea are common and potentially serious adverse events associated with abemaciclib in advanced breast cancer (ABC), and the risk factors have been minimally explored. The study aimed to develop clinical prediction tools that allow personalized predictions of neutropenia and diarrhoea following abemaciclib initiation. Materials and methods: Data was pooled from MONARCH 1, 2 and 3 trials investigating abemaciclib. Cox proportional hazard analysis was used to assess the association between pre-treatment clinicopathological data and grade ≥3 diarrhoea and neutropenia occurring within the first 365 days of abemaciclib use. Results: Older age was associated with increased risk of grade ≥3 diarrhoea [HR [95%CI] for age > 70: 1.72 [1.14–2.58]; P = 0.009]. A clinical prediction tool for abemaciclib induced grade ≥3 neutropenia was optimally defined by race, ECOGPS and white blood cell count. Large discrimination between subgroups was observed; the highest risk subgroup had a 64% probability of grade ≥3 neutropenia within the first 365 days of abemaciclib (150 mg twice daily) + fulvestrant/NSAI, compared to 5% for the lowest risk subgroup. Conclusion: The study identified advanced age as significantly associated with an increased risk of abemaciclib induced grade ≥ 3 diarrhoea. A clinical prediction tool, defined by race, ECOGPS and pre-treatment white blood cell count, was able to discriminate subgroups with significantly different risks of grade ≥3 neutropenia following abemaciclib initiation. The tool may enable improved interpretation of personalized risks and the risk-benefit ratio of abemaciclib

Preclinical and Clinical Applications of Metabolomics and Proteomics in Glioblastoma Research

Glioblastoma (GB) is a primary malignancy of the central nervous system that is classified by the WHO as a grade IV astrocytoma. Despite decades of research, several aspects about the biology of GB are still unclear. Its pathogenesis and resistance mechanisms are poorly understood, and methods to optimize patient diagnosis and prognosis remain a bottle neck owing to the heterogeneity of the malignancy. The field of omics has recently gained traction, as it can aid in understanding the dynamic spatiotemporal regulatory network of enzymes and metabolites that allows cancer cells to adjust to their surroundings to promote tumor development. In combination with other omics techniques, proteomic and metabolomic investigations, which are a potent means for examining a variety of metabolic enzymes as well as intermediate metabolites, might offer crucial information in this area. Therefore, this review intends to stress the major contribution these tools have made in GB clinical and preclinical research and highlights the crucial impacts made by the integrative “omics” approach in reducing some of the therapeutic challenges associated with GB research and treatment. Thus, our study can purvey the use of these powerful tools in research by serving as a hub that particularly summarizes studies employing metabolomics and proteomics in the realm of GB diagnosis, treatment, and prognosis

Evaluation of Two Simultaneous Metabolomic and Proteomic Extraction Protocols Assessed by Ultra-High-Performance Liquid Chromatography Tandem Mass Spectrometry

Untargeted multi-omics analysis of plasma is an emerging tool for the identification of novel biomarkers for evaluating disease prognosis, and for developing a better understanding of molecular mechanisms underlying human disease. The successful application of metabolomic and proteomic approaches relies on reproducibly quantifying a wide range of metabolites and proteins. Herein, we report the results of untargeted metabolomic and proteomic analyses from blood plasma samples following analyte extraction by two frequently-used solvent systems: chloroform/methanol and methanol-only. Whole blood samples were collected from participants (n = 6) at University Hospital Sharjah (UHS) hospital, then plasma was separated and extracted by two methods: (i) methanol precipitation and (ii) 4:3 methanol:chloroform extraction. The coverage and reproducibility of the two methods were assessed by ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). The study revealed that metabolite extraction by methanol-only showed greater reproducibility for both metabolomic and proteomic quantifications than did methanol/chloroform, while yielding similar peptide coverage. However, coverage of extracted metabolites was higher with the methanol/chloroform precipitation

Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms

Skin cancer, including malignant melanoma (MM) and keratinocyte carcinoma (KC), historically named non-melanoma skin cancers (NMSC), represents the most common type of cancer among the white skin population. Despite decades of clinical research, the incidence rate of melanoma is increasing globally. Therefore, a better understanding of disease pathogenesis and resistance mechanisms is considered vital to accomplish early diagnosis and satisfactory control. The “Omics” field has recently gained attention, as it can help in identifying and exploring metabolites and metabolic pathways that assist cancer cells in proliferation, which can be further utilized to improve the diagnosis and treatment of skin cancer. Although skin tissues contain diverse metabolic enzymes, it remains challenging to fully characterize these metabolites. Metabolomics is a powerful omics technique that allows us to measure and compare a vast array of metabolites in a biological sample. This technology enables us to study the dermal metabolic effects and get a clear explanation of the pathogenesis of skin diseases. The purpose of this literature review is to illustrate how metabolomics technology can be used to evaluate the metabolic profile of human skin cancer, using a variety of analytical platforms including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance (NMR). Data collection has not been based on any analytical method