Outcomes of second-tier rapid response activations in a tertiary referral hospital: A prospective observational study

Introduction: A second-tier rapid response team (RRT) is activated for patients who do not respond to first-tier measures. The premise of a tiered response is that first-tier responses by a ward team may identify and correct early states of deterioration or establish goals of care, thereby reducing unnecessary escalation of care to the RRT. Currently, utilisation and outcomes of tiered RRTs remain poorly described. Methods: A prospective observational study of adult patients (age ≥18 years) who required RRT activations was conducted from February 2018 to December 2019. Results: There were 951 consecutive RRT activations from 869 patients and 76.0% patients had a National Early Warning Score (NEWS) ≥5 at the time of RRT activation. The majority (79.8%) of patients required RRT interventions that included endotracheal intubation (12.7%), point-of-care ultrasound (17.0%), discussing goals of care (14.7%) and intensive care unit (ICU) admission (24.2%). Approximately 1 in 3 (36.6%) patients died during hospitalisation or within 30 days of RRT activation. In multivariate analysis, age ≥65 years, NEWS ≥7, ICU admission, longer hospitalisation days at RRT activation, Eastern Cooperative Oncology Group performance scores ≥3 (OR [odds ratio] 2.24, 95% CI [confidence interval] 1.45–3.46), metastatic cancer (OR 2.64, 95% CI 1.71–4.08) and haematological cancer (OR 2.78, 95% CI 1.84–4.19) were independently associated with mortality. Conclusion: Critical care interventions and escalation of care are common with second-tier RRTs. This supports the need for dedicated teams with specialised critical care services. Poor functional status, metastatic and haematological cancer are significantly associated with mortality, independent of age, NEWS and ICU admission. These factors should be considered during triage and goals of care discussion. Keywords: Clinical deterioration, critical care, intensive care, mortality, rapid response system, rapid response team

Efficacy of COVID-19 vaccines in immunocompromised patients: A systematic review and meta-analysis

Objective To compare the efficacy of COVID 19 vaccines between those with immunocompromised medical conditions and those who are immunocompetent Design Systematic review and meta-analysis Data sources PubMed, EMBASE, CENTRAL, CORD-19 and WHO COVID-19 research databases were searched for eligible comparative studies published between 1st December 2020 and 3rd September 2021. ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform were searched in August 2021 to identify registered yet unpublished or ongoing studies. Study selection Prospective observational studies which compared the efficacy of COVID-19 vaccination between those with immunocompromising medical conditions and those who were immunocompetent were included. Two reviewers independently screened for potentially eligible studies. Data extraction The primary outcomes of interest were cumulative incidence of seroconversion after first and second doses of COVID vaccination. Secondary outcomes included SARS-CoV-2 antibody titre level after first and second doses of COVID-19 vaccination. After duplicate data abstraction, a frequentist random effects meta-analysis was conducted. Risk of bias was assessed using the ROBINS-I tool. Certainty of evidence was assessed using the GRADE approach. Results After screening 3283 studies, 42 studies that met our inclusion criteria were identified. 18 immunocompromised cohorts from 17 studies reported seroconversion in immunocompromised patients compared to healthy controls after the first dose and 30 immunocompromised cohorts in 28 studies reporting data after the second dose. Among immunocompromised groups, in incremental order, transplant recipients had the lowest pooled risk ratio of 0.06 (95%CI: 0.04 to 0.09, I^2=0%, p=0.81) (GRADE=Moderate) followed by haematological cancer patients at 0.36 (95%CI: 0.21 to 0.62, I^2 = 89%, p<0.01) (GRADE=Moderate), solid cancer patients at 0.40 (95%CI: 0.31 to 0.52, I^2 = 63%, p=0.03) (GRADE=Moderate) and IMID patients at 0.66 (95%CI: 0.48 to 0.91, I^2=81%, p<0.01) (GRADE=Moderate). After the second dose, the lowest pooled risk ratio was again seen in transplant recipients at 0.29 (95%CI: 0.21 to 0.40, I^2=91%, p<0.01) (GRADE=Moderate), haematological cancer patients at 0.68 (95%CI: 0.57 to 0.80, I^2=68%, p=0.02) (GRADE=Low), IMID patients at 0.79 (95%CI: 0.72 to 0.786, I^2=87%, p<0.01) (GRADE=Low) and solid cancer at 0.92 (95%CI: 0.89 to 0.95, I^2=26%, p=0.25) (GRADE=Low). Conclusion Seroconversion rates and serological titres are significantly lower in immunocompromised patients with transplant recipients having the poorest outcomes. Additional strategies on top of the conventional 2-dose regimen will likely be warranted, such as a booster dose of the vaccine. Systematic review registration PROSPERO CRD42021272088

Transcriptional Regulation by the NFAT Family in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is a haematological cancer with poor outcomes due to a lack of efficacious targeted therapies. The Nuclear Factor of Activated T Cells (NFAT) family of transcription factors is well characterised as a regulator of the cell cycle and differentiation in the myeloid lineage. Recent evidence has demonstrated that NFAT family members may have roles in regulating AML leukemogenesis and resistance to targeted therapy in myeloid leukaemia. Furthermore, gene expression data from patient samples show that some NFATs are more highly expressed in poorly differentiated AML and after disease relapse, implying that the NFAT family may have roles in specific types of AML. This review outlines the evidence for the role of NFAT in healthy myeloid tissue and explores how NFAT might regulate AML pathogenesis, highlighting the potential to target specific NFAT proteins therapeutically in AML.

Risk of COVID-19 death in cancer patients: an analysis from Guy’s Cancer Centre and King’s College Hospital in London

Background Using an updated dataset with more patients and extended follow-up, we further established cancer patient characteristics associated with COVID-19 death. Methods Data on all cancer patients with a positive reverse transcription-polymerase chain reaction swab for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) at Guy’s Cancer Centre and King’s College Hospital between 29 February and 31 July 2020 was used. Cox proportional hazards regression was performed to identify which factors were associated with COVID-19 mortality. Results Three hundred and six SARS-CoV-2-positive cancer patients were included. Seventy-one had mild/moderate and 29% had severe COVID-19. Seventy-two patients died of COVID-19 (24%), of whom 35 died <7 days. Male sex [hazard ratio (HR): 1.97 (95% confidence interval (CI): 1.15–3.38)], Asian ethnicity [3.42 (1. 59–7.35)], haematological cancer [2.03 (1.16–3.56)] and a cancer diagnosis for >2–5 years [2.81 (1.41–5.59)] or ≥5 years were associated with an increased mortality. Age >60 years and raised C-reactive protein (CRP) were also associated with COVID-19 death. Haematological cancer, a longer-established cancer diagnosis, dyspnoea at diagnosis and raised CRP were indicative of early COVID-19-related death in cancer patients (<7 days from diagnosis). Conclusions Findings further substantiate evidence for increased risk of COVID-19 mortality for male and Asian cancer patients, and those with haematological malignancies or a cancer diagnosis >2 years. These factors should be accounted for when making clinical decisions for cancer patients.

In-hospital mortality among immunosuppressed patients with COVID-19: Analysis from a national cohort in Spain

Background Whether immunosuppressed (IS) patients have a worse prognosis of COVID-19 compared to non-IS patients is not known. The aim of this study was to evaluate the clinical characteristics and outcome of IS patients hospitalized with COVID-19 compared to non-IS patients. Methods We designed a retrospective cohort study. We included all patients hospitalized with laboratory-confirmed COVID-19 from the SEMI-COVID-19 Registry, a large multicentre national cohort in Spain, from March 27th until June 19th, 2020. We used multivariable logistic regression to assess the adjusted odds ratios (aOR) of in-hospital death among IS compared to non-IS patients. Results Among 13 206 included patients, 2 111 (16.0%) were IS. A total of 166 (1.3%) patients had solid organ (SO) transplant, 1081 (8.2%) had SO neoplasia, 332 (2.5%) had hematologic neoplasia, and 570 (4.3%), 183 (1.4%) and 394 (3.0%) were receiving systemic steroids, biological treatments, and immunosuppressors, respectively. Compared to non-IS patients, the aOR (95% CI) for in-hospital death was 1.60 (1.43–1.79) for all IS patients, 1.39 (1.18–1.63) for patients with SO cancer, 2.31 (1.76–3.03) for patients with haematological cancer and 3.12 (2.23–4.36) for patients with SO transplant. The aOR (95% CI) for death for patients who were receiving systemic steroids, biological treatments and immunosuppressors compared to non-IS patients were 2.16 (1.80–2.61), 1.97 (1.33–2.91) and 2.06 (1.64–2.60), respectively. IS patients had a higher odds than non-IS patients of in-hospital acute respiratory distress syndrome, heart failure, myocarditis, thromboembolic disease and multiorgan failure. Conclusions IS patients hospitalized with COVID-19 have a higher odds of in-hospital complications and death compared to non-IS patients.

A Computer-Based Methodology to Design Non-Standard Peptides Potentially Able to Prevent HOX-PBX1-Associated Cancer Diseases

In the last decades, HOX proteins have been extensively studied due to their pivotal role in transcriptional events. HOX proteins execute their activity by exploiting a cooperative binding to PBX proteins and DNA. Therefore, an increase or decrease in HOX activity has been associated with both solid and haematological cancer diseases. Thus, inhibiting HOX-PBX interaction represents a potential strategy to prevent these malignancies, as demonstrated by the patented peptide HTL001 that is being studied in clinical trials. In this work, a computational study is described to identify novel potential peptides designed by employing a database of non-natural amino acids. For this purpose, residue scanning of the HOX minimal active sequence was performed to select the mutations to be further processed. According to these results, the peptides were point-mutated and used for Molecular Dynamics (MD) simulations in complex with PBX1 protein and DNA to evaluate complex binding stability. MM-GBSA calculations of the resulting MD trajectories were exploited to guide the selection of the most promising mutations that were exploited to generate twelve combinatorial peptides. Finally, the latter peptides in complex with PBX1 protein and DNA were exploited to run MD simulations and the ΔGbinding average values of the complexes were calculated. Thus, the analysis of the results highlighted eleven combinatorial peptides that will be considered for further assays.

CORONET; COVID-19 in Oncology evaluatiON Tool: Use of machine learning to inform management of COVID-19 in patients with cancer.

1505 Background: Patients (pts) with cancer are at increased risk of severe COVID-19 infection and death. Due to COVID-19 outcome heterogeneity, accurate assessment of pts is crucial. Early identification of pts who are likely to deteriorate allows timely discussions regarding escalation of care. Likewise, safe home management will reduce risk of nosocomial infection. To aid clinical decision-making, we developed a model to help determine which pts should be admitted vs. managed as an outpatient and which pts are likely to have severe COVID-19. Methods: Pts with active solid or haematological cancer presenting with symptoms/asymptomatic and testing positive for SARS-CoV-2 in Europe and USA were identified following institutional board approval. Clinical and laboratory data were extracted from pt records. Clinical outcome measures were discharge within 24 hours, requirement for oxygen at any stage during admission and death. Random Forest (RF) algorithm was used for model derivation as it compared favourably vs. lasso regression. Relevant clinical features were identified using recursive feature elimination based on SHAP. Internal validation (bootstrapping) with multiple imputations for missing data (maximum ≤2) were used for performance evaluation. Cost function determined cut-offs were defined for admission/death. The final CORONET model was trained on the entire cohort. Results: Model derivation set comprised 672 pts (393 male, 279 female, median age 71). 83% had solid cancers, 17% haematological. Predictive features were selected based on clinical relevance and data availability, supported by recursive feature elimination based on SHAP. RF model using haematological cancer, solid cancer stage, no of comorbidities, National Early Warning Score 2 (NEWS2), neutrophil:lymphocyte ratio, platelets, CRP and albumin achieved AUROC for admission 0.79 (+/-0.03) and death 0.75 (+/-0.02). RF explanation using SHAP revealed NEWS2 and C-reactive protein as the most important features predicting COVID-19 severity. In the entire cohort, CORONET recommended admission of 96% of patients requiring oxygen and 99% of patients who died. We then built a decision support tool using the model, which aids clinical decisions by presenting model predictions and explaining key contributing features. Conclusions: We have developed a model and tool available at https://coronet.manchester.ac.uk/ to predict which pts with cancer and COVID-19 require hospital admission and are likely to have a severe disease course. CORONET is being continuously refined and validated over time.