Inflammatory phenotypes may be more important than age and comorbidities in predicting clinical outcomes in hospitalised patients with COVID-19.

Objectives: In critically ill patients with COVID-19, distinct hyperinflammatory and hypoinflammatory phenotypes have been described, with different outcomes and responses to therapy. We investigated if similar phenotypes exist in non-severe illness. Methods: Consecutive patients with polymerase chain reaction (PCR) confirmed SARS-CoV-2 were examined. Baseline demographics and laboratory investigations were tabulated, including serum C-reactive protein. Patients were divided into those who were hyperinflammatory (defined as C-reactive protein >17 mg/l) or hypoinflammatory. Adverse outcomes, defined as requiring oxygenation, intensive care, or death, were recorded during the hospital stay. Clinical characteristics and outcomes were compared. Results: Of the 1781 patients examined, 276 (15.5%) had a hyperinflammatory phenotype. They were older (51.8 ± 17.2 vs 40.3 ± 13.8 years, P <0.001), had a lower PCR cycle threshold (PCR cycle threshold value 19.3 ± 6.3 vs 22.7 ± 15.4, P = 0.025) at presentation, and more medical comorbidities. The hyperinflammatory phenotype was independently associated with adverse clinical outcomes, even after adjusting for age, medical history and viral load on multivariable analyses (adjusted odds ratio 5.78, 95% confidence interval 2.86-11.63). Conclusion: Even in non-severe COVID-19, there are distinct hyper- and hypoinflammatory phenotypes, with the hyperinflammatory phenotype strongly associated with adverse clinical outcomes, that could be distinguished with a simple biomarker.

Frailty scoring in vascular and endovascular surgery: A systematic review.

One in 10 independently living adults aged 65 years old and older is considered frail, and frailty is associated with poor postoperative outcomes. This systematic review aimed to examine the association between frailty assessments and postoperative outcomes in patients with vascular disease. Electronic databases - MEDLINE, Embase, and the Cochrane Library - were searched from inception until January 2022, resulting in 648 articles reviewed for potential inclusion and 16 studies selected. Demographic data, surgery type, frailty measure, and postoperative outcomes predicted by frailty were extracted from the selected studies. The risk of bias was assessed using the Newcastle-Ottawa Scale. The selected studies (mean age: 56.1-76.3 years) had low-to-moderate risk of bias and included 16 vascular (elective and nonelective) surgeries and eight frailty measures. Significant associations (p < 0.05) were established between mortality (30-day, 90-day, 1-year, 5-year), 30-day morbidity, nonhome discharge, adverse events, failure to rescue, patient requiring care after discharge, and amputation following critical limb ischaemia. The strongest evidence was found between 30-day mortality and frailty. Composite 30-day morbidity and mortality, functional status at discharge, length of stay, spinal cord deficit, and access site complications were found to be nonsignificantly associated with frailty. With frailty being significantly associated with several adverse postoperative outcomes, preoperative frailty assessments can potentially be clinically useful in helping practitioners predict and guide the pre-, peri-, and postoperative management of frail with vascular disease.

Acute medical units during the first wave of the COVID-19 pandemic: a cross-national exploratory study of impact and responses.

BACKGROUND: The COVID-19 pandemic represents one of the greatest ever challenges for healthcare. In the UK and beyond, acute medical units (AMUs) are the first point of assessment and care for the majority of medical inpatients. By their design and systems, they inevitably played an important role in the COVID-19 response but to date little has been published on how the COVID-19 pandemic has affected how AMUs have reorganised their resources, processes and structure. METHODS: This retrospective study in August 2020 of 10 AMUs across Europe and Australasia used a standardised questionnaire to investigate existing practice and structure of AMUs, the national context of local hospital experience, changes to practice during the COVID-19 pandemic and views regarding future practice. RESULTS: Changes to AMU structure, process and organisation are described in two contexts: preventing and controlling the spread of COVID-19 and adding value to the patient's acute care journey in the local context. We describe novel practices that have arisen and highlight areas of concern. CONCLUSIONS: The AMUs were able to adapt to meet the demands of acute care delivery during the first wave of the COVID-19 pandemic. Operational planning and prioritisation of resources must be optimised to ensure sustainability of these services for future waves.

Validating a methodology to measure frailty syndromes at hospital level utilising administrative data.

BACKGROUND: Identifying older people with clinical frailty, reliably and at scale, is a research priority. We measured frailty in older people using a novel methodology coding frailty syndromes on routinely collected administrative data, developed on a national English secondary care population, and explored its performance of predicting inpatient mortality and long length of stay at a single acute hospital. METHODOLOGY: We included patient spells from Secondary User Service (SUS) data for those ≥65 years with attendance to the emergency department or admission to West Middlesex University Hospital between 01 July 2016 to 01 July 2017. We created eight groups of frailty syndromes using diagnostic coding groups. We used descriptive statistics and logistic regression to explore performance of diagnostic coding groups for the above outcomes. RESULTS: We included 17,199 patient episodes in the analysis. There was at least one frailty syndrome present in 7,004 (40.7%) patient episodes. The resultant model had moderate discrimination for inpatient mortality (area under the receiver operating characteristic curve (AUC) 0.74; 95% confidence interval (CI) 0.72-0.76) and upper quartile length of stay (AUC 0.731; 95% CI 0.722-0.741). There was good negative predictive value for inpatient mortality (98.1%). CONCLUSIONS: Coded frailty syndromes significantly predict outcomes. Model diagnostics suggest the model could be used for screening of elderly patients to optimise their care.

Patients' and providers' perceptions of the preventability of hospital readmission: a prospective, observational study in four European countries.

OBJECTIVES: Because of fundamental differences in healthcare systems, US readmission data cannot be extrapolated to the European setting: To investigate the opinions of readmitted patients, their carers, nurses and physicians on predictability and preventability of readmissions and using majority consensus to determine contributing factors that could potentially foresee (preventable) readmissions. DESIGN: Prospective observational study. Readmitted patients, their carers, and treating professionals were surveyed during readmission to assess the discharge process and the predictability and preventability of the readmission. Cohen's Kappa measured pairwise agreement of considering readmission as predictable/preventable by patients, carers and professionals. Subsequently, multivariable logistic regressionidentified factors associated with predictability/preventability. SETTING: 15 hospitals in four European countries PARTICIPANTS: 1398 medical patients readmitted unscheduled within 30 days MAIN OUTCOMES AND MEASURES: (1) Agreement between the interviewed groups on considering readmissions likely predictable or preventable;(2) Factors distinguishing predictable from non-predictable and preventable from non-preventable readmissions. RESULTS: The majority deemed 27.8% readmissions potentially predictable and 14.4% potentially preventable. The consensus on predictability and preventability was poor, especially between patients and professionals (kappas ranged from 0.105 to 0.173). The interviewed selected different factors as potentially associated with predictability and preventability. When a patient reported that he was ready for discharge during index admission, the readmission was deemed less likely by the majority (predictability: OR 0.55; 95% CI 0.40 to 0.75; preventability: OR 0.35; 95% CI 0.24 to 0.49). CONCLUSIONS: There is no consensus between readmitted patients, their carers and treating professionals about predictability and preventability of readmissions, nor associated risk factors. A readmitted patient reporting not feeling ready for discharge at index admission was strongly associated with preventability/predictability. Therefore, healthcare workers should question patients' readiness to go home timely before discharge.