Changes in COVID-19 in-hospital mortality in hospitalised adults in England over the first seven months of the pandemic: An observational study using administrative data

A COVID-19 specific multiparametric and ECG-based score for the prediction of in-hospital mortality: ELCOVID score

We aimed to develop and validate a COVID-19 specific scoring system, also including some ECG features, to predict all-cause in-hospital mortality at admission. Patients were retrieved from the ELCOVID study (ClinicalTrials.gov identifier: NCT04367129), a prospective, multicenter Italian study enrolling COVID-19 patients between May to September 2020. For the model validation, we randomly selected two-thirds of participants to create a derivation dataset and we used the remaining one-third of participants as the validation set. Over the study period, 1014 hospitalized COVID-19 patients (mean age 74 years, 61% males) met the inclusion criteria and were included in this analysis. During a median follow-up of 12 (IQR 7-22) days, 359 (35%) patients died. Age (HR 2.25 [95%CI 1.72-2.94], p < 0.001), delirium (HR 2.03 [2.14-3.61], p = 0.012), platelets (HR 0.91 [0.83-0.98], p = 0.018), D-dimer level (HR 1.18 [1.01-1.31], p = 0.002), signs of right ventricular strain (RVS) (HR 1.47 [1.02-2.13], p = 0.039) and ECG signs of previous myocardial necrosis (HR 2.28 [1.23-4.21], p = 0.009) were independently associated to in-hospital all-cause mortality. The derived risk-scoring system, namely EL COVID score, showed a moderate discriminatory capacity and good calibration. A cut-off score of ≥ 4 had a sensitivity of 78.4% and 65.2% specificity in predicting all-cause in-hospital mortality. ELCOVID score represents a valid, reliable, sensitive, and inexpensive scoring system that can be used for the prognostication of COVID-19 patients at admission and may allow the earlier identification of patients having a higher mortality risk who may be benefit from more aggressive treatments and closer monitoring.

Risk of COVID-19 death for people with a pre-existing cancer diagnosis prior to COVID-19-vaccination: A systematic review and meta-analysis

While previous reviews found a positive association between pre-existing cancer diagnosis and COVID-19-related death, most early studies did not distinguish long-term cancer survivors from those recently diagnosed/treated, nor adjust for important confounders including age. We aimed to consolidate higher-quality evidence on risk of COVID-19-related death for people with recent/active cancer (compared to people without) in the pre-COVID-19-vaccination period. We searched the WHO COVID-19 Global Research Database (20 December 2021), and Medline and Embase (10 May 2023). We included studies adjusting for age and sex, and providing details of cancer status. Risk-of-bias assessment was based on the Newcastle-Ottawa Scale. Pooled adjusted odds or risk ratios (aORs, aRRs) or hazard ratios (aHRs) and 95% confidence intervals (95% CIs) were calculated using generic inverse-variance random-effects models. Random-effects meta-regressions were used to assess associations between effect estimates and time since cancer diagnosis/treatment. Of 23 773 unique title/abstract records, 39 studies were eligible for inclusion (2 low, 17 moderate, 20 high risk of bias). Risk of COVID-19-related death was higher for people with active or recently diagnosed/treated cancer (general population: aOR = 1.48, 95% CI: 1.36-1.61, I2  = 0; people with COVID-19: aOR = 1.58, 95% CI: 1.41-1.77, I2  = 0.58; inpatients with COVID-19: aOR = 1.66, 95% CI: 1.34-2.06, I2  = 0.98). Risks were more elevated for lung (general population: aOR = 3.4, 95% CI: 2.4-4.7) and hematological cancers (general population: aOR = 2.13, 95% CI: 1.68-2.68, I2  = 0.43), and for metastatic cancers. Meta-regression suggested risk of COVID-19-related death decreased with time since diagnosis/treatment, for example, for any/solid cancers, fitted aOR = 1.55 (95% CI: 1.37-1.75) at 1 year and aOR = 0.98 (95% CI: 0.80-1.20) at 5 years post-cancer diagnosis/treatment. In conclusion, before COVID-19-vaccination, risk of COVID-19-related death was higher for people with recent cancer, with risk depending on cancer type and time since diagnosis/treatment.

Regional variation of COVID-19 admissions, acute kidney injury and mortality in England - a national observational study using administrative data

BACKGROUND

This study explores regional variations in COVID-19 hospitalization rates, in-hospital mortality, and acute kidney injury (AKI) in England. We investigated the influence of population demographic characteristics, viral strain changes, and therapeutic advances on clinical outcomes.

METHODS

Using hospital episode statistics, we conducted a retrospective cohort study with 749,844 admissions in 337,029 adult patients with laboratory-confirmed COVID-19 infection (March 1, 2020, to March 31, 2021). Multivariable logistic regression identified factors predicting AKI and mortality in COVID-19 hospitalized patients.

RESULTS

London had the highest number of COVID-19 admissions (131,338, 18%), followed by the North-west region (122,683, 16%). The North-west had the highest population incidence of COVID-19 hospital admissions (21,167 per million population, pmp), while the South-west had the lowest (9,292 admissions pmp). Patients in London were relatively younger (67.0 ± 17.7 years) than those in the East of England (72.2 ± 16.8 years). The shortest length of stay was in the North-east (12.2 ± 14.9 days), while the longest was in the North-west (15.2 ± 17.9 days). All eight regions had higher odds of death compared to London, ranging from OR 1.04 (95% CI 1.00, 1.07) in the South-west to OR 1.24 (95% CI 1.21, 1.28) in the North-west. Older age, Asian ethnicity, emergency admission, transfers from other hospitals, AKI presence, ITU admission, social deprivation, and comorbidity were associated with higher odds of death. AKI incidence was 30.3%, and all regions had lower odds of developing AKI compared to London. Increasing age, mixed and black ethnicity, emergency admission, transfers from other providers, ITU care, and different levels of comorbidity were associated with higher odds of developing AKI.

CONCLUSIONS

London exhibited higher hospital admission numbers and AKI incidence, but lower odds of death compared to other regions in England.

TRIAL REGISTRATION

Registered on National Library of Medicine website ( www.

CLINICALTRIALS

gov ) with registration number NCT04579562 on 8/10/2020.

Expert Consensus: Main Risk Factors for Poor Prognosis in COVID-19 and the Implications for Targeted Measures against SARS-CoV-2

The clinical evolution of patients infected with the Severe Acute Respiratory Coronavirus type 2 (SARS-CoV-2) depends on the complex interplay between viral and host factors. The evolution to less aggressive but better-transmitted viral variants, and the presence of immune memory responses in a growing number of vaccinated and/or virus-exposed individuals, has caused the pandemic to slowly wane in virulence. However, there are still patients with risk factors or comorbidities that put them at risk of poor outcomes in the event of having the coronavirus infectious disease 2019 (COVID-19). Among the different treatment options for patients with COVID-19, virus-targeted measures include antiviral drugs or monoclonal antibodies that may be provided in the early days of infection. The present expert consensus is based on a review of all the literature published between 1 July 2021 and 15 February 2022 that was carried out to establish the characteristics of patients, in terms of presence of risk factors or comorbidities, that may make them candidates for receiving any of the virus-targeted measures available in order to prevent a fatal outcome, such as severe disease or death. A total of 119 studies were included from the review of the literature and 159 were from the additional independent review carried out by the panelists a posteriori. Conditions found related to strong recommendation of the use of virus-targeted measures in the first days of COVID-19 were age above 80 years, or above 65 years with another risk factor; antineoplastic chemotherapy or active malignancy; HIV infection with CD4+ cell counts < 200/mm³; and treatment with anti-CD20 immunosuppressive drugs. There is also a strong recommendation against using the studied interventions in HIV-infected patients with a CD4+ nadir <200/mm³ or treatment with other immunosuppressants. Indications of therapies against SARS-CoV-2, regardless of vaccination status or history of infection, may still exist for some populations, even after COVID-19 has been declared to no longer be a global health emergency by the WHO.

Analysing COVID-19 treatment outcomes in dedicated wards at a large university hospital in northern Poland: a result-based observational study

OBJECTIVES

Presenting outcomes of patients hospitalised for COVID-19 should be put in context and comparison with other facilities. However, varied methodology applied in published studies can impede or even hinder a reliable comparison. The aim of this study is to share our experience in pandemic management and highlight previously under-reported factors affecting mortality. We present outcomes of COVID-19 treatment in our facility that will allow for an intercentre comparison. We use simple statistical parameters-case fatality ratio (CFR) and length of stay (LOS).

SETTING

Large clinical hospital in northern Poland serving over 120 000 patients annually.

PARTICIPANTS

Data were collected from patients hospitalised in COVID-19 general and intensive care unit (ICU) isolation wards from November 2020 to June 2021. The sample consisted of 640 patients-250 (39.1 %) were women and 390 (60.9 %) were men, with a median age of 69 (IQR 59-78) years.

RESULTS

Values of LOS and CFR were calculated and analysed. Overall CFR for the analysed period was 24.8%, varying from 15.9 % during second quarter 2021 to 34.1% during fourth quarter 2020. The CFR was 23.2% in the general ward and 70.7% in the ICU. All ICU patients required intubation and mechanical ventilation, and 44 (75.9 %) of them developed acute respiratory distress syndrome. The average LOS was 12.6 (±7.5) days.

CONCLUSIONS

We highlighted the importance of some of the under-reported factors affecting CFR, LOS and thus, mortality. For further multicentre analysis, we recommend broad analysis of factors affecting mortality in COVID-19 using simple and transparent statistical and clinical parameters.

A Meta-Analysis on the Association between Peptic Ulcer Disease and COVID-19 Severity

The association between peptic ulcer disease and the severity of coronavirus disease 2019 (COVID-19) is inconclusive across individual studies. Thus, this study aimed to investigate whether there was a significant association between peptic ulcer disease and COVID-19 severity through a meta-analysis. The electronic databases (Web of Science, Wiley, Springer, EMBASE, Elsevier, Cochrane Library, Scopus and PubMed) were retrieved for all eligible studies. The Stata 11.2 software was used for all statistical analyses. The pooled odds ratio (OR) with a 95% confidence interval (CI) was calculated by a random-effects meta-analysis model. The heterogeneity was evaluated by the inconsistency index (I²) and Cochran's Q test. Egger's analysis and Begg's analysis were conducted to evaluate the publication bias. Meta-regression analysis and subgroup analysis were done to explore the potential source of heterogeneity. Totally, our findings based on confounding variables-adjusted data indicated that there was no significant association between peptic ulcer disease and the higher risk for COVID-19 severity (pooled OR = 1.17, 95% CI: 0.97-1.41) based on 15 eligible studies with 4,533,426 participants. When the subgroup analysis was performed by age (mean or median), there was a significant association between peptic ulcer disease and a higher risk for COVID-19 severity among studies with age ≥ 60 years old (pooled OR = 1.15, 95% CI: 1.01-1.32), but not among studies with age < 60 years old (pooled OR = 1.16, 95% CI: 0.89-1.50). Our meta-analysis showed that there was a significant association between peptic ulcer disease and a higher risk for COVID-19 severity among older patients but not among younger patients.

COVID-19 inpatient treatments and outcomes during the conflict in Syria: an observational cohort study

Background

During the COVID-19 pandemic, countries undergoing conflict have faced difficulties in mounting an effective health response. This observational cohort study describes the treatments and outcomes for inpatients with COVID-19 in the Syrian city of Latakia.

Design and methods

A single-centre observational cohort study was conducted at Tishreen University Hospital, involving all patients over 18 admitted between October 1 and December 31, 2021 with a positive RT-PCR test for SARS-CoV-2. Clinical features, investigations, treatments, and outcomes were reported.

Results

In total, 149 patients fitted the study criteria. Only one patient was double vaccinated against COVID-19. Oxygen supplementation was required in 87% (n = 130) of participants. Invasive mechanical ventilation was required in 4% (n = 5). Therapeutic anticoagulation was administered in 97.3% (n = 144). Intravenous dexamethasone was received by 97.3% (n = 145) of participants. All patients received empiric antibiotic treatment. In-hospital mortality was 48.4% (n = 72), while only 40.9% (n = 61) were discharged during the study period.

Conclusion

The pandemic has placed a compromised Syrian healthcare system under more significant strain. This requires urgent international relief efforts from health agencies in order to aid the pandemic response.

Role of hospital strain in determining outcomes for people hospitalised with COVID-19 in England

BACKGROUND

In England, reported COVID-19 mortality rates increased during winter 2020/21 relative to earlier summer and autumn months. This study aimed to examine the association between COVID-19-related hospital bed-strain during this time and patient outcomes.

METHODS

This was a retrospective observational study using Hospital Episode Statistics data for England. All unique patients aged ≥18 years in England with a diagnosis of COVID-19 who had a completed (discharged alive or died in hospital) hospital stay with an admission date between 1 July 2020 and 28 February 2021 were included. Bed-strain was calculated as the number of beds occupied by patients with COVID-19 divided by the maximum COVID-19 bed occupancy during the study period. Bed-strain was categorised into quartiles for modelling. In-hospital mortality was the primary outcome of interest and length of stay a secondary outcome.

RESULTS

There were 253 768 unique hospitalised patients with a diagnosis of COVID-19 during a hospital stay. Patient admissions peaked in January 2021 (n=89 047), although the crude mortality rate peaked slightly earlier in December 2020 (26.4%). After adjustment for covariates, the mortality rate in the lowest and highest quartile of bed-strain was 23.6% and 25.3%, respectively (OR 1.13, 95% CI 1.09 to 1.17). For the lowest and the highest quartile of bed-strain, adjusted mean length of stay was 13.2 days and 11.6 days, respectively in survivors and was 16.5 days and 12.6 days, respectively in patients who died in hospital.

CONCLUSIONS

High levels of bed-strain were associated with higher in-hospital mortality rates, although the effect was relatively modest and may not fully explain increased mortality rates during winter 2020/21 compared with earlier months. Shorter hospital stay during periods of greater strain may partly reflect changes in patient management over time.

Combined effect of early diagnosis and treatment on the case fatality risk of COVID-19 in Japan, 2020

Japanese government initially enforced restrictions on outpatient attendances among febrile individuals suspected of having COVID-19, asking everyone to remain at home for at least 4 days from the onset of fever. This restriction was cancelled on 8 May 2020, and a new antiviral, remdesivir, was approved from 7 May 2020. To investigate how this policy change influenced the prognosis of people with COVID-19, we estimated the case fatality risk as a function of the date of illness onset from April to June 2020. We used an interrupted time-series analysis model with an intervention date of 8 May 2020, and estimated time-dependent case fatality risk by age group. The case fatality risk showed a decreasing trend in all groups, and models were favored accounting for an abrupt causal effect, i.e., immediate decline in fatality risk. The trend was estimated at - 1.1% (95% CI [confidence interval]: - 3.9, 3.0) among people aged 60-69 years, - 7.2% (95% CI - 11.2, - 2.4) among those aged 70-79 years, - 7.4% (95% CI - 14.2, 0.2) among those aged 80-89 years, and - 10.3% (95% CI - 21.1, 2.7) among those aged 90 and over. Early diagnosis and treatment greatly contributed to reducing the case fatality risk.

Cancer, more than a “COVID-19 co-morbidity”

Patients with cancer represent a particularly vulnerable population at risk of adverse outcomes related to COVID-19. Collectively, the initial studies, including patients with and without cancer, confirmed that patients with cancer had a higher risk of complications and death related to COVID-19. Subsequent studies on patients with COVID-19 and cancer investigated patient and disease-related factors associated with COVID-19 severity and morality. Multiple interconnected factors include demographics, comorbidities, cancer-associated variables, treatment side effects, and other parameters. However, there is a lack of clarity on the contributions of any one factor. In this commentary, we deconvolute the data of specific risk factors associated with worse outcomes due to COVID-19 in cancer patients and focus on understanding the recommended guidelines to mitigate COVID-19 risk in this vulnerable population. In the first section, we highlight the key parameters, including age and race, cancer status, type of malignancy, cancer therapy, smoking status and comorbidities that impact outcomes for cancer patients with COVID-19. Next, we discuss efforts made at the patient, health system, and population levels to mitigate the effects of the ongoing outbreak for patients with cancer, including (1) screening, barrier and isolation strategies (2), Masking/PPE (3), vaccination, and (4) systemic therapies (e.g., evusheld) to prevent disease onset in patients. In the last section, we discuss optimal treatment strategies for COVID-19, including additional therapies for patients with COVID-19 and cancer. Overall, this commentary focuses on articles with high yield and impact on understanding the evolving evidence of risk factors and management guidelines in detail. We also emphasize the ongoing collaboration between clinicians, researchers, health system administrators and policymakers and how its role will be important in optimizing care delivery strategies for patients with cancer. Creative patient-centered solutions will be critical in the coming years, post the pandemic.

Ethnic disparities in COVID-19 outcomes: a multinational cohort study of 20 million individuals from England and Canada

BACKGROUND

Heterogeneous studies have demonstrated ethnic inequalities in the risk of SARS-CoV-2 infection and adverse COVID-19 outcomes. This study evaluates the association between ethnicity and COVID-19 outcomes in two large population-based cohorts from England and Canada and investigates potential explanatory factors for ethnic patterning of severe outcomes.

METHODS

We identified adults aged 18 to 99 years in the QResearch primary care (England) and Ontario (Canada) healthcare administrative population-based datasets (start of follow-up: 24th and 25th Jan 2020 in England and Canada, respectively; end of follow-up: 31st Oct and 30th Sept 2020, respectively). We harmonised the definitions and the design of two cohorts to investigate associations between ethnicity and COVID-19-related death, hospitalisation, and intensive care (ICU) admission, adjusted for confounders, and combined the estimates obtained from survival analyses. We calculated the 'percentage of excess risk mediated' by these risk factors in the QResearch cohort.

RESULTS

There were 9.83 million adults in the QResearch cohort (11,597 deaths; 21,917 hospitalisations; 2932 ICU admissions) and 10.27 million adults in the Ontario cohort (951 deaths; 5132 hospitalisations; 1191 ICU admissions). Compared to the general population, pooled random-effects estimates showed that South Asian ethnicity was associated with an increased risk of COVID-19 death (hazard ratio: 1.63, 95% CI: 1.09-2.44), hospitalisation (1.53; 1.32-1.76), and ICU admission (1.67; 1.23-2.28). Associations with ethnic groups were consistent across levels of deprivation. In QResearch, sociodemographic, lifestyle, and clinical factors accounted for 42.9% (South Asian) and 39.4% (Black) of the excess risk of COVID-19 death.

CONCLUSION

International population-level analyses demonstrate clear ethnic inequalities in COVID-19 risks. Policymakers should be cognisant of the increased risks in some ethnic populations and design equitable health policy as the pandemic continues.

COVID-19 Severity and Survival over Time in Patients with Hematologic Malignancies: A Population-Based Registry Study

Mortality rates for COVID-19 have declined over time in the general population, but data in patients with hematologic malignancies are contradictory. We identified independent prognostic factors for COVID-19 severity and survival in unvaccinated patients with hematologic malignancies, compared mortality rates over time and versus non-cancer inpatients, and investigated post COVID-19 condition. Data were analyzed from 1166 consecutive, eligible patients with hematologic malignancies from the population-based HEMATO-MADRID registry, Spain, with COVID-19 prior to vaccination roll-out, stratified into early (February-June 2020; n = 769 (66%)) and later (July 2020-February 2021; n = 397 (34%)) cohorts. Propensity-score matched non-cancer patients were identified from the SEMI-COVID registry. A lower proportion of patients were hospitalized in the later waves (54.2%) compared to the earlier (88.6%), OR 0.15, 95%CI 0.11-0.20. The proportion of hospitalized patients admitted to the ICU was higher in the later cohort (103/215, 47.9%) compared with the early cohort (170/681, 25.0%, 2.77; 2.01-3.82). The reduced 30-day mortality between early and later cohorts of non-cancer inpatients (29.6% vs. 12.6%, OR 0.34; 0.22-0.53) was not paralleled in inpatients with hematologic malignancies (32.3% vs. 34.8%, OR 1.12; 0.81-1.5). Among evaluable patients, 27.3% had post COVID-19 condition. These findings will help inform evidence-based preventive and therapeutic strategies for patients with hematologic malignancies and COVID-19 diagnosis.

Clinical Characteristics and Outcomes of Patients with Acute Respiratory Failure Due to SARS-CoV-2 Interstitial Pneumonia Treated with CPAP in a Medical Intermediate Care Setting: A Retrospective Observational Study on Comparison of Four Waves

BACKGROUND

In COVID-19 patients non-invasive-positive-pressure-ventilation (NIPPV) has held a challenging role to reduce mortality and the need for invasive mechanical ventilation (IMV). The aim of this study was to compare the characteristics of patients admitted to a Medical Intermediate Care Unit for acute respiratory failure due to SARS-CoV-2 pneumonia throughout four pandemic waves.

METHODS

The clinical data of 300 COVID-19 patients treated with continuous positive airway pressure (CPAP) were retrospectively analysed, from March-2020 to April-2022.

RESULTS

Non-survivors were older and more comorbid, whereas patients transferred to ICU were younger and had fewer pathologies. Patients were older (from 65 (29-91) years in I wave to 77 (32-94) in IV, p < 0.001) and with more comorbidities (from Charlson's Comorbidity Index = 3 (0-12) in I to 6 (1-12) in IV, p < 0.001). No statistical difference was found for in-hospital mortality (33.0%, 35.8%, 29.6% and 45.9% in I, II, III and IV, p = 0.216), although ICU-transfers rate decreased from 22.0% to 1.4%.

CONCLUSIONS

COVID-19 patients have become progressively older and with more comorbidities even in critical care area; from risk class analyses by age and comorbidity burden, in-hospital mortality rates remain high and are thus consistent over four waves while ICU-transfers have significantly reduced. Epidemiological changes need to be considered to improve the appropriateness of care.

Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data

BACKGROUND

Older adults have worse outcomes following hospitalization with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes.

OBJECTIVE

We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed for machine learning algorithms.

METHODS

This was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed; the first model included the demographic, hospital-related, and disease-related items described above, as well as individual GFS domains and CCI items. The second model was similar to the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy.

RESULTS

In total, 215,831 patients were included. The model using the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model using the HFRS had similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures, and pressure ulcers/weight loss. The most important comorbidity items in the CCI were cancer, heart failure, and renal disease.

CONCLUSIONS

The physical manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.

Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19

BACKGROUND

The evolution of patients hospitalized with coronavirus disease 2019 (COVID-19) is still hard to predict, even after several months of dealing with the pandemic.

AIMS

To develop and validate a score to predict outcomes in patients hospitalized with COVID-19.

METHODS

All consecutive adults hospitalized for COVID-19 from February to April 2020 were included in a nationwide observational study. Primary composite outcome was transfer to an intensive care unit from an emergency department or conventional ward, or in-hospital death. A score that estimates the risk of experiencing the primary outcome was constructed from a derivation cohort using stacked LASSO (Least Absolute Shrinkage and Selection Operator), and was tested in a validation cohort.

RESULTS

Among 2873 patients analysed (57.9% men; 66.6±17.0 years), the primary outcome occurred in 838 (29.2%) patients: 551 (19.2%) were transferred to an intensive care unit; and 287 (10.0%) died in-hospital without transfer to an intensive care unit. Using stacked LASSO, we identified 11 variables independently associated with the primary outcome in multivariable analysis in the derivation cohort (n=2313), including demographics (sex), triage vitals (body temperature, dyspnoea, respiratory rate, fraction of inspired oxygen, blood oxygen saturation) and biological variables (pH, platelets, C-reactive protein, aspartate aminotransferase, estimated glomerular filtration rate). The Critical COVID-19 France (CCF) risk score was then developed, and displayed accurate calibration and discrimination in the derivation cohort, with C-statistics of 0.78 (95% confidence interval 0.75-0.80). The CCF risk score performed significantly better (i.e. higher C-statistics) than the usual critical care risk scores.

CONCLUSIONS

The CCF risk score was built using data collected routinely at hospital admission to predict outcomes in patients with COVID-19. This score holds promise to improve early triage of patients and allocation of healthcare resources.

Preparing for pandemics: a systematic review of pandemic influenza clinical management guidelines

BACKGROUND

The COVID-19 pandemic has highlighted the importance of evidence-based clinical decision-making. Clinical management guidelines (CMGs) may help reduce morbidity and mortality by improving the quality of clinical decisions. This systematic review aims to evaluate the availability, inclusivity, and quality of pandemic influenza CMGs, to identify gaps that can be addressed to strengthen pandemic preparedness in this area.

METHODS

Ovid Medline, Ovid Embase, TRIP (Turning Research Into Practice), and Guideline Central were searched systematically from January 2008 to 23rd June 2022, complemented by a grey literature search till 16th June 2022. Pandemic influenza CMGs including supportive care or empirical treatment recommendations were included. Two reviewers independently extracted data from the included studies and assessed their quality using AGREE II (Appraisal of Guidelines for Research & Evaluation). The findings are presented narratively.

RESULTS

Forty-eight CMGs were included. They were produced in high- (42%, 20/48), upper-middle- (40%, 19/48), and lower-middle (8%, 4/48) income countries, or by international organisations (10%, 5/48). Most CMGs (81%, 39/48) were over 5 years old. Guidelines included treatment recommendations for children (75%, 36/48), pregnant women (54%, 26/48), people with immunosuppression (33%, 16/48), and older adults (29%, 14/48). Many CMGs were of low quality (median overall score: 3 out of 7 (range 1-7). All recommended oseltamivir; recommendations for other neuraminidase inhibitors and supportive care were limited and at times contradictory. Only 56% (27/48) and 27% (13/48) addressed oxygen and fluid therapy, respectively.

CONCLUSIONS

Our data highlights the limited availability of up-to-date pandemic influenza CMGs globally. Of those identified, many were limited in scope and quality and several lacked recommendations for specific at-risk populations. Recommendations on supportive care, the mainstay of treatment, were limited and heterogeneous. The most recent guideline highlighted that the evidence-base to support antiviral treatment recommendations is still limited. There is an urgent need for trials into treatment and supportive care strategies including for different risk populations. New evidence should be incorporated into globally accessible guidelines, to benefit patient outcomes. A 'living guideline' framework is recommended and further research into guideline implementation in different resourced settings, particularly low- and middle-income countries.

COVID-19 hospital activity and in-hospital mortality during the first and second waves of the pandemic in England: an observational study

INTRODUCTION

We aimed to examine the profile of, and outcomes for, all people hospitalised with COVID-19 across the first and second waves of the pandemic in England.

METHODS

This was an exploratory retrospective analysis of observational data from the Hospital Episode Statistics data set for England. All patients aged ≥18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed between 1 March 2020 and 31 March 2021 were included. In-hospital mortality was the primary outcome of interest. The second wave was identified as starting on 1 September 2020. Multilevel logistic regression modelling was used to investigate the relationship between mortality and demographic, comorbidity and temporal covariates.

RESULTS

Over the 13 months, 374 244 unique patients had a diagnosis of COVID-19 during a hospital stay, of whom 93 701 (25%) died in hospital. Adjusted mortality rates fell from 40%-50% in March 2020 to 11% in August 2020 before rising to 21% in January 2021 and declining steadily to March 2021. Improvements in mortality rates were less apparent in older and comorbid patients. Although mortality rates fell for all ethnic groups from the first to the second wave, declines were less pronounced for Bangladeshi, Indian, Pakistani, other Asian and black African ethnic groups.

CONCLUSIONS

There was a substantial decline in adjusted mortality rates during the early part of the first wave which was largely maintained during the second wave. The underlying reasons for consistently higher mortality risk in some ethnic groups merits further study.

Data consistency in the English Hospital Episodes Statistics database

BACKGROUND

To gain maximum insight from large administrative healthcare datasets it is important to understand their data quality. Although a gold standard against which to assess criterion validity rarely exists for such datasets, internal consistency can be evaluated. We aimed to identify inconsistencies in the recording of mandatory International Statistical Classification of Diseases and Related Health Problems, tenth revision (ICD-10) codes within the Hospital Episodes Statistics dataset in England.

METHODS

Three exemplar medical conditions where recording is mandatory once diagnosed were chosen: autism, type II diabetes mellitus and Parkinson's disease dementia. We identified the first occurrence of the condition ICD-10 code for a patient during the period April 2013 to March 2021 and in subsequent hospital spells. We designed and trained random forest classifiers to identify variables strongly associated with recording inconsistencies.

RESULTS

For autism, diabetes and Parkinson's disease dementia respectively, 43.7%, 8.6% and 31.2% of subsequent spells had inconsistencies. Coding inconsistencies were highly correlated with non-coding of an underlying condition, a change in hospital trust and greater time between the spell with the first coded diagnosis and the subsequent spell. For patients with diabetes or Parkinson's disease dementia, the code recording for spells without an overnight stay were found to have a higher rate of inconsistencies.

CONCLUSIONS

Data inconsistencies are relatively common for the three conditions considered. Where these mandatory diagnoses are not recorded in administrative datasets, and where clinical decisions are made based on such data, there is potential for this to impact patient care.

Modelling the first wave of COVID-19 in India

Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. Here, we use a 9-component, age-stratified, contact-structured epidemiological compartmental model, which we call the INDSCI-SIM model, to analyse the first wave of COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to daily reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions (NPIs), an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05—0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 35% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

Making sense of publicly available epidemiological data for the COVID-19 pandemic in India presents multiple challenges, largely to do with the quality of the data. Here, we describe ways of addressing these questions by studying the data using a well-parameterised, detailed compartmental model together with Bayesian methods, alongside information derived from pan-India serological surveys. We focus on the first wave of the Indian pandemic, across the interval Jan 30, 2020 to Feb 15, 2021. We estimate that deaths were under-counted by a factor between 2 and 5 across the span of the first wave and that cases were under-counted by a factor of between 20 and 25 towards its end. We estimate an infection fatality ratio (IFR) in the range 0.05—0.15. We find that approximately 35% of India had been infected overall by the end of the first wave, a number that helps us better understand the context in which the second and later waves unfolded.

Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, a cohort study

Widespread vaccination campaigns have changed the landscape for COVID-19, vastly altering symptoms and reducing morbidity and mortality. We estimate trends in mortality by month of admission and vaccination status among those hospitalised with COVID-19 in England between March 2020 to September 2021, controlling for demographic factors and hospital load. Among 259,727 hospitalised COVID-19 cases, 51,948 (20.0%) experienced mortality in hospital. Hospitalised fatality risk ranged from 40.3% (95% confidence interval 39.4–41.3%) in March 2020 to 8.1% (7.2–9.0%) in June 2021. Older individuals and those with multiple co-morbidities were more likely to die or else experienced longer stays prior to discharge. Compared to unvaccinated people, the hazard of hospitalised mortality was 0.71 (0.67–0.77) with a first vaccine dose, and 0.56 (0.52–0.61) with a second vaccine dose. Compared to hospital load at 0–20% of the busiest week, the hazard of hospitalised mortality during periods of peak load (90–100%), was 1.23 (1.12–1.34). The prognosis for people hospitalised with COVID-19 in England has varied substantially throughout the pandemic and according to case-mix, vaccination, and hospital load. Our estimates provide an indication for demands on hospital resources, and the relationship between hospital burden and outcomes.

This study investigates trends in mortality and length of stay for people hospitalised with COVID-19 in England until September 2021. It shows that risks were higher for unvaccinated people and those with multiple comorbidities, and that busier hospitals had higher mortality rates at the start of the pandemic but this effect lessened over time.

Factors associated with, and variations in, COVID-19 hospital death rates in England's first two waves: observational study

OBJECTIVES

To assess patient-level and hospital-level predictors of death and variation in death rates following admission for COVID-19 in England's first two waves after accounting for random variation. To quantify the correlation between hospitals' first and second wave death rates.

DESIGN

Observational study using administrative data.

SETTING

Acute non-specialist hospitals in England.

PARTICIPANTS

All patients admitted with a primary diagnosis of COVID-19.

PRIMARY AND SECONDARY OUTCOMES

In-hospital death.

RESULTS

Hospital Episode Statistics (HES) data were extracted for all acute hospitals in England for COVID-19 admissions from March 2020 to March 2021. In wave 1 (March to July 2020), there were 74 484 admissions and 21 883 deaths (crude rate 29.4%); in wave 2 (August 2020 to March 2021), there were 165 642 admissions and 36 040 deaths (21.8%). Wave 2 patients were younger, with more hypertension and obesity but lower rates of other comorbidities. Mortality improved for all ages; in wave 2, it peaked in December 2020 at 24.2% (lower than wave 1's peak) but halved by March 2021. In multiple multilevel modelling combining HES with hospital-level data from Situational Reports, wave 2 and wave 1 variables significantly associated with death were mostly the same. The median odds ratio for wave 1 was just 1.05 and for wave 2 was 1.07. At 99.8% control limits, 3% of hospitals were high and 7% were low funnel plot outliers in wave 1; these figures were 9% and 12% for wave 2. Four hospitals were (low) outliers in both waves. The correlation between hospitals' adjusted mortality rates between waves was 0.45 (p<0.0001). Length of stay was similar in each wave.

CONCLUSIONS

England's first two COVID-19 waves were similar regarding predictors and moderate interhospital variation. Despite the challenges, variation in death rates and length of stay between hospitals was modest and might be accounted for by unobserved patient factors.

Changes of Acute Kidney Injury Epidemiology during the COVID-19 Pandemic: A Retrospective Cohort Study

To evaluate the impact of the Coronavirus Disease-19 (COVID-19) pandemic on the epidemiology of acute kidney injury (AKI) in hospitalized patients, we performed a retrospective cohort study comparing data of patients hospitalized from January 2016 to December 2019 (pre-COVID-19 period) and from January to December 2020 (COVID-19 period, including both severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-negative and positive patients). AKI was classified by evaluating the kinetics of creatinine levels. A total of 51,681 patients during the pre-COVID-19 period and 10,062 during the COVID-19 period (9026 SARS-CoV-2-negative and 1036 SARS-CoV-2-positive) were analyzed. Patients admitted in the COVID-19 period were significantly older, with a higher prevalence of males. In-hospital AKI incidence was 31.7% during the COVID-19 period (30.5% in SARS-CoV-2-negative patients and 42.2% in SARS-CoV-2-positive ones) as compared to 25.9% during the pre-COVID-19 period (p < 0.0001). In the multivariate analysis, AKI development was independently associated with both SARS-CoV-2 infection and admission period. Moreover, evaluating the pre-admission estimated glomerular filtration rate (eGFR) we found that during the COVID-19 period, there was an increase in AKI stage 2−3 incidence both in patients with pre-admission eGFR < 60 mL/min/1.73 m2 and in those with eGFR ≥ 60 mL/min/1.73 m2 (“de novo” AKI). Similarly, clinical outcomes evaluated as intensive care unit admission, length of hospital stay, and mortality were significantly worse in patients admitted in the COVID-19 period. Additionally, in this case, the mortality was independently correlated with the admission during the COVID-19 period and SARS-CoV-2 infection. In conclusion, we found that during the COVID-19 pandemic, in-hospital AKI epidemiology has changed, not only for patients affected by COVID-19. These modifications underline the necessity to rethink AKI management during health emergencies.

Mortality rates among COVID-19 patients hospitalised during the first three waves of the epidemic in Milan, Italy: A prospective observational study

Introduction

This paper describes how mortality among hospitalised COVID-19 patients changed during the first three waves of the epidemic in Italy.

Methods

This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral centre in Milan, Italy, during the three consecutive periods of: 21 February-31 July 2020 (first wave, W1), 1 August 2020–31 January 2021 (second wave, W2), and 1 February-30 April 2021 (third wave, W3). Cox models were used to examine the association between death and the period of admission after adjusting for age, biological sex, the time from symptom onset to admission, disease severity upon admission, obesity, and the comorbidity burden.

Results

Of the 2,023 COVID-19 patients admitted to our hospital during the study period, 553 (27.3%) were admitted during W1, 838 (41.5%) during W2, and 632 (31.2%) during W3. The crude mortality rate during W1, W2 and W3 was respectively 21.3%, 23.7% and 15.8%. After adjusting for potential confounders, hospitalisation during W2 or W3 was independently associated with a significantly lower risk of death than hospitalisation during W1 (adjusted hazard ratios [AHRs]: 0.75, 95% confidence interval [CI] 0.59–0.95, and 0.58, 95% CI 0.44–0.77). Among the patients aged >75 years, there was no significant difference in the probability of death during the three waves (AHRs during W2 and W3 vs W1: 0.93, 95% CI 0.65–1.33, and 0.88, 95% CI 0.59–1.32), whereas those presenting with critical disease during W2 and W3 were at significantly lower risk of dying than those admitted during W1 (AHRs 0.61, 95% CI 0.43–0.88, and 0.44, 95% CI 0.28–0.70).

Conclusions

Hospitalisation during W2 and W3 was associated with a reduced risk of COVID-19 death in comparison with W1, but there was no difference in survival probability in patients aged >75 years.

Predictors for Early and Late Death in Adult Patients with COVID-19: A Cohort Study

The timing of death in patients with coronavirus disease 2019 (COVID-19) varied by their comorbidities and severity of illness. However, few studies have determined predictors of mortality with respect to the timing of death in infectious patients. This cohort study aimed to identify the factors associated with early and late death in hospitalized COVID-19 patients. From 14 May to 31 July 2021, this study consecutively recruited laboratory-confirmed COVID-19 patients admitted to Taipei City Hospital. All patients with COVID-19 were followed up until death or discharge from the hospital or till 13 August 2021. Mortality in such patients was categorized as early death (death within the first two weeks of hospitalization) or late death (mortality later than two weeks after hospitalization), based on the timing of death. Multinomial logistic regression was used to determine the factors associated with early and late death among such patients. Of 831 recruited patients, the overall mean age was 59.3 years, and 12.2% died during hospitalization. Of the 101 deceased, 66 (65.3%) and 35 (34.7%) died early and late, respectively. After adjusting for demographics and comorbidities, independent predictors for early death included age ≥ 65 years (adjusted odds ratio (AOR) = 5.27; 95% confidence interval (CI): 2.88–9.65), heart failure (AOR = 10.32; 95% CI: 2.28–46.65), and end-stage renal disease (AOR = 11.97; 95% CI: 3.53–40.55). This study found that two thirds of COVID-19 deaths occurred within two weeks of hospitalization. It suggests that hospitalized patients with COVID-19 should be treated carefully and monitored closely for the progression of clinical conditions during treatment, particularly in older patients and in those with comorbidities.

Utilization of Palliative Care Screening Tool to Early Identify Patients with COVID-19 Needing Palliative Care: A Cohort Study

There are very few programs that identify patients with coronavirus disease 2019 (COVID-19) who need palliative care. This cohort study presents a model to use a validated palliative care screening tool (PCST) to systematically identify hospitalized patients with COVID-19 in need of palliative care. In this prospective study, we consecutively recruited patients with COVID-19 admitted to Taipei City Hospital between 1 January and 30 July 2021. Patients’ palliative care needs were determined by using the PCST. Advance care planning (ACP) and advance directives (AD) were systemically provided for all patients with a PCST score ≥ 4. Of 897 patients, 6.1% had a PCST score ≥ 4. During the follow-up period, 106 patients died: 75 (8.9%) with a PCST score < 4 and 31 (56.4%) with a PCST score ≥ 4. The incidence of mortality was 2.08 and 0.58/100 person-days in patients with PCST scores ≥ 4 and <4, respectively. After controlling for other covariates, a PCST score ≥ 4 was associated with a higher risk of mortality in patients with COVID-19 (adjusted HR = 2.08; 95% CI: 1.22–3.54; p < 0.001). During hospitalization, 55 patients completed an ACP discussion with their physicians, which led to 15 of them completing the AD. Since hospitalized patients with COVID-19 had a high mortality rate, it is imperative to implement a comprehensive palliative care program to early identify patients needing palliative care and promotion of AD and ACP.

Characteristics and Clinical Outcomes of 116,539 Patients Hospitalized with COVID-19-Poland, March-December 2020

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus Disease 2019 (COVID-19). This study aimed to characterize patients hospitalized with COVID-19 in Poland between March and December 2020, as well as to identify factors associated with COVID 19-related risk of in-hospital death. This retrospective analysis was based on data from the hospital discharge reports on COVID-19 patients hospitalized in Poland between March and December 2020. A total of 116,539 discharge reports on patients hospitalized with COVID-19 were analyzed. Among patients with COVID-19, 21,490 (18.4%) died during hospitalization. Patients over 60 years of age (OR = 7.74; 95%CI: 7.37-8.12; p < 0.001), men (OR = 1.42; 95%CI: 1.38-1.47; p < 0.001) as well as those with cardiovascular diseases (OR = 1.51; 95%CI: 1.46-1.56; p < 0.001) or disease of the genitourinary system (OR = 1.39; 95%CI: 1.31-1.47; p < 0.001) had much higher odds of COVID 19-related risk of in-hospital death. The presence of at least one comorbidity more than doubled the COVID 19-related risk of in-hospital death (OR = 2.23; 95%CI: 2.14-2.32; p < 0.01). The following predictors of admission to ICU were found in multivariable analysis: age over 60 years (OR: 2.03; 95%CI: 1.90-2.16), male sex (OR: 1.79; 95%CI: 1.69-1.89), presence of at least one cardiovascular disease (OR: 1.26; 95%CI: 1.19-1.34), presence of at least one endocrine, nutritional and metabolic disease (OR: 1.17; 95%CI: 1.07-1.28).