Risk Factors Associated with Mortality in Hospitalized Patients with Laboratory Confirmed SARS-CoV-2 Infection During the Period of Omicron (B.1.1.529) Variant Predominance

The outcome and related risk factors of unvaccinated patients with end-stage kidney disease during the Omicron pandemic: a multicentre retrospective study

OBJECTIVES

The study aims to identify the outcome and the related factors of unvaccinated patients with end-stage kidney disease during the Omicron pandemic.

DESIGN

A multicentre retrospective study of patients with end-stage kidney disease undergone maintenance haemodialysis (HD) in China.

SETTING

6 HD centres in China.

PARTICIPANTS

A total of 654 HD patients who tested positive for SARS-CoV-2 were ultimately included in the study.

OUTCOME MEASURES

The primary outcomes of interest were adverse outcomes, including hospitalisation due to COVID-19 and all-cause mortality.

RESULTS

The average age of the patients was 57 years, with 33.6% of them being over 65 years. Among the patients, 57.5% were male. During the follow-up period, 158 patients (24.2%) experienced adverse outcomes, and 93 patients (14.2%) died. The majority of patients (88/158) developed adverse outcomes within 30 days, and most deaths (77/93) occurred within 1 month. An advanced multivariable Cox regression analysis identified that adverse outcomes were associated with various factors while all-cause mortality was related to advanced age, male gender, high levels of C reactive protein (CRP) and low levels of prealbumin. The Kaplan-Meier curves demonstrated significantly higher all-cause mortality rates in the older, male, high CRP and low prealbumin subgroups.

CONCLUSIONS

Among unvaccinated HD patients with confirmed Omicron infections, various factors were found to be linked to adverse outcomes. Notably, age, sex, CRP and prealbumin had a substantial impact on the risk of all-cause mortality.

Demystifying COVID-19 mortality causes with interpretable data mining

While COVID-19 becomes periodical, old individuals remain vulnerable to severe disease with high mortality. Although there have been some studies on revealing different risk factors affecting the death of COVID-19 patients, researchers rarely provide a comprehensive analysis to reveal the relationships and interactive effects of the risk factors of COVID-19 mortality, especially in the elderly. Through retrospectively including 1917 COVID-19 patients (102 were dead) admitted to Xiangya Hospital from December 2022 to March 2023, we used the association rule mining method to identify the risk factors leading causes of death among the elderly. Firstly, we used the Affinity Propagation clustering to extract key features from the dataset. Then, we applied the Apriori Algorithm to obtain 6 groups of abnormal feature combinations with significant increments in mortality rate. The results showed a relationship between the number of abnormal feature combinations and mortality rates within different groups. Patients with "C-reactive protein > 8 mg/L", "neutrophils percentage > 75.0 %", "lymphocytes percentage < 20%", and "albumin < 40 g/L" have a 2 × mortality rate than the basic one. When the characteristics of "D-dimer > 0.5 mg/L" and "WBC > 9.5 × 10 9 /L" are continuously included in this foundation, the mortality rate can be increased to 3 × or 4 × . In addition, we also found that liver and kidney diseases significantly affect patient mortality, and the mortality rate can be as high as 100%. These findings can support auxiliary diagnosis and treatment to facilitate early intervention in patients, thereby reducing patient mortality.

Impact of T-Cell Engagers on COVID-19-Related Mortality in B-Cell Lymphoma Patients Receiving B-Cell Depleting Therapy

PURPOSE

B-cell depleting therapies, including T-cell engager (TCE), are increasingly used for patients with hematologic malignancies, including during the coronavirus disease 2019 (COVID-19) pandemic. We aimed to evaluate the relationship between TCE therapy and COVID-19-related outcomes among patients with COVID-19 and B-cell lymphomas receiving B-cell depleting therapy.

MATERIALS AND METHODS

This retrospective cohort study included patients with B-cell lymphoma, who were admitted to Seoul Natio-nal University Hospital with COVID-19 between September 2021 and February 2023, and received B-cell depleting therapy before COVID-19 diagnosis. Multivariable logistic regression was used to identify factors associated with severe to critical COVID-19 and COVID-19-related mortality.

RESULTS

Of 54 patients with B-cell lymphomas and COVID-19 who received B-cell depleting therapy, 14 were treated with TCE (TCE group) and 40 with rituximab (RTX group). COVID-19-related mortality was higher in the TCE group than in the RTX group (57.1% vs. 12.5%, p=0.002). In multivariable analyses, TCE therapy (adjusted odds ratio [aOR], 7.08; 95% confidence interval [CI], 1.29 to 38.76; p=0.024) and older age (aOR, 1.06; 95% CI, 1.00 to 1.13; p=0.035) were associated with severe to critical COVID-19. TCE therapy (aOR, 8.98; 95% CI, 1.48 to 54.40; p=0.017), older age (aOR, 1.13; 95% CI, 1.02 to 1.26; p=0.022), and prior bendamustine therapy (aOR, 7.78; 95% CI, 1.17 to 51.65; p=0.034) were independent risk factors for COVID-19-related mortality.

CONCLUSION

B-cell lymphoma patients treated with TCE had significantly worse outcomes from COVID-19 than those treated with RTX. TCE therapy should be used with caution in B-cell lymphoma patients during the COVID-19 epidemic.

Effectiveness of full (booster) COVID-19 vaccination against severe outcomes and work absenteeism in hospitalized patients with COVID-19 during the Delta and Omicron waves in Greece

AIM

We estimated vaccine effectiveness (VE) of full (booster) vaccination against severe outcomes in hospitalized COVID-19 patients during the Delta and Omicron waves.

METHODS

The study extended from November 15, 2021 to April 17, 2022. Full vaccination was defined as a primary vaccination plus a booster ≥ 6 months later.

RESULTS

We studied 1138 patients (mean age: 66.6 years), of whom 826 (72.6 %) had > 1 comorbidity. Of the 1138 patients, 75 (6.6 %) were admitted to intensive care unit (ICU), 64 (5.6 %) received mechanical ventilation, and 172 (15.1 %) died. There were 386 (33.9 %) fully vaccinated, 172 (15.1 %) partially vaccinated, and 580 (51 %) unvaccinated patients. Unvaccinated patients were absent from work for longer periods compared to partially or fully vaccinated patients (mean absence of 20.1 days versus 12.3 and 17.3 days, respectively; p-value = 0.03). Compared to unvaccinated patients, fully vaccinated patients were less likely to be admitted to ICU [adjusted relative risk (ARR: 0.49; 95 % CI: 0.29-0.84)], mechanically ventilated (ARR: 0.43; 95 % CI: 0.23-0.80), and die (ARR: 0.57; 95 % CI: 0.42-0.78), while they were hospitalized for significantly shorter periods (ARR: 0.79; 95 % CI: 0.70-0.89). The adjusted full VE was 48.8 % (95 % CI: 42.7 %-54.9 %) against ICU admission, 55.4 % (95 % CI: 52.0 %-56.2 %) against mechanical ventilation, and 22.6 % (95 % CI: 7.4 %-34.8 %) against death. For patients with ≥ 3 comorbidities, VE was 56.2 % (95 % CI: 43.9 %-67.1 %) against ICU admission, 60.2 % (95 % CI: 53.7 %-65.4 %) against mechanical ventilation, and 43.9 % (95 % CI: 19.9 %-59.7 %) against death.

CONCLUSIONS

Full (booster) COVID-19 vaccination conferred protection against severe outcomes, prolonged hospitalization, and prolonged work absenteeism.

Value of internet of things-based diagnosis-treatment model in improving the quality of medical services during COVID-19 outbreak

OBJECTIVE

To demonstrate the value of Internet of things (IoT)-based diagnosis-treatment model in improving medical service quality during the novel coronavirus pneumonia (COVID-19) outbreak.

METHODS

In this retrospective analysis, 483 patients with chronic diseases treated between January 2020 and March 2021 were selected and grouped as follows based on different intervention methods: a research group (the Res group) with 229 patients that were given IoT-based diagnosis and treatment, and a control group (the Con group) with 254 patients that were treated with routine diagnosis and treatment. The qualified rate of medical records, the missing rate of medical records, and the incidence of doctor-patient disputes were compared between the two groups. In addition, investigations were made regarding patients' daily living ability, psychological state, health behavior, self-care ability, quality of life, as well as treatment satisfaction.

RESULTS

There was no difference in the qualified rate of medical records between the Res group and the Con group (P>0.05), but the missing rate of medical records and the incidence of doctor-patient disputes were lower in the Res group (both P<0.05). An obviously improved living ability was observed in both groups after the treatment (both P<0.05), with no statistical significance between groups (P>0.05). Besides, the Res group presented lower scores of SAS and SDS but higher scores of SRAHP, ES-CA and SF-36 than the Con group after treatment (all P<0.05). Finally, according to the satisfaction survey, more patients in the Res group were very satisfied but fewer cases were dissatisfied with the medical service they received as compared with the Con group (both P<0.05).

CONCLUSIONS

The IoT-based diagnosis-treatment model can effectively improve the quality of medical services and patients' self-care ability, which is extremely important and promising for addressing the current medical limitations during the COVID-19 epidemic.