COVID-19–Related Hospitalization Rates and Severe Outcomes Among Veterans From 5 Veterans Affairs Medical Centers: Hospital-Based Surveillance Study

BackgroundCOVID-19 has disproportionately affected older adults and certain racial and ethnic groups in the United States. Data quantifying the disease burden, as well as describing clinical outcomes during hospitalization among these groups, are needed.ObjectiveWe aimed to describe interim COVID-19 hospitalization rates and severe clinical outcomes by age group and race and ethnicity among US veterans by using a multisite surveillance network.MethodsWe implemented a multisite COVID-19 surveillance platform in 5 Veterans Affairs Medical Centers located in Atlanta, Bronx, Houston, Palo Alto, and Los Angeles, collectively serving more than 396,000 patients annually. From February 27 to July 17, 2020, we actively identified inpatient cases with COVID-19 by screening admitted patients and reviewing their laboratory test results. We then manually abstracted the patients'' medical charts for demographics, underlying medical conditions, and clinical outcomes. Furthermore, we calculated hospitalization incidence and incidence rate ratios, as well as relative risk for invasive mechanical ventilation, intensive care unit admission, and case fatality rate after adjusting for age, race and ethnicity, and underlying medical conditions.ResultsWe identified 621 laboratory-confirmed, hospitalized COVID-19 cases. The median age of the patients was 70 years, with 65.7% (408/621) aged ≥65 years and 94% (584/621) male. Most COVID-19 diagnoses were among non-Hispanic Black (325/621, 52.3%) veterans, followed by non-Hispanic White (153/621, 24.6%) and Hispanic or Latino (112/621, 18%) veterans. Hospitalization rates were the highest among veterans who were ≥85 years old, Hispanic or Latino, and non-Hispanic Black (430, 317, and 298 per 100,000, respectively). Veterans aged ≥85 years had a 14-fold increased rate of hospitalization compared with those aged 18-29 years (95% CI: 5.7-34.6), whereas Hispanic or Latino and Black veterans had a 4.6- and 4.2-fold increased rate of hospitalization, respectively, compared with non-Hispanic White veterans (95% CI: 3.6-5.9). Overall, 11.6% (72/621) of the patients required invasive mechanical ventilation, 26.6% (165/621) were admitted to the intensive care unit, and 16.9% (105/621) died in the hospital. The adjusted relative risk for invasive mechanical ventilation and admission to the intensive care unit did not differ by age group or race and ethnicity, but veterans aged ≥65 years had a 4.5-fold increased risk of death while hospitalized with COVID-19 compared with those aged <65 years (95% CI: 2.4-8.6).ConclusionsCOVID-19 surveillance at the 5 Veterans Affairs Medical Centers across the United States demonstrated higher hospitalization rates and severe outcomes among older veterans, as well as higher hospitalization rates among Hispanic or Latino and non-Hispanic Black veterans than among non-Hispanic White veterans. These findings highlight the need for targeted prevention and timely treatment for veterans, with special attention to older aged, Hispanic or Latino, and non-Hispanic Black veterans.     

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis

BackgroundPrior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe.ObjectiveOur goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions.MethodsWe developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory’s COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground.ResultsForecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs.ConclusionsReal-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.     

Healthcare Resource Utilization of Patients With COVID-19 Visiting US Hospitals

ObjectivesSevere cases of COVID-19 have overwhelmed hospital systems across the nation. This study aimed to describe the healthcare resource utilization of patients with COVID-19 from hospital visit to 30 days after discharge for inpatients and hospital-based outpatients in the United States.MethodsA retrospective cohort study was conducted using Premier Healthcare Database COVID-19 Special Release, a large geographically diverse all-payer hospital administrative database. Adult patients (age ≥ 18 years) were identified by their first, or “index,” visit between April 1, 2020, and February 28, 2021, with a principal or secondary discharge diagnosis of COVID-19.ResultsOf 1 454 780 adult patients with COVID-19, 33% (n = 481 216) were inpatients and 67% (n = 973 564) were outpatients. Among inpatients, mean age was 64.4 years and comorbidities were common. Most patients (80%) originated from home, 10% from another acute care facility, and 95% were admitted through the emergency department. Of these patients, 23% (n = 108 120) were admitted to intensive care unit and 14% (n = 66 706) died during index hospitalization; 44% were discharged home, 15% to nursing or rehabilitation facility, and 12% to home health. Among outpatients, mean age was 48.8 years, 44% were male, and 60% were emergency department outpatients (n = 586 537). During index outpatient visit, 79% were sent home but 10% had another outpatient visit and 4% were hospitalized within 30 days.ConclusionsCOVID-19 is associated with high level of healthcare resource utilization and in-hospital mortality. More than one-third of inpatients required post hospital healthcare services. Such information may help healthcare providers better allocate resources for patients with COVID-19 during the pandemic.     

Design and Implementation of a Skilled Nursing Facility COVID-19 Unit

Coronavirus disease 2019 (COVID-19) has challenged the health care system's capacity to care for acutely ill patients. In a collaborative partnership between a health system and a skilled nursing facility (SNF), we developed and implemented an SNF COVID-19 unit to allow expedited hospital discharge of COVID-positive older adults who are clinically improving, and to provide an alternative to hospitalization for those who require SNF care but do not require or necessarily desire aggressive disease-modifying interventions.     

新型冠状病毒肺炎患者出院管理的流行病学探讨

目的 分析新型冠状病毒肺炎(简称新冠肺炎)患者出院后的传染风险。方法 收集整理分析郴州市40例确诊病例、6例无症状感染者诊断、住院治疗、出院及复查的资料,判定患者出院后是否安全。结果 截至2020年5月31日,按照《诊疗方案》(试行第六版)解除隔离和出院标准,40例确诊病例全部治愈出院,确诊后住院天数7～29 d,平均12.50 d,≤14 d 29例,>14 d 11例;6例无症状感染者均治愈出院,确诊后住院天数6～16 d,平均14 d,≤14 d 5例,>14 d 1例;均经过两次以上咽拭子核酸检测阴性。6例确诊病例出院后复检核酸阳性。结论 新冠肺炎患者出院后,可能仍有传染风险,建议完善出院标准,加强患者出院后管理,及时规范随访、复诊。     

A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe

BackgroundThe exponential increase in SARS-CoV-2 infections during the first wave of the pandemic created an extraordinary overload and demand on hospitals, especially intensive care units (ICUs), across Europe. European countries have implemented different measures to address the surge ICU capacity, but little is known about the extent. The aim of this paper is to compare the rates of hospitalised COVID-19 patients in acute and ICU care and the levels of national surge capacity for intensive care beds across 16 European countries and Lombardy region during the first wave of the pandemic (28 February to 31 July).MethodsFor this country level analysis, we used data on SARS-CoV-2 cases, current and/or cumulative hospitalised COVID-19 patients and current and/or cumulative COVID-19 patients in ICU care. To analyse whether capacities were exceeded, we also retrieved information on the numbers of hospital beds, and on (surge) capacity of ICU beds during the first wave of the COVID-19 pandemic from the COVID-19 Health System Response Monitor (HSRM). Treatment days and mean length of hospital stay were calculated to assess hospital utilisation.ResultsHospital and ICU capacity varied widely across countries. Our results show that utilisation of acute care bed capacity by patients with COVID-19 did not exceed 38.3% in any studied country. However, the Netherlands, Sweden, and Lombardy would not have been able to treat all patients with COVID-19 requiring intensive care during the first wave without an ICU surge capacity. Indicators of hospital utilisation were not consistently related to the number of SARS-CoV-2 infections. The mean number of hospital days associated with one SARS-CoV-2 case ranged from 1.3 (Norway) to 11.8 (France).ConclusionIn many countries, the increase in ICU capacity was important to accommodate the high demand for intensive care during the first COVID-19 wave.     

Influenza and pneumococcal vaccinations are not associated to COVID-19 outcomes among patients admitted to a university hospital

In order to reduce the burden on healthcare systems and to support differential diagnosis with COVID-19, influenza and pneumococcal vaccinations were strongly recommended during the COVID-19 pandemic, especially in vulnerable groups. However, no univocal and conclusive evidence on the relationship between influenza and pneumococcal vaccinations and COVID‐19 outcomes exists. We evaluated the association between such vaccinations, COVID-19 hospitalization, intensive care unit admissions and deaths in a cohort (N = 741) of COVID-19 patients who had access to the emergency room of a large Italian University hospital between March 1, 2020 and June 1, 2020. Results show that influenza and pneumococcal vaccinations did not affect hospitalization, intensive care unit admission and deaths in COVID-19 patients in the overall sample and in those ≥65 years. The same pattern of results was confirmed considering timing of influenza vaccine administration, vaccination type, and number of uptakes in the last five vaccination campaigns. In conclusion, our study does not support an impact of influenza and pneumococcal vaccinations on COVID-19 outcomes.     

Providing health services effectively during the first wave of COVID-19: A cross-country comparison on planning services,managing cases,and maintaining essential services

The COVID-19 pandemic triggered abrupt challenges for health care providers, requiring them to simultaneously plan for and manage a rise of COVID-19 cases while maintaining essential health services. Since March 2020, the COVID-19 Health System Response Monitor, a joint initiative of the European Observatory on Health Systems and Policies, the WHO Regional Office for Europe, and the European Commission, has documented country responses to COVID-19 using a structured template which includes a section on provision of care. Using the information available on the platform, this paper analyzes how countries planned services for potential surge capacity, designed patient flows ensuring separation between COVID-19 and non-COVID-19 patients, and maintained routine services in both hospital and ambulatory settings. Despite very real differences in the organization of health and care services, there were many similarities in country responses. These include transitioning the management of COVID-19 mild cases from hospitals to outpatient settings, increasing the use of remote consultations, and cancelling or postponing non-urgent services during the height of the first wave. In the immediate future, countries will have to continue balancing care for COVID-19 and non-COVID-19 patients to minimize adverse health outcomes, ideally with supporting guidelines and COVID-19-specific care zones. Looking forward, policymakers will have to consider whether strategies adopted during the COVID-19 pandemic will become permanent features of care provision.     

Evolution of Nutritional Status after Early Nutritional Management in COVID-19 Hospitalized Patients

Background & Aims: SARS-CoV2 infection is associated with an increased risk of malnutrition. Although there are numerous screening and nutritional management protocols for malnutrition, only few studies have reported nutritional evolution after COVID-19. The objectives of this study were to describe the evolution of nutritional parameters between admission and 30 days after hospital discharge, and to determine predictive factors of poor nutritional outcome after recovery in adult COVID-19 patients. Methods: In this observational longitudinal study, we report findings after discharge in 91 out of 114 patients initially admitted for COVID-19 who received early nutritional management. Nutritional status was defined using GLIM criteria and compared between admission and day 30 after discharge. Baseline predictors of nutritional status at day 30 were assessed using logistic regression. Results: Thirty days after discharge, 28.6% of patients hospitalized for COVID-19 were malnourished, compared to 42.3% at admission. Half of malnourished patients (53%) at admission recovered a normal nutritional status after discharge. Weight trajectories were heterogeneous and differed if patients had been transferred to an intensive care unit (ICU) during hospitalization (p = 0.025). High oxygen requirement during hospitalization (invasive ventilation p = 0.016 (OR 8.3 [1.6–61.2]) and/or oxygen therapy over 5 L/min p = 0.021 (OR 3.2 [1.2–8.9]) were strong predictors of malnutrition one month after discharge. Conclusions: With early nutritional management, most patients hospitalized for COVID-19 improved nutritional parameters after discharge. These findings emphasize the importance of nutritional care in COVID-19 patients hospitalized in medicine departments, especially in those transferred from ICU.     

Vaccine effectiveness against COVID-19 related hospital admission in the Netherlands: A test-negative case-control study

IntroductionReal-world vaccine effectiveness (VE) estimates are essential to identify potential groups at higher risk of break-through infections and to guide policy. We assessed the VE of COVID-19 vaccination against COVID-19 hospitalization, while adjusting and stratifying for patient characteristics.MethodsWe performed a test-negative case-control study in six Dutch hospitals. The study population consisted of adults eligible for COVID-19 vaccination hospitalized between May 1 and June 28, 2021 with respiratory symptoms. Cases were defined as patients who tested positive for SARS-CoV-2 by PCR during the first 48 h of admission or within 14 days prior to hospital admission. Controls were patients tested negative at admission and did not have a positive test during the 2 weeks prior to hospitalization. VE was calculated using multivariable logistic regression, adjusting for calendar week, sex, age, comorbidity and nursing home residency. Subgroup analysis was performed for age, sex and different comorbidities. Secondary endpoints were ICU-admission and mortality.Results379 cases and 255 controls were included of whom 157 (18%) were vaccinated prior to admission. Five cases (1%) and 40 controls (16%) were fully vaccinated (VE: 93%; 95% CI: 81 – 98), and 40 cases (11%) and 70 controls (27%) were partially vaccinated (VE: 70%; 95% CI: 50–82). A strongly protective effect of vaccination was found in all comorbidity subgroups. No ICU-admission or mortality were reported among fully vaccinated cases. Of unvaccinated cases, mortality was 10% and 19% was admitted at the ICU.ConclusionCOVID-19 vaccination provides a strong protective effect against COVID-19 related hospital admission, in patients with and without comorbidity.     

新型冠状病毒肺炎疫情期间大型医疗机构医院感染综合防控策略及评价

目的旨在为大型医疗机构应对突发传染病暴发疫情提供参考。方法本文总结了某大型医疗机构新型冠状病毒肺炎疫情期间如何从门诊、住院部、工作人员防护等方面进行风险排查,以及如何将国家规范指南落地的管理经验。结果2020-01-23/02-26该院共上报新型冠状病毒肺炎疑似病例41例,其中确诊病例4例,确诊病例出现时间点与成都市疫情高峰相吻合。疫情期间患者和工作人员未发生新型冠状病毒肺炎院内感染。结论通过各级风险排查和执行综合防控策略,新冠流行期间该院医院感染防控工作成效较好。     

Evaluation of patients' satisfaction with hospital-at-home care

On July 1, 1997, in the Canton of Vaud, Switzerland, a pilot experiment of Hospital-at-Home Care (H-Hcare) was set up for a 2-year period at four sites to measure patients' satisfaction with this type of health care. Out of 174 patients referred to the H-Hcare program for a wide range of treatments, 107 were medical patients admitted for heart failure, community acquired pneumonia, or for an infectious disease requiring i.v.-antibiotherapy; 95 of these agreed to express H-Hcare satisfaction and dissatisfactions during a semistructured interview conducted 6 weeks after admission. H-Hcare was considered a viable alternative to hospitalization when the illness is not too serious, and for patients who are still independent and need little care. When patients are more severely ill, they prefer to go to hospital to avoid overburdening their caregivers and to feel more secure.     

Telehealth transition in a comprehensive care unit for eating disorders: Challenges and long-term benefits

The 2019 novel coronavirus disease (COVID-19) pandemic has forced many eating disorder medical stabilization units to consider adjustments that uphold both the quality of care delivered to patients while also observing social distancing public health directives for patients and staff. To date, inpatient facilities for eating disorders (both medical stabilization units and higher level of care facilities) have not needed to consider how to translate services to electronic platforms, given that most of these programs have in-person staff. We outline our transition to telehealth broadly, emphasizing some unexpected benefits of using telehealth services that we plan on integrating into our work-flow post COVID-19. These may be useful for other higher level of care eating disorder programs, including medical stabilization units, residential, partial hospitalization, and intensive outpatient programs. We also highlight aspects of transition that have been more challenging for this particular patient population, warranting the need for in-person services.     

COVID-19疫情对北京市公立医院住院服务量的影响及其恢复情况分析

目的：了解COVID-19疫情对北京市公立医院住院服务的影响,为卫生健康管理决策提供参考。方法：采用描述性方法分析2020年上半年北京市公立医院出院量的变化情况,并利用ARIMA乘积季节模型假设未发生COVID-19情况下对2020—2021年的出院量进行预测,通过比较其与实际状态下出院量的差异,评估COVID-19疫情对住院服务的潜在影响。结果：2020年1—6月出院总量较2019年同期减少69.1万人次（48.0%）;外地患者出院量较去年同期下降28.2万人次（65.5%）,其中循环系统疾病与恶性肿瘤患者出院人次数下降最多。ARIMA模型结果显示,2020年1—6月实际出院总量与外地患者出院量较预测值分别减少77.3万人次（50.8%）与33.2万人次（69.1%）,住院服务的恢复压力不断增加。结论：疫情后期北京市住院医疗服务秩序的恢复将面临更为复杂的挑战,建议卫生健康部门充分利用互联网与现代化信息技术手段,在做好常态化防控的同时,重点做好外地患者与重点专科医院的住院需求应对。     

Algorithm for Individual Prediction of COVID-19–Related Hospitalization Based on Symptoms: Development and Implementation Study

BackgroundThe COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes.ObjectiveThis study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization.MethodsA predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed.ResultsThe predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept –0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients.ConclusionsA simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.     

Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

     

Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients With COVID-19: Retrospective Analysis

BackgroundRisk assessment of patients with acute COVID-19 in a telemedicine context is not well described. In settings of large numbers of patients, a risk assessment tool may guide resource allocation not only for patient care but also for maximum health care and public health benefit.ObjectiveThe goal of this study was to determine whether a COVID-19 telemedicine risk assessment tool accurately predicts hospitalizations.MethodsWe conducted a retrospective study of a COVID-19 telemedicine home monitoring program serving health care workers and the community in Atlanta, Georgia, with enrollment from March 24 to May 26, 2020; the final call range was from March 27 to June 19, 2020. All patients were assessed by medical providers using an institutional COVID-19 risk assessment tool designating patients as Tier 1 (low risk for hospitalization), Tier 2 (intermediate risk for hospitalization), or Tier 3 (high risk for hospitalization). Patients were followed with regular telephone calls to an endpoint of improvement or hospitalization. Using survival analysis by Cox regression with days to hospitalization as the metric, we analyzed the performance of the risk tiers and explored individual patient factors associated with risk of hospitalization.ResultsProviders using the risk assessment rubric assigned 496 outpatients to tiers: Tier 1, 237 out of 496 (47.8%); Tier 2, 185 out of 496 (37.3%); and Tier 3, 74 out of 496 (14.9%). Subsequent hospitalizations numbered 3 out of 237 (1.3%) for Tier 1, 15 out of 185 (8.1%) for Tier 2, and 17 out of 74 (23%) for Tier 3. From a Cox regression model with age of 60 years or older, gender, and reported obesity as covariates, the adjusted hazard ratios for hospitalization using Tier 1 as reference were 3.74 (95% CI 1.06-13.27; P=.04) for Tier 2 and 10.87 (95% CI 3.09-38.27; P<.001) for Tier 3.ConclusionsA telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified populations with low, intermediate, and high risk of hospitalization.     

Immunocompromise among vaccinated versus unvaccinated COVID-19 cases admitted to critical care in Ireland,July to October 2021

As the COVID-19 pandemic progressed, so too did the proportion of cases admitted to critical care in Ireland who were fully vaccinated. Reporting of this observation has public health implications as incorrect interpretation may affect public confidence in COVID-19 vaccines. A potential explanation is the reduced ability of those who are immunocompromised to produce an adequate, sustained immune response to vaccination. We conducted an analysis of the association between COVID-19 vaccination status and underlying degree of immunocompromise among a cohort of critical care patients all with a confirmed diagnosis of COVID-19 admitted to critical care between July and October 2021. Multinomial logistic regression was used to estimate an odds ratio of immunocompromise among vaccinated COVID-19 cases in critical care compared to unvaccinated cases. In this study, we found a statistically significant association between the vaccination status of severe COVID-19 cases requiring critical care admission and underlying immunocompromise. Fully vaccinated patients were significantly more likely to be highly (OR = 19.3, 95 % CI 7.7–48.1) or moderately immunocompromised (OR = 9.6, 95 % CI 5.0–18.1) compared to unvaccinated patients with COVID-19. These findings support our hypothesis, that highly immunocompromised patients are less likely to produce an adequate and sustained immune response to COVID-19 vaccination, and are therefore more likely to require critical care admission for COVID-19 infection.     

新冠疫情期间肿瘤医院住院患者医院感染及相关因素分析

目的观察新型冠状病毒肺炎疫情防控期间,肿瘤专科医院住院患者医院感染相关变化情况。方法收集2020年1―6月新型冠状病毒疫情防控期间及2019年和2018年同期住院患者医院感染监测数据,对相关数据进行比较分析。结果与2019和2018年同期相比,新型冠状病毒肺炎疫情防控期间住院患者收治同比下降41.51%和34.54%,手术例数同比下降46.43%和43.51%,医院感染率和现患率下降至0.52%和0.80%,医院感染漏报率上升至30.16%,腹盆腔内组织感染和下呼吸道感染仍是最主要医院感染部位,医院感染致病菌中铜绿假单胞菌构成比上升至16.22%,大肠埃希菌构成比下降至10.81%,抗菌药物使用率和送检率有所下降。结论新型冠状病毒肺炎疫情对肿瘤患者住院及接受手术治疗方面产生较大影响。各项医院感染防控措施的强化落实有助于降低医院感染率和抗菌药物使用率,含氯消毒剂消毒频次增加能有效降低肿瘤患者发生大肠埃希菌感染的风险。新型冠状病毒肺炎疫情防控常态化管理下,临床需关注肿瘤患者发生铜绿假单胞菌感染的风险、及时上报医院感染病例以降低医院感染暴发风险。