Pandemic influenza and excess intensive-care workload

Global predictors of tracheostomy-related pressure injury in the COVID-19 era: A study of secondary data

OBJECTIVES

To determine the incidence and risk factors of tracheostomy-related pressure injuries (TRPI) and examine the COVID-19 pandemic's impact on TRPI incidence.

DESIGN

Secondary analysis of Global Tracheostomy Collaborative database and a multi-center hospital system's electronic medical records.

SETTING

27 hospitals, primarily in the United States, United Kingdom, and Australasia.

PATIENTS

6,400 adults and 2,405 pediatric patients hospitalized with tracheostomy between 1 January 2019 and 31 December 2021.

MEASUREMENT

TRPI as a binary outcome, reported as odds ratios.

RESULTS

TRPI incidence was 4.69 % in adults and 5.65 % in children. For adults, associated risks were female sex (OR: 0.64), severe obesity (OR: 2.62), ICU admission (OR: 2.05), cuffed tracheostomy (OR: 1.49), fenestrated tracheostomy (OR: 15.37), percutaneous insertion (OR: 2.03) and COVID-19 infection (OR: 1.66). For children, associated risks were diabetes mellitus (OR: 4.31) and ICU admission (OR: 2.68). TRPI odds increased rapidly in the first 60 days of stay. Age was positively associated with TRPI in adults (OR: 1.014) and children (OR: 1.060). Black patients had higher TRPI incidence than white patients; no moderating effects of race were found. Hospital cluster effects (adults ICC: 0.227; children ICC: 0.138) indicated unmeasured hospital-level factors played a significant role.

CONCLUSIONS

Increasing age and length of stay up to 60 days are TRPI risk factors. Other risks for adults were female sex, severe obesity, cuffed/fenestrated tracheostomy, percutaneous insertion, and COVID-19; for children, diabetes mellitus and FlexTend devices were risks. Admission during the COVID-19 pandemic had contrasting effects for adults and children. Additional research is needed on unmeasured hospital-level factors.

IMPLICATIONS FOR CLINICAL PRACTICE

These findings can guide targeted interventions to reduce TRPI incidence and inform tracheostomy care during public health crises. Hospital benchmarking of tracheostomy-related pressure injuries is needed.

Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study

Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, with fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of 3 seasonal measures of influenza hospitalizations in the United States: peak hospitalization rate, peak hospitalization week, and cumulative hospitalization rate. We trained an ensemble machine learning algorithm on 15,000 simulated hospitalization curves and generated weekly predictions. We compared the performance of the ensemble (weighted combination of predictions from multiple prediction algorithms), the best-performing individual prediction algorithm, and a naive prediction (median of a simulated outcome distribution). Ensemble predictions performed similarly to the naive predictions early in the season but consistently improved as the season progressed for all prediction targets. The best-performing prediction algorithm in each week typically had similar predictive accuracy compared with the ensemble, but the specific prediction algorithm selected varied by week. An ensemble super learner improved predictions of influenza-related hospitalizations, relative to a naive prediction. Future work should examine the super learner's performance using additional empirical data on influenza-related predictors (e.g., influenza-like illness). The algorithm should also be tailored to produce prospective probabilistic forecasts of selected prediction targets.

ICU Resource Limitations During Peak Seasonal Influenza: Results of a 2018 National Feasibility Study

OBJECTIVES

Demonstrate the feasibility of weekly data collection and analysis of public health emergency (PHE) data. Assess fluctuations in, and challenges of, resource matching and potential effect on patient care for influenza in ICUs.

DESIGN

Multicenter prospective noninterventional study testing effectiveness of leveraging the Discovery Critical Care Research Network Program for Resilience and Emergency Preparedness (Discovery-PREP) in performing PHE research. A 20-question internet survey was developed to prospectively assess ICU influenza-related resource stress. An informatics tool was designed to track responses; data were analyzed within 24 hours of weekly survey completion by the team biostatistician for timely reporting.

PARTICIPANTS

Critical care and Emergency Medicine Discovery-PREP network investigators self-selected to participate in the voluntary query.

SETTING

ICUs of 13 hospitals throughout the United States, 12 academic, and one community.

INTERVENTIONS

ICU physicians were electronically surveyed weekly over 17 weeks during the influenza season (January 2018-April 2018). Responses were collected for 48 hours after each email query.

MEASUREMENTS AND MAIN RESULTS

The average weekly response among the sites was 79% (range, 65-100%). Significant stress, defined as alterations in ICU staffing and/or resource allocation, occurred in up to 41% of sites during the national peak of influenza activity. These alterations included changes in staffing, not accepting external patient transfers, and canceling elective surgery. During this same period, up to 17% of the sites indicated that these changes might not have been sufficient to prevent potentially avoidable patient harm.

CONCLUSIONS

This novel approach to querying ICU operational stress indicated that almost half of participating sites experienced critical care resource limitations during peak influenza season and required process and/or staffing changes to better balance resources with patient care demands. This weekly national reporting infrastructure could be adapted and expanded to better inform providers, hospital emergency management teams, and government leaders during PHEs.

Readiness of Medical Teams Caring for COVID-19 in the Intensive Care Units: A National Web-Based Survey in Indonesia

BACKGROUND: The coronavirus disease 2019 (COVID)-19 pandemic is a challenge for the intensive care unit (ICU) medical team. It requires management of space, stuff (medical equipment including drugs), staff, and system readiness (4S) to deal with the surge in the number of patients. AIM: This survey aims to describe the current readiness efforts among ICU medical team at the COVID-19 referral hospitals in Indonesia; space, stuff readiness, staff, and systems readiness. METHODS: We conducted a cross-sectional national web-based survey of ICUs across referral hospitals during pandemic COVID-19 in Indonesia from June to October 2020. The medical teams survey included 53 questions in multiple parts addressing five dimensions. A linear regression model was applied to determine the factors related with readiness. RESULTS: A total of 459 participants (83.6%) agreed to join in this study. The participants’ average age was 40.43 years (SD = 5.78). About 62.53% were male, 51.20% had bachelor degree, and 55.77% lived outside of Java Island. The mean of total score of medical team readiness was 2.76 (SD = 0.320) and the highest (maximum score) mean score of medical team readiness domain was stuff (2.81, SD = 7.72). Education, working experience, training, perception of risk of contracting COVID-19, and residence had a substantial effect on the readiness, with R2 values of 0.378, p < 0.05. CONCLUSIONS: This study provides an initial view of current preparedness efforts among a group of ICUs in Indonesia’s leading hospital during the first wave of pandemic. Interventions must be developed and implemented quickly to increase the medical team’s readiness to care for a future pandemic.

The Experience of a Single NHS England Trust on the Impact of the COVID-19 Pandemic on Junior and Middle-Grade Doctors: What Is Next?

The COVID-19 pandemic has undoubtedly affected all national healthcare systems at different levels. In countries heavily hit by the pandemic, it was reported that healthcare workers were asked to work long hours, had increased workload, were faced with difficult decisions, and that the resources were stretched. As such, the COVID-19 pandemic would create the perfect storm for burnout in healthcare workers. Within this context, we conducted a survey in a district general hospital in Southeast England. We focused on doctors in training, in different specialties. This survey included parts of the Maslach Burnout Inventory for healthcare professionals, along with other relevant questions, such as the financial impact and seeking of psychological support. The results showed moderate levels of emotional exhaustion, but high levels of personal satisfaction, a positive impact on doctors finances and very low levels of seeking support.

Health workforce surge capacity during the COVID‐19 pandemic and other global respiratory disease outbreaks: A systematic review of health system requirements and responses

Health system decision‐makers need comprehensive evidence to mitigate surges in the demand for human resources for health (HRH) during infectious disease outbreaks. This study aimed to assess the state of the evidence on policy and planning responses to HRH surge capacity during the coronavirus disease (COVID‐19) pandemic and other viral respiratory disease outbreaks of global significance in the 21st century. We systematically searched eight bibliographic databases to extract primary research articles published between January 2000 and June 2020 capturing temporal changes in health workforce requirements and responses surrounding respiratory virus pandemics. Following the Preferred Reporting Items for Systematic Reviews and Meta‐analyses standard, 16 studies met our inclusion criteria. Five focused on COVID‐19, three on H1N1, and eight modelled a hypothetical pandemic. Investigations of different training, mobilization, and redeployment options to address pandemic‐time health system capacity were reviewed; however, few scenarios drew on observational HRH data, and heterogeneity of study approaches and outcomes generally precluded comparability across contexts. Notable evidence gaps included occupational and psychosocial factors affecting healthcare workers' absenteeism and risk of burnout, gendered considerations of HRH capacity, evaluations in low‐ and lower‐middle income countries, and policy‐actionable assessments to inform post‐pandemic recovery and sustainability of services for noncommunicable disease management.

Mental workload of frontline nurses aiding in the COVID-19 pandemic: A latent profile analysis

Aims

To investigate the mental workload level of nurses aiding the most affected area during the Coronavirus disease 2019 (COVID‐19) pandemic and explore the subtypes of nurses regarding their mental workload.

Design

Cross‐sectional study.

Methods

A sample of 446 frontline nurses participated from March 8 to 19, 2020. A latent profile analysis was performed to identify clusters based on the six subscales of the Chinese version of the National Aeronautics and Space Administration Task Load Index. The differences among the classes and the variables including sociodemographic characteristics, psychological capital and coping style were explored.

Results

The level of mental workload indicates that the nurses had high self‐evaluations of their performance while under extremely intensive task loads. The following three latent subtypes were identified: ‘low workload & low self‐evaluation’ (8.6%); ‘medium workload & medium self‐evaluation’ (35.3%) and ‘high workload & high self‐evaluation’ (56.1%) (Classes 1, 2, and 3, respectively). Nurses with shared accommodations, fewer years of practice, junior professional titles, lower incomes, nonmanagement working positions, lower psychological capital levels and negative coping styles had a higher likelihood of belonging to Class 1. In contrast, senior nurses with higher psychological capital and positive coping styles were more likely to belong to Classes 2 and 3.

Conclusion

The characteristics of the ‘low workload & low self‐evaluation’ subtype suggest that attention should be paid to the work pressure and psychological well‐being of junior nurses. Further research on regular training program of public health emergency especially for novices is needed. Personnel management during public health events should be focused on the allocation between novice and senior frontline nurses.

Impact

This study addresses the level of mental workload of frontline nurses who aid in the most severe area of the COVID‐19 pandemic in China and delineates the characteristics of the subtypes of these nurses.

Role of time-normalized laboratory findings in predicting COVID-19 outcome

Objectives The pandemic COVID-19 currently reached 213 countries worldwide with nearly 9 million infected people and more than 460,000 deaths. Although several Chinese studies, describing the laboratory findings characteristics of this illness have been reported, European data are still scarce. Furthermore, previous studies often analyzed the averaged laboratory findings collected during the entire hospitalization period, whereas monitoring their time-dependent variations should give more reliable prognostic information. Methods We analyzed the time-dependent variations of 14 laboratory parameters in two groups of COVID-19 patients with, respectively, a positive (40 patients) or a poor (42 patients) outcome, admitted to the San Raffaele Hospital (Milan, Italy). We focused mainly on laboratory parameters that are routinely tested, thus, prognostic information would be readily available even in low-resource settings. Results Statistically significant differences between the two groups were observed for most of the laboratory findings analyzed. We showed that some parameters can be considered as early prognostic indicators whereas others exhibit statistically significant differences only at a later stage of the disease. Among them, earliest indicators were: platelets, lymphocytes, lactate dehydrogenase, creatinine, alanine aminotransferase, C-reactive protein, white blood cells and neutrophils. Conclusions This longitudinal study represents, to the best of our knowledge, the first study describing the laboratory characteristics of Italian COVID-19 patients on a normalized time-scale. The time-dependent prognostic value of the laboratory parameters analyzed in this study can be used by clinicians for the effective treatment of the patients and for the proper management of intensive care beds, which becomes a critical issue during the pandemic peaks.

[Contingency plan for the intensive care services for the COVID-19 pandemic]

In January 2020, the Chinese authorities identified a new virus of the Coronaviridae family as the cause of several cases of pneumonia of unknown aetiology. The outbreak was initially confined to Wuhan City, but then spread outside Chinese borders. On 31 January 2020, the first case was declared in Spain. On 11 March 2020, The World Health Organization (WHO) declared the coronavirus outbreak a pandemic. On 16 March 2020, there were 139 countries affected. In this situation, the Scientific Societies SEMICYUC and SEEIUC have decided to draw up this Contingency Plan to guide the response of the Intensive Care Services. The objectives of this plan are to estimate the magnitude of the problem and identify the necessary human and material resources. This is to provide the Spanish Intensive Medicine Services with a tool to programme optimal response strategies.

Contingency plan for the intensive care services for the COVID-19 pandemic

In January 2020, the Chinese authorities identified a new virus of the Coronaviridae family as the cause of several cases of pneumonia of unknown aetiology. The outbreak was initially confined to Wuhan City, but then spread outside Chinese borders. On 31 January 2020, the first case was declared in Spain. On 11 March 2020, The World Health Organization (WHO) declared the coronavirus outbreak a pandemic. On 16 March 2020, there were 139 countries affected. In this situation, the Scientific Societies SEMICYUC and SEEIUC, have decided to draw up this Contingency Plan to guide the response of the Intensive Care Services. The objectives of this plan are to estimate the magnitude of the problem and identify the necessary human and material resources. This is to provide the Spanish Intensive Medicine Services with a tool to programme optimal response strategies.

Managing ICU surge during the COVID-19 crisis: rapid guidelines

Given the rapidly changing nature of COVID-19, clinicians and policy makers require urgent review and summary of the literature, and synthesis of evidence-based guidelines to inform practice. The WHO advocates for rapid reviews in these circumstances. The purpose of this rapid guideline is to provide recommendations on the organizational management of intensive care units caring for patients with COVID-19 including: planning a crisis surge response; crisis surge response strategies; triage, supporting families, and staff.

COVID-19: lessons for junior doctors redeployed to critical care

Approximately 4% of patients with coronavirus disease 2019 (COVID-19) will require admission to an intensive care unit (ICU). Governments have cancelled elective procedures, ordered new ventilators and built new hospitals to meet this unprecedented challenge. However, intensive care ultimately relies on human resources. To enhance surge capacity, many junior doctors have been redeployed to ICU despite a relative lack of training and experience. The COVID-19 pandemic poses additional challenges to new ICU recruits, from the practicalities of using personal protective equipment to higher risks of burnout and moral injury. In this article, we describe lessons for junior doctors responsible for managing patients who are critically ill with COVID-19 based on our experiences at an urban teaching hospital.

Contingency Plan for the Intensive Care Services for the COVID-19 pandemic

In January 2020, the Chinese authorities identified a new virus of the Coronaviridae family as the cause of several cases of pneumonia of unknown aetiology. The outbreak was initially confined to Wuhan City, but then spread outside Chinese borders. On 31 January 2020, the first case was declared in Spain. On 11 March 2020, The World Health Organisation (WHO) declared the coronavirus outbreak a pandemic. On 16 March 2020, there were 139 countries affected. In this situation, the Scientific Societies SEMICYUC and SEEIUC, have decided to draw up this Contingency Plan to guide the response of the intensive care services. The objectives of this plan are to estimate the magnitude of the problem and identify the necessary human and material resources. This is to provide the Spanish Intensive Medicine Services with a tool to programme optimal response strategies.

Contingency Plan for the Intensive Care Services for the COVID-19 pandemic

In January 2020, the Chinese authorities identified a new virus of the Coronaviridae family as the cause of several cases of pneumonia of unknown aetiology. The outbreak was initially confined to Wuhan City, but then spread outside Chinese borders. On 31 January 2020, the first case was declared in Spain. On 11 March 2020, The World Health Organization (WHO) declared the coronavirus outbreak a pandemic. On 16 March 2020, there were 139 countries affected. In this situation, the Scientific Societies SEMICYUC and SEEIUC, have decided to draw up this Contingency Plan to guide the response of the Intensive Care Services. The objectives of this plan are to estimate the magnitude of the problem and identify the necessary human and material resources. This is to provide the Spanish Intensive Medicine Services with a tool to programme optimal response strategies.

An algorithm to diagnose influenza infection: evaluating the clinical importance and impact on hospital costs of screening with rapid antigen detection tests

Rapid antigen detection tests (RADTs) are immunoassays that produce results in 15 min or less, have low sensitivity (50 %), but high specificity (95 %). We studied the clinical impact and laboratory savings of a diagnostic algorithm for influenza infection using RADTs as a first-step technique during the influenza season. From January 15th to March 31st 2014, we performed a diagnostic algorithm for influenza infection consisting of an RADT for all respiratory samples received in the laboratory. We studied all the patients with positive results for influenza infection, dividing them into two groups: Group A with a negative RADT but positive reference tests [reverse transcription polymerase chain reaction (RT-PCR) and/or culture] and Group B with an initial positive RADT. During the study period, we had a total of 1,156 patients with suspicion of influenza infection. Of them, 217 (19 %) had a positive result for influenza: 132 (11 %) had an initial negative RADT (Group A) and 85 (7 %) had a positive RADT (Group B). When comparing patients in Group A and Group B, we found significant differences, as follows: prescribed oseltamivir (67 % vs. 82 %; p = 0.02), initiation of oseltamivir before 24 h (89 % vs. 97 %; p = 0.03), antibiotics prescribed (89 % vs. 67 %; p = <0.01), intensive care unit (ICU) admissions after diagnosis (23 % vs. 14 %; p = 0.05), and need for supplementary oxygen (61 % vs. 47 %; p = 0.01). An influenza algorithm including RADTs as the first step improves the time of administration of proper antiviral therapy, reduces the use of antibiotics and ICU admissions, and decreases hospital costs.

Health systems' "surge capacity": state of the art and priorities for future research

CONTEXT

Over the past decade, a number of high-impact natural hazard events, together with the increased recognition of pandemic risks, have intensified interest in health systems' ability to prepare for, and cope with, "surges" (sudden large-scale escalations) in treatment needs. In this article, we identify key concepts and components associated with this emerging research theme. We consider the requirements for a standardized conceptual framework for future research capable of informing policy to reduce the morbidity and mortality impacts of such incidents. Here our objective is to appraise the consistency and utility of existing conceptualizations of health systems' surge capacity and their components, with a view to standardizing concepts and measurements to enable future research to generate a cumulative knowledge base for policy and practice.

METHODS

A systematic review of the literature on concepts of health systems' surge capacity, with a narrative summary of key concepts relevant to public health.

FINDINGS

The academic literature on surge capacity demonstrates considerable variation in its conceptualization, terms, definitions, and applications. This, together with an absence of detailed and comparable data, has hampered efforts to develop standardized conceptual models, measurements, and metrics. Some degree of consensus is evident for the components of surge capacity, but more work is needed to integrate them. The overwhelming concentration in the United States complicates the generalizability of existing approaches and findings.

CONCLUSIONS

The concept of surge capacity is a useful addition to the study of health systems' disaster and/or pandemic planning, mitigation, and response, and it has far-reaching policy implications. Even though research in this area has grown quickly, it has yet to fulfill its potential to generate knowledge to inform policy. Work is needed to generate robust conceptual and analytical frameworks, along with innovations in data collection and methodological approaches that enhance health systems' readiness for, and response to, unpredictable high-consequence surges in demand.

Pilot-testing an applied competency-based approach to health human resources planning

A competency-based approach to health human resources (HHR) planning is one that explicitly considers the spectrum of knowledge, skills and judgement (competencies) required for the health workforce based on the health needs of the relevant population in some specific circumstances. Such an approach is of particular benefit to planners challenged to make optimal use of limited HHR as it allows them to move beyond simply estimating numbers of certain professionals required and plan instead according to the unique mix of competencies available from the existing health workforce. This kind of flexibility is particularly valuable in contexts where healthcare providers are in short supply generally (e.g. in many developing countries) or temporarily due to a surge in need (e.g. a pandemic or other disease outbreak). A pilot application of this approach using the context of an influenza pandemic in one health district of Nova Scotia, Canada, is described, and key competency gaps identified. The approach is also being applied using other conditions in other Canadian jurisdictions and in Zambia.

Epidemiologic modeling with FluSurge for pandemic (H1N1) 2009 outbreak, Queensland, Australia

At the beginning of the pandemic (H1N1) 2009 outbreak, we estimated the potential surge in demand for hospital-based services in 4 Health Service Districts of Queensland, Australia, using the FluSurge model. Modifications to the model were made on the basis of emergent evidence and results provided to local hospitals to inform resource planning for the forthcoming pandemic. To evaluate the fit of the model, a comparison between the model's predictions and actual hospitalizations was made. In early 2010, a Web-based survey was undertaken to evaluate the model's usefulness. Predictions based on modified assumptions arising from the new pandemic gained better fit than results from the default model. The survey identified that the modeling support was helpful and useful to service planning for local hospitals. Our research illustrates an integrated framework involving post hoc comparison and evaluation for implementing epidemiologic modeling in response to a public health emergency.

Pandemic influenza and pediatric intensive care

OBJECTIVE

To assess the adequacy of preparedness planning for an influenza pandemic by modeling the pediatric surge capacity of healthcare facility and pediatric intensive care unit (PICU) requirements over time. Governments and Public Health authorities have planned preparedness activities and training for a flu pandemic. PICU facilities will be the limiting factor in healthcare provision for children but detailed analyses for needs and demands in PICU care have not been published.

DESIGN

Based on the Center for Disease Control and Prevention and World Health Organization estimates and published models of the expected evolution of pandemic flu, we modeled the pediatric surge capacity of healthcare facility and PICU requirements over time. Various scenarios with different assumptions were explored. We compared these demands with estimates of maximal PICU capacity factoring in healthcare worker absenteeism as well as reported and more realistic estimates derived from semistructured telephone interviews with key stakeholders in ICUs in the study area.

SETTING

All hospitals and intensive care facilities in the Northern Region in The Netherlands with near 1.7 million inhabitants, of whom approximately 25% is <18 yrs.

MEASUREMENTS AND MAIN RESULTS

Using well-established modeling techniques, evidence-based medicine, and incorporating estimates from the Centers for Disease Control and Prevention and World Health Organization, we show that PICU capacity may suffice during an influenza pandemic. Even during the peak of the pandemic, most children requiring PICU admission may be served, even those who have nonflu-related conditions, provided that robust indications and decision rules are maintained, both for admission, as well as continuation (or discontinuation) of life support.

CONCLUSIONS

We recommend that a model, with assumptions that can be adapted with new information obtained during early stages of the pandemic that is evolving, be an integral part of a preparedness plan for a pandemic influenza with new human transmissible agent like influenza A virus.

Nurses' contacts and potential for infectious disease transmission

These data can help predict staff availability and provide information for pandemic preparedness planning.

Nurses’ contacts with potentially infectious persons probably place them at higher risk than the general population for infectious diseases. During an influenza pandemic, illness among nurses might result in staff shortage. We aimed to show the value of individual data from the healthcare sector for mathematical modeling of infectious disease transmission. Using a paper diary approach, we compared nurses’ daily contacts (2-way conversation with >2 words or skin-to-skin contact) with those of matched controls from a representative population survey. Nurses (n = 129) reported a median of 40 contacts (85% work related), and controls (n = 129) reported 12 contacts (33% work related). For nurses, 51% of work-related contacts were with patients (74% involving skin-to-skin contact, and 63% lasted <15 minutes); 40% were with staff members (29% and 36%, respectively). Our data, used with simulation models, can help predict staff availability and provide information for pandemic preparedness planning.