Does PELOD measure organ dysfunction...and is organ function a valid surrogate for death?

Associative memory retrieval modulates upcoming perceptual decisions

Expectations can inform fast, accurate decisions. But what informs expectations? Here we test the hypothesis that expectations are set by dynamic inference from memory. Participants performed a cue-guided perceptual decision task with independently-varying memory and sensory evidence. Cues established expectations by reminding participants of past stimulus-stimulus pairings, which predicted the likely target in a subsequent noisy image stream. Participant's responses used both memory and sensory information, in accordance to their relative reliability. Formal model comparison showed that the sensory inference was best explained when its parameters were set dynamically at each trial by evidence sampled from memory. Supporting this model, neural pattern analysis revealed that responses to the probe were modulated by the specific content and fidelity of memory reinstatement that occurred before the probe appeared. Together, these results suggest that perceptual decisions arise from the continuous sampling of memory and sensory evidence.

Variation in Initial U.S. Hospital Responses to the Coronavirus Disease 2019 Pandemic

OBJECTIVES

The coronavirus disease 2019 pandemic has strained many healthcare systems. In response, U.S. hospitals altered their care delivery systems, but there are few data regarding specific structural changes. Understanding these changes is important to guide interpretation of outcomes and inform pandemic preparedness. We sought to characterize emergency responses across hospitals in the United States over time and in the context of local case rates early in the coronavirus disease 2019 pandemic.

DESIGN

We surveyed hospitals from a national acute care trials group regarding operational and structural changes made in response to the coronavirus disease 2019 pandemic from January to August 2020. We collected prepandemic characteristics and changes to hospital system, space, staffing, and equipment during the pandemic. We compared the timing of these changes with county-level coronavirus disease 2019 case rates.

SETTING AND PARTICIPANTS

U.S. hospitals participating in the Prevention and Early Treatment of Acute Lung Injury Network Coronavirus Disease 2019 Observational study. Site investigators at each hospital collected local data.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Forty-five sites participated (94% response rate). System-level changes (incident command activation and elective procedure cancellation) occurred at nearly all sites, preceding rises in local case rates. The peak inpatient census during the pandemic was greater than the prior hospital bed capacity in 57% of sites with notable regional variation. Nearly half (49%) expanded ward capacity, and 63% expanded ICU capacity, with nearly all bed expansion achieved through repurposing of clinical spaces. Two-thirds of sites adapted staffing to care for patients with coronavirus disease 2019, with 48% implementing tiered staffing models, 49% adding temporary physicians, nurses, or respiratory therapists, and 30% changing the ratios of physicians or nurses to patients.

CONCLUSIONS

The coronavirus disease 2019 pandemic prompted widespread system-level changes, but front-line clinical care varied widely according to specific hospital needs and infrastructure. Linking operational changes to care delivery processes is a necessary step to understand the impact of the coronavirus disease 2019 pandemic on patient outcomes.

Adult ICU Triage During the Coronavirus Disease 2019 Pandemic: Who Will Live and Who Will Die? Recommendations to Improve Survival

OBJECTIVES

Coronavirus disease 2019 patients are currently overwhelming the world's healthcare systems. This article provides practical guidance to front-line physicians forced to make critical rationing decisions.

DATA SOURCES

PubMed and Medline search for scientific literature, reviews, and guidance documents related to epidemic ICU triage including from professional bodies.

STUDY SELECTION

Clinical studies, reviews, and guidelines were selected and reviewed by all authors and discussed by internet conference and email.

DATA EXTRACTION

References and data were based on relevance and author consensus.

DATA SYNTHESIS

We review key challenges of resource-driven triage and data from affected ICUs. We recommend that once available resources are maximally extended, triage is justified utilizing a strategy that provides the greatest good for the greatest number of patients. A triage algorithm based on clinical estimations of the incremental survival benefit (saving the most life-years) provided by ICU care is proposed. "First come, first served" is used to choose between individuals with equal priorities and benefits. The algorithm provides practical guidance, is easy to follow, rapidly implementable and flexible. It has four prioritization categories: performance score, ASA score, number of organ failures, and predicted survival. Individual units can readily adapt the algorithm to meet local requirements for the evolving pandemic. Although the algorithm improves consistency and provides practical and psychologic support to those performing triage, the final decision remains a clinical one. Depending on country and operational circumstances, triage decisions may be made by a triage team or individual doctors. However, an experienced critical care specialist physician should be ultimately responsible for the triage decision. Cautious discharge criteria are proposed acknowledging the difficulties to facilitate the admission of queuing patients.

CONCLUSIONS

Individual institutions may use this guidance to develop prospective protocols that assist the implementation of triage decisions to ensure fairness, enhance consistency, and decrease provider moral distress.

Risk factors associated to noninvasive ventilation failure in primary influenza A pneumonia in the critical care setting

OBJECTIVE

To evaluate the risk factors associated to noninvasive mechanical ventilation (NIV) failure in patients with primary pneumonia due to influenza A (H1N1)pdm09 virus admitted to the intensive care unit (ICU), and to demonstrate the association of NIV failure to increased mortality and longer stays.

DESIGN

A cohort study was carried out.

SCOPE

A mixed ICU (16 beds) in a teaching hospital.

PATIENTS

Adult patients admitted to the ICU with a diagnosis of pneumonia due to influenza A (H1N1)pdm09 virus requiring mechanical ventilation.

MEASUREMENTS

Age, sex, severity scores, administration of corticosteroids, oseltamivir within 72h of symptoms onset, days of symptoms prior to admission, affected quadrants, hemodynamic parameters, renal failure, laboratory test data on admission, mortality and stay in ICU and in hospital.

RESULTS

A total of 54 patients were admitted to the ICU and 49 were ventilated; 29 were females (59.2%), and the mean age±standard deviation was 66.77±14.77 years. Forty-three patients (87.75%) were ventilated with NIV, and 18 (41.9%) of them failed. Patients with NIV failure were younger (63 vs. 74 years; p=0.04), with a higher SOFA score (7 vs. 4; p=0.01) and greater early hemodynamic failure (61.1 vs. 8%; p=0.01). In addition, they presented longer ICU (26.28 vs. 6.88 days; p=0.01) and hospital stay (32.78 vs. 18.8 days; p=0.01). The ICU mortality rate was also higher in the NIV failure group (38.9 vs. 0%; p=0.02). In the multivariate analysis, corticosteroid therapy (OR 7.08; 95% CI 1.23-40.50) and early hemodynamic failure (OR 14.77; 95% CI 2.34-92.97) were identified as independent risk factors for NIV failure.

CONCLUSIONS

Treatment with corticosteroids and early hemodynamic failure were associated to NIV failure in patients with primary pneumonia due to influenza A (H1N1)pdm09 virus infection admitted to the ICU. The failure of NIV was associated to increased mortality.

Risk factors associated to noninvasive ventilation failure in primary influenza A pneumonia in the critical care setting

OBJECTIVE

To evaluate the risk factors associated to noninvasive mechanical ventilation (NIV) failure in patients with primary pneumonia due to influenza A (H1N1)pdm09 virus admitted to the intensive care unit (ICU), and to demonstrate the association of NIV failure to increased mortality and longer stays.

DESIGN

A cohort study was carried out.

SCOPE

A mixed ICU (16 beds) in a teaching hospital.

PATIENTS

Adult patients admitted to the ICU with a diagnosis of pneumonia due to influenza A (H1N1)pdm09 virus requiring mechanical ventilation.

MEASUREMENTS

Age, sex, severity scores, administration of corticosteroids, oseltamivir within 72h of symptoms onset, days of symptoms prior to admission, affected quadrants, hemodynamic parameters, renal failure, laboratory test data on admission, mortality and stay in ICU and in hospital.

RESULTS

A total of 54 patients were admitted to the ICU and 49 were ventilated; 29 were females (59.2%), and the mean age±standard deviation was 66.77±14.77 years. Forty-three patients (87.75%) were ventilated with NIV, and 18 (41.9%) of them failed. Patients with NIV failure were younger (63 vs. 74 years; P=.04), with a higher SOFA score (7 vs. 4; P=.01) and greater early hemodynamic failure (61.1 vs. 8%; P=.01). In addition, they presented longer ICU (26.28 vs. 6.88 days; P=.01) and hospital stay (32.78 vs. 18.8 days; P=.01). The ICU mortality rate was also higher in the NIV failure group (38.9 vs. 0%; P=.02). In the multivariate analysis, corticosteroid therapy (OR 7.08; 95% CI 1.23-40.50) and early hemodynamic failure (OR 14.77; 95% CI 2.34-92.97) were identified as independent risk factors for NIV failure.

CONCLUSIONS

Treatment with corticosteroids and early hemodynamic failure were associated to NIV failure in patients with primary pneumonia due to influenza A (H1N1)pdm09 virus infection admitted to the ICU. The failure of NIV was associated to increased mortality.

Assisting radiologists with reporting urgent findings to referring physicians: A machine learning approach to identify cases for prompt communication

Radiologists are expected to expediently communicate critical and unexpected findings to referring clinicians to prevent delayed diagnosis and treatment of patients. However, competing demands such as heavy workload along with lack of administrative support resulted in communication failures that accounted for 7% of the malpractice payments made from 2004 to 2008 in the United States. To address this problem, we have developed a novel machine learning method that can automatically and accurately identify cases that require prompt communication to referring physicians based on analyzing the associated radiology reports. This semi-supervised learning approach requires a minimal amount of manual annotations and was trained on a large multi-institutional radiology report repository from three major external healthcare organizations. To test our approach, we created a corpus of 480 radiology reports from our own institution and double-annotated cases that required prompt communication by two radiologists. Our evaluation on the test corpus achieved an F-score of 74.5% and recall of 90.0% in identifying cases for prompt communication. The implementation of the proposed approach as part of an online decision support system can assist radiologists in identifying radiological cases for prompt communication to referring physicians to avoid or minimize potential harm to patients.

Ebola preparedness: a rapid needs assessment of critical care in a tertiary hospital

BACKGROUND

The current outbreak of Ebola has been declared a public health emergency of international concern. We performed a rigorous and rapid needs assessment to identify the desired results, the gaps in current practice, and the barriers and facilitators to the development of solutions in the provision of critical care to patients with suspected or confirmed Ebola.

METHODS

We conducted a qualitative study with an emergent design at a tertiary hospital in Ontario, Canada, recently designated as an Ebola centre, from Oct. 21 to Nov. 7, 2014. Participants included physicians, nurses, respiratory therapists, and staff from infection control, housekeeping, waste management, administration, facilities, and occupational health and safety. Data collection included document analysis, focus groups, interviews and walk-throughs of critical care areas with key stakeholders.

RESULTS

Fifteen themes and 73 desired results were identified, of which 55 had gaps. During the study period, solutions were implemented to fully address 8 gaps and partially address 18 gaps. Themes identified included the following: screening; response team activation; personal protective equipment; postexposure to virus; patient placement, room setup, logging and signage; intrahospital patient movement; interhospital patient movement; critical care management; Ebola-specific diagnosis and treatment; critical care staffing; visitation and contacts; waste management, environmental cleaning and management of linens; postmortem; conflict resolution; and communication.

INTERPRETATION

This investigation identified widespread gaps across numerous themes; as such, we have been able to develop a set of credible and measureable results. All hospitals need to be prepared for contact with a patient with Ebola, and the preparedness plan will need to vary based on local context, resources and site designation.

Surge capacity principles: care of the critically ill and injured during pandemics and disasters: CHEST consensus statement

BACKGROUND

This article provides consensus suggestions for expanding critical care surge capacity and extension of critical care service capabilities in disasters or pandemics. It focuses on the principles and frameworks for expansion of intensive care services in hospitals in the developed world. A companion article addresses surge logistics, those elements that provide the capability to deliver mass critical care in disaster events. The suggestions in this article are important for all who are involved in large-scale disasters or pandemics with injured or critically ill multiple patients, including front-line clinicians, hospital administrators, and public health or government officials.

METHODS

The Surge Capacity topic panel developed 23 key questions focused on the following domains: systems issues; equipment, supplies, and pharmaceuticals; staffing; and informatics. Literature searches were conducted to identify evidence on which to base key suggestions. Most reports were small scale, were observational, or used flawed modeling; hence, the level of evidence on which to base recommendations was poor and did not permit the development of evidence-based recommendations. Therefore, the panel developed expert opinion-based suggestions using a modified Delphi process. Suggestions from the previous task force were also included for validation by the expert panel.

RESULTS

This article presents 10 suggestions pertaining to the principles that should guide surge capacity and capability planning for mass critical care, including the role of critical care in disaster planning; the surge continuum; targets of surge response; situational awareness and information sharing; mitigating the impact on critical care; planning for the care of special populations; and service deescalation/cessation (also considered as engineered failure).

CONCLUSIONS

Future reports on critical care surge should emphasize population-based outcomes as well as logistical details. Planning should be based on the projected number of critically ill or injured patients resulting from specific scenarios. This should include a consideration of ICU patient care requirements over time and must factor in resource constraints that may limit the ability to provide care. Standard ICU management forms and patient data forms to assess ICU surge capacity impacts should be created and used in disaster events.