The COVID-19 pandemic placed considerable strain on critical care resources. How US hospitals responded to this crisis is unknown.
What actions did US hospitals take to prepare for a potential surge ...in demand for critical care services in the context of the COVID-19 pandemic?
From September to November 2020, the chief nursing officers of a representative sample of US hospitals were surveyed regarding organizational actions taken to increase or maintain critical care capacity during the COVID-19 pandemic. Weighted proportions of hospitals for each potential action were calculated to create estimates across the entire population of US hospitals, accounting for both the sampling strategy and nonresponse. Also examined was whether the types of actions taken varied according to the cumulative regional incidence of COVID-19 cases.
Responses were received from 169 of 540 surveyed US hospitals (response rate, 31.3%). Almost all hospitals canceled or postponed elective surgeries (96.7%) and nonsurgical procedures (94.8%). Few hospitals created new medical units in areas not typically dedicated to health care (12.9%), and almost none adopted triage protocols (5.6%) or protocols to connect multiple patients to a single ventilator (4.8%). Actions to increase or preserve ICU staff, including use of ICU telemedicine, were highly variable, without any single dominant strategy. Hospitals experiencing a higher incidence of COVID-19 did not consistently take different actions compared with hospitals facing lower incidence.
Responses of hospitals to the mass need for critical care services due to the COVID-19 pandemic were highly variable. Most hospitals canceled procedures to preserve ICU capacity and scaled up ICU capacity using existing clinical space and staffing. Future research linking hospital response to patient outcomes can inform planning for additional surges of this pandemic or other events in the future.
Patients with chronic illness frequently use Physician Orders for Life-Sustaining Treatment (POLST) to document treatment limitations.
To evaluate the association between POLST order for medical ...interventions and intensive care unit (ICU) admission for patients hospitalized near the end of life.
Retrospective cohort study of patients with POLSTs and with chronic illness who died between January 1, 2010, and December 31, 2017, and were hospitalized 6 months or less before death in a 2-hospital academic health care system.
POLST order for medical interventions ("comfort measures only" vs "limited additional interventions" vs "full treatment"), age, race/ethnicity, education, days from POLST completion to admission, histories of cancer or dementia, and admission for traumatic injury.
The primary outcome was the association between POLST order and ICU admission during the last hospitalization of life; the secondary outcome was receipt of a composite of 4 life-sustaining treatments: mechanical ventilation, vasopressors, dialysis, and cardiopulmonary resuscitation. For evaluating factors associated with POLST-discordant care, the outcome was ICU admission contrary to POLST order for medical interventions during the last hospitalization of life.
Among 1818 decedents (mean age, 70.8 SD, 14.7 years; 41% women), 401 (22%) had POLST orders for comfort measures only, 761 (42%) had orders for limited additional interventions, and 656 (36%) had orders for full treatment. ICU admissions occurred in 31% (95% CI, 26%-35%) of patients with comfort-only orders, 46% (95% CI, 42%-49%) with limited-interventions orders, and 62% (95% CI, 58%-66%) with full-treatment orders. One or more life-sustaining treatments were delivered to 14% (95% CI, 11%-17%) of patients with comfort-only orders and to 20% (95% CI, 17%-23%) of patients with limited-interventions orders. Compared with patients with full-treatment POLSTs, those with comfort-only and limited-interventions orders were significantly less likely to receive ICU admission (comfort only: 123/401 31% vs 406/656 62%, aRR, 0.53 95% CI, 0.45-0.62; limited interventions: 349/761 46% vs 406/656 62%, aRR, 0.79 95% CI, 0.71-0.87). Across patients with comfort-only and limited-interventions POLSTs, 38% (95% CI, 35%-40%) received POLST-discordant care. Patients with cancer were significantly less likely to receive POLST-discordant care than those without cancer (comfort only: 41/181 23% vs 80/220 36%, aRR, 0.60 95% CI, 0.43-0.85; limited interventions: 100/321 31% vs 215/440 49%, aRR, 0.63 95% CI, 0.51-0.78). Patients with dementia and comfort-only orders were significantly less likely to receive POLST-discordant care than those without dementia (23/111 21% vs 98/290 34%, aRR, 0.44 95% CI, 0.29-0.67). Patients admitted for traumatic injury were significantly more likely to receive POLST-discordant care (comfort only: 29/64 45% vs 92/337 27%, aRR, 1.52 95% CI, 1.08-2.14; limited interventions: 51/91 56% vs 264/670 39%, aRR, 1.36 95% CI, 1.09-1.68). In patients with limited-interventions orders, older age was significantly associated with less POLST-discordant care (aRR, 0.93 per 10 years 95% CI, 0.88-1.00).
Among patients with POLSTs and with chronic life-limiting illness who were hospitalized within 6 months of death, treatment-limiting POLSTs were significantly associated with lower rates of ICU admission compared with full-treatment POLSTs. However, 38% of patients with treatment-limiting POLSTs received intensive care that was potentially discordant with their POLST.
Palliative care is associated with improved survival and quality of life among patients with lung cancer; however, its influence on health-care utilization and quality of care is unclear.
Is ...palliative care, and the setting in which it occurs, associated with health-care resource utilization and quality of care among patients with advanced lung cancer?
This was a retrospective cohort study of 23,142 patients with stage IIIB/IV lung cancer in the Veterans Affairs HealthCare System between 2007 and 2013. Exposures included the receipt of specialist-delivered palliative care, and the setting of the initial palliative care encounter (inpatient or outpatient) received after cancer diagnosis. Primary outcomes included rates of ED visits, along with rates of hospitalization and odds of ICU admission within the last 30 days of life. Secondary outcomes included any health-care utilization (ED, hospital, or ICU) related to chemotherapy toxicity. We used propensity score methods to perform Poisson and logistic regression modeling.
Among the 23,142 patients, 57% received palliative care, and 36% of initial palliative care encounters were outpatient. Compared with no palliative care, initial palliative care encounter in the outpatient setting was associated with reduced rates of ED visits (adjusted incidence rate ratio aIRR, 0.86; 95% CI, 0.77-0.96) and hospitalizations in the last 30 days of life (aIRR, 0.64; 95% CI, 0.59-0.70). Initial palliative care encounters in both inpatient (adjusted OR aOR, 0.63; 95% CI, 0.53-0.75) and outpatient (aOR, 0.42; 95% CI, 0.35-0.52) settings were associated with reduced odds of ICU admission in the last 30 days of life. Palliative care was also associated with reduced health-care utilization related to chemotherapy toxicity (aOR, 0.88; 95% CI, 0.82-0.95).
Palliative care (particularly in outpatient settings) is associated with reduced health-care utilization at the end of life and may improve the quality of care among patients with advanced lung cancer. These findings support the role of palliative care as an important component of comprehensive cancer care and highlight the potential benefits of outpatient palliative care services.
The COVID-19 pandemic resulted in unprecedented adjustments to ICU organization and care processes globally.
Did hospital emergency responses to the COVID-19 pandemic differ depending on hospital ...setting? Which strategies worked well to mitigate strain as perceived by intensivists?
Between August and November 2020, we carried out semistructured interviews of intensivists from tertiary and community hospitals across six regions in the United States that experienced early or large surges of COVID-19 patients, or both. We identified themes of hospital emergency responses using the four S framework of acute surge planning: space, staff, stuff, system.
Thirty-three intensivists from seven tertiary and six community hospitals participated. Clinicians across both settings believed that canceling elective surgeries was helpful to increase ICU capabilities and that hospitals should establish clearly defined thresholds at which surgeries are limited during future surge events. ICU staff was the most limited resource; staff shortages were improved by the use of tiered staffing models, just-in-time training for non-ICU clinicians, designated treatment teams, and deployment of trainees. Personal protective equipment (PPE) shortages and reuse were widespread, causing substantial distress among clinicians; hands-on PPE training was helpful to reduce clinicians' anxiety. Transparency and involvement of frontline clinicians as stakeholders were important components of effective emergency responses and helped to maintain trust among staff.
We identified several strategies potentially to mitigate strain as perceived by intensivists working in both tertiary and community hospital settings. Our study also demonstrated the importance of trust and transparency between frontline staff and hospital leadership as key components of effective emergency responses during public health crises.
Background
Despite its widespread implementation, it is unclear whether Physician Orders for Life‐Sustaining Treatment (POLST) are safe and improve the delivery of care that patients desire. We ...sought to systematically review the influence of POLST on treatment intensity among patients with serious illness and/or frailty.
Methods
We performed a systematic review of POLST and similar programs using MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database for Systematic Reviews, and PsycINFO, from inception through February 28, 2020. We included adults with serious illness and/or frailty with life expectancy <1 year. Primary outcomes included place of death and receipt of high‐intensity treatment (i.e., hospitalization in the last 30‐ and 90‐days of life, ICU admission in the last 30‐days of life, and number of care setting transitions in last week of life).
Results
Among 104,554 patients across 20 observational studies, 27,090 had POLST. No randomized controlled trials were identified. The mean age of POLST users was 78.7 years, 55.3% were female, and 93.0% were white. The majority of POLST users (55.3%) had orders for comfort measures only. Most studies showed that, compared to full treatment orders on POLST, treatment limitations were associated with decreased in‐hospital death and receipt of high‐intensity treatment, particularly in pre‐hospital settings. However, in the acute care setting, a sizable number of patients likely received POLST‐discordant care. The overall strength of evidence was moderate based on eight retrospective cohort studies of good quality that showed a consistent, similar direction of outcomes with moderate‐to‐large effect sizes.
Conclusion
We found moderate strength of evidence that treatment limitations on POLST may reduce treatment intensity among patients with serious illness. However, the evidence base is limited and demonstrates potential unintended consequences of POLST. We identify several important knowledge gaps that should be addressed to help maximize benefits and minimize risks of POLST.
Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' ...shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts.
We performed clustering analysis using data from patients' hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population.
Kaiser Permanente Northern California, a healthcare delivery system serving 3.9 million members.
ICU patients 18 years old or older with an ICU admission between January 1, 2012, and December 31, 2012, at one of 21 Kaiser Permanente Northern California hospitals.
None.
We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables.
A machine learning approach revealed important differences between empirically derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients' shared needs.
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.
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.
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.
None.
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.
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.
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