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1.
BMJ Open ; 12(2): e058171, 2022 02 15.
Article in English | MEDLINE | ID: covidwho-1799217

ABSTRACT

INTRODUCTION: COVID-19 first struck New York City in the spring of 2020, resulting in an unprecedented strain on our healthcare system and triggering multiple changes in public health policy governing hospital operations as well as therapeutic approaches to COVID-19. We examined inpatient mortality at our centre throughout the course of the pandemic. METHODS: This is a retrospective chart review of clinical characteristics, treatments and outcome data of all patients admitted with COVID-19 from 1 March 2020 to 28 February 2021. Patients were grouped into 3-month quartiles. Hospital strain was assessed as per cent of occupied beds based on a normal bed capacity of 1491. RESULTS: Inpatient mortality decreased from 25.0% in spring to 10.8% over the course of the year. During this time, use of remdesivir, steroids and anticoagulants increased; use of hydroxychloroquine and other antibiotics decreased. Daily bed occupancy ranged from 62% to 118%. In a multivariate model with all year's data controlling for demographics, comorbidities and acuity of illness, percentage of bed occupancy was associated with increased 30-day in-hospital mortality of patients with COVID-19 (0.7% mortality increase for each 1% increase in bed occupancy; HR 1.007, CI 1.001 to 1.013, p=0.004) CONCLUSION: Inpatient mortality from COVID-19 was associated with bed occupancy. Early reduction in epicentre hospital bed occupancy to accommodate acutely ill and resource-intensive patients should be a critical component in the strategic planning for future pandemics.


Subject(s)
COVID-19 , Pandemics , Bed Occupancy , Cohort Studies , Hospital Mortality , Hospitals , Humans , Inpatients , Intensive Care Units , Retrospective Studies , Risk Factors , SARS-CoV-2
2.
Crit Care Med ; 50(3): 353-362, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1708946

ABSTRACT

OBJECTIVES: The coronavirus disease 2019 pandemic has disrupted critical care services across the world. In anticipation of surges in the need for critical care services, governments implemented "lockdown" measures to preserve and create added critical care capacity. Herein, we describe the impact of lockdown measures on the utilization of critical care services and patient outcomes compared with nonlockdown epochs in a large integrated health region. DESIGN: This was a population-based retrospective cohort study. SETTING: Seventeen adult ICUs across 14 acute care hospitals in Alberta, Canada. PATIENTS: All adult (age ≥ 15 yr) patients admitted to any study ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main exposure was ICU admission during "lockdown" occurring between March 16, 2020, and June 30, 2020. This period was compared with two nonpandemic control periods: "year prior" (March 16, 2019, to June 30, 2019) and "pre lockdown" immediately prior (November 30, 2019, to March 15, 2020). The primary outcome was the number of ICU admissions. Secondary outcomes included the following: daily measures of ICU utilization, ICU duration of stay, avoidable delay in ICU discharge, and occupancy; and patient outcomes. Mixed multilevel negative binomial regression and interrupted time series regression were used to compare rates of ICU admissions between periods. Multivariable regressions were used to compare patient outcomes between periods. During the lockdown, there were 3,649 ICU admissions (34.1 [8.0] ICU admissions/d), compared with 4,125 (38.6 [9.3]) during the prelockdown period and 3,919 (36.6 [8.7]) during the year prior. Mean bed occupancy declined significantly during the lockdown compared with the nonpandemic periods (78.7%, 95.9%, and 96.4%; p < 0.001). Avoidable ICU discharge delay also decreased significantly (42.0%, 53.2%, and 58.3%; p < 0.001). During the lockdown, patients were younger, had fewer comorbid diseases, had higher acuity, and were more likely to be medical admissions compared with the nonpandemic periods. Adjusted ICU and hospital mortality and ICU and hospital lengths of stay were significantly lower during the lockdown compared with nonpandemic periods. CONCLUSIONS: The coronavirus disease 2019 lockdown resulted in substantial changes to ICU utilization, including a reduction in admissions, occupancy, patient lengths of stay, and mortality.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control/statistics & numerical data , Intensive Care Units/statistics & numerical data , APACHE , Adult , Age Factors , Aged , Alberta/epidemiology , Bed Occupancy , Comorbidity , Critical Care , Female , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Pandemics , Patient Discharge , Public Health , Retrospective Studies , SARS-CoV-2 , Sex Factors
5.
PLoS One ; 17(1): e0262462, 2022.
Article in English | MEDLINE | ID: covidwho-1630364

ABSTRACT

Remdesivir and dexamethasone are the only drugs providing reductions in the lengths of hospital stays for COVID-19 patients. We assessed the impacts of remdesivir on hospital-bed resources and budgets affected by the COVID-19 outbreak. A stochastic agent-based model was combined with epidemiological data available on the COVID-19 outbreak in France and data from two randomized control trials. Strategies involving treating with remdesivir only patients with low-flow oxygen and patients with low-flow and high-flow oxygen were examined. Treating all eligible low-flow oxygen patients during the entirety of the second wave would have decreased hospital-bed occupancy in conventional wards by 4% [2%; 7%] and intensive care unit (ICU)-bed occupancy by 9% [6%; 13%]. Extending remdesivir use to high-flow-oxygen patients would have amplified reductions in ICU-bed occupancy by up to 14% [18%; 11%]. A minimum remdesivir uptake of 20% was required to observe decreases in bed occupancy. Dexamethasone had effects of similar amplitude. Depending on the treatment strategy, using remdesivir would, in most cases, generate savings (up to 722€) or at least be cost neutral (an extra cost of 34€). Treating eligible patients could significantly limit the saturation of hospital capacities, particularly in ICUs. The generated savings would exceed the costs of medications.


Subject(s)
Adenosine Monophosphate/analogs & derivatives , Alanine/analogs & derivatives , Antiviral Agents/economics , Bed Occupancy/economics , Dexamethasone/economics , Adenosine Monophosphate/economics , Adenosine Monophosphate/therapeutic use , Alanine/economics , Alanine/therapeutic use , Antiviral Agents/therapeutic use , Bed Occupancy/statistics & numerical data , COVID-19/drug therapy , COVID-19/economics , COVID-19/virology , Dexamethasone/therapeutic use , France , Hospitalization/economics , Hospitalization/statistics & numerical data , Humans , Intensive Care Units , Length of Stay , Models, Statistical , SARS-CoV-2/isolation & purification
6.
QJM ; 114(11): 773-779, 2022 Jan 05.
Article in English | MEDLINE | ID: covidwho-1612642

ABSTRACT

BACKGROUND: The Acute Medical Unit (AMU) provides care for unscheduled hospital admissions. Seven-day consultant presence and morning AMU discharges have been advocated to improve hospital bed management. AIMS: To determine whether a later time of daily peak AMU occupancy correlates with measures of hospital stress; whether 7-day consultant presence, for COVID-19, abolished weekly periodicity of discharges. DESIGN: Retrospective cohort analysis. METHODS: : Anonymised AMU admission and discharge times were retrieved from the Profile Information Management System (PIMS), at a large, urban hospital from 14 April 2014 to 31 December 2018 and 20 March to 2 May 2020 (COVID-19 peak). Minute-by-minute admission and discharge times were combined to construct a running total of AMU bed occupancy. Fourier transforms were used to determine periodicity. We tested association between (i) average AMU occupancy and (ii) time of peak AMU occupancy, with measures of hospital stress (total medical bed occupancy and 'medical outliers' on non-medical wards). RESULTS: : Daily, weekly and seasonal patterns of AMU bed occupancy were evident. Timing of AMU peak occupancy was unrelated to each measure of hospital stress: total medical inpatients (Spearman's rho, rs = 0.04, P = 0.24); number of medical outliers (rs = -0.06, P = 0.05). During COVID-19, daily bed occupancy was similar, with continuation of greater Friday and Monday discharges than the weekend. CONCLUSIONS: : Timing of peak AMU occupancy did not alter with hospital stress. Efforts to increase morning AMU discharges are likely to have little effect on hospital performance. Seven-day consultant presence did not abolish weekly periodicity of discharges-other factors influence weekend discharges.


Subject(s)
COVID-19 , Bed Occupancy , Hospitals , Humans , Length of Stay , Periodicity , Retrospective Studies , SARS-CoV-2
7.
MMWR Morb Mortal Wkly Rep ; 70(46): 1613-1616, 2021 Nov 19.
Article in English | MEDLINE | ID: covidwho-1524681

ABSTRACT

Surges in COVID-19 cases have stressed hospital systems, negatively affected health care and public health infrastructures, and degraded national critical functions (1,2). Resource limitations, such as available hospital space, staffing, and supplies led some facilities to adopt crisis standards of care, the most extreme operating condition for hospitals, in which the focus of medical decision-making shifted from achieving the best outcomes for individual patients to addressing the immediate care needs of larger groups of patients (3). When hospitals deviated from conventional standards of care, many preventive and elective procedures were suspended, leading to the progression of serious conditions among some persons who would have benefitted from earlier diagnosis and intervention (4). During March-May 2020, U.S. emergency department visits declined by 23% for heart attacks, 20% for strokes, and 10% for diabetic emergencies (5). The Cybersecurity & Infrastructure Security Agency (CISA) COVID Task Force* examined the relationship between hospital strain and excess deaths during July 4, 2020-July 10, 2021, to assess the impact of COVID-19 surges on hospital system operations and potential effects on other critical infrastructure sectors and national critical functions. The study period included the months during which the highly transmissible SARS-CoV-2 B.1.617.2 (Delta) variant became predominant in the United States.† The negative binomial regression model used to calculate estimated deaths predicted that, if intensive care unit (ICU) bed use nationwide reached 75% capacity an estimated 12,000 additional excess deaths would occur nationally over the next 2 weeks. As hospitals exceed 100% ICU bed capacity, 80,000 excess deaths would be expected in the following 2 weeks. This analysis indicates the importance of controlling case growth and subsequent hospitalizations before severe strain. State, local, tribal, and territorial leaders could evaluate ways to reduce strain on public health and health care infrastructures, including implementing interventions to reduce overall disease prevalence such as vaccination and other prevention strategies, as well as ways to expand or enhance capacity during times of high disease prevalence.


Subject(s)
COVID-19/epidemiology , Hospitals/statistics & numerical data , Mortality/trends , Pandemics , Adult , Bed Occupancy/statistics & numerical data , COVID-19/mortality , COVID-19/therapy , Humans , Intensive Care Units/statistics & numerical data , United States/epidemiology
8.
Crit Care Med ; 49(11): 1895-1900, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1467429

ABSTRACT

OBJECTIVES: To determine whether the previously described trend of improving mortality in people with coronavirus disease 2019 in critical care during the first wave was maintained, plateaued, or reversed during the second wave in United Kingdom, when B117 became the dominant strain. DESIGN: National retrospective cohort study. SETTING: All English hospital trusts (i.e., groups of hospitals functioning as single operational units), reporting critical care admissions (high dependency unit and ICU) to the Coronavirus Disease 2019 Hospitalization in England Surveillance System. PATIENTS: A total of 49,862 (34,336 high dependency unit and 15,526 ICU) patients admitted between March 1, 2020, and January 31, 2021 (inclusive). INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: The primary outcome was inhospital 28-day mortality by calendar month of admission, from March 2020 to January 2021. Unadjusted mortality was estimated, and Cox proportional hazard models were used to estimate adjusted mortality, controlling for age, sex, ethnicity, major comorbidities, social deprivation, geographic location, and operational strain (using bed occupancy as a proxy). Mortality fell to trough levels in June 2020 (ICU: 22.5% [95% CI, 18.2-27.4], high dependency unit: 8.0% [95% CI, 6.4-9.6]) but then subsequently increased up to January 2021: (ICU: 30.6% [95% CI, 29.0-32.2] and high dependency unit, 16.2% [95% CI, 15.3-17.1]). Comparing patients admitted during June-September 2020 with those admitted during December 2020-January 2021, the adjusted mortality was 59% (CI range, 39-82) higher in high dependency unit and 88% (CI range, 62-118) higher in ICU for the later period. This increased mortality was seen in all subgroups including those under 65. CONCLUSIONS: There was a marked deterioration in outcomes for patients admitted to critical care at the peak of the second wave of coronavirus disease 2019 in United Kingdom (December 2020-January 2021), compared with the post-first-wave period (June 2020-September 2020). The deterioration was independent of recorded patient characteristics and occupancy levels. Further research is required to determine to what extent this deterioration reflects the impact of the B117 variant of concern.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Bed Occupancy , Comorbidity , Critical Care , Female , Humans , Length of Stay , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , United Kingdom/epidemiology , Young Adult
9.
PLoS One ; 16(10): e0257235, 2021.
Article in English | MEDLINE | ID: covidwho-1456081

ABSTRACT

During the early months of the current COVID-19 pandemic, social distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of these countries, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. By smart testing we mean the testing protocol that is automatically designed by our optimization platform for a given time period, knowing the available number of tests, the current availability of ICU beds and the initial epidemiological situation. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a data-driven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social distancing measures, even when testing capacity is limited.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19 Testing , COVID-19/diagnosis , COVID-19/prevention & control , Critical Care , COVID-19/epidemiology , Humans , Pandemics/prevention & control
10.
Crit Care Med ; 50(3): 353-362, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1398157

ABSTRACT

OBJECTIVES: The coronavirus disease 2019 pandemic has disrupted critical care services across the world. In anticipation of surges in the need for critical care services, governments implemented "lockdown" measures to preserve and create added critical care capacity. Herein, we describe the impact of lockdown measures on the utilization of critical care services and patient outcomes compared with nonlockdown epochs in a large integrated health region. DESIGN: This was a population-based retrospective cohort study. SETTING: Seventeen adult ICUs across 14 acute care hospitals in Alberta, Canada. PATIENTS: All adult (age ≥ 15 yr) patients admitted to any study ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main exposure was ICU admission during "lockdown" occurring between March 16, 2020, and June 30, 2020. This period was compared with two nonpandemic control periods: "year prior" (March 16, 2019, to June 30, 2019) and "pre lockdown" immediately prior (November 30, 2019, to March 15, 2020). The primary outcome was the number of ICU admissions. Secondary outcomes included the following: daily measures of ICU utilization, ICU duration of stay, avoidable delay in ICU discharge, and occupancy; and patient outcomes. Mixed multilevel negative binomial regression and interrupted time series regression were used to compare rates of ICU admissions between periods. Multivariable regressions were used to compare patient outcomes between periods. During the lockdown, there were 3,649 ICU admissions (34.1 [8.0] ICU admissions/d), compared with 4,125 (38.6 [9.3]) during the prelockdown period and 3,919 (36.6 [8.7]) during the year prior. Mean bed occupancy declined significantly during the lockdown compared with the nonpandemic periods (78.7%, 95.9%, and 96.4%; p < 0.001). Avoidable ICU discharge delay also decreased significantly (42.0%, 53.2%, and 58.3%; p < 0.001). During the lockdown, patients were younger, had fewer comorbid diseases, had higher acuity, and were more likely to be medical admissions compared with the nonpandemic periods. Adjusted ICU and hospital mortality and ICU and hospital lengths of stay were significantly lower during the lockdown compared with nonpandemic periods. CONCLUSIONS: The coronavirus disease 2019 lockdown resulted in substantial changes to ICU utilization, including a reduction in admissions, occupancy, patient lengths of stay, and mortality.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control/statistics & numerical data , Intensive Care Units/statistics & numerical data , APACHE , Adult , Age Factors , Aged , Alberta/epidemiology , Bed Occupancy , Comorbidity , Critical Care , Female , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Pandemics , Patient Discharge , Public Health , Retrospective Studies , SARS-CoV-2 , Sex Factors
11.
Am J Med ; 134(11): 1380-1388.e3, 2021 11.
Article in English | MEDLINE | ID: covidwho-1397151

ABSTRACT

BACKGROUND: Whether the volume of coronavirus disease 2019 (COVID-19) hospitalizations is associated with outcomes has important implications for the organization of hospital care both during this pandemic and future novel and rapidly evolving high-volume conditions. METHODS: We identified COVID-19 hospitalizations at US hospitals in the American Heart Association COVID-19 Cardiovascular Disease Registry with ≥10 cases between January and August 2020. We evaluated the association of COVID-19 hospitalization volume and weekly case growth indexed to hospital bed capacity, with hospital risk-standardized in-hospital case-fatality rate (rsCFR). RESULTS: There were 85 hospitals with 15,329 COVID-19 hospitalizations, with a median hospital case volume was 118 (interquartile range, 57, 252) and median growth rate of 2 cases per 100 beds per week but varied widely (interquartile range: 0.9 to 4.5). There was no significant association between overall hospital COVID-19 case volume and rsCFR (rho, 0.18, P = .09). However, hospitals with more rapid COVID-19 case-growth had higher rsCFR (rho, 0.22, P = 0.047), increasing across case growth quartiles (P trend = .03). Although there were no differences in medical treatments or intensive care unit therapies (mechanical ventilation, vasopressors), the highest case growth quartile had 4-fold higher odds of above median rsCFR, compared with the lowest quartile (odds ratio, 4.00; 1.15 to 13.8, P = .03). CONCLUSIONS: An accelerated case growth trajectory is a marker of hospitals at risk of poor COVID-19 outcomes, identifying sites that may be targets for influx of additional resources or triage strategies. Early identification of such hospital signatures is essential as our health system prepares for future health challenges.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19 , Hospital Bed Capacity/statistics & numerical data , Intensive Care Units/statistics & numerical data , Mortality , Quality Improvement/organization & administration , COVID-19/mortality , COVID-19/therapy , Civil Defense , Health Care Rationing/organization & administration , Health Care Rationing/standards , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Outcome Assessment, Health Care , Registries , Risk Assessment , SARS-CoV-2 , Triage/organization & administration , United States/epidemiology
12.
BMC Med ; 19(1): 213, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1379790

ABSTRACT

BACKGROUND: The literature paints a complex picture of the association between mortality risk and ICU strain. In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain. METHODS: A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease). RESULTS: One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)]. CONCLUSION: Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19/mortality , Cause of Death , Critical Care/statistics & numerical data , Hospital Mortality , Intensive Care Units , Ventilators, Mechanical , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Young Adult
13.
Epidemiol Infect ; 149: e102, 2021 04 27.
Article in English | MEDLINE | ID: covidwho-1279797

ABSTRACT

Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds' demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients' hospitalised, without parametric priors and adjusting for individual covariates. We applied a non-parametric mixture cure model and compared its performance in estimating hospital ward (HW)/ICU LoS to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and HW LoS estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting HW and ICU occupancy, as well as discharge or death outcomes.


Subject(s)
COVID-19/epidemiology , Forecasting/methods , Length of Stay/trends , Models, Statistical , Age Factors , Bed Occupancy/statistics & numerical data , Bed Occupancy/trends , Hospital Mortality/trends , Hospitals , Humans , Intensive Care Units/statistics & numerical data , Intensive Care Units/trends , Length of Stay/statistics & numerical data , Patient Discharge/statistics & numerical data , Patient Discharge/trends , SARS-CoV-2 , Sex Factors , Spain/epidemiology , Statistics, Nonparametric , Survival Analysis
14.
Chest ; 161(1): 121-129, 2022 01.
Article in English | MEDLINE | ID: covidwho-1272334

ABSTRACT

BACKGROUND: During the first wave of the COVID-19 pandemic, shortages of ventilators and ICU beds overwhelmed health care systems. Whether early tracheostomy reduces the duration of mechanical ventilation and ICU stay is controversial. RESEARCH QUESTION: Can failure-free day outcomes focused on ICU resources help to decide the optimal timing of tracheostomy in overburdened health care systems during viral epidemics? STUDY DESIGN AND METHODS: This retrospective cohort study included consecutive patients with COVID-19 pneumonia who had undergone tracheostomy in 15 Spanish ICUs during the surge, when ICU occupancy modified clinician criteria to perform tracheostomy in Patients with COVID-19. We compared ventilator-free days at 28 and 60 days and ICU- and hospital bed-free days at 28 and 60 days in propensity score-matched cohorts who underwent tracheostomy at different timings (≤ 7 days, 8-10 days, and 11-14 days after intubation). RESULTS: Of 1,939 patients admitted with COVID-19 pneumonia, 682 (35.2%) underwent tracheostomy, 382 (56%) within 14 days. Earlier tracheostomy was associated with more ventilator-free days at 28 days (≤ 7 days vs > 7 days [116 patients included in the analysis]: median, 9 days [interquartile range (IQR), 0-15 days] vs 3 days [IQR, 0-7 days]; difference between groups, 4.5 days; 95% CI, 2.3-6.7 days; 8-10 days vs > 10 days [222 patients analyzed]: 6 days [IQR, 0-10 days] vs 0 days [IQR, 0-6 days]; difference, 3.1 days; 95% CI, 1.7-4.5 days; 11-14 days vs > 14 days [318 patients analyzed]: 4 days [IQR, 0-9 days] vs 0 days [IQR, 0-2 days]; difference, 3 days; 95% CI, 2.1-3.9 days). Except hospital bed-free days at 28 days, all other end points were better with early tracheostomy. INTERPRETATION: Optimal timing of tracheostomy may improve patient outcomes and may alleviate ICU capacity strain during the COVID-19 pandemic without increasing mortality. Tracheostomy within the first work on a ventilator in particular may improve ICU availability.


Subject(s)
COVID-19/therapy , Intensive Care Units , Pneumonia, Viral/therapy , Respiration, Artificial , Tracheostomy , Aged , Bed Occupancy/statistics & numerical data , COVID-19/epidemiology , Female , Humans , Length of Stay/statistics & numerical data , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Propensity Score , Retrospective Studies , Spain/epidemiology
15.
BMC Health Serv Res ; 21(1): 566, 2021 Jun 09.
Article in English | MEDLINE | ID: covidwho-1262505

ABSTRACT

BACKGROUND: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. METHODS: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. RESULTS: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. CONCLUSIONS: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. TRIAL REGISTRATION: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.


Subject(s)
Bed Occupancy , COVID-19 , England , Humans , Length of Stay , SARS-CoV-2
16.
Ciênc. Saúde Colet ; 26(4): 1441-1456, abr. 2021. tab, graf
Article in Portuguese | WHO COVID, LILACS (Americas) | ID: covidwho-1216975

ABSTRACT

Resumo Mesmo no período em que a pandemia de Covid-19 encontrava-se em crescimento no Nordeste do Brasil, iniciou-se a adoção de medidas de flexibilização do distanciamento social. O objetivo do estudo é o de avaliar a pertinência das propostas de flexibilização, tomando-se em conta a situação da pandemia em cada local e o momento em que foram adotadas. Tendo como referência as diretrizes da OMS, foram construídos e analisados indicadores operacionais para cada diretriz, no contexto da região Nordeste. Para análise do comportamento da epidemia, conforme indicadores selecionados, foram usadas técnicas de Joinpoint Trend Analysis, mapas de calor, razão de taxas e comparação da tendência temporal entre capitais e interior dos estados. O pico do crescimento semanal ocorreu em maio-julho/2020 (semanas epidemiológicas 19 a 31). Na maioria das capitais não se observou tendência decrescente simultânea do número de casos e óbitos nos 14 dias prévios à flexibilização. Em todos os estados o quantitativo de testes realizados foi insuficiente. Na semana epidemiológica 24 os percentuais estaduais de ocupação de leitos de UTI/Covid-19 foram próximos ou superiores 70%. A situação epidemiológica das nove capitais dos estados do Nordeste, no momento em que a decisão de flexibilização foi tomada, mostra que nenhuma delas atendia aos critérios e parâmetros recomendados pela OMS.


Abstract Even in the period when the Covid-19 pandemic was on the rise in the Northeast of Brazil, the relaxation of social distancing measures was introduced. The scope of the study is to assess, in the light of the epidemiological-sanitary situation in the region, the suitability of relaxation of social distancing measures. Based on the WHO guidelines for relaxation of social distancing, operational indicators were created and analyzed for each guideline in the context of the Northeast. To analyze the behavior of the epidemic, according to selected indicators, Joinpoint trend analysis techniques, heat maps, rate ratios and time trends between capitals and the state interior were compared. The weekly growth peak of the epidemic occurred in May-July 2020 (epidemiological weeks 19 to 31). In most capitals, there was no simultaneous downward trend in the number of cases and deaths in the 14 days prior to flexibilization. In all states the number of tests performed was insufficient. In epidemiological week 24, the state percentages of ICU/Covid-19 bed occupancy were close to or above 70%. The epidemiological situation of the nine Northeastern state capitals analyzed here did not meet criteria and parameters recommended by the World Health Organization for the relaxation of social distancing measures.


Subject(s)
Humans , Pandemics , Physical Distancing , COVID-19/epidemiology , Bed Occupancy/statistics & numerical data , World Health Organization , Brazil/epidemiology , Communicable Disease Control , COVID-19/prevention & control
18.
Pediatr Ann ; 50(4): e172-e177, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1211970

ABSTRACT

Severe acute respiratory syndrome coronavirus 2, the virus causing the pandemic illness coronavirus disease 2019, was first detected in the United States in January 2020. As the illness spread across the country, all aspects and venues of health care were significantly impacted. This article explores the challenges and response of one children's emergency medicine division related to surge planning, personal protective equipment, screening, testing, staffing, and other operational challenges, and describes the impact and implications thus far. [Pediatr Ann. 2021;50(4):e172-e177.].


Subject(s)
COVID-19/diagnosis , COVID-19/therapy , Emergency Service, Hospital , Bed Occupancy/statistics & numerical data , Child , Humans , Personal Protective Equipment , Personnel Staffing and Scheduling , SARS-CoV-2 , United States
19.
Rev Esp Quimioter ; 34(3): 238-244, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1173137

ABSTRACT

OBJECTIVE: In some patients the immune response triggered by SARS-CoV-2 is unbalanced, presenting an acute respiratory distress syndrome which in many cases requires intensive care unit (ICU) admission. The limitation of ICU beds has been one of the major burdens in the management around the world; therefore, clinical strategies to avoid ICU admission are needed. We aimed to describe the influence of tocilizumab on the need of transfer to ICU or death in non-critically ill patients. METHODS: A retrospective study of 171 patients with SARS-CoV-2 infection that did not qualify as requiring transfer to ICU during the first 24h after admission to a conventional ward, were included. The criteria to receive tocilizumab was radiological impairment, oxygen demand or an increasing of inflammatory parameters, however, the ultimate decision was left to the attending physician judgement. The primary outcome was the need of ICU admission or death whichever came first. RESULTS: A total of 77 patients received tocilizumab and 94 did not. The tocilizumab group had less ICU admissions (10.3% vs. 27.6%, P=0.005) and need of invasive ventilation (0 vs 13.8%, P=0.001). In the multivariable analysis, tocilizumab remained as a protective variable (OR: 0.03, CI 95%: 0.007-0.1, P=0.0001) of ICU admission or death. CONCLUSIONS: Tocilizumab in early stages of the inflammatory flare could reduce an important number of ICU admissions and mechanical ventilation. The mortality rate of 10.3% among patients receiving tocilizumab appears to be lower than other reports. This is a non-randomized study and the results should be interpreted with caution.


Subject(s)
Antibodies, Monoclonal, Humanized/therapeutic use , COVID-19/mortality , Hospitalization/statistics & numerical data , Intensive Care Units/statistics & numerical data , Bed Occupancy , COVID-19/immunology , Female , Humans , Male , Middle Aged , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS-CoV-2
20.
Acta Paul. Enferm. (Online) ; 34: eAPE02531, 2021. tab
Article in Portuguese | WHO COVID, LILACS (Americas) | ID: covidwho-1171641

ABSTRACT

Resumo Objetivo: Analisar a tendência temporal da incidência, mortalidade, cobertura de enfermarias e leitos de terapia intensiva e rígido isolamento social no estado do Ceará e correlacioná-los. Métodos: Estudo ecológico, cuja variável de desfecho foi a taxa de mortalidade. Os preditores foram a incidência, a taxa de ocupação de enfermarias e leitos de terapia intensiva e a taxa de isolamento social. Foi realizada uma regressão linear múltipla considerada significativa quando p <0,05. Resultados: Observou-se tendência de aumento da incidência e mortalidade por COVID-19 no estado do Ceará (p = 0,01). Por outro lado, observou-se tendência de diminuição na ocupação de enfermarias e leitos de terapia intensiva (p = 0,02). A taxa de isolamento social diminuiu significativamente durante o período (p = 0,001). Na regressão linear múltipla, o isolamento social manteve-se inversamente relacionado à mortalidade pela COVID-19 (β = -0,08; p = 0,02). Conclusão: Verificou-se o efeito do rígido isolamento social durante a pandemia de COVID-19. A implementação antecipada do mesmo, com outras ações de saúde pública, mostrou-se relevante para garantir a continuidade de seus benefícios.


Resumen Objetivo: Analizar la tendencia temporal de la incidencia, mortalidad, ocupación de enfermerías y camas de terapia intensiva y el rígido aislamiento social en el estado de Ceará y correlacionarlos. Métodos: Estudio ecológico, cuya variable de criterio de valoración fue el índice de mortalidad. Los predictores fueron la incidencia, el índice de ocupación de enfermerías y camas de terapia intensiva y el índice de aislamiento social. Se realizó una regresión lineal múltiple considerada significativa cuando p < 0,05. Resultados: Se observó una tendencia de aumento de la incidencia y mortalidad por COVID-19 en el estado de Ceará (p = 0,01). Por otro lado, se observó una tendencia de reducción de ocupación de enfermerías y camas de terapia intensiva (p = 0,02). El índice de aislamiento social se redujo significativamente durante el período (p = 0,001). En la regresión lineal múltiple, el aislamiento social se mantuvo inversamente relacionado con la mortalidad por COVID-19 (β = -0,08; p = 0,02). Conclusión: Se verificó el efecto del aislamiento social rígido durante la pandemia de COVID-19. La implementación anticipada de esta medida, junto con otras acciones de salud pública, demostró ser relevante para garantizar la continuidad de sus beneficios.


Abstract Objective: This study aimed to analyze the temporal trend of incidence, mortality, coverage of wards and intensive care beds, and rigid social isolation in the Ceará State and correlate them. Methods: Ecological study, which outcome variable was the mortality rate. Predictors were incidence, occupation rate of bed wards and intensive care beds, and social isolation rate. It was performed a multiple linear regression considering significant when p<0.05. Results: It was observed an increasing trend of incidence and mortality by COVID-19 in the Ceará State (p=0.01). On the other hand, it was seen a decreasing trend in the occupation of wards and intensive care beds (p=0.02). The social isolation rate significantly decreased during the period (p=0.001). In the multiple linear regression, social isolation remained inversely related to mortality by COVID-19 (β=-0.08; p=0,02). Conclusion: It was seen the effect of rigid social isolation during the COVID-19 pandemics. The anticipated implementation of it, with other public health actions, showed relevance to guarantee the continuity of its benefits.


Subject(s)
Social Isolation , Coronavirus Infections/mortality , Coronavirus Infections/epidemiology , Intensive Care Units , Bed Occupancy , Brazil , Ecological Studies
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