What Is the Potential Value of a Randomized Trial of Different Thresholds to Initiate Invasive Ventilation? A Health Economic Analysis.

OBJECTIVES: To estimate the expected value of undertaking a future randomized controlled trial of thresholds used to initiate invasive ventilation compared with usual care in hypoxemic respiratory failure. PERSPECTIVE: Publicly funded healthcare payer. SETTING: Critical care units capable of providing invasive ventilation and unconstrained by resource limitations during usual (nonpandemic) practice. METHODS: We performed a model-based cost-utility estimation with individual-level simulation and value-of-information analysis focused on adults, admitted to critical care, receiving noninvasive oxygen. In the primary scenario, we compared hypothetical threshold A to usual care, where threshold A resulted in increased use of invasive ventilation and improved survival compared with usual care. In the secondary scenario, we compared hypothetical threshold B to usual care, where threshold B resulted in decreased use of invasive ventilation and similar survival compared with usual care. We assumed a willingness-to-pay of 100,000 Canadian dollars (CADs) per quality-adjusted life year. RESULTS: In the primary scenario, threshold A was cost-effective compared with usual care due to improved hospital survival (78.1% vs. 75.1%), despite more use of invasive ventilation (62% vs. 30%) and higher lifetime costs (86,900 vs. 75,500 CAD). In the secondary scenario, threshold B was cost-effective compared with usual care due to similar survival (74.5% vs. 74.6%) with less use of invasive ventilation (20.2% vs. 27.6%) and lower lifetime costs (71,700 vs. 74,700 CAD). Value-of-information analysis showed that the expected value to Canadian society over 10 years of a 400-person randomized trial comparing a threshold for invasive ventilation to usual care in hypoxemic respiratory failure was 1.35 billion CAD or more in both scenarios. CONCLUSIONS: It would be highly valuable to society to identify thresholds that, in comparison to usual care, either increase survival or reduce invasive ventilation without reducing survival.

Reducing the burden of preventable deaths from sepsis in Canada: A need for a national sepsis action plan.

Sepsis is a global health threat with significant morbidity and mortality. Despite clinical practice guidelines and developed health systems, sepsis is often unrecognized or misdiagnosed, leading to preventable harm. In Canada, sepsis is responsible for 1 in 20 deaths and is a significant driver of health system costs. Despite being a signatory to the World Health Organization's Resolution WHA 70.7, adopted in 2017, Canada has not lived up to its commitment. Many existing sepsis policies were developed in response to a specific tragedy, and there is no national sepsis action plan. In this article, we describe the burden of sepsis, provide examples of existing, context-specific, reactionary sepsis policies, and urge a coordinated, proactive Canadian sepsis action plan to reduce the burden of sepsis.

Sepsis policy, guidelines and standards in Canada: a jurisdictional scoping review protocol.

INTRODUCTION: To our knowledge, this study is the first to identify and describe current sepsis policies, clinical practice guidelines, and health professional training standards in Canada to inform evidence-based policy recommendations. METHODS AND ANALYSIS: This study will be designed and reported according to the Arksey and O'Malley framework for scoping reviews and the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews. EMBASE, CINAHL, Medline, Turning Research Into Practice and Policy Commons will be searched for policies, clinical practice guidelines and health professional training standards published or updated in 2010 onwards, and related to the identification, management or reporting of sepsis in Canada. Additional sources of evidence will be identified by searching the websites of Canadian organisations responsible for regulating the training of healthcare professionals and reporting health outcomes. All potentially eligible sources of evidence will be reviewed for inclusion, followed by data extraction, independently and in duplicate. The included policies will be collated and summarised to inform future evidence-based sepsis policy recommendations. ETHICS AND DISSEMINATION: The proposed study does not require ethics approval. The results of the study will be submitted for publication in a peer-reviewed journal and presented at local, national and international forums.

Evaluating the Impact of Ex Vivo Lung Perfusion on Organ Transplantation: A Retrospective Cohort Study.

BACKGROUND: Ex vivo lung perfusion (EVLP) sustains and allows advanced assessment of potentially useable donor lungs before transplantation, potentially relieving resource constraints. OBJECTIVE: We sought to characterize the effect of EVLP on organ utilization and patient outcomes. METHODS: We performed a retrospective, before-after cohort study using linked institutional data sources of adults wait-listed for lung transplant and donor organs transplanted in Ontario, Canada between 2005 and 2019. We regressed the annual number of transplants against year, EVLP use, and organ characteristics. Time-to-transplant, waitlist mortality, primary graft dysfunction, tracheostomy insertion, in-hospital mortality, and chronic lung allograft dysfunction were evaluated using propensity score-weighted regression. RESULTS: EVLP availability ( P =0.01 for interaction) and EVLP use ( P <0.001 for interaction) were both associated with steeper increases in transplantation than expected by historical trends. EVLP was associated with more donation after circulatory death and extended-criteria donors transplanted, while the numbers of standard-criteria donors remained relatively stable. Significantly faster time-to-transplant was observed after EVLP was available (hazard ratio=1.64 [1.41-1.92]; P <0.001). Fewer patients died on the waitlist after EVLP was available, but no difference in the hazard of waitlist mortality was observed (HR=1.19 [0.81-1.74]; P =0.176). We observed no difference in the likelihood of chronic lung allograft dysfunction before versus after EVLP was available. CONCLUSIONS: We observed a significant increase in organ transplantation since EVLP was introduced into practice, predominantly from increased acceptance of donation after circulatory death and extended-criteria lungs. Our findings suggest that EVLP-associated increases in organ availability meaningfully alleviated some barriers to transplant.

Determining the impact of ex-vivo lung perfusion on hospital costs for lung transplantation: A retrospective cohort study.

INTRODUCTION: Ex-vivo lung perfusion (EVLP) has improved organ utilization for lung transplantation, but it is not yet known whether the benefits of this technology offset its additional costs. We compared the institutional costs of lung transplantation before vs after EVLP was available to identify predictors of costs and determine the health-economic impact of EVLP. METHODS: We performed a retrospective, before-after, propensity-score weighted cohort study of patients wait-listed for lung transplant at University Health Network (UHN) in Ontario, Canada, between January 2005 and December 2019 using institutional administrative data. We compared costs, in 2019 Canadian Dollars ($), between patients referred for transplant before EVLP was available (Pre-EVLP) to after (Modern EVLP). Cumulative costs were estimated using a novel application of multistate survival models. Predictors of costs were identified using weighted log-gamma generalized linear regression. RESULTS: A total of 1,199 patients met inclusion criteria (352 Pre-EVLP; 847 Modern EVLP). Mean total costs for the transplant hospitalization were $111,878 ($94,123-$130,767) in the Pre-EVLP era and $110,969 ($87,714-$136,000) in the Modern EVLP era. Cumulative five-year costs since referral were $278,777 ($82,575-$298,135) in the Pre-EVLP era and $293,680 ($252,832-$317,599) in the Modern EVLP era. We observed faster progression to transplantation when EVLP was available. EVLP availability was not a predictor of waitlist (cost ratio [CR] 1.04 [0.81-1.37]; p = 0.354) or transplant costs (CR 1.02 [0.80-1.29]; p = 0.425) but was associated with lower costs during posttransplant years 1&2 (CR 0.75 [0.58-1.06]; p = 0.05) and posttransplant years 3+ (CR 0.43 [0.26-0.74]; p = 0.001). CONCLUSIONS: At our center, EVLP availability was associated with faster progression to transplantation at no significant marginal cost.

Health-related quality-of-life and health-utility reporting in critical care.

Mortality is a well-established patient-important outcome in critical care studies. In contrast, morbidity is less uniformly reported (given the myriad of critical care illnesses and complications of each) but may have a common end-impact on a patient's functional capacity and health-related quality-of-life (HRQoL). Survival with a poor quality-of-life may not be acceptable depending on individual patient values and preferences. Hence, as mortality decreases within critical care, it becomes increasingly important to measure intensive care unit (ICU) survivor HRQoL. HRQoL measurements with a preference-based scoring algorithm can be converted into health utilities on a scale anchored at 0 (representing death) and 1 (representing full health). They can be combined with survival to calculate quality-adjusted life-years (QALY), which are one of the most widely used methods of combining morbidity and mortality into a composite outcome. Although QALYs have been use for health-technology assessment decision-making, an emerging and novel role would be to inform clinical decision-making for patients, families and healthcare providers about what expected HRQoL may be during and after ICU care. Critical care randomized control trials (RCTs) have not routinely measured or reported HRQoL (until more recently), likely due to incapacity of some patients to participate in patient-reported outcome measures. Further differences in HRQoL measurement tools can lead to non-comparable values. To this end, we propose the validation of a gold-standard HRQoL tool in critical care, specifically the EQ-5D-5L. Both combined health-utility and mortality (disaggregated) and QALYs (aggregated) can be reported, with disaggregation allowing for determination of which components are the main drivers of the QALY outcome. Increased use of HRQoL, health-utility, and QALYs in critical care RCTs has the potential to: (1) Increase the likelihood of finding important effects if they exist; (2) improve research efficiency; and (3) help inform optimal management of critically ill patients allowing for decision-making about their HRQoL, in additional to traditional health-technology assessments.

Variation in the risk of death due to COVID-19: An international multicenter cohort study of hospitalized adults.

BACKGROUND: There is wide variation in mortality among patients hospitalized with COVID-19. Whether this is related to patient or hospital factors is unknown. OBJECTIVE: To compare the risk of mortality for patients hospitalized with COVID-19 and to determine whether the majority of that variation was explained by differences in patient characteristics across sites. DESIGN, SETTING, AND PARTICIPANTS: An international multicenter cohort study of hospitalized adults with laboratory-confirmed COVID-19 enrolled from 10 hospitals in Ontario, Canada and 8 hospitals in Copenhagen, Denmark between January 1, 2020 and November 11, 2020. MAIN OUTCOMES AND MEASURES: Inpatient mortality. We used a multivariable multilevel regression model to compare the in-hospital mortality risk across hospitals and quantify the variation attributable to patient-level factors. RESULTS: There were 1364 adults hospitalized with COVID-19 in Ontario (n = 1149) and in Denmark (n = 215). In Ontario, the absolute risk of in-hospital mortality ranged from 12.0% to 39.8% across hospitals. Ninety-eight percent of the variation in mortality in Ontario was explained by differences in the characteristics of the patients. In Denmark, the absolute risk of inpatients ranged from 13.8% to 20.6%. One hundred percent of the variation in mortality in Denmark was explained by differences in the characteristics of the inpatients. CONCLUSION: There was wide variation in inpatient COVID-19 mortality across hospitals, which was largely explained by patient-level factors, such as age and severity of presenting illness. However, hospital-level factors that could have affected care, including resource availability and capacity, were not taken into account. These findings highlight potential limitations in comparing crude mortality rates across hospitals for the purposes of reporting on the quality of care.

Demographic characteristics, acute care resource use and mortality by age and sex in patients with COVID-19 in Ontario, Canada: a descriptive analysis.

BACKGROUND: Understanding resource use for coronavirus disease 2019 (COVID-19) is critical. We conducted a descriptive analysis using public health data to describe age- and sex-specific acute care use, length of stay (LOS) and mortality associated with COVID-19. METHODS: We conducted a descriptive analysis using Ontario's Case and Contact Management Plus database of individuals who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Ontario from Mar. 1 to Sept. 30, 2020, to determine age- and sex-specific hospital admissions, intensive care unit (ICU) admissions, use of invasive mechanical ventilation, LOS and mortality. We stratified analyses by month of infection to study temporal trends and conducted subgroup analyses by long-term care residency. RESULTS: During the observation period, 56 476 individuals testing positive for SARS-CoV-2 were reported; 41 049 (72.7%) of these were younger than 60 years, and 29 196 (51.7%) were female. Proportion of cases shifted from older populations (> 60 yr) to younger populations (10-39 yr) over time. Overall, 5383 (9.5%) of individuals were admitted to hospital; of these, 1183 (22.0%) were admitted to the ICU, and 712 (60.2%) of these received invasive mechanical ventilation. Mean LOS for individuals in the ward, ICU without invasive mechanical ventilation and ICU with invasive mechanical ventilation was 12.8 (standard deviation [SD] 15.4), 8.5 (SD 7.8) and 20.5 (SD 18.1) days, respectively. Among patients receiving care in the ward, ICU without invasive mechanical ventilation and ICU with invasive mechanical ventilation, 911/3834 (23.8%), 124/418 (29.7%) and 287/635 (45.2%) died, respectively. All outcomes varied by age and decreased over time, overall and within age groups. INTERPRETATION: This descriptive study shows use of acute care and mortality varying by age and decreasing between March and September 2020 in Ontario. Improvements in clinical practice and changing risk distributions among those infected may contribute to fewer severe outcomes.

Simulation-Based Estimation of SARS-CoV-2 Infections Associated With School Closures and Community-Based Nonpharmaceutical Interventions in Ontario, Canada.

Importance: Resurgent COVID-19 cases have resulted in the reinstitution of nonpharmaceutical interventions, including school closures, which can have adverse effects on families. Understanding the associations of school closures with the number of incident and cumulative COVID-19 cases is critical for decision-making. Objective: To estimate the association of schools being open or closed with the number of COVID-19 cases compared with community-based nonpharmaceutical interventions. Design, Setting, and Participants: This decision analytical modelling study developed an agent-based transmission model using a synthetic population of 1 000 000 individuals based on the characteristics of the population of Ontario, Canada. Members of the synthetic population were clustered into households, neighborhoods, or rural districts, cities or rural regions, day care facilities, classrooms (ie, primary, elementary, or high school), colleges or universities, and workplaces. Data were analyzed between May 5, 2020, and October 20, 2020. Exposures: School reopening on September 15, 2020, vs schools remaining closed under different scenarios for nonpharmaceutical interventions. Main Outcomes and Measures: Incident and cumulative COVID-19 cases between September 1, 2020, and October 31, 2020. Results: Among 1 000 000 simulated individuals, the percentage of infections among students and teachers acquired within schools was less than 5% across modeled scenarios. Incident COVID-19 case numbers on October 31, 2020, were 4414 (95% credible interval [CrI], 3491-5382) cases in the scenario with schools remaining closed and 4740 (95% CrI, 3863-5691) cases in the scenario for schools reopening, with no other community-based nonpharmaceutical intervention. In scenarios with community-based nonpharmaceutical interventions implemented, the incident case numbers on October 31 were 714 (95% CrI, 568-908) cases for schools remaining closed and 780 (95% CrI, 580-993) cases for schools reopening. When scenarios applied the case numbers observed in early October in Ontario, the cumulative case numbers were 777 (95% CrI, 621-993) cases for schools remaining closed and 803 (95% CrI, 617-990) cases for schools reopening. In scenarios with implementation of community-based interventions vs no community-based interventions, there was a mean difference of 39 355 cumulative COVID-19 cases by October 31, 2020, while keeping schools closed vs reopening them yielded a mean difference of 2040 cases. Conclusions and Relevance: This decision analytical modeling study of a synthetic population of individuals in Ontario, Canada, found that most COVID-19 cases in schools were due to acquisition in the community rather than transmission within schools and that the changes in COVID-19 case numbers associated with school reopenings were relatively small compared with the changes associated with community-based nonpharmaceutical interventions.

Estimation de l'épuisement des ressources hospitalières attribuable à la COVID-19 en Ontario, au Canada.

CONTEXTE: La propagation à l'échelle planétaire de la maladie à coronavirus 2019 (COVID-2019) se poursuit dans plusieurs pays, mettant à rude épreuve les systèmes de santé. Cette étude avait pour but de prédire les répercussions de la pandémie sur les issues des patients et l'utilisation des ressources hospitalières en Ontario (Canada). MÉTHODES: Nous avons conçu un modèle de simulation axé sur les personnes illustrant le flux de patients atteints de la COVID-19 dans les hôpitaux ontariens. Nous avons simulé diverses combinaisons de trajectoires épidémiques et de capacités de soins hospitaliers. Les paramètres à l'étude étaient le nombre de patients devant être admis au service d'hospitalisation ou à l'unité des soins intensifs (USI) ­avec ou sans respirateur mécanique ­, le nombre de jours jusqu'à l'épuisement des ressources, le nombre de patients en attente de ressources et le nombre de décès. RÉSULTATS: Nous avons constaté que la mise en place rapide de mesures de santé publique efficaces éviterait l'épuisement des ressources hospitalières. Les simulations dans lesquelles les mesures d'éloignement sanitaire étaient inefficaces ou adoptées tardivement ont montré que l'épuisement des ressources prendrait de 14 à 26 jours et qu'il y aurait, dans le pire des cas, 13 321 décès de personnes en attente de ressources. Cet épuisement pourrait être évité ou retardé par la mise en place de mesures rigoureuses visant à améliorer la capacité des hôpitaux en matière de soins intensifs, de respirateurs mécaniques et de soins hospitaliers. INTERPRÉTATION: Sans l'adoption de mesures d'éloignement sanitaire rigoureuses, le système de santé ontarien n'aurait pas eu les ressources nécessaires pour prendre en charge le nombre attendu de patients atteints de la COVID-19, même en cas d'augmentation rapide de sa capacité hospitalière. Les pénuries auraient fait augmenter le taux de mortalité. En ralentissant la transmission de la maladie par la mise en place de mesures de santé publique et l'augmentation de la capacité des hôpitaux, l'Ontario a probablement évité que ces derniers subissent une pression catastrophique.

COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia / COVID-19: Predicción de las necesidades de recursos sanitarios en el Valle del Cauca, Colombia

Abstract Background: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. Methods: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. Results: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. Conclusion: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making

Resumen Introducción: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. Métodos: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. Resultados: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.

Estimation of COVID-19-induced depletion of hospital resources in Ontario, Canada.

BACKGROUND: The global spread of coronavirus disease 2019 (COVID-19) continues in several jurisdictions, causing substantial strain to health care systems. The purpose of our study was to predict the effect of the COVID-19 pandemic on patient outcomes and use of hospital resources in Ontario, Canada. METHODS: We developed an individual-level simulation to model the flow of patients with COVID-19 through the hospital system in Ontario. We simulated different combined scenarios of epidemic trajectory and hospital health care capacity. Our outcomes included the number of patients who needed admission to the ward or to the intensive care unit (ICU) with or without the need for mechanical ventilation, number of days to resource depletion, number of patients awaiting resources and number of deaths. RESULTS: We found that with effective early public health measures, hospital system resources would not be depleted. For scenarios with late or ineffective implementation of physical distancing, hospital resources would be depleted within 14-26 days, and in the worst case scenario, 13 321 patients would die while waiting for needed resources. Resource depletion would be avoided or delayed with aggressive measures to increase ICU, ventilator and acute care hospital capacities. INTERPRETATION: We found that without aggressive physical distancing measures, the Ontario hospital system would have been inadequately equipped to manage the expected number of patients with COVID-19 despite a rapid increase in capacity. This lack of hospital resources would have led to an increase in mortality. By slowing the spread of the disease using public health measures and by increasing hospital capacity, Ontario may have avoided catastrophic stresses to its hospitals.

The Use of Decision Modelling to Inform Timely Policy Decisions on Cardiac Resource Capacity During the COVID-19 Pandemic.

In Ontario on March 16, 2020, a directive was issued to all acute care hospitals to halt nonessential procedures in anticipation of a potential surge in COVID-19 patients. This included scheduled outpatient cardiac surgical and interventional procedures that required the use of intensive care units, ventilators, and skilled critical care personnel, given that these procedures would draw from the same pool of resources required for critically ill COVID-19 patients. We adapted the COVID-19 Resource Estimator (CORE) decision analytic model by adding a cardiac component to determine the impact of various policy decisions on the incremental waitlist growth and estimated waitlist mortality for 3 key groups of cardiovascular disease patients: coronary artery disease, valvular heart disease, and arrhythmias. We provided predictions based on COVID-19 epidemiology available in real-time, in 3 phases. First, in the initial crisis phase, in a worst case scenario, we showed that the potential number of waitlist related cardiac deaths would be orders of magnitude less than those who would die of COVID-19 if critical cardiac care resources were diverted to the care of COVID-19 patients. Second, with better local epidemiology data, we predicted that across 5 regions of Ontario, there may be insufficient resources to resume all elective outpatient cardiac procedures. Finally in the recovery phase, we showed that the estimated incremental growth in waitlist for all cardiac procedures is likely substantial. These outputs informed timely data-driven decisions during the COVID-19 pandemic regarding the provision of cardiovascular care.

Estimating healthcare resource needs for COVID-19 patients in Nigeria.

INTRODUCTION: continuous assessment of healthcare resources during the COVID-19 pandemic will help in proper planning and to prevent an overwhelming of the Nigerian healthcare system. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria. METHODS: we adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology in Nigeria by September 2020. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included the expected cumulative number of cases, days until depletion resources and the number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon. RESULTS: in our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, assuming all three resource scenarios, hospital resources would not be expended within the 60-days time horizon. In our worst-case epidemic trajectory, assuming conservative resource scenario, only ventilated ICU beds would be depleted after 39 days and 16 patients were projected to die while waiting for ventilated ICU bed. Acute care resources were only sufficient in the three epidemic trajectory scenarios when combined with a substantial increase in healthcare resources. CONCLUSION: substantial increase in hospital resources is required to manage the COVID-19 pandemic in Nigeria, even as the infection growth rate declines. Given Nigeria's limited health resources, it is imperative to focus on maintaining aggressive public health measures as well as increasing hospital resources to reduce COVID-19 transmission further.

COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia.

BACKGROUND: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. METHODS: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. RESULTS: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. CONCLUSION: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making.

INTRODUCCIÓN: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. MÉTODOS: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. RESULTADOS: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.

Estimating healthcare resource needs for COVID-19 patients in Nigeria

Introduction: continuous assessment of healthcare resources during the COVID-19 pandemic will help in proper planning and to prevent an overwhelming of the Nigerian healthcare system. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria.Methods: we adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology in Nigeria by September 2020. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included the expected cumulative number of cases, days until depletion resources and the number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon.Results: in our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, assuming all three resource scenarios, hospital resources would not be expended within the 60-days time horizon. In our worst-case epidemic trajectory, assuming conservative resource scenario, only ventilated ICU beds would be depleted after 39 days and 16 patients were projected to die while waiting for ventilated ICU bed. Acute care resources were only sufficient in the three epidemic trajectory scenarios when combined with a substantial increase in healthcare resources.Conclusion: substantial increase in hospital resources is required to manage the COVID-19 pandemic in Nigeria, even as the infection growth rate declines. Given Nigeria's limited health resources, it is imperative to focus on maintaining aggressive public health measures as well as increasing hospital resources to reduce COVID-19 transmission further

Economic Evaluation of Venovenous Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome.

OBJECTIVES: Venovenous extracorporeal membrane oxygenation is increasingly being used to support patients with severe acute respiratory distress syndrome, but its cost-effectiveness is unknown. We assessed the cost-utility of venovenous extracorporeal membrane oxygenation for severe acute respiratory distress syndrome in adults compared with standard lung protective ventilation from the perspective of the healthcare system. DESIGN: We conducted a cost-utility analysis with a cohort state transition decision model using a lifetime time horizon, 1.5% discount rate, and outcomes reported as cost per quality-adjusted life year. Literature reviews were conducted to inform the model variables. Deterministic and probabilistic sensitivity analyses were conducted to assess uncertainty in the model. SETTING: Canadian publicly funded healthcare system. PATIENTS: Hypothetical cohort of adults with severe acute respiratory distress syndrome. INTERVENTIONS: Venovenous extracorporeal membrane oxygenation or standard lung protective ventilation. MEASUREMENTS AND MAIN RESULTS: In our model, the use of venovenous extracorporeal membrane oxygenation compared with lung protective ventilation resulted in a gain of 5.2 life years and 4.05 quality-adjusted life years, at an additional lifetime cost of $145,697 Canadian dollars. The incremental cost-effectiveness ratio was $36,001/quality-adjusted life year. Sensitivity analyses show that the incremental cost-effectiveness ratio is sensitive to the efficacy of extracorporeal membrane oxygenation therapy and costs. CONCLUSIONS: Based on current data, venovenous extracorporeal membrane oxygenation is cost-effective for patients with severe acute respiratory distress syndrome. Additional evidence on the efficacy of venovenous extracorporeal membrane oxygenation for acute respiratory distress syndrome and in different subgroups of patients will allow for greater certainty in its cost-effectiveness.

Predicting neurologic outcome after targeted temperature management for cardiac arrest: systematic review and meta-analysis.

OBJECTIVES: Targeted temperature management improves survival and neurologic outcomes for adult out-of-hospital cardiac arrest survivors but may alter the accuracy of tests for predicting neurologic outcome after cardiac arrest. DATA SOURCES: We systematically searched Medline, Embase, CINAHL, and CENTRAL from database inception to September 2012. STUDY SELECTION: Citations were screened for studies that examined diagnostic tests to predict poor neurologic outcome or death following targeted temperature management in adult cardiac arrest survivors. DATA EXTRACTION: Data on study outcomes and quality were abstracted in duplicate. We constructed contingency tables for each diagnostic test and calculated sensitivity, specificity, and positive and negative likelihood ratios. DATA SYNTHESIS: Of 2,737 citations, 20 studies (n = 1,845) met inclusion criteria. Meta-analysis showed that three tests accurately predicted poor neurologic outcome with low false-positive rates: bilateral absence of pupillary reflexes more than 24 hours after a return of spontaneous circulation (false-positive rate, 0.02; 95% CI, 0.01-0.06; summary positive likelihood ratio, 10.45; 95% CI, 3.37-32.43), bilateral absence of corneal reflexes more than 24 hours (false-positive rate, 0.04; 95% CI, 0.01-0.09; positive likelihood ratio, 6.8; 95% CI, 2.52-18.38), and bilateral absence of somatosensory-evoked potentials between days 1 and 7 (false-positive rate, 0.03; 95% CI, 0.01-0.07; positive likelihood ratio, 12.79; 95% CI, 5.35-30.62). False-positive rates were higher for a Glasgow Coma Scale motor score showing extensor posturing or worse (false-positive rate, 0.09; 95% CI, 0.06-0.13; positive likelihood ratio, 7.11; 95% CI, 5.01-10.08), unfavorable electroencephalogram patterns (false-positive rate, 0.07; 95% CI, 0.04-0.12; positive likelihood ratio, 8.85; 95% CI, 4.87-16.08), myoclonic status epilepticus (false-positive rate, 0.05; 95% CI, 0.02-0.11; positive likelihood ratio, 5.58; 95% CI, 2.56-12.16), and elevated neuron-specific enolase (false-positive rate, 0.12; 95% CI, 0.06-0.23; positive likelihood ratio, 4.14; 95% CI, 1.82-9.42). The specificity of available tests improved when these were performed beyond 72 hours. Data on neuroimaging, biomarkers, or combination testing were limited and inconclusive. CONCLUSION: Simple bedside tests and somatosensory-evoked potentials predict poor neurologic outcome for survivors of cardiac arrest treated with targeted temperature management, and specificity improves when performed beyond 72 hours. Clinicians should use caution with these predictors as they carry the inherent risk of becoming self-fulfilling.