icumonitoring.ch: a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland.

In Switzerland, the COVID-19 epidemic is progressively slowing down owing to “social distancing” measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch, a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations, hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.

Spatial Analysis of COVID-19 cases and intensive care beds in the State of Ceará, Brazil. / Análise Espacial dos Casos de COVID-19 e leitos de terapia intensiva no estado do Ceará, Brasil.

The geographical distribution of COVID-19 through Geographic Information Systems resources is hardly explored. We aimed to analyze the distribution of COVID-19 cases and the exclusive intensive care beds in the state of Ceará, Brazil. This is an ecological study with the geographic distribution of the case detection coefficient in 184 municipalities. Maps of crude and estimated values (global and local Bayesian method) were developed, calculating the Moran index and using BoxMap and MoranMap. Intensive care beds were distributed through geolocalized points. In total, 3,000 cases and 459 beds were studied. The highest rates were found in the capital Fortaleza, the Metropolitan Region (MR), and the south of this region. A positive spatial autocorrelation has been identified in the local Bayesian rate (I = 0.66). The distribution of beds superimposed on the BoxMap shows clusters with a High-High pattern of number of beds (capital, MR, northwestern part). However, a similar pattern is found in the far east or transition areas with insufficient beds. The MoranMap shows clusters statistically significant in the state. COVID-19 interiorization in Ceará requires contingency measures geared to the distribution of specific intensive care beds for COVID-19 cases in order to meet the demand.

A distribuição geográfica da COVID-19 por meio de recursos de Sistemas de Informação Geográfica é pouco explorada. O objetivo foi analisar a distribuição de casos da COVID-19 e de leitos de terapia intensiva exclusivos para a doença no estado do Ceará, Brasil. Estudo ecológico, com distribuição geográfica do coeficiente de detecção de casos da doença em 184 municípios. Construíram-se mapas dos valores brutos e estimados (método bayesiano global e local), com cálculo do índice de Moran e utilização do "BoxMap" e "MoranMap" Os leitos foram distribuídos por meio de pontos geolocalizados. Estudaram-se 3.000 casos e 459 leitos. As maiores taxas encontram-se na capital Fortaleza, região metropolitana (RM) e ao sul dessa região. Há autocorrelação espacial positiva na taxa bayesiana local (I = 0,66). A distribuição dos leitos de terapia intensiva sobreposta ao "BoxMap" evidenciou aglomerados com padrão Alto-Alto apresentando número de leitos (capital, RM, porção noroeste); porém, há o mesmo padrão (extremo leste) e em áreas de transição com insuficiência de leito. O "MoranMap" evidenciou "clusters" estatisticamente significativos no estado. A interiorização da COVID-19 no Ceará demanda medidas de contingência voltadas à distribuição dos leitos de terapia intensiva específicos para casos de COVID19 para atender à demanda.

Design of COVID-19 staged alert systems to ensure healthcare capacity with minimal closures.

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France's ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As proof-of-concept, we describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in a large US city (Austin, Texas) since May 2020. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.

Surgical Residents at the Forefront of the COVID-19 Pandemic: Perspectives on Redeployment.

The COVID-19 pandemic has led many of us to re-evaluate our approaches to disaster management, reflect on our experiences, and be reminded of the strong resolve for our work. This article details a resident's perspective on redeployment of surgical residents to the COVID-19 frontline setting, using the example of the COVID-19 intensive care unit. Redeployment during a pandemic brings the unique opportunity to collaborate with colleagues on the frontlines and learn alongside one another about the evolving management of this disease. During this ongoing pandemic, it is incumbent upon us as clinicians to work together in a multidisciplinary manner and reflect on ways this pandemic impacts the delivery of patient care.

Mechanical-Ventilation Supply and Options for the COVID-19 Pandemic. Leveraging All Available Resources for a Limited Resource in a Crisis.

The novel coronavirus disease (COVID-19) has exposed critical supply shortages both in the United States and worldwide, including those in intensive care unit (ICU) and hospital bed supply, hospital staff, and mechanical ventilators. Many of those who are critically ill have required days to weeks of supportive invasive mechanical ventilation (IMV) as part of their treatment. Previous estimates set the U.S. availability of mechanical ventilators at approximately 62,000 full-featured ventilators, with 98,000 non-full-featured devices (including noninvasive devices). Given the limited availability of this resource both in the United States and in low- and middle-income countries, we provide a framework to approach the shortage of IMV resources. Here we discuss evidence and possibilities to reduce overall IMV needs, discuss strategies to maximize the availability of IMV devices designed for invasive ventilation, discuss the underlying methods in the literature to create and fashion new sources of potential ventilation that are available to hospitals and front-line providers, and discuss the staffing needs necessary to support IMV efforts. The pandemic has already pushed cities like New York and Boston well beyond previous ICU capacity in its first wave. As hot spots continue to develop around the country and the globe, it is evident that issues may arise ahead regarding the efficient and equitable use of resources. This unique challenge may continue to stretch resources and require care beyond previously set capacities and boundaries. The approaches presented here provide a review of the known evidence and strategies for those at the front line who are facing this challenge.

Hospital bed capacity and usage across secondary healthcare providers in England during the first wave of the COVID-19 pandemic: a descriptive analysis.

OBJECTIVE: In this study, we describe the pattern of bed occupancy across England during the peak of the first wave of the COVID-19 pandemic. DESIGN: Descriptive survey. SETTING: All non-specialist secondary care providers in England from 27 March27to 5 June 2020. PARTICIPANTS: Acute (non-specialist) trusts with a type 1 (ie, 24 hours/day, consultant-led) accident and emergency department (n=125), Nightingale (field) hospitals (n=7) and independent sector secondary care providers (n=195). MAIN OUTCOME MEASURES: Two thresholds for 'safe occupancy' were used: 85% as per the Royal College of Emergency Medicine and 92% as per NHS Improvement. RESULTS: At peak availability, there were 2711 additional beds compatible with mechanical ventilation across England, reflecting a 53% increase in capacity, and occupancy never exceeded 62%. A consequence of the repurposing of beds meant that at the trough there were 8.7% (8508) fewer general and acute beds across England, but occupancy never exceeded 72%. The closest to full occupancy of general and acute bed (surge) capacity that any trust in England reached was 99.8% . For beds compatible with mechanical ventilation there were 326 trust-days (3.7%) spent above 85% of surge capacity and 154 trust-days (1.8%) spent above 92%. 23 trusts spent a cumulative 81 days at 100% saturation of their surge ventilator bed capacity (median number of days per trust=1, range: 1-17). However, only three sustainability and transformation partnerships (aggregates of geographically co-located trusts) reached 100% saturation of their mechanical ventilation beds. CONCLUSIONS: Throughout the first wave of the pandemic, an adequate supply of all bed types existed at a national level. However, due to an unequal distribution of bed utilisation, many trusts spent a significant period operating above 'safe-occupancy' thresholds despite substantial capacity in geographically co-located trusts, a key operational issue to address in preparing for future waves.

[Expanding ICU facilities in a pandemic scenario]. / La criticidad de las Unidades de Intensivo. Ampliando las Unidades en tiempos de pandemia.

Our country is suffering the effects of the ongoing pandemic of coronavirus disease (COVID-19). Because the vulnerability of healthcare systems, especially the intensive care areas they can rapidly be overloaded. That challenge the ICUs simultaneously on multiple fronts making urgent to increase the number of beds, without lowering the standards of care. The purpose of this article is to discuss some aspects of the national situation and to provide recommendations on the organizational management of intensive care units such as isolation protocols, surge in ICU bed capacity, ensure adequate supplies, protect and train healthcare workers maintaining quality clinical management.

Mortality and renal outcomes of patients with severe COVID-19 treated in a provisional intensive care unit.

COVID-19 has created an enormous health crisis and this spring New York City had a severe outbreak that pushed health and critical care resources to the limit. A lack of adequate space for mechanically ventilated patients induced our hospital to convert operating rooms into critical care areas (OR-ICU). A large number of COVID-19 will develop acute kidney injury that requires renal replacement therapy (RRT). We included 116 patients with COVID-19 who required mechanical ventilation and were cared for in our OR-ICU. At 90 days and at discharge 35 patients died (30.2%). RRT was required by 45 of the 116 patients (38.8%) and 18 of these 45 patients (40%) compared to 17 with no RRT (23.9%, ns) died during hospitalization and after 90 days. Only two of the 27 patients who required RRT and survived required RRT at discharge and 90 days. When defining renal recovery as a discharge serum creatinine within 150% of baseline, 68 of 78 survivors showed renal recovery (87.2%). Survival was similar to previous reports of patients with severe COVID-19 for patients cared for in provisional ICUs compared to standard ICUs. Most patients with severe COVID-19 and AKI are likely to recover full renal function.

Cancellation of Elective Surgery and Intensive Care Unit Capacity in New York State: A Retrospective Cohort Analysis.

BACKGROUND: In response to the coronavirus disease 2019 (COVID-19) pandemic, New York State ordered the suspension of all elective surgeries to increase intensive care unit (ICU) bed capacity. Yet the potential impact of suspending elective surgery on ICU bed capacity is unclear. METHODS: We retrospectively reviewed 5 years of New York State data on ICU usage. Descriptions of ICU utilization and mechanical ventilation were stratified by admission type (elective surgery, emergent/urgent/trauma surgery, and medical admissions) and by geographic location (New York metropolitan region versus the rest of New York State). Data are presented as absolute numbers and percentages and all adult and pediatric ICU patients were included. RESULTS: Overall, ICU admissions in New York State were seen in 10.1% of all hospitalizations (n = 1,232,986/n = 12,251,617) and remained stable over a 5-year period from 2011 to 2015. Among n = 1,232,986 ICU stays, sources of ICU admission included elective surgery (13.4%, n = 165,365), emergent/urgent admissions/trauma surgery (28.0%, n = 345,094), and medical admissions (58.6%, n = 722,527). Ventilator utilization was seen in 26.3% (n = 323,789/n = 1232,986) of all ICU patients of which 6.4% (n = 20,652), 32.8% (n = 106,186), and 60.8% (n = 196,951) was for patients from elective, emergent, and medical admissions, respectively. New York City holds the majority of ICU bed capacity (70.0%; n = 2496/n = 3566) in New York State. CONCLUSIONS: Patients undergoing elective surgery comprised a small fraction of ICU bed and mechanical ventilation use in New York State. Suspension of elective surgeries in response to the COVID-19 pandemic may thus have a minor impact on ICU capacity when compared to other sources of ICU admission such as emergent/urgent admissions/trauma surgery and medical admissions. More study is needed to better understand how best to maximize ICU capacity for pandemics requiring heavy use of critical care resources.

Analysis of COVID-19 Cases' Spatial Dependence in US Counties Reveals Health Inequalities.

On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I = 0.174 and 0.264, p < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran I = 0.264, p < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran I = -0.203, p < 0.0001) and deaths (Moran I = -0.137, p < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I = 0.08, p < 0.0001) and deaths (Moran I = 0.15, p < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease.

Early indicators of intensive care unit bed requirement during the COVID-19 epidemic: A retrospective study in Ile-de-France region, France.

The aim of our retrospective study was to evaluate the earliest COVID19-related signal to anticipate requirements of intensive care unit (ICU) beds. Although the number of ICU beds is crucial during the COVID-19 epidemic, there is no recognized early indicator to anticipate it. In the Ile-de-France region, from February 20 to May 5, 2020, emergency medical service (EMS) calls and the response provided (ambulances) together the percentage of positive reverse transcriptase polymerase chain reaction (RT-PCR) tests, general practitioner (GP) and emergency department (ED) visits, and hospital admissions of COVID-19 patients were recorded daily and compared to the number of ICU patients. Correlation curve analysis was performed to determine the best correlation coefficient, depending on the number of days the indicator has been shifted. Primary endpoint was the number of ICU patients. EMS calls, percentage of positive RT-PCR tests, ambulances used, ED and GP visits of COVID-19 patients were strongly associated (R2 ranging between 0.79 to 0.99, all P<0.001) with COVID-19 ICU patients with an anticipation delay of 23, 15, 14, 13, and 12 days respectively. Hospitalization did not anticipate ICU bed requirement. A qualitative analysis of the onset of the second wave period of the epidemic (August 1 to September 15, 2020) in the same region provided similar results. The daily number of COVID19-related telephone calls received by the EMS and corresponding dispatch ambulances, and the proportion of positive RT-PCR tests were the earliest indicators of the number of COVID19 patients requiring ICU care during the epidemic crisis, rapidly followed by ED and GP visits. This information may help health authorities to anticipate a future epidemic, including a second wave of COVID19, or decide additional social measures.

A flexible method for optimising sharing of healthcare resources and demand in the context of the COVID-19 pandemic.

As the number of cases of COVID-19 continues to grow, local health services are at risk of being overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data from the United Kingdom and Spain. In the UK, we consider the National Health Service at the level of trusts and define a 4-regular geometric graph which indicates the four nearest neighbours of any given trust. In Spain we coarse-grain the healthcare system at the level of autonomous communities, and extract similar contact networks. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity. Our framework is general and flexible allowing for additional criteria, alternative cost functions, and can be extended to other resources beyond ICU units or ventilators. Assuming a uniform ICU demand, we show that it is possible to enable access to ICU for up to 1000 additional cases in the UK in a single step of the algorithm. Under a more realistic and heterogeneous demand, our method is able to balance about 600 beds per step in the Spanish system only using local sharing, and over 1300 using countrywide sharing, potentially saving a large percentage of these lives that would otherwise not have access to ICU.

Early prediction of level-of-care requirements in patients with COVID-19.

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.

The new coronavirus (now named SARS-CoV-2) causing the disease pandemic in 2019 (COVID-19), has so far infected over 35 million people worldwide and killed more than 1 million. Most people with COVID-19 have no symptoms or only mild symptoms. But some become seriously ill and need hospitalization. The sickest are admitted to an Intensive Care Unit (ICU) and may need mechanical ventilation to help them breath. Being able to predict which patients with COVID-19 will become severely ill could help hospitals around the world manage the huge influx of patients caused by the pandemic and save lives. Now, Hao, Sotudian, Wang, Xu et al. show that computer models using artificial intelligence technology can help predict which COVID-19 patients will be hospitalized, admitted to the ICU, or need mechanical ventilation. Using data of 2,566 COVID-19 patients from five Massachusetts hospitals, Hao et al. created three separate models that can predict hospitalization, ICU admission, and the need for mechanical ventilation with more than 86% accuracy, based on patient characteristics, clinical symptoms, laboratory results and chest x-rays. Hao et al. found that the patients' vital signs, age, obesity, difficulty breathing, and underlying diseases like diabetes, were the strongest predictors of the need for hospitalization. Being male, having diabetes, cloudy chest x-rays, and certain laboratory results were the most important risk factors for intensive care treatment and mechanical ventilation. Laboratory results suggesting tissue damage, severe inflammation or oxygen deprivation in the body's tissues were important warning signs of severe disease. The results provide a more detailed picture of the patients who are likely to suffer from severe forms of COVID-19. Using the predictive models may help physicians identify patients who appear okay but need closer monitoring and more aggressive treatment. The models may also help policy makers decide who needs workplace accommodations such as being allowed to work from home, which individuals may benefit from more frequent testing, and who should be prioritized for vaccination when a vaccine becomes available.

[Predictive modeling to estimate the demand for intensive care hospital beds nationwide in the context of the COVID-19 pandemic]. / Modelamiento predictivo para el cálculo de demanda de camas hospitalarias de cuidados intensivos a nivel nacional en el marco de la pandemia por COVID-19.

INTRODUCTION: SARS CoV-2 pandemic is pressing hard on the responsiveness of health systems worldwide, notably concerning the massive surge in demand for intensive care hospital beds. AIM: This study proposes a methodology to estimate the saturation moment of hospital intensive care beds (critical care beds) and determine the number of units required to compensate for this saturation. METHODS: A total of 22,016 patients with diagnostic confirmation for COVID-19 caused by SARS-CoV-2 were analyzed between March 4 and May 5, 2020, nationwide. Based on information from the Chilean Ministry of Health and ministerial announcements in the media, the overall availability of critical care beds was estimated at 1,900 to 2,000. The Gompertz function was used to estimate the expected number of COVID-19 patients and to assess their exposure to the available supply of intensive care beds in various possible scenarios, taking into account the supply of total critical care beds, the average occupational index, and the demand for COVID-19 patients who would require an intensive care bed. RESULTS: A 100% occupancy of critical care beds could be reached between May 11 and May 27. This condition could be extended for around 48 days, depending on how the expected over-demand is managed. CONCLUSION: A simple, easily interpretable, and applicable to all levels (nationwide, regionwide, municipalities, and hospitals) model is offered as a contribution to managing the expected demand for the coming weeks and helping reduce the adverse effects of the COVID-19 pandemic.

INTRODUCCIÓN: La pandemia por SARS CoV-2 está presionando fuertemente la capacidad de respuesta de los sistemas de salud en todo el mundo, siendo uno de los aspectos más importantes el aumento masivo de pacientes que requerirán utilizar camas hospitalarias de cuidados intensivos. OBJETIVO: Este estudio propone una metodología para estimar el momento de saturación de las camas de cuidados intensivos hospitalarios (camas críticas) y determinar el número de unidades requeridas para compensar dicha saturación. MÉTODO: Se analizaron 22 016 pacientes con confirmación diagnóstica para COVID-19 provocada por SARS-CoV-2, entre el 4 de marzo y el 5 de mayo de 2020 a nivel nacional. Sobre la base de información del Ministerio de Salud de Chile y a anuncios ministeriales en medios de prensa, se estimó una disponibilidad total actual de 1900 a 2200 camas críticas totales. Se utilizó la función de Gompertz para estimar el número esperado de pacientes COVID-19 y evaluar su exposición a la oferta disponible de camas de cuidados intensivos en varios escenarios posibles. Para ello se tomó en cuenta la oferta de camas críticas totales, el índice ocupacional promedio, y la demanda de pacientes COVID-19 que requerirán cama de cuidados intensivos. RESULTADOS: Considerando diferentes escenarios, entre el 11 y el 27 de mayo podría ser alcanzado el 100% de ocupación de camas críticas totales. Esta condición podría extenderse por unos 48 días dependiendo como se maneje la sobredemanda esperada. CONCLUSIÓN: Se puede establecer una ventana de operaciones relativamente estrecha, de 4 a 8 semanas, para mitigar la inminente saturación de camas críticas hospitalarias, producto de la demanda de pacientes COVID-19.

COVID-19 and Italy: what next?

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected. There is now grave concern regarding the Italian national health system's capacity to effectively respond to the needs of patients who are infected and require intensive care for SARS-CoV-2 pneumonia. The percentage of patients in intensive care reported daily in Italy between March 1 and March 11, 2020, has consistently been between 9% and 11% of patients who are actively infected. The number of patients infected since Feb 21 in Italy closely follows an exponential trend. If this trend continues for 1 more week, there will be 30 000 infected patients. Intensive care units will then be at maximum capacity; up to 4000 hospital beds will be needed by mid-April, 2020. Our analysis might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. If the Italian outbreak follows a similar trend as in Hubei province, China, the number of newly infected patients could start to decrease within 3-4 days, departing from the exponential trend. However, this cannot currently be predicted because of differences between social distancing measures and the capacity to quickly build dedicated facilities in China.

The efficiency in the ordinary hospital bed management in Italy: An in-depth analysis of intensive care unit in the areas affected by COVID-19 before the outbreak.

Since the end of February 2020 a severe diffusion of COVID-19 has affected Italy and in particular its northern regions, resulting in a high demand of hospitalizations in particular in the intensive care units (ICUs). Hospitals are suffering the high degree of patients to be treated for respiratory diseases and the majority of the health structures, especially in the north of Italy, are or are at risk of saturation. Therefore, the question whether and to what extent the reduction of hospital beds occurred in the past years has biased the management of the emergency has come to the front in the public debate. In our opinion, to start a robust analysis it is necessary to consider the Italian health system capacity prior to the emergency. Therefore, the aim of this study is to analyse the availability of hospital beds across the country as well as to determine their management in terms of complexity and performance of cases treated at regional level. The results of this study underlines that, despite the reduction of beds for the majority of the hospital wards, ICUs availabilities did not change between 2010 and 2017. Moreover, this study confirms that the majority of the Italian regions have a routinely efficient management of their facilities allowing hospitals to treat patients without the risk of having an overabundance of patients and a scarcity of beds. In fact, this analysis shows that, in normal situations, the management of hospital and ICU beds has no critical levels.

Estimating Shortages in Capacity to Deliver Continuous Kidney Replacement Therapy During the COVID-19 Pandemic in the United States.

RATIONALE & OBJECTIVE: During the coronavirus disease 2019 (COVID-19) pandemic, New York encountered shortages in continuous kidney replacement therapy (CKRT) capacity for critically ill patients with acute kidney injury stage 3 requiring dialysis. To inform planning for current and future crises, we estimated CKRT demand and capacity during the initial wave of the US COVID-19 pandemic. STUDY DESIGN: We developed mathematical models to project nationwide and statewide CKRT demand and capacity. Data sources included the Institute for Health Metrics and Evaluation model, the Harvard Global Health Institute model, and published literature. SETTING & POPULATION: US patients hospitalized during the initial wave of the COVID-19 pandemic (February 6, 2020, to August 4, 2020). INTERVENTION: CKRT. OUTCOMES: CKRT demand and capacity at peak resource use; number of states projected to encounter CKRT shortages. MODEL, PERSPECTIVE, & TIMEFRAME: Health sector perspective with a 6-month time horizon. RESULTS: Under base-case model assumptions, there was a nationwide CKRT capacity of 7,032 machines, an estimated shortage of 1,088 (95% uncertainty interval, 910-1,568) machines, and shortages in 6 states at peak resource use. In sensitivity analyses, varying assumptions around: (1) the number of pre-COVID-19 surplus CKRT machines available and (2) the incidence of acute kidney injury stage 3 requiring dialysis requiring CKRT among hospitalized patients with COVID-19 resulted in projected shortages in 3 to 8 states (933-1,282 machines) and 4 to 8 states (945-1,723 machines), respectively. In the best- and worst-case scenarios, there were shortages in 3 and 26 states (614 and 4,540 machines). LIMITATIONS: Parameter estimates are influenced by assumptions made in the absence of published data for CKRT capacity and by the Institute for Health Metrics and Evaluation model's limitations. CONCLUSIONS: Several US states are projected to encounter CKRT shortages during the COVID-19 pandemic. These findings, although based on limited data for CKRT demand and capacity, suggest there being value during health care crises such as the COVID-19 pandemic in establishing an inpatient kidney replacement therapy national registry and maintaining a national stockpile of CKRT equipment.