Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 45
Filtrar
Añadir filtros

Intervalo de año
3.
BMJ Open ; 11(1): e042945, 2021 01 26.
Artículo en Inglés | MEDLINE | ID: covidwho-1050402

RESUMEN

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.


Asunto(s)
/epidemiología , Capacidad de Camas en Hospitales , Hospitales/provisión & distribución , Capacidad de Reacción , Ventiladores Mecánicos/provisión & distribución , Ocupación de Camas/estadística & datos numéricos , Inglaterra/epidemiología , Personal de Salud , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Medicina Estatal
4.
Rev. méd. Chile ; 148(5): 674-683, mayo 2020. tab, graf
Artículo en Español | LILACS (Américas) | ID: covidwho-1006910

RESUMEN

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.


Asunto(s)
Humanos , Infecciones por Coronavirus/epidemiología , Pandemias , Unidades de Cuidados Intensivos/organización & administración , Unidades de Cuidados Intensivos/provisión & distribución , Capacidad de Reacción
5.
J Crit Care ; 62: 172-175, 2021 04.
Artículo en Inglés | MEDLINE | ID: covidwho-988303

RESUMEN

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.


Asunto(s)
Lesión Renal Aguda/etiología , Lesión Renal Aguda/mortalidad , Lesión Renal Aguda/terapia , /mortalidad , Terapia de Reemplazo Renal , Anciano , Estudios de Cohortes , Femenino , Hospitalización , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Masculino , Persona de Mediana Edad , Ciudad de Nueva York/epidemiología , Recuperación de la Función , Respiración Artificial , Estudios Retrospectivos
6.
Anesth Analg ; 131(5): 1337-1341, 2020 11.
Artículo en Inglés | MEDLINE | ID: covidwho-983117

RESUMEN

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.


Asunto(s)
Citas y Horarios , Infecciones por Coronavirus/terapia , Cuidados Críticos , Prestación Integrada de Atención de Salud , Procedimientos Quirúrgicos Electivos , Unidades de Cuidados Intensivos/provisión & distribución , Admisión del Paciente , Neumonía Viral/terapia , Capacidad de Reacción , Infecciones por Coronavirus/diagnóstico , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/virología , Bases de Datos Factuales , Necesidades y Demandas de Servicios de Salud , Humanos , Evaluación de Necesidades , New York/epidemiología , Sistemas de Información en Quirófanos , Pandemias , Neumonía Viral/diagnóstico , Neumonía Viral/epidemiología , Neumonía Viral/virología , Respiración Artificial , Factores de Tiempo , Ventiladores Mecánicos/provisión & distribución
7.
PLoS One ; 15(10): e0241027, 2020.
Artículo en Inglés | MEDLINE | ID: covidwho-883688

RESUMEN

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.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/terapia , Recursos en Salud/provisión & distribución , Modelos Teóricos , Neumonía Viral/epidemiología , Neumonía Viral/terapia , Algoritmos , Infecciones por Coronavirus/virología , Cuidados Críticos , Capacidad de Camas en Hospitales , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Pandemias , Transferencia de Pacientes , Neumonía Viral/virología , España/epidemiología , Reino Unido/epidemiología , Ventiladores Mecánicos/provisión & distribución
8.
Elife ; 92020 10 12.
Artículo en Inglés | MEDLINE | ID: covidwho-844205

RESUMEN

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.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/terapia , Necesidades y Demandas de Servicios de Salud , Pandemias , Neumonía Viral/terapia , Adulto , Anciano , Área Bajo la Curva , Índice de Masa Corporal , Comorbilidad , Infecciones por Coronavirus/epidemiología , Diabetes Mellitus/epidemiología , Femenino , Hospitalización/estadística & datos numéricos , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Unidades de Cuidados Intensivos/provisión & distribución , Masculino , Massachusetts/epidemiología , Persona de Mediana Edad , Dinámicas no Lineales , Neumonía Viral/epidemiología , Utilización de Procedimientos y Técnicas , Curva ROC , Respiración Artificial/estadística & datos numéricos , Factores de Riesgo , Ventiladores Mecánicos/provisión & distribución
9.
Medwave ; 20(9)30-10-2020.
Artículo en Español | LILACS (Américas) | ID: covidwho-841474

RESUMEN

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.


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.


Asunto(s)
Humanos , Neumonía Viral/epidemiología , Modelos Estadísticos , Infecciones por Coronavirus/epidemiología , Capacidad de Camas en Hospitales/estadística & datos numéricos , Unidades de Cuidados Intensivos/provisión & distribución , Chile/epidemiología , Pandemias
10.
Lancet ; 395(10231): 1225-1228, 2020 04 11.
Artículo en Inglés | MEDLINE | ID: covidwho-830460

RESUMEN

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.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Neumonía Viral/epidemiología , Adulto , Anciano , Anciano de 80 o más Años , Infecciones por Coronavirus/terapia , Femenino , Salud Global , Política de Salud/tendencias , Capacidad de Camas en Hospitales/estadística & datos numéricos , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Unidades de Cuidados Intensivos/provisión & distribución , Italia/epidemiología , Masculino , Persona de Mediana Edad , Modelos Biológicos , Pandemias , Neumonía Viral/terapia , Respiración Artificial/estadística & datos numéricos
11.
PLoS One ; 15(9): e0239249, 2020.
Artículo en Inglés | MEDLINE | ID: covidwho-788880

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/terapia , Capacidad de Camas en Hospitales/estadística & datos numéricos , Unidades de Cuidados Intensivos/provisión & distribución , Neumonía Viral/terapia , Betacoronavirus , Infecciones por Coronavirus/epidemiología , Prestación de Atención de Salud/normas , Brotes de Enfermedades , Capacidad de Camas en Hospitales/normas , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Italia/epidemiología , Pandemias , Manejo de Atención al Paciente/normas , Neumonía Viral/epidemiología
12.
Am J Kidney Dis ; 76(5): 696-709.e1, 2020 11.
Artículo en Inglés | MEDLINE | ID: covidwho-676627

RESUMEN

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.


Asunto(s)
Lesión Renal Aguda , Defensa Civil , Terapia de Reemplazo Renal Continuo/métodos , Infecciones por Coronavirus , Enfermedad Crítica , Necesidades y Demandas de Servicios de Salud/organización & administración , Unidades de Cuidados Intensivos/provisión & distribución , Pandemias , Neumonía Viral , Reserva Estratégica/métodos , Lesión Renal Aguda/etiología , Lesión Renal Aguda/terapia , Betacoronavirus , Defensa Civil/métodos , Defensa Civil/organización & administración , Infecciones por Coronavirus/complicaciones , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/terapia , Enfermedad Crítica/epidemiología , Enfermedad Crítica/terapia , Humanos , Modelos Teóricos , Neumonía Viral/complicaciones , Neumonía Viral/epidemiología , Neumonía Viral/terapia , Utilización de Procedimientos y Técnicas/estadística & datos numéricos , Medición de Riesgo/métodos , Estados Unidos/epidemiología
14.
CMAJ ; 192(44): E1347-E1356, 2020 11 02.
Artículo en Inglés | MEDLINE | ID: covidwho-740406

RESUMEN

BACKGROUND: To mitigate the effects of coronavirus disease 2019 (COVID-19), jurisdictions worldwide ramped down nonemergent surgeries, creating a global surgical backlog. We sought to estimate the size of the nonemergent surgical backlog during COVID-19 in Ontario, Canada, and the time and resources required to clear the backlog. METHODS: We used 6 Ontario or Canadian population administrative sources to obtain data covering part or all of the period between Jan. 1, 2017, and June 13, 2020, on historical volumes and operating room throughput distributions by surgery type and region, and lengths of stay in ward and intensive care unit (ICU) beds. We used time series forecasting, queuing models and probabilistic sensitivity analysis to estimate the size of the backlog and clearance time for a +10% (+1 day per week at 50% capacity) surge scenario. RESULTS: Between Mar. 15 and June 13, 2020, the estimated backlog in Ontario was 148 364 surgeries (95% prediction interval 124 508-174 589), an average weekly increase of 11 413 surgeries. Estimated backlog clearance time is 84 weeks (95% confidence interval [CI] 46-145), with an estimated weekly throughput of 717 patients (95% CI 326-1367) requiring 719 operating room hours (95% CI 431-1038), 265 ward beds (95% CI 87-678) and 9 ICU beds (95% CI 4-20) per week. INTERPRETATION: The magnitude of the surgical backlog from COVID-19 raises serious implications for the recovery phase in Ontario. Our framework for modelling surgical backlog recovery can be adapted to other jurisdictions, using local data to assist with planning.


Asunto(s)
Procedimientos Quirúrgicos Cardíacos/estadística & datos numéricos , Infecciones por Coronavirus , Neoplasias/cirugía , Trasplante de Órganos/estadística & datos numéricos , Pandemias , Neumonía Viral , Procedimientos Quirúrgicos Vasculares/estadística & datos numéricos , Betacoronavirus , Procedimientos Quirúrgicos Electivos/estadística & datos numéricos , Predicción , Capacidad de Camas en Hospitales/estadística & datos numéricos , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Tiempo de Internación/estadística & datos numéricos , Modelos Estadísticos , Ontario , Quirófanos/provisión & distribución , Pediatría/estadística & datos numéricos , Factores de Tiempo
15.
Ann Glob Health ; 86(1): 100, 2020 08 13.
Artículo en Inglés | MEDLINE | ID: covidwho-736810

RESUMEN

Background: Brazil faces some challenges in the battle against the COVID-19 pandemic, including: the risks for cross-infection (community infection) increase in densely populated areas; low access to health services in areas where the number of beds in intensive care units (ICUs) is scarce and poorly distributed, mainly in states with low population density. Objective: To describe and intercorrelate epidemiology and geographic data from Brazil about the number of intensive care unit (ICU) beds at the onset of COVID-19 pandemic. Methods: The epidemiology and geographic data were correlated with the distribution of ICU beds (public and private health systems) and the number of beneficiaries of private health insurance using Pearson's Correlation Coefficient. The same data were correlated using partial correlation controlled by gross domestic product (GDP) and number of beneficiaries of private health insurance. Findings: Brazil has a large geographical area and diverse demographic and economic aspects. This diversity is also present in the states and the Federal District regarding the number of COVID-19 cases, deaths and case fatality rate. The effective management of severe COVID-19 patients requires ICU services, and the scenario was also dissimilar as for ICU beds and ICU beds/10,000 inhabitants for the public (SUS) and private health systems mainly at the onset of COVID-19 pandemic. The distribution of ICUs was uneven between public and private services, and most patients rely on SUS, which had the lowest number of ICU beds. In only a few states, the number of ICU beds at SUS was above 1 to 3 by 10,000 inhabitants, which is the number recommended by the World Health Organization (WHO). Conclusions: Brazil needed to improve the number of ICU beds units to deal with COVID-19 pandemic, mainly for the SUS showing a late involvement of government and health authorities to deal with the COVID-19 pandemic.


Asunto(s)
Infecciones por Coronavirus , Accesibilidad a los Servicios de Salud/organización & administración , Unidades de Cuidados Intensivos/provisión & distribución , Pandemias , Manejo de Atención al Paciente , Neumonía Viral , Sector Privado/estadística & datos numéricos , Sector Público/estadística & datos numéricos , Ocupación de Camas/estadística & datos numéricos , Betacoronavirus , Brasil/epidemiología , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/prevención & control , Infecciones por Coronavirus/terapia , Necesidades y Demandas de Servicios de Salud , Humanos , Control de Infecciones/organización & administración , Control de Infecciones/normas , Innovación Organizacional , Pandemias/prevención & control , Manejo de Atención al Paciente/organización & administración , Manejo de Atención al Paciente/normas , Neumonía Viral/epidemiología , Neumonía Viral/prevención & control , Neumonía Viral/terapia , Índice de Severidad de la Enfermedad
17.
Oncology (Williston Park) ; 34(8): 317-319, 2020 08 12.
Artículo en Inglés | MEDLINE | ID: covidwho-713075

RESUMEN

A 78-year-old man had a medical history of hypertension, atrial fibrillation, chronic kidney disease, and metastatic castration-resistant prostate cancer (CRPC). He had progressed to first-line therapy for CRPC with abiraterone plus androgen-deprivation therapy (ADT) and as second-line therapy he was being treated with docetaxel, with biochemical progression in his last prostate specific antigen measurement. He was admitted to the hospital on April 2020, in the middle of the coronavirus disease 2019 (COVID-19) pandemic, because of painful bone lesions and deterioration of renal function.


Asunto(s)
Anticoagulantes/uso terapéutico , Neoplasias Óseas/tratamiento farmacológico , Infecciones por Coronavirus/terapia , Cuidados Paliativos , Neumonía Viral/terapia , Neoplasias de la Próstata Resistentes a la Castración/tratamiento farmacológico , Insuficiencia Respiratoria/terapia , Anciano , Antagonistas de Andrógenos/uso terapéutico , Androstenos/uso terapéutico , Antineoplásicos/uso terapéutico , Betacoronavirus , Conservadores de la Densidad Ósea/uso terapéutico , Neoplasias Óseas/complicaciones , Neoplasias Óseas/secundario , Dolor en Cáncer/complicaciones , Dolor en Cáncer/terapia , Infecciones por Coronavirus/complicaciones , Progresión de la Enfermedad , Docetaxel/uso terapéutico , Combinación de Medicamentos , Determinación de la Elegibilidad , Heparina de Bajo-Peso-Molecular/uso terapéutico , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Lopinavir/uso terapéutico , Masculino , Terapia por Inhalación de Oxígeno , Pandemias , Neumonía Viral/complicaciones , Neoplasias de la Próstata Resistentes a la Castración/complicaciones , Neoplasias de la Próstata Resistentes a la Castración/patología , Insuficiencia Renal , Insuficiencia Respiratoria/etiología , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa , Ritonavir/uso terapéutico , Índice de Severidad de la Enfermedad , Ácido Zoledrónico/uso terapéutico
18.
Eur J Epidemiol ; 35(8): 733-742, 2020 Aug.
Artículo en Inglés | MEDLINE | ID: covidwho-708706

RESUMEN

Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the "ground truth" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.


Asunto(s)
Infecciones por Coronavirus/mortalidad , Predicción/métodos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Pandemias/prevención & control , Neumonía Viral/mortalidad , Ocupación de Camas , Betacoronavirus , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Modelos Estadísticos , Mortalidad/tendencias , New York/epidemiología , Salud Pública
19.
J Biol Dyn ; 14(1): 621-632, 2020 12.
Artículo en Inglés | MEDLINE | ID: covidwho-680239

RESUMEN

We model the extent to which age-targeted protective sequestration can be used to reduce ICU admissions caused by novel coronavirus COVID-19. Using demographic data from New Zealand, we demonstrate that lowering the age threshold to 50 years of age reduces ICU admissions drastically and show that for sufficiently strict isolation protocols, sequestering one-third of the countries population for a total of 8 months is sufficient to avoid overwhelming ICU capacity throughout the entire course of the epidemic. Similar results are expected to hold for other countries, though some minor adaption will be required based on local age demographics and hospital facilities.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Modelos Biológicos , Pandemias , Neumonía Viral/epidemiología , Cuarentena/métodos , Adolescente , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Niño , Preescolar , Control de Enfermedades Transmisibles/métodos , Simulación por Computador , Infecciones por Coronavirus/prevención & control , Infecciones por Coronavirus/transmisión , Cuidados Críticos , Femenino , Hospitalización , Humanos , Lactante , Recién Nacido , Unidades de Cuidados Intensivos/provisión & distribución , Masculino , Persona de Mediana Edad , Nueva Zelanda/epidemiología , Pandemias/prevención & control , Aislamiento de Pacientes/métodos , Neumonía Viral/prevención & control , Neumonía Viral/transmisión , Factores de Riesgo , Adulto Joven
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA