Endovascular Repair of Infrarenal Abdominal Aortic Aneurysm Results in Higher Hospital Expenses than Open Surgical Repair: Evidence from a Tertiary Hospital in Brazil.

BACKGROUND: Endovascular aneurysm repair (EVAR) has become the preferred approach for the treatment of infrarenal abdominal aortic aneurysm (IRAAA) in detriment of open surgical repair (OSR). EVAR results in lower mortality rates within 30 days, but rates tend to be the same after longer periods. Moreover, reduced use of hospital resources with EVAR does not necessarily offset the costs of the endoprosthesis. We aimed, in this study, to estimate hospital expenses after OSR or EVAR, including early and late readmissions. METHODS: Retrospective analysis of hospital expenses (2005-2012) with elective IRAAA surgeries performed in a tertiary hospital, including 127 patients divided into 2 groups, EVAR (n = 102) and OSR (n = 25). RESULTS: One perioperative death occurred in each group. EVAR interventions lasted 145 vs. 210 min of OSR (P < 0.001). Among OSR patients, 68% required packed red blood cells. Among EVAR patients, this proportion was 7.8% (P < 0.001). Median hospitalization time differed significantly for EVAR (4 days) and OSR (8 days; P < 0.001, intervals EVAR: 1-17 days, OSR: 2-442 days). The median and mean expenses with EVAR were US $53,080.95 and US $56,289.49, respectively. The median and mean expenses with OSR were US $37,116.04 and US $68,788.54, respectively. Early readmissions reached 11.2%. None of the OSR patients required late reinterventions, but 10 (9.9%) EVAR patients did, one of whom died. CONCLUSIONS: EVAR resulted in higher expenses with the exclusion of one outlier. Late reinterventions, with elevated costs, were only required by EVAR patients. Thus, when patients are eligible to undergo either intervention, OSR seems to have lower costs and better long-term results.

Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

BACKGROUND: Gabaldi et al. utilized telemedicine data, web search trends, hospitalized patient characteristics, and resource usage data to estimate bed occupancy during the COVID-19 pandemic. The results showcase the potential of data-driven strategies to enhance resource allocation decisions for an effective pandemic response. OBJECTIVE: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. METHODS: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. RESULTS: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. CONCLUSION: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources. BACKGROUND: Developed models to forecast bed occupancy for up to 14 days and monitored errors for 365 days. BACKGROUND: Telemedicine calls from COVID-19 patients correlated with the number of patients hospitalized in the next 8 days.

Patient-level cost analysis of intensive care unit acquired infections: A prospective cohort study.

INTRODUCTION: Hospital-associated infections (HAIs) are associated with increased mortality and prolonged hospital length-of-stay (LOS). Although some studies have shown that HAIs are associated with increased costs, these studies only used cost estimates, were carried out in a small number of centres, or only in high-income countries. METHODS: We carried out a prospective cohort study in ten Brazilian intensive care units (ICUs) selected from a collaborative platform study (IMPACTO MR). We included all patients aged 18 years or older admitted from October 2019 to December 2021 and who had an ICU LOS of at least two days. The costs were adjusted for official inflation until December 2022 and converted into international dollars using the 2021 purchasing power parity (PPP) conversion rate. We used a propensity score matching method to compare patients with HAIs and patients without HAIs, and patients with and without ventilator-associated pneumonia (VAP), central-line bloodstream infection (CLABSI), catheter-associated urinary tract infection (CA-UTI) and multidrug-resistant (MDR) HAIs. RESULTS: We included 7,953 patients in the study, of whom 574 (7.2%) had an HAI during their ICU stay. After propensity-score matching, patients with HAIs had ICU costs that were more than three times higher than those of patients without HAIs [$ 19,642 (IQR; 12,884-35,134) vs. 6,086 (IQR; 3,268-12,550); p <0.001). Patients with VAP, CLABSI, and CA-UTI, but not with MDR-HAIs also had higher total ICU costs. CONCLUSIONS: HAIs acquired in the ICU are associated with higher ICU costs. These findings were consistent across specific types of infection.

Quality improvement project to reduce unplanned extubations in a paediatric intensive care unit.

BACKGROUND: Unplanned extubations are recurrent adverse events in mechanically ventilated children and have been the focus of quality and safety improvement in paediatric intensive care units (ICUs). LOCAL PROBLEM: To reduce the rate of unplanned extubation in the paediatric ICU by 66% (from 2.02 to 0.7). METHODS: This is a quality improvement project that was conducted in a paediatric ICU of a private hospital at the quaternary level. All hospitalised patients who used invasive mechanical ventilation between October 2018 and August 2019 were included. INTERVENTIONS: The project was based on the Improvement Model methodology of the Institute for Healthcare Improvement to implement change strategies. The main ideas of change were innovation in the endotracheal tube fixation model, evaluation of the endotracheal tube positioning, good practices of physical restraint, sedation monitoring, family education and engagement and checklist for prevention of unplanned extubation, with Plan-Do-Study-Act, the tool chosen to test and implement ideas for change. RESULTS: The actions reduced the unplanned extubation rate to zero in our institution and sustained this result for a period of 2 years, totalling 743 days without any event. An estimate was made comparing cases with unplanned extubation and controls without the occurrence of this adverse event, which resulted in savings of R$955 096.65 (US$179 540.41) during the 2 years after the implementation of the improvement actions. CONCLUSION: The improvement project conducted in the 11-month period reduced the unplanned extubation rate to zero in our institution and sustained this result for a period of 743 days. Adherence to the new fixation model and the creation of a new restrictor model, which enabled the implementation of good practices of physical restraint were the ideas of change that had the greatest impact in achieving this result.

Impact of a quality programme on overindication of surgeries for endometriosis and cholecystectomies.

Approximately 45% of patients receive medical services with minimal or no benefit (low-value care). In addition to the increasing costs to the health system, performing invasive procedures without an indication poses a potentially preventable risk to patient safety. This study aimed to determine whether a managed quality improvement programme could prevent cholecystectomy and surgery for endometriosis treatment with minimal or no benefit to patients.This before-and-after study was conducted at a private hospital in São Paulo, Brazil, which has a main medical remuneration model of fee for service. All patients who underwent cholecystectomy or surgery for endometriosis between 1 August 2020 and 31 May 2021 were evaluated.The intervention consisted of allowing the performance of procedures that met previously defined criteria or for which the indications were validated by a board of experts.A total of 430 patients were included in this analysis. The programme prevented the unnecessary performance of 13% of cholecystectomies (p=0.0001) and 22.2% (p=0.0006) of surgeries for the treatment of endometriosis. This resulted in an estimated annual cost reduction to the health system of US$466 094.93.In a hospital with a private practice and fee-for-service medical remuneration, the definition of clear criteria for indicating surgery and the analysis of cases that did not meet these criteria by a board of reputable experts at the institution resulted in a statistically significant reduction in low-value cholecystectomies and endometriosis surgeries.

Prevalence, predictors, and patient-reported outcomes of long COVID in hospitalized and non-hospitalized patients from the city of São Paulo, Brazil.

Background: Robust data comparing long COVID in hospitalized and non-hospitalized patients in middle-income countries are limited. Methods: A retrospective cohort study was conducted in Brazil, including hospitalized and non-hospitalized patients. Long COVID was diagnosed at 90-day follow-up using WHO criteria. Demographic and clinical information, including the depression screening scale (PHQ-2) at day 30, was compared between the groups. If the PHQ-2 score is 3 or greater, major depressive disorder is likely. Logistic regression analysis identified predictors and protective factors for long COVID. Results: A total of 291 hospitalized and 1,118 non-hospitalized patients with COVID-19 were included. The prevalence of long COVID was 47.1% and 49.5%, respectively. Multivariable logistic regression showed female sex (odds ratio [OR] = 4.50, 95% confidence interval (CI) 2.51-8.37), hypertension (OR = 2.90, 95% CI 1.52-5.69), PHQ-2 > 3 (OR = 6.50, 95% CI 1.68-33.4) and corticosteroid use during hospital stay (OR = 2.43, 95% CI 1.20-5.04) as predictors of long COVID in hospitalized patients, while female sex (OR = 2.52, 95% CI 1.95-3.27) and PHQ-2 > 3 (OR = 3.88, 95% CI 2.52-6.16) were predictors in non-hospitalized patients. Conclusion: Long COVID was prevalent in both groups. Positive depression screening at day 30 post-infection can predict long COVID. Early screening of depression helps health staff to identify patients at a higher risk of long COVID, allowing an early diagnosis of the condition.

Longer-term effectiveness of a heterologous coronavirus disease 2019 (COVID-19) vaccine booster in healthcare workers in Brazil.

Objective: To compare the long-term vaccine effectiveness between those receiving viral vector [Oxford-AstraZeneca (ChAdOx1)] or inactivated viral (CoronaVac) primary series (2 doses) and those who received an mRNA booster (Pfizer/BioNTech) (the third dose) among healthcare workers (HCWs). Methods: We conducted a retrospective cohort study among HCWs (aged ≥18 years) in Brazil from January 2021 to July 2022. To assess the variation in the effectiveness of booster dose over time, we estimated the effectiveness rate by taking the log risk ratio as a function of time. Results: Of 14,532 HCWs, coronavirus disease 2019 (COVID-19) was confirmed in 56.3% of HCWs receiving 2 doses of CoronaVac vaccine versus 23.2% of HCWs receiving 2 doses of CoronaVac vaccine with mRNA booster (P < .001), and 37.1% of HCWs receiving 2 doses of ChAdOx1 vaccine versus 22.7% among HCWs receiving 2 doses of ChAdOx1 vaccine with mRNA booster (P < .001). The highest vaccine effectiveness with mRNA booster was observed 30 days after vaccination: 91% for the CoronaVac vaccine group and 97% for the ChAdOx1 vaccine group. Vacine effectiveness declined to 55% and 67%, respectively, at 180 days. Of 430 samples screened for mutations, 49.5% were SARS-CoV-2 delta variants and 34.2% were SARS-CoV-2 omicron variants. Conclusions: Heterologous COVID-19 vaccines were effective for up to 180 days in preventing COVID-19 in the SARS-CoV-2 delta and omicron variant eras, which suggests the need for a second booster.

Risk factors for long coronavirus disease 2019 (long COVID) among healthcare personnel, Brazil, 2020-2022.

OBJECTIVE: To determine risk factors for the development of long coronavirus disease 2019 (COVID-19) in healthcare personnel (HCP). METHODS: We conducted a case-control study among HCP who had confirmed symptomatic COVID-19 working in a Brazilian healthcare system between March 1, 2020, and July 15, 2022. Cases were defined as those having long COVID according to the Centers for Disease Control and Prevention definition. Controls were defined as HCP who had documented COVID-19 but did not develop long COVID. Multiple logistic regression was used to assess the association between exposure variables and long COVID during 180 days of follow-up. RESULTS: Of 7,051 HCP diagnosed with COVID-19, 1,933 (27.4%) who developed long COVID were compared to 5,118 (72.6%) who did not. The majority of those with long COVID (51.8%) had 3 or more symptoms. Factors associated with the development of long COVID were female sex (OR, 1.21; 95% CI, 1.05-1.39), age (OR, 1.01; 95% CI, 1.00-1.02), and 2 or more SARS-CoV-2 infections (OR, 1.27; 95% CI, 1.07-1.50). Those infected with the SARS-CoV-2 δ (delta) variant (OR, 0.30; 95% CI, 0.17-0.50) or the SARS-CoV-2 o (omicron) variant (OR, 0.49; 95% CI, 0.30-0.78), and those receiving 4 COVID-19 vaccine doses prior to infection (OR, 0.05; 95% CI, 0.01-0.19) were significantly less likely to develop long COVID. CONCLUSIONS: Long COVID can be prevalent among HCP. Acquiring >1 SARS-CoV-2 infection was a major risk factor for long COVID, while maintenance of immunity via vaccination was highly protective.

IMPACTO-MR: a Brazilian nationwide platform study to assess infections and multidrug resistance in intensive care units. / IMPACTO-MR: um estudo brasileiro de plataforma nacional para avaliar infecções e multirresistência em unidades de terapia intensiva.

OBJECTIVE: To describe the IMPACTO-MR, a Brazilian nationwide intensive care unit platform study focused on the impact of health care-associated infections due to multidrug-resistant bacteria. METHODS: We described the IMPACTO-MR platform, its development, criteria for intensive care unit selection, characterization of core data collection, objectives, and future research projects to be held within the platform. RESULTS: The core data were collected using the Epimed Monitor System® and consisted of demographic data, comorbidity data, functional status, clinical scores, admission diagnosis and secondary diagnoses, laboratory, clinical, and microbiological data, and organ support during intensive care unit stay, among others. From October 2019 to December 2020, 33,983 patients from 51 intensive care units were included in the core database. CONCLUSION: The IMPACTO-MR platform is a nationwide Brazilian intensive care unit clinical database focused on researching the impact of health care-associated infections due to multidrug-resistant bacteria. This platform provides data for individual intensive care unit development and research and multicenter observational and prospective trials.

OBJETIVO: Descrever o IMPACTO-MR, um estudo brasileiro de plataforma nacional em unidades de terapia intensiva focado no impacto das infecções por bactérias multirresistentes relacionadas à assistência à saúde. MÉTODOS: Descrevemos a plataforma IMPACTO-MR, seu desenvolvimento, critérios para seleção das unidades de terapia intensiva, caracterização da coleta de dados, objetivos e projetos de pesquisa futuros a serem realizados na plataforma. RESULTADOS: Os dados principais foram coletados por meio do Epimed Monitor System® e consistiram em dados demográficos, dados de comorbidades, estado funcional, escores clínicos, diagnóstico de internação e diagnósticos secundários, dados laboratoriais, clínicos e microbiológicos e suporte de órgãos durante a internação na unidade de terapia intensiva, entre outros. De outubro de 2019 a dezembro de 2020, 33.983 pacientes de 51 unidades de terapia intensiva foram incluídos no banco de dados principal. CONCLUSÃO: A plataforma IMPACTO-MR é um banco de dados clínico brasileiro de unidades de terapia intensiva focado na pesquisa do impacto das infecções por bactérias multirresistentes relacionadas à assistência à saúde. Essa plataforma fornece dados para o desenvolvimento e pesquisa de unidades de terapia intensiva individuais e ensaios clínicos observacionais e prospectivos multicêntricos.

Impact of the COVID-19 pandemic on emergency department visits: experience of a Brazilian reference center.

OBJECTIVE: To analyze the impact of COVID-19 on emergency department metrics at a large tertiary reference hospital in Brazil. METHODS: A retrospective analysis of consecutive emergency department visits, from January 1, 2020, to November 21, 2020, was performed and compared to the corresponding time frame in 2018 and 2019. The volume of visits and patients' demographic and clinic characteristics were compared. All medical conditions were included, except confirmed cases of COVID-19. RESULTS: A total of 138,138 emergency department visits occurred during the study period, with a statistically significant (p<0.01) reduction by 52% compared to both 2018 and 2019. This decrease was more pronounced for pediatric visits - a drop by 71% in comparison to previous years. Regarding clinical presentation, there was a decrease of severe cases by 34.7% and 37.6%, whereas mild cases decreased by 55.2% and 56.2% when comparing 2020 to 2018 and 2019, respectively. A 30% fall in the total volume of hospital admission from emergency department patients was observed during the study period, but accompanied by a proportional increase in monthly admission rates since April 2020. CONCLUSION: The COVID-19 pandemic led to a 52% fall in attendance at our emergency department for other conditions, along with a proportional increase in hospital admission rates of COVID-19 patients. Healthcare providers should raise patient awareness not to delay seeking medical treatment of severe conditions that require care at the emergency department.

Impact of COVID-19 pandemic on care of oncological patients: experience of a cancer center in a Latin American pandemic epicenter.

OBJECTIVE: Since the rising of coronavirus disease 2019 (COVID-19) pandemic, there is uncertainty regarding the impact of transmission to cancer patients. Evidence on increased severity for patients undergoing antineoplastic treatment is posed against deferring oncologic treatment. We aimed to evaluate the impact of COVID-19 pandemic on patient volumes in a cancer center in an epicenter of the pandemic. METHODS: Outpatient and inpatient volumes were extracted from electronic health record database. Two intervals were compared: pre-COVID-19 (March to May 2019) and COVID-19 pandemic (March to May 2020) periods. RESULTS: The total number of medical appointments declined by 45% in the COVID-19 period, including a 56.2% decrease in new visits. There was a 27.5% reduction in the number of patients undergoing intravenous systemic treatment and a 57.4% decline in initiation of new treatments. Conversely, there was an increase by 309% in new patients undergoing oral chemotherapy regimens and a 5.9% rise in new patients submitted to radiation therapy in the COVID-19 period. There was a 51.2% decline in length of stay and a 60% reduction in the volume of surgical cases during COVID-19. In the stem cell transplant unit, we observed a reduction by 36.5% in length of stay and a 62.5% drop in stem cell transplants. CONCLUSION: A significant decrease in the number of patients undergoing cancer treatment was observed after COVID-19 pandemic. Although this may be partially overcome by alternative therapeutic options, avoiding timely health care due to fear of getting COVID-19 infection might impact on clinical outcomes. Our findings may help support immediate actions to mitigate this hypothesis.

Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. Results: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. Conclusion: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources.

IMPACTO-MR: um estudo brasileiro de plataforma nacional para avaliar infecções e multirresistência em unidades de terapia intensiva / IMPACTO-MR: a Brazilian nationwide platform study to assess infections and multidrug resistance in intensive care units

RESUMO Objetivo: Descrever o IMPACTO-MR, um estudo brasileiro de plataforma nacional em unidades de terapia intensiva focado no impacto das infecções por bactérias multirresistentes relacionadas à assistência à saúde. Métodos: Descrevemos a plataforma IMPACTO-MR, seu desenvolvimento, critérios para seleção das unidades de terapia intensiva, caracterização da coleta de dados, objetivos e projetos de pesquisa futuros a serem realizados na plataforma. Resultados: Os dados principais foram coletados por meio do Epimed Monitor System® e consistiram em dados demográficos, dados de comorbidades, estado funcional, escores clínicos, diagnóstico de internação e diagnósticos secundários, dados laboratoriais, clínicos e microbiológicos e suporte de órgãos durante a internação na unidade de terapia intensiva, entre outros. De outubro de 2019 a dezembro de 2020, 33.983 pacientes de 51 unidades de terapia intensiva foram incluídos no banco de dados principal. Conclusão: A plataforma IMPACTO-MR é um banco de dados clínico brasileiro de unidades de terapia intensiva focado na pesquisa do impacto das infecções por bactérias multirresistentes relacionadas à assistência à saúde. Essa plataforma fornece dados para o desenvolvimento e pesquisa de unidades de terapia intensiva individuais e ensaios clínicos observacionais e prospectivos multicêntricos.

ABSTRACT Objective: To describe the IMPACTO-MR, a Brazilian nationwide intensive care unit platform study focused on the impact of health care-associated infections due to multidrug-resistant bacteria. Methods: We described the IMPACTO-MR platform, its development, criteria for intensive care unit selection, characterization of core data collection, objectives, and future research projects to be held within the platform. Results: The core data were collected using the Epimed Monitor System® and consisted of demographic data, comorbidity data, functional status, clinical scores, admission diagnosis and secondary diagnoses, laboratory, clinical, and microbiological data, and organ support during intensive care unit stay, among others. From October 2019 to December 2020, 33,983 patients from 51 intensive care units were included in the core database. Conclusion: The IMPACTO-MR platform is a nationwide Brazilian intensive care unit clinical database focused on researching the impact of health care-associated infections due to multidrug-resistant bacteria. This platform provides data for individual intensive care unit development and research and multicenter observational and prospective trials.

Impact of COVID-19 pandemic on care of oncological patients: experience of a cancer center in a Latin American pandemic epicenter / Impacto da COVID-19 sobre o atendimento de pacientes oncológicos: experiência de um centro oncológico localizado em um epicentro Latino-Americano da pandemia

ABSTRACT Objective Since the rising of coronavirus disease 2019 (COVID-19) pandemic, there is uncertainty regarding the impact of transmission to cancer patients. Evidence on increased severity for patients undergoing antineoplastic treatment is posed against deferring oncologic treatment. We aimed to evaluate the impact of COVID-19 pandemic on patient volumes in a cancer center in an epicenter of the pandemic. Methods Outpatient and inpatient volumes were extracted from electronic health record database. Two intervals were compared: pre-COVID-19 (March to May 2019) and COVID-19 pandemic (March to May 2020) periods. Results The total number of medical appointments declined by 45% in the COVID-19 period, including a 56.2% decrease in new visits. There was a 27.5% reduction in the number of patients undergoing intravenous systemic treatment and a 57.4% decline in initiation of new treatments. Conversely, there was an increase by 309% in new patients undergoing oral chemotherapy regimens and a 5.9% rise in new patients submitted to radiation therapy in the COVID-19 period. There was a 51.2% decline in length of stay and a 60% reduction in the volume of surgical cases during COVID-19. In the stem cell transplant unit, we observed a reduction by 36.5% in length of stay and a 62.5% drop in stem cell transplants. Conclusion A significant decrease in the number of patients undergoing cancer treatment was observed after COVID-19 pandemic. Although this may be partially overcome by alternative therapeutic options, avoiding timely health care due to fear of getting COVID-19 infection might impact on clinical outcomes. Our findings may help support immediate actions to mitigate this hypothesis.

RESUMO Objetivo Desde o surgimento da pandemia da doença pelo coronavírus 2019 (COVID-19), há incerteza quanto ao impacto da transmissão para pacientes com câncer. As evidências sobre o aumento da gravidade para pacientes submetidos a tratamento antineoplásico são contra o adiamento do tratamento oncológico. Nosso objetivo foi avaliar o impacto da pandemia de COVID-19 em volumes de pacientes em um centro oncológico, em um epicentro da pandemia. Métodos Os volumes de pacientes ambulatoriais e de internação foram extraídos do banco de dados de prontuários eletrônicos. Dois intervalos foram comparados: períodos pré-COVID-19 (março a maio de 2019) e pandemia COVID-19 (março a maio de 2020). Resultados O número total de consultas médicas diminuiu 45% no período pandemia COVID-19, inclusive com redução de 56,2% nas novas consultas. Houve redução de 27,5% no número de pacientes em tratamento sistêmico intravenoso e de 57,4% no início de novos tratamentos. Por outro lado, ocorreram aumento de 309% em novos pacientes submetidos a regimes de quimioterapia oral e elevação de 5,9% em novos pacientes submetidos à radioterapia no período pandemia COVID-19. Observaram-se queda de 51,2% nos dias de internação e redução de 60% no volume de casos cirúrgicos durante a COVID-19. Na unidade de transplante de células-tronco, a redução foi de 36,5% nos dias de internação e de 62,5% nos transplantes de células-tronco. Conclusão Foi observado declínio significativo no número de pacientes em tratamento de câncer após a pandemia de COVID-19. Embora isso possa ser parcialmente superado por opções terapêuticas alternativas, evitar cuidados de saúde oportunos devido ao medo de contrair COVID-19 pode impactar nos resultados clínicos. Nossos resultados podem ajudar a apoiar ações imediatas para mitigar essa hipótese.

Impact of the COVID-19 pandemic on emergency department visits: experience of a Brazilian reference center / Impacto da pandemia da COVID-19 no volume de atendimentos no pronto atendimento: experiência de um centro de referência no Brasil

ABSTRACT Objective To analyze the impact of COVID-19 on emergency department metrics at a large tertiary reference hospital in Brazil. Methods A retrospective analysis of consecutive emergency department visits, from January 1, 2020, to November 21, 2020, was performed and compared to the corresponding time frame in 2018 and 2019. The volume of visits and patients' demographic and clinic characteristics were compared. All medical conditions were included, except confirmed cases of COVID-19. Results A total of 138,138 emergency department visits occurred during the study period, with a statistically significant (p<0.01) reduction by 52% compared to both 2018 and 2019. This decrease was more pronounced for pediatric visits - a drop by 71% in comparison to previous years. Regarding clinical presentation, there was a decrease of severe cases by 34.7% and 37.6%, whereas mild cases decreased by 55.2% and 56.2% when comparing 2020 to 2018 and 2019, respectively. A 30% fall in the total volume of hospital admission from emergency department patients was observed during the study period, but accompanied by a proportional increase in monthly admission rates since April 2020. Conclusion The COVID-19 pandemic led to a 52% fall in attendance at our emergency department for other conditions, along with a proportional increase in hospital admission rates of COVID-19 patients. Healthcare providers should raise patient awareness not to delay seeking medical treatment of severe conditions that require care at the emergency department.

RESUMO Objetivo Analisar o impacto da pandemia da COVID-19 nas métricas do pronto atendimento de um hospital terciário de referência no Brasil. Métodos Uma análise retrospectiva das visitas consecutivas ao pronto atendimento, de 1o de janeiro de 2020 a 21 de novembro de 2020, foi realizada e comparada ao mesmo intervalo nos anos de 2018 e 2019. O volume de atendimentos e as características clínicas e demográficas dos pacientes foram comparados. Todos os diagnósticos foram incluídos, exceto os casos confirmados de COVID-19. Resultados Um total de 138.138 visitas ao pronto atendimento ocorreu durante o período do estudo, com redução estatisticamente significativa (p<0,01) de 52% do volume comparado tanto a 2018 como a 2019. Essa queda foi mais pronunciada nos atendimentos de pediatria, com redução de 71% se comparada aos números de anos anteriores. Em relação ao quadro clínico, houve redução dos casos graves em 34,7% e 37,6%, enquanto os casos leves caíram 55,2% e 56,2%, quando comparado 2020 a 2018 e a 2019, respectivamente. Uma queda de 30% foi vista no volume de admissões hospitalares originadas dessas visitas, porém houve aumento percentual da taxa de admissão mensal em relação ao volume desde abril de 2020. Conclusão O impacto da pandemia da COVID-19 gerou redução de 52% no volume de atendimento do pronto atendimento por outras condições clínicas, bem como aumento proporcional na taxa de admissão hospitalar de pacientes com COVID-19. Os profissionais de saúde devem orientar seus pacientes a não atrasar a procura por atendimento médico de condições graves que precisem de cuidados no pronto atendimento.