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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.

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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.

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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.

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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.

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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.

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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.

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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.

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