Healthcare-associated infections in the Intensive Care Unit during the COVID-19 pandemic

Zimmermann, Guilherme dos Santos; Freitas, Aline Alcantara de; Abraão, Lígia Maria; Bohomol, Elena

doi:10.37689/acta-ape/2025AO0003152

Abstract

Objective: To describe the occurrence of healthcare-associated infections in the first and second waves of the COVID-19 pandemic and analyze their correlations with management indicators in an intensive care unit.

Methods: Retrospective documentary study conducted during the epidemiological waves of the COVID-19 pandemic using the database of patients admitted to the intensive care unit of a private hospital in São Paulo. Inclusion criteria comprised patients admitted between March 2020 and August 2021, aged over 18 years, diagnosed with COVID-19, and presenting healthcare-associated infections. Descriptive analysis was performed for demographic and clinical variables, and Pearson’s chi-square and Fisher’s exact tests were used for associations. A significance level of 5% was considered in the analyzes.

Results: Ventilator-associated pneumonia is highlighted as the most prevalent topography among infections. A considerable increase in infection rates and use of invasive devices were also observed from the first to the second wave, as well as a positive correlation between infections and bed/day and patient/day occupancy indicators.

Conclusion: Infections and the use of invasive devices increased from the first to the second wave of COVID-19, as well as the occurrence of adverse events related to care between the periods. The increase in the number of beds, hospitalizations and occupancy also showed a positive impact on the increase in infections in the unit.

Resumo

Objetivo: Descrever a ocorrência de infecções relacionadas à assistência em saúde nas primeira e segunda ondas da pandemia da COVID-19 e analisar suas relações com os indicadores gerenciais em uma unidade de terapia intensiva.

Métodos: Estudo documental retrospectivo, realizado durante as fases epidemiológicas da pandemia de COVID-19 utilizando a base de dados dos pacientes internados na unidade de terapia intensiva de um hospital da iniciativa privada em São Paulo. Os critérios de inclusão foram os pacientes internados no período de março de 2020 a agosto de 2021, com idade acima de 18 anos, diagnóstico de COVID-19 e apresentaram infecções relacionadas à assistência. Foram realizados análise descritiva para as variáveis demográficas e clínicas e testes de Qui-quadrado de Pearson e exato de Fisher para as associações. As análises consideraram o nível de significância de 5%.

Resultados: Destacamos a pneumonia associada à ventilação mecânica como a topografia mais prevalente entre as infecções. Foram também observados considerável aumento nos índices de infecção e uso de dispositivos invasivos desde a primeira à segunda ondas e uma correlação positiva das infecções com os indicadores de ocupação leitos/dia e pacientes/dia.

Conclusão: Aumentaram as infecções e uso de dispositivos invasivos da primeira para a segunda ondas da COVID-19 com elevação na ocorrência de eventos adversos relacionados à assistência entre os períodos observados. O aumento nos números de leitos, internações e ocupação também mostrou um impacto positivo no aumento das infecções na unidade.

Descritores
Segurança do paciente; Infecção hospitalar; Infecções por coronavírus; Pandemias; Unidades de Terapia Intensiva

Resumen

Objetivo: Describir la incidencia de infecciones relacionadas con la atención de salud en la primera y segunda ola de la pandemia de COVID-19 y analizar la relación con los indicadores gerenciales en una Unidad de Cuidados Intensivos.

Métodos: Estudio documental retrospectivo, realizado durante las fases epidemiológicas de la pandemia de COVID-19, utilizando la base de datos de los pacientes internados en la Unidad de Cuidados Intensivos de un hospital privado en São Paulo. Los criterios de inclusión fueron: pacientes internados durante el período de marzo de 2020 a agosto de 2021, edad superior a 18 años, diagnóstico de COVID-19 y presentación de infecciones asociadas a la atención. Se realizó un análisis descriptivo para las variables demográficas y clínicas, y pruebas de ji cuadrado de Pearson y exacto de Fisher para las asociaciones. Los análisis consideraron un nivel de significación del 5 %.

Resultados: Se destacó la neumonía asociada a la ventilación mecánica como la topografía más predominante de las infecciones. También se observó un aumento considerable de los índices de infecciones y el uso de dispositivos invasivos de la primera a la segunda ola, y una correlación positiva de las infecciones con los indicadores de ocupación de camas/día y pacientes/día.

Conclusión: Las infecciones y el uso de dispositivos invasivos aumentaron de la primera a la segunda ola de COVID-19, con un aumento de la incidencia de eventos adversos asociados a la atención durante los períodos observados. El aumento de la cantidad de camas, internaciones y ocupación también mostró un impacto positivo en el aumento de las infecciones en la unidad.

Descriptores
Seguridad del paciente; Infección hospitalaria; Infecciones por coronavirus; Pandemias; Unidades de cuidado intensivo

Introduction

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a considerable impact on healthcare worldwide. After rapid spread and given the severity of the disease, the pandemic affected millions of people, leading to an overload in hospital institutions, especially intensive care units (ICUs).(¹)

In more complex processes, the situation of patients with COVID-19 required rapid decision-making due to the need to establish safety measures and assess the risks of exposure and infection for both the professional team and other patients.(², ³) As occurred in several countries, COVID-19 cases in Brazil have increased since the first case diagnosed in the city of São Paulo. According to notification data from the Ministry of Health, the increase in the number of deaths occurred in waves: the first, from the 9^th to the 45^th epidemiological weeks of 2020 and the second, longer and more lethal, from the 46^th week of 2020 to the 51^st week of 2021.(⁴)

Patients affected by the disease required strict monitoring and high levels of care. These included ventilatory care such as high-flow nasal oxygen, noninvasive mechanical ventilation, intubation and invasive mechanical ventilation; in addition, the use of central venous catheters to administer medications and bladder devices to monitor urinary output, thus increasing the risk of healthcare-associated infection (HAI).(², ⁵, ⁶)

Healthcare-associated infections are relatively common in critically ill patients, representing adverse events to the safety of care; their occurrence increases morbidity and mortality and the costs of care. Studies have shown an estimated cost to the global health system of US$8.3-11.5 billion per year.(⁷) Other studies have shown that the increase in HAIs during the pandemic was related to patient risk factors, such as age and comorbidities, use of devices, mechanical ventilation and immunosuppressive agents, in addition to the illness of professionals.(¹, ², ⁸, ⁹)

Understanding the incidence of HAIs in ICUs during the pandemic is essential to assess the impact of COVID-19 on infection control practices and improvement processes. In addition, the impact challenges managers to implement safe strategies for situations that affect the health system. However, such studies are scarce and there are still no studies on the temporal evolution through the epidemiological waves that occurred in Brazil.(¹⁰) This motivated the following research questions: what is the occurrence of HAIs in the epidemiological waves of the COVID-19 pandemic and what is the relationship of these infections with ICU indicators? Therefore, the objectives of this study were to describe the occurrence of HAIs in the first and second waves of the COVID-19 pandemic and analyze their correlation with management indicators in an ICU.

Methods

This retrospective, descriptive, exploratory documentary study was conducted during the first and second waves of COVID-19 using the database of critical care units of a private, high complexity hospital located in the central region of the city of São Paulo, in which care for adults with clinical and surgical conditions is provided. The systematization proposed in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) tool was used in the methodological design of the study.

This ICU had the capacity to serve 44 beds. However, during the actions to combat the COVID-19 pandemic, its capacity was increased to 89 beds, totaling seven ICUs dedicated exclusively to patients with COVID-19. The time frame of hospitalizations that occurred between March 2020 and July 2021 (considered the first wave of infection cases from March 2020 to November 2020) and from December 2020 to August 2021 for the second wave were used in this analysis.

The selected sample was non-probabilistic, consisting of information on the care of all patients admitted to the unit during the analysis period. The inclusion criteria were the infection criteria defined by the National Health Surveillance Agency (ANVISA): age ≥18 years; diagnosis or test with the reverse transcriptase reaction followed by the polymerase chain reaction (RT-PCR) for COVID-19 and presence of HAIs; a central line-associated bloodstream infection (CLABSI); ventilator-associated pneumonia (VAP) and catheter associated urinary tract infection (CAUTI), totaling 107 patients.

A database (Microsoft Excel® spreadsheet) was created to extract hospital data from the following sources: (a) Epimed Solutions® platforms for managing hospital clinical and epidemiological information and (b) Business Intelligence Tableau Software®. All data extracted through the data intelligence software were kept anonymous, and patients were identified only by sequential numbers of care service. The researcher did not have access to patients’ medical records, any health history or personal data that could identify them.

A descriptive analysis (mean, standard deviation, median and absolute and relative frequencies) was performed for demographic and clinical variables. The assumption of normality was not met when evaluating numerical variables using the Shapiro-Wilk test. Pearson’s chi-square and Fisher’s exact tests were performed for associations. The nonparametric Kruskall-Wallis test and Spearman correlation were used for numerical variables, considering coefficients <0.30 (weak magnitude), 0.30-0.49 (moderate magnitude) and ≥0.50 (strong magnitude). The analyzes were performed using R software (v. 4.0.4). A significance level of 5% was considered in all analyzes.

The project was approved by the hospital’s Research Ethics Committee, Certificate of Presentation of Ethical Appreciation: no. 51449121.8.0000.0070, Opinion: no. 4.977.607. Exemption from the Informed Consent form was granted given the observational and epidemiological nature of the study.

Results

The results of the study revealed a complex and multifaceted picture of HAIs in an ICU during the first and second waves of the COVID-19 pandemic. A total of 107 patients with HAIs were identified during the first or second waves of COVID-19. Although the demographic results did not show statistically significant differences, a predominance of male HAI patients was observed in the first and second waves, as well as a lower mean age and a longer mean length of stay in the ICU in the second wave. Admissions to the ICU from inpatient units predominated in the second wave (p>0.005) (Table 1).

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Table 1
Distribution of demographic and clinical variables of patients with healthcare-associated infection in the ICU in the first and second waves of COVID-19

The occurrence of HAIs, the use of devices and the incidence density of HAIs in the first and second waves of COVID were compared. The results indicate a significant increase in the use of invasive devices (mechanical ventilators, central catheters and indwelling urinary catheters) and an increase in the occurrence of HAIs from the first to the second wave (p<0.05) (Table 2).

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Table 2
Comparison between the occurrence of healthcare-associated infection and the use of invasive devices in the first and second waves of COVID-19

Through correlation tests, we also sought to assess possible positive or negative associations between the occurrence of HAIs, use of invasive devices, infection incidence density and ICU management indicators (such as turnover, absenteeism, patient/day, bed/day, occupancy and headcount). The results showed moderate and strong positive correlations between the patient/day, bed/day and occupancy indicators and a moderate negative correlation with absenteeism (Table 3).

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Table 3
Correlation between healthcare-associated infections, use of invasive devices and management indicators in the COVID-19 pandemic

Discussion

Healthcare-associated infections in ICUs represented a major challenge to organizations, with a high spread of COVID infections, a constant need to adapt processes, and lack of knowledge about the disease. Safety protocols sometimes became weak to prevent nosocomial infection, in addition to the risk of care-related infection.(¹¹)

In the present study, a significant increase in the use of invasive devices was evidenced from the first to the second wave, especially in central venous catheter (CVC)/day and mechanical ventilation (MV)/day. We highlight the steep increase in the number of cases and deaths in Brazil due to COVID-19 from the first to the second wave. The number of new cases reported increased by 161% between waves. In the first wave, 7,677 deaths were reported per week; in the second wave, which was longer and more lethal, three times the number of deaths was observed (21,141 deaths in one week). In this context, Brazil experienced a lack of essential supplies to maintain patient care, in addition to the overload on health systems.(⁴, ¹²) The scarcity of resources and the increase in the severity of patients’ conditions also caused an increase in the rates of HAIs (CLABSI, VAP and CAUTI) both in Brazil and in other countries around the world. Potential factors that contributed to an increased risk of HAIs associated with devices during the pandemic (including the second wave) were longer hospital stays, increased comorbidities, greater severity of patients, and longer periods of use of invasive devices.(², ¹³, ¹⁴, ¹⁵, ¹⁶, ¹⁷) In a study published by the Centers for Disease Control and Prevention (CDC), which sought to identify the impact of the COVID-19 pandemic on the incidence of HAIs in US hospitals, the use rates of all devices [indwelling urinary catheter (IUC), CVC, and MV] increased significantly during the pandemic.(¹⁸)

Our results showed a strong positive correlation between the use of MV and the occurrence of VAP, as well as in relation to the use of CVC and the occurrence of CLABSI. Thus, the prevalence of VAP was significantly higher from the first to the second wave (increasing from 11 to 42 cases), followed by a significant increase in cases of CLABSI (with a prevalence of eight to 28 infections from the first to the second wave). A study to evaluate the clinical characteristics of HAIs in severe and critically ill patients with COVID-19 in Italy showed that the occurrence of VAP was also higher in relation to cases of CLABSI.(¹⁹) In contrast, an ecological study carried out in 21 different Brazilian hospitals compared adult patients admitted to the ICU during the second wave with those in the period before the pandemic, finding a higher prevalence of CLABSI, with no difference between the incidence of VAP in the two periods.(¹⁰)

In patients with COVID-19, the increased risk of VAP may be related to several factors, including: less rigorous use of standardized infection prevention strategies during COVID-19, patient immunodeficiency associated with the disease and therapy, prolonged illness, duration of mechanical ventilation, prolonged use of sedation, need for more frequent ventilation, prone positioning, and increased risk of pulmonary infarction with associated superinfection.(²⁰) In addition, some studies have observed an increased risk in conditions associated with mechanical ventilation in patients severely affected by COVID-19.(¹⁹)

According to the CDC publication, the prolonged length of patient hospitalization, additional comorbidities, higher levels of severity, and longer duration of use of invasive devices may have contributed to an overall increase in the risk of device-associated infection during the pandemic in 2020 (second wave period).(¹⁸)

A relevant finding was the fact that in the second wave, ICU admissions generally occurred in patients from inpatient units. They probably did not present clinical signs justifying ICU admission upon arrival at the hospital. Thus, we can infer that the clinical deterioration of these patients was more frequent in the second wave compared to the first. A study conducted in an emergency department in India used the National Early Warning Score 2 scale to assess clinical deterioration in the second wave of COVID. Their data indicated that 25.0% of patients required continuous monitoring and 12.7% of them developed worsening of their clinical condition up to 24 h after admission.(²¹) In another study conducted in an emergency care unit in São Paulo, the deterioration of patients upon emergency admission was assessed by the Modified Early Warning Score, showing a higher occurrence of clinical deterioration in the first 24 h in patients with COVID-19.(²²)

With the advance in the spread of COVID infections in Brazil, the need to greatly increase the number of ICU beds throughout the country was confirmed.(²³) The hospital addressed in this study needed to double the number of beds to better serve patients requiring intensive care support. When we assessed the correlation between ICU occupancy and the number of beds/day with the occurrence of HAIs during the COVID-19 waves, a moderate to strong positive correlation of these indicators with CLABSI, VAP, and CAUTI was highlighted.

Studies exploring the occurrence of HAIs during the pandemic are very limited, especially when we assess the correlation between events and clinical or management metrics. A systematic review (2012) highlighted the evidence that high hospital occupancy increases the occurrence of HAIs, especially at occupancies above 80%.(²⁴) The present study agrees with another investigation carried out in a hospital in California. A positive association between the occupancy of patients with COVID-19 and the incidence of infections with an increase in bloodstream infections by Methicillin-resistant Staphyloccocus aureus was highlighted.(²⁵)

We hope this study can contribute to the development of public and organizational policies, promoting not only patient safety but also considering the profile of patients with COVID-19 and their clinical specificities, expanding knowledge about the impacts on patient safety during the pandemic and seeking to improve the response to future public calamity situations. As a limitation of the study, we highlight its single-center characteristic that prevents generalization to other patient profiles and critical care units.

Conclusion

The findings allowed us to identify the increase in infections and use of invasive devices from the first to the second wave of COVID-19 and a positive correlation in the increase in infections and ICU beds, hospitalizations and occupancy. The management of critically ill patients at high risk for infections should follow standardized measures and ideas to prevent and control HAIs. The prevalence of these infections can be a relevant adverse event even in institutions with consolidated quality processes and infection control practices. The incidence of HAIs can be minimized by optimizing the use of devices such as MV, IUC and CVC, and by managing physical and human resources based on quality and patient safety.

Acknowledgements

We would like to thank Hospital Alemão Oswaldo Cruz for the technological and epidemiological support provided during the data collection process.

References

¹ Rouzé A, Martin-Loeches I, Povoa P, Makris D, Artigas, Bouchereau M, et al. Relationship between SARS-CoV-2 infection and the incidence of ventilator-associated lower respiratory tract infections: a European multicenter cohort study. Intensive Care Med. 2021;47(2):188–98. Erratum in: Intensive Care Med. 2022 Apr;48(4):514–5.
² Fakih MG, Bufalino A, Sturm L, Huang RH, Ottenbacher A, Saake K, et al. Coronavirus disease 2019 (COVID-19) pandemic, central-line-associated bloodstream infection (CLABSI), and catheter-associated urinary tract infection (CAUTI): The urgent need to refocus on hardwiring prevention efforts. Infect Control Hosp Epidemiol. 2022; 43(1):26–31.
³ Coelho MM, Cavalcante VM, Cabral RL, Oliveira RM, Nogueira PS, Silva FA, et al. Contexto de trabalho e manifestações clínicas da COVID-19 em profissionais de saúde. Acta Paul Enferm. 2022;35: eAPE0163345
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⁵ Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al. (2020) Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ. 2020; 369:m1985.
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⁷ Peters A, Schmid MN, Parneix P, Lebowitz D, de Kraker M, Sauser J, et al. Impact of environmental hygiene interventions on healthcare-associated infections and patient colonization: a systematic review. Antimicrob Resist Infect Control. 2022;11(1):38.
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¹⁰ Porto AP, Borges IC, Buss L, Machado A, Bassetti BR, Cocentino B, et al. Healthcare-associated infections on the intensive care unit in 21 Brazilian hospitals during the early months of the coronavirus disease 2019 (COVID-19) pandemic: An ecological study. Infect Control Hosp Epidemiol. 2023;44(2):284–90.
¹¹ Begum H, Neto AS, Alliegro P, Broadley T, Trapani T, Campbell LT, et al. People in intensive care with COVID-19: demographic and clinical features during the first, second, and third pandemic waves in Australia. Med J Aust. 2022; 217(7):352–60.
¹² Silva FP, Correia KC, Araujo RM, Oliveira EC, Oliveira RC, Pereira RB, et al. Incident notification and patient safety in times of a pandemic. Acta Paul Enferm. 2023;36:eAPE00952.
¹³ Fram DS, Ferreira DB, Matias LD, Coelho WE, Escudero DV, Antonelli TS, et al. Perfil epidemiológico das IRAS notificadas em um hospital universitário durante a pandemia da COVID-19. Braz J Infect Dis. 2021;25:101063.
¹⁴ Hyte M, Clark C, Pandey R, Redden D, Roderick M, Brock K. How COVID-19 impacted CAUTI and CLABSI rates in Alabama. Am J Infect Control. 2023: S0196-6553(23)00381-4.
¹⁵ Baker MA, Sands KE, Huang SS, Kleinman K, Septimus EJ, Varma N et al. The Impact of Coronavirus Disease 2019 (COVID-19) on Healthcare-Associated Infections. Clin Infect Dis. 2022 ;74(10):1748-54.
¹⁶ LeRose J, Sandhu A, Polistico J, Ellsworth J, Cranis M, Jabbo L, et al. The impact of coronavirus disease 2019 (COVID-19) response on central-line-associated bloodstream infections and blood culture contamination rates at a tertiary-care center in the Greater Detroit area. Infect Control Hosp Epidemiol. 2021;42(8):997-1000.
¹⁷ Shukla BS, Warde PR, Knott E, Arenas S, Pronty D, Ramirez R, et al. Bloodstream Infection Risk, Incidence, and Deaths for Hospitalized Patients during Coronavirus Disease Pandemic. Emerg Infect Dis. 2021;27(10):2588-94. .
¹⁸ Weiner-Lastinger LM, Pattabiraman V, Konnor RY, Patel PR, Wong E, Xu SY, et al. The impact of coronavirus disease 2019 (COVID-19) on healthcare-associated infections in 2020: A summary of data reported to the National Healthcare Safety Network. Infect Control Hosp Epidemiol. 2022; 43(1):12-25.
¹⁹ Fakhreddine S, Fawaz M, Hassanein S, Al Khatib A. Prevalence and mortality rate of healthcare-associated infections among COVID-19 patients: a retrospective cohort community-based approach. Front Public Health. 2023; 11:1235636
²⁰ Wicky PH, Niedermann MS, Timsit JF. Ventilator-associated pneumonia in the era of COVID-19 pandemic: How common and what is the impact? Crit Care. 2021; 25(1):153.
²¹ Khuraijam S, Gangurde A, Shetty V. Utility of National Early Warning Score 2 to risk-stratify coronavirus disease of 2019 patients in the emergency department: A retrospective cohort study. Int J Crit Illn Inj Sci. 2022;12(3):133-137.
²² Neiman AE, Campanharo CR, Lopes MC, Piacezzi LH, Batista RE. COVID-19: Association of risk classification with the Modified Early Warning Score and hospital outcomes. Rev Lat Am Enfermagem. 2023; 31 e3977.
²³ Freitas CM, Barcellos C, Villela DA, Portela MC, Guimarães LC, Xavier DR, et al. Covid-19 Fiocruz Observatory - an analysis of the evolution of the pandemic from February 2020 to April 2022. Ciênc. Saúde Coletiva. 2023; 28(1): 2845-5.
²⁴ Kaier K, Mutters NT, Frank U. Bed occupancy rates and hospital-acquired infections--should beds be kept empty? Clin Microbiol Infect. 2012;18(10):941-5.
²⁵ Parriott AM, Kazerouni NN, Epson EE. Association of the coronavirus disease 2019 (COVID-19) pandemic with the incidence of healthcare-associated infections in California hospitals. Infect Control Hosp Epidemiol. 2023;44(9):1429-36.

Edited by

Associate Editor
Juliana de Lima Lopes, (https://orcid.org/0000-0001-6915-6781), Escola Paulista de Enfermagem, Universidade Federal de São Paulo, São Paulo, SP, Brasil

Publication Dates

Publication in this collection
28 Mar 2025
Date of issue
2025

History

Received
02 Dec 2023
Accepted
21 Oct 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

[1] ¹ Rouzé A, Martin-Loeches I, Povoa P, Makris D, Artigas, Bouchereau M, et al. Relationship between SARS-CoV-2 infection and the incidence of ventilator-associated lower respiratory tract infections: a European multicenter cohort study. Intensive Care Med. 2021;47(2):188–98. Erratum in: Intensive Care Med. 2022 Apr;48(4):514–5.

[2] ² Fakih MG, Bufalino A, Sturm L, Huang RH, Ottenbacher A, Saake K, et al. Coronavirus disease 2019 (COVID-19) pandemic, central-line-associated bloodstream infection (CLABSI), and catheter-associated urinary tract infection (CAUTI): The urgent need to refocus on hardwiring prevention efforts. Infect Control Hosp Epidemiol. 2022; 43(1):26–31.

[3] ³ Coelho MM, Cavalcante VM, Cabral RL, Oliveira RM, Nogueira PS, Silva FA, et al. Contexto de trabalho e manifestações clínicas da COVID-19 em profissionais de saúde. Acta Paul Enferm. 2022;35: eAPE0163345

[4] ⁴ Moura EC, Cortez-Escalante J, Cavalcante FV, Barreto IC, Sanchez MN, Santos LM. Covid-19: evolução temporal e imunização nas três ondas epidemiológicas, Brasil, 2020-2022. Rev Saude Publica. 2022; 56:105.

[5] ⁵ Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al. (2020) Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ. 2020; 369:m1985.

[6] ⁶ Azevedo CC, Almeida LF, Fonseca CT, Paula VG, Pereira SE, Henrique DM. Uso do cateter vesical de demora em uma unidade de terapia intensiva: estudo transversal. Rev Enferm UERJ. 2021;29(1): e57284.

[7] ⁷ Peters A, Schmid MN, Parneix P, Lebowitz D, de Kraker M, Sauser J, et al. Impact of environmental hygiene interventions on healthcare-associated infections and patient colonization: a systematic review. Antimicrob Resist Infect Control. 2022;11(1):38.

[8] ⁸ Cormier H, Brangier A, Lefeuvre C, Asfar M, Annweiler C, Legeay C. Lessons learnt from a nosocomial COVID-19 outbreak in a geriatric acute care ward with a high attack rate. Maturitas. 2021;149:34–6.

[9] ⁹ Cunha QB, Freitas EO, Pai DD, Santos JL, Lourenção LG, Silva RM, et al. Factors associated with the SARS-CoV-2 infection among health professionals from university hospitals. Rev Lat Am Enfermagem. 2023; 31:e3917.

[10] ¹⁰ Porto AP, Borges IC, Buss L, Machado A, Bassetti BR, Cocentino B, et al. Healthcare-associated infections on the intensive care unit in 21 Brazilian hospitals during the early months of the coronavirus disease 2019 (COVID-19) pandemic: An ecological study. Infect Control Hosp Epidemiol. 2023;44(2):284–90.

[11] ¹¹ Begum H, Neto AS, Alliegro P, Broadley T, Trapani T, Campbell LT, et al. People in intensive care with COVID-19: demographic and clinical features during the first, second, and third pandemic waves in Australia. Med J Aust. 2022; 217(7):352–60.

[12] ¹² Silva FP, Correia KC, Araujo RM, Oliveira EC, Oliveira RC, Pereira RB, et al. Incident notification and patient safety in times of a pandemic. Acta Paul Enferm. 2023;36:eAPE00952.

[13] ¹³ Fram DS, Ferreira DB, Matias LD, Coelho WE, Escudero DV, Antonelli TS, et al. Perfil epidemiológico das IRAS notificadas em um hospital universitário durante a pandemia da COVID-19. Braz J Infect Dis. 2021;25:101063.

[14] ¹⁴ Hyte M, Clark C, Pandey R, Redden D, Roderick M, Brock K. How COVID-19 impacted CAUTI and CLABSI rates in Alabama. Am J Infect Control. 2023: S0196-6553(23)00381-4.

[15] ¹⁵ Baker MA, Sands KE, Huang SS, Kleinman K, Septimus EJ, Varma N et al. The Impact of Coronavirus Disease 2019 (COVID-19) on Healthcare-Associated Infections. Clin Infect Dis. 2022 ;74(10):1748-54.

[16] ¹⁶ LeRose J, Sandhu A, Polistico J, Ellsworth J, Cranis M, Jabbo L, et al. The impact of coronavirus disease 2019 (COVID-19) response on central-line-associated bloodstream infections and blood culture contamination rates at a tertiary-care center in the Greater Detroit area. Infect Control Hosp Epidemiol. 2021;42(8):997-1000.

[17] ¹⁷ Shukla BS, Warde PR, Knott E, Arenas S, Pronty D, Ramirez R, et al. Bloodstream Infection Risk, Incidence, and Deaths for Hospitalized Patients during Coronavirus Disease Pandemic. Emerg Infect Dis. 2021;27(10):2588-94. .

[18] ¹⁸ Weiner-Lastinger LM, Pattabiraman V, Konnor RY, Patel PR, Wong E, Xu SY, et al. The impact of coronavirus disease 2019 (COVID-19) on healthcare-associated infections in 2020: A summary of data reported to the National Healthcare Safety Network. Infect Control Hosp Epidemiol. 2022; 43(1):12-25.

[19] ¹⁹ Fakhreddine S, Fawaz M, Hassanein S, Al Khatib A. Prevalence and mortality rate of healthcare-associated infections among COVID-19 patients: a retrospective cohort community-based approach. Front Public Health. 2023; 11:1235636

[20] ²⁰ Wicky PH, Niedermann MS, Timsit JF. Ventilator-associated pneumonia in the era of COVID-19 pandemic: How common and what is the impact? Crit Care. 2021; 25(1):153.

[21] ²¹ Khuraijam S, Gangurde A, Shetty V. Utility of National Early Warning Score 2 to risk-stratify coronavirus disease of 2019 patients in the emergency department: A retrospective cohort study. Int J Crit Illn Inj Sci. 2022;12(3):133-137.

[22] ²² Neiman AE, Campanharo CR, Lopes MC, Piacezzi LH, Batista RE. COVID-19: Association of risk classification with the Modified Early Warning Score and hospital outcomes. Rev Lat Am Enfermagem. 2023; 31 e3977.

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Variables	n	1^st wave	2^nd wave	p-value
Sex (107)
Female	33	9(27.3)	24(72.7)	0.374 (v=0.11)
Male	74	13(17.6)	61(82.4)
Age (107)
N		22	85	0.082
Mean		70.5	63.7
Median		71	66
SD		14.0	15.1
Reason for discharge (107)
Discharge	42	9(21.4)	33(78.6)	1.000
Death	63	13(20.6)	50(79.4)
Hospital transfer	2	0(0)	2(100)
ICU outcome (107)
Discharge to inpatient unit	56	11(19.6)	4 (80.4)	0.882
Death	49	11(22.5)	38(77.6)
Hospital transfer	2	0(0)	2(100)
Length of stay in ICU (107)
N		22	85	0.963
Mean		34.5	38.2
Median		31.5	31
SD		18.6	33.8
Unit of admission (107)
Surgical room	1	1(100)	0(0)	0.012
Emergency room	20	7(35.0)	13(65)
Hospital transfer	8	2(25)	6(75.0)
Inpatient unit	77	11(14.3)	66(85.7)
ICU	1	1(100)	0(0)
SAPS 3
N		22	85	0.250
Mean		53	52.4
Median		53.5	49
SD		5.7	10.2
SOFA
N		22	85	0.239
Mean		3.6	3.1
Median		3	2
SD		2.6	2.8

Variables	1^st wave	2^nd wave	p-value
VAP
N	11	42	0.017 (0.27)
Mean	1.2	4.7
Median	1	4
SD	0.9	3.8
MV-day
Mean	227.4	579.9	<0.001 (0.62)
Median	270	529
SD	101.3	184.3
Incidence of VAP/1000 MV-day
Mean	6.8	7.9	0.859
Median	9.8	7.2
SD	5.3	5.5
CLABSI
N	8	28	0.001 (0.54)
Mean	0.9	3.1
Median	1	3
SD	0.6	1.6
CVC-day
Mean	339.9	663	0.002 (0.52)
Median	360	606
SD	144.1	203.5
Incidence of CLABSI/1000 CVC-day
Mean	2.9	4.5	0.092
Median	2.8	4.1
SD	2.1	1.4
CAUTI
N	2	10	0,014 (0,29)
Mean	0,2	1,1
Median	0	1
SD	0,4	0,8
IUC-day
Mean	310,8	717,7	0,002 (0,52)
Median	293	681
SD	140	233,3
CAUTI/1000 IUC-day
Mean	0,6	1,7	0,089
Median	0	2
SD	1,3	1,2

	Correlation	2.5% CI	97% CI	p-value	Strength of correlation
ICU turnover
VAP	−0.1356	−0.5666	0.3536	0.5915	No correlation
MV-day	0.0116	−0.4577	0.4759	0.9634	No correlation
Incidence of PAV/1000 MV-day	−0.2023	−0.6114	0.2922	0.4208	No correlation
CLABSI	−0.0996	−0.5413	0.3851	0.6940	No correlation
CVC-day	0.1265	−0.3618	0.5603	0.6170	No correlation
Incidence of CLABSI /1000 CVC-day	−0.1933	−0.6055	0.3007	0.4422	No correlation
CAUTI	−0.0077	−0.4729	0.4608	0.9758	No correlation
IUC-day	0.0493	−0.4274	0.5045	0.8460	No correlation
Incidence of CAUTI /1000 IUC-day	−0.0915	−0.5355	0.3922	0.7181	No correlation
Absenteeism
VAP	−0.3839	−0.7215	0.1011	0.1157	No correlation
MV-day	−0.5726	−0.8202	−0.1443	0.0130	No correlation
Incidence of VAP/1000 MV-day	−0.1103	−0.5489	0.3759	0.6631	No correlation
CLABSI	−0.5259	−0.7971	−0.0783	0.0250	Moderate Negative Correlation
CVC-day	−0.4517	−0.7586	0.0193	0.0599	No correlation
Incidence of CLABSI /1000 CVC-day	−0.4867	−0.7770	−0.0257	0.0405	Moderate Negative Correlation
CAUTI	−0.5803	−0.8240	−0.1556	0.0116	Moderate Negative Correlation
IUC-day	−0.4911	−0.7793	−0.0315	0.0385	Moderate Negative Correlation
Incidence of CAUTI /1000 IUC-day	−0.4308	−0.7474	0.0451	0.0743	No correlation
Patient-day
VAP	0.7097	0.3632	0.8838	0.0010	Strong Positive Correlation
MV-day	0.8924	0.7295	0.9595	0.0000	Strong Positive Correlation
Incidence of VAP/1000 MV-day	0.1693	−0.3231	0.5896	0.5018	No correlation
CLABSI	0.8640	0.6656	0.9483	0.0000	Strong Positive Correlation
CVC-day	0.8330	0.5991	0.9359	0.0000	Strong Positive Correlation
Incidence of CLABSI /1000 CVC-day	0.5641	0.1320	0.8161	0.0147	Moderate Positive Correlation
CAUTI	0.5504	0.1124	0.8093	0.0179	Moderate Positive Correlation
IUC-day	0.8543	0.6445	0.9445	0.0000	Strong Positive Correlation
Incidence of CAUTI /1000 IUC-day	0.3199	−0.1728	0.6845	0.1956	No correlation
Bed-day
VAP	0.6067	0.1951	0.8366	0.0076	Moderate Positive Correlation
MV-day	0.7833	0.4988	0.9154	0.0001	Strong Positive Correlation
Incidence of VAP/1000 MV-day	0.0754	−0.4058	0.5238	0.7663	No correlation
CLABSI	0.7474	0.4309	0.9002	0.0004	Strong Positive Correlation
CVC-day	0.7663	0.4662	0.9082	0.0002	Strong Positive Correlation
Incidence of CLABSI /1000 CVC-day	0.4334	−0.0420	0.7488	0.0724	Moderate Positive Correlation
CAUTI	0.4729	0.0078	0.7698	0.0475	Moderate Positive Correlation
IUC-day	0.7537	0.4426	0.9029	0.0003	Strong Positive Correlation
Incidence of CAUTI /1000 IUC-day	0.3639	−0.1240	0.7101	0.1376	No correlation
Occupation
VAP	0.5438	0.1031	0.8060	0.0197	Moderate Positive Correlation
MV-day	0.6568	0.2740	0.8600	0.0031	Moderate Positive Correlation
Incidence of VAP/1000 MV-day	0.2593	−0.2362	0.6477	0.2988	No correlation
CLABSI	0.6492	0.2616	0.8565	0.0036	Moderate Positive Correlation
CVC-day	0.5442	0.1036	0.8062	0.0196	Moderate Positive Correlation
Incidence of CLABSI /1000 CVC-day	0.5486	0.1099	0.8084	0.0184	Moderate Positive Correlation
CAUTI	0.4550	−0.0151	0.7604	0.0578	No correlation
IUC-day	0.6087	0.1982	0.8376	0.0073	Moderate Positive Correlation
Incidence of CAUTI /1000 IUC-day	0.1658	−0.3263	0.5872	0.5109	No correlation
ICU Headcount
VAP	0.6227	0.2197	0.8442	0.0058	No correlation
MV-day	0.8282	0.5891	0.9339	0.0000	No correlation
Incidence of VAP/1000 MV-day	0.0718	−0.4087	0.5212	0.7770	No correlation
CLABSI	0.7611	0.4564	0.9060	0.0002	No correlation
CVC-day	0.7718	0.4766	0.9106	0.0002	No correlation
Incidence of CLABSI /1000 CVC-day	0.4972	0.0395	0.7824	0.0358	No correlation
CAUTI	0.5661	0.1348	0.8170	0.0143	No correlation
IUC-day	0.7958	0.5234	0.9206	0.0001	No correlation
Incidence of CAUTI /1000 IUC-day	0.4298	−0.0464	0.7468	0.0751	No correlation