www.nature.com/scientificreports OPEN Risk factors for developing ventilator‑associated lower respiratory tract infection in patients with severe COVID‑19: a multinational, multicentre study, prospective, observational study Luis Felipe Reyes 1,2,3,90*, Alejandro Rodriguez 4,90, Yuli V. Fuentes 1,2, Sara Duque 1, Esteban García‑Gallo 1, Alirio Bastidas 1, Cristian C. Serrano‑Mayorga 1,2, Elsa D. Ibáñez‑Prada 1, Gerard Moreno 4, Paula C. Ramirez‑Valbuena 1, Gustavo Ospina‑Tascon 5, Glenn Hernandez 6, Edwin Silva 7, Ana Maria Díaz 8, Manuel Jibaja 8, Magdalena Vera‑Alarcon 6, Emili Díaz 9, María Bodí 4, Jordi Solé‑Violán 10,11, Ricard Ferrer 12, Antonio Albaya‑Moreno 13, Lorenzo Socias 14, William Figueroa 1, Jose L. Lozano‑Villanueva 7, Fabio Varón‑Vega 15, Ángel Estella 16, Ana Loza‑Vazquez 17, Ruth Jorge‑García 18, Isabel Sancho 19, Manu Shankar‑Hari 20,21,91, Ignacio Martin‑Loeches 22,91, LIVEN-Covid-19 * & SEMICYUC Study Group * Around one-third of patients diagnosed with COVID-19 develop a severe illness that requires admission to the Intensive Care Unit (ICU). In clinical practice, clinicians have learned that patients admitted to the ICU due to severe COVID-19 frequently develop ventilator-associated lower respiratory tract infections (VA-LRTI). This study aims to describe the clinical characteristics, the factors associated with VA-LRTI, and its impact on clinical outcomes in patients with severe COVID- 19. This was a multicentre, observational cohort study conducted in ten countries in Latin America and Europe. We included patients with confirmed rtPCR for SARS-CoV-2 requiring ICU admission and endotracheal intubation. Only patients with a microbiological and clinical diagnosis of VA-LRTI were included. Multivariate Logistic regression analyses and Random Forest were conducted to determine the risk factors for VA-LRTI and its clinical impact in patients with severe COVID-19. In our study 1 Unisabana Center for Translational Science, Universidad de La Sabana, Chía, Colombia. 2Clinica Universidad de La Sabana, Chía, Colombia. 3Pandemic Sciences Institute, University of Oxford, Oxford, UK. 4Critical Care Department, URV/IISPV/CIBERES, Hospital Universitari Joan XXIII, Tarragona, Spain. 5Fundación Valle del Lili, Cali, Colombia. 6Critical Care Department, Pontificia Universidad Católica de Chile, Santiago, Chile. 7Fundación Clínica Shaio, Bogotá, Colombia. 8Eugenio Espejo Hospital of Specialties, Quito, Pichincha, Ecuador. 9Critical Care Department, Hospital Universitari Parc Taulí, Universitat Autonoma Barcelona, Sabadell, Spain. 10Hospital Universitario Dr Negrín, Las Palmas de Gran Canaria, Spain. 11Universidad Fernando Pessoa, Canarias, Spain. 12Vall d’Hebron Hospital Universitari, Barcelona, Spain. 13Guadalajara University Hospital, Guadalajara, Spain. 14Son Llatzer University Hospital, Palma de Mallorca, Spain. 15Fundación Neumológica Colombiana, La Cardio, Bogotá, Colombia. 16Jerez University Hospital, Jerez de la Frontera, Spain. 17Critical Care Department, Hospital Universitario Virgen del Valme, Sevilla, Spain. 18Hospital Providencial Nuestra Señora de Gracia, Zaragoza, Spain. 19Critical Care Department, Hospital Universitario Miguel Servet, Zaragoza, Spain. 20Intensive Care Unit, Royal Infirmary of Edinburgh, Little France Crescent, Edinburgh, UK. 21Centre for Inflammation Research, The University of Edinburgh, Edinburgh, Scotland, UK. 22Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St. James’s Hospital, Dublin, UK. 90These authors contributed equally: Luis Felipe Reyes and Alejandro Rodriguez. 91These authors jointly supervised this work: Manu Shankar-Hari and Ignacio Martin-Loeches. *Lists of authors and their affiliations appear at the end of the paper. *email: luis.reyes5@ unisabana.edu.co Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 1 Vol.:(0123456789) www.nature.com/scientificreports/ cohort of 3287 patients, VA-LRTI was diagnosed in 28.8% [948/3287]. The cumulative incidence of ventilator-associated pneumonia (VAP) was 18.6% [610/3287], followed by ventilator-associated tracheobronchitis (VAT) 10.3% [338/3287]. A total of 1252 bacteria species were isolated. The most frequently isolated pathogens were Pseudomonas aeruginosa (21.2% [266/1252]), followed by Klebsiella pneumoniae (19.1% [239/1252]) and Staphylococcus aureus (15.5% [194/1,252]). The factors independently associated with the development of VA-LRTI were prolonged stay under invasive mechanical ventilation, AKI during ICU stay, and the number of comorbidities. Regarding the clinical impact of VA-LRTI, patients with VAP had an increased risk of hospital mortality (OR [95% CI] of 1.81 [1.40–2.34]), while VAT was not associated with increased hospital mortality (OR [95% CI] of 1.34 [0.98–1.83]). VA-LRTI, often with difficult-to-treat bacteria, is frequent in patients admitted to the ICU due to severe COVID-19 and is associated with worse clinical outcomes, including higher mortality. Identifying risk factors for VA-LRTI might allow the early patient diagnosis to improve clinical outcomes. Trial registration: This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable. Abbreviations ICU Intensive care unit VA-LRTI Ventilator-associated lower respiratory tract infection COVID-19 Coronavirus disease-19 HIV/AIDS Human immunodeficiency virus/acquired immunodeficiency syndrome SARS-CoV-2 Severe Respiratory Syndrome Coronavirus 2 VAP Ventilator-associated pneumonia VAT Ventilator-associated tracheobronchitis rt-PCR Reverse transcription-polymerase chain reaction IMV Invasive Mechanical Ventilation ERS European Respiratory Society ESICM European Society of Intensive Care Medicine ESCMID European Society of Clinical Microbiology and Infectious Diseases ALAT Asociación Latinoamericana del Tórax ETA Endotracheal aspirates LOS Length of stay RF Random forest AUROC The area under the model’s receiver operating curve ORs Odds ratios IQR Interquartile range CRP C reactive protein AKI Acute renal injury HRs Hazard ratio The Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has affected over 500 million people worldwide, and almost 6 million people have died of this infection and its complications (https://​covid​19.​who.​ int)1,2. SARS-CoV-2 causes coronavirus disease-19 (COVID-19), a multi-system illness with high morbidity and mortality related to progressive life-threatening ­pneumonia3,4. Patients frequently need ventilatory support due to the advanced disease s­ everity5–7. Even though ventilatory supports are life-saving treatments, these are commonly associated with infectious and non-infectious ­complications8–10. Patients treated with ventilator sup- port that develop secondary infections have more extended ICU and hospital length of stay, worsening clinical ­outcomes11–15. Up to 50.5% of COVID-19 patients develop ventilator-associated lower respiratory tract infection (VA- LRTI), compared to 30.3% in patients with other severe pulmonary infections, such as i­ nfluenza16–18. VA-LRTI is frequently categorised as ventilator-associated pneumonia (VAP) or ventilator-associated tracheobronchitis (VAT) in clinical practice and by international treatment ­guidelines19. Early reports highlighted that potentially difficult-to-treat bacteria (i.e., Pseudomonas aeruginosa, Staphylococcus aureus, and Klebsiella pneumoniae, among others) are common in VA-LRTI associated with COVID-19 in small studies mainly in high-income c­ ountries20. However, the underlying microbiology, the associated risk factors, and the impact on clinical outcomes for VA- LRTI in COVID-19 patients remain uncertain, especially in lower-income countries. Understanding risk factors related to VA-LRTI will suggest targeted prevention strategies, particularly within strained healthcare systems. In this context, we conducted a multicentre, multinational, prospective observational cohort study in Latin America and Europe to characterise the cumulative incidence of VA-LRTI in COVID-19 patients, its underlying microbiology, associated risk factors, and its association with clinical outcomes. Materials and methods This was a retrospective analysis of two prospective observational cohort studies of patients admitted to 84 ICUs due to severe SARS-CoV-2 infection in ten countries (i.e., Spain, Ireland, Andorra, Colombia, Chile, Ecuador, Mexico, Argentina, Uruguay, and Brazil) between March 2020 and January 2021. The patients were included in Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 2 Vol:.(1234567890) www.nature.com/scientificreports/ a voluntary registry created by the Latin American Intensive Care Network (Red LIVEN—https://w ​ ww.r​ edliv​ en.​ org/​web/)21 and the Spanish Society of Intensive Care Medicine (SEMICYUC)21,22. Data were collected prospec- tively by the attending physicians by reviewing medical records, laboratory data, and radiological records. All consecutive cases admitted who met the entry criteria were included in the registry. Both the Ethics Committee of the Clínica Universidad de La Sabana (IRB#2020AN28) and Hospital Joan XXIII (IRB#CEIM/066/2020) approved the study. All data were anonymized, allowing the requirement for informed consent to be waived. The ICU admission criteria and treatment decisions were left to the discretion of the attending physicians and were not provided in the study’s protocol. The cohort includes patients older than 18 years hospitalised in the ICU due to severe SARS-CoV-2 infection and receiving invasive mechanical ventilation (IMV). Confirmed SARS-CoV-2 infection was determined by reverse transcription-polymerase chain reaction (rt-PCR) in a respiratory sample at each hospital based on local protocols. All COVID-19 patients admitted to the ICU with the clinical diagnosis of VAP or VAT were included in the VA-LRTI group. No patient received antibiotic treatment before the initiation of IMV. We excluded patients without pathogen isolated at the moment of the clinical diagnosis of interest (i.e., VAP or VAT). Patients that received IMV for less than 72 h were excluded. Finally, patients with a coinfection documented within the first 48 h of admission were excluded. All other patients were included and analysed in this study. The attending physician determined each patient’s treatments and microbiological identification strategy. The following variables were recorded during ICU admission: demographic data, comorbidities, symptoms, physiological variables collected during the first 24 h of ICU admission, systemic complications, and treatments used during the admission. For comparisons, patients admitted between February 22 and July 1, 2020, were clas- sified in the subgroup of the “first wave,” while those admitted to the ICU until February 28, 2021 were considered a “second wave”. Variables with more than 30% missing data were discarded. VA-LRTI was defined as a clinical syndrome of VAP or VAT in ventilated patients, as defined by the Euro- pean Respiratory Society (ERS), European Society of Intensive Care Medicine (ESICM), European Society of Clinical Microbiology and Infectious Diseases (ESCMID), and Asociación Latinoamericana del Tórax (ALAT) ­guidelines19. VAP was defined as pneumonia that arises more than 48 h after endotracheal intubation; VAT as fever with no other recognizable cause, with new or increased sputum production, positive endotracheal aspirates (ETA) culture (> 106 CFU/mL) yielding a new bacterium and no radiographic evidence of nosocomial pneu- monia. Additionally, only patients with the isolation of at least one respiratory pathogen known to be capable of causing pneumonia in a respiratory sample (e.g., tracheal aspirate, bronchoalveolar lavage, or pleural fluid) after the first 48 h of ICU admission were considered to have VA-LRTI. The study aimed to determine the cumulative incidence, clinical characteristics, and outcomes of ventilated COVID-19 patients diagnosed with VA-LRTI. Moreover, to identify the risk factors associated with the develop- ment of VA-LRTI, the risk of hospital mortality among these patients, and describe the etiological microbiology. Statistical analysis. Categorical variables are presented in counts (percentages) and were evaluated through the Chi-square test. Continuous variables were expressed as median (interquartile ranges) or mean (standard deviation). T-Student test or Wilcoxon–Mann–Whitney test was performed according to data distribution. A random forest (RF) model was used to predict the probability of hospital mortality. A total of 500 esti- mators were used in this model to calculate the area under the model’s receiver operating curve (AUROC), cross-validation was performed. A recursive elimination feature was carried out to select the smallest possible subset of variables that generate a model with adequate performance based on its AUROC. According to the Gini importance, the elimination is done by removing the variable with the least important from the dataset. To interpret the contribution of the optimal subset of variables in the model, the Python Treeinterpreter library was used. A multivariate logistic regression model was carried out with sociodemographic, admission data, and clinical outcomes variables identified as relevant in the final RF model (independent variables) to determine the Odds ratios (ORs) related to hospital mortality. Another logistic regression model was developed to quantify the adjusted risk of VA-LRTI with all the soci- odemographic, admission data, and clinical outcomes variables. A significance level of 0.05 and a confidence level of 95% were chosen. All statistical analysis was carried out in R studio 1.3.1056, Python 3.9.5–3.7.9, and IBM SPSS 28 for MAC. A more detailed description of the models and statistical analyses are presented in the online supplement. Results A total of 3287 patients were included in this study (Fig. 1, Table 1). The median [IQR] age was 63.0 years [54.5–71.0], and 70.5% [2317/3287] were male. Common comorbidities were hypertension 46.6% [1532/3287], obesity 36.4% [1193/3287], and diabetes mellitus 24.9% [819/3287]. Cumulative incidence of VA‑LRTI by pandemic waves and by geographic region. A total of 28.8% [948/3287] patients were diagnosed with VA-LRTI. VAP cumulative incidence was 18.6% [610/3287], and VAT cumulative incidence was 10.3% [338/3287]. Most of the patients in our study were enrolled during the first pandemic wave (76.5% [2516/3287]), with a higher VAP cumulative incidence observed during the second wave (14.7% [371/2516] vs. 31.0% [239/771], p < 0.001). Also, VA-LRTI proportion was higher in Latin America than Europe (36.7% [368/1004] vs. 25.4% [580/2283], p < 0.001). Microbiology. A total of 1252 bacteria species were isolated. The most frequently identified pathogens were Pseudomonas aeruginosa (21.2% [266/1253]), followed by Klebsiella pneumoniae (19.1% [239/1253]), Staphy- lococcus aureus (15.5% [194/1253]), and Citrobacter freundii (6.4% [80/1253]). For VAT, the most frequently Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 3 Vol.:(0123456789) www.nature.com/scientificreports/ Figure 1.  Flow chart of patients included in the analysis. identified pathogens were K. pneumoniae (29.7% [127/427]), followed by P. aeruginosa (15% [64/427]), and for VAP most frequent pathogens were P. aeruginosa (24.5% [202/826]) followed by S. aureus (17.8% [147/826]). Notably, most identified pathogens were Gram-negative roots and potentially difficult-to-treat bacteria (Fig. 2). We did not collect information on antibiotic resistance patterns in these pathogens. Several differences were found between Latin America’s and Europe’s microbiology. Regarding the most fre- quently identified bacterial pathogens in Latin America were Klebsiella pneumoniae (33.5% [164/489), followed by Pseudomonas aeruginosa (12.5% [61/489]), and Staphylococcus aureus (10.6% [52/489). While in Europe, Pseudomonas aeruginosa (26.8% [205/764]), Staphylococcus aureus (18.6% [142/764]), and Citrobacter freundii (10.1% [77/764]) were the most frequently identified bacteria. Notably, Acinetobacter baumannii was Latin America’s fourth most prevalent bacteria (10.6% [52/489]). In contrast, this bacteria was not as predominant in Europe (2.1% [16/764]) (Table 2). VA‑LRTI clinical characteristics and outcomes. Patients who developed VAP and VAT had similar age (median [IQR] VAP: 65.0 years [57.0–72.0]; VAT: 62.0 years [54.0–69.8]), and were most often male (VAP: 72.5% [442/610]; VAT: 69.8% [236/338]), and had similar laboratory results (Table 1). The most prevalent comorbidi- ties were hypertension, obesity, and diabetes in both groups (Table 1). Regarding treatments during hospital admission, those who developed VAP more often received corticoster- oid treatment (VAP: 74.9% [457/610]; VAT: 70.4% [238/338]), while those with VAT more frequently received lopinavir/ritonavir (VAP: 1.9% [50/610]; VAT: 0.9% [25/338]). The antimicrobial usage was similar among groups. Patients who develop VAP had longer hospital LOS when compared to VAT (median [IQR] VAP: 40 days [27.0–57.0]; VAT: 24.5 days [16.0–44.0]) and more days under IMV (median [IQR] VAP: 25.9 days [16.0–38.0]; VAT: 15.0 days [11.0–25.0]). Finally, the prevalence of acute kidney injury (AKI) during ICU stay was similar among both groups (VAP: 42.0% [256/610]; VAT: 41.4% [140/338]) as well the mortality rates (VAP: 43.9% [268/610]; VAT: 47.3% [160/338]) (Table 3). Impact of VA‑LRTI on hospital mortality. The Random Forest (RF) analyses identified AKI, VAP devel- opment, prone position ventilation, number of comorbidities, and days under IMV as risk factors associated with higher hospital mortality. Immunomodulant and antiviral treatments showed less than 1% importance according to the Gini feature importance; thus, these were unrelated to the addressed outcome. The ROC curve of the constructed model using the selected variables in the RF had a mean [SD] of 0.87 [± 0.05] to predict hospi- tal mortality (Fig. 3). Moreover, in the multivariate logistic regression analysis, we found an OR [95% CI] of 2.79 [2.31–3.38] for AKI during ICU stay and 1.81 [1.40–2.34] for VAP. However, the association between VAT and hospital mortality was not statistically significant, despite the higher point estimates (1.34 [0.98–1.83]) (Fig. 4). Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 4 Vol:.(1234567890) www.nature.com/scientificreports/ All cohort No VAP No VAT (n = 3287) VAP (n = 610) (n = 2677) p-value VAT (n = 338) (n = 2949) p-value Age, median 63.0 (54.5–71.0) 65.0 (57.0–72.0) 63.0 (54.0–71.0) < 0.001 62.0 (54.0–69.8) 64.0 (55.0–71.0) 0.09 (IQR) Male, n (%) 2317 (70.5) 442 (72.5) 1875 (70.0) 0.26 236 (69.8) 2081 (70.6) 0.83 Health worker, 79 (2.4) 12 (2.0) 67 (2.5) 0.53 9 (2.7) 70 (2.4) 0.89 n (%) Influenza vaccine, 59 (1.8) 1 (0.2) 58 (2.2) 0.001 29 (8.6) 30 (1.0) < 0.001 n (%) First wave, n (%) 2516 (76.5) 371 (60.8) 2145 (80.1) < 0.001 302 (89.4) 2214 (75.1) < 0.001 Second wave, 771 (23.5) 239 (39.2) 532 (19.9) < 0.001 36 (10.6) 735 (24.9) < 0.001 n (%) Comorbid condition, n (%) Congestive heart 172 (5.2) 22 (3.6) 150 (5.6) 0.06 23 (6.8) 149 (5.1) 0.21 failure Hypertension 1532 (46.6) 311 (51.0) 1221 (45.6) 0.018 145 (42.9) 1387 (47.0) 0.17 COPD 250 (7.6) 51 (8.4) 199 (7.4) 0.49 22 (6.5) 228 (7.7) 0.49 Asthma 140 (4.3) 33 (5.4) 107 (4.0) 0.15 4(1.2) 136 (4.6) 0.004 Chronic kidney 185 (5.6) 26 (4.3) 159 (5.9) 0.13 30 (8.9) 155 (5.3) 0.009 disease Neurologic 47 (1.4) 7 (1.2) 40 (1.5) 0.64 7 (2.1) 40 (1.4) 0.42 disease Haematological 90 (2.7) 29 (4.8) 61 (2.3) 0.001 5 (1.5) 85 (2.9) 0.19 disease HIV-AIDS 10 (0.3) 2 (0.3) 8 (0.3) 0.77 4 (1.2) 6 (0.2) 0.010 Obesity 11,960 (36.4) 221 (36.2) 975 (36.4) 0.97 85 (25.2) 1111 (37.7) < 0.001 Rheumatological 105 (3.2) 17 (2.8) 88 (3.3) 0.61 4 (1.2) 101 (3.4) 0.039 disease Diabetes 819 (24.9) 156 (25.6) 662 (24.7) 0.70 93 (27.5.3) 725 (24.6) 0.27 ARDS at admis- 721 (2.9) 59 (9.7) 662 (24.7) < 0.001 183 (54.1) 538 (18.2) < 0.001 sion Number of comorbidities, 1.0 (0.0–2.0) 1.0 (1.0–2.0) 1.0 (0.0–2.0) 0.06 1.0 (0.0–2.0) 1.0 (0.0–2.0) 0.028 median (IQR) Laboratory testing, median (IQR) Creatinine 0.9 (0.7–1.2) 0.9 (0.7–1.2) 0.9 (0.7–1.2) 0.72 0.9 (0.7–1.2) 0.9 (0.7–1.2) 0.60 Leucocytes 9.3 (6.6–13.1) 9.4 (6.7–13.2) 9.3 (6.6–13.1) 0.66 11.1 (7.8–15.8) 9.1 (6.5–12.9) < 0.001 CRP 16.7 (9.0–28.5) 15.6 (97.8–25.0) 17.0 (9.1–29.3) < 0.001 27.2 (12.0–152.5) 16.0 (8.7–27.0) < 0.001 Procalcitonin 0.3 (0.2–0.4) 0.3 (0.1–0.4) 0.3 (0.2–0.4) 0.003 0.3 (0.3–0.8) 0.3 (0.2–0.4) < 0.001 Table 1.  Characteristics and laboratory findings on the admission of the general cohort and VA-LRTI discriminated by patients that developed VAP and VAT. IQR interquartile range, COPD chronic pulmonary injury, HIV-AIDS: human immunodeficiency virus-acquired immunodeficiency syndrome, Leucocytes leucocytes 109 cells/L, C-RP C-reactive protein mg/dL. Risk factors associated with the development of VA‑LRTI. In the RF analysis and the logistic regres- sion analysis carried out to identify the variables associated with the development of VA-LRTI, we found that the development of AKI during ICU stay (OR, 95% CI 1.32 [1.11–1.57]), along with days under IMV (OR, 95% CL 1.06 [1.05–1.07]) and high CRP serum concentration on ICU admission (OR% CL: 1.01 [1.01–1.01]) were significantly associated with the development of this VA-LRTI (Fig. 4). Notably, prior history of Diabetes Mel- litus, hypertension, and obesity were not associated with the development of VA-LRTI. Ethics approval and consent to participate. Both the Ethics Committee of the Clínica Universidad de La Sabana (IRB#2020AN28) and Hospital Joan XXIII (IRB#CEIM/066/2020) approved the study. All patients signed informed consent to participate in the study. Local guidelines and international regulations were taken into account, and the Declarations of Helsinki and Colombian Resolution No 008430 of 1993. Discussion We found that VA-LRTI occurs in more than a quarter of patients with severe COVID-19 admitted to the ICU, often with potentially difficult-to-treat pathogens, and regional differences in incidence (higher incidence in Latin America). The most frequently isolated pathogen was Pseudomonas aeruginosa. AKI during ICU stay, longer duration under IMV, and higher CRP levels at ICU admission were risk factors independently associated with the development of VA-LRTI. Notably, severe COVID-19 patients who developed VAP had higher mortality, while the association between VAT and hospital mortality was not statistically significant. Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 5 Vol.:(0123456789) www.nature.com/scientificreports/ Figure 2.  Aetiological pathogens. Here is presented the frequency (in counts) of the etiological isolations in the whole cohort of patients with VA-LRTI. (A) Shows microorganisms isolated in the general cohort. (B) Represents the VAP cohort and Panel C depicts the VAT cohort. Within the “Other” group are microorganisms with less than five isolates (Achromobacter xylosoxidans, Acinetobacter lwoffii, Citrobacter koseri, Hafnia alvei, Moraxella catharralis, Neisseria cinerea, Providencia rettgeri, Raoultella planticola, Raoultella ornithinolytica, Streptococcus anginosus, and Streptococcus pyogenes). Microbiology identification All cohort Latin America Europe Microrganism n = 1253 n = 489 n = 764 Pseudomonas aeruginosa 266 (21.2) 61 (12.5) 205 (26.8) Klebsiella pneumoniae 239 (19.1) 164 (33.5) 142 (18.6) Staphylococcus aureus 194 (15.5) 52 (10.6) 77 (10.1) Citrobacter freundii 80 (6.4) 3 (0.6) 75 (9.8) Acinetobacter baumannii 68 (5.4) 52 (10.6) 47 (6.2) Escherichia coli 65 (5.2) 26 (5.3) 39 (5.1) Serratia marcescens 58 (4.6) 11 (2.2) 36 (4.7) Stenotrophomonas maltophilia 51 (4.1) 15 (3.1) 34 (4.5) Enterobacter cloacae 47 (3.8) 13 (2.7) 28 (3.7) Enterococcus faecalis 34 (2.7) 6 (1.2) 16 (2.1) Klebsiella oxytoca 34 (2.7) 23 (4.7) 11 (1.4) Burkholderia cepacia 26 (2.1) 24 (4.9) 8 (1.0) Enterobacter aerogenes 19 (1.5) 11 (2.2) 8 (1.0) Proteus mirabilis 11 (0.9) 6 (1.2) 7 (0.9) Enterococcus faecium 10 (0.8) 3 (0.6) 7 (0.9) Morganella morganii 10 (0.8) 2 (0.4) 5 (0.7) Haemophilus influenzae 10 (0.8) 3 (0.6) 4 (0.5) Streptococcus pneumoniae 7 (0.6) 4 (0.8) 3 (0.4) Achromobacter xylosoxidans 4 (0.3) 0 (0.0) 3 (0.4) Acinetobacter lwoffii 4 (0.3) 3 (0.6) 2 (0.3) Streptococcus pyogenes 4 (0.3) 4 (0.8) 2 (0.3) Citrobacter koseri 3 (0.2) 1 (0.2) 1 (0.1) Hafnia alvei 3 (0.2) 0 (0.0) 1 (0.1) Moraxella catharralis 1 (0.1) 0 (0.0) 1 (0.1) Neisseria cinerea 1 (0.1) 0 (0.0) 1 (0.1) Providencia rettgeri 1 (0.1) 0 (0.0) 1 (0.1) Raoultella planticola 1 (0.1) 1 (0.2) 0 (0.0) Raoultella ornithinolytica 1 (0.1) 1 (0.2) 0 (0.0) Streptococcus anginosus 1 (0.1) 0 (0.0) 0 (0.0) Table 2.  Identified microbiology in the general cohort and VA-LRTI discriminated by Latin American and European patients. Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 6 Vol:.(1234567890) www.nature.com/scientificreports/ Treatments, interventions and clinical outcomes All cohort No VAP No VAT (n = 3287) VAP (n = 610) (n = 2677) p-value VAT (n = 338) (n = 2949) p-value ECMO, n (%) 92 (2.8) 21 (3.4) 71 (2.6) 0.35 7 (2.1) 85 (2.9) 0.49 Prone position, 2498 (76.0) 507 (83.1) 1991 (74.4) < 0.001 277 (82.0) 2221 (75.3) 0.008 n (%) Corticosteroids 2413 (73.4) 460 (75.4) 1953 (73.0) 0.23 249 (73.7) 2164 (73.4) 0.96 use, n (%) One corticoster- 2363 (71.9) 457 (74.9) 1906 (71.2) 0.18 238 (70.4) 2125 (72.1) < 0.001 oid, n (%) Two corticoster- 92 (2.8) 16 (2.6) 76 (2.8) 0.18 50 (14.8) 42 (1.4) < 0.001 oid, n (%) Hydrocortisone, 200 (6.1) 36 (5.9) 164 (6.1) 0.91 66 (19.5) 134 (4.5) < 0.001 n (%) Methylpredniso- 1096 (31.4) 200 (32.8) 896(33.5) 0.78 98 (29.0) 998 (33.8) 0.08 lone, n (%) Dexamethasone, 1251 (38.1) 2530 (41.5) 998 (37.3) 0.06 174 (51.5) 1077 (36.5) < 0.001 n (%) Dexametha- sone + hydrocorti- 61 (1.9) 12 (2.0) 49 (1–8) 0.95 41 (12.1) 20 (0.7) < 0.001 sone, n (%) Dexametha- sone + methyl- 22 (0.7) 1 (0.2) 21 (0.8) 0.16 9 (2.7) 13 (0.4) < 0.001 prednisolone, n (%) Methylpredniso- lone + hydrocorti- 9 (0.3) 3 (0.5) 6 (0.2) 0.48 0 (0.0) 9 (0.3) 0.64 sone, n (%) Lopinavir/ritona- 51 (1.6) 50 (1.9) 1 (0.2) < 0.01 25 (0.9) 26 (7.7) < 0.001 vir, n (%) Interferon beta, 1 (0.0) 1 (0.0) 0 (0.0) 0.42 1 (0.0) 0 (0.0) 0.19 n (%) Neuraminidase 58 (1.8) 58 (2.2) 0 (0.0) < 0.001 57 (1.3) 1 (0.3) 0.05 inhibitor, n (%) Ampicilin/sulbac- 5 (0.2) 5 (0.2) 0 (0.0) 0.62 5 (0.2) 0 (0.0) 0.98 tam, n (%) Amoxacilin/clavu- 3 (0.1) 3 (0.1) 0 (0.0) 0.93 3 (0.1) 0 (0.0) 0.72 lanate, n (%) Ampicilin, n (%) 3 (0.1) 3 (0.1) 0 (0.0) 0.93 3 (0.1) 0 (0.0) 0.72 Azitromicine, 3 (0.1) 3 (0.1) 0 (0.0) 0.93 3 (0.1) 0 (0.0) 0.72 n (%) Claritromycine, 5 (0.2) 5 (0.2) 0 (0.0) 0.62 5 (0.2) 0 (0.0) 0.98 n (%) Amikacine, n (%) 6 (0.2) 6 (0.2) 0 (0.0) 0.52 1 (0.0) 5 (1.5) < 0.001 Ceftriaxone, n (%) 1 (0.0) 1 (0.0) 0 (0.0) 0.42 1 (0.0) 0 (0.0) 0.19 Vancomicine, 1 (0.0) 1 (0.0) 0 (0.0) 0.42 1 (0.0) 0 (0.0) 0.19 n (%) Linezolid, n (%) 2 (0.1) 2 (0.1) 0 (0.0) 0.81 2 (0.1) 0 (0).0 0.49 Tocilizumab, 21 (0.6) 21 (0.8) 0 (0.0) 0.06 14 (0.5) 7 (2.1) < 0.01 n (%) Rendesivir, n (%) 9 (0.3) 7 (0.3) 2 (0.3) 0.88 5 (0.2) 4 (1.2) < 0.01 Hydroxychlo- roquine/chloro- 82 (2.5) 81 (3.0) 1 (0.2) < 0.001 63 (2.1) 19 (5.6) < 0.001 quine, n (%) Outcomes AKI, n (%) 1147 (34.9) 256 (42.0) 891 (33.3) < 0.001 140 (41.4) 1007 (34.2) 0.027 Deaths, n (%) 1285 (39.1) 268 (43.9) 1017 (38.0) 0.007 160 (47.3) 1125 (38.2) 0.001 Hospital LOS, 26.0 (17.0–42.5) 40.0 (27.0–57.0) 24.0 (15.0–18.0) < 0.001 24.5 (16.0–44.0) 27.0 (17.0–42.0) 0.66 median (IQR) LOS, median 17.0 (10.0–28.0) 29.0 (19.0–45.0) 15.0 (9.9 -23.0) < 0.001 18.0 (12.0–28.0) 16.0 (10.0–28.0) 0.007 (IQR) IMV Days, 14.0 (8.0–24.0) 25.0 (16.0–38.0) 12.0 (7.0–20.0) < 0.001 15.0 (11.0–25.0) 14.0 (8.0–24.0) 0.004 median (IQR) Table 3.  Treatment, interventions, and outcomes of the general cohort and VA-LRTI discriminated by patients that developed VAP and VAT. Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 7 Vol.:(0123456789) www.nature.com/scientificreports/ Figure 3.  An automatized model to determine the impact of ventilator-associated lower respiratory tract infection on hospital mortality. (A) Presents variables more strongly associated with hospital mortality according to the Gini importance. (B) Represents the contribution of the variables to the output; the red values indicate a high-value contribution of the variable, and the blue values are a low-value contribution. The positive values in the plot indicate a high probability of hospital mortality, and negative values indicate a low likelihood of hospital mortality. (C) Presents each cross-validation trial’s receiver operative curve (ROC) for the subset of the selected variables. The blue curve represents the average of the ROC curves of each test, and the average area under de ROC is also presented. The most significant variables associated with hospital mortality according to their Gini importance and contribution to the output were Hospital-LOS, higher days under IMV, old age, higher creatinine levels on admission, and lower PaO2/FiO2 on ICU admission. Also, the development of AKI during ICU stays and VA-LRTI were risk factors associated with hospital mortality. Figure 4.  Logistic regression models to identify factors associated with ventilator-associated lower respiratory tract infection (VA-LRTI) (A) and the impact of VA-LRTI on hospital mortality (B). Logistic regression was performed with the optimal subset of variables obtained with the random forest model for the two outcomes (VA-LRTI and hospital mortality). The odds ratios (OR) are graphically represented in the Forest plot for better medical interpretability. (A) Presents the odd proportions of the risk for VA-LRTI, and (B) is shown the odds ratios for the hospital mortality. Our study found a cumulative incidence of 28.8% for VA-LRTI in patients with severe COVID-19 that required IMV and admission to the ICU, which is consistent with the systematic reviews that highlight an inci- dence varying between 21 to 64%23,24. Pulmonary superinfections are reported to be more frequent in COVID- 19 patients compared to other viral a­ etiologies18,25,26. Of note, the cumulative incidence of VAT is estimated to be 14% in the European ­population18. Importantly, we found that the cumulative incidence of VA-LRTI (i.e., VAP and VAT) was higher in patients enrolled in Latin America when compared to the incidence in European Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 8 Vol:.(1234567890) www.nature.com/scientificreports/ Countries during the same study period, which is a novel finding. To the best of our knowledge, these differences have not been described in the literature. Notably, these differences build into the argument that each country and hospital should have local protocols and guidelines; and that one size does not fit all when discussing infec- tion prevention. We found that the most frequent etiological microorganisms of VA-LRTI isolated in the respiratory samples were Pseudomonas aeruginosa, Klebsiella pneumoniae, and Staphylococcus aureus, which are considered poten- tially challenging to treat pathogens as they tend to either generate new or have resistance to carbapenems, and beta-lactams, amongst other antibiotics used in this c­ ontext27–31. Although a similar prevalence of difficult-to- treat pathogens has been reported ­previously18,32, these were smaller cohorts, restricted to European countries. Moreover, the microbiology observed in Latin America and Europe was notably different. Klebsiella pneumoniae was the principal bacteria in Latin America. This data is concerning since this microorganism may become fre- quently resistant and constitutes a challenging pathogen to treat, especially in countries with limited resources and no new antibiotics being widely available. It is important to highlight that Klebsiella pneumoniae has been highly reported in patients with COVID-19 in Latin A ­ merica33,34. Interestingly, the prevalence of Acinetobacter baumannii was particularly higher in Latin America, which is also concerning. Rangel et al. found one of the highest Acinetobacter baumannii resistance rates in Latin America. This infection is increasing in COVID-19 patients due to disease severity and has been associated with prolonged hospitalization and associated immune ­dysfunction35. Thus, our study adds to this key literature by highlighting geographic differences and including patients in limited-resource settings such as South America. We found that patients who developed VAP but not VAT were independently associated with increased hos- pital mortality, which is consistent with previous ­studies23,36. Interestingly, the higher risk of mortality observed in patients ventilated in the prone position was unexpected, given the potential value of the prone position in severe ARDS. However, this study was not intended to explore the effect of this therapy on clinical outcomes. We hypothesise that the prone position might facilitate the dissemination of microorganisms to the lower res- piratory ­tract37–39, leading to a greater risk for VAP development. Nonetheless, a 2022 systematic review and network meta-analysis including 20 randomised clinical trials state that the prone position did not increase the VAP ­incidence40. Thus, more studies are required to understand this finding further. Of note, factors associ- ated with mortality were similar to those variables associated with VA-LTRI, such as AKI during admission, days under mechanical ventilation, and the number of comorbidities. Identifying risk factors for the develop- ment of VA-LRTIVAP is pivotal to optimizing preventive strategies and hypothesizing potential underlying ­mechanisms25,41–43. Since the pandemic’s beginning, several studies have attempted to identify the factors associated with increased mortality in patients with severe COVID-19. The most frequent factors associated with worse clinical outcomes in patients with severe COVID-19 are older age, male gender, several comorbid conditions, dexamethasone treatment, the development of AKI during admission, and more days under mechanical v­ entilation12,14,22,44–47. For instance, a study conducted with 9657 patients showed an increased incidence of AKI in COVID-19 patients that was associated with a significant increase in the risk of death (HRs of 3.4 [95% CI 3.0–3.9] for A ­ KI25,41,42. Since COVID-19 patients have a greater risk of AKI ­development48, it is critical to prevent this complication. In this regard, the 25th Acute Disease Quality Initiative (ADQI) Workgroup recommends healthcare professionals individualise fluid and haemodynamic management based on a dynamic assessment of the cardiovascular status and limit nephrotoxic drug exposure where p ­ ossible49. We report the largest cohort of COVID-19 patients that evaluate risk factors and mortality associated with VA-LRTI. Our cohort patients from very different health care settings in 10 countries, from Europe and Latin America, involving different COVID-19 waves, giving external validity. We adopted the international consensus case definitions for our exposure of interest (VA-LRTI), had positive culture to inform LRTI diagnoses, and used an outcome without ascertainment bias (hospital mortality). Although our study did not include standardized protocols to prevent and diagnose VA-LRTI, the participating centres used internationally accepted guidelines to prevent and diagnose VA-LRTI in patients with severe COVID-19, including pneumonia zero protocols. We did not collect time from intubation to the development of VA-LRTI or the resistance patterns of the identified pathogens, which is a limitation and should be taken into account for future research. However, we did include the number of days patients remained under IMV, which may serve as a surrogate. Finally, although our study did not include standardized protocols for the treatment of COVID-19 patients between the different ICUs, the WHO recommendations were broadly followed in the participating centres. 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Author contributions L.F.R., A.R., M.S.H., I.M.L. contributions to conception; L.F.R., A.R., M.S.H., and I.M.L. design of work; L.F.R., A.R., M.S.H., I.M.L. acquisition; L.F.R., A.R., Y.V.F., S.D., R.G.G., A.B., C.C.S.M., E.D.I.P., M.S.H., I.M.L. analysis L.F.R., A.R., M.S.H., and I.M.L. interpretation data; L.F.R., A.R., Y.V.F., S.D., E.G.G., A.B., M.S.H., I.M.L. crea- tion of software; L.F.R., A.R., Y.V.F., S.D., E.G.G., A.B., C.S.M., E.D.I.P., G.M., G.O.T., G.H., W.S., A.M.D., M.J., M.V.A., E.D., M.B., J.S.V., R.F., A.A.M., L.S., W.F., J.L.L.V., F.V.V., A.E., A.L.V., R.J.G., I.S., M.S.H., I.M.L. drafted and revised the work. All authors have approved the submitted version and agreed to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work. Funding This work was supported by the SEMICYUC (Spanish Society of Intensive Care Medicine and Coronary Units) and the Universidad de La Sabana, Chía, Colombia (LFR). All authors were not precluded from accessing data in the study, and they accept responsibility for submitting it for publication. Competing interests The authors declare no competing interests. Additional information Supplementary Information The online version contains supplementary material available at https://​doi.​org/​ 10.​1038/​s41598-​023-​32265-5. Correspondence and requests for materials should be addressed to L.F.R. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. © The Author(s) 2023 LIVEN-Covid-19 Luis Antonio Gorordo23, Ricardo Buitrago7, Marcela Poveda7, Lina Maria Saucedo7, Edwin Silva7, Elisa Estenssoro24, Guillermo Ortiz25, Nicolas Nin26, Alfonso Jose Arango1, Alvaro Aguilar1, Andrea Lizeth Ayala1, Andrea Viviana Bayona1, Andrea Lizeth Ayala1, Angelica Rodriguez1, Carol Viviana Aponte1, Carolina Forero‑Carreño1, Conny Stefanny Muñoz1, Cristian Augusto Estrada1, Cristopher Romero1, Danilo Trujillo1, Diego Holguin1, Jesus Chavez‑Villegas1, Faure Rodriguez1, Francisco Franco1, Hernan Sánchez1, Janett Vanessa Moncayo1, Jennifer A. Pinedo1, Jesica Valeria Bravo1, Jose David Cruz1, Jose Miguel Angel1, Jovany Castro‑Lara1, Karen Andrea Mantilla1, Lorena Garcia1, Lorena Pabón1, Luis Arturo Lopez1, Luis Fernando Mamani1, Marisa Lucrecia Yupa1 & Valeria Catalina Quevedo1 23 Critical Care Unit, Hospital Juárez de México, Mexico, Mexico. 24Hospital Interzonal de Agudos San Martín de La Plata, La Plata, Argentina. 25Hospital Santa Clara, Bogota, Colombia. 26Intensive Care Unit, Hospital Español, Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 11 Vol.:(0123456789) www.nature.com/scientificreports/ Montevideo, Uruguay. SEMICYUC Study Group Andalucía Ana Loza27, Diego Matallana Zapata28, Isabel Díaz Torres29, Sonia Ibañez Cuadros29, María Recuerda Nuñez29, Maria Luz Carmona Pérez29, Jorge Gómez Ramos29, Alba Villares Casas29, María Luisa Cantón30, José Javier González Contreras30, Helena Pérez Chomón30, Nerissa Alvarez Chicote30, Alberto Sousa González30 & María De Alba Aparicio31 27 UCI Hospital Universitario Virgen de Valme, Seville, Spain. 28UCI Hospital Quirón, Huelva, Spain. 29UCI Hospital Universitario Puerto Real, Cádiz, Spain. 30UCI Hospital Universitario Virgen de la Macarena, Seville, Spain. 31UCI Hospital Universitario Reina Sofía, Córdoba, Spain. Aragón Ruth Jorge García32, Laura Sánchez Montori33, Sandra Herrero García33, Paula Abanses Moreno33, Carlos Mayordomo García33, Tomás Mallor Bonet34, Paula Omedas Bonafonte34, Enric Franquesa Gonzalez34, Nestor Bueno Vidales34, Paula Ocabo Buil34, Carlos Serón Arbeloa34, Isabel Sancho19, Pablo Guerrero Ibañez19, Pablo Gutierrez19, María Concepción Valdovinos35, Raquel Canto35, Ruth Jorge García32, Laura Sánchez Montori33, Sandra Herrero García33, Paula Abanses Moreno33, Carlos Mayordomo García33, Tomás Mallor Bonet34, Paula Omedas Bonafonte34, Enric Franquesa Gonzalez34, Nestor Bueno Vidales34, Paula Ocabo Buil34, Carlos Serón Arbeloa34, Isabel Sancho19, Pablo Guerrero Ibañez19, Pablo Gutierrez19, María Concepción Valdovinos35 & Raquel Canto35 32 UCI Hospital Nuestra Señora de Gracia, Zaragoza, Spain. 33UCI Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain. 34UCI Hospital General San Jorge, Huesca, Spain. 35UCI Hospital Obispo Polanco, Teruel, Spain. Asturias Ana Luz Balán Mariño36, María José Gutiérrez Fernández36, Marta Martín Cuadrado36, Belén García Arias36, Lorena Forcelledo Espina37, Lucía Viña Soria37, Lorena Martín Iglesias37, Lucía López Amor37, Elisabet Fernández Rey37, Emilio García Prieto37, Débora Fernández Ruíz38 & Carla Martínez González38 36 UCI Hospital Universitario San Agustín, Avilés, Spain. 37UCI Hospital Universitario Central de Asturias, Oviedo, Spain. 38UCI Hospital Cabueñes, Gijón, Spain. Baleares Lorenzo Socias14, Marcio Borges‐Sá14, María Aranda Pérez14, Antonia Socias14, José Ma Bonell Goytisolo39, Inmaculada Alcalde Mayayo39, Carlos Corradini39, Isabel Ceniceros39, Edwin Rodríguez39, Jose Ignacio Ayestarán Rota40, Mariana Andrea Novo40, Joaquim Colomina Climent40, Albert Figueras Castilla40, Tomàs Leal Rullan40, Maria Magdalena Garcias Sastre40, Rossana Pérez Senoff41 & Ramón Fernández42 39 UCI Hospital Quirón Salud Palmaplanas, Palma de Mallorca, Spain. 40UCI Hospital Universitario Son Espases, Palma de Mallorca, Spain. 41UCI Hospital Comarcal d’Inca, Inca, Spain. 42UCI Hospital Mateu Orfila, Mao, Spain. Canarias Juan Carlos Martín González43, Carmen Pérez Ortiz43, José Luciano Cabrera Santana43, Juan José Cáceres Agra43, Domingo González Romero43, Ana Casamitjana Ortega43, Luis Alberto Ramos Gómez44, Carolina Montelongo Ojeda44 & Jordi Solé‑Violán10 43 44 UCI Complejo Hospitalario Universitario Insular—Materno Infantil, Las Palmas de Gran Canaria, Spain. UCI Hospital General de la Palma, Tenerife, Spain. Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 12 Vol:.(1234567890) www.nature.com/scientificreports/ Cataluña Alejandro Rodríguez45, María Bodí45, Gerard Moreno45, Sandra Trefler45, Laura Claverias45, Raquel Carbonell45, Erika Esteve45, Montserrat Olona45, Xavier Teixidó45, Monserrat Vallverdú Vidal46, Begoña Balsera Garrido46, Elisabeth Papiol Gallofré12, Raquel Albertos Martell12, Rosa Alcaráz Peñarrocha12, Xavier Nuvials Casals12, Ricard Ferrer Roca12, Eric Adrián Mayor Vázquez47, Ferrán Roche Campo47, Pablo Concha Martínez47, Diego Franch Llasat47, Joan Ramón Masclanz48, Judith Marín‐ Corral48, Purificación Pérez48, Rosana Muñoz48, Clara Vila48, Francisco Javier González de Molina49, Elisabeth Navas Moya49, Josep Trenado49, Imma Vallverdú50, Eric Castañé50, Emili Díaz Santos51, Gemma Goma51 & Edg51 45 UCI Hospital Universitari de Tarragona Joan XXIII, Tarragona, Spain. 46UCI Hospital Universitari Arnau de Vilanova, Lleida, Spain. 47UCI Hospital Verge de la Cinta, Tortosa, Spain. 48UCI Hospital del Mar, Barcelona, Spain. 49 UCI Hospital Mutua de Terrasa, Terrasa, Spain. 50UCI Hospital Sant Joan, Reus, Spain. 51UCI Hospital Parc Tauli, Sabadell, Spain. Cantabria Borja Suberviola52 52 UCI Hospital Universitario Marqués de Valdecillas, Santander, Spain. Castilla La Mancha Antonio Albaya Moreno53, Carlos Marian Crespo53, Carmen Carolina Sena Pérez54 & Francisca Arbol Linde54 53 UCI Hospital Universitario de Guadalajara, Guadalajara, Spain. 54UCI Hospital Nuestra Señora del Prado, Toledo, Spain. Castilla y León Diana Monge Donaire55, Vega Losada Martínez55, Nuria Rodrigo Castroviejo55, Gerardo Ferrigno55, Reyes Beltrán55, Carolina Sanmartino55, Concepción Tarancón Maján55, Alfredo Marcos Gutiérrez55, Virginia Hidalgo Valverde56, Caridad Martín López56, Oihane Badallo57, María del Valle Ortiz57, Rebeca Vara Arlanzón57, David Iglesias Posadilla57, María Teresa Recio58 & Juan Carlos Ballesteros58 55 UCI Hospital Virgen de la Concha, Zamora, Spain. 56UCI Complejo Asistencial de Segovia, Segovia, Spain. 57UCI Hospital universitario de Burgos, Burgos, Spain. 58UCI Hospital Clínica de Salamanca, Salamanca, Spain. Ceuta Enrique Laza59 59 UCI Hospital Universitario de Ceuta, Ceuta, Spain. Extremadura Elena Gallego Curto60, Ma Car‐men Sánchez García60, Miguel Díaz‐Tavora61 & Rosa Mancha61 60 UCI Hospital San Pedro de Alcántara, Cáceres, Spain. 61UCI Hospital de Mérida, Mérida, Spain. Galicia Ana Ortega Montes62, Isabel Gallego Barbachano62, Eva Sanmartín Mantiñán62, María Lourdes Cordero63, Raquel María Rodríguez García63, Jorge Gámez Zapata63, María Gestal Vázquez63, María José Castro Orjales64, María Isabel Álvarez Diéguez64, Carmen Rivero Velasco65, Beatriz Lence Massa65, María Gestal Vázquez66 & Ignacio Martí67 62 UCI Hospital Montecelo, Pontevedra, Spain. 63UCI CHUAC A Coruña, A Coruña, Spain. 64UCI Centro Hospital Universitario de Ferrol, Ferrol, Spain. 65UCI Hospitalario Clínico Universitario de Santiago, Santiago de Compostela, Spain. 66REA CHUAC A Coruña, A Coruña, Spain. 67UCI Hospital Lucus Augusti, Lugo, Spain. Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 13 Vol.:(0123456789) www.nature.com/scientificreports/ Huelva Diego Matallana Zapata68 68 UCI Hospital Infanta Elena, Huelva, Spain. Madrid Alberto Hernández Tejedor69, Esther Ma López Ramos70, Laura Alcázar Sánchez Elvira70, Rocío Molina Montero70, Ma Consuelo Pintado Delgado70, María Trascasa Muñoz de la Peña70, Yaiza Betania Ortiz de Zárate Ansotegui70, Alejandra Acha Aranda70, Juan Higuera Lucas70, Juan Antonio Sanchez Giralt71, Marta Chicot Llano71, Nuria Arevalillo Fernández71, Marta Sánchez Galindo71, Ricardo Andino Ruiz71, Alfonso Canabal Berlanga71, Miguel Sánchez72, Mercedes Nieto72, Eduardo Arias Sarmiento73, Adoración Bueno Blázquez73, Rosa María de la Casa73, Fátima Martín73 & Samuel González73 69 UCI IFEMA, Madrid, Spain. 70UCI Hospital Príncipe de Asturias, Madrid, Spain. 71UCI Hospital de la Princesa, Madrid, Spain. 72UCI Hospital Clinico San Carlos, Madrid, Spain. 73UCI Hospital HLA la Moncloa, Madrid, Spain. Murcia Elena Martínez Quintana74, Bernardo Gil Rueda74, Áurea Higon Cañigral74, Laura López Gómez74, Pablo Safwat Bayoumi Delis74, Augusto Montenegro Muore74, Ángel Andrés Agamez Luengas74, Enriqueta Andreu Soler75, Ana Beatriz Pérez75, José Higinio de Gea García75, Rubén Jara Rubio75, Silvia Sánchez Cámara75, Alba Moreno Flores75, José Moya Sánchez75, Daniel Fran‐cisco Pérez Martínez75, Ma Desamparados del Rey Carrión75, María José Rico Lledó76, Juana María Serrano Navarro76, Juan Fran‐cisco Martín Ruíz76, Julián Triviño Hidalgo76, África López Ferrer76, Isabel Cremades Navalón76, Josefa Murcia Payá77, J. M. Allegre Gallego77 & María del Carmen Lorente78 74 UCI Hospital Morales Meseguer, Murcia, Spain. 75UCI Hospital Clínico Universitario Virgen de la Arrixaca, Murcia, Spain. 76UCI Hospital Reina Sofía, Murcia, Spain. 77UCI Hospital Santa Lucía, Cartagena, Spain. 78UCI Hospital Rafael Méndez, Lorca, Spain. Navarra Ruth González Natera79, Raquel Garrido López de Murillo79, Tania Ojuel Gros79, Raquel Flecha Viguera79, Isabel López González79 & Adriana García Herrera80 79 UCI Hospital Reina Sofía, Tudela, Spain. 80UCI Hospital García Orcoyen, Estella‐Lizarra, Spain. País Vasco Loreto Vidaur Tello81, Maialen Aseguinolaza81 & Itziar Eguibar81 81 UCI Hospital Universitario de Donostia, Donostia, Spain. Valencia Asunción Marqués Parra82, Sergio García Marti82, Alberto Lorenzo Aguilar82, Laura Bellver Bosch82, Victor Gascón Sanchez82, Sonia De la Guía Ortega82, Martín Parejo Montell83, Alberto Belenguer Muncharaz83, Hector Hernández Garces83, Victor Ramírez Montero83, Mónica Crespo Gómez83, Verónica Martí Algarra83, Susana Sancho Chinesta84, Joaquin Arguedas Cervera84, Faustino Álvarez Cebrian84, Begoña Balerdi Pérez84, Rosa Jannone Fores84, Javier Botella de Maglia84, Nieves Carbonell Monleón85, Jose Ferreres Franco85, Ainhoa Serrano Lazaro85, Mar Juan Díaz85, María Luisa Blasco Cortés85, Laura Fayos86, Julia Giménez86, Gaspar Soriano86, Ricardo Navarro86, Sonia Mas87, Elena Bisbal87, Laura Albert87, Johncard Romero87, Juan Fernández Cabreara87 & Andrea Ortíz88 82 UCI Hospital Universitario de La Ribera, Alzira, Spain. 83UCI Hospital Dr. Peset, Valencia, Spain. 84UCI Hospital Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 14 Vol:.(1234567890) www.nature.com/scientificreports/ Universitari i Politècnic La Fe, Valencia, Spain. 85UCI Hospital Clínico Universitario de Valencia, Valencia, Spain. 86 UCI Hospital Virgen de los Lirios de Alcoy, Alicante, Spain. 87UCI Hospital Arnau de Vilanova, Valencia, Spain. 88 UCI Hospital Comarcal de Vinarós, Vinarós, Spain. Principado de Andorra Antonio Margarit Ribas89 & Neus Guasch89 89 ICU Hospital nostra Señyora de Meritxell, Les Esclades, Spain. Scientific Reports | (2023) 13:6553 | https://doi.org/10.1038/s41598-023-32265-5 15 Vol.:(0123456789)