Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.

BACKGROUND: Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster. METHODS: Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3. RESULTS: Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3. CONCLUSIONS: During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis.

Effects of intubation timing in patients with COVID-19 throughout the four waves of the pandemic: a matched analysis.

BACKGROUND: The primary aim of our study was to investigate the association between intubation timing and hospital mortality in critically ill patients with coronavirus disease 2019 (COVID-19)-associated respiratory failure. We also analysed both the impact of such timing throughout the first four pandemic waves and the influence of prior noninvasive respiratory support on outcomes. METHODS: This is a secondary analysis of a multicentre, observational and prospective cohort study that included all consecutive patients undergoing invasive mechanical ventilation due to COVID-19 from across 58 Spanish intensive care units (ICUs) participating in the CIBERESUCICOVID project. The study period was between 29 February 2020 and 31 August 2021. Early intubation was defined as that occurring within the first 24 h of ICU admission. Propensity score matching was used to achieve a balance across baseline variables between the early intubation cohort and those patients who were intubated after the first 24 h of ICU admission. Differences in outcomes between early and delayed intubation were also assessed. We performed sensitivity analyses to consider a different time-point (48 h from ICU admission) for early and delayed intubation. RESULTS: Of the 2725 patients who received invasive mechanical ventilation, a total of 614 matched patients were included in the analysis (307 for each group). In the unmatched population, there were no differences in mortality between the early and delayed groups. After propensity score matching, patients with delayed intubation presented higher hospital mortality (27.3% versus 37.1%; p=0.01), ICU mortality (25.7% versus 36.1%; p=0.007) and 90-day mortality (30.9% versus 40.2%; p=0.02) compared with the early intubation group. Very similar findings were observed when we used a 48-h time-point for early or delayed intubation. The use of early intubation decreased after the first wave of the pandemic (72%, 49%, 46% and 45% in the first, second, third and fourth waves, respectively; first versus second, third and fourth waves p<0.001). In both the main and sensitivity analyses, hospital mortality was lower in patients receiving high-flow nasal cannula (HFNC) (n=294) who were intubated earlier. The subgroup of patients undergoing noninvasive ventilation (n=214) before intubation showed higher mortality when delayed intubation was set as that occurring after 48 h from ICU admission, but not when after 24 h. CONCLUSIONS: In patients with COVID-19 requiring invasive mechanical ventilation, delayed intubation was associated with a higher risk of hospital mortality. The use of early intubation significantly decreased throughout the course of the pandemic. Benefits of such an approach occurred more notably in patients who had received HFNC.

A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study.

BACKGROUND: The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. METHODS: This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. RESULTS: Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. CONCLUSIONS: A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.

Microbiological diagnosis of pulmonary invasive aspergillosis in critically ill patients with severe SARS-CoV-2 pneumonia: a bronchoalveolar study.

BACKGROUND: Diagnosing COVID-19-associated pulmonary aspergillosis (CAPA) can be challenging since radiological and clinical criteria in the critically ill patient are nonspecific. Microbiological diagnostic support is therefore crucial. The aim of this study was to document the incidence of aspergillosis using bronchoalveolar lavage (BAL) as the diagnostic method and to determine the performance of the current mycological diagnostic tests most widely used for the diagnosis of CAPA, together with evaluation of the Asp lateral flow device (LFD). METHODS: Prospective cohort study conducted between March 2020 and June 2022. Inclusion criteria were critically ill patients admitted to the ICU with SARS-CoV-2 pneumonia requiring invasive mechanical ventilation. Diagnostic bronchoscopy and BAL were performed at the beginning of invasive mechanical ventilation. The sensitivity, specificity, positive and negative predictive value (PPV and NPV), positive and negative likelihood ratio (LR + and LR-) of BAL culture, direct examination with calcofluor white stain, ELISA (Platelia) and LFD (AspLFD) for detection of galactomannan (GM) were evaluated. Aspergillus-qPCR was applied when discrepancies between diagnostic tests arose. RESULTS: Of the 244 critically ill patients with SARS-CoV-2 pneumonia admitted to the ICU, the majority (n = 200, 82%) required invasive mechanical ventilation. Diagnostic bronchoscopic procedures were performed in 160 patients (80%), who were enrolled in this study. The incidence of CAPA was 18.7% (n = 30). LFD-GM demonstrated a sensitivity of 84%, specificity of 99%, PPV 94%, NPV 97%, LR(+) of 84, and LR(-) of 0.16. At GM-ELISA indices of ≥ 0.5 and ≥ 1.0, sensitivity was 92% and 79%, specificity was 95% and 99%, PPV 76% and 91%, NPV 99% and 96%, LR(+) 18 and 79, and LR(-) 0.08 and 0.21, respectively. The optimal cut-off index from the ROC curve was 0.48, with sensitivity of 95% and specificity of 95%. CONCLUSIONS: Using a diagnostic strategy based on bronchoscopy and BAL, we documented a high incidence of pulmonary aspergillosis in patients with severe SARS-CoV-2 pneumonia. Asp-LFD showed moderate sensitivity and excellent specificity, with a high PPV, and could be used for rapid diagnosis of patients with suspected CAPA.

Temporal changes in the epidemiology, management, and outcome from acute respiratory distress syndrome in European intensive care units: a comparison of two large cohorts.

BACKGROUND: Mortality rates for patients with ARDS remain high. We assessed temporal changes in the epidemiology and management of ARDS patients requiring invasive mechanical ventilation in European ICUs. We also investigated the association between ventilatory settings and outcome in these patients. METHODS: This was a post hoc analysis of two cohorts of adult ICU patients admitted between May 1-15, 2002 (SOAP study, n = 3147), and May 8-18, 2012 (ICON audit, n = 4601 admitted to ICUs in the same 24 countries as the SOAP study). ARDS was defined retrospectively using the Berlin definitions. Values of tidal volume, PEEP, plateau pressure, and FiO2 corresponding to the most abnormal value of arterial PO2 were recorded prospectively every 24 h. In both studies, patients were followed for outcome until death, hospital discharge or for 60 days. RESULTS: The frequency of ARDS requiring mechanical ventilation during the ICU stay was similar in SOAP and ICON (327[10.4%] vs. 494[10.7%], p = 0.793). The diagnosis of ARDS was established at a median of 3 (IQ: 1-7) days after admission in SOAP and 2 (1-6) days in ICON. Within 24 h of diagnosis, ARDS was mild in 244 (29.7%), moderate in 388 (47.3%), and severe in 189 (23.0%) patients. In patients with ARDS, tidal volumes were lower in the later (ICON) than in the earlier (SOAP) cohort. Plateau and driving pressures were also lower in ICON than in SOAP. ICU (134[41.1%] vs 179[36.9%]) and hospital (151[46.2%] vs 212[44.4%]) mortality rates in patients with ARDS were similar in SOAP and ICON. High plateau pressure (> 29 cmH2O) and driving pressure (> 14 cmH2O) on the first day of mechanical ventilation but not tidal volume (> 8 ml/kg predicted body weight [PBW]) were independently associated with a higher risk of in-hospital death. CONCLUSION: The frequency of and outcome from ARDS remained relatively stable between 2002 and 2012. Plateau pressure > 29 cmH2O and driving pressure > 14 cmH2O on the first day of mechanical ventilation but not tidal volume > 8 ml/kg PBW were independently associated with a higher risk of death. These data highlight the continued burden of ARDS and provide hypothesis-generating data for the design of future studies.

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.

BACKGROUND: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. METHODS: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. RESULTS: The database included a total of 2022 patients (mean age 64 [IQR 5-71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10-17]) and SOFA score (5 [IQR 3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. CONCLUSION: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a "one-size-fits-all" model in practice.

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients.

BACKGROUND: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. METHODS: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. RESULTS: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). CONCLUSIONS: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation.

Compassionate Communication and End-of-Life Care for Critically Ill Patients with SARS-CoV-2 Infection.

Public health strategies recommend isolating patients with SARS-CoV-2 infection. But compassionate care in the intensive care unit (ICU) is an ethical obligation of modern medicine that cannot be justified by the risk of infection or the lack of personal protective equipment. This article describes the experiences of clinicians in ICUs in the south of Spain promoted by the Andalusian Society of Intensive Care SAMIUC, in the hope it will serve to improve the conditions in which these patients die, and to help their families who suffer when they cannot say good-bye to their loved ones. In the south of Spain, healthcare professionals use daily videoconferencing to improve communication between clinicians, patients, and their relatives who cannot visit them in the ICU. This close communication allows families to see their loved ones and extends communication between healthcare professionals, patients, and their relatives. To allow family members to accompany patients at the end of life, it is possible to adapt public health rules to the epidemic situation.

Colistin versus meropenem in the empirical treatment of ventilator-associated pneumonia (Magic Bullet study): an investigator-driven, open-label, randomized, noninferiority controlled trial.

BACKGROUND: Colistin is recommended in the empirical treatment of ventilator-associated pneumonia (VAP) with a high prevalence of carbapenem-resistant gram-negative bacilli (CR-GNB). However, the efficacy and safety of colistin are not well defined. METHODS: A multicenter prospective randomized trial conducted in 32 European centers compared the efficacy and safety of colistin (4.5 million unit loading dose followed by a maintenance dose of 3 million units every 8 h) versus meropenem (2 g every 8 h), both in combination with levofloxacin (500 mg every 12 h) for 7-14 days in patients with late VAP. Between May 2012 and October 2015, 232 patients were randomly assigned to the 2 treatment groups. The primary endpoint was mortality at 28 days after randomization in the microbiologically modified intention-to-treat (mMITT) population. Secondary outcomes included clinical and microbiological cure, renal function at the end of the treatment, and serious adverse events. The study was interrupted after the interim analysis due to excessive nephrotoxicity in the colistin group; therefore, the sample size was not achieved. RESULTS: A total of 157 (67.7%) patients were included in the mMITT population, 36 of whom (22.9%) had VAP caused by CR-GNB. In the mMITT population, no significant difference in mortality between the colistin group (19/82, 23.2%) and the meropenem group (19/75, 25.3%) was observed, with a risk difference of - 2.16 (- 15.59 to 11.26, p = 0.377); the noninferiority of colistin was not demonstrated due to early termination and limited number of patients infected by carbapenem-resistant pathogens. Colistin plus levofloxacin increased the incidence of renal failure (40/120, 33.3%, versus 21/112, 18.8%; p = 0.012) and renal replacement therapy (11/120, 9.1%, versus 2/112, 1.8%; p = 0.015). CONCLUSIONS: This study did not demonstrate the noninferiority of colistin compared with meropenem, both combined with levofloxacin, in terms of efficacy in the empirical treatment of late VAP but demonstrated the greater nephrotoxicity of colistin. These findings do not support the empirical use of colistin for the treatment of late VAP due to early termination. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01292031. Registered 9 February 2011.

Changes in End-of-Life Practices in European Intensive Care Units From 1999 to 2016.

IMPORTANCE: End-of-life decisions occur daily in intensive care units (ICUs) around the world, and these practices could change over time. OBJECTIVE: To determine the changes in end-of-life practices in European ICUs after 16 years. DESIGN, SETTING, AND PARTICIPANTS: Ethicus-2 was a prospective observational study of 22 European ICUs previously included in the Ethicus-1 study (1999-2000). During a self-selected continuous 6-month period at each ICU, consecutive patients who died or had any limitation of life-sustaining therapy from September 2015 until October 2016 were included. Patients were followed up until death or until 2 months after the first treatment limitation decision. EXPOSURES: Comparison between the 1999-2000 cohort vs 2015-2016 cohort. MAIN OUTCOMES AND MEASURES: End-of-life outcomes were classified into 5 mutually exclusive categories (withholding of life-prolonging therapy, withdrawing of life-prolonging therapy, active shortening of the dying process, failed cardiopulmonary resuscitation [CPR], brain death). The primary outcome was whether patients received any treatment limitations (withholding or withdrawing of life-prolonging therapy or shortening of the dying process). Outcomes were determined by senior intensivists. RESULTS: Of 13 625 patients admitted to participating ICUs during the 2015-2016 study period, 1785 (13.1%) died or had limitations of life-prolonging therapies and were included in the study. Compared with the patients included in the 1999-2000 cohort (n = 2807), the patients in 2015-2016 cohort were significantly older (median age, 70 years [interquartile range {IQR}, 59-79] vs 67 years [IQR, 54-75]; P < .001) and the proportion of female patients was similar (39.6% vs 38.7%; P = .58). Significantly more treatment limitations occurred in the 2015-2016 cohort compared with the 1999-2000 cohort (1601 [89.7%] vs 1918 [68.3%]; difference, 21.4% [95% CI, 19.2% to 23.6%]; P < .001), with more withholding of life-prolonging therapy (892 [50.0%] vs 1143 [40.7%]; difference, 9.3% [95% CI, 6.4% to 12.3%]; P < .001), more withdrawing of life-prolonging therapy (692 [38.8%] vs 695 [24.8%]; difference, 14.0% [95% CI, 11.2% to 16.8%]; P < .001), less failed CPR (110 [6.2%] vs 628 [22.4%]; difference, -16.2% [95% CI, -18.1% to -14.3%]; P < .001), less brain death (74 [4.1%] vs 261 [9.3%]; difference, -5.2% [95% CI, -6.6% to -3.8%]; P < .001) and less active shortening of the dying process (17 [1.0%] vs 80 [2.9%]; difference, -1.9% [95% CI, -2.7% to -1.1%]; P < .001). CONCLUSIONS AND RELEVANCE: Among patients who had treatment limitations or died in 22 European ICUs in 2015-2016, compared with data reported from the same ICUs in 1999-2000, limitations in life-prolonging therapies occurred significantly more frequently and death without limitations in life-prolonging therapies occurred significantly less frequently. These findings suggest a shift in end-of-life practices in European ICUs, but the study is limited in that it excluded patients who survived ICU hospitalization without treatment limitations.

Initial Antifungal Strategy Reduces Mortality in Critically Ill Patients With Candidemia: A Propensity Score-Adjusted Analysis of a Multicenter Study.

OBJECTIVE: The objective of this study was to evaluate the impact of the empirical therapy with fluconazole or an echinocandin on 30- and 90-day mortality in critically ill patients with candidemia. The outcome of patients in whom the empirical echinocandin was deescalated to fluconazole was also assessed. DESIGN: Retrospective, observational multicenter study. SETTING: Medical and surgical ICUs in nine Spanish hospitals. PATIENTS: Adult patients (≥ 18 yr) with an episode of Candida bloodstream infection during ICU admission from January 2011 to April 2016. INTERVENTIONS: Patient characteristics, infection-related variables, therapeutic interventions, and metastatic complications were reviewed. A propensity score-adjusted multivariable analysis was performed to identify the risk factors significantly associated with 30-day and 90-day mortality. MEASUREMENTS AND MAIN RESULTS: A total of 294 patients were diagnosed of candidemia in the participant ICUs. Sixty patients were excluded (other antifungals in the primary therapy or the patient died without empirical antifungal therapy). The study group comprised 115 patients who received fluconazole (30-day mortality, 37.4%) and 119 patients treated empirically with an echinocandin (30-day mortality, 31.9%). The use of an echinocandin in the empirical therapy was a protective factor for 30-day (odds ratio, 0.32; 95% CI, 0.16-0.66; p = 0.002) and 90-day mortality (odds ratio, 0.50; 95% CI, 0.27-0.93; p = 0.014) in the propensity score- adjusted multivariable analysis. Deescalation of the empirical echinocandin to fluconazole was not associated with a higher mortality or the occurrence of long-term complications. CONCLUSIONS: Empirical use of an echinocandin in critically ill patients with documented candidemia reduces mortality at 30 and 90 days significantly. Deescalation of the empirical echinocandin to fluconazole is safe and effective in fluconazole-susceptible infections.

Latest developments in early diagnosis and specific treatment of severe influenza infection.

Influenza pandemics are unpredictable recurrent events with global health, economic, and social consequences. The objective of this review is to provide an update on the latest developments in early diagnosis and specific treatment of the disease and its complications, particularly with regard to respiratory organ failure. Despite advances in treatment, the rate of mortality in the intensive care unit remains approximately 30%. Therefore, early identification of potentially severe viral pneumonia is extremely important to optimize treatment in these patients. The pathogenesis of influenza virus infection depends on viral virulence and host response. Thus, in some patients, it is associated with an excessive systemic response mediated by an authentic cytokine storm. This process leads to severe primary pneumonia and acute respiratory distress syndrome. Initial prognostication in the emergency department based on comorbidities, vital signs, and biomarkers (e.g., procalcitonin, ferritin, human leukocyte antigen-DR, mid-regional proadrenomedullin, and lactate) is important. Identification of these biomarkers on admission may facilitate clinical decision-making to determine early admission to the hospital or the intensive care unit. These decisions are reached considering pathophysiological circumstances that are associated with a poor prognosis (e.g., bacterial co-infection, hyperinflammation, immune paralysis, severe endothelial damage, organ dysfunction, and septic shock). Moreover, early implementation is important to increase treatment efficacy. Based on a limited level of evidence, all current guidelines recommend using oseltamivir in this setting. The possibility of drug resistance should also be considered. Alternative options include other antiviral drugs and combination therapies with monoclonal antibodies. Importantly, it is not recommended to use corticosteroids in the initial treatment of these patients. Furthermore, the implementation of supportive measures for respiratory failure is essential. Current recommendations are limited, heterogeneous, and not regularly updated. Early intubation and mechanical ventilation is the basic treatment for patients with severe respiratory failure. Prone ventilation should be promptly performed in patients with acute respiratory distress syndrome, while early tracheostomy should be considered in case of planned prolonged mechanical ventilation. Clinical trials on antiviral treatment and respiratory support measures specifically for these patients, as well as specific recommendations for different at-risk populations, are necessary to improve outcomes.

Limitation of life support treatments in Spanish Intensive Care Units: Analysis of the ETHICUS II study.

OBJECTIVE: The aim of this study is to describe the results of Spanish ICUs in ETHICUS II study. DESIGN: Planned substudy of patients from ETHICUS II study. SETTING: 12 Spanish ICU. PATIENTS OR PARTICIPANTS: Patients admitted to Spanish ICU who died or in whom a limitation of life-sustaining treatment (LLST) was decided during a recruitment period of 6 months. INTERVENTIONS: Follow-up of patients was performed until discharge from the ICU and 2 months after the decision of LLST or death. MAIN VARIABLES OF INTEREST: Demographic characteristics, clinical profile, type of decision of LLST, time and form in which it was adopted. Patients were classified into 4 categories according to the ETHICUS II study protocol: withholding or withdrawing life-sustaining therapy, active shortening of the dying process, failed cardiopulmonary resuscitation and patients with brain death. RESULTS: A total of 795 patients were analyzed; 129 patients died after CPR, 129 developed brain death. LLST was decided in 537 patients, 485 died in the ICU, 90.3%. The mean age was 66.19 years ± 14.36, 63.8% of male patients. In 221 (41%) it was decided to withdraw life-sustaining treatments and in 316 (59%) withholding life-sustaining treatments. Nineteen patients (2.38%) had advance living directives. CONCLUSIONS: The predominant clinical profile when LTSV was established was male patients over 65 years with mostly cardiovascular comorbidity. We observed that survival was higher in LLST decisions involving withholding of treatments compared to those in which withdrawal was decided. Spain has played a leading role in both patient and ICU recruitment participating in this worldwide multicenter study.

A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective.

Background: Bacterial/fungal coinfections (COIs) are associated with antibiotic overuse, poor outcomes such as prolonged ICU stay, and increased mortality. Our aim was to develop machine learning-based predictive models to identify respiratory bacterial or fungal coinfections upon ICU admission. Methods: We conducted a secondary analysis of two prospective multicenter cohort studies with confirmed influenza A (H1N1)pdm09 and COVID-19. Multiple logistic regression (MLR) and random forest (RF) were used to identify factors associated with BFC in the overall population and in each subgroup (influenza and COVID-19). The performance of these models was assessed by the area under the ROC curve (AUC) and out-of-bag (OOB) methods for MLR and RF, respectively. Results: Of the 8902 patients, 41.6% had influenza and 58.4% had SARS-CoV-2 infection. The median age was 60 years, 66% were male, and the crude ICU mortality was 25%. BFC was observed in 14.2% of patients. Overall, the predictive models showed modest performances, with an AUC of 0.68 (MLR) and OOB 36.9% (RF). Specific models did not show improved performance. However, age, procalcitonin, CRP, APACHE II, SOFA, and shock were factors associated with BFC in most models. Conclusions: Machine learning models do not adequately predict the presence of co-infection in critically ill patients with pandemic virus infection. However, the presence of factors such as advanced age, elevated procalcitonin or CPR, and high severity of illness should alert clinicians to the need to rule out this complication on admission to the ICU.

Association of obesity on the outcome of critically ill patients affected by COVID-19.

OBJECTIVE: To evaluate the impact of obesity on ICU mortality. DESIGN: Observational, retrospective, multicentre study. SETTING: Intensive Care Unit (ICU). PATIENTS: Adults patients admitted with COVID-19 and respiratory failure. INTERVENTIONS: None. PRIMARY VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. Body mass index (BMI) impact on ICU mortality was studied as (1) a continuous variable, (2) a categorical variable obesity/non-obesity, and (3) as categories defined a priori: underweight, normal, overweight, obesity and Class III obesity. The impact of obesity on mortality was assessed by multiple logistic regression and Smooth Restricted cubic (SRC) splines for Cox hazard regression. RESULTS: 5,206 patients were included, 20 patients (0.4%) as underweight, 887(17.0%) as normal, 2390(46%) as overweight, 1672(32.1) as obese and 237(4.5%) as class III obesity. The obesity group patients (n = 1909) were younger (61 vs. 65 years, p < 0.001) and with lower severity scores APACHE II (13 [9-17] vs. 13[10-17, p < 0.01) than non-obese. Overall ICU mortality was 28.5% and not different for obese (28.9%) or non-obese (28.3%, p = 0.65). Only Class III obesity (OR = 2.19, 95%CI 1.44-3.34) was associated with ICU mortality in the multivariate and SRC analysis. CONCLUSIONS: COVID-19 patients with a BMI > 40 are at high risk of poor outcomes in the ICU. An effective vaccination schedule and prolonged social distancing should be recommended.

Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients.

OBJECTIVE: To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves. DESIGN: Observational, retrospective, multicentre study. SETTING: Intensive Care Unit (ICU). PATIENTS: Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model's performance was measured using accuracy test and area under curve (AUC) ROC. RESULTS: A total of 2330 patients (mean age 63 [53-82] years, 1643 (70.5%) male, median APACHE II score (12 [9-16]) and SOFA score (4 [3-6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was -0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC -0.08). CONCLUSION: Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation.

Managing the Next Wave of Influenza and/or SARS-CoV-2 in the ICU-Practical Recommendations from an Expert Group for CAPA/IAPA Patients.

The aim of this study was to establish practical recommendations for the diagnosis and treatment of influenza-associated invasive aspergillosis (IAPA) based on the available evidence and experience acquired in the management of patients with COVID-19-associated pulmonary aspergillosis (CAPA). The CAPA/IAPA expert group defined 14 areas in which recommendations would be made. To search for evidence, the PICO strategy was used for both CAPA and IAPA in PubMed, using MeSH terms in combination with free text. Based on the results, each expert developed recommendations for two to three areas that they presented to the rest of the group in various meetings in order to reach consensus. As results, the practical recommendations for the management of CAPA/IAPA patients have been grouped into 12 sections. These recommendations are presented for both entities in the following situations: when to suspect fungal infection; what diagnostic methods are useful to diagnose these two entities; what treatment is recommended; what to do in case of resistance; drug interactions or determination of antifungal levels; how to monitor treatment effectiveness; what action to take in the event of treatment failure; the implications of concomitant corticosteroid administration; indications for the combined use of antifungals; when to withdraw treatment; what to do in case of positive cultures for Aspergillus spp. in a patient with severe viral pneumonia or Aspergillus colonization; and how to position antifungal prophylaxis in these patients. Available evidence to support the practical management of CAPA/IAPA patients is very scarce. Accumulated experience acquired in the management of CAPA patients can be very useful for the management of IAPA patients. The expert group presents eminently practical recommendations for the management of CAPA/IAPA patients.

Anticipatory Antifungal Treatment in Critically Ill Patients with SARS-CoV-2 Pneumonia.

BACKGROUND: The aim of this study was to investigate the incidence of COVID-19-associated pulmonary aspergillosis (CAPA) in critically ill patients and the impact of anticipatory antifungal treatment on the incidence of CAPA in critically ill patients. METHODS: Before/after observational study in a mixed intensive care unit (ICU) of a university teaching hospital. The study took place between March 2020 and June 2022. Inclusion criteria were critically ill patients with severe SARS-CoV-2 pneumonia requiring invasive mechanical ventilation. Two analysis periods were compared according to whether or not antifungal therapy was given early. RESULTS: A total of 160 patients with severe SARS-CoV-2 pneumonia and invasive mechanical ventilation were included. The incidence of CAPA in the first study period was 19 out of 58 patients (32.75%); during the second period, after implementation of the intervention (anticipatory antifungal therapy), the incidence of CAPA decreased to 10.78% (11 out of 102 patients). In patients with CAPA under invasive mechanical ventilation, the mortality rate decreased from 100% to 64%. CONCLUSIONS: Anticipating antifungal treatment in patients with SARS-CoV-2 pneumonia under invasive mechanical ventilation was associated with a decrease in the incidence and mortality of pulmonary aspergillosis.