COVID-19 associated bacterial infections in intensive care unit: a case control study.

We described the secondary bacterial infections (SBI) among COVID-19 patients in comparison with non-COVID-19 patients. We performed a retrospective case-control study between January 01, 2020 and April 01, 2022. Including the adult patients, who stayed ≥ 72 h in intensive care unit (ICU). In total 405 patients were included, 135 had (33.3%) COVID-19, with similar age and gender. The length of stay in ICU was not different (11.4 vs 8.2, p = 0.109), however mean intubation days were higher among COVID-19 cases (6.5 vs 3.8, p = 0.005), SBI were more common among COVID-19 cases (34% vs 10.7%, p < 0.001). Among the patients with pneumonia, the rate of gram-positive bacteria was higher in COVID-19 group than the control group (39% vs 5%, p = 0.006). The predictors for SBI were having COVID-19 (OR: 2.3, Cl 1.25-4.32, p = 0.008), days of intubation (OR: 1.05, Cl 1.01-1.10, p = 0.004), and being male (OR: 2, Cl 1.12-3.58, p = 0.018). The predictors of mortality were COVID-19 (OR: 2.38, Cl 1.28-4.42, p = 0.006), days of intubation (OR: 1.06, Cl 1.03-1.09, p < 0.001), active hematologic malignancy (OR: 3.1, Cl: 1.33-7.28, p = 0.09), active solid tumors (OR: 2.44, Cl 1.21-4.91, p = 0.012), and coronary artery diseases (OR: 1.8, Cl 1.01-3.52, p = 0.045). The most common SBI in COVID-19 patients were methicillin-sensitive Staphylococcus aureus. No carbapenem-resistant Enterobacterales related infections were detected in COVID-19 patients.

The power of mechanical ventilation may predict mortality in critically ill patients.

BACKGROUND: Mechanical power (MP) is the amount of energy transferred from the ventilator to the patient within a unit of time. It has been emphasized in ventilation-induced lung injury (VILI) and mortality. However, its measurement and use in clinical practice are challenging. "Electronic recording systems (ERS)" using mechanical ventilation parameters provided by the ventilator can be helpful to measure and record the MP. The MP (J/minutes) formula is 0.098 x tidal volume x respiratory rate x (Ppeak - ½ ∆P), in which ∆P is the driving pressure and Ppeak is the peak pressure. We aimed to define the association between MP values and ICU mortality, mechanical ventilation days, and intensive care unit length of stay (ICU-LOS). The secondary outcome was to determine the most potent or essential component of power in the equation that has a role in mortality. METHODS: This retrospective study was performed in two centers (VKV American Hospital and Bakirköy Sadi Konuk Hospital ICUs) that used ERS (Metavision IMDsoft) between 2014 and 2018. We uploaded the power formula (MP (J/minutes)=0.098×VT×RR×(Ppeak - ½ ∆P) to ERS (METAvision, iMDsoft, and Consult Orion Health) and calculated the MP value by using MV parameters automatically sent from the ventilator. (∆P; driving pressure, VT; tidal volume, RR; respiratory rate and Ppeak; peak pressure). RESULTS: A total of 3042 patients were included in the study. The median value of MP was 11.3 J/min. Mortality in MP<11.3 J/min was 35.4%, and 49.1% in MP>11.3J/min.; P<0.001. Mechanical ventilation days and ICU-LOS were also statistically longer in the MVP>11.3 J/min group. CONCLUSIONS: The first 24 h MP maybe a predictive value for the ICU patients' prognosis. This implies that MP may be used as a decision-making system to define the clinical approach and as a scoring system to predict patient prognosis.

The association between Acinetobacter baumannii infections and the COVID-19 pandemic in an intensive care unit.

We aimed to describe the increased rate of Acinetobacter baumannii infections during the COVID-19 pandemic and define its significance within the last five years. This study was performed in a tertiary hospital with 280 beds and included all patients infected with A. baumannii in the intensive care unit between January 1, 2018, and June 30, 2022. A. baumannii-infected patients in the intensive care unit 27 months before the pandemic and 27 months during the pandemic were included. Pulsed-field gel electrophoresis was performed to assess clonal relatedness. The infection control measures were specified based on the findings and targeted elimination. In total, 5718 patients were admitted to the intensive care unit from January 1st, 2018, to June 30th, 2022. A. baumannii infection was detected in 81 patients. Compared to the pre-pandemic era, the rate of A. baumannii infection during the pandemic was 1.90 times higher (OR: 1.90, 95% CI: [1.197, 3.033]). Clonality assessment of multidrug-resistant A. baumannii samples revealed eight clusters with one main cluster comprising 14/27 isolates between 2021 and 2022. The case fatality rate of the pre-pandemic and pandemic era was not different statistically (83.33% vs. 81.48%, p = 0.835). Univariate analysis revealed the association of mechanical ventilation (p = 0.002) and bacterial growth in tracheal aspirate (p = 0.001) with fatality. During the COVID-19 pandemic, potential deficits in infection control measures may lead to persistent nosocomial outbreaks. In this study, the introduction of enhanced and customized infection control measures has resulted in the containment of an A. baumannii outbreak.

Weaning Outcomes in Patients with Brain Injury.

BACKGROUND: Despite the need for specific weaning strategies in neurological patients, evidence is generally insufficient or lacking. We aimed to describe the evolution over time of weaning and extubation practices in patients with acute brain injury compared with patients who are mechanically ventilated (MV) due to other reasons. METHODS: We performed a secondary analysis of three prospective, observational, multicenter international studies conducted in 2004, 2010, and 2016 in adults who had need of invasive MV for more than 12 h. We collected data on baseline characteristics, variables related to management ventilator settings, and complications while patients were ventilated or until day 28. RESULTS: Among the 20,929 patients enrolled, we included 12,618 (60%) who started the weaning from MV, of whom 1722 (14%) were patients with acute brain injury. In the acutely brain-injured cohort, 538 patients (31%) did not undergo planned extubation, defined as the need for a tracheostomy without an attempt of extubation, accidental extubation, and death. Among the 1184 planned extubated patients with acute brain injury, 202 required reintubation (17%). Patients with acute brain injury had a higher odds for unplanned extubation (odds ratio [OR] 1.35, confidence interval for 95% [CI 95%] 1.19-1.54; p < 0.001), a higher odds of failure after the first attempt of weaning (spontaneous breathing trial or gradual reduction of ventilatory support; OR 1.14 [CI 95% 1.01-1.30; p = 0.03]), and a higher odds for reintubation (OR 1.41 [CI 95% 1.20-1.66; p < 0.001]) than patients without brain injury. Patients with hemorrhagic stroke had the highest odds for unplanned extubation (OR 1.47 [CI 95% 1.22-1.77; p < 0.001]), of failed extubation after the first attempt of weaning (OR 1.28 [CI 95% 1.06-1.55; p = 0.009]), and for reintubation (OR 1.49 [CI 95% 1.17-1.88; p < 0.001]). In relation to weaning evolution over time in patients with acute brain injury, the risk for unplanned extubation showed a downward trend; the risk for reintubation was not associated to time; and there was a significant increase in the percentage of patients who underwent extubation after the first attempt of weaning from MV. CONCLUSIONS: Patients with acute brain injury, compared with patients without brain injury, present higher odds of undergoing unplanned extubated after weaning was started, lower odds of being extubated after the first attempt, and a higher risk of reintubation.

Simplified calculation of mechanical power for pressure controlled ventilation in Covid-19 ARDS patients.

BACKGROUND: Mechanical power (MP) is a promising tool for guidance of lung protective ventilation. Different equations have been proposed to calculate MP in pressure control ventilation (PCV). The aim of this study is to introduce an easy to use MP equation MPpcv(m-simpl) and compare it to an equation proposed by Van der Meijden et al. (MPpcv) which considered as the reference equation in PCV. METHODS: Ventilatory parameters of 206 Covid-19 ARDS patients recorded between 24-72 hours after admission to intensive care unit. The PCV data from these patients were retrospectively investigated. MP in PCV was calculated with a modified equation (MPpcv(m-simpl)) derived from the equation (MPpcv) of Van der Meijden et al.: 0.098xRRx∆Vx(PEEP+∆Pinsp - 1). The results from MPpcv(slope), MPpcv(simpl), and MPpcv(m-simpl) were compared to MPpcv at 15 cmH2O ∙ s/L inspiratory resistance levels by univariable regression and Bland-Altman analysis. RESULTS: Inspiratory resistance levels at 15 cmH2O s/L was found to be correlated between the power values calculated by MPpcv(simpl)/MPpcv(m-simpl) and the MPpcv(slope)/MPpcv based on univariable logistic regression (R2≥98) analyses. In the comparison of all patients average MP values computed by the MPpcv(m-simpl) equation and the MPpcv reference equation. Bland-Altman analysis mean difference and p values at 15 cmH2O s/L inspiratory resistance values (J/min) were found to be MPpcv(m-simpl) vs MPpcv=-0,04 (P=0.014); MPpcv(slope) vs. MPpcv=0.63 (P<0.0001); MPpcv(simpl) vs. MPpcv=0.64 J/min (P<0.0001), respectively. CONCLUSIONS: The results of this study confirmed that the MPpcv(m-simpl) equation can be used easily to calculate MP at bedside in pressure control ventilated COVID-19 ARDS patients.

Driving Pressure Is a Risk Factor for ARDS in Mechanically Ventilated Subjects Without ARDS.

BACKGROUND: Driving pressure (ΔP) has been described as a risk factor for mortality in patients with ARDS. However, the role of ΔP in the outcome of patients without ARDS and on mechanical ventilation has received less attention. Our objective was to evaluate the association between ΔP on the first day of mechanical ventilation with the development of ARDS. METHODS: This was a post hoc analysis of a multicenter, prospective, observational, international study that included subjects who were on mechanical ventilation for > 12 h. Our objective was to evaluate the association between ΔP on the first day of mechanical ventilation with the development of ARDS. To assess the effect of ΔP, a logistic regression analysis was performed when adjusting for other potential risk factors. Validation of the results obtained was performed by using a bootstrap method and by repeating the same analyses at day 2. RESULTS: A total of 1,575 subjects were included, of whom 65 (4.1%) developed ARDS. The ΔP was independently associated with ARDS (odds ratio [OR] 1.12, 95% CI 1.07-1.18 for each cm H2O of ΔP increase, P < .001). The same results were observed at day 2 (OR 1.14, 95% CI 1.07-1.21; P < .001) and after bootstrap validation (OR 1.13, 95% CI 1.04-1.22; P < .001). When taking the prevalence of ARDS in the lowest quartile of ΔP (≤9 cm H2O) as a reference, the subjects with ΔP > 12-15 cm H2O and those with ΔP > 15 cm H2O presented a higher probability of ARDS (OR 3.65, 95% CI 1.32-10.04 [P = .01] and OR 7.31, 95% CI, 2.89-18.50 [P < .001], respectively). CONCLUSIONS: In the subjects without ARDS, a higher level of ΔP on the first day of mechanical ventilation was associated with later development of ARDS. (ClinicalTrials.gov registration NCT02731898.).

Comparison of Respiratory and Hemodynamic Parameters of COVID-19 and Non-COVID-19 ARDS Patients.

BACKGROUND: COVID-19 can cause a clinical spectrum from asymptomatic disease to life-threatening respiratory failure and acute respiratory distress syndrome (ARDS). There is an ongoing discussion whether the clinical presentation and ventilatory parameters are the same as typical ARDS or not. There is no clear understanding of how the hemodynamic parameters have been affected in COVID-19 ARDS patients. We aimed to compare hemodynamic and respiratory parameters of moderate and severe COVID-19 and non-COVID-19 ARDS patients. These patients were monitored with an advanced hemodynamic measurement system by the transpulmonary thermodilution method in prone and supine positions. PATIENTS AND METHODS: Data of 17 patients diagnosed with COVID-19 and 16 patients diagnosed with other types of diseases with moderate and severe ARDS, mechanically ventilated, placed in a prone position, had advanced hemodynamic measurements with PiCCO, and stayed in the intensive care unit for more than a week were analyzed retrospectively. Patient characteristics and arterial blood gases analysis recorded at admission and respiratory and advanced hemodynamic parameters during the first week were compared in prone and supine positions. RESULTS: No difference was observed in the respiratory parameters including respiratory system compliance between COVID-19 and non-COVD-19 patients in prone and supine positions. In comparison of advanced hemodynamic parameters in the first week of intensive care, the extravascular lung water and pulmonary vascular permeability indexes measured in supine position of COVID-19 ARDS patients were found to be significantly higher than non-COVID-19 patients. Duration of prone position was significantly longer in patients diagnosed with COVID-19 ARDS. CONCLUSIONS: The results of this study suggested that COVID-19 ARDS is a variant of typical ARDS with a different pathophysiology. HOW TO CITE THIS ARTICLE: Asar S, Acicbe Ö, Sabaz MS, Tontu F, Canan E, Cukurova Z, et al. Comparison of Respiratory and Hemodynamic Parameters of COVID-19 and Non-COVID-19 ARDS Patients. Indian J Crit Care Med 2021;25(6):704-708.

Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation.

BACKGROUND: Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid-base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters. METHODS: We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders. RESULTS: Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders. CONCLUSION: The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes. TRIAL REGISTRATION: NCT02731898 ( https://clinicaltrials.gov/ct2/show/NCT02731898 ), prospectively registered on April 8, 2016.

Evolution Over Time of Ventilatory Management and Outcome of Patients With Neurologic Disease.

OBJECTIVES: To describe the changes in ventilator management over time in patients with neurologic disease at ICU admission and to estimate factors associated with 28-day hospital mortality. DESIGN: Secondary analysis of three prospective, observational, multicenter studies. SETTING: Cohort studies conducted in 2004, 2010, and 2016. PATIENTS: Adult patients who received mechanical ventilation for more than 12 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among the 20,929 patients enrolled, we included 4,152 (20%) mechanically ventilated patients due to different neurologic diseases. Hemorrhagic stroke and brain trauma were the most common pathologies associated with the need for mechanical ventilation. Although volume-cycled ventilation remained the preferred ventilation mode, there was a significant (p < 0.001) increment in the use of pressure support ventilation. The proportion of patients receiving a protective lung ventilation strategy was increased over time: 47% in 2004, 63% in 2010, and 65% in 2016 (p < 0.001), as well as the duration of protective ventilation strategies: 406 days per 1,000 mechanical ventilation days in 2004, 523 days per 1,000 mechanical ventilation days in 2010, and 585 days per 1,000 mechanical ventilation days in 2016 (p < 0.001). There were no differences in the length of stay in the ICU, mortality in the ICU, and mortality in hospital from 2004 to 2016. Independent risk factors for 28-day mortality were age greater than 75 years, Simplified Acute Physiology Score II greater than 50, the occurrence of organ dysfunction within first 48 hours after brain injury, and specific neurologic diseases such as hemorrhagic stroke, ischemic stroke, and brain trauma. CONCLUSIONS: More lung-protective ventilatory strategies have been implemented over years in neurologic patients with no effect on pulmonary complications or on survival. We found several prognostic factors on mortality such as advanced age, the severity of the disease, organ dysfunctions, and the etiology of neurologic disease.

Propensity-Adjusted Comparison of Mortality of Elderly Versus Very Elderly Ventilated Patients.

BACKGROUND: The growing proportion of elderly intensive care patients constitutes a public health challenge. The benefit of critical care in these patients remains unclear. We compared outcomes in elderly versus very elderly subjects receiving mechanical ventilation. METHODS: In total, 5,557 mechanically ventilated subjects were included in our post hoc retrospective analysis, a subgroup of the VENTILA study. We divided the cohort into 2 subgroups on the basis of age: very elderly subjects (age ≥ 80 y; n = 1,430), and elderly subjects (age 65-79 y; n = 4,127). A propensity score on being very elderly was calculated. Evaluation of associations with 28-d mortality was done with logistic regression analysis. RESULTS: Very elderly subjects were clinically sicker as expressed by higher SAPS II scores (53 ± 18 vs 50 ± 18, P < .001), and their rates of plateau pressure < 30 cm H2O were higher, whereas other parameters did not differ. The 28-d mortality was higher in very elderly subjects (42% vs 34%, P < .001) and remained unchanged after propensity score adjustment (adjusted odds ratio 1.31 [95% CI 1.16-1.49], P < .001). CONCLUSIONS: Age was an independent and unchangeable risk factor for death in mechanically ventilated subjects. However, survival rates of very elderly subjects were > 50%. Denial of critical care based solely on age is not justified. (ClinicalTrials.gov registration NCT02731898.).

Is elective cancer surgery feasible during the lock-down period of the COVID-19 pandemic? Analysis of a single institutional experience of 404 consecutive patients.

BACKGROUND: We aimed to assess the feasibility and short-term clinical outcomes of surgical procedures for cancer at an institution using a coronavirus disease 2019 (COVID-19)-free surgical pathway during the peak phase of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. MATERIALS AND METHODS: This was a single-center study, including cancer patients from all surgical departments, who underwent elective surgical procedures during the first peak phase between March 10 and June 30, 2020. The primary outcomes were the rate of postoperative SARS-CoV-2 infection and 30-day pulmonary or non-pulmonary related morbidity and mortality associated with SARS-CoV-2 disease. RESULTS: Four hundred and four cancer patients fulfilling inclusion criteria were analyzed. The rate of patients who underwent open and minimally invasive procedures was 61.9% and 38.1%, respectively. Only one (0.2%) patient died during the study period due to postoperative SARS-CoV2 infection because of acute respiratory distress syndrome. The overall non-SARS-CoV2 related 30-day morbidity and mortality rates were 19.3% and 1.7%, respectively; whereas the overall SARS-CoV2 related 30-day morbidity and mortality rates were 0.2% and 0.2%, respectively. CONCLUSIONS: Under strict institutional policies and measures to establish a COVID-19-free surgical pathway, elective and emergency cancer operations can be performed with acceptable perioperative and postoperative morbidity and mortality.

Physical rehabilitation in Intensive Care Unit in acute respiratory distress syndrome patients with COVID-19.

BACKGROUND: The risk of muscle weakness is high in the survivors of acute respiratory distress syndrome with COVID-19 following discharge from intensive care unit. AIM: To evaluate the effects of early rehabilitation program in intensive care unit in patients with acute respiratory distress syndrome secondary to COVID-19. DESIGN: The design of the study is observational. SETTING: The setting of the study is inpatient. POPULATION: Thirty-five patients with acute respiratory distress syndrome secondary to COVID-19 were enrolled. METHODS: This study was performed in an intensive care unit of a university hospital. Early rehabilitation program consisting of passive or active range of motion exercises and neuromuscular electrical stimulation in addition to standard intensive care (N.=18) compared to standard intensive care (N.=17). Primary outcome was hand grip strength following discharge. RESULTS: Rehab group had higher prevalence of chronic pulmonary diseases and neurologic diseases. There was no difference in hand grip or manual muscle strength following discharge between rehab and non-rehab groups. No adverse event was noted. CONCLUSIONS: The results did not support the beneficial effects of early rehabilitation in intensive care unit on improving muscle strength. More patients with pulmonary and neurologic diseases in rehab group might impede the impact of rehabilitation on outcomes. On the other hand, these comorbidities underline the role and need of rehabilitation. It is safe both for the patients and the health care workers when necessary precautions are taken. CLINICAL REHABILITATION IMPACT: This study guide how to rehabilitate patients with acute respiratory distress syndrome with COVID-19 during intensive care unit in a safe way.

Appropriate use of tocilizumab in COVID-19 infection.

OBJECTIVE: This study aimed to describe the effectiveness and optimum use of tocilizumab (TCZ) treatment by the support of clinical, laboratory and radiologic observations. METHODS: All patients were followed up in the hospital with daily interleukin-6 (IL-6), C-reactive protein (CRP), ferritin, d-dimer, full blood count, and procalcitonin. Thoracic computed tomography (CT) was performed on admission, when oxygen support was necessary, and seven days after TCZ started. Disease course of the patients was grouped as severe or critical, according to their clinical, laboratory and radiologic evaluations. RESULTS: Forty-three patients were included: 70% were male; the median age was 64 years (minimum-maximum: 27-94); and six (14%) patients died. The median duration of oxygen support before the onset of TCZ was shorter among the severe patient group than the critical patient group (1 vs. 4 days, p < 0.001). Three cases of 21 (14%) who received TCZ in the ward were transferred to ICU, and none of them died. The levels of IL-6, CRP, ferritin, d-dimer, and procalcitonin were significantly lower in the severe cases group than the critical cases group (p = 0.025, p = 0.002, p = 0.008, p = 0.002, and p = 0.001, respectively). Radiological improvement was observed in severe cases on the seventh day of TCZ. Secondary bacterial infection was detected in 41% of critical cases, but none of the severe ones. CONCLUSION: Earlier use of TCZ in COVID-19 infection was beneficial for survival, length of hospitalization and duration of oxygen support. The recommendation for administration of TCZ was based on an increase in requirement of oxygen support, progression in thoracic CT, and elevation of inflammation markers, including IL-6, CRP, ferritin, and d-dimer, and decrease in % lymphocytes.

Bedside dynamic calculation of mechanical power: A validation study.

PURPOSE: To develop an equation to calculate the bedside dynamic mechanical power (MPdyn) for modern ventilators using the Work of Breathing ventilator (WOBv) parameter. MATERIALS AND METHODS: We developed an equation based on mechanical power values, which is equal to WOBv x minute volume. To measure mechanical power with this equation forty adult patients, hospitalized with the diagnosis of Acute Respiratory Distress Syndrome and underwent invasive mechanical ventilation, were used. To be able compare our results with Gattinoni's standart mechanical power equation (MPstd) the contribution of the PEEP was included in our equation. Then results obtained from MPdyn and MPstd were compared using univariable regression and Bland-Altman analysis. This comparison was performed at different I:E ratios, PEEP levels and tidal volumes. RESULTS: Analysis of the results for each condition showed that MPdyn and MPstd equation correlated with R2 ≥ 0.98. Additionally, there was no statistically significant difference between MPdyn and MPstd for patient power means were 0.04 J/min (p = .42) using Bland-Altman analysis. CONCLUSIONS: Physicians can easily calculate mechanical power by using MPdyn at the bedside of patients on volume control mode.

Inter-country variability over time in the mortality of mechanically ventilated patients.

PURPOSE: Variations in clinical characteristics and management and in the mortality of mechanically ventilated patients have not been sufficiently evaluated. We hypothesized that mortality shows a variability associated with country after adjustment for clinical characteristics and management. METHODS: Analysis of four studies carried out at 6-year intervals over an 18-year period. The studies included 26,024 patients (5183 in 1998, 4968 in 2004, 8108 in 2010, and 7765 in 2016) admitted to 1253 units from 38 countries. The primary outcome was 28-day mortality. We performed analyses using multilevel logistic modeling with mixed-random effects, including country as a random variable. To evaluate the effect of management strategies on mortality, a mediation analysis was performed. RESULTS: Adjusted 28-day mortality decreased significantly over time (first study as reference): 2004: odds ratio 0.82 (95% confidence interval [CI] 0.72-0.93); 2010: 0.63 (95% CI 0.53-0.75); 2016: 0.49 (95% CI 0.39-0.61). A protective ventilatory strategy and the use of continuous sedation mediated a moderate fraction of the effect of time on mortality in patients with moderate hypoxemia and without hypoxemia, respectively. Logistic multilevel modeling showed a significant effect of country on mortality: median odds ratio (MOR) in 1998: 2.02 (95% CI 1.57-2.48); in 2004: 1.76 (95% CI 1.47-2.06); in 2010: 1.55 (95% CI 1.37-1.74), and in 2016: 1.39 (95% CI 1.25-1.54). CONCLUSIONS: These findings suggest that country could contribute, independently of confounder variables, to outcome. The magnitude of the effect of country decreased over time. Clinical trials registered with http://www.clinicaltrials.gov (NCT02731898).

Bortezomib induced pulmonary toxicity: a case report and review of the literature.

Bortezomib is widely used in the treatment of Multiple Myeloma. While the most common side effects are neurological and gastrointestinal related complications, severe pulmonary problems are rarely described. The present case is a 72-year old male with multiple myeloma, who received Lenalidomide, Bortezomib, and Dexamethasone (RVD) combination regimen. He underwent 30 Gy palliative radiotherapy to the thoracic 5-9 and lumbar L1-3 vertebra due to pain and fracture risk. During the third cycle, he was admitted to hospital with dyspnea and dizziness. The thoracic CT revealed bilateral pleural effusions, a diffuse reticular pattern on the parenchyma, and ground-glass opacities that were compatible with drug-induced lung injury. The microbiological and molecular analysis excluded infectious disease, and lung biopsy confirmed the diagnosis of Bortezomib Lung Injury. The time from the first dose of Bortezomib to the lung injury was 57 days, and it was five days from the last dose of Bortezomib. His symptoms were refractory to IV steroids and supportive care. Our patient was lost despite steroids and intensive care support. Even Bortezomib induced lung injury is a rare adverse effect, based on high mortality rate, we would like to emphasize the clinical importance of this clinical scenario in light of the published literature and our presented case.

Easy prognostic assessment of concomitant organ failure in critically ill patients undergoing mechanical ventilation.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a life-threatening disease. We evaluated the prognostic utility of Model for End-stage Liver Disease excluding INR (MELD-XI) score for predicting mortality in a cohort of critically ill patients on mechanical ventilation. METHODS: In total, 11,091 mechanically ventilated patients were included in our post-hoc retrospective analysis, a subgroup of the VENTILA study (NCT02731898). Evaluation of associations with mortality was done by logistic and Cox regression analysis, an optimal cut-off was calculated using the Youden Index. We divided the cohort in two sub-groups based on their MELD-XI score at the optimal cut-off (12 score points). RESULTS: Peak-, plateau- and positive end-expiratory pressure were higher in patients with MELD-XI>12. Patients with MELD-XI>12 had higher driving pressures (14 ±â€¯6 cmH2O versus 13 ±â€¯6; p < 0.001). MELD-XI was associated with 28-day mortality after correction for relevant cofounders including SAPS II and ventilation pressures (HR 1.04 95%CI 1.03-1.05; p < 0.001. Patients with MELD-XI>12 evidenced both increased hospital (46% versus 27%; p < 0.001) and 28-day mortality (39% versus 22%). CONCLUSIONS: MELD-XI is independently associated with mortality and constitutes a useful and easily applicable tool for risk stratification in critically ill patients receiving mechanical ventilation. TRIAL REGISTRATION: NCT02731898, registered 4 April 2016.

Relationship between arterial oxygen tension and mortality of patients in intensive care unit on mechanical ventilation support.

BACKGROUND: Although there are studies demonstrating hyperoxia may be an independent risk factor for increased mortality and morbidity, this issue remains unclear. Our research then aimed to examine the relationship between arterial oxygen tension, arterial carbon dioxide tension, and in-hospital mortality of critically ill patients in intensive care unit (ICU). METHODS: After obtaining ethics committee approval, we analyzed a retrospective data of patients over the age of 18 who survived at least 24 hours in the ICU on mechanical ventilatory support between year 2008 and 2012. The demographic properties, mechanical ventilation, and blood gas parameters were studied. We defined hyperoxia group as PaO2 value of ≥120 mmHg and normoxemia group as PaO2 of 60-120 mmHg. Patients with PaCO2 value <30 mmHg were determined to have hypocapnia, those with 30-50 mmHg normocapnia, and those with >50 mmHg hypercapnia. RESULTS: Between 2008 and 2012, a total of 7689 patients were admitted to the ICU. Of 450 patients meeting the inclusion criteria of the study, 263 (58.4%) were male and 187 (41.6%) were female. Normoxia was observed in 232 (51.5%) patients and hyperoxia in 218 (48.5%) patients. The mean PaO2 was 16.2 kPa (121.50 mmHg), and FiO2 was 60%. 254 (56%) of the patients had died during the five-year period. There was no statistically significant difference in mortality between PaO2 levels and PCO2 levels (p>0.05). According to the classification of PaO2 and FiO2, there was no statistically significant difference in mortality (p>0.05) among patients. In addition, no statistically significant difference was found between the survival rates according to PCO2 classification (p=0.602, p>0.05). CONCLUSION: There was no significant association between mortality and oxygen and carbon dioxide of patients in ICU on mechanical ventilatory support.

The association between the APACHE-II scores and age groups for predicting mortality in an intensive care unit: a retrospective study.

BACKGROUND AND AIMS: In this study, we aimed to evaluate whether the age or the APACHE-II score was a better predictor of mortality in each group. The secondary objective was to investigate the factors affecting the mortality in each individual age group. METHODS: We designed this retrospective study between 2016-2017. Age groups were classified into 3 classes: Patients < 60 years were Group 1, patients between 60-70 years were Group 2, and patients > 70 years were Group 3. We recorded patients' age, ICU indication, demographic data, APACHE-II, ASA, length of hospital stays and mortality. RESULTS: We analysed 150 patients and reported mortality for 58 patients (38.7%). We did not detect any association between age and mortality for all groups. ASA, length of ICU stays and predicted mortality rate, were significantly higher for exitus patients (p < 0.001). The ROC curve for the APACHE-II score, with a cut-off point of 23, demonstrated 74.14% sensitivity, 60.87% specificity, an area under the curve (AUC) of 67.3%, with 4.5% standard deviation (SD). The ODDS ratio for APACHE-II scores was 4.459 (95% CI: 2.167-9.176). For the adjusted mortality rate, ROC analysis identified a cut-off of 60.8 with 70.69% sensitivity, 52.17% specificity, AUC of 61.2% and 4.6% SD. The ODDS ratio for the adjusted mortality rate was 2.631 (95% CI: 1.309-5.287). CONCLUSION: We could not demonstrate any correlation between age and mortality. We consider APACHE-II as a valuable scoring system to predict mortality. We do not consider age as a predictor of mortality. Therefore, we do not suggest its use as a sole prognostic marker in ICU patients.