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Purpose To use natural language processing (NLP) to study the incidence, characteristics, trajectory, associations, and outcomes of hallucinations and disturbed behaviour in intensive care unit (ICU) patients. Methods We used NLP to scan clinical progress notes of a large cohort of ICU patients to detect words indicating that a patient had experienced hallucinations. We also used NLP to detected disturbed behaviour during ICU stay. Moreover, we studied the use of antipsychotic medications in a nested cohort. Finally, we obtained the demographics, trajectory, associations, and outcome of these patients. Results We conducted a non-interventional, observational study of 7525 patients. We found that 625 (8.31%) had experienced hallucinations. Among these, 623 (99.7%) also had NLP-diagnosed behavioural disturbance (NLP-Dx-BD). In contrast, in patients without hallucinations, only 3274 (47.4%) were NLP-Dx-BD positive. Among the 2904 nested cohort patients with electronic medications data, 252 (8.7%) experienced hallucinations. Of these, 60 (23.8%) received medications compared with 147 (5.5%) ( p  < 0.001) patients without hallucinations. There was no difference on outcomes in patients with or without hallucination. Conclusions Hallucinations affect one in 12 ICU patients and are strongly associated with disturbed behaviour, and the use of antipsychotic medications. Hallucinations may represent another phenotype of critical illness associated neurocognitive dysfunction and require a dedicated research program.

Importance Maneuvers assessing fluid responsiveness before an intravascular volume expansion may limit useless fluid administration, which in turn may improve outcomes. Objective To describe maneuvers for assessing fluid responsiveness in mechanically ventilated patients. Registration The protocol was registered at PROSPERO: CRD42019146781. Information sources and search PubMed, EMBASE, CINAHL, SCOPUS, and Web of Science were search from inception to 08/08/2023. Study selection and data collection Prospective and intervention studies were selected . Statistical analysis Data for each maneuver were reported individually and data from the five most employed maneuvers were aggregated. A traditional and a Bayesian meta-analysis approach were performed. Results A total of 69 studies, encompassing 3185 fluid challenges and 2711 patients were analyzed. The prevalence of fluid responsiveness was 49.9%. Pulse pressure variation (PPV) was studied in 40 studies, mean threshold with 95% confidence intervals (95% CI) = 11.5 (10.5–12.4)%, and area under the receiver operating characteristics curve (AUC) with 95% CI was 0.87 (0.84–0.90). Stroke volume variation (SVV) was studied in 24 studies, mean threshold with 95% CI = 12.1 (10.9–13.3)%, and AUC with 95% CI was 0.87 (0.84–0.91). The plethysmographic variability index (PVI) was studied in 17 studies, mean threshold = 13.8 (12.3–15.3)%, and AUC was 0.88 (0.82–0.94). Central venous pressure (CVP) was studied in 12 studies, mean threshold with 95% CI = 9.0 (7.7–10.1) mmHg, and AUC with 95% CI was 0.77 (0.69–0.87). Inferior vena cava variation (∆IVC) was studied in 8 studies, mean threshold = 15.4 (13.3–17.6)%, and AUC with 95% CI was 0.83 (0.78–0.89). Conclusions Fluid responsiveness can be reliably assessed in adult patients under mechanical ventilation. Among the five maneuvers compared in predicting fluid responsiveness, PPV, SVV, and PVI were superior to CVP and ∆IVC. However, there is no data supporting any of the above mentioned as being the best maneuver. Additionally, other well-established tests, such as the passive leg raising test, end-expiratory occlusion test, and tidal volume challenge, are also reliable.

Background Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients. Methods We obtained electronic clinical notes, demographic information, outcomes, and treatment data from three medical-surgical ICUs. Using NLP, we screened for behavioural disturbance phenotypes based on words suggestive of an agitated state, a non-agitated state, or a combination of both. Results We studied 2931 patients. Of these, 225 (7.7%) were NLP-Dx-BD positive for the agitated phenotype, 544 (18.6%) for the non-agitated phenotype and 667 (22.7%) for the combined phenotype. Patients with these phenotypes carried multiple clinical baseline differences. On time-dependent multivariable analysis to compensate for immortal time bias and after adjustment for key outcome predictors, agitated phenotype patients were more likely to receive antipsychotic medications (odds ratio [OR] 1.84, 1.35–2.51, p  < 0.001) compared to non-agitated phenotype patients but not compared to combined phenotype patients (OR 1.27, 0.86–1.89, p  = 0.229). Moreover, agitated phenotype patients were more likely to die than other phenotypes patients (OR 1.57, 1.10–2.25, p  = 0.012 vs non-agitated phenotype; OR 4.61, 2.14–9.90, p  < 0.001 vs. combined phenotype). This association was strongest in patients receiving mechanical ventilation when compared with the combined phenotype (OR 7.03, 2.07–23.79, p  = 0.002). A similar increased risk was also seen for patients with the non-agitated phenotype compared with the combined phenotype (OR 6.10, 1.80–20.64, p  = 0.004). Conclusions NLP-Dx-BD screening enabled identification of three behavioural disturbance phenotypes with different characteristics, prevalence, trajectory, treatment, and outcome. Such phenotype identification appears relevant to prognostication and trial design. Graphical abstract

[...]in this pandemic setting, the ethics of ensuring compassionate drug use to all patients were considered a priority. [...]before considering controlled trials, evidence of some physiological safety was considered important. [...]under the extraordinary pressures of the most dramatic health disaster in Italy’s history in a century, this study was the best possible under the circumstances. [...]we provide the first observational cohort study of ANGII infusion in ventilated patients with COVID-19-associated vasodilatory shock.

Readmission to the intensive care unit (ICU) is associated with poor clinical outcomes, increased length of ICU and hospital stay, and higher costs. Nevertheless, knowledge of epidemiology of ICU readmissions, risk factors, and attributable outcomes is restricted to developed countries. To determine the effect of ICU readmissions on in-hospital mortality, determine incidence of ICU readmissions, identify predictors of ICU readmissions and hospital mortality, and compare resource use and outcomes between readmitted and nonreadmitted patients in a developing country. This retrospective single-center cohort study was conducted in a 40-bed, open medical-surgical ICU of a private, tertiary care hospital in São Paulo, Brazil. The Local Ethics Committee at Hospital Israelita Albert Einstein approved the study protocol, and the need for informed consent was waived. All consecutive adult (≥18 yr) patients admitted to the ICU between June 1, 2013 and July 1, 2015 were enrolled in this study. Comparisons were made between patients readmitted and not readmitted to the ICU. Logistic regression analyses were performed to identify predictors of ICU readmissions and hospital mortality. Out of 5,779 patients admitted to the ICU, 576 (10%) were readmitted to the ICU during the same hospitalization. Compared with nonreadmitted patients, patients readmitted to the ICU were more often men (349 of 576 patients [60.6%] vs. 2,919 of 5,203 patients [56.1%]; P = 0.042), showed a higher (median [interquartile range]) severity of illness (Simplified Acute Physiology III score) at index ICU admission (50 [41-61] vs. 42 [32-54], respectively, for readmitted and nonreadmitted patients; P < 0.001), and were more frequently admitted due to medical reasons (425 of 576 [73.8%] vs. 2,998 of 5,203 [57.6%], respectively, for readmitted and nonreadmitted patients; P < 0.001). Simplified Acute Physiology III score (P < 0.001), ICU admission from the ward (odds ratio [OR], 1.907; 95% confidence interval [CI], 1.463-2.487; P < 0.001), vasopressors need during index ICU stay (OR, 1.391; 95% CI, 1.130-1.713; P = 0.002), and length of ICU stay (P = 0.001) were independent predictors of ICU readmission. After adjusting for severity of illness, ICU readmission (OR, 4.103; 95% CI, 3.226-5.518; P < 0.001), admission source, presence of cancer, use of vasopressors, mechanical ventilation or renal replacement therapy, length of ICU stay, and nighttime ICU discharge were associated with increased risk of in-hospital death. Readmissions to the ICU were frequent and strongly related to poor outcomes. The degree to which ICU readmissions are preventable as well as the main causes of preventable ICU readmissions need to be further determined.

Studies in patients receiving invasive ventilation show important differences in use of low tidal volume (V.sub.T) ventilation (LTVV) between females and males. The aims of this study were to describe temporal changes in V.sub.T and to determine what factors drive the sex difference in use of LTVV. This is a posthoc analysis of 2 large longitudinal projects in 59 ICUs in the United States, the 'Medical information Mart for Intensive Care III' (MIMIC III) and the 'eICU Collaborative Research DataBase'. The proportion of patients under LTVV (median V.sub.T < 8 ml/kg PBW), was the primary outcome. Mediation analysis, a method to dissect total effect into direct and indirect effects, was used to understand which factors drive the sex difference. We included 3614 (44%) females and 4593 (56%) males. Median V.sub.T declined over the years, but with a persistent difference between females (from median 10.2 (9.1 to 11.4) to 8.2 (7.5 to 9.1) ml/kg PBW) vs. males (from median 9.2 [IQR 8.2 to 10.1] to 7.3 [IQR 6.6 to 8.0] ml/kg PBW) (P < .001). In females versus males, use of LTVV increased from 5 to 50% versus from 12 to 78% (difference, -27% [-29% to -25%]; P < .001). The sex difference was mainly driven by patients' body height and actual body weight (adjusted average causal mediation effect, -30% [-33% to -27%]; P < .001, and 4 [3% to 4%]; P < .001). While LTVV is increasingly used in females and males, females continue to receive LTVV less often than males. The sex difference is mainly driven by patients' body height and actual body weight, and not necessarily by sex. Use of LTVV in females could improve by paying more attention to a correct calculation of V.sub.T, i.e., using the correct body height.

The Coronavirus Disease 2019 (COVID-19) outbreak is evolving rapidly worldwide. Data on the mobility level of patients with COVID-19 in the intensive care unit (ICU) are needed. To describe the mobility level of patients with COVID-19 admitted to the ICU and to address factors associated with mobility level at the time of ICU discharge. Single center, retrospective cohort study. Consecutive patients admitted to the ICU with confirmed COVID-19 infection were analyzed. The mobility status was assessed by the Perme Score at admission and discharge from ICU with higher scores indicating higher mobility level. The Perme Mobility Index (PMI) was calculated [PMI = ΔPerme Score (ICU discharge-ICU admission)/ICU length of stay]. Based on the PMI, patients were divided into two groups: \"Improved\" (PMI > 0) and \"Not improved\" (PMI ≤ 0). A total of 136 patients were included in this analysis. The hospital mortality rate was 16.2%. The Perme Score improved significantly when comparing ICU discharge with ICU admission [20.0 (7-28) points versus 7.0 (0-16) points; P < 0.001]. A total of 88 patients (64.7%) improved their mobility level during ICU stay, and the median PMI of these patients was 1.5 (0.6-3.4). Patients in the improved group had a lower duration of mechanical ventilation [10 (5-14) days versus 15 (8-24) days; P = 0.021], lower hospital length of stay [25 (12-37) days versus 30 (11-48) days; P < 0.001], and lower ICU and hospital mortality rate. Independent predictors for mobility level were lower age, lower Charlson Comorbidity Index, and not having received renal replacement therapy. Patients' mobility level was low at ICU admission; however, most patients improved their mobility level during ICU stay. Risk factors associated with the mobility level were age, comorbidities, and use of renal replacement therapy.

Clinical trials focusing on critically ill patients with metabolic acidosis, a common exclusion criterion is the presence of a PaCO2 > 45 mmHg. The aim of this study was to assess the impact of mild hypercapnia on patient characteristics, severity, and clinical outcomes in critically ill patients with metabolic acidosis. Multicentre, retrospective, observational study conducted in 12 intensive care units (ICUs) in Queensland, Australia. Patients with metabolic acidosis and concurrent vasopressor requirement were included and the exposure of interest was the PaCO2 level at the time of meeting the eligibility criteria divided in two groups: PaCO2 ≤ 45 mmHg and PaCO2 46–50 mmHg. Primary clinical outcome was major adverse kidney events within 30 days (MAKE30). We studied 5601 patients, with 3605 (64.4 %) in the PaCO2 ≤ 45 mmHg group and 1996 (35.6 %) in the PaCO2 46–50 mmHg group. The incidence of MAKE30 was lower in the PaCO2 46–50 mmHg group (29 % vs. 34 %; OR, 0.79 [95 %CI, 0.69 to 0.90]; p < 0.001) as was the use of renal replacement therapy, and the incidence of acute kidney injury. After adjustment for confounders, no outcome was different between the groups. The maximum fall of pH associated with an increase of 1 mmHg of PaCO2 in the PaCO2 46–50 mmHg group was 0.006. In patients with metabolic acidosis, after adjustment for potential confounders, mild hypercapnia does not increase the MAKE-30 rate and does not have a major impact on pH. •Patients with mild hypercapnia and metabolic acidosis have different characteristics.•Patients with mild hypercapnia and metabolic acidosis have different outcomes.•The decrease in pH with mild hypercapnia is small and appeared of limited clinical relevance.

Mechanical power of ventilation, a summary parameter reflecting the energy transferred from the ventilator to the respiratory system, has associations with outcomes. INTELLiVENT-Adaptive Support Ventilation is an automated ventilation mode that changes ventilator settings according to algorithms that target a low work-and force of breathing. The study aims to compare mechanical power between automated ventilation by means of INTELLiVENT-Adaptive Support Ventilation and conventional ventilation in critically ill patients. International, multicenter, randomized crossover clinical trial in patients that were expected to need invasive ventilation > 24 hours. Patients were randomly assigned to start with a 3-hour period of automated ventilation or conventional ventilation after which the alternate ventilation mode was selected. The primary outcome was mechanical power in passive and active patients; secondary outcomes included key ventilator settings and ventilatory parameters that affect mechanical power. A total of 96 patients were randomized. Median mechanical power was not different between automated and conventional ventilation (15.8 [11.5-21.0] versus 16.1 [10.9-22.6] J/min; mean difference -0.44 (95%-CI -1.17 to 0.29) J/min; P = 0.24). Subgroup analyses showed that mechanical power was lower with automated ventilation in passive patients, 16.9 [12.5-22.1] versus 19.0 [14.1-25.0] J/min; mean difference -1.76 (95%-CI -2.47 to -10.34J/min; P < 0.01), and not in active patients (14.6 [11.0-20.3] vs 14.1 [10.1-21.3] J/min; mean difference 0.81 (95%-CI -2.13 to 0.49) J/min; P = 0.23). In this cohort of unselected critically ill invasively ventilated patients, automated ventilation by means of INTELLiVENT-Adaptive Support Ventilation did not reduce mechanical power. A reduction in mechanical power was only seen in passive patients. Clinicaltrials.gov (study identifier NCT04827927), April 1, 2021. https://clinicaltrials.gov/study/NCT04827927?term=intellipower&rank=1.