Mar 23
Mar 23
Background: Acute kidney injury (AKI) is common in critically ill patients admitted to intensive care units (ICU) and is frequently associated with poorer outcomes. Hence, if an indicator is available for predicting severe AKI within the first few hours of admission, management strategies can be put into place to improve outcomes. Materials and methods: This was a prospective, observational study, involving 63 critically ill patients, that aimed to explore the diagnostic accuracy of different Doppler parameters in predicting AKI in critically ill patients from a mixed ICU. Participants were enrolled at ICU admission. All underwent ultrasonographic examinations and hemodynamic assessment. Renal Doppler resistive index (RDRI), venous impedance index (VII), arterial systolic time intervals (a-STI), and venous systolic time intervals (v-STI) were measured within 2 h from ICU admission. Results: Cox proportional hazards models, including a-STI, v-STI, VII, and RDRI as independent variables, returned a-STI as the only putative predictor for the development of AKI or severe AKI. An overall statistically significant difference (p < 0.001) was observed in the Kaplan–Meier plots for cumulative AKI events between patients with a-STI higher or equal than 0.37 and for cumulative severe AKI-3 between patients with a-STI higher or equal than 0.63. As assessed by the area under the receiver operating curves (ROC) curves, a-STI performed best in diagnosing any AKI and/or severe AKI-3. Positive correlations were found between a-STI and the N-terminal brain natriuretic peptide precursor (NT-pro BNP) (ρ = 0.442, p < 0.001), the sequential organ failure assessment (SOFA) score (ρ: 0.361, p = 0.004), and baseline serum creatinine (ρ: 0.529, p < 0.001). Conclusions: Critically ill patients who developed AKI had statistically significant different a-STI (on admission to ICU), v-STI, and VII than those who did not. Moreover, a-STI was associated with the development of AKI at day 5 and provided the best diagnostic accuracy for the diagnosis of any AKI or severe AKI compared with RDRI, VII, and v-STI.
Feb 23
Feb 23
Feb 23
Jan 23
Mechanical ventilation is a lifesaving treatment for critically ill patients in an Intensive Care Unit (ICU) or during surgery. However, one potential harm of mechanical ventilation is related to patient–ventilator asynchrony (PVA). PVA can cause discomfort to the patient, damage to the lungs, and an increase in the length of stay in the ICU and on the ventilator. Therefore, automated detection algorithms are being developed to detect and classify PVAs, with the goal of optimizing mechanical ventilation. However, the development of these algorithms often requires large labeled datasets; these are generally difficult to obtain, as their collection and labeling is a time-consuming and labor-intensive task, which needs to be performed by clinical experts.
In this work, we aimed to develop a computer algorithm for the automatic detection and classification of PVA. The algorithm employs a neural network for the detection of the breath of the patient. The development of the algorithm was aided by simulations from a recently published model of the patient-ventilator interaction.
The proposed method was effective, providing an algorithm with reliable detection and classification results of over 90% accuracy. Besides presenting a detection and classification algorithm for a variety of PVAs, here we show that using simulated data in combination with clinical data increases the variability in the training dataset, leading to a gain in performance and generalizability.
In the future, these algorithms can be utilized to gain a better understanding of the clinical impact of PVAs and help clinicians to better monitor their ventilation strategies.
Jan 23
Jan 23
Dec 22
Few studies have reported the implications and adverse events of performing endotracheal intubation for critically ill COVID-19 patients admitted to intensive care units. The aim of the present study was to determine the adverse events related to tracheal intubation in COVID-19 patients, defined as the onset of hemodynamic instability, severe hypoxemia, and cardiac arrest.
Tertiary care medical hospitals, dual-centre study performed in Northern Italy from November 2020 to May 2021.
Adult patients with positive SARS-CoV-2 PCR test, admitted for respiratory failure and need of advanced invasive airways management.
Endotracheal Intubation Adverse Events.
The primary endpoint was to determine the occurrence of at least 1 of the following events within 30 minutes from the start of the intubation procedure and to describe the types of major adverse peri-intubation events: severe hypoxemia defined as an oxygen saturation as measured by pulse-oximetry <80%; hemodynamic instability defined as a SBP 65 mmHg recoded at least once or SBP < 90 mmHg for 30 minutes, a new requirement or increase of vasopressors, fluid bolus >15 mL/kg to maintain the target blood pressure; cardiac arrest.
Among 142 patients, 73.94% experienced at least one major adverse peri-intubation event. The predominant event was cardiovascular instability, observed in 65.49% of all patients undergoing emergency intubation, followed by severe hypoxemia (43.54%). 2.82% of the patients had a cardiac arrest.
In this study of intubation practices in critically ill patients with COVID-19, major adverse peri-intubation events were frequent.
Dec 22