Dissipation-induced symmetry breaking in a driven optical lattice

We analyze the atomic dynamics in an ac driven periodic optical potential which is symmetric in both time and space. We experimentally demonstrate that in the presence of dissipation the symmetry is broken, and a current of atoms through the optical lattice is generated as a result

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury:A Systematic Review

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.</p

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury:A Systematic Review

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.</p

Circular material flow in the intensive care unit-environmental effects and identification of hotspots

PurposeThe healthcare sector is responsible for 6–7% of CO2 emissions. The intensive care unit (ICU) contributes to these CO2 emissions and a shift from a linear system to a circular system is needed. The aim of our research was to perform a material flow analysis (MFA) in an academic ICU. Secondary aims were to obtain information and numbers on mass, carbon footprint, agricultural land occupation and water usage and to determine so-called “environmental hotspots” in the ICU.MethodsA material flow analysis was performed over the year 2019, followed by an environmental footprint analysis of materials and environmental hotspot identification.Results2839 patients were admitted to our ICU in 2019. The average length of stay was 4.6 days. Our MFA showed a material mass inflow of 247,000 kg in 2019 for intensive care, of which 50,000 kg is incinerated as (hazardous) hospital waste. The environmental impact per patient resulted in 17 kg of mass, 12 kg CO2 eq, 300 L of water usage and 4 m2 of agricultural land occupation per day. Five hotspots were identified: non-sterile gloves, isolation gowns, bed liners, surgical masks and syringes (including packaging).ConclusionThis is the first material flow analysis that identified environmental risks and its magnitude in the intensive care unit

Failure of target attainment of beta-lactam antibiotics in critically ill patients and associated risk factors: a two-center prospective study (EXPAT)

Background: Early and appropriate antibiotic dosing is associated with improved clinical outcomes in critically ill patients, yet target attainment remains a challenge. Traditional antibiotic dosing is not suitable in critically ill patients, since these patients undergo physiological alterations that strongly affect antibiotic exposure. For betalactam antibiotics, the unbound plasma concentrations above at least one to four times the minimal inhibitory concentration (MIC) for 100% of the dosing interval (100%ƒT>1–4×MIC) have been proposed as pharmacodynamic targets (PDTs) to maximize bacteriological and clinical responses. The objectives of this study are to describe the PDT attainment in critically ill patients and to identify risk factors for target non-attainment. Methods: This prospective observational study was performed in two ICUs in the Netherlands. We enrolled adult patients treated with the following beta-lactam antibiotics: amoxicillin (with or without clavulanic acid), cefotaxime, ceftazidime, ceftriaxone, cefuroxime, and meropenem. Based on five samples within a dosing interval at day 2 of therapy, the time unbound concentrations above the epidemiological cut-off (ƒT > MICECOFF and ƒT > 4×MICECOFF) were determined. Secondary endpoints were estimated multivariate binomial and binary logistic regression models, for examining the association of PDT attainment with patient characteristics and clinical outcomes. Results: A total of 147 patients were included, of whom 63.3% achieved PDT of 100%ƒT > MICECOFF and 36.7% achieved 100%ƒT > 4×MICECOFF. Regression analysis identified male gender, estimated glomerular filtration rate (eGFR) ≥ 90 mL/min/1.73 m2 , and high body mass index (BMI) as risk factors for target non-attainment. Use of continuous renal replacement therapy (CRRT) and high serum urea significantly increased the probability of target attainment. In addition, we found a significant association between the 100%ƒT > MICECOFF target attainment and ICU length of stay (LOS), but no significant correlation was found for the 30-day survival. Conclusions: Traditional beta-lactam dosing results in low target attainment in the majority of critically ill patients. Male gender, high BMI, and high eGFR were significant risk factors for target non-attainment. These predictors, together with therapeutic drug monitoring, may help ICU clinicians in optimizing beta-lactam dosing in critically ill patients

Post COVID-19 Pandemic Increased Detection of Mycoplasma Pneumoniae in Adults Admitted to the Intensive Care

Background: Mycoplasma pneumoniae (M. pneumoniae) infections can progress to severe respiratory complications, necessitating intensive care treatment. Recent post COVID-19 pandemic surges underscore the need for timely diagnosis, given potential diagnostic method limitations. Methods: A retrospective case series analysis was conducted on M. pneumonia PCR-positive patients admitted to two Dutch secondary hospitals’ ICUs between January 2023 and February 2024. Clinical presentations, treatments, outcomes, and mechanical ventilation data were assessed. Results: Seventeen ICU-admitted patients were identified, with a median age of 44 years, primarily due to hypoxia. Non-invasive ventilation was effective for most, while five required invasive mechanical ventilation. None of the patients required extracorporeal membrane oxygenation. No fatalities occurred. Post-PCR, treatment was adjusted to doxycycline or azithromycin; seven received steroid treatment. Discussion: Increased ICU admissions for M. pneumoniae infection were observed. Diverse clinical and radiological findings emphasize heightened clinical awareness. Early molecular diagnostics and tailored antibiotic regimens are crucial since beta-lactam antibiotics are ineffective. Conclusion: This study highlights the escalating challenge of severe M. pneumoniae infections in ICUs, necessitating a multifaceted approach involving accurate diagnostics, vigilant monitoring, and adaptable treatment strategies for optimal patient outcomes.</p