Lower or Higher Oxygenation Targets for Acute Hypoxemic Respiratory Failure.

BACKGROUND: Patients with acute hypoxemic respiratory failure in the intensive care unit (ICU) are treated with supplemental oxygen, but the benefits and harms of different oxygenation targets are unclear. We hypothesized that using a lower target for partial pressure of arterial oxygen (Pao2) would result in lower mortality than using a higher target. METHODS: In this multicenter trial, we randomly assigned 2928 adult patients who had recently been admitted to the ICU (≤12 hours before randomization) and who were receiving at least 10 liters of oxygen per minute in an open system or had a fraction of inspired oxygen of at least 0.50 in a closed system to receive oxygen therapy targeting a Pao2 of either 60 mm Hg (lower-oxygenation group) or 90 mm Hg (higher-oxygenation group) for a maximum of 90 days. The primary outcome was death within 90 days. RESULTS: At 90 days, 618 of 1441 patients (42.9%) in the lower-oxygenation group and 613 of 1447 patients (42.4%) in the higher-oxygenation group had died (adjusted risk ratio, 1.02; 95% confidence interval, 0.94 to 1.11; P = 0.64). At 90 days, there was no significant between-group difference in the percentage of days that patients were alive without life support or in the percentage of days they were alive after hospital discharge. The percentages of patients who had new episodes of shock, myocardial ischemia, ischemic stroke, or intestinal ischemia were similar in the two groups (P = 0.24). CONCLUSIONS: Among adult patients with acute hypoxemic respiratory failure in the ICU, a lower oxygenation target did not result in lower mortality than a higher target at 90 days. (Funded by the Innovation Fund Denmark and others; HOT-ICU ClinicalTrials.gov number, NCT03174002.).

Lower vs Higher Oxygenation Target and Days Alive Without Life Support in COVID-19: The HOT-COVID Randomized Clinical Trial.

Importance: Supplemental oxygen is ubiquitously used in patients with COVID-19 and severe hypoxemia, but a lower dose may be beneficial. Objective: To assess the effects of targeting a Pao2 of 60 mm Hg vs 90 mm Hg in patients with COVID-19 and severe hypoxemia in the intensive care unit (ICU). Design, Setting, and Participants: Multicenter randomized clinical trial including 726 adults with COVID-19 receiving at least 10 L/min of oxygen or mechanical ventilation in 11 ICUs in Europe from August 2020 to March 2023. The trial was prematurely stopped prior to outcome assessment due to slow enrollment. End of 90-day follow-up was June 1, 2023. Interventions: Patients were randomized 1:1 to a Pao2 of 60 mm Hg (lower oxygenation group; n = 365) or 90 mm Hg (higher oxygenation group; n = 361) for up to 90 days in the ICU. Main Outcomes and Measures: The primary outcome was the number of days alive without life support (mechanical ventilation, circulatory support, or kidney replacement therapy) at 90 days. Secondary outcomes included mortality, proportion of patients with serious adverse events, and number of days alive and out of hospital, all at 90 days. Results: Of 726 randomized patients, primary outcome data were available for 697 (351 in the lower oxygenation group and 346 in the higher oxygenation group). Median age was 66 years, and 495 patients (68%) were male. At 90 days, the median number of days alive without life support was 80.0 days (IQR, 9.0-89.0 days) in the lower oxygenation group and 72.0 days (IQR, 2.0-88.0 days) in the higher oxygenation group (P = .009 by van Elteren test; supplemental bootstrapped adjusted mean difference, 5.8 days [95% CI, 0.2-11.5 days]; P = .04). Mortality at 90 days was 30.2% in the lower oxygenation group and 34.7% in the higher oxygenation group (risk ratio, 0.86 [98.6% CI, 0.66-1.13]; P = .18). There were no statistically significant differences in proportion of patients with serious adverse events or in number of days alive and out of hospital. Conclusion and Relevance: In adult ICU patients with COVID-19 and severe hypoxemia, targeting a Pao2 of 60 mm Hg resulted in more days alive without life support in 90 days than targeting a Pao2 of 90 mm Hg. Trial Registration: ClinicalTrials.gov Identifier: NCT04425031.

Assimilating flow and level data into an urban drainage surrogate model for forecasting flows and overflows.

It is crucial to be able to forecast flows and overflows in urban drainage systems to build good and effective real-time control and warning systems. Due to computational constraints, it may often be unfeasible to employ detailed 1D hydrodynamic models for real-time purposes, and surrogate models can be used instead. In rural hydrology, forecast models are usually built or calibrated using long historical time series of, for example, flow or level observations, but such series are typically not available for the ever-changing urban drainage systems. In the current study, we therefore used a fast, reservoir-based surrogate forecast model constructed from a 1D hydrodynamic urban drainage model. Thus, we did not rely directly on historical time series data. Forecast models should preferably be able to update their internal states based on observations to ensure the best initial conditions for each forecast. We therefore used the Ensemble Kalman filter to update the surrogate model before each forecast. Water level or flow observations were assimilated into the model either directly, or indirectly using rating curves. The model forecasts were validated against observed flows and overflows. The results showed that model updating improved the forecasts up to 2 h ahead, but also that updating using water level observations resulted in better flow forecasts than assimilation based on flow data. Furthermore, updating with water level observations was insensitive to changes in the noise formulation used for the Ensemble Kalman filter, meaning that the method is suitable for operational settings where there is often little time and data for fine-tuning.

Robust model for estimating pumping station characteristics and sewer flows from standard pumping station data.

Flow data represent crucial input for reliable diagnostics of sewer functions and identification of potential problems such as unwanted inflow and infiltration. Flow estimates from pumping stations, which are an integral part of most separate sewer systems, might help in this regard. A robust model and an associated optimization procedure is proposed for estimating inflow to a pumping station using only registered water levels in the pump sump and power consumption. The model was successfully tested on one month of data from a single upstream station. The model is suitable for identification of pump capacity and volume thresholds for switching the pump on and off. These are parameters which are required for flow estimation during periods with high inflows or during periods with flow conditions triggering pump switching on and off at frequencies close to the temporal resolution of monitored data. The model is, however, sensitive within the transition states between emptying and filling to observation errors in volume and on inflow/outflow variability.

Evaluating catchment response to artificial rainfall from four weather generators for present and future climate.

The technical lifetime of urban water infrastructure has a duration where climate change has to be considered when alterations to the system are planned. Also, models for urban water management are reaching a very high complexity level with, for example, decentralized stormwater control measures being included. These systems have to be evaluated under as close-to-real conditions as possible. Long term statistics (LTS) modelling with observational data is the most close-to-real solution for present climate conditions, but for future climate conditions artificial rainfall time series from weather generators (WGs) have to be used. In this study, we ran LTS simulations with four different WG products for both present and future conditions on two different catchments. For the present conditions, all WG products result in realistic catchment responses when it comes to the number of full flowing pipes and the number and volume of combined sewer overflows (CSOs). For future conditions, the differences in the WGs representation of the expectations to climate change is evident. Nonetheless, all future results indicate that the catchments will have to handle more events that utilize the full capacity of the drainage systems. Generally, WG products are relevant to use in planning of future changes to sewer systems.

Comparing the impact of time displaced and biased precipitation estimates for online updated urban runoff models.

When an online runoff model is updated from system measurements, the requirements of the precipitation input change. Using rain gauge data as precipitation input there will be a displacement between the time when the rain hits the gauge and the time where the rain hits the actual catchment, due to the time it takes for the rain cell to travel from the rain gauge to the catchment. Since this time displacement is not present for system measurements the data assimilation scheme might already have updated the model to include the impact from the particular rain cell when the rain data is forced upon the model, which therefore will end up including the same rain twice in the model run. This paper compares forecast accuracy of updated models when using time displaced rain input to that of rain input with constant biases. This is done using a simple time-area model and historic rain series that are either displaced in time or affected with a bias. The results show that for a 10 minute forecast, time displacements of 5 and 10 minutes compare to biases of 60 and 100%, respectively, independent of the catchments time of concentration.

Comparison of short-term rainfall forecasts for model-based flow prediction in urban drainage systems.

Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real-time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 h. The best performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times.

Biotransformation kinetics and sorption of cocaine and its metabolites and the factors influencing their estimation in wastewater.

The quantitative analysis of human urinary metabolites as biomarkers in wastewater streams has been used to estimate the rates of illicit drug use in the wider community. The primary underlying assumption in such studies is that a sample of wastewater is equivalent to a cumulative sample of urine. Drug metabolism in humans is predominantly enzymatically mediated, but these processes are not exclusive to the human body, and are found to occur in the environment and the sewer network. Understanding what happens to drugs and their urinary metabolites in the sewer system between the point of excretion and sampling is particularly important since it is possible that in-sewer transformation may influence final biomarker concentration. The present study uses batch experiments to measure and assess the biotransformation processes of cocaine and its two major human metabolites, benzoylecgonine and ecgonine methyl ester. The activated sludge modelling framework for xenobiotic organic micro-pollutants (ASM-X) is used for model structure identification and calibration. Biotransformation was observed to follow pseudo first-order kinetics. The biodegradation kinetics of cocaine, benzoylecgonine and ecgonine methyl ester is not significantly affected by the availability of dissolved oxygen. Results obtained in this study show that omitting in-pipe biotransformation affects the accuracy of back-calculated cocaine use estimates. This varies markedly depending on the in-sewer hydraulic retention time, total biomass concentration and the relative concentration of each metabolite. However, back-calculated cocaine use estimates derived from wastewater concentrations of benzoylecgonine and ecgonine methyl ester do show very close agreement if ex-vivo biotransformation of these compounds is considered.