Predictive Modeling of COVID-19 Intensive Care Unit Patient Flows and Nursing Complexity: A Monte Carlo Simulation Study.

This study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status. The simulation was performed 1000 times to generate a scenario, and the mean outcome was compared with the observed outcome. The model also allowed for a 7-day forecast of ICU occupancy. The simulation output had a good fit with the actual data, with an R2 of 0.998 and a root mean square error of 0.22. The study demonstrated the usefulness of the Monte Carlo simulation model for predicting the demand for ICU beds and could help optimize resource allocation during a pandemic. The model's extrinsic validity was confirmed using open data from the French Public Health Authority. This study provides a valuable tool for healthcare systems to anticipate and manage surges in ICU demand during pandemics.

Value of assessment of multivoxel proton chemical shift imaging to predict long term outcome in patients after out-of-hospital cardiac arrest: A preliminary prospective observational study.

BACKGROUND: Existing methods to predict recovery after out-of-hospital cardiac arrest (OHCA) lack of accuracy. The aim of this study was to determine whether quantitative proton chemical shift imaging (1H-CSI) during the subacute stage of OHCA can predict neurological outcome of such patients. METHODS: This monocentric prospective observational study was conducted in a Intensive Care Unit of a teaching hospital. Forty consecutive patients with OHCA were enrolled between January 1st 2011-December 31st 2013. Multivoxel 1H-CSI values were compared to structural magnetic resonance imaging (MRI) sequences (fluid-attenuated inversion recovery and diffusion-weighted imaging). Ratios of N-acetyl-aspartate (NAA) to creatine (Cr) and choline compounds were analyzed using region of interest in bilateral lenticular cores and thalami. The outcome evaluated was the Cerebral Performance Category (CPC) at 6 months, dichotomized as favorable (CPC 1-2) and unfavorable outcome (CPC 3-5). The performance was compared by area under the receiver operating characteristic (ROCAUC) curves analysis. RESULTS: Twenty nine OHCA had an interpretable MRI. Eight patients (28%) had favorable outcome at 6 months. The worst NAA/Cr in lenticular cores was the best 1H-CSI marker, with 80% sensitivity (95% confidence interval (CI), 57-94) and a 100% specificity (95% CI, 63-100) with a positive predictive value of 100%. Prognostic accuracy, as quantified by the ROCAUC, was higher with the worst NAA/Cr in lenticular cores (ROCAUC 0.88; 95% CI, 0.70-0.97) than with the structural MRI sequences. CONCLUSION: In this preliminary study we found that multivoxel 1H-CSI in lenticular cores was highly predictive of unfavorable outcome at 6 months.