Inhaled Amikacin to Prevent Ventilator-Associated Pneumonia.

BACKGROUND: Whether preventive inhaled antibiotics may reduce the incidence of ventilator-associated pneumonia is unclear. METHODS: In this investigator-initiated, multicenter, double-blind, randomized, controlled, superiority trial, we assigned critically ill adults who had been undergoing invasive mechanical ventilation for at least 72 hours to receive inhaled amikacin at a dose of 20 mg per kilogram of ideal body weight once daily or to receive placebo for 3 days. The primary outcome was a first episode of ventilator-associated pneumonia during 28 days of follow-up. Safety was assessed. RESULTS: A total of 850 patients underwent randomization, and 847 were included in the analyses (417 assigned to the amikacin group and 430 to the placebo group). All three daily nebulizations were received by 337 patients (81%) in the amikacin group and 355 patients (83%) in the placebo group. At 28 days, ventilator-associated pneumonia had developed in 62 patients (15%) in the amikacin group and in 95 patients (22%) in the placebo group (difference in restricted mean survival time to ventilator-associated pneumonia, 1.5 days; 95% confidence interval [CI], 0.6 to 2.5; P = 0.004). An infection-related ventilator-associated complication occurred in 74 patients (18%) in the amikacin group and in 111 patients (26%) in the placebo group (hazard ratio, 0.66; 95% CI, 0.50 to 0.89). Trial-related serious adverse effects were seen in 7 patients (1.7%) in the amikacin group and in 4 patients (0.9%) in the placebo group. CONCLUSIONS: Among patients who had undergone mechanical ventilation for at least 3 days, a subsequent 3-day course of inhaled amikacin reduced the burden of ventilator-associated pneumonia during 28 days of follow-up. (Funded by the French Ministry of Health; AMIKINHAL ClinicalTrials.gov number, NCT03149640; EUDRA Clinical Trials number, 2016-001054-17.).

Prevalence of post-acute coronavirus disease 2019 symptoms twelve months after hospitalization in participants retained in follow-up: analyses stratified by gender from a large prospective cohort.

OBJECTIVES: Persistent post-acute coronavirus disease 2019 (COVID-19) symptoms (PACSs) have been reported up to 6 months after hospital discharge. Herein we assessed the symptoms that persisted 12 months (M12) after admission for COVID-19 in the longitudinal prospective national French coronavirus disease cohort. METHODS: Hospitalized patients with a confirmed virological diagnosis of COVID-19 were enrolled. Follow-up was planned until M12 after admission. Associations between persistence of ≥3 PACSs at M12 and clinical characteristics at admission were assessed through logistic regression according to gender. RESULTS: We focused on participants enrolled between 24 January 2020 and 15 July 2020, to allow M12 follow-up. The M12 data were available for 737 participants. Median age was 61 years, 475 (64%) were men and 242/647 (37%) were admitted to intensive care units during the acute phase. At M12, 27% (194/710) of the participants had ≥3 persistent PACS, mostly fatigue, dyspnoea and joint pain. Among those who had a professional occupation before the acute phase, 91 out of 339 (27%) were still on sick leave at M12. Presence of ≥3 persistent PACS was associated with female gender, both anxiety and depression, impaired health-related quality of life and Medical Muscle Research Council Scale <57. Compared with men, women more often reported presence of ≥3 persistent PACSs (98/253, 39% vs. 96/457, 21%), depression and anxiety (18/152, 12% vs. 17/268, 6% and 33/156, 21% vs. 26/264, 10%, respectively), impaired physical health-related quality of life (76/141, 54% vs. 120/261, 46%). Women had less often returned to work than men (77/116, 66% vs. 171/223, 77%). CONCLUSIONS: One fourth of the individuals admitted to hospital for COVID-19 still had ≥3 persistent PACSs at M12 post-discharge. Women reported more often ≥3 persistent PACSs, suffered more from anxiety and depression and had less often returned to work than men.

A Hybrid Model Associating Population Pharmacokinetics with Machine Learning: A Case Study with Iohexol Clearance Estimation.

BACKGROUND: Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic model is frequently used to estimate pharmacokinetic parameters in individuals, however with some uncertainty (bias). Recent works have shown that the performance in individual estimation or pharmacokinetic parameters can be improved by combining population pharmacokinetic and machine learning algorithms. OBJECTIVE: The objective of this work was to investigate the use of a hybrid machine learning/population pharmacokinetic approach to improve individual iohexol clearance estimation. METHODS: The reference iohexol clearance values were derived from 500 simulated profiles (samples collected between 0.1 and 24.7 h) using a population pharmacokinetic model we recently developed in Monolix and obtained using all the concentration timepoints available. Xgboost and glmnet algorithms able to predict the error of MAP-BE clearance estimates based on a limited sampling strategy (0.1 h, 1 h, and 9 h) versus reference values were developed in a training subset (75%) and were evaluated in a testing subset (25%) and in 36 real patients. RESULTS: The MAP-BE limited sampling strategy estimated clearance was corrected by the machine learning predicted error leading to a decrease in root mean squared error by 29% and 24%, and in the percentage of profiles with the mean prediction error out of the ± 20% bias by 60% and 40% in the external validation dataset for the glmnet and Xgboost machine learning algorithms, respectively. These results were attributable to a decrease in the eta-shrinkage (shrinkage for a MAP-BE limited sampling strategy = 32.4%, glmnet = 18.2%, and Xgboost = 19.4% in the external dataset). CONCLUSIONS: In conclusion, this hybrid algorithm represents a significant improvement in comparison to MAP-BE estimation alone.