The reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for COVID-19 diagnosis among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model’s performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis.
To assess the neurological prognosis of comatose survivors of cardiac arrest by early transcranial Doppler sonography (TCD). Methods: This was a prospective study performed between May 2016 and October 2017 in a medical intensive care unit (ICU) and a cardiac ICU of a university teaching hospital. All patients older than 18 years who were successfully resuscitated from an out-of-hospital cardiac arrest (OHCA) with persistent coma after the return of spontaneous circulation (ROSC) were eligible. We excluded patients for whom OHCA was associated with traumatic brain injury, no possibility of TCD measurements, or who were dead before establishing the neurological prognosis. We measured the pulsatility index (PI) and diastolic flow velocity (DFV) of the right and left middle cerebral arteries within 12 h after ICU admission. The lowest DFV and highest PI values were used for the statistical analysis. The neurological outcome at hospital discharge was evaluated by the cerebral performance category. Results: Forty-two patients were included in the final analysis: 15 had good and 27 poor neurological outcomes. The PI was higher in the poor outcome (1.49 vs. 1.12, p = 0.01) than good outcome group and the DFV was lower in the poor outcome group (17.3 cm.s-1 vs. 26.0 cm.s-1 ; p = 0.01). Conclusion: Data provided by early TCD after ROSC are associated with neurological outcome. The use of TCD could help guide interventions to improve cerebral perfusion after ROSC in patients resuscitated from OHCA.
Objectives: Unhealthy use of alcohol and acute kidney injury are major public health problems, but little is known about the impact of excessive alcohol consumption on kidney function in critically ill patients. We aimed to determine whether at-risk drinking is independently associated with acute kidney injury in the ICU and at ICU discharge. Design: Prospective observational cohort study. Setting: A 21-bed polyvalent ICU in a university hospital. Patients: A total of 1,107 adult patients admitted over a 30-month period who had an ICU stay of greater than or equal to 3 days and in whom alcohol consumption could be assessed. Interventions: None. Measurements and Main Results: We assessed Kidney Disease Improving Global Outcomes stages 2–3 acute kidney injury in 320 at-risk drinkers (29%) and 787 non–at-risk drinkers (71%) at admission to the ICU, within 4 days after admission and at ICU discharge. The proportion of patients with stages 2–3 acute kidney injury at admission to the ICU (42.5% vs 18%; p < 0.0001) was significantly higher in at-risk drinkers than in non–at-risk drinkers. Within 4 days and after adjustment on susceptible and predisposing factors for acute kidney injury was performed, at-risk drinking was significantly associated with acute kidney injury for the entire population (odds ratio, 2.15; 1.60–2.89; p < 0.0001) in the subgroup of 832 patients without stages 2–3 acute kidney injury at admission to the ICU (odds ratio, 1.44; 1.02–2.02; p = 0.04) and in the subgroup of 971 patients without known chronic kidney disease (odds ratio, 1.92; 1.41–2.61; p < 0.0001). Among survivors, 22% of at-risk drinkers and 9% of non–at-risk drinkers were discharged with stages 2–3 acute kidney injury (p < 0.001). Conclusions: Our results suggest that chronic and current alcohol misuse in critically ill patients is associated with kidney dysfunction. The systematic and accurate identification of patients with alcohol misuse may allow for the prevention of acute kidney injury.
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