Empiric antibiotic therapy was acceptable for severe sepsis and septic shock patients treated in the ICU. The appropriate selection of empiric antibiotics was related to a greater rate of de-escalation and better survival. The risk of multi-drug-resistant bacterial infections was not as high as expected, but will need further attention in the future.
Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients. Patients (n = 12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for outcomes (primary outcome, mortality; secondary outcome, length of ICU stay). The rank of the variables that contributed to the machine learning prediction was confirmed, and cluster analysis of the patients with risk factors of mortality was performed to identify the important variables associated with patient outcomes. Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945 (95% confidence interval [CI] 0.922–0.977). In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 (95% CI 0.876–0.908) and 0.889 (95% CI 0.849–0.936), respectively. Lactate dehydrogenase (LDH) was identified as a variable contributing to the precise prediction in machine learning for both mortality and length of ICU stay. LDH was also identified as a contributing variable to classify patients into sub-populations based on different risk factors of mortality. The machine learning algorithm could predict mortality and length of stay in ICU patients with high accuracy. LDH was identified as a contributing variable in mortality and length of ICU stay prediction and could be used to classify patients based on mortality risk.
Some coronavirus disease 2019 (COVID-19) patients develop rapidly progressive acute respiratory distress syndrome and require veno-venous extracorporeal membrane oxygenation (V-V ECMO). A previous study recommended the transfer of ECMO patients to ECMO centers. However, because of the pandemic, a limited number of ECMO centers are available for patient transfer. The safe long-distance interhospital transport of these patients is a concern. To minimize transportation time, helicopter use is a suitable choice. We report the first case of a COVID-19 patient on V-V ECMO, transferred to our ECMO center by helicopter. A 45-year-old man with rheumatoid arthritis history, treated with immunosuppressants, presented with fever and sore throat. He was diagnosed with COVID-19 following a positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction test result and was subsequently prescribed favipiravir. However, his respiratory failure progressively worsened. On day 10 of hospitalization at the previous hospital, he was intubated, and we received a request for ECMO transport on the next day. The ECMO team, who wore personal protective equipment (N95 respirators, gloves, gowns, and face shields), initiated V-V ECMO in the referring hospital and safely transported the patient by helicopter. The flight time was 7 min. He was admitted to the intensive care unit of our hospital and received tocilizumab. He was discharged on hospital day 31 with no significant sequelae. In this case report, we discuss important factors for the safe and appropriate interhospital transportation of COVID-19 patients on ECMO as well as staff and patient safety during helicopter transportation.
Background Severe pregabalin intoxication may cause serious symptoms, such as coma. Since pregabalin is a small molecule with no protein binding sites and has low volume of distribution, hemodialysis can be effective in eliminating pregabalin from the blood. However, in cases of emergency, it is not always possible to perform hemodialysis because of limited availability and time delay associated with using the plumbing equipment. Continuous hemodiafiltration (CHDF) can be performed without plumbing equipment; however, the knowledge on the effectiveness of CHDF in pregabalin elimination is insufficient. Case presentation A septuagenarian woman with normal renal function was found in a collapsed state with symptoms of coma and miosis. Empty medical bags of pregabalin (2350 mg), bepotastine besilate (600 mg), celecoxib (4600 mg), quetiapine fumarate (87.5 mg), clotiazepam (180 mg), and teprenone (50 mg) were found around her. During the patient's transfer to our hospital, her cognition worsened and she developed glossoptosis necessitating her emergent intubation upon arrival. We considered that the coma was mainly caused by pregabalin intoxication and were concerned about the consequent critical comorbidities. Thus, we performed CHDF in a high-flow setting in our intensive care unit for pregabalin elimination. After 8 h of CHDF, the patient regained consciousness, and after 6.5 h we extubated her. At a later date, we measured her serum pregabalin levels during the clinical course and estimated the blood pregabalin clearance levels depending on her metabolism as 76.8 mL/min and depending on CHDF itself as 65.1 mL/min. Based on these findings, we concluded that CHDF contributed to reducing blood pregabalin levels in this patient. Conclusions Our case revealed that pregabalin clearance using CHDF is similar to metabolic clearance in patients with normal renal function, indicating that CHDF decreases blood pregabalin levels and can be a potential treatment for severe pregabalin intoxication.
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