Feasibility of Somato-Cognitive Coordination Therapy Using Virtual Reality for Patients with Advanced Severe Parkinson's Disease.

 This feasibility study enrolled 20 patients with advanced severe Parkinson's disease (PD) to evaluate somato-cognitive coordination therapy (SCCT) using virtual reality. Focusing on the safety and tolerability of SCCT, 17 patients (76±9 years old and 64.7% male) completed the 3-month trial. Key observations included absence of adverse events and tolerability of the participants to SCCT despite initial apprehensions and minor adjustments in medication. Physical functions showed no significant deterioration, suggesting the safety of SCCT. In conclusion, SCCT emerges as feasible and well-tolerated intervention in advanced severe PD, requiring further research to assess its therapeutic efficacy.

Outcomes after early versus delayed antibiotic treatment of liver abscess in Japan: A nationwide retrospective cohort study.

BACKGROUND: The optimal timing of antibiotic administration in patients with a liver abscess undergoing liver aspiration or drainage is unknown. METHODS: This was a retrospective cohort study using the Diagnosis Procedure Combination database, a national inpatient database in Japan. RESULTS: A total of 34,424 patients who were emergently hospitalized due to liver abscess between July 2010 and March 2020 were included. Of these, 31,248 (90.8%) received antibiotics on the day of admission (early antibiotics group), and 3176 (9.2%) did not (delayed antibiotics group). Multivariable logistic regression analyses showed that in-hospital mortality of patients in the early antibiotics group was significantly lower than that in the delayed antibiotics group (odds ratio, 0.61; 95% confidence interval, 0.52-0.72; p <0.001). Patients in the early antibiotics group had a significantly lower proportion of clinical deterioration (odds ratio, 0.73; 95% confidence interval, 0.63-0.84; p <0.001) and shorter length of stay (adjusted difference, -5.2 days; 95% confidence interval, -6.2 to -4.1 days; p <0.001) than those in the delayed antibiotics group. CONCLUSIONS: Starting antibiotic treatment on the day of admission was associated with lower mortality, a lower proportion of clinical deterioration, and a shorter length of hospital stay.

Impact of antibiotic timing relative to percutaneous aspiration on culture positivity rate and clinical outcomes: A retrospective study of patients with pyogenic liver abscess.

The optimal timing of antibiotic administration relative to liver abscess aspiration is debatable. This retrospective cohort study investigated whether the timing affects the abscess culture positivity rate and clinical outcomes. Twenty-nine patients with 30 percutaneously drained liver abscess cases were analyzed. Antibiotics were administered before aspiration (pre-aspiration antibiotics) in 22 cases and following aspiration (post-aspiration antibiotics) in 8 cases (i.e., 1 patient underwent aspiration twice, both before and following antibiotics). Both groups demonstrated similar patient characteristics, short time to aspiration, and high antibiotic appropriateness. Most patients were immunocompetent and non-septic. Pre-aspiration antibiotics did not reduce the culture yield (95% with pre-aspiration antibiotics vs. 100% with post-aspiration antibiotics; p = 1). Post-aspiration antibiotics were not associated with higher mortality, longer length of hospitalization, or an increased rate of clinical deterioration following aspiration. With appropriate antibiotic and aspiration, antibiotics timing can be either before or after aspiration without compromising the culture positivity rate and clinical outcomes.

Machine Learning Approach to Predict Positive Screening of Methicillin-Resistant Staphylococcus aureus During Mechanical Ventilation Using Synthetic Dataset From MIMIC-IV Database.

Background: Mechanically ventilated patients are susceptible to nosocomial infections such as ventilator-associated pneumonia. To treat ventilated patients with suspected infection, clinicians select appropriate antibiotics. However, decision-making regarding the use of antibiotics for methicillin-resistant Staphylococcus aureus (MRSA) is challenging, because of the lack of evidence-supported criteria. This study aims to derive a machine learning model to predict MRSA as a possible pathogen responsible for infection in mechanically ventilated patients. Methods: Data were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database (an openly available database of patients treated at the Beth Israel Deaconess Medical Center in the period 2008-2019). Of 26,409 mechanically ventilated patients, 809 were screened for MRSA during the mechanical ventilation period and included in the study. The outcome was positivity to MRSA on screening, which was highly imbalanced in the dataset, with 93.9% positive outcomes. Therefore, after dividing the dataset into a training set (n = 566) and a test set (n = 243) for validation by stratified random sampling with a 7:3 allocation ratio, synthetic datasets with 50% positive outcomes were created by synthetic minority over-sampling for both sets individually (synthetic training set: n = 1,064; synthetic test set: n = 456). Using these synthetic datasets, we trained and validated an XGBoost machine learning model using 28 predictor variables for outcome prediction. Model performance was evaluated by area under the receiver operating characteristic (AUROC), sensitivity, specificity, and other statistical measurements. Feature importance was computed by the Gini method. Results: In validation, the XGBoost model demonstrated reliable outcome prediction with an AUROC value of 0.89 [95% confidence interval (CI): 0.83-0.95]. The model showed a high sensitivity of 0.98 [CI: 0.95-0.99], but a low specificity of 0.47 [CI: 0.41-0.54] and a positive predictive value of 0.65 [CI: 0.62-0.68]. Important predictor variables included admission from the emergency department, insertion of arterial lines, prior quinolone use, hemodialysis, and admission to a surgical intensive care unit. Conclusions: We were able to develop an effective machine learning model to predict positive MRSA screening during mechanical ventilation using synthetic datasets, thus encouraging further research to develop a clinically relevant machine learning model for antibiotics stewardship.