BACKGROUND

Large electronic databases have been widely used in recent years; however, they can be susceptible to bias due to incomplete information. To address this, validation studies have been conducted to assess the accuracy of disease diagnoses defined in databases. However, such studies may be constrained by potential misclassification in references and the interdependence between diagnoses from the same data source.

METHODS

This study employs latent class modeling with Bayesian inference to estimate the sensitivity, specificity, and positive/negative predictive values of different diagnostic definitions. Four models are defined with/without assumptions of the gold standard and conditional independence, and then compared with breast cancer study data as a motivating example. Additionally, simulations that generated data under various true values are used to compare the performance of each model with bias, Pearson-type goodness-of-fit statistics, and widely applicable information criterion.

RESULTS

The model assuming conditional dependence and non-gold standard references exhibited the best predictive performance among the four models in the motivating example data analysis. The disease prevalence was slightly higher than that in previous findings, and the sensitivities were significantly lower than those of the other models. Additionally, bias evaluation showed that the Bayesian models with more assumptions and the frequentist model performed better under the true value conditions. The Bayesian model with fewer assumptions performed well in terms of goodness of fit and widely applicable information criteria.

CONCLUSIONS

The current assessments of outcome validation can introduce bias. The proposed approach can be adopted broadly as a valuable method for validation studies.

BACKGROUND

Approximately 30% of coronavirus disease 2019 COVID-19 patients develop fatigue and psychological symptoms. We previously demonstrated the efficacy of donepezil, an acetylcholinesterase inhibitor that is widely used to treat dementia, in basic research.

METHODS

This is a multicenter, double-blind, randomized, controlled, phase II clinical trial in which 120 patients with COVID-19 will be randomized in a 1:1 ratio to a donepezil or placebo group. Inclusion criteria are as follows: (1) Adult. (2) With COVID-19 infection who had an upper respiratory tract infection, fever, or cough in the acute phase. (3) With a global binary fatigue score ≥4 on the Chalder Fatigue Scale assessment (4) Within 52 weeks of the onset of COVID-19. (5) Patients who provide consent themselves. In the donepezil group, a low dose (3 mg/day) is administered for the first week and is increased to 5 mg/day for 2 weeks. The control group receives placebo for 3 weeks. The primary endpoint is a change in and the absolute value of the Chalder Fatigue Scale score after 3 weeks of treatment. Secondary endpoints are a change in and the absolute value of the Chalder Fatigue Scale score after 8 weeks of treatment, the other mental scores after 3 and 8 weeks of treatment, a symptom survey, adverse events, and medication compliance rate.

RESULTS

This study protocol is ongoing and the results will be analyzed in April 2024.

CONCLUSIONS

The off-label use of donepezil at the default dose for dementia has potential for the treatment of post-COVID-19 condition.

BACKGROUND

Many patients who get discharged from the intensive care unit experience physical dysfunction that persists even after discharge. Physical dysfunction is associated with skeletal muscle atrophy and accompanying intensive care unit-acquired weakness in the early stages of intensive care unit admission, and early diagnosis and prevention with early mobilization are crucial. However, the amount of physical activity required for early mobilization remains controversial in critically ill patients. This study aims to reveal the optimal mobilization quantification score dose associated with physical dysfunction after hospital discharge.

METHODS

This is a multicenter prospective cohort study planned in 22 facilities; all consecutive patients admitted to the participating facilities between June 2024 and May 2025 will be included. Adult patients on ventilator management for at least 2 days and who will consent to this study will be included. Patients’ mobility level and duration will be documented by the mobilization quantification score during their intensive care unit stay, and physical dysfunction will be assessed using muscle mass changes from day one to seven with ultrasonography and the Short-Form 12 Health Survey at 3 months after hospital discharge. The primary outcome is physical dysfunction at 3 months.

RESULTS AND CONCLUSION

Mobilization quantification score dose and muscle mass evaluation with ultrasonography will enable the quantification of the early mobilization intervention. This study will lay the foundation for future randomised studies.

The Ministry of Health, Labor, and Welfare, Japan, launched the Diagnosis Procedure Combination system in 2002. Detailed information on the Diagnosis Procedure Combination data was reported in Annals of Clinical Epidemiology in 2019. In this report, I provide updated information on the Diagnosis Procedure Combination. The data included the discharge abstracts and administrative claims data for each inpatient. Several entities (including the Ministry, academic groups, and private companies) independently collected anonymized Diagnosis Procedure Combination data. The advantages of Diagnosis Procedure Combination data include detailed process and clinical data, which enable researchers to conduct clinical epidemiology and health services research. Diagnoses are recorded using the International Classification of Diseases-10th Revision codes, and several indices based on these codes can be used. Several clinical measures are available for specific diseases including stroke, respiratory failure, heart failure, pneumonia, liver cirrhosis, pancreatitis, burns, and multiple organ failure. Scores for consciousness, activities of daily living, functional independence, and dementia are also available. Studies that use Diagnosis Procedure Combination data are interdisciplinary and include clinical medicine, epidemiology, statistics, and medical informatics.