Validation of the Saga Fall Injury Risk Model.

Background: Predicting fall injuries can mitigate the sequelae of falls and potentially utilize medical resources effectively. This study aimed to externally validate the accuracy of the Saga Fall Injury Risk Model (SFIRM), consisting of six factors including age, sex, emergency transport, medical referral letter, Bedriddenness Rank, and history of falls, assessed upon admission. Methods: This was a two-center, prospective, observational study. We included inpatients aged 20 years or older in two hospitals, an acute and a chronic care hospital, from October 2018 to September 2019. The predictive performance of the model was evaluated by calculating the area under the curve (AUC), 95% confidence interval (CI), and shrinkage coefficient of the entire study population. The minimum sample size of this study was 2,235 cases. Results: A total of 3,549 patients, with a median age of 78 years, were included in the analysis, and men accounted for 47.9% of all the patients. Among these, 35 (0.99%) had fall injuries. The performance of the SFIRM, as measured by the AUC, was 0.721 (95% CI: 0.662-0.781). The observed fall incidence closely aligned with the predicted incidence calculated using the SFIRM, with a shrinkage coefficient of 0.867. Conclusions: The external validation of the SFIRM in this two-center, prospective study showed good discrimination and calibration. This model can be easily applied upon admission and is valuable for fall injury prediction.

The Administration of Lemborexant at Admission is Not Associated with Inpatient Falls: A Multicenter Retrospective Observational Study.

Purpose: There has been no large-scale investigation into the association between the use of lemborexant, suvorexant, and ramelteon and falls in a large population. This study, serving as a pilot investigation, was aimed at examining the relationship between inpatient falls and various prescribed hypnotic medications at admission. Patients and Methods: This study was a sub-analysis of a multicenter retrospective observational study conducted over a period of 3 years. The target population comprised patients aged 20 years or above admitted to eight hospitals, including chronic care, acute care, and tertiary hospitals. We extracted data on the types of hypnotic medications prescribed at admission, including lemborexant, suvorexant, ramelteon, benzodiazepines, Z-drugs, and other hypnotics; the occurrence of inpatient falls during the hospital stay; and patients' background information. To determine the outcome of inpatient falls, items with low collinearity were selected and included as covariates in a forced-entry binary logistic regression analysis. Results: Overall, 150,278 patients were included in the analysis, among whom 3,458 experienced falls. The median age of the entire cohort was 70 years, with men constituting 53.1%. Binary logistic regression analysis revealed that the prescription of lemborexant, suvorexant, and ramelteon at admission was not significantly associated with inpatient falls. Conclusion: The administration of lemborexant, suvorexant, and ramelteon at admission may not be associated with inpatient falls.

Effects of a 60-Minute Lecture About Diagnostic Errors for Medical Students: A Single-Center Interventional Study.

INTRODUCTION: The danger of diagnostic errors exists in daily medical practice, and doctors are required to avoid such errors as much as possible. Although various factors, including cognitive, system-related, and patient-related factors, are involved in the occurrence of diagnostic errors, the percentage of doctors with insufficient medical knowledge among those factors is extremely low. Therefore, lectures on diagnostic errors might also be useful for medical students without experience working as doctors. This study investigated whether a 60-minute lecture on diagnostic errors would enable Japanese medical students to consider the factors involved in diagnostic errors and how their perceptions of diagnostic errors change. METHODS AND MATERIALS: This single-center interventional study was conducted in October 2022 among fourth-year medical students at the Faculty of Medicine, Saga University. A questionnaire survey was conducted before and immediately after the lecture to investigate changes in the perceptions of medical students regarding diagnostic errors. One mock case question was given on an exam the day after the lecture, and the number of responses to cognitive biases and system-related and patient-related factors involved in diagnostic errors were calculated. RESULTS: A total of 83 students were analyzed. After the lecture, medical students were significantly more aware of the existence of the concept of diagnostic error, the importance of learning about it, their willingness to continue learning about it, and their perception that learning about diagnostic errors improves their clinical skills. They were also significantly less likely to feel blame or shame over diagnostic errors. The mean numbers of responses per student for cognitive bias, system-related factors, and patient-related factors were 1.9, 3.4, and 0.9, respectively. The mean number of responses per student for all factors was 5.6. CONCLUSION: A 60-minute lecture on diagnostic errors among medical students is beneficial because it significantly changes their perception of diagnostic errors. The results of the present study also suggest that lectures may enable Japanese medical students to consider the factors involved in diagnostic errors.

Validation and Improvement of the Saga Fall Risk Model: A Multicenter Retrospective Observational Study.

Purpose: We conducted a pilot study in an acute care hospital and developed the Saga Fall Risk Model 2 (SFRM2), a fall prediction model comprising eight items: Bedriddenness rank, age, sex, emergency admission, admission to the neurosurgery department, history of falls, independence of eating, and use of hypnotics. The external validation results from the two hospitals showed that the area under the curve (AUC) of SFRM2 may be lower in other facilities. This study aimed to validate the accuracy of SFRM2 using data from eight hospitals, including chronic care hospitals, and adjust the coefficients to improve the accuracy of SFRM2 and validate it. Patients and Methods: This study included all patients aged ≥20 years admitted to eight hospitals, including chronic care, acute care, and tertiary hospitals, from April 1, 2018, to March 31, 2021. In-hospital falls were used as the outcome, and the AUC and shrinkage coefficient of SFRM2 were calculated. Additionally, SFRM2.1, which was modified from the coefficients of SFRM2 using logistic regression with the eight items comprising SFRM2, was developed using two-thirds of the data randomly selected from the entire population, and its accuracy was validated using the remaining one-third portion of the data. Results: Of the 124,521 inpatients analyzed, 2,986 (2.4%) experienced falls during hospitalization. The median age of all inpatients was 71 years, and 53.2% were men. The AUC of SFRM2 was 0.687 (95% confidence interval [CI]:0.678-0.697), and the shrinkage coefficient was 0.996. SFRM2.1 was created using 81,790 patients, and its accuracy was validated using the remaining 42,731 patients. The AUC of SFRM2.1 was 0.745 (95% CI: 0.731-0.758). Conclusion: SFRM2 showed good accuracy in predicting falls even on validating in diverse populations with significantly different backgrounds. Furthermore, the accuracy can be improved by adjusting the coefficients while keeping the model's parameters fixed.

Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research.

Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.