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Background: The Late-Stage Elderly Questionnaire has been incorporated into health assessments for older adults in Japan, encompassing three self-administered questions on subjective physical function: subjective gait speed decline, recent fall history, and exercise habits. Nevertheless, its efficacy in predicting new fracture occurrences remains uncertain. Methods: This retrospective cohort study utilized Japan's DeSC database, a large commercially available claims database. Participants were older adults aged =75 years and provided complete responses to the Late-Stage Elderly Questionnaire at health check-ups. We performed two Cox regression analyses for new fractures based on the responses to the three questions (Model 1) and on age, sex, and responses to the three questions (Model 2). The predictive abilities of the 1-year occurrence of new fractures were compared between the two models. Results: Of 11 683 eligible older adults, 927 (7.93%) experienced new fractures. Model 1 revealed significant associations between new fractures and subjective gait speed decline (hazard ratio [HR], 1.63; 95% confidence interval [CI], 1.40-1.89), recent fall history (HR, 2.03; 95% CI, 1.77-2.33), and absence of exercise habits (HR, 1.29; 95% CI, 1.13-1.47). Model 2 demonstrated superior predictive ability (area under the curve, 0.677; 95% CI, 0.659-0.695) compared with Model 1 (area under the curve, 0.633; 95% CI, 0.614-0.652), with a net reclassification improvement of 0.383 (95% CI, 0.317-0.449). Conclusion: Three subjective physical well-being factors were significantly associated with new fracture development in older adults. These results suggest that the three-question assessment may be a valuable screening tool for identifying new fractures. [ABSTRACT FROM AUTHOR]

Context: Insulinoma is the most common pancreatic functional neuroendocrine neoplasm, yet little information on recent clinical practice in patients with insulinoma, especially malignant insulinoma, is available.Objective: This work aims to clarify the characteristics and practice patterns in patients with insulinoma using a national inpatient database.Methods: Using the Japanese Diagnosis Procedure Combination database, we retrospectively identified patients with insulinoma admitted between 2010 and 2018. We compared background characteristics and therapeutic interventions between patients with benign and malignant insulinoma. We also estimated the incidence of insulinoma using the number of patients with newly diagnosed insulinoma in 2012.Results: We identified 844 patients with benign insulinoma and 102 patients with malignant insulinoma. Patients with malignant insulinoma were younger (median, 55.5 vs 66.0 years, P < .001) and less likely to be female (55.9% vs 65.3%, P = .061) than patients with benign insulinoma. Analysis of therapeutic interventions revealed that patients with malignant insulinoma more frequently received medications (71.6% vs 49.6%, P < .001) but less frequently underwent pancreatic surgery (57.8% vs 72.0%, P = .003). Older patients were a smaller proportion of those undergoing surgery and a larger proportion of those managed with medications without surgery (P < .001). The incidence of insulinoma was estimated to be 3.27 (95% CI, 2.93-3.61) individuals per million Japanese adult population per year.Conclusion: The present study using a nationwide database had a larger sample size than previous studies and revealed definitive differences in patient characteristics and therapeutic patterns between benign and malignant insulinoma. [ABSTRACT FROM AUTHOR]

Context: Guidelines worldwide recommend potassium replacement of 10 to 40 mmol/L in the initial fluid therapy for patients with diabetic ketoacidosis. However, evidence is lacking as to the association between infused potassium concentration and mortality.Objective: We aimed to determine the association between infused potassium concentration and in-hospital mortality.Methods: Using the Japanese Diagnosis Procedure Combination database, we retrospectively identified inpatients admitted for treatment of diabetic ketoacidosis from July 2010 to March 2018. Patients with kidney dysfunction or serum potassium abnormalities were excluded. We evaluated the association of the potassium concentration in the total infused solutions in the first 2 days of hospitalization with 28-day in-hospital mortality using multivariable regression analysis with a cubic spline model. We also assessed the association between potassium concentration and occurrence of hyperkalemia.Results: We identified 14 216 patients with diabetic ketoacidosis and observed 261 deaths. The quartile cut-points for potassium concentration were 7.7, 11.4, and 16.1 mmol/L. Within the range of approximately 10 to 40 mmol/L, potassium concentration was not associated with occurrence of hyperkalemia or death. Lower potassium concentrations were associated with higher 28-day in-hospital mortality; the odds ratio for patients receiving 8 mmol/L was 1.69 (95% CI, 1.03 to 2.78; reference: 20 mmol/L), and the odds ratio increased monotonically as potassium concentration decreased further.Conclusion: Patients receiving potassium replacement at concentrations of 10 to 40 mmol/L had similar in-hospital mortality rates, whereas lower concentrations were associated with higher mortality. [ABSTRACT FROM AUTHOR]

Laser microwelding has been an essential tool with a reputation of rapidity and precision for joining miniaturized metal parts. In industrial applications, an accurate prediction of weld bead geometry is required in automation systems to enhance productivity of laser microwelding. The present work was conducted to establish an intelligent algorithm to build a simplified relationship between process parameters and weld bead geometry that can be easily used to predict the weld bead geometry with a wide range of process parameters through an artificial neural network (ANN) in laser microwelding of thin steel sheet. The back propagation with the Levenberg-Marquardt training algorithm was used to train the neural network model. The accuracy of neural network model has been tested by comparing the simulated data with actual data from the laser microwelding experiments. The predictions of the neural network model showed excellent agreement with the experimental results, indicating that the neural network model is a viable means for predicting weld bead geometry. Furthermore, a comparison was made between the neural network and mathematical model. It was found that the developed neural network model has better prediction capability compared to the regression analysis model. [ABSTRACT FROM AUTHOR]