The prediction of corticosteroid responses in coronavirus disease 2019 (COVID-19) patients is crucial in clinical practice, and exploring the role of artificial intelligence (AI)-assisted analysis of chest radiographs (CXR) is warranted. This retrospective case–control study involving mild-to-moderate COVID-19 patients treated with corticosteroids was conducted from 4 September 2021, to 30 August 2022. The primary endpoint of the study was corticosteroid responsiveness, defined as the advancement of two or more of the eight-categories-ordinal scale. Serial abnormality scores for consolidation and pleural effusion on CXR were obtained using a commercial AI-based software based on days from the onset of symptoms. Amongst the 258 participants included in the analysis, 147 (57%) were male. Multivariable logistic regression analysis revealed that high pleural effusion score at 6–9 days from onset of symptoms (adjusted odds ratio of (aOR): 1.022, 95% confidence interval (CI): 1.003–1.042, p = 0.020) and consolidation scores up to 9 days from onset of symptoms (0–2 days: aOR: 1.025, 95% CI: 1.006–1.045, p = 0.010; 3–5 days: aOR: 1.03 95% CI: 1.011–1.051, p = 0.002; 6–9 days: aOR; 1.052, 95% CI: 1.015–1.089, p = 0.005) were associated with an unfavorable corticosteroid response. AI-generated scores could help intervene in the use of corticosteroids in COVID-19 patients who would not benefit from them.

Serum creatinine is used to measure the estimated glomerular filtration rate (eGFR); however, it is influenced by muscle mass and may therefore overestimate renal function in patients with sarcopenia. We examined calf circumference (CC) as a convenient muscle mass evaluation tool that can potentially indicate the need to test for cystatin C instead of creatinine in elderly inpatients. We retrospectively reviewed the electronic health record of 271 inpatients aged 65 or over. CC was determined by measuring the thickest part of the nondominant calf. eGFRcys and eGFRcr were calculated using cystatin C and creatinine levels, respectively. We evaluated optimum CC cutoff values using the eGFRcys/eGFRcr ratio for detecting hidden renal impairment (HRI, defined as eGFRcr ≥ 60 mL/min/1.73 m² but eGFRcys < 60 mL/min/1.73 m²). CC showed a significant positive correlation with the eGFRcys/eGFRcr ratio in both sexes. The areas under the receiver operating characteristic curve were 0.725 and 0.681 for males and females, respectively. CC cutoffs with a sensitivity or specificity of 90% or 95% might be used to detect HRI in males. In conclusion, utilizing the optimum cutoff, CC could be a cost-effective screening tool for detecting HRI in elderly male patients using cystatin C as an add-on test.

Background/Objectives: Hyperglycemia in hospitalized patients is associated with an increased risk of complications, morbidity, mortality, and healthcare costs, regardless of a prior diagnosis of diabetes. The hospitalist system can improve various outcomes, including length of stay, medical costs, patient satisfaction, and mortality rates. However, the effects of hospitalist care on blood glucose control in hospitalized patients remain unclear. This study aimed to assess the specific effects of hospitalist services on blood glucose control in hospitalized patients, with a focus on hyperglycemia management and patient outcomes. Methods: This retrospective study reviewed the electronic medical records of patients diagnosed with diabetes at Yonsei Severance Hospital in Yongin, between March 2020 and February 2022. It included adults aged ≥20 years who were hospitalized and had undergone blood glucose measurements during hospitalization. Glycemic control was assessed using hemoglobin A1c, and the blood glucose levels were measured four times daily during hospitalization. Variability was quantified using the coefficient of variation and compared between hospitalist-led and traditional specialty care groups, over a 14-day hospitalization period. Results: Despite a higher baseline risk profile, patients receiving hospitalist-led care experienced significantly more stable glycemic variability over time (p = 0.002), suggesting better inpatient glucose management than those receiving traditional specialty care. Conclusions: Hospitalist-led care was associated with more stable glycemic variability over time in hospitalized patients with diabetes, despite a higher baseline burden of comorbidities and poorer glycemic control at admission.

The prediction of corticosteroid responses in coronavirus disease 2019 (COVID-19) patients is crucial in clinical practice, and exploring the role of artificial intelligence (AI)-assisted analysis of chest radiographs (CXR) is warranted. This retrospective case-control study involving hospitalized COVID-19 patients treated with corticosteroids was conducted from September 4th, 2021, to August 30th, 2022. The primary endpoint of the study was corticosteroid responsiveness, defined as the advancement of two or more of the eight-categories-ordinal scale. Serial abnormality scores for consolidation and pleural effusion on CXR were obtained using a commercial AI-based software based on days from onset of symptoms. Amongst the 258 participants included in the analysis, 147 (57%) were male. Multivariable logistic regression analysis revealed that high pleural effusion score at 6–9 days from onset of symptoms (adjusted odds ratio of [aOR]: 1.022, 95% confidence interval [CI]: 1.003-1.042, p=0.020) and consolidation scores up to 9 days from onset of symptoms (0-2 days: aOR: 1.025, 95% CI: 1.006-1.045, p=0.010; 3-5 days: aOR: 1.03 95% CI: 1.011-1.051, p=0.002; 6-9 days: aOR; 1.052, 95% CI: 1.015-1.089, p=0.005) were associated with an unfavorable corticosteroid response. AI-generated scores could help intervene in the use of corticosteroids in COVID-19 patients who would not benefit from them