Higher mortality and longer length of stay in hospitalized patients with newly diagnosed diabetes.

AIMS: We investigated the association between diabetes status at admission and in-hospital outcomes in all hospitalized patients, regardless of the reason for admission. METHODS: All individuals aged 20 years or older who were admitted to Yongin Severance Hospital between March 2020 and February 2022 were included in study. Subjects were categorized into three groups: non-DM, known DM, and newly diagnosed DM, based on medical history, anti-diabetic medications use, and laboratory test. Hospitalization-related outcomes, including in-hospital mortality and length of hospital stay, were compared between groups. RESULTS: 33,166 participants were enrolled. At hospitalization, 6,572 (19.8 %) subjects were classified as known DM, and another 2,634 (7.9 %) subjects were classified as newly diagnosed DM. In-hospital mortality was highest in newly diagnosed DM (HR 1.89, 95% CI 1.58-2.26, p < 0.001) followed by known DM (HR 1.41, 95% CI 1.18-1.69, p < 0.001) compared to non-DM. Length of hospital stay was significantly longer in newly diagnosed DM (median [IQR] 9.0 [5.0-18.0],days) than known DM (median [IQR] 5.0 [3.0-10.0],days)(p < 0.001) and non-DM (median [IQR] 4.0 [2.0-7.0],days). After adjusting for multiple covariates, newly diagnosed diabetes was independently associated with increased in-hospital mortality (p < 0.001). CONCLUSIONS: Diabetes status at admission was closely linked to hospitalization-related outcomes. Notably, individuals with newly diagnosed diabetes demonstrated a higher risk of in-hospital mortality and a prolonged length of hospital stay.

Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography.

Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely available, low-cost test) can potentially solve this problem. In this study, we develop deep learning scores to detect osteoporosis and VF based on lateral spine radiography and investigate whether their use can improve referral of high-risk individuals to bone-density testing. The derivation cohort consisted of patients aged 50 years or older who underwent lateral spine radiography in Severance Hospital, Korea, from January 2007 to December 2018, providing a total of 26,299 lateral spine plain X-rays for 9276 patients (VF prevalence, 18.6%; osteoporosis prevalence, 40.3%). Two individual deep convolutional neural network scores to detect prevalent VF (VERTE-X pVF score) and osteoporosis (VERTE-X osteo score) were tested on an internal test set (20% hold-out set) and external test set (another hospital cohort [Yongin], 395 patients). VERTE-X pVF, osteo scores, and clinical models to detect prevalent VF or osteoporosis were compared in terms of the areas under the receiver-operating-characteristics curves (AUROCs). Net reclassification improvement (NRI) was calculated when using deep-learning scores to supplement clinical indications for classification of high-risk individuals to dual-energy X-ray absorptiometry (DXA) testing. VERTE-X pVF and osteo scores outperformed clinical models in both the internal (AUROC: VF, 0.93 versus 0.78; osteoporosis, 0.85 versus 0.79) and external (VF, 0.92 versus 0.79; osteoporosis, 0.83 versus 0.65; p < 0.01 for all) test sets. VERTE-X pVF and osteo scores improved the reclassification of individuals with osteoporosis to the DXA testing group when applied together with the clinical indications for DXA testing in both the internal (NRI 0.10) and external (NRI 0.14, p < 0.001 for all) test sets. The proposed method could detect prevalent VFs and osteoporosis, and it improved referral of individuals at high risk of fracture to DXA testing more than clinical indications alone. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Risk of Carotid Atherosclerosis in Subjects with Prediabetes Overlapping Metabolic Syndrome.

Background: While the number of individuals with prediabetes and metabolic syndrome (MetS) is increasing, only a few studies have reported differences in cardiovascular risk according to the presence or absence of MetS in individuals with prediabetes. Here, we examined differences in carotid intima-media thickness (CIMT) and carotid plaques in individuals with prediabetes with or without MetS among subjects who visited a single center in Seoul (Huh Diabetes Center). Methods: A total of 328 participants aged ≥20 years, including the group with normoglycemia, were enrolled in the analysis, of which 273 had prediabetes. Individuals with prediabetes were defined as those who met one or more of the following two criteria: fasting plasma glucose of 100-125 mg/dL and/or HbA1c level of 5.7%-6.4%. Carotid atherosclerosis was determined by mean and maximal CIMT and by the presence of carotid plaques. Results: Eighty-nine subjects (32.6% of prediabetes group) were categorized as having MetS. Those with MetS had significantly higher mean CIMT and maximal CIMT than those without (P < 0.05). Moreover, the group with MetS had a significantly higher prevalence of carotid plaques than the group without MetS [odds ratio (OR): 2.45, 95% confidence interval (CI): 1.43-4.19; P = 0.001]. After adjusting for age, sex, body mass index, and low-density lipoprotein cholesterol, individuals with MetS still had greater mean and maximal CIMT than individuals without MetS (P < 0.05), and the presence of MetS was significantly associated with a higher risk of carotid plaques (OR: 2.55, 95% CI: 1.06-6.15; P = 0.037). Conclusion: These results suggest that MetS is independently associated with increased CIMT and the presence of carotid plaques in prediabetes. Our study indicates that the risk of cardiovascular disease (CVD) is high in prediabetic individuals with MetS, and that more attention is needed on the risk of CVD in these individuals.

Association Between Low Serum Phosphate Level and Risk of Falls in Hospitalized Patients Over 50 Years of Age: A Retrospective Observational Cohort Study.

Purpose: Falls are the leading cause of injury among hospitalized patients, particularly among older patients. We investigated the association between serum phosphate (s-phosphate) levels and the risk of in-hospital falls. Patients and Methods: This retrospective observational cohort study included all patients aged over 50 years who were admitted to Yongin Severance Hospital in South Korea between January 2018 and March 2021. Demographic, anthropometric, and biochemical parameters were recorded on admission. S-phosphate levels were classified into three groups: below normal (<2.8 mg/dL), normal (2.8-4.4 mg/dL), and above normal (≥4.5 mg/dL). The normal group was further stratified into tertiles (2.8-3.2, 3.3-3.7, and 3.8-4.4 mg/dL). The incidence of in-hospital falls was compared between the five groups. Logistic regression analyses were performed to assess the association between s-phosphate levels and the incidence of falls during the hospital stay, with clinical factors included as covariates in the multivariable models. Results: A total of 15,485 patients (female: 52.1%) with a median age of 70.0 years (interquartile range: 60.0-79.0 years) were included in the analysis, of whom 295 (1.9%) experienced a fall during the hospital stay. The incidence of falls was significantly higher among patients with lower s-phosphate levels, and this relationship also applied among patients with s-phosphate levels within the normal range as well. The association between lower s-phosphate levels and increased risk of falls remained significant in the adjusted analyses. Conclusion: A lower s-phosphate level on admission was independently associated with an increased risk of in-hospital falls. Further studies are needed to determine whether the s-phosphate level on admission could improve prediction of the risk of in-hospital falls.