Background: Inactivity is frequent among older patients during hospitalization. It is unknown how patients' daily activity pattern (diurnal profile) vary between hospitalization and after discharge. This study aims to describe and compare the distribution of physical activity and sedentary behavior in acutely hospitalized older patients during hospitalization and after discharge. Methods: We included data on 80 patients (+65 years) admitted with acute medical illness from the STAND-Cph trial. Physical activity and sedentary behavior were measured as daily number of steps, uptime (walking/standing) and sedentary behavior (lying/sitting) with an activity monitor (activPAL3, PAL Technologies Ltd). The patients wore the monitor for three periods of one week: during hospitalization, after discharge, and four weeks after discharge. Results: The patients’ median age was 80 years [IQR: 75;88], 68% were female and the median De Morton Mobility Index (DEMMI) was 57 [IQR: 48;67]. The daily median uptime was 1.7 h [IQR: 1;2.8] during hospitalization, 4.0 h [IQR: 2.7;5.4] after discharge and 4.0 h [IQR: 2.8;5.8] four weeks after discharge. The daily median number of steps was 728 [IQR: 176;2089], 2207 [IQR: 1433;3148], and 2622 [IQR: 1714;3865], respectively, and median daily sedentary behavior was 21.4 h (IQR: 20.7;22.4), 19.5 h (IQR: 18.1;21.0) and 19.6 h (IQR: 18.0;20.8), respectively. During hospitalization, a small activity peak was observed between 9-11 AM without any notable variation after. At discharge and four weeks after discharge, a peak in physical activity was seen between 9-12 AM and at 5 PM. Conclusion: Older hospitalized patients spend most of their time being sedentary with their highest activity between 9-11 AM. Daily activity doubles after discharge with one extra peak in the afternoon. Daily routines might be disrupted, and older patients have the potential to be more physically active during hospitalization. Interventions that encourage physical activity during hospitalization are warranted.

The aging process is often accompanied by high risk of malnutrition and elevated levels of growth differentiation factor 15 (GDF15). GDF15 is an increasingly recognized biomarker for regulation of metabolism, but few studies have investigated the connection between GDF15 and malnutrition in older age and how it relates to other features of aging such as decreased appetite and physical function. Therefore, we investigated the associations between GDF15 levels and nutritional status, appetite, and physical function in acutely admitted older adults. Plasma GDF15 levels were measured using immunoassays in 302 older adults (≥65 years) admitted to the emergency department (ED). Nutritional status was evaluated with the Mini Nutritional Assessment Short-Form (MNA®-SF), appetite was evaluated with the Simplified Nutritional Appetite Questionnaire (SNAQ), and physical function was evaluated with handgrip strength (HGS), 30-s chair stand test (30s-RSS), and gait speed (GS). Associations between GDF15 and each outcome was determined by logistic regression adjusted for age, sex, and C-reactive protein (CRP). Each doubling in plasma GDF15 level was associated with an adjusted odds ratio (OR) (95% confidence interval) of 1.59 (1.10-2.29, P = 0.01) for risk of malnutrition compared to normal nutrition and 1.19 (0.85-1.69, P = 0.3)) for malnutrition compared to risk of malnutrition. Each doubling in GDF15 was associated with an adjusted OR of 1.63 (1.21-2.23)) for having poor appetite, 1.46 (1.07-1.99) for having low HGS, 1.74 (1.23-2.51) for having low 30s-RSS, and 1.99 (1.39-2.94) for having low GS. Among older adults admitted to the ED, higher GDF15 levels were significantly associated with malnutrition, poor appetite, and low physical function independent of age, sex, and CRP.

Diagnosis of acute kidney injury (AKI) based on plasma creatinine often lags behind actual changes in renal function. Here, we investigated early detection of AKI using the plasma soluble urokinase plasminogen activator receptor (suPAR) and neutrophil gelatinase-sssociated lipocalin (NGAL) and observed the impact of early detection on prescribing recommendations for renally-eliminated medications. This study is a secondary analysis of data from the DISABLMENT cohort on acutely admitted older (≥65 years) medical patients (n = 339). Presence of AKI according to kidney disease: improving global outcomes (KDIGO) criteria was identified from inclusion to 48 h after inclusion. Discriminatory power of suPAR and NGAL was determined by receiver-operating characteristic (ROC). Selected medications that are contraindicated in AKI were identified in Renbase^®. A total of 33 (9.7%) patients developed AKI. Discriminatory power for suPAR and NGAL was 0.69 and 0.78, respectively, at a cutoff of 4.26 ng/mL and 139.5 ng/mL, respectively. The interaction of suPAR and NGAL yielded a discriminatory power of 0.80, which was significantly higher than for suPAR alone (p = 0.0059). Among patients with AKI, 22 (60.6%) used at least one medication that should be avoided in AKI. Overall, suPAR and NGAL levels were independently associated with incident AKI and their combination yielded excellent discriminatory power for risk determination of AKI.

Several scores predicting mortality at the emergency department have been developed. However, all with shortcomings either simple and applicable in a clinical setting, with poor performance, or advanced, with high performance, but clinically difficult to implement. This study aimed to explore if machine learning algorithms could predict all-cause short- and long-term mortality based on the routine blood test collected at admission. Methods: We analyzed data from a retrospective cohort study, including patients > 18 years admitted to the Emergency Department (ED) of Copenhagen University Hospital Hvidovre, Denmark between November 2013 and March 2017. The primary outcomes were 3-, 10-, 30-, and 365-day mortality after admission. PyCaret, an automated machine learning library, was used to evaluate the predictive performance of fifteen machine learning algorithms using the area under the receiver operating characteristic curve (AUC). Results: Data from 48,841 admissions were analyzed, of these 34,190 (70%) were randomly divided into training data, and 14,651 (30%) were in test data. Eight machine learning algorithms achieved very good to excellent results of AUC on test data in a of range 0.85–0.93. In prediction of short-term mortality, lactate dehydrogenase (LDH), leukocyte counts and differentials, Blood urea nitrogen (BUN) and mean corpuscular hemoglobin concentration (MCHC) were the best predictors, whereas prediction of long-term mortality was favored by age, LDH, soluble urokinase plasminogen activator receptor (suPAR), albumin, and blood urea nitrogen (BUN). Conclusion: The findings suggest that measures of biomarkers taken from one blood sample during admission to the ED can identify patients at high risk of short-and long-term mortality following emergency admissions.

The accuracy of multi-frequency (MF) bioelectrical impedance analysis (BIA) to estimate low muscle mass in older hospitalized patients remains unclear. This study aimed to describe the ability of MF-BIA to identify low muscle mass as proposed by The Global Leadership Initiative on Malnutrition (GLIM) and The European Working Group on Sarcopenia in Older People (EWGSOP-2) and examine the association between muscle mass, dehydration, malnutrition, and poor appetite in older hospitalized patients. In this prospective exploratory cohort study, low muscle mass was estimated with MF-BIA against dual-energy X-ray absorptiometry (DXA) in 42 older hospitalized adults (≥65 years). The primary variable for muscle mass was appendicular skeletal muscle mass (ASM), and secondary variables were appendicular skeletal muscle mass index (ASMI) and fat-free mass index (FFMI). Cut-off values for low muscle mass were based on recommendations by GLIM and EWGSOP-2. MF-BIA was evaluated against DXA on the ability to estimate absolute values of muscle mass by mean bias, limits of agreement (LOA), and accuracy (5% and 10% levels). Agreement between MF-BIA and DXA to identify low muscle mass was evaluated with sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). The association between muscle mass, dehydration, malnutrition, and poor appetite was visually examined with boxplots. MF-BIA overestimated absolute values of ASM with a mean bias of 0.63 kg (CI: −0.20:1.46, LOA: −4.61:5.87). Agreement between MF-BIA and DXA measures of ASM showed a sensitivity of 86%, specificity of 94%, PPV of 75% and NPV of 97%. Boxplots indicate that ASM is lower in patients with malnutrition. This was not observed in patients with poor appetite. We observed a tendency toward higher ASM in patients with dehydration. Estimation of absolute ASM values with MF-BIA should be interpreted with caution, but MF-BIA might identify low muscle mass in older hospitalized patients.