Jan Olof Nehlin

Background: Pulmonary function impairment significantly affects quality of life, work ability, and healthcare utilization. Among patients with COVID-19, respiratory symptoms vary in severity. This study aimed to assess whether biomarkers related to respiratory function and inflammation at emergency department (ED) admittance can predict long-term pulmonary function impairment in COVID-19 survivors. Methods: This prospective single-center study recruited patients 4–5 months post-COVID-19 infection using consecutive sampling. All attendees at the respiratory outpatient clinic were invited to participate. Pulmonary function tests, including diffusing capacity of the lungs for carbon monoxide (DL_CO), total lung capacity (TLC), forced expiratory volume in the first second (FEV1), and forced vital capacity (FVC), were performed, with DL_CO < 80% as the key indicator of impairment. Baseline biomarkers—C-Reactive Protein (CRP), leukocyte counts, and soluble urokinase Plasminogen Activator Receptor (suPAR)—were correlated with post-discharge DL_CO values. Results: This study enrolled 110 patients with COVID-19; 58.2% were female, the median age was 61.5, and the average BMI was 27.2. Smoking history showed that 53.7% were never smokers, 43.5% were former smokers, and 2.8% were current smokers. A diffusion deficit (DL_CO < 80%) was present in 48.6% of patients. Leukocyte counts and suPAR had the highest sensitivity (>0.80) for predicting DL_CO impairment but showed low specificity and a positive predictive value (PPV) of around 0.50. However, combining all biomarkers improved prediction accuracy, with a negative predictive value (NPV) of 0.93. Conclusions: The chosen inflammatory biomarkers by themselves had a limited ability to predict long-term pulmonary function impairment in COVID-19 survivors. However, when combined, they demonstrated a high negative predictive value (NPV) for identifying DL_CO impairment. This strategy could help clinicians better tailor follow-up care for patients with COVID-19.

Background/Objectives: Treatment of knee osteoarthritis (OA) with autologous stem cells from microfragmented adipose tissue (MFAT) has shown promising but varying results. Multiple stem cell types, including CD34⁺, CD146⁺, and CD271⁺ stem cells, have been identified within MFAT. Patient-specific heterogeneity in stem cell populations and the content of highly potent cells may be determining factors for a successful treatment outcome. The current study aimed to identify the most promising stem cell type in MFAT to treat OA, focusing on their chondrogenic and osteogenic differentiation performance. Methods: CD34⁺, CD146⁺, and CD271⁺ stem cells from the MFAT of eight patients with knee OA were separated using magnetic-activated cell sorting (MACS) and analyzed as subtypes. Unsorted cells were used as a control. Chondrogenic and osteogenic in vitro differentiation were assessed through Safranin-O and H&E staining, pellet size, and qPCR for chondrogenesis, as well as Alizarin Red S staining and qPCR for osteogenesis. Results: CD34⁺, CD146⁺, and CD271⁺ stem cells were doubled using MACS. All subtypes were able to undergo osteogenic differentiation with Alizarin Red S staining, revealing a significant increase in calcium deposits of induced cells compared to non-induced controls. CD146⁺ stem cells showed higher calcium deposition compared to CD34⁺, CD271⁺, and unsorted stem cells. All cell types could form chondrogenic pellets. CD271⁺ stem cells produced more proteoglycans, as shown by Safranin-O staining, than CD34⁺ and CD146⁺ stem cells, but not more than the unsorted stem cells. After differentiation induction, all cell types showed an upregulation of most chondrogenic and osteogenic biomarkers. Conclusions: CD146⁺ stem cells showed the highest osteogenic differentiation performance for calcium deposition, while CD271⁺ stem cells showed the greatest chondrogenic differentiation performance for proteoglycan formation. The prevalence of these stem cell types may play a critical role in the clinical effectiveness when treating OA.

Background: Predicting mortality in emergency departments (EDs) using machine learning models presents challenges, particularly in balancing simplicity with performance. This study aims to develop models that are both simple and effective for predicting short- and long-term mortality in ED patients. Our approach uses a minimal set of variables derived from one single blood sample obtained at admission. Methods: Data from three cohorts at two large Danish university hospitals were analyzed, including one retrospective and two prospective cohorts where prognostic models were applied to predict individual mortality risk, spanning the years 2013–2022. Routine biochemistry analyzed in blood samples collected at admission was the primary data source for the prediction models. The outcomes were mortality at 10, 30, 90, and 365 days after admission to the ED. The models were developed using Light Gradient Boosting Machines. The evaluation of mortality predictions involved metrics such as Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, specificity, negative predictive values, positive predictive values, and Matthews correlation coefficient (MCC). Results: A total of 43,648 unique patients with 65,484 admissions were analyzed. The models showed high accuracy, with very good to excellent AUC values between 0.87 and 0.93 across different time intervals. Conclusions: This study demonstrates that a single assessment of routine clinical biochemistry upon admission can serve as a powerful predictor for both short-term and long-term mortality in ED admissions.

Background: Hospitalized patients are at an increased risk of developing kidney disease after discharge, often despite the absence of any clinical indicators during hospitalization. Soluble urokinase plasminogen activator receptor (suPAR) is a marker of systemic chronic inflammation that can be measured from routine blood samples. We determined whether elevated suPAR during hospitalization is associated with a decline in estimated glomerular filtration rate (eGFR) after discharge. Methods: This was a retrospective longitudinal cohort study of patients without detectable kidney disease presenting to the emergency department on two separate occasions during a 3-year period. The association between suPAR and a decline in eGFR was assessed by linear mixed models for repeated measures adjusting for age, sex, C-reactive protein, sodium, diabetes, hypertension and cardiovascular disease. Results: In total, 5124 patients (median age 65.9 years, 51.0% female) were included. The median suPAR was 2.9 ng/mL, the median time to readmission was 144 days and the expected rate of eGFR decline over this period was 5.1 mL/min/1.73 m2/year. Adjusting for other risk factors, patients with suPAR <3, 3–6 or ≥6 ng/mL had an expected eGFR decline of 4.3, 5.2 or 9.0 mL/min/1.73 m2/year, respectively. Similarly, patients with suPAR in the lowest (<2.4 ng/mL), middle (2.4–3.6 ng/mL) or highest (≥3.6 ng/mL) tertile had an expected eGFR decline of 4.2, 4.6 or 6.5 mL/min/1.73 m2/year, respectively. In both cases, a higher suPAR level was significantly and independently associated with a higher rate of eGFR decline (P < .001). Conclusions: A higher suPAR level was associated with accelerated eGFR decline among patients presenting to the emergency department, suggesting that routine suPAR measurements may have utility for the early detection of kidney disease.

Objectives: Elevated soluble urokinase Plasminogen Activator Receptor (suPAR) is a biomarker associated with adverse outcomes. We aimed to investigate the associations between plasma suPAR levels (testing the cut-offs ⩽4, 4-6, and ⩾6 ng/mL) with risk of 14-day mortality, and with the risk of mechanical ventilation in patients that tested positive for SARS-CoV-2. Methods: Observational cohort study of patients presenting with symptoms of COVID-19 at Department of Emergency Medicine, Amager and Hvidovre Hospital, Denmark from March 19th, 2020 to April 3rd, 2020. Plasma suPAR was measured using suPARnostic technologies. Patients were followed for development of mechanical ventilation and mortality for 14 days. Validation of our findings were carried out in a similar sized COVID-19 patient cohort from Mikkeli Central Hospital, Finland. Results: Among 386 patients with symptoms of COVID-19, the median (interquartile range) age was 64 years (46-77), 57% were women, median suPAR was 4.0 ng/mL (2.7-5.9). In total, 35 patients (9.1%) died during the 14 days follow-up. Patients with suPAR ⩽4 ng/mL (N = 196; 50.8%) had a low risk of mortality (N = 2; 1.0%; negative predictive value of 99.0%, specificity 55.3%, sensitivity 95.2%, positive predictive value 17.4%). Among patients with suPAR ⩾6 ng/mL (N = 92; 23.8%), 16 died (17.4%). About 99 patients (25.6%) tested positive for SARS CoV-2 and of those 12 (12.1%) developed need for mechanical ventilation. None of the SARS-CoV-2 positive patients with suPAR ⩽4 ng/mL (N = 28; 38.8%) needed mechanical ventilation or died. The Mikkeli Central Hospital validation cohort confirmed our findings concerning suPAR cut-offs for risk of development of mechanical ventilation and mortality. Conclusions: Patients with symptoms of COVID-19 and suPAR ⩽4 or ⩾6 ng/mL had low or high risk, respectively, concerning the need for mechanical ventilation or mortality. We suggest cut-offs for identification of risk groups in patients presenting to the ED with symptoms of or confirmed COVID-19.