Diagnostic Performance of Plasma SP-D, KL-6, and CC16 in Acutely Hospitalised Patients Suspected of Having Community-Acquired Pneumonia-A Diagnostic Accuracy Study.

Community-acquired pneumonia is a common cause of acute hospitalisation. Identifying patients with community-acquired pneumonia among patients suspected of having the disease can be a challenge, which causes unnecessary antibiotic treatment. We investigated whether the circulatory pulmonary injury markers surfactant protein D (SP-D), Krebs von den Lungen-6 (KL-6), and Club cell protein 16 (CC16) could help identify patients with community-acquired pneumonia upon acute admission. In this multi-centre diagnostic accuracy study, SP-D, KL-6, and CC16 were quantified in plasma samples from acutely hospitalised patients with provisional diagnoses of community-acquired pneumonia. The area under the receiver operator characteristics curve (AUC) was calculated for each marker against the following outcomes: patients' final diagnoses regarding community-acquired pneumonia assigned by an expert panel, and pneumonic findings on chest CTs. Plasma samples from 339 patients were analysed. The prevalence of community-acquired pneumonia was 63%. AUCs for each marker against both final diagnoses and chest CT diagnoses ranged between 0.50 and 0.56. Thus, SP-D, KL-6, and CC16 demonstrated poor diagnostic performance for community-acquired pneumonia in acutely hospitalised patients. Our findings indicate that the markers cannot readily assist physicians in confirming or ruling out community-acquired pneumonia.

Analytical Performance Specifications for Input Variables: Investigation of the Model of End-Stage Liver Disease.

BACKGROUND: Artificial intelligence models constitute specific uses of analysis results and, therefore, necessitate evaluation of analytical performance specifications (APS) for this context specifically. The Model of End-stage Liver Disease (MELD) is a clinical prediction model based on measurements of bilirubin, creatinine, and the international normalized ratio (INR). This study evaluates the propagation of error through the MELD, to inform choice of APS for the MELD input variables. METHODS: A total of 6093 consecutive MELD scores and underlying analysis results were retrospectively collected. "Desirable analytical variation" based on biological variation as well as current local analytical variation was simulated onto the data set as well as onto a constructed data set, representing a worst-case scenario. Resulting changes in MELD score and risk classification were calculated. RESULTS: Biological variation-based APS in the worst-case scenario resulted in 3.26% of scores changing by ≥1 MELD point. In the patient-derived data set, the same variation resulted in 0.92% of samples changing by ≥1 MELD point, and 5.5% of samples changing risk category. Local analytical performance resulted in lower reclassification rates. CONCLUSIONS: Error propagation through MELD is complex and includes population-dependent mechanisms. Biological variation-derived APS were acceptable for all uses of the MELD score. Other combinations of APS can yield equally acceptable results. This analysis exemplifies how error propagation through artificial intelligence models can become highly complex. This complexity will necessitate that both model suppliers and clinical laboratories address analytical performance specifications for the specific use case, as these may differ from performance specifications for traditional use of the analyses.

Heterogeneity in the perirenal region of humans suggests presence of dormant brown adipose tissue that contains brown fat precursor cells.

OBJECTIVE: Increasing the amounts of functionally competent brown adipose tissue (BAT) in adult humans has the potential to restore dysfunctional metabolism and counteract obesity. In this study, we aimed to characterize the human perirenal fat depot, and we hypothesized that there would be regional, within-depot differences in the adipose signature depending on local sympathetic activity. METHODS: We characterized fat specimens from four different perirenal regions of adult kidney donors, through a combination of qPCR mapping, immunohistochemical staining, RNA-sequencing, and pre-adipocyte isolation. Candidate gene signatures, separated by adipocyte morphology, were recapitulated in a murine model of unilocular brown fat induced by thermoneutrality and high fat diet. RESULTS: We identified widespread amounts of dormant brown adipose tissue throughout the perirenal depot, which was contrasted by multilocular BAT, primarily found near the adrenal gland. Dormant BAT was characterized by a unilocular morphology and a distinct gene expression profile, which partly overlapped with that of subcutaneous white adipose tissue (WAT). Brown fat precursor cells, which differentiated into functional brown adipocytes were present in the entire perirenal fat depot, regardless of state. We identified SPARC as a candidate adipokine contributing to a dormant BAT state, and CLSTN3 as a novel marker for multilocular BAT. CONCLUSIONS: We propose that perirenal adipose tissue in adult humans consists mainly of dormant BAT and provide a data set for future research on factors which can reactivate dormant BAT into active BAT, a potential strategy for combatting obesity and metabolic disease.