Search

Objective Accurate and prompt identification of periprosthetic joint infections (PJIs) is critical prior to re‐revision arthroplasty to ensure optimal surgical outcomes. Among routinely measured blood indices, red blood cell distribution width (RDW) and platelet count (PLT) have shown strong correlations with infection presence. This study aimed to assess the utility of RDW and PLT for diagnosing PJI in patients scheduled for re‐revision arthroplasty. Methods This retrospective research encompassed all patients who underwent re‐revision hip or knee arthroplasty at our institution from 2008 to 2022. Participants were categorized into either the PJI (n = 41) or the non‐PJI (n = 47) group following the guidelines established in the 2013 International Consensus Meeting on PJI. In this analysis, RDW and PLT counts were evaluated alongside conventional inflammatory markers, including C‐reactive protein (CRP) and erythrocyte sedimentation rate (ESR). The efficacy of these diagnostics was evaluated by the area under the receiver operating characteristic (ROC) curve ([area under the curve AUC]). Results RDW demonstrated a modest AUC of 0.678 with sensitivity at 61.0% and specificity at 71.7%, using a threshold of 14.5%. PLT was on par with ESR, showing an AUC of 0.773, and both sensitivity and specificity around 73% at a threshold of 201 × 109/L. CRP presented the highest diagnostic accuracy with an AUC of 0.815, achieving a sensitivity of 82.9% and specificity of 73.9% at a 6.9 mg/L threshold, surpassing ESR's AUC of 0.754. None of the biomarkers, individually or combined, outperformed CRP alone (p > 0.05). Conclusions In the context of re‐revision arthroplasty, RDW and PLT demonstrate limited efficacy as diagnostic biomarkers for PJI. However, CRP retains its reliability as a biomarker when the diagnostic threshold is appropriately recalibrated.

Abstract Mesoscale convective systems (MCSs) are important air water sources to the Three‐river‐source (TRS) region known as the “Chinese water tower.” Using hourly equivalent blackbody temperature (TBB) data from geostationary satellites of Chinese Fengyun‐2 series during the warm season (May–August) in 2005–2020 and an objective algorithm, MCSs in the TRS are divided into meso‐α (MαCS), meso‐β (MβCS), and meso‐γ (MγCS), and MαCS and MβCS are subdivided into larger meso‐α (LMαCS), smaller meso‐α (SMαCS), larger meso‐β (LMβCS), and smaller meso‐β (SMβCS). Results show that a high‐frequency zone of MCSs in the TRS distributes along the source of the rivers. Most MCSs, except LMαCS, develop and dissipate in situ. The interannual variation in MCS frequency exhibits a decreasing trend, especially after 2013, mainly due to the decrease in MCSs in the source region of the Yellow–Lancang River. The occurrence of MCSs peaks in August, but MCSs are most likely to produce precipitation in July and usually generate between 1600–2200 h LST (UTC + 8). The precipitation caused by MCSs to the total precipitation (precipitation ratio, PR) accounts for about 40%; MCS PR is closely related to, and increases with, the horizontal scale of the MCS, with MαCS PR being the highest, exceeding 67%. The contribution of MCSs to precipitation is mainly reflected in weak precipitation, smaller than 10.0 mm/h. Most of the maximum precipitation of MCSs appears after MCSs reach their prime, with the maximum lag by MαCS up to 2 h.