Surgical site infection (SSI) following spine surgery is a dreaded complication with significant morbidity and economic burden. SSIs following spine surgery can be superficial, characterized by obvious wound drainage, or deep-seated with a healed wound. Staphylococcus aureus remains the principal causal agent. There are certain pre-operative risk factors that increase the risk of SSI, mainly diabetes, smoking, steroids, and peri-operative transfusions. Additionally, intra-operative risk factors include surgical invasiveness, type of fusion, implant use, and traditional instead of minimally invasive approach. A high level of suspicion is crucial to attaining an early definitive diagnosis and initiating appropriate management. The most common presenting symptom is back pain, usually manifesting 2 to 4 weeks and up to 3 months after a spinal procedure. Scheduling a follow-up visit between weeks 2 to 4 after surgery is therefore necessary for early detection. Inflammatory markers are important diagnostic tools, and comparing pre-operative with post-operative levels should be done when suspecting SSIs following spine surgery. Particularly, Serum Amyloid A (SAA) is a novel inflammatory marker that can expedite the diagnosis of SSIs. Magnetic resonance imaging remains the diagnostic modality of choice when suspecting a SSI following spine surgery. While 18F-fluorodeoxyglucose-positron emission tomography is not widely used, it may be useful in challenging cases. Despite their low yield, blood cultures should be collected before initiating antibiotic therapy. Samples from wound drainage should be sent for Gram stain and cultures. When there is a high clinical suspicion of SSI and in the absence of superficial wound drainage, CT guided aspiration of paraspinal collections is warranted. Unless the patient is hemodynamically compromised, antibiotics should be deferred until proper specimens for culture are secured.

Introduction In clear cell renal cell carcinoma (ccRCC), tumor-associated macrophage (TAM) induction of CD8+T cells into a terminally exhausted state has been implicated as a major mechanism of immunotherapy resistance, but a deeper biological understanding is necessary. Methods Primary ccRCC tumor samples were obtained from 97 patients between 2004 and 2018. Multiplex immunofluorescence using lymphoid and myeloid markers was performed in seven regions of interest per patient across three predefined zones, and geospatial analysis was performed using Ripley’s K analysis, a methodology adapted from ecology. Results Clustering of CD163+M2 like TAMs into the stromal compartment at the tumor–stroma interface was associated with worse clinical stage (tumor/CD163+nK(75): stage I/II: 4.4 (IQR −0.5 to 5.1); stage III: 1.4 (IQR −0.3 to 3.5); stage IV: 0.6 (IQR −2.1 to 2.1); p=0.04 between stage I/II and stage IV), and worse overall survival (OS) and cancer-specific survival (CSS) (tumor/CD163+nK(75): median OS–hi=149 months, lo=86 months, false-discovery rate (FDR)-adj. Cox p<0.001; median CSS–hi=174 months, lo=85 months; FDR-adj. Cox p<0.001). An RNA-seq differential gene expression score was developed using this geospatial metric, and was externally validated in multiple independent cohorts of patients with ccRCC including: TCGA KIRC, and the IMmotion151, IMmotion150, and JAVELIN Renal 101 clinical trials. In addition, this CD163+ geospatial pattern was found to be associated with a higher TIM-3+ proportion of CD8+T cells, indicative of terminal exhaustion (tumor-core: 0.07 (IQR 0.04–0.14) vs 0.40 (IQR 0.15–0.66), p=0.05). Conclusions Geospatial clustering of CD163+M2 like TAMs into the stromal compartment at the tumor–stromal interface was associated with poor clinical outcomes and CD8+T cell terminal exhaustion.