Title : Comparative Assessment of Diagnostic Modalities in Retrospective Cases of Necrotizing Fasciitis (NF) in a District General Hospital
Abstract:
Background: Necrotizing Fasciitis (NF) is a rare but rapidly progressive and life-threatening soft tissue infection, presenting significant diagnostic challenges due to its heterogeneous clinical presentation and the limitations of current diagnostic modalities. Early recognition and intervention are critical to improving patient outcomes, particularly in high-risk populations such as trauma and orthopaedic patients.
Methods: This retrospective study reviewed nine histopathologically confirmed cases of NF managed over a two-year period in a district general hospital. Data on patient demographics, comorbidities, clinical features, laboratory findings—including the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score and lactate levels—imaging results, and outcomes were collected and analyzed.
Results: The cohort consisted predominantly of older adults, with a mean age of 57.6 years and a high prevalence of diabetes mellitus (67%). The lower extremities, particularly the thigh, were the most commonly affected sites. Pain, erythema, and swelling were the most frequent presenting symptoms, while classic features such as bullae and shock were less common. The LRINEC score demonstrated limited sensitivity, correctly identifying only 56% of cases as high risk. MRI was highly sensitive in the limited cases performed, whereas CT and X-ray were less reliable. All patients underwent surgical debridement, with an in- hospital mortality rate of 22%. The incidence of NF in this cohort (9.0 per 100,000 person- years) was notably higher than national averages, likely reflecting the elevated risk profile of the trauma and orthopaedic patient population.
Conclusions: The findings highlight the variability in clinical and laboratory presentation of NF and the limitations of relying solely on the LRINEC score for diagnosis. A high index of suspicion and a multimodal diagnostic approach are essential for timely identification and management.Further research, including the integration of artificial intelligence and advanced analytics, may enhance early detection and improve outcomes in this challenging condition.
