Development and validation of the Baseline Recurrence Risk in Cellulitis (BRRISC) score

Cellulitis is often treated with antibiotics for longer than recommended by guidelines. Prolonged therapy may reduce recurrence in certain patients, but it is not known which patients are at greatest risk. Our objective was to develop and temporally validate a risk prediction score to identify patients attending hospital with cellulitis at highest risk of recurrence.

We included UK adult patients with cellulitis attending hospital in an electronic health records (EHR) study to identify demographic, comorbid, physiological, and laboratory factors predicting recurrence (before death) within 90 days, using multivariable logistic regression with backwards elimination in complete cases. A points-based risk score integerised model coefficients for selected predictors. Performance was assessed using the C-index in development and temporal validation samples.

The final model included 4938 patients treated for median 8 days (IQR 6-11); 8.8% (n = 436) experienced hospitalisation-associated recurrence. A risk score using eight variables (age, heart rate, urea, platelets, albumin, previous cellulitis, venous insufficiency, and liver disease) ranged from 0-15, with C-index = 0.65 (95%CI: 0.63-0.68). Categorising as low (score 0-1), medium (2-5) and high (6-15) risk, recurrence increased fourfold; 3.2% (95%CI: 2.3-4.4%), 9.7% (8.7-10.8%), and 16.6% (13.3-20.4%). Performance was maintained in the validation sample (C-index = 0.63 (95%CI: 0.58-0.67)). Among patients at high risk, four distinct clinical phenotypes were identified using hierarchical clustering 1) young, acutely unwell with liver disease; 2) comorbid with previous cellulitis and venous insufficiency; 3) chronic renal disease with severe renal impairment; and 4) acute severe illness, with substantial inflammatory responses.

Risk of cellulitis recurrence varies markedly according to individual patient factors captured in the Baseline Recurrence Risk in Cellulitis (BRRISC) score. Further work is needed to optimise the score, considering baseline and treatment response variables not captured in EHR data, and establish the utility of risk-based approaches to guide optimal antibiotic duration.