Addison A, Paul C, Kuo R, Lamyman A, Martinez-Devesa P, Hettige R
Otorhinolaryngology, John Radcliffe Hospital, Oxford, Headley Way, United Kingdom.
Rhinology. 2017 Jun 1;55(2):99-105. doi: 10.4193/Rhin16.259.
To derive and validate a predictive scoring tool (RHINO-ooze score) with good sensitivity and specificity in identifying patients with epistaxis at high risk of 30 day readmission and to enable risk stratification for possible definitive intervention.
Using medical databases, we searched for factors influencing recurrent epistaxis. The information ascertained together with our analysis of retrospective data on patients admitted with epistaxis between October 2013 and September 2014, was used as the derivation cohort to develop the predictive scoring model (RHINO-ooze score). The tool was validated by performing statistical analysis on the validation cohort of patients admitted with epistaxis between October 2014 and October 2015. Multiple linear regressions with backwards elimination was used to derive the predictive model. The area under the curve (AUC), sensitivity and specificity were calculated.
834 admissions were encountered within the study period. Using the derivative cohort (n= 302) the RHINO-ooze score with a maximum score of 8 from five variables (Recent admission, Haemorrhage point unidentified, Increasing age over 70, posterior Nasal packing, Oral anticoagulant) was developed. The RHINO-ooze score had a chi-square value of 99.72 with a significance level of smaller than 0.0001 and hence an overall good model fit. Comparison between the derivative and validation groups revealed similar rates of 30-day readmission between the cohorts. The sensitivity and specificity of predicting 30-day readmission in high risk patients with recurrent epistaxis (RHINO-ooze score equal/larger than 6) was 81% and 84%, respectively.
The RHINO-ooze scoring tool demonstrates good specificity and sensitivity in predicting the risk of 30 day readmission in patients with epistaxis and can be used as an adjunct to clinical decision making with regards to timing of operative intervention in order to reduce readmission rates.
