INTRODUCTION: Early identification of prognostic factors is essential to improve the grim prognosis associated with left-sided infective endocarditis. This group identified three independent risk factors obtained within 72 h of admission, (Staphylococcus aureus, heart failure and periannular complications) for inhospital mortality or urgent surgery in a series of 317 patients diagnosed at five tertiary centres (derivation sample). A stratification score was constructed for the test cohort by a simple arithmetic sum of the number of variables present. The goal was to validate this model internally and externally in a prospective manner with two different cohorts of patients. METHODS: The appropriateness of the model was tested prospectively on predicting events in two cohorts of patients with left-sided endocarditis: internally with the 263 consecutive patients diagnosed at the same centres where the model was derived (internal validation sample), and externally with 264 patients admitted at another hospital (external validation sample). RESULTS: The discriminatory power of the model, expressed as the area under the receiver operating characteristic curve was similar between derivation and both validation samples (internal 0.67 vs 0.68, p=0.79; external 0.67 vs p=0.74, p=0.09). There was a progressive, significant pattern of increasing event rates as the risk stratification score increased in both validation cohorts (p<0.001 by χ² for trend). CONCLUSIONS: The early risk stratification model derived, based on variables obtained within 72 h of admission, is applicable to different populations with left-sided endocarditis. A simple bedside assessment tool is provided to clinicians that identifies patients at high risk of having an adverse event.
INTRODUCTION: Early identification of prognostic factors is essential to improve the grim prognosis associated with left-sided infective endocarditis. This group identified three independent risk factors obtained within 72 h of admission, (Staphylococcus aureus, heart failure and periannular complications) for inhospital mortality or urgent surgery in a series of 317 patients diagnosed at five tertiary centres (derivation sample). A stratification score was constructed for the test cohort by a simple arithmetic sum of the number of variables present. The goal was to validate this model internally and externally in a prospective manner with two different cohorts of patients. METHODS: The appropriateness of the model was tested prospectively on predicting events in two cohorts of patients with left-sided endocarditis: internally with the 263 consecutive patients diagnosed at the same centres where the model was derived (internal validation sample), and externally with 264 patients admitted at another hospital (external validation sample). RESULTS: The discriminatory power of the model, expressed as the area under the receiver operating characteristic curve was similar between derivation and both validation samples (internal 0.67 vs 0.68, p=0.79; external 0.67 vs p=0.74, p=0.09). There was a progressive, significant pattern of increasing event rates as the risk stratification score increased in both validation cohorts (p<0.001 by χ² for trend). CONCLUSIONS: The early risk stratification model derived, based on variables obtained within 72 h of admission, is applicable to different populations with left-sided endocarditis. A simple bedside assessment tool is provided to clinicians that identifies patients at high risk of having an adverse event.