BACKGROUND AND PURPOSE: Reliable identification of stroke patients in the field by prehospital personnel could expedite delivery of acute stroke therapy. The Los Angeles Prehospital Stroke Screen (LAPSS) is a 1-page instrument designed to allow prehospital personnel to rapidly identify acute stroke patients in the field. We performed a prospective, in-the-field validation study of the LAPSS. METHODS: Paramedics assigned to 3 University of California at Los Angeles-based advanced life support units were trained and certified in use of the LAPSS. Over 7 months, paramedics completed the LAPSS on noncomatose, nontrauma patients with complaints suggestive of neurological disease. LAPSS form stroke identification results were compared with emergency department and final hospital discharge diagnoses. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and likelihood ratios were calculated for LAPSS identification of ischemic stroke, currently symptomatic transient ischemic attack, and intracerebral hemorrhage. RESULTS: Of a total of 1298 runs, 34% were for nontraumatic, noncomatose neurologically relevant complaints. Thirty-six of these patients (3% of all transports) had a final diagnosis of acute symptomatic cerebrovascular disease (21 ischemic strokes, 7 transient ischemic attacks, and 8 intracerebral hemorrhages). LAPSS forms were completed on 206 patients. Paramedic performance when completing the LAPSS demonstrated sensitivity of 91% (95% CI, 76% to 98%), specificity of 97% (95% CI, 93% to 99%), positive predictive value of 86% (95% CI, 70% to 95%), and negative predictive value of 98% (95% CI, 95% to 99%). With correction for the 4 documentation errors, positive predictive value increased to 97% (95% CI, 84% to 99%). CONCLUSIONS: The LAPSS allows prehospital personnel to identify patients with acute cerebral ischemia and intracerebral hemorrhage with a high degree of sensitivity and specificity.
BACKGROUND AND PURPOSE: Reliable identification of strokepatients in the field by prehospital personnel could expedite delivery of acute stroke therapy. The Los Angeles Prehospital Stroke Screen (LAPSS) is a 1-page instrument designed to allow prehospital personnel to rapidly identify acute strokepatients in the field. We performed a prospective, in-the-field validation study of the LAPSS. METHODS: Paramedics assigned to 3 University of California at Los Angeles-based advanced life support units were trained and certified in use of the LAPSS. Over 7 months, paramedics completed the LAPSS on noncomatose, nontrauma patients with complaints suggestive of neurological disease. LAPSS form stroke identification results were compared with emergency department and final hospital discharge diagnoses. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and likelihood ratios were calculated for LAPSS identification of ischemic stroke, currently symptomatic transient ischemic attack, and intracerebral hemorrhage. RESULTS: Of a total of 1298 runs, 34% were for nontraumatic, noncomatose neurologically relevant complaints. Thirty-six of these patients (3% of all transports) had a final diagnosis of acute symptomatic cerebrovascular disease (21 ischemic strokes, 7 transient ischemic attacks, and 8 intracerebral hemorrhages). LAPSS forms were completed on 206 patients. Paramedic performance when completing the LAPSS demonstrated sensitivity of 91% (95% CI, 76% to 98%), specificity of 97% (95% CI, 93% to 99%), positive predictive value of 86% (95% CI, 70% to 95%), and negative predictive value of 98% (95% CI, 95% to 99%). With correction for the 4 documentation errors, positive predictive value increased to 97% (95% CI, 84% to 99%). CONCLUSIONS: The LAPSS allows prehospital personnel to identify patients with acute cerebral ischemia and intracerebral hemorrhage with a high degree of sensitivity and specificity.
Authors: Christopher W Seymour; David Carlbom; Ruth A Engelberg; Jonathan Larsen; Eileen M Bulger; Michael K Copass; Thomas D Rea Journal: J Emerg Med Date: 2011-11-08 Impact factor: 1.484
Authors: Brian S Katz; Opeolu Adeoye; Heidi Sucharew; Joseph P Broderick; Jason McMullan; Pooja Khatri; Michael Widener; Kathleen S Alwell; Charles J Moomaw; Brett M Kissela; Matthew L Flaherty; Daniel Woo; Simona Ferioli; Jason Mackey; Sharyl Martini; Felipe De Los Rios la Rosa; Dawn O Kleindorfer Journal: Stroke Date: 2017-07-12 Impact factor: 7.914