OBJECTIVE: Bacteremia is relatively common in patients with skin and skin-structure infection (SSSI) severe enough to require hospitalization. We used selected demographic and clinical characteristics easily assessable at initial evaluation to develop a model for the early identification of patients with SSSI who are at higher risk for bacteremia. PARTICIPANTS: A large database of adults hospitalized with SSSI at 97 hospitals in the United States during the period from 2003 through 2007 and from whom blood samples were obtained for culture at admission. METHODS: We compared selected candidate predictor variables for patients shown to have bacteremia and patients with no demonstrated bacteremia. Using stepwise logistic regression to identify independent risk factors for bacteremia, we derived a model by using 75% of a randomly split cohort, converted the model coefficients into a risk score system, and then we validated it by using the remaining 25% of the cohort. RESULTS: Bacteremia was documented in 1,021 (11.7%) of the 8,747 eligible patients. Independent predictors of bacteremia (P<.001) were infected device or prosthesis, respiratory rate less than 10 or more than 29 breaths per minute, pulse rate less than 49 or more than 125 beats per minute, temperature less than 35.6 degrees C or at least 38.0 degrees C, white blood cell band percentage of 7% or more, white blood cell count greater than 11x10(9)/L, healthcare-associated infection, male sex, and older age. The bacteremia rates ranged from 3.7% (lowest decile) to 30.6% (highest decile) (P<.001). The model C statistic was 0.71; the Hosmer-Lemeshow test P value was .36, indicating excellent model calibration. CONCLUSIONS: Using data available at hospital admission, we developed a risk score that differentiated SSSI patients at low risk for bacteremia from patients at high risk. This score may help clinicians identify patients who require more intensive monitoring or antimicrobial regimens appropriate for treating bacteremia.
OBJECTIVE:Bacteremia is relatively common in patients with skin and skin-structure infection (SSSI) severe enough to require hospitalization. We used selected demographic and clinical characteristics easily assessable at initial evaluation to develop a model for the early identification of patients with SSSI who are at higher risk for bacteremia. PARTICIPANTS: A large database of adults hospitalized with SSSI at 97 hospitals in the United States during the period from 2003 through 2007 and from whom blood samples were obtained for culture at admission. METHODS: We compared selected candidate predictor variables for patients shown to have bacteremia and patients with no demonstrated bacteremia. Using stepwise logistic regression to identify independent risk factors for bacteremia, we derived a model by using 75% of a randomly split cohort, converted the model coefficients into a risk score system, and then we validated it by using the remaining 25% of the cohort. RESULTS:Bacteremia was documented in 1,021 (11.7%) of the 8,747 eligible patients. Independent predictors of bacteremia (P<.001) were infected device or prosthesis, respiratory rate less than 10 or more than 29 breaths per minute, pulse rate less than 49 or more than 125 beats per minute, temperature less than 35.6 degrees C or at least 38.0 degrees C, white blood cell band percentage of 7% or more, white blood cell count greater than 11x10(9)/L, healthcare-associated infection, male sex, and older age. The bacteremia rates ranged from 3.7% (lowest decile) to 30.6% (highest decile) (P<.001). The model C statistic was 0.71; the Hosmer-Lemeshow test P value was .36, indicating excellent model calibration. CONCLUSIONS: Using data available at hospital admission, we developed a risk score that differentiated SSSI patients at low risk for bacteremia from patients at high risk. This score may help clinicians identify patients who require more intensive monitoring or antimicrobial regimens appropriate for treating bacteremia.
Authors: Benjamin A Goldstein; Ann Marie Navar; Michael J Pencina; John P A Ioannidis Journal: J Am Med Inform Assoc Date: 2016-05-17 Impact factor: 4.497
Authors: Loren G Miller; Robert S Daum; C Buddy Creech; David Young; Michele D Downing; Samantha J Eells; Stephanie Pettibone; Rebecca J Hoagland; Henry F Chambers Journal: N Engl J Med Date: 2015-03-19 Impact factor: 91.245
Authors: Robert S Daum; Loren G Miller; Lilly Immergluck; Stephanie Fritz; C Buddy Creech; David Young; Neha Kumar; Michele Downing; Stephanie Pettibone; Rebecca Hoagland; Samantha J Eells; Mary G Boyle; Trisha Chan Parker; Henry F Chambers Journal: N Engl J Med Date: 2017-06-29 Impact factor: 91.245
Authors: Loren G Miller; Debra F Eisenberg; Honghu Liu; Chun-Lan Chang; Yan Wang; Rakesh Luthra; Anna Wallace; Christy Fang; Joseph Singer; Jose A Suaya Journal: BMC Infect Dis Date: 2015-08-21 Impact factor: 3.090
Authors: J Collazos; B de la Fuente; J de la Fuente; A García; H Gómez; C Menéndez; H Enríquez; P Sánchez; M Alonso; I López-Cruz; M Martín-Regidor; A Martínez-Alonso; J Guerra; A Artero; M Blanes; V Asensi Journal: BMC Infect Dis Date: 2020-03-12 Impact factor: 3.090
Authors: M Patel; S I Lee; R K Akyea; D Grindlay; N Francis; N J Levell; P Smart; J Kai; K S Thomas Journal: Br J Dermatol Date: 2019-06-28 Impact factor: 9.302