title: What is the best method for estimating the burden of severe sepsis in the United States?
abstract: OBJECTIVE
  The aim of the study was to compare estimates of hospitalizations, outcomes, and costs produced by 2 approaches for defining severe sepsis.
  
  
  METHODS
  We used the Nationwide Inpatient Sample to study adults hospitalized in the United States in 2007. We defined severe sepsis using 2 previously published algorithms: (1) the presence of a principal or secondary diagnosis of septicemia combined with organ dysfunction or (2) the presence of a principal or secondary diagnosis of septicemia or another infection (eg, pneumonia) combined with organ dysfunction. For each approach, we calculated the weighted frequency of hospitalizations, population-based mortality rates, and geometric mean costs.
  
  
  RESULTS
  A total of 719099 (SD, 16676) hospitalizations had a diagnosis of septicemia and a diagnosis of organ dysfunction. A total of 2.5 million hospitalizations were recorded, with a diagnosis code for either septicemia or infection combined with a diagnosis code for organ dysfunction. Hospitalizations without a diagnosis code for septicemia had lower rates of respiratory failure (35% vs 51%, P < .001) or shock (20% vs 46%, P < .001), lower in-hospital mortality (8% vs 29%, P < .001), and lower mean costs.
  
  
  CONCLUSIONS
  An approach that requires a diagnosis code for septicemia and a diagnosis code for organ dysfunction yields estimates of disease burden and outcomes that are more consistent with chart-based studies.
authors.affiliation: Center for Quality of Care Research, Baystate Medical Center, Springfield, MA 01199, USA. lagutc@gmail.com
authors.forename: Tara
authors.initials: T
authors.lastname: Lagu
authors.forename: Michael B
authors.initials: MB
authors.lastname: Rothberg
authors.forename: Meng-Shiou
authors.initials: MS
authors.lastname: Shieh
authors.forename: Penelope S
authors.initials: PS
authors.lastname: Pekow
authors.forename: Jay S
authors.initials: JS
authors.lastname: Steingrub
authors.forename: Peter K
authors.initials: PK
authors.lastname: Lindenauer
mesh_terms: D000367:Age Factors; D017281:Cost of Illness; D003625:Data Collection; D005102:Health Expenditures; D006760:Hospitalization / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D038801:International Classification of Diseases / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000191:economics / Q000706:statistics & numerical data; D063305:Organ Dysfunction Scores; D044469:Racial Groups; D018805:Sepsis / Q000191:economics / Q000453:epidemiology* / Q000628:therapy; D012737:Sex Factors; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology