• Medicine · Jul 2015

    Randomized Controlled Trial Multicenter Study

    Risk Models of Operative Morbidities in 16,930 Critically Ill Surgical Patients Based on a Japanese Nationwide Database.

    • Zenichiro Saze, Hiroaki Miyata, Hiroyuki Konno, Mitsukazu Gotoh, Takayuki Anazawa, Ai Tomotaki, Go Wakabayashi, and Masaki Mori.
    • From the Japanese Society of Gastroenterological Surgery (JSGS) (ZS, TA, MM), JSGS Database Committee (HM, HK, MG, GW), and National Clinical Database (HM, AT), Tokyo, Japan.
    • Medicine (Baltimore). 2015 Jul 1;94(30):e1224.

    AbstractThe aim of the study was to evaluate preoperative variables predictive of lethal morbidities in critically ill surgical patients at a national level.There is no report of risk stratification for morbidities associated with mortality in critically ill patients with acute diffuse peritonitis (ADP).We examined data from 16,930 patients operated during 2011 and 2012 in 1546 different hospitals for ADP identified in the National Clinical Database of Japan. We analyzed morbidities significantly associated with operative mortality. Based on 80% of the population, we calculated independent predictors for these morbidities. The risk factors were validated using the remaining 20%.The operative mortality was 14.1%. Morbidity of any grade occurred in 40.2% of patients. Morbidities correlated with mortality, including septic shock, progressive renal insufficiency, prolonged ventilation >48 hours, systemic sepsis, central nervous system (CNS) morbidities, acute renal failure and pneumonia, and surgical site infection (SSI), were selected for risk models. A total of 18 to 29 preoperative variables were selected per morbidity and yielded excellent C-indices for each (septic shock: 0.851; progressive renal insufficiency: 0.878; prolonged ventilation >48 h: 0.849; systemic sepsis: 0.839; CNS morbidities: 0.848; acute renal failure: 0.868; pneumonia: 0.830; and SSI: 0.688).We report the first risk stratification study on lethal morbidities in critically ill patients with ADP using a nationwide surgical database. These risk models will contribute to patient counseling and help predict which patients require more aggressive surgical and novel pharmacological interventions.

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