4.5 Article

Models predicting the risks of six life-threatening morbidities and bile leakage in 14,970 hepatectomy patients registered in the National Clinical Database of Japan

期刊

MEDICINE
卷 95, 期 49, 页码 -

出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MD.0000000000005466

关键词

hepatectomy; morbidity; risk model

资金

  1. Grants-in-Aid for Scientific Research [16K10437] Funding Source: KAKEN

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To construct a robust morbidity risk-prediction model based on a Japanese nationwide web-based database of patients who underwent liver surgery. Although liver resection has become safer, patient mortality and morbidity still occur. This study investigated postoperative morbidity risks in patients who underwent hepatectomy in Japan at institutions registered in the National Clinical Database. This analysis involved 14,970 patients who underwent hepatectomy of more than 1 section, except for left lateral sectionectomy, during 2011 and 2012 at 1192 hospitals in Japan. Patients were randomized into 2 subsets, with 80% of patients analyzed for model development and the remaining 20% for model validation. Rates of 90-day inhospital mortality and overall morbidity were 3.7% and 25.7%, respectively. Rates of surgical site infection and bile leakage were 9.0% and 8.0%, respectively, but these morbidities showed little association with mortality. Rates of nonsurgical complications, including postoperative transfusion over 5 units, unexpected intubation, renal failure, cardiac events, septic shock, and postoperative pneumonia, ranged from 0.2% to 2.6%. These complications were highly associated with mortality, suggesting they were life-threatening. Risk models for morbidity yielded high C-indices for transfusion of over 5 units (0.758), unplanned intubation (0.755), renal failure (0.80), cardiac events (0.779), septic shock (0.783), pneumonia (0.768), and bile leakage (0.676). Preoperative parameters/comorbidities can accurately predict life-threatening complications after hepatectomy. These models allow early identification of patients at risk of mortality and may be useful in deciding on surgical interventions and in improving surgical quality.

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