In-Hospital Mortality for Liver Resection for Metastases: A Simple Risk Score
ABSTRACT Surgical management of liver metastases from various primaries is increasingly common. The mortality of such procedures is not well-defined. Accurate predictions for perioperative risk could augment decision-making.
The Nationwide Inpatient Sample was queried (1998-2005) for patient-discharges for hepatic procedures for metastases. Logistic regression and bootstrap methods were used to create an integer score for estimating the risk of in-hospital mortality using patient demographics, comorbidities, procedure, and hospital type. A randomly selected sample of 80% of the cohort was used to create the risk score, with validation of the score in the remaining 20%.
For the total 50,537 patient-discharges, overall in-hospital mortality was 2.6%. Factors included in the model were age, sex, Charlson comorbidity score, procedure type, and teaching hospital status. Integer values were assigned for calculating an additive score. Four score groups were assembled to stratify risk, with a 15-fold gradient of mortality ranging from 0.9% to 14.7% (P<0.0001). In the derivation and the validation set, the score discriminated well, with a c-statistic of 0.72 and 0.72, respectively.
An integer-based risk score can be used to predict in-hospital mortality after hepatic procedure for metastases, and may be useful for preoperative patient counseling.
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ABSTRACT: Tesis Univ. Granada. Departamento de Radiología y Medicina Física. Leída el 7 de abril de 2010
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ABSTRACT: Reported morbidity varies widely for laparoscopic cholecystectomy (LC). A reliable method to determine complication risk may be useful to optimize care. We developed an integer-based risk score to determine the likelihood of major complications following LC. Using the Nationwide Inpatient Sample 1998-2006, patient discharges for LC were identified. Using previously validated methods, major complications were assessed. Preoperative covariates including patient demographics, disease characteristics, and hospital factors were used in logistic regression/bootstrap analyses to generate an integer score predicting postoperative complication rates. A randomly selected 80% was used to create the risk score, with validation in the remaining 20%. Patient discharges (561,923) were identified with an overall complication rate of 6.5%. Predictive characteristics included: age, sex, Charlson comorbidity score, biliary tract inflammation, hospital teaching status, and admission type. Integer values were assigned and used to calculate an additive score. Three groups stratifying risk were assembled, with a fourfold gradient for complications ranging from 3.2% to 13.5%. The score discriminated well in both derivation and validation sets (c-statistic of 0.7). An integer-based risk score can be used to predict complications following LC and may assist in preoperative risk stratification and patient counseling.Journal of Gastrointestinal Surgery 09/2009; 13(11):1929-36. DOI:10.1007/s11605-009-0979-3 · 2.39 Impact Factor
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ABSTRACT: Hepatic resections represent a standard procedure for both benign and malignant liver diseases. Nevertheless, typical complications arise follow-ing hepatic surgery. Besides common problems, such as bile leakage or impaired wound healing, rare complications, like progressive liver failure or portal vein thrombosis are observed. Mortality and morbidity after liver resection depend on the preoperative constitution of the patient, on the state of the liver parenchyma and on the re-main-ing liver volume. In particular, a marked steatosis increases both morbidity and mortality of hepatic resections. The advances of modern chemotherapy increases the number of surgical patients, who were previously not resectable. However, the chemotherapy induced hepatotoxicity implies additional problems, thus increasing the morbidity of liver resections. Therefore, before planning hepatic surgery, the individual situation of the patient has to be evaluated in order to maximise the security of the operative procedure.Zentralblatt für Chirurgie 04/2010; 135(2):112-20. DOI:10.1055/s-0030-1247331 · 1.19 Impact Factor