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: Background Liver resection is considered to offer the only hope of cure for patients with liver malignancy. However, there are concerns about its safety, particularly in view of the increasing efficacy of less invasive strategies. No systematic review of prognostic research in liver resections has yet been performed.MethodsA systematic search identified articles published between 1999 and 2012 that performed a risk prediction analysis in patients undergoing liver resection. Studies were included if an outcome occurring within 90 days of surgery was identified, multivariable analysis performed and regression coefficients provided. The main endpoints were the outcomes and predictors chosen by the investigators, their definition, the performance and validity of the models, and the quality of the study as assessed using the QUIPS (quality in prognosis studies) tool.ResultsA total of 91 studies were included. Eleven were prospective, but only two of these were registered. Twenty-eight endpoints were identified. These focused on postoperative morbidity or mortality, but many were redundant or ill defined and other relevant patient-reported outcomes were lacking. Predictors were not standardized, were poorly defined and overlapped. Only nine studies assessed the performance of their models and seven made an internal or temporal validation, but none reported an external validation or impact analysis. The median QUIPS score was 34 out of 50, indicating a high risk for bias.Conclusion Prognostic research in liver resection is still at the developmental stage.HPB 10/2014; 17(3). DOI:10.1111/hpb.12346 · 2.05 Impact Factor
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ABSTRACT: The aim of this study was to identify risk factors associated with unplanned readmissions after hepatectomies. Patients who underwent hepatectomies between January and December of 2011 were identified using the ACS-NSQIP database. A multivariate logistic regression analysis was performed to determine predictors of unplanned readmissions related to the procedure within 30 days. Unplanned readmissions occurred in 10.5 % of all patients who received a hepatectomy. On multivariate analysis, transfusion within 72 h after surgery (odds ratio [OR] 1.74, p < 0.001), complexity of procedure (extended, OR 1.84, p = 0.004; right hepatectomy, OR 1.66, p = 0.003), and longer operative time (>median 320 min, OR 2.43, p < 0.001) were independent perioperative predictors of unplanned readmissions. Independent preoperative risk factors included elevated alkaline phosphatase (OR 1.45, p = 0.017), bleeding disorders (OR 1.72, p = 0.051), and lower albumin levels (OR 1.30, p = 0.036). Transfusion, complexity of procedure, and duration of operation were the strongest predictors of unplanned readmissions after liver resection.Journal of Gastrointestinal Surgery 12/2014; 19(2). DOI:10.1007/s11605-014-2713-z · 2.39 Impact Factor
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ABSTRACT: Adequate comorbidity risk adjustment is central for reliable outcome prediction and provider performance evaluation. The two most commonly employed risk-adjustment methods in orthopaedic surgery were not originally validated in this patient population. We sought (1) to develop a single numeric comorbidity score for predicting inpatient mortality in patients undergoing orthopaedic surgery by combining and reweighting the conditions included in the Charlson and Elixhauser measures, and to compare its predictive performance to each of the separate component scores. We also (2) evaluated the new score separately for spine surgery, adult reconstruction, hip fracture, and musculoskeletal oncology admissions. Data from the National Hospital Discharge Survey for the years 1990 through 2007 were obtained. A comorbidity score for predicting inpatient mortality was developed by combining conditions from the Charlson and Elixhauser measures. Weights were derived from a random sample of 80 % of the cohort (n = 26,454,972), and the predictive ability of the new score was internally validated on the remaining 20 % (n = 6,739,169). Performance of scores was assessed and compared using the area under the receiver operating characteristic curve (AUC) derived from multivariable logistic regression models. The new combined comorbidity score (AUC = 0.858, 95 % CI 0.856-0.859) performed 58 % better than the Charlson score (AUC = 0.794, 95 % CI 0.792-0.796) and 12 % better than the Elixhauser score (AUC = 0.845, 95 % CI 0.844-0.847). Of the seven conditions that received the highest weights in the new combined score, only three of them were included in both the Charlson and the Elixhauser indices. The new combined score achieved higher discriminatory power for all orthopaedic admission subgroups. A single numeric comorbidity score combining conditions from the Charlson and Elixhauser models provided better discrimination of inpatient mortality than either of its constituent scores. Future research should test this score in other populations and data settings.International Orthopaedics 02/2015; DOI:10.1007/s00264-015-2702-1 · 2.02 Impact Factor