Recurrence After Operative Management of Intrahepatic Cholangiocarcinoma

INTRODUCTION: Data on recurrence after operation for intrahepatic cholangiocarcinoma (ICC) are limited. We sought to investigate rates and patterns of recurrence in patients after operative intervention for ICC. METHODS: We identified 301 patients who underwent operation for ICC between 1990 and 2011 from an international, multi-institutional database. Clinicopathologic data, recurrence patterns, and recurrence-free survival (RFS) were analyzed. RESULTS: During the median follow up duration of 31 months (range 1-208), 53.5% developed a recurrence. Median RFS was 20.2 months and 5-year actuarial disease-free survival, 32.1%. The most common site for initial recurrence after operation of ICC was intrahepatic (n = 98; 60.9%), followed by simultaneous intra- and extrahepatic disease (n = 30; 18.6%); 33 (21.0%) patients developed extrahepatic recurrence only as the first site of recurrence. Macrovascular invasion (hazard ratio [HR], 2.08; 95% confidence interval [CI], 1.34-3.21; P < .001), nodal metastasis (HR, 1.55; 95% CI, 1.01-2.45; P = .04), unknown nodal status (HR, 1.57; 95% CI, 1.10-2.25; P = .04), and tumor size ≥5 cm (HR, 1.84; 95% CI, 1.28-2.65; P < .001) were independently associated with increased risk of recurrence. Patients were assigned a clinical score from 0 to 3 according to the presence of these risk factors. The 5-year RFS for patients with scores of 0, 1, 2, and 3 was 61.8%, 36.2%, 19.5%, and 9.6%, respectively. CONCLUSION: Recurrence after operative intervention for ICC was common. Disease recurred both at intra- and extrahepatic sites with roughly the same frequency. Factors such as lymph node metastasis, tumor size, and vascular invasion predict highest risk of recurrence.

Sepsis Mortality Prediction Based on Predisposition, Infection and Response

OBJECTIVE: To empirically test, based on a large multicenter, multinational database, whether a modified PIRO (predisposition, insult, response, and organ dysfunction) concept could be applied to predict mortality in patients with infection and sepsis. DESIGN: Substudy of a multicenter multinational cohort study (SAPS 3). PATIENTS: A total of 2,628 patients with signs of infection or sepsis who stayed in the ICU for >48 h. Three boxes of variables were defined, according to the PIRO concept. Box 1 (Predisposition) contained information about the patient's condition before ICU admission. Box 2 (Injury) contained information about the infection at ICU admission. Box 3 (Response) was defined as the response to the infection, expressed as a Sequential Organ Failure Assessment score after 48 h. INTERVENTIONS: None. MAIN MEASUREMENTS AND RESULTS: Most of the infections were community acquired (59.6%); 32.5% were hospital acquired. The median age of the patients was 65 (50-75) years, and 41.1% were female. About 22% (n=576) of the patients presented with infection only, 36.3% (n=953) with signs of sepsis, 23.6% (n=619) with severe sepsis, and 18.3% (n=480) with septic shock. Hospital mortality was 40.6% overall, greater in those with septic shock (52.5%) than in those with infection (34.7%). Several factors related to predisposition, infection and response were associated with hospital mortality. CONCLUSION: The proposed three-level system, by using objectively defined criteria for risk of mortality in sepsis, could be used by physicians to stratify patients at ICU admission or shortly thereafter, contributing to a better selection of management according to the risk of death.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.

Characterizing the Risk Profiles of Intensive Care Units

OBJECTIVE: To develop a new method to evaluate the performance of individual ICUs through the calculation and visualisation of risk profiles. METHODS: The study included 102,561 patients consecutively admitted to 77 ICUs in Austria. We customized the function which predicts hospital mortality (using SAPS II) for each ICU. We then compared the risks of hospital mortality resulting from this function with the risks which would be obtained using the original function. The derived risk ratio was then plotted together with point-wise confidence intervals in order to visualise the individual risk profile of each ICU over the whole spectrum of expected hospital mortality. MAIN MEASUREMENTS AND RESULTS: We calculated risk profiles for all ICUs in the ASDI data set according to the proposed method. We show examples how the clinical performance of ICUs may depend on the severity of illness of their patients. Both the distribution of the Hosmer-Lemeshow goodness-of-fit test statistics and the histogram of the corresponding P values demonstrated a good fit of the individual risk models. CONCLUSIONS: Our risk profile model makes it possible to evaluate ICUs on the basis of the specific risk for patients to die compared to a reference sample over the whole spectrum of hospital mortality. Thus, ICUs at different levels of severity of illness can be directly compared, giving a clear advantage over the use of the conventional single point estimate of the overall observed-to-expected mortality ratio.