A Communication Intervention for Training Southern European Oncologists to Recognize Psychosocial Morbidity in Cancer. I - Development of the Model and Preliminary Results on Physicians' Satisfaction

BACKGROUND: The detection of psychosocial distress is a significant communication problem in Southern Europe and other countries. Work in this area is hampered by a lack of data. Because not much is known about training aimed at improving the recognition of psychosocial disorders in cancer patients, we developed a basic course model for medical oncology professionals. METHODS: A specific educational and experiential model (12 hours divided into 2 modules) involving formal teaching (ie, journal articles, large-group presentations), practice in small groups (ie, small-group exercises and role playing), and discussion in large groups was developed with the aim of improving the ability of oncologists to detect emotional disturbances in cancer patients (ie, depression, anxiety, and adjustment disorders). RESULTS: A total of 30 oncologists from 3 Southern European countries (Italy, Portugal, and Spain) participated in the workshop. The training course was well accepted by most participants who expressed general satisfaction and a positive subjective perception of the utility of the course for clinical practice. Of the total participants, 28 physicians (93.3%) thought that had they been exposed to this material sooner, they would have incorporated the techniques received in the workshop into their practices; 2 participants stated they would likely have done so. Half of the doctors (n = 15) believed that their clinical communication techniques were improved by participating in the workshop, and the remaining half thought that their abilities to communicate with cancer patients had improved. CONCLUSIONS: This model is a feasible approach for oncologists and is easily applicable to various oncology settings. Further studies will demonstrate the effectiveness of this method for improving oncologists skills in recognizing emotional disorders in their patients with cancer.

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.

Psychosocial Determinants of Quality of Life 6 Months After Transplantation: Longitudinal Prospective Study

OBJECTIVES: We sought to investigate the psychosocial determinants of quality of life at 6 months after transplantation. METHODS: A sample of liver transplant candidates (n = 60), composed of consecutive patients (25% with familial amyloid polyneuropathy [FAP]) attending outpatient clinics was assessed in the pretransplant period using the Neo Five Factor Inventory, Hospital Anxiety and depression Scale (HADS), Brief COPE, and SF-36, a quality-of-life, self-rating questionnaire. Six months after transplantation, these patients were assessed by means of the SF-36. RESULTS: Psychosocial predictors where found by means of multiple regression analysis. The physical component of quality of life at 6 months after transplantation was determined based upon coping strategies and physical quality of life in the pretransplant period (this model explained 32% of variance). The mental component at 6 months after transplantation was determined by depression in the pretransplant period and by clinical diagnoses of patients. Because FAP patients show a lower mental component of quality of life, this diagnosis explained 25% of the variance. CONCLUSIONS: Our findings suggested that coping strategies and depression measured in the pretransplant period are important determinants of quality of life at 6 months after liver transplantation.

Acute Kidney Injury after Pediatric Cardiac Surgery: Risk Factors and Outcomes. Proposal for a Predictive Model

Objectives: To characterize the epidemiology and risk factors for acute kidney injury (AKI) after pediatric cardiac surgery in our center, to determine its association with poor short-term outcomes, and to develop a logistic regression model that will predict the risk of AKI for the study population. Methods: This single-center, retrospective study included consecutive pediatric patients with congenital heart disease who underwent cardiac surgery between January 2010 and December 2012. Exclusion criteria were a history of renal disease, dialysis or renal transplantation. Results: Of the 325 patients included, median age three years (1 day---18 years), AKI occurred in 40 (12.3%) on the first postoperative day. Overall mortality was 13 (4%), nine of whom were in the AKI group. AKI was significantly associated with length of intensive care unit stay, length of mechanical ventilation and in-hospital death (p<0.01). Patients’ age and postoperative serum creatinine, blood urea nitrogen and lactate levels were included in the logistic regression model as predictor variables. The model accurately predicted AKI in this population, with a maximum combined sensitivity of 82.1% and specificity of 75.4%. Conclusions: AKI is common and is associated with poor short-term outcomes in this setting. Younger age and higher postoperative serum creatinine, blood urea nitrogen and lactate levels were powerful predictors of renal injury in this population. The proposed model could be a useful tool for risk stratification of these patients.