Comparison and combination of a hemodynamics/biomarkers-based model with simplified PESI score for prognostic stratification of acute pulmonary embolism: findings from a real world study

Background: Prognostic stratification is of utmost importance for management of acute Pulmonary Embolism (PE) in clinical practice. Many prognostic models have been proposed, but which is the best prognosticator in real life remains unclear. The aim of our study was to compare and combine the predictive values of the hemodynamics/biomarkers based prognostic model proposed by European Society of Cardiology (ESC) in 2008 and simplified PESI score (sPESI).Methods: Data records of 452 patients discharged for acute PE from Internal Medicine wards of Tuscany (Italy) were analysed. The ESC model and sPESI were retrospectively calculated and compared by using Areas under Receiver Operating Characteristics (ROC) Curves (AUCs) and finally the combination of the two models was tested in hemodinamically stable patients. All cause and PE-related in-hospital mortality and fatal or major bleedings were the analyzed endpointsResults: All cause in-hospital mortality was 25% (16.6% PE related) in high risk, 8.7% (4.7%) in intermediate risk and 3.8% (1.2%) in low risk patients according to ESC model. All cause in-hospital mortality was 10.95% (5.75% PE related) in patients with sPESI score ≥1 and 0% (0%) in sPESI score 0. Predictive performance of sPESI was not significantly different compared with 2008 ESC model both for all cause (AUC sPESI 0.711, 95% CI: 0.661-0.758 versus ESC 0.619, 95% CI: 0.567-0.670, difference between AUCs 0.0916, p=0.084) and for PE-related mortality (AUC sPESI 0.764, 95% CI: 0.717-0.808 versus ESC 0.650, 95% CI: 0.598-0.700, difference between AUCs 0.114, p=0.11). Fatal or major bleedings occurred in 4.30% of high risk, 1.60% of intermediate risk and 2.50% of low risk patients according to 2008 ESC model, whereas these occurred in 1.80% of high risk and 1.45% of low risk patients according to sPESI, respectively. Predictive performance for fatal or major bleeding between two models was not significantly different (AUC sPESI 0.658, 95% CI: 0.606-0.707 versus ESC 0.512, 95% CI: 0.459-0.565, difference between AUCs 0.145, p=0.34). In hemodynamically stable patients, the combined endpoint in-hospital PE-related mortality and/or fatal or major bleeding (adverse events) occurred in 0% of patients with low risk ESC model and sPESI score 0, whilst it occurred in 5.5% of patients with low-risk ESC model but sPESI ≥1. In intermediate risk patients according to ESC model, adverse events occurred in 3.6% of patients with sPESI score 0 and 6.65% of patients with sPESI score ≥1.Conclusions: In real world, predictive performance of sPESI and the hemodynamic/biomarkers-based ESC model as prognosticator of in-hospital mortality and bleedings is similar. Combination of sPESI 0 with low risk ESC model may identify patients with very low risk of adverse events and candidate for early hospital discharge or home treatment.

Use of the Flugelman index for identifying patients who are difficult to discharge from the hospital

Introduction: To evaluate the use of multidimensional assessment based on the Fluegelman Index (FI) to identify internal medicine patients who are likely to be difficult to discharge from the hospital. Materials and methods: Have been evaluated all patients admitted to the medical wards of the District General Hospital of Arezzo from September 1 to October 31, 2007. We collected data on age, sex, socioeconomic condition, cause of admission, comorbidity score preadmission functional status (Barthel Index), incontinence, feeding problems, length of hospitalization, condition at discharge, and type of discharge. The FI cut off for difficult discharge was > 17. Results: Of the 413 patients (mean age 80 + 11.37 years; percentage of women, 56.1%) included in the study, 109 (26.39%) had Flugelman Index > 17. These patients were significantly older than the patients with lower FIs (85 + 9.35 vs 78 + 11.58 years, p < 0.001), more likely to be admitted for pneumonia (22% vs. 4.9% of those with lower FIs; p < 0,001). They also had more comorbidity, loss of autonomy, cognitive impairment, social frailty, and nursing care needs. The subgroup with FIs>17 had significantly higher in-hospital mortality (30.28% vs 6.25%, p < 0.001), longer hospital stay (13 vs. 10 days, p < 0.05), and higher rates of discharge to nursing homes. Conclusions: Evaluation of internal medicine patients with the Flugelman Index may be helpful for identifying more critical patients likely to require longer hospitalization and to detect factors affecting the hospital stay. This information can be useful for more effective discharge planning