In-hospital mortality in stroke patients

Risk Factors for Pulmonary Embolism in Patients with Paralysis and Deep Venous Thrombosis

Background. Venous thromboembolism is a frequent complication and an important cause of death in patients with paralysis. We aimed to investigate predictors of pulmonary embolism (PE) and the impact of PE on the survival of patients with paralysis in comparison to those with deep venous thrombosis or thrombophlebitis (DVT). Methods: Patients were selected by screening the German nationwide inpatient sample (2005–2017) for paralysis, and were stratified for venous thromboembolism (VTE) and the VTE-sub-entity PE (ICD-code I26). Impact of PE on mortality and predictors for PE were analyzed. Results: Overall, 7,873,769 hospitalizations of patients with paralysis were recorded in Germany 2005–2017, of whom 1.6% had VTE and 7.0% died. While annual hospitalizations increased (2005: 520,357 to 2017: 663,998) (β 12,421 (95% CI 10,807 to 14,034), p < 0.001), in-hospital mortality decreased from 7.5% to 6.7% (β −0.08% (95% CI −0.10% to −0.06%), p < 0.001). When focusing on 82,558 patients with paralysis hospitalized due to VTE (51.8% females; 58.3% aged ≥ 70 years) in 2005–2017, in-hospital mortality was significantly higher in patients with paralysis and PE than in those with DVT only (23.8% vs. 6.3%, p < 0.001). Cancer (OR 2.18 (95% CI 2.09–2.27), p < 0.001), heart failure (OR 1.83 (95% CI 1.76–1.91), p < 0.001), COPD (OR 1.63 (95% CI 1.53–1.72), p < 0.001) and obesity (OR 1.42 (95% CI 1.35–1.50), p < 0.001) were associated with PE. PE (OR 4.28 (95% CI 4.07–4.50), p < 0.001) was a strong predictor of in-hospital mortality. Conclusions: In Germany, annual hospitalizations of patients with paralysis increased in 2005–2017, in whom VTE and especially PE substantially affected in-hospital mortality. Cancer, heart failure, COPD, obesity and acute paraplegia were risk factors of PE.

Structural Equation Model (SEM) of Stroke Mortality in Spanish Inpatient Hospital Settings: The Role of Individual and Contextual Factors

Introduction: Traditionally, predictive models of in-hospital mortality in ischemic stroke have focused on individual patient variables, to the neglect of in-hospital contextual variables. In addition, frequently used scores are betters predictors of risk of sequelae than mortality, and, to date, the use of structural equations in elaborating such measures has only been anecdotal. Aims: The aim of this paper was to analyze the joint predictive weight of the following: (1) individual factors (age, gender, obesity, and epilepsy) on the mediating factors (arrhythmias, dyslipidemia, hypertension), and ultimately death (exitus); (2) contextual in-hospital factors (year and existence of a stroke unit) on the mediating factors (number of diagnoses, procedures and length of stay, and re-admission), as determinants of death; and (3) certain factors in predicting others. Material and Methods: Retrospective cohort study through observational analysis of all hospital stays of Diagnosis Related Group (DRG) 14, non-lysed ischemic stroke, during the time period 2008-2012. The sample consisted of a total of 186,245 hospital stays, taken from the Minimum Basic Data Set (MBDS) upon discharge from Spanish hospitals. MANOVAs were carried out to establish the linear effect of certain variables on others. These formed the basis for building the Structural Equation Model (SEM), with the corresponding parameters and restrictive indicators. Results: A consistent model of causal predictive relationships between the postulated variables was obtained. One of the most interesting effects was the predictive value of contextual variables on individual variables, especially the indirect effect of the existence of stroke units on reducing number of procedures, readmission and in-hospital mortality. Conclusion: Contextual variables, and specifically the availability of stroke units, made a positive impact on individual variables that affect prognosis and mortality in ischemic stroke. Moreover, it is feasible to determine this impact through the use of structural equation methodology. We analyze the methodological and clinical implications of this type of study for hospital policies.