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

Structural Model of Biomedical and Contextual Factors Predicting In-Hospital Mortality due to Heart Failure

Background: Among the clinical predictors of a heart failure (HF) prognosis, different personal factors have been established in previous research, mainly age, gender, anemia, renal insufficiency and diabetes, as well as mediators (pulmonary embolism, hypertension, chronic obstructive pulmonary disease (COPD), arrhythmias and dyslipidemia). We do not know the role played by contextual and individual factors in the prediction of in-hospital mortality. Methods: The present study has added hospital and management factors (year, type of hospital, length of stay, number of diagnoses and procedures, and readmissions) in predicting exitus to establish a structural predictive model. The project was approved by the Ethics Committee of the province of Almeria. Results: A total of 529,606 subjects participated, through databases of the Spanish National Health System. A predictive model was constructed using correlation analysis (SPSS 24.0) and structural equation models (SEM) analysis (AMOS 20.0) that met the appropriate statistical values (chi-square, usually fit indices and the root-mean-square error approximation) which met the criteria of statistical significance. Individual factors, such as age, gender and chronic obstructive pulmonary disease, were found to positively predict mortality risk. Isolated contextual factors (hospitals with a greater number of beds, especially, and also the number of procedures performed, which negatively predicted the risk of death. Conclusions: It was, therefore, possible to introduce contextual variables to explain the behavior of mortality in patients with HF. The size or level of large hospital complexes, as well as procedural effort, are key contextual variables in estimating the risk of mortality in HF.

Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment

Background: Stroke is the second cause of mortality worldwide and the first in women. The aim of this study is to develop a predictive model to estimate the risk of mortality in the admission of patients who have not received reperfusion treatment. Methods: A retrospective cohort study was conducted of a clinical–administrative database, reflecting all cases of non-reperfused ischaemic stroke admitted to Spanish hospitals during the period 2008–2012. A predictive model based on logistic regression was developed on a training cohort and later validated by the “hold-out” method. Complementary machine learning techniques were also explored. Results: The resulting model had the following nine variables, all readily obtainable during initial care. Age (OR 1.069), female sex (OR 1.202), readmission (OR 2.008), hypertension (OR 0.726), diabetes (OR 1.105), atrial fibrillation (OR 1.537), dyslipidaemia (0.638), heart failure (OR 1.518) and neurological symptoms suggestive of posterior fossa involvement (OR 2.639). The predictability was moderate (AUC 0.742, 95% CI: 0.737–0.747), with good visual calibration; Pearson’s chi-square test revealed non-significant calibration. An easily consulted risk score was prepared. Conclusions: It is possible to create a predictive model of mortality for patients with ischaemic stroke from which important advances can be made towards optimising the quality and efficiency of care. The model results are available within a few minutes of admission and would provide a valuable complementary resource for the neurologist.

Association of consciousness impairment and mortality in people with COVID-19

BACKGROUND

To investigate the association between impairment of consciousness and risk of death in people with COVID-19.

METHODS

In this multicentre retrospective study, we enrolled people with confirmed COVID-19 from 44 hospitals in Wuhan and Sichuan, China, between 18 January and 30 March 2020. We extracted demographics, clinical, laboratory data and consciousness level (as measured by the Glasgow Coma Scale (GCS) score) from medical records. We used Cox proportional hazards regression, structural equation modelling and survival time analysis to compare people with different progressions of impaired consciousness.

RESULTS

We enrolled 1,143 people (average age 51.3 ± standard deviation 17.1-year-old; 50.3% males), of whom 76 died. Increased mortality risk was identified in people with GCS score between 9 and 14 (hazard ratio (HR) 46.76, p < .001) and below 9 (HR 65.86, p < .001). Pathway analysis suggested a significant direct association between consciousness level and death. Other factors, including age, oxygen saturation level and pH, had indirect associations with death mediated by GCS scores. People who developed impaired consciousness more rapidly either from symptoms onset (<10 days vs. 10-19 days, p = .025, <10 days vs. ≥20 days and 10-19 days vs. ≥20 days, <.001) or deterioration of oxygen saturation (≤2 days vs.>2 days, p = .028) had shorter survival times.

CONCLUSION

Altered consciousness and its progression had a direct link with death in COVID-19. Interactions with age, oxygen saturation level and pH suggest possible pathophysiology. Further work to confirm these findings explore prevention strategies and interventions to decrease mortality is warranted.