Background: Various models have been proposed to predict mortality rates for hospital patients undergoing colorectal cancer surgery. However, none have been developed in Spain using clinical administrative databases and none are based exclusively on the variables available upon admission. Our study aim is to detect factors associated with in-hospital mortality in patients undergoing surgery for colorectal cancer and, on this basis, to generate a predictive mortality score. Methods: A population cohort for analysis was obtained as all hospital admissions for colorectal cancer during the period 2008–2014, according to the Spanish Minimum Basic Data Set. The main measure was actual and expected mortality after the application of the considered mathematical model. A logistic regression model and a mortality score were created, and internal validation was performed. Results: 115,841 hospitalization episodes were studied. Of these, 80% were included in the training set. The variables associated with in-hospital mortality were age (OR: 1.06, 95%CI: 1.05–1.06), urgent admission (OR: 4.68, 95% CI: 4.36–5.02), pulmonary disease (OR: 1.43, 95%CI: 1.28–1.60), stroke (OR: 1.87, 95%CI: 1.53–2.29) and renal insufficiency (OR: 7.26, 95%CI: 6.65–7.94). The level of discrimination (area under the curve) was 0.83. Conclusions: This mortality model is the first to be based on administrative clinical databases and hospitalization episodes. The model achieves a moderate–high level of discrimination.

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.

Objetivos. Estudiar las diferencias de comportamiento entre ingresos urgentes y programados en los procesos más prevalentes y el resto en la casuística hospitalaria española y su relación con la edad de los pacientes, comparando los años 2002 y 2007. Métodos. Diseño observacional y descriptivo durante esos años. Se clasifican los grupos relacionados de diagnóstico (GRD) en alta prevalencia (25 más frecuentes) y resto, se confeccionan cuatro subgrupos según ésta (alta o baja) e ingreso (electivo o no). Se analiza estancia media, peso relativo, número de diagnósticos y procedimientos y mortalidad en función de la edad, por medio del test de Student y/o ANOVA, según proceda, para variables cuantitativas y el de χ 2 de Pearson para cualitativas, comparando ambas según análisis de datos ya tabulados. Se asume significación estadística para p<0,05. Resultados. El subgrupo de alta prevalencia e ingreso urgente presenta mayor edad, proporción de varones, estancia media, mortalidad, número de diagnósticos y procedimientos (todo con p<0,0001), tanto en 2002 como en 2007. La gravedad, complejidad y consumo de recursos-medidos con esas variables-alcanza su máximo en las cohortes de 65-69 y 70-74 años, respectivamente, con ingreso urgente. Conclusiones. Existen claras diferencias entre los procesos, según su prevalencia y acceso, y debe priorizarse el conocimiento y la información de los más frecuentes y urgentes para mejorar la efectividad, la eficiencia y la calidad.

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.