External Validation and Recalibration of a Mortality Prediction Model for Patients with Ischaemic Stroke.

BACKGROUND: Stroke is a highly prevalent disease that can provoke severe disability. We evaluate a predictive model based on the Minimum Basic Data Set (MBDS) compiled by the Spain Health Ministry, obtained for the period 2008-2012 for patients with ischaemic stroke in Spain, to establish the model's validity and to optimise its calibration. The MBDS is the main clinical-administrative database for hospitalisations recorded in Spain, and to our knowledge, no predictive models for stroke mortality have previously been developed using this resource. The main study aim is to perform an external validation and recalibration of the coefficients of this predictive model with respect to a chronologically later cohort. MATERIAL AND METHODS: External validation (testing the model on a different cohort to assess its performance) and recalibration (validation with optimisation of model coefficients) were performed using the MBDS for patients admitted for ischaemic stroke in the period 2016-2018. A cohort study was designed, in which a recalibrated model was obtained by applying the variables of the original model without their coefficients. The variables from the original model were then applied to the subsequent cohort, together with the coefficients from the initial model. The areas under the curve (AUC) of the recalibration and the external validation procedure were compared. RESULTS: The recalibrated model produced an AUC of 0.743 and was composed of the following variables: age (odds ratio, OR:1.073), female sex (OR:1.143), ischaemic heart disease (OR:1.192), hypertension (OR:0.719), atrial fibrillation (OR:1.414), hyperlipidaemia (OR:0.652), heart failure (OR:2.133) and posterior circulation stroke (OR: 0.755). External validation produced an AUC of 0.726. CONCLUSIONS: The recalibrated clinical model thus obtained presented moderate-high discriminant ability and was generalisable to predict death for patients with ischaemic stroke. Rigorous external validation slightly decreased the AUC but confirmed the validity of the baseline model for the chronologically later cohort.

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.

Predictive Model of the Risk of In-Hospital Mortality in Colorectal Cancer Surgery, Based on the Minimum Basic Data Set.

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.

[Detection of violence against women in the family doctor clinic]. / Detección de violencia contra la mujer en la consulta del médico de familia.

OBJECTIVES: To find out the magnitude of violence against female partners among patients who visit their family doctor. To study frequency and acceptance of its investigation by the family doctor and to assess the effectiveness of a screening question on abuse. DESIGN: Descriptive, cross-sectional study. SETTING: Primary care, 4 samples from 2 urban health centres in Jaén, Spain. PARTICIPANTS: Who participated 170 women randomly selected from the female consulting population. MEASUREMENTS: Interviews by means of the Bradley modified test and the anxiety and depression Goldberg scales. Perceived health, frequency of detection of domestic violence, by the family doctor, and female opinions were also studied. RESULTS: During the last year, abuse against women was detected in 22.9% of the female population consulting their family doctor (95% confidence interval [95% CI], 16.6-29.2). Abused women had a worse perception of health (odds ratio [OR] =4.2; 95% CI, 1.02-17.5) and a higher probability of depression (OR=4.7; 95% CI, 1.8-12.5) independently from the rest of variables. The question "How are the things going with your partner?" as a screening of abuse does obtain a positive probability quotient of 6.23 (95% CI, 3.6-10.9), a specificity of 89% and a negative predictive value of 90%. Of those interviewed, 96.5% would not mind if their family doctor approached the couple's relationships, a situation that occurs in 24.7% of cases. CONCLUSIONS: Some degree of abuse was detected in almost a quarter of women who consult their family doctor. Family doctors do not usually ask about family and partner relationships and environment, although for almost all women it is well appreciated and the item has an increased likelihood ratio and high negative predictive value in detecting abuse.