Predictive models for all-cause and cardiovascular mortality in type 2 diabetic inpatients. A cohort study

Abstract

This study provides tools to predict premature mortality in type 2 diabetic inpatients. However, before their general application they require joint validation by the internal medicine unit, emergency department, primary healthcare unit and endocrinology service to enable better prediction of the prognosis and more adequate decision-taking.

Construction and internal validation of a new mortality risk score for patients admitted to the intensive care unit

“…This was assessed through the computer system available in our hospital, which automatically records patient mortality. These data were corroborated by the clinical history, allowing us to minimise information bias …”

“…The combination providing a greater AUC was selected, i.e. that combination which best determined which patients died in the ICU . Having estimated the model with the best possible combination, this was adapted to a points system using the Framingham Heart Study methodology.…”

“…These data were corroborated by the clinical history, allowing us to minimise information bias. [16][17][18] To predict mortality from all causes, explanatory variables used at baseline (time of admission) were: gender, Fried criteria for frailty (none, prefrail and frail), 19 function scale (independent, dependent and disability), 20,21 admission (medical or surgical), cardiac arrest, cardiology admission, sepsis, mechanical ventilation, inotropic support, age (years), frailty index and clinical frailty scale. 20 All these variables were collected through routine clinical practice and corroborated by medical records.…”

Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

“…Also, nonsignificant variables remained in the points system. We must keep in mind that we were assessing the comprehensiveness of the model when making the prediction and not each variable separately (Ramírez-Prado et al, 2015b; Palazón-Bru et al, 2016; Piqueras-Rodríguez et al, 2016). On the other hand, variables that others have shown to be associated with poorer PPI adherence were not included (Dal-Paz et al, 2012; Lanas et al, 2012).…”

Nonadherence to guidelines for prescribing antibiotic therapy to patients with tonsillitis or pharyngotonsillitis: a cross-sectional study

“…To determine the factors associated with nonadherence, a binary logistic regression model was implemented with all the independent variables (not a stepwise model, as we had a sufficient number of events in our sample to introduce all the independent variables in the model) 12 . In this way the adjusted ORs were obtained.…”