Clinical Deterioration Early Warning System Score

Phase 1. Derivation of the predictive model Data from patients admitted to the participating centres during 2017 will be extracted from the OBI electronic medical record and the laboratory data management system. The data analyst contracted in this project, together with clinical collaborators, from the health management unit and the research unit, will be responsible for cleaning and cleaning the data and for the generation and internal validation of the predictive model.

Phase 2: Implementation in the electronic medical record: Once the model and the risk scale have been generated, it will be implemented as an alarm system in Osabide Global in the participating centres. To this end, an action guide will be created by the HGU's UGS, which, once validated, will then be exported to the other two centres. The system will operate on the basis of the RIC elements associated with vital signs, and will guide professionals through the process, so that the physician will be shown the analytical data that have been shown to be necessary to calculate the risk, or in the event that the patient has not been asked for that specific laboratory test, recommendations for extraction. Once the system has all the data, a warning will pop up on the screen with the results of the risk scale and the mild-moderate-severe risk category and a report with recommendations for action.Recommendations based on the ViEWS scale, from the physician informing the nurse in charge at the start of continuous monitoring and visit in less than 15 minutes by the responsible attending physician who will also inform the ICU physician to the transfer of the patient to a unit where he/she will be monitored and acted upon, are given in annex 1. The alarm system will be presented in the HGU by the clinical collaborators, GSU and data analyst. The data analyst will be, together with the co-researchers, the person in charge of generating the alarm system, of participating in the meetings in which it is presented in the centres and subsequently, will carry out a constant exploration of the use and adherence of the risk scale, will collect doubts and problems that may arise from the implementation of the system, and will also be responsible for the implementation of the risk scale.

Phase 3: External validation of the predictive model. Comparison of clinical outcomes: The predictive model will be validated once 100 events per centre have occurred (hopefully once about 8000 patients have been admitted). During this time, data will be collected during and after implementation in order to compare clinical deteriorations observed before and after implementation. The data analyst will be extracting the information, sorting and cleaning it in order to be able to carry out the corresponding statistical analyses later on.

STATISTICAL ANALSIS

The data processing procedure of the present project will be established following the following steps, divided into two sections:

A) Development and validation of predictive models of clinical deterioration, transfer to the critical care unit, cardiorespiratory arrest or death during hospitalisation.

Descriptive analysis of the model derivation/internal validation cohort consisting of patients from the three participating centres of the retrospective cohort. Information on losses in the variables recorded in the study will be collected. Information on excluded patients will also be collected. The possibility of applying imputation techniques to the missing data in the variables recorded will be assessed.

2.- The following points will be followed for the creation of the predictive models: a. The sample collected shall be divided into two subsamples: Derivation group 1: The total sample will be divided into 60% for the derivation of the predictive models for the main outcome variable - clinical deterioration; Group 2 of validation of the predictive rules: the models will be validated in this sample (40% of the sample). Analyses will also be performed disaggregated by gender. b. In Group 1, risk factors for clinical deterioration will be identified, as well as for transfer to the critical care unit, cardiorespiratory arrest or death during hospitalisation. A bivariate analysis will be performed to study which of the possible predictor variables are related to each outcome parameter. Those variables with a p-value \<0.20 will be identified as potential predictors to be entered into a multilevel multivariate logistic regression model. Those variables that are statistically significant will be chosen for the final scale. The categories of variables in this model will be assigned a score in relation to the parameter β obtained in that multivariate model. In this way, a total score and an ROC curve will be obtained for it. From this scale, X risk categories will be created.

3.- Goodness of fit and comparison of the predictive models developed. Parameters will be found to evaluate the discriminative and calibration capacity of the predictive models developed in section 2. On the one hand, in the case of dichotomous dependent variables, the area under the ROC curve (AUC, discriminative capacity) will be calculated, considering a value >0.80 to be a robust predictive model. In addition to the AUC, the calibration of the model will be estimated through the Hosmer-Lemeshow test (good calibration for a p-value ≥0.05).

4.- Internal validation of the predictive models The validation of the predictive model will be carried out in validation group 2. The predictive model and the scale will be validated in this group by musing the predicted values obtained in the derivation sample (group 1). Sensitivity, specificity and area under the curve will be obtained by comparing them with the results obtained in the derivation sample. The calibration capability of the models will be assessed by the Hosmer-Lemeshow test and the discrimination capability by ROC curves. As a form of additional internal validation, a validation by bootstrapping methods will be performed.

5.-External validation of predictive models External validation of the models developed in the retrospective cohort will be applied to the prospective cohort. The linear predictor (LP) will first be found in the remaining cohorts by considering the regression coefficients obtained in the original models. Logistic regression models will be developed. Three main procedures will be considered for the external validation of these models: model fit, discrimination and calibration. First, to verify model fit, we will develop a logistic regression model on the remaining cohorts, considering LP as a covariate to estimate its beta regression coefficient. If the beta regression coefficient of the slope is 1, it will be considered a good fit.Secondly, to assess the discrimination of the model, the AUC will be calculated, by bootstrapping and cross-validation methods. An AUC value >0.80 will be considered a robust predictive model. Thirdly, the calibration will be evaluated by calculating the overall calibration, the calibration slope (both derived from the calibration plots) and the Hosmer-Lemeshow test.

B) Evaluation of the EWS for clinical impairment 6.- Comparison of results: For the comparison of results after the implementation of the alarm system, a comparison will be made of percentages of patients suffering clinical deterioration before and after its implementation the HGU and also between the percentages appearing in the HGU, where the alarm system will be implemented and the HUDs and HUAs where they will continue with their usual practice during this evaluation period. In order to evaluate the possibility of bias occurring when forming the cohorts, a propensity score will be calculated to explain the probability of being assigned to one (HGU patients) or the other cohort (HUD+HUA) conditional on the independent variables observed and explained in the section "Exposure variables". This propensity score will be entered as an independent variable in a replication of the final models together with exposure and the coefficients obtained in both cases will be compared.