The IRCU of University Hospital fundacion Jimenez Diaz is a high complexity unit with 15% of patients requiring complicated postoperative weaning procedures, while vasoactive treatment prevalence is approximately 26%. The unit has 5 beds (1:5 patient nurse ratio), being able to reach 8 in the periods of greatest need for care (1:4 patient nurse ratio in this scenario). Our unit is an open room to guarantee the global supervision of the patients that are completely telemonitorized. Supplementary Figures 1–3 describe the material and human resources, as well as its complexity according to the average GRD value of the unit. The study was aproved by our Ethic Committee. De-identified medical records were confidentially collected from a total of 1966 patients admitted to the FJD-Hospital IRCU (Madrid, Spain) from January 2007 to December 2017. A second cohort of 230 new patients with confidential records recruited from October 2018 to September 2019 in the same IRCU, were used to measure the performance of the new IRCU protocol based on the conclusions of this work. This is an observational and retrospective study. All the patients admitted to our IRCU were selected for the study and no exclusión criteria was imposed.

Vital signs of patients were recorded upon arrival at the hospital, before being admitted to the IRCU: respiratory rate (RR), temperature (T), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Laboratory findings including hemoglobin (Hb), platelets (PL), leucocytes (Leucos), the International Normalized Ratio (INR), blood glucose (GLU), blood potassium (K), sodium (NA) and creatinine (CREAT) were also registered. Blood gases at the time of hospital admission were recorded (Fi02 21%), including partial oxygen pressure (P02), partial carbon dioxide pressure (PC02), PH, bicarbonates (SBC) and base excess (BE). Age and individual mortality or survival upon discharge from the IRCU was extracted from medical histories. The data was collected following standard protocols by the same personnel over the whole time period and in the two cohorts included in the study.

A criteria for variable exclusion was defined in order to avoid introduction of statistical noise that may introduce bias in final results. Parameters with a high percentage (>25%) of missing values were excluded from the analysis. For the rest of the parameters we perform the K-nearest neighbors (KNN) imputation method implemented in the DMwR R package.

Principal component analysis (PCA) was performed on the matrix with missing data imputation, using the pca3d R library.

Using ROC curves and Youden's J statistic we obtained the cut-off point that best discriminates the mortality status for each of the parameters. Based on these cut-off points we defined a binary classification for each parameter (P-value <.2) to feed a multivariate logistic regression model. Area Under the Curve (AUC), sensitivity and specificity were calculated using a ROC curve. An internal validation of the model was performed by bootstrap method to correct for optimistic prediction. A total of 1000 bootstrap samples with replacement were generated, and the differences in AUC on the bootstrap samples and the original sample were calculated. The goodness of fit of the model was evaluated by the Hosmer and Lemeshow test.

A NN model was applied using the caret R package. Data matrix was normalized using the min-max scale method. Ten re-sampled matrices were calculated having samples with mortality=1 and a random selection of the same number of samples with mortality=0. Every re-sampled matrix is then subject to a 10-fold cross validation process (90% training and 10% testing). Thus, a total of 100 neural network models are performed using “nnet” method with automatic selection of: (i) the optimal number of units per hidden layer between 1 and 5, and (ii) the optimal value for the regularization parameter (between 0.1 and 0.5 with increments of 0.1). The “twoClassSummary” method was used to compute sensitivity, specificity and the AUC. A ROC curve is calculated for the set of predictions and real values within the loop, using the 10-fold cross validation process. The final AUC, accuracy, sensitivity and specificity were calculated as the mean of the 100 neural network models performed in total. Our R script is available at https://github.com/pminguez/MachineLearning4UnbalancedData.

Using the KNN data matrix, we calculate the Spearman correlation between every pair of variables. In order to build the network, we select the pairs that have a correlation coefficient >±0.3 and a P-value <.01. The selected pairs of variables represent the edges of the correlation network.

In order to compare the failure before and after the new criteria to detect high risk patients was implemented, we fit Poisson regression models to calculate the annual trend of failure risk in the two periods. The relatives risks (RR) were compared using the Wald test, taking the coefficients of the models (logarithm of RR) and its standard errors.

A total of 1966 patients were included in the analysis having a global local IRCU mortality rate of 1.68% (33 patients) and a global failure (IRCU mortality+ICU transfer+outside IRCU delayed mortality) of about 5.39% (106 patients). Table 1 describes the study population with epidemiologic data and treatment used and Table 2 shows mean and standard deviation values for each of the variable used (INR was excluded). A PCA of the patients’ features did not show observable differences between patient's classifications according to mortality status (Fig. 1).