DOI: 10.5061/dryad.1vhhmgr2g
title: Feature statistics for the prediction of postoperative delirium in the recovery room

description: Background: Postoperative delirium (POD) contributes to severe outcomes
 such as death or development of dementia. Thus, it is desirable to
 identify vulnerable patients in advance during the perioperative phase.
 Previous studies mainly investigated risk factors for delirium during
 hospitalization and further used a linear logistic regression (LR)
 approach with time-invariant data. Studies have not investigated patients’
 fluctuating conditions to support POD precautions. Objective: In this
 single-center study, we aimed to predict POD in a recovery room setting
 with a non-linear machine learning (ML) technique using pre-, intra-, and
 postoperative data. Methods: The target variable POD was defined with the
 Nursing Screening Delirium Scale (Nu-DESC) ≥ 1. Feature selection was
 conducted based on robust univariate test statistics and L1
 regularization. Non-linear multi-layer perceptron (MLP) as well as
 tree-based models were trained and evaluated – with the receiver operating
 characteristics curve (AUROC), the area under precision recall curve
 (AUPRC), and additional metrics – against LR and published models on
 bootstrapped testing data. Results:The prevalence of POD was 8.2% in a
 sample of 73,181 surgeries performed between 2017 and 2020. Significant
 univariate impact factors were the preoperative ASA status, the
 intraoperative amount of given remifentanil, and the postoperative Aldrete
 score. The best model used pre-, intra-, and postoperative data. The
 tree-based model achieved a mean AUROC of 0.854 and a mean AUPRC of 0.418
 outperforming linear LR, well as best applied and retrained baseline
 models. Conclusions: Overall, non-linear machine learning models using
 data from multiple perioperative time phases were superior to traditional
 ones in predicting POD in the recovery room. Class imbalance was seen as a
 main impediment for model application in clinical practice.
description: This open data repository presents descriptive statistics for
 inputs used by machine learning models from the manuscript
 "Predicting postoperative delirium assessed by the Nursing Screening
 Delirium Scale in the recovery room for non-cardiac surgeries without
 craniotomy: A retrospective study using a machine learning approach".
 Data were extracted from the clinical information systems (CIS) of three
 sides at our clinical center. Based on literature review and clinical
 expertise, a total of 549 clinical variables were identified with respect
 to different time phases in our source systems. From these variables, 375
 were available for at least 10% of the included patients. Multiple values
 for one surgery recorded during one time phase were aggregated as 10th,
 50th, and 90th percentiles as well as the median absolute deviation. For
 volumes and amounts of given medications, we calculated the
 sums. 
description: # Descriptive Statistics of Feature Sets
 [https://doi.org/10.5061/dryad.1vhhmgr2g](https://doi.org/10.5061/dryad.1vhhmgr2g) We created this dataset for performing robust univariate MWU statistics that contributed to feature explanations for our trained machine learning models. Models were trained with binary targets discriminating POD and no POD surgeries. Our models ingest aggregated data in form of percentiles and other aggregations that summarize multiple values recorded during one surgery.  ## Description of the data and file structure We submit our data as separate files. Please find metrics describing our cohort on the first one. Descriptive statistics of extracted raw data that could comprise multiple values per surgery in the next file. The last file describes concrete model inputs that were derived by summary statistics like percentiles calculated on raw extracted data.  On the first tab "Table 1" the following columns are provided: | Domain | Variable | Unit | All  | POD Positives (y=1) | POD Negatives (y=0) | | :----- | :------- | :--- | :--- | :------------------ | :------------------ | Here, the domain describes the clinical context of the variable that is more specified with a unit by following columns. The column "All" represents summary statistics as counts or mean [1st,2nd,3rd] quartiles, the following columns include the statistics for "POD Positives" or "POD Negatives". Hence, numerical variables e.g. age as cited with 56, [42, 60, 73] years in the Table 1 reflects 56 years as the mean and 42,60,73 as 1st, 2nd, and 3rd quartile. Additional data fields, included in the table, hold fractions, e.g., 53% female, 57% male as the sex distribution.  In the Extracted Data sheet, we include the columns: | feature | set | domain | unit | tl | missingness | min | max | mean | std | 10\_perc | 25\_perc | 50\_perc | 75\_perc | 90\_perc | mad | | :------ | :-- | :----- | :--- | :- | :---------- | :-- | :-- | :--- | :-- | :------- | :------- | :------- | :------- | :------- | :-- | We describe the feature in sets (train/test) and the clinical domain as well as units and corresponding time line (tl). The missingness as a fraction of all patients having missing values and further summary statistics are provided to extensively report possible biases between sets. We include summary statistics as columns covering min, max, mean, the standard-deviation (std), as well as 10th, 25th, 50th, 75th, and 90th percentiles (perc). The last metrics denotes the mean absolute deviation as mad.  For the aggregated data sheet, we provide: | feature | mean | std | min | 25\_perc | 50\_perc | 75\_perc | max | tl | dataset | | :------ | :--- | :-- | :-- | :------- | :------- | :------- | :-- | :- | :------ | As summary statistics after aggregation also per tl to show differences of test and train sets. Like before, we include summary statistics as columns. To gain an overview in differences between train and testing sets (dataset), we provided the mean, the standard-deviation (std), min, as well as 25th, 50th, 75th and the max per corresponding time line (tl). ## Sharing/Access information Data is required to be downloaded for researchers for understanding and validating our research results. There is no restriction on how to use our data for secondary analysis. ## Code/Software We used python code that is described in more detail under [https://github.com/ngiesa/icdep. ](https://github.com/ngiesa/icdep.)