This toolkit is designed to guide you through the data extraction and processing for SMARTT. The data sources are the ICCA reporting server and an extract from your local ICNARC dataset.
A Streamlit application (The Sniffer), running locally on your computer, will guide you through the process, enabling any participating site to produce data in the correct format to allow federated validation of the SMARTT algorithm(s).
See the accompanying instructional video for example usage (link to follow).
Please work through the steps below in order (click on the step to expand the instructions).
If at any stage you encounter a problem please send a description to [email protected] along with any associated error messages.
Step 1.
In this step you will setup the environment, install required packages and test the installation.
The system and user requirements are as follows:
- You need to have admin rights to download and install software from the internet on your machine (specifically python packages using pip and Git).
- System installation of python 3.11 (it is recommended to install this manually and not use Anaconda).
- NHS Trust user account with read access rights on (a copy of) the ICCA reporting database.
- Ability to leave your computer running for an extended period of time (e.g. overnight), for the application to perform the required data extracts.
Please note that the SQL queries used by the Sniffer, assume that the patient fact tables in ICCA
(e.g. PtAssessment) all have multi-column indexes on encounterId and attributeId. If this is not
the case, the queries may be prohibitively slow.
Installation instructions:
The following instructions assume that you are working on Windows. The procedure for other systems should be very similar.
You will first need to clone this repository and then open the command prompt to enter to following commands:
cd <directory of cloned repository>(replace <...> with the path to your directory)git checkout latest-releasegit pull origin latest-releasepython -m venv venvvenv\Scripts\activatepython -m pip install --upgrade pippython -m pip install -r streamlit_requirements.txtcd streamlitstreamlit run app.y
This should launch the Sniffer application in your web browser. You can then follow the instructions to set up a new project and connect to the ICCA database.
Step 2.
In this step you will link to an extract of the ICNARC data, in order to synthesise outcome variables.
Instructions to follow soon....
Data description.
Our original model used only 15 physiological variables in order to provide a fair test against a set of nurse-led discharge critera. To improve on our original model we will add more variables and also engineer additional features by processing and combining these variables in different ways, in order to improve the predictive performance. As our starting point we take the set of variables that were used in the model published by Pacmed. These variables are listed in the table below and also defined in the schema spreadsheet. Note that the `number of features' refers to the number of features that were derived from these variables for input to the model (using their feature engineering approach described here).
| Feature Category | Feature Name | Number of Features |
|---|---|---|
| General information | ||
| Patient characteristics | Age, gender, and weight at admission | 3 |
| Admission information | Origin department | 3 |
| Laboratory results | ||
| Blood gas analysis | pH, Paco2, Pao2, actual bicarbonate, base excess, and arterial oxygen saturation | 15 |
| Hematology | Hemoglobin, WBC count, platelet count, activated partial thromboplastin time, and prothrombin time | 16 |
| Routine chemistry | Sodium, potassium, creatinine, ureum, creatinine/ureum ratio, chloride, ionized calcium, magnesium, phosphate, lactate dehydrogenase, glucose, lactate, C-reactive protein, and albumin | 43 |
| Cardiac enzymes | Creatinine kinase and troponin-T | 5 |
| Liver and pancreas tests | Bilirubin, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, Gamma-glutamyltransferase, and amylase | 11 |
| Vital signs and device data | ||
| Circulation | Heart rate, arterial blood pressure (systolic/diastolic/mean), noninvasive blood pressure (systolic/diastolic), cardiac output, temperature, and central venous pressure | 34 |
| Respiration | Fio2, positive end-expiratory pressure, tidal volume, respiratory rate, peripheral oxygen saturation, and rapid shallow breathing index | 18 |
| Clinical observations and scores | ||
| Neurology | Glasgow Coma Scale score, Richmond Agitation-Sedation Scale, pupil response, and pupil diameter | 9 |
| Respiration | Bronchial suctioning, coughing reflex, and Pao2/Fio2 | 10 |
| Nephrology | Urine output | 2 |
| Diagnostics and therapeutics | ||
| Lines, drains and tubes | Endotracheal tube and urine catheter | 3 |
| Interventions | Supplemental oxygen, continuous renal replacement therapy, and tube feeding | 8 |
| Total | 180 |