Intensive Care Unit Risk Score (ICURS)

Retrospective Analysis - Scoring Systems in Intensive Care Medicine

Subject of the planned project is the retrospective analysis of routine data of digital patient files of the Department for Anaesthesiology and Surgical Intensive Care Medicine, to test whether the predictive values of intensive care scoring systems with regard to perioperative mortality and morbidity can be improved by continuous score calculation and by using machine learning and time series analysis methods.

Study Overview

• Status: Recruiting
• Conditions: Alarm Fatigue; Mortality in Intensive Care Units; Complications Infection

Detailed Description

A scoring system usually consists of two parts - a score (a number reflecting the severity of the disease) and a probability model (equation indicating the probability of an event, e.g. the death of the patient in hospital). Scoring systems have been used in intensive care medicine for decades and can help to assess the effectiveness of treatment or identify comparable patients for study purposes. Scoring systems that are used in intensive care medicine are for example

• Acute Physiology, Age, Chronic Health Evaluation II (APACHE II)
• Simplified Acute Physiology Score II (SAPS II)
• Multiple Organ Dysfunction Score (MODS)
• Sequential Organ Failure Assessment (SOFA)
• Logistic Organ Dysfunction System (LODS)
• MPM II-Admission (Mortality Probability Models (MPM II)
• Organ Dysfunction and Infection score (ODIN)
• Three-Day Recalibrating ICU Outcomes (TRIOS)
• Glasgow coma score (GCS)
• Discharge Readiness Score (DRS) The above-mentioned scoring systems are already being collected regularly in the respective hospital's departments. In a recent study by Badawi et al. it could be shown that scoring systems allow more accurate predictions when calculated continuously. However, due to the patient collectives investigated, these results can only be transferred to other patient groups to a limited extent. Furthermore, only the scoring systems APACHE, SOFA and DRS were analyzed.

Therefore, in the present study, all of the above scoring systems will be calculated continuously (once per minute) using routine data from the digital patient records and optimized by applying machine learning and methods of time series analysis.

On the anesthesiologically managed intensive care units of the respective hospital, there is no campus-wide standard with regard to alarm management. Accordingly, we estimate the rate of alarm fatigue (ignoring alarms due to many false alarms) to be very high. In order to optimize the alarm management, alarms from the patient monitoring devices will be evaluated retrospectively and combined with the data mentioned above to determine, for example, whether more frequent alarms are to be expected for certain types of diseases (e.g. sepsis), or scores (e.g., high APACHE score) and how the alarm limit setting can be optimized.

• Study Type: Observational
• Enrollment (Estimated): 60000

Contacts and Locations

• Study Contact: Name: Felix Balzer, Prof; Email: data-science@charite.de
• Study Contact Backup: Name: Akira S Poncette, MD; Email: data-science@charite.de

Study Locations

• Germany
  • Berlin, Germany, 10117
    • Recruiting
    • Charite Universtitaetsmedizin
    • Contact: Felix Balzer, Prof. Dr. Dr.

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

Age from 18 years. The respective intensive care department carries out approximately 5000 intensive care treatments per year on persons of each sex.

Description

Inclusion Criteria:

- Patients with admission between 01.01.2006 and 30.09.2023

Exclusion Criteria:

• Patients under 18 years of age.
• Incomplete patient records.
• Intensive stay of less than 24 hours.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Prediction of patient outcome	Identification of scores with a high on impact mortality, complications and length of stay in the intensive care unit	2006 - 2023

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Predictive model for alarm load	Identification of items leading to a high alarm load measured by number of alarm per day per bed in the intensive care unit	2020 - 2023
Predictive model for actionable alarms	Identification of items leading to a high number of actionable alarms measured by number of actionable alarms per day per bed in the intensive care unit	2020 - 2023

Collaborators and Investigators

• Sponsor: Charite University, Berlin, Germany
• Investigators: Principal Investigator: Felix Balzer, Prof, Charite University, Berlin, Germany

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2006
• Primary Completion (Estimated): September 30, 2025
• Study Completion (Estimated): December 31, 2025

Study Registration Dates

• First Submitted: December 4, 2020
• First Submitted That Met QC Criteria: December 4, 2020
• First Posted (Actual): December 10, 2020

Study Record Updates

• Last Update Posted (Actual): September 8, 2023
• Last Update Submitted That Met QC Criteria: September 7, 2023
• Last Verified: September 1, 2023

More Information

Terms related to this study

• Keywords: intensive care unit; patient monitoring; scores; alarm management
• Additional Relevant MeSH Terms: Fatigue
• Other Study ID Numbers: ICURS

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No