Computerized Decision Support System for Traumatic Brain Injury Management

Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design

BACKGROUND

Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patients' status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinician's cognitive processes and clinical decision-making skills.

OBJECTIVE

In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow.

METHODS

First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs.

RESULTS

We created a structure with 3 levels of abstraction-unit level, patient level, and system level-to optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure.

CONCLUSIONS

The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction.

Exploring the clinical relevance of vital signs statistical calculations from a new-generation clinical information system

New information on the intensive care applications of new generation 'high-density data clinical information systems' (HDDCIS) is increasingly being published in the academic literature. HDDCIS avoid data loss from bedside equipment and some provide vital signs statistical calculations to promote quick and easy evaluation of patient information. Our objective was to study whether manual records of continuously monitored vital signs in the Paediatric Intensive Care Unit could be replaced by these statistical calculations. Here we conducted a prospective observational clinical study in paediatric patients with severe diabetic ketoacidosis, using a Medlinecare® HDDCIS, which collects information from bedside equipment (1 data point per parameter, every 3-5 s) and automatically provides hourly statistical calculations of the central trend and sample dispersion. These calculations were compared with manual hourly nursing records for patient heart and respiratory rates and oxygen saturation. The central tendency calculations showed identical or remarkably similar values and strong correlations with manual nursing records. The sample dispersion calculations differed from the manual references and showed weaker correlations. We concluded that vital signs calculations of central tendency can replace manual records, thereby reducing the bureaucratic burden of staff. The significant sample dispersion calculations variability revealed that automatic random measurements must be supervised by healthcare personnel, making them inefficient.

Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis

BACKGROUND

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI management. This study aimed to identify ML models with demonstrated effectiveness in predicting TBI outcomes.

METHODS

We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. In total, 15 articles were identified using the search strategy. Patient demographics, clinical status, ML outcome variables, and predictive characteristics were extracted. A small meta-analysis of mortality prediction was performed, and a meta-analysis of diagnostic accuracy was conducted for ML algorithms used across multiple studies.

RESULTS

ML algorithms including support vector machine (SVM), artificial neural networks (ANN), random forest, and Naïve Bayes were compared to logistic regression (LR). Thirteen studies found significant improvement in prognostic capability using ML versus LR. The accuracy of the above algorithms was consistently over 80% when predicting mortality and unfavorable outcome measured by Glasgow Outcome Scale. Receiver operating characteristic curves analyzing the sensitivity of ANN, SVM, decision tree, and LR demonstrated consistent findings across studies. Lower admission Glasgow Coma Scale (GCS), older age, elevated serum acid, and abnormal glucose were associated with increased adverse outcomes and had the most significant impact on ML algorithms.

CONCLUSION

ML algorithms were stronger than traditional regression models in predicting adverse outcomes. Admission GCS, age, and serum metabolites all have strong predictive power when used with ML and should be considered important components of TBI risk stratification.

A data-driven approach to clinical decision support in tinnitus retraining therapy

Background

Tinnitus, known as “ringing in the ears”, is a widespread and frequently disabling hearing disorder. No pharmacological treatment exists, but clinical management techniques, such as tinnitus retraining therapy (TRT), prove effective in helping patients. Although effective, TRT is not widely offered, due to scarcity of expertise and complexity because of a high level of personalization. Within this study, a data-driven clinical decision support tool is proposed to guide clinicians in the delivery of TRT.

Methods

This research proposes the formulation of data analytics models, based on supervised machine learning (ML) techniques, such as classification models and decision rules for diagnosis, and action rules for treatment to support the delivery of TRT. A knowledge-based framework for clinical decision support system (CDSS) is proposed as a UI-based Java application with embedded WEKA predictive models and Java Expert System Shell (JESS) rule engine with a pattern-matching algorithm for inference (Rete). The knowledge base is evaluated by the accuracy, coverage, and explainability of diagnostics predictions and treatment recommendations.

Results

The ML methods were applied to a clinical dataset of tinnitus patients from the Tinnitus and Hyperacusis Center at Emory University School of Medicine, which describes 555 patients and 3,000 visits. The validated ML classification models for diagnosis and rules: association and actionable treatment patterns were embedded into the knowledge base of CDSS. The CDSS prototype was tested for accuracy and explainability of the decision support, with preliminary testing resulting in an average of 80% accuracy, satisfactory coverage, and explainability.

Conclusions

The outcome is a validated prototype CDS system that is expected to facilitate the TRT practice.

Creating a High-Frequency Electronic Database in the PICU: The Perpetual Patient

OBJECTIVE

Our objective was to construct a prospective high-quality and high-frequency database combining patient therapeutics and clinical variables in real time, automatically fed by the information system and network architecture available through fully electronic charting in our PICU. The purpose of this article is to describe the data acquisition process from bedside to the research electronic database.

DESIGN

Descriptive report and analysis of a prospective database.

SETTING

A 24-bed PICU, medical ICU, surgical ICU, and cardiac ICU in a tertiary care free-standing maternal child health center in Canada.

PATIENTS

All patients less than 18 years old were included at admission to the PICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Between May 21, 2015, and December 31, 2016, 1,386 consecutive PICU stays from 1,194 patients were recorded in the database. Data were prospectively collected from admission to discharge, every 5 seconds from monitors and every 30 seconds from mechanical ventilators and infusion pumps. These data were linked to the patient's electronic medical record. The database total volume was 241 GB. The patients' median age was 2.0 years (interquartile range, 0.0-9.0). Data were available for all mechanically ventilated patients (n = 511; recorded duration, 77,678 hr), and respiratory failure was the most frequent reason for admission (n = 360). The complete pharmacologic profile was synched to database for all PICU stays. Following this implementation, a validation phase is in process and several research projects are ongoing using this high-fidelity database.

CONCLUSIONS

Using the existing bedside information system and network architecture of our PICU, we implemented an ongoing high-fidelity prospectively collected electronic database, preventing the continuous loss of scientific information. This offers the opportunity to develop research on clinical decision support systems and computational models of cardiorespiratory physiology for example.