Dataset Information

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms.

ABSTRACT:

Background

This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD).

Methodology

This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers.

Discussion

This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences.

Trial registration

Systematic review registration: PROSPERO CRD42022334988.

SUBMITTER: Rostam Niakan Kalhori S

PROVIDER: S-EPMC10016632 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms.

Rostam Niakan Kalhori Sharareh S Deserno Thomas M TM Haghi Mostafa M Ganapathy Nagarajan N

PloS one 20230315 3

<h4>Background</h4>This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD).<h4>Methodology</h4>This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reportin ...[more]

PMID: 36920960

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Project description:ObjectiveTo assess (1) how well validated existing paediatric track and trigger tools (PTTT) are for predicting adverse outcomes in hospitalised children, and (2) how effective broader paediatric early warning systems are at reducing adverse outcomes in hospitalised children.DesignSystematic review.Data sourcesBritish Nursing Index, Cumulative Index of Nursing and Allied Health Literature, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effectiveness, EMBASE, Health Management Information Centre, Medline, Medline in Process, Scopus and Web of Knowledge searched through May 2018.Eligibility criteriaWe included (1) papers reporting on the development or validation of a PTTT or (2) the implementation of a broader early warning system in paediatric units (age 0-18 years), where adverse outcome metrics were reported. Several study designs were considered.Data extraction and synthesisData extraction was conducted by two independent reviewers using template forms. Studies were quality assessed using a modified Downs and Black rating scale.Results36 validation studies and 30 effectiveness studies were included, with 27 unique PTTT identified. Validation studies were largely retrospective case-control studies or chart reviews, while effectiveness studies were predominantly uncontrolled before-after studies. Metrics of adverse outcomes varied considerably. Some PTTT demonstrated good diagnostic accuracy in retrospective case-control studies (primarily for predicting paediatric intensive care unit transfers), but positive predictive value was consistently low, suggesting potential for alarm fatigue. A small number of effectiveness studies reported significant decreases in mortality, arrests or code calls, but were limited by methodological concerns. Overall, there was limited evidence of paediatric early warning system interventions leading to reductions in deterioration.ConclusionThere are several fundamental methodological limitations in the PTTT literature, and the predominance of single-site studies carried out in specialist centres greatly limits generalisability. With limited evidence of effectiveness, calls to make PTTT mandatory across all paediatric units are not supported by the evidence base.Prospero registration numberCRD42015015326.

Project description:BackgroundEarly warning tools have been widely implemented without evidence to guide (a) recognition and (b) response team expertise optimisation. With growing databases from MET-calls and digital hospitals, we now have access to guiding information. The Queensland Adult-Deterioration-Detection-System (Q-ADDS) is widely used and requires validation.AimCompare the accuracy of Q-ADDS to National Early Warning Score (NEWS), Between-the-Flags (BTF) and the electronic Cardiac Arrest Risk Triage Score (eCART)).MethodsData from the Chicago University hospital database were used. Clinical deterioration was defined as unplanned admission to ICU or death. Currently used NEWS, BTF and eCART trigger thresholds were compared with a clinically endorsed Q-ADDS variant.ResultsOf 224,912 admissions, 11,706 (5%) experienced clinical deterioration. Q-ADDS (AUC 0.71) and NEWS (AUC 0.72) had similar predictive accuracy, BTF (AUC 0.64) had the lowest, and eCART (AUC 0.76) the highest. Early warning alert (advising ward MO review) had similar NPV (99.2-99.3%), for all the four tools however sensitivity varied (%: Q-ADDS = 47/NEWS = 49/BTF = 66/eCART = 40), as did alerting rate (% vitals sets: Q-ADDS = 1.4/NEWS = 3.5/BTF = 4.1/eCART = 3.4). MET alert (advising MET/critical-care review) had similar NPV for all the four tools (99.1-99.2%), however sensitivity varied (%: Q-ADDS = 14/NEWS = 24/BTF = 19/eCART = 29), as did MET alerting rate (%: Q-ADDS = 1.4/NEWS = 3.5/BTF = 4.1/eCART = 3.4). High-severity alert (advising advanced ward review, Q-ADDS only): NPV = 99.1%, sensitivity = 26%, alerting rate = 3.5%.ConclusionThe accuracy of Q-ADDS is comparable to NEWS, and higher than BTF, with eCART being the most accurate. Q-ADDS provides an additional high-severity ward alert, and generated significantly fewer MET alerts. Impacts of increased ward awareness and fewer MET alerts on actual MET call numbers and patient outcomes requires further evaluation.

Project description:ObjectiveTo develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR).MethodsWe conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal EDR data for the following follow-up times: (1) None; (2) Up to 5 years; (3) > 5 and ≤ 10 years; and (4) >10 and ≤ 15 years.ResultsThirty-four percent (n = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) did not have a disease status change between their first and last visits, 669 (13%) patients' disease status progressed, and 589 (11%) patients' disease improved.ConclusionsThis study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods.

Project description:BACKGROUND:Changes to physiological parameters precede deterioration of ill patients. Early warning and track and trigger systems (TTS) use routine physiological measurements with pre-specified thresholds to identify deteriorating patients and trigger appropriate and timely escalation of care. Patients presenting to the emergency department (ED) are undiagnosed, undifferentiated and of varying acuity, yet the effectiveness and cost-effectiveness of using early warning systems and TTS in this setting is unclear. We aimed to systematically review the evidence on the use, development/validation, clinical effectiveness and cost-effectiveness of physiologically based early warning systems and TTS for the detection of deterioration in adult patients presenting to EDs. METHODS:We searched for any study design in scientific databases and grey literature resources up to March 2016. Two reviewers independently screened results and conducted quality assessment. One reviewer extracted data with independent verification of 50% by a second reviewer. Only information available in English was included. Due to the heterogeneity of reporting across studies, results were synthesised narratively and in evidence tables. RESULTS:We identified 6397 citations of which 47 studies and 1 clinical trial registration were included. Although early warning systems are increasingly used in EDs, compliance varies. One non-randomised controlled trial found that using an early warning system in the ED may lead to a change in patient management but may not reduce adverse events; however, this is uncertain, considering the very low quality of evidence. Twenty-eight different early warning systems were developed/validated in 36 studies. There is relatively good evidence on the predictive ability of certain early warning systems on mortality and ICU/hospital admission. No health economic data were identified. CONCLUSIONS:Early warning systems seem to predict adverse outcomes in adult patients of varying acuity presenting to the ED but there is a lack of high quality comparative studies to examine the effect of using early warning systems on patient outcomes. Such studies should include health economics assessments.

Project description:BackgroundTimely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs-based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results.ObjectiveThis study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning-based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings.MethodsPubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to "vital signs," "clinical deterioration," and "machine learning." Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines.ResultsWe identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97.ConclusionsIn studies that compared performance, reported results suggest that machine learning-based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

Project description:BackgroundThe National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop dynamic early warning scores (DEWS) to address these deficiencies, and tested their accuracy in patients with respiratory disease for predicting (1) death or intensive care unit admission, occurring within 24 h (D/ICU), and (2) clinically significant deterioration requiring urgent intervention, occurring within 4 h (CSD).MethodsClinical observations data were extracted from electronic records for 31,590 respiratory in-patient episodes from April 2015 to December 2020 at a large acute NHS Trust. The timing of D/ICU was extracted for all episodes. 1100 in-patient episodes were annotated manually to record the timing of CSD, defined as a specific event requiring a change in treatment. Time series features were entered into logistic regression models to derive DEWS for each of the clinical outcomes. Area under the receiver operating characteristic curve (AUROC) was the primary measure of model accuracy.ResultsAUROC (95% confidence interval) for predicting D/ICU was 0.857 (0.852-0.862) for NEWS-2 and 0.906 (0.899-0.914) for DEWS in the validation data. AUROC for predicting CSD was 0.829 (0.817-0.842) for NEWS-2 and 0.877 (0.862-0.892) for DEWS. NEWS-2 ≥ 5 had sensitivity of 88.2% and specificity of 54.2% for predicting CSD, while DEWS ≥ 0.021 had higher sensitivity of 93.6% and approximately the same specificity of 54.3% for the same outcome. Using these cut-offs, 315 out of 347 (90.8%) CSD events were detected by both NEWS-2 and DEWS, at the time of the event or within the previous 4 h; 12 (3.5%) were detected by DEWS but not by NEWS-2, while 4 (1.2%) were detected by NEWS-2 but not by DEWS; 16 (4.6%) were not detected by either scoring system.ConclusionWe have developed DEWS that display greater accuracy than NEWS-2 for predicting clinical deterioration events in patients with respiratory disease. Prospective validation studies are required to assess whether DEWS can be used to reduce missed deteriorations and false alarms in real-life clinical settings.

Dataset Information

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms.

Background

Methodology

Discussion

Trial registration

Publications

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms.

Similar Datasets

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