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Patient or family perceived deterioration in functional status and outcome after intensive care admission: a retrospective cohort analysis of routinely collected data.

ABSTRACT: OBJECTIVE:To explore the association of patient or family reported functional deterioration (defined by a single question) in the preceding year, with mortality outcome for those admitted to the intensive care unit (ICU). DESIGN:Retrospective observational analysis of a routinely collected data source. PARTICIPANTS:Patients that were admitted to the ICU at Northwick Park and St Marks Hospitals, London North West University Healthcare NHS Trust between 01 October 2017 to 15 June 2019 were included. Patients were excluded if they had a prior ICU admission during the existing hospital episode or if information on functional deterioration could not be retrieved from either the patient or their advocate. PRIMARY OUTCOMES:Mortality at the point of hospital discharge and 1 year following admission to the ICU. RESULTS:Of the 1006 patients who were admitted to the ICU during the study period, information on functional deterioration was available for 621 patients who were included in the analysis. From these, 251 (40.4%) patients had patient or family reported functional deterioration in the preceding year, while 370 (59.6%) patients had a perceived stable functional baseline. Comparing the two groups, mortality was significantly higher in those who had functionally deteriorated compared with those with stable baseline function, at the point of hospital discharge (45.4% vs 25.9%; p<0.0001) and at 1 year (59.4% vs 33.0%; p<0.0001). CONCLUSION:Patient or family reported functional deterioration was significantly associated with higher mortality at the point of hospital discharge and at 1 year. The concept of functional deterioration in the lead up to ICU admission warrants further exploration.

SUBMITTER: Gross JL

PROVIDER: S-EPMC7545658 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Patient or family perceived deterioration in functional status and outcome after intensive care admission: a retrospective cohort analysis of routinely collected data.

Gross Jamie L JL Borkowski Jacek J Brett Stephen J SJ

BMJ open 20201008 10

<h4>Objective</h4>To explore the association of patient or family reported functional deterioration (defined by a single question) in the preceding year, with mortality outcome for those admitted to the intensive care unit (ICU).<h4>Design</h4>Retrospective observational analysis of a routinely collected data source.<h4>Participants</h4>Patients that were admitted to the ICU at Northwick Park and St Marks Hospitals, London North West University Healthcare NHS Trust between 01 October 2017 to 15 ...[more]

PMID: 33033096

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Project description:BackgroundIntensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients' journeys into and through intensive care are now collected and stored in electronic health records: machine learning has been used to analyse such data in order to provide decision support to clinicians.MethodsSystematic review of the applications of machine learning to routinely collected ICU data. Web of Science and MEDLINE databases were searched to identify candidate articles: those on image processing were excluded. The study aim, the type of machine learning used, the size of dataset analysed, whether and how the model was validated, and measures of predictive accuracy were extracted.ResultsOf 2450 papers identified, 258 fulfilled eligibility criteria. The most common study aims were predicting complications (77 papers [29.8% of studies]), predicting mortality (70 [27.1%]), improving prognostic models (43 [16.7%]), and classifying sub-populations (29 [11.2%]). Median sample size was 488 (IQR 108-4099): 41 studies analysed data on > 10,000 patients. Analyses focused on 169 (65.5%) papers that used machine learning to predict complications, mortality, length of stay, or improvement of health. Predictions were validated in 161 (95.2%) of these studies: the area under the ROC curve (AUC) was reported by 97 (60.2%) but only 10 (6.2%) validated predictions using independent data. The median AUC was 0.83 in studies of 1000-10,000 patients, rising to 0.94 in studies of > 100,000 patients. The most common machine learning methods were neural networks (72 studies [42.6%]), support vector machines (40 [23.7%]), and classification/decision trees (34 [20.1%]). Since 2015 (125 studies [48.4%]), the most common methods were support vector machines (37 studies [29.6%]) and random forests (29 [23.2%]).ConclusionsThe rate of publication of studies using machine learning to analyse routinely collected ICU data is increasing rapidly. The sample sizes used in many published studies are too small to exploit the potential of these methods. Methodological and reporting guidelines are needed, particularly with regard to the choice of method and validation of predictions, to increase confidence in reported findings and aid in translating findings towards routine use in clinical practice.

Dataset Information

Patient or family perceived deterioration in functional status and outcome after intensive care admission: a retrospective cohort analysis of routinely collected data.

Publications

Patient or family perceived deterioration in functional status and outcome after intensive care admission: a retrospective cohort analysis of routinely collected data.

Similar Datasets

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