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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:BACKGROUND:Intensive 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. METHODS:Systematic 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. RESULTS:Of 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%]). CONCLUSIONS:The 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.

Project description:Antipsychotics are licensed for psychosis and are also prescribed for behavior control. This study aims to examine characteristics and outcomes of children prescribed antipsychotics.A cohort study using general practice and hospital records linked with education records for 1,488,936 children living in Wales between 1999 and 2015. The characteristics of the children who were prescribed antipsychotics are presented using descriptive statistics and outcomes such as respiratory illness, diabetes, and injury were analyzed using multilevel logistic regression and the prior event rate ratio (PERR).Children with intellectual difficulty/autism were more likely to be prescribed antipsychotics (2.8% have been prescribed an antipsychotic [75% with autism] compared with 0.15% of children without intellectual difficulty). Those with intellectual disabilities/autism were prescribed antipsychotics at a younger age and for a longer period. Antipsychotic use was associated with a higher rate of respiratory illness for all (PERR of hospital admission: 1.55 [95% CI: 1.51-1.598] or increase in rate of 2 per 100 per year in those treated), and for those with intellectual difficulty/autism, there was a higher rate of injury and hospitalized depression. However, among those without intellectual difficulty/autism, there were lower rates of depression (PERR: 0.55 [95% CI: 0.51-0.59]).This work shows real-world use of antipsychotics and provides information on the rate of possible adverse events in children treated. Antipsychotics are predominantly used for those with intellectual difficulty/autism rather than those with a psychotic diagnosis. There is evidence that rates of respiratory disease, epilepsy, and diabetes are also higher postantipsychotic use for all. In those with intellectual difficulty/autism, hospital-admitted depression and injury are higher postantipsychotic use. The use of antipsychotics for behavioral management is likely to have increased cost implications to the healthcare system.

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.

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