Analysis of multi-dimensional contemporaneous EHR data to refine delirium assessments

Delirium detection tools show varying completion rates and positive score rates when used at scale in routine practice in general hospital settings: A systematic review

BACKGROUND

Multiple short delirium detection tools have been validated in research studies and implemented in routine care, but there has been little study of these tools in real-world conditions. This systematic review synthesized literature reporting completion rates and/or delirium positive score rates of detection tools in large clinical populations in general hospital settings.

METHODS

PROSPERO (CRD42022385166). Medline, Embase, PsycINFO, CINAHL, and gray literature were searched from 1980 to December 31, 2022. Included studies or audit reports used a validated delirium detection tool performed directly with the patient as part of routine care in large clinical populations (n ≥ 1000) within a general acute hospital setting. Narrative synthesis was performed.

RESULTS

Twenty-two research studies and four audit reports were included. Tools used alone or in combination were the Confusion Assessment Method (CAM), 4 'A's Test (4AT), Delirium Observation Screening Scale (DOSS), Brief CAM (bCAM), Nursing Delirium Screening Scale (NuDESC), and Intensive Care Delirium Screening Checklist (ICDSC). Populations and settings varied and tools were used at different stages and frequencies in the patient journey, including on admission only; inpatient, daily or more frequently; on admission and as inpatient; inpatient post-operatively. Tool completion rates ranged from 19% to 100%. Admission positive score rates ranged from: CAM 8%-51%; 4AT 13%-20%. Inpatient positive score rates ranged from: CAM 2%-20%, DOSS 6%-42%, and NuDESC 5-13%. Postoperative positive score rates were 21% and 28% (4AT). All but two studies had moderate-high risk of bias.

CONCLUSIONS

This systematic review of delirium detection tool implementation in large acute patient populations found clinically important variability in tool completion rates, and in delirium positive score rates relative to expected delirium prevalence. This study highlights a need for greater reporting and analysis of relevant healthcare systems data. This is vital to advance understanding of effective delirium detection in routine care.

Advancing specificity in delirium: The delirium subtyping initiative

BACKGROUND

Delirium, a common syndrome with heterogeneous etiologies and clinical presentations, is associated with poor long-term outcomes. Recording and analyzing all delirium equally could be hindering the field's understanding of pathophysiology and identification of targeted treatments. Current delirium subtyping methods reflect clinically evident features but likely do not account for underlying biology.

METHODS

The Delirium Subtyping Initiative (DSI) held three sessions with an international panel of 25 experts.

RESULTS

Meeting participants suggest further characterization of delirium features to complement the existing Diagnostic and Statistical Manual of Mental Disorders Fifth Edition Text Revision diagnostic criteria. These should span the range of delirium-spectrum syndromes and be measured consistently across studies. Clinical features should be recorded in conjunction with biospecimen collection, where feasible, in a standardized way, to determine temporal associations of biology coincident with clinical fluctuations.

DISCUSSION

The DSI made recommendations spanning the breadth of delirium research including clinical features, study planning, data collection, and data analysis for characterization of candidate delirium subtypes.

HIGHLIGHTS

Delirium features must be clearly defined, standardized, and operationalized. Large datasets incorporating both clinical and biomarker variables should be analyzed together. Delirium screening should incorporate communication and reasoning.

Positive scores on the 4AT delirium assessment tool at hospital admission are linked to mortality, length of stay and home time: two-centre study of 82,770 emergency admissions

BACKGROUND

Studies investigating outcomes of delirium using large-scale routine data are rare. We performed a two-centre study using the 4 'A's Test (4AT) delirium detection tool to analyse relationships between delirium and 30-day mortality, length of stay and home time (days at home in the year following admission).

METHODS

The 4AT was performed as part of usual care. Data from emergency admissions in patients ≥65 years in Lothian, UK (n = 43,946) and Salford, UK (n = 38,824) over a period of $\sim$3 years were analysed using logistic regression models adjusted for age and sex.

RESULTS

4AT completion rates were 77% in Lothian and 49% in Salford. 4AT scores indicating delirium (≥4/12) were present in 18% of patients in Lothian, and 25% of patients in Salford. Thirty-day mortality with 4AT ≥4 was 5.5-fold greater than the 4AT 0/12 group in Lothian (adjusted odds ratio (aOR) 5.53, 95% confidence interval [CI] 4.99-6.13) and 3.4-fold greater in Salford (aOR 3.39, 95% CI 2.98-3.87). Length of stay was more than double in patients with 4AT scores of 1-3/12 (indicating cognitive impairment) or ≥ 4/12 compared with 4AT 0/12. Median home time at 1 year was reduced by 112 days (Lothian) and 61 days (Salford) in the 4AT ≥4 group (P < 0.001).

CONCLUSIONS

Scores on the 4AT used at scale in practice are strongly linked with 30-day mortality, length of hospital stay and home time. The findings highlight the need for better understanding of why delirium is linked with poor outcomes and also the need to improve delirium detection and treatment.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Innovation to enhance health in care homes and evaluation of tools for measuring outcomes of care: rapid evidence synthesis

Background

Flexible, integrated models of service delivery are being developed to meet the changing demands of an ageing population. To underpin the spread of innovative models of care across the NHS, summaries of the current research evidence are needed. This report focuses exclusively on care homes and reviews work in four specific areas, identified as key enablers for the NHS England vanguard programme.

Aim

To conduct a rapid synthesis of evidence relating to enhancing health in care homes across four key areas: technology, communication and engagement, workforce and evaluation.

Objectives

(1) To map the published literature on the uses, benefits and challenges of technology in care homes; flexible and innovative uses of the nursing and support workforce to benefit resident care; communication and engagement between care homes, communities and health-related organisations; and approaches to the evaluation of new models of care in care homes. (2) To conduct rapid, systematic syntheses of evidence to answer the following questions. Which technologies have a positive impact on resident health and well-being? How should care homes and the NHS communicate to enhance resident, family and staff outcomes and experiences? Which measurement tools have been validated for use in UK care homes? What is the evidence that staffing levels (i.e. ratio of registered nurses and support staff to residents or different levels of support staff) influence resident outcomes?

Data sources

Searches of MEDLINE, CINAHL, Science Citation Index, Cochrane Database of Systematic Reviews, DARE (Database of Abstracts of Reviews of Effects) and Index to Theses. Grey literature was sought via Google™ (Mountain View, CA, USA) and websites relevant to each individual search.

Design

Mapping review and rapid, systematic evidence syntheses.

Setting

Care homes with and without nursing in high-income countries.

Review methods

Published literature was mapped to a bespoke framework, and four linked rapid critical reviews of the available evidence were undertaken using systematic methods. Data were not suitable for meta-analysis, and are presented in narrative syntheses.

Results

Seven hundred and sixty-one studies were mapped across the four topic areas, and 65 studies were included in systematic rapid reviews. This work identified a paucity of large, high-quality research studies, particularly from the UK. The key findings include the following. (1) Technology: some of the most promising interventions appear to be games that promote physical activity and enhance mental health and well-being. (2) Communication and engagement: structured communication tools have been shown to enhance communication with health services and resident outcomes in US studies. No robust evidence was identified on care home engagement with communities. (3) Evaluation: 6 of the 65 measurement tools identified had been validated for use in UK care homes, two of which provide general assessments of care. The methodological quality of all six tools was assessed as poor. (4) Workforce: joint working within and beyond the care home and initiatives that focus on staff taking on new but specific care tasks appear to be associated with enhanced outcomes. Evidence for staff taking on traditional nursing tasks without qualification is limited, but promising.

Limitations

This review was restricted to English-language publications after the year 2000. The rapid methodology has facilitated a broad review in a short time period, but the possibility of omissions and errors cannot be excluded.

Conclusions

This review provides limited evidential support for some of the innovations in the NHS vanguard programme, and identifies key issues and gaps for future research and evaluation.

Future work

Future work should provide high-quality evidence, in particular experimental studies, economic evaluations and research sensitive to the UK context.

Study registration

This study is registered as PROSPERO CRD42016052933, CRD42016052933, CRD42016052937 and CRD42016052938.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Prediction of Incident Delirium Using a Random Forest classifier

Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visits to train and test a model predicting delirium arising in hospital. Incident delirium was defined as the first instance of a positive CAM occurring at least 48 h into a hospital stay. A Random Forest machine learning algorithm was used with demographic data, comorbidities, medications, procedures, and physiological measures. The data set was randomly partitioned 80% / 20% for training and validating the predictive model, respectively. Of the 51,240 patients in the training set, 2774 (5.4%) experienced delirium during their hospital stay; and of the 12,798 patients in the validation set, 701 (5.5%) experienced delirium. Under-sampling of the delirium negative population was used to address the class imbalance. The Random Forest predictive model yielded an area under the receiver operating characteristic curve (ROC AUC) of 0.909 (95% CI 0.898 to 0.921). Important variables in the model included previously identified predisposing and precipitating risk factors. This machine learning approach displayed a high degree of accuracy and has the potential to provide a clinically useful predictive model for earlier intervention in those patients at greatest risk of developing delirium.