Geriatric Delirium Care: Using Chart Audits to Target Improvement Strategies

Characterizing medical patients with delirium: A cohort study comparing ICD-10 codes and a validated chart review method

BACKGROUND

Delirium is a major cause of preventable mortality and morbidity in hospitalized adults, but accurately determining rates of delirium remains a challenge.

OBJECTIVE

To characterize and compare medical inpatients identified as having delirium using two common methods, administrative data and retrospective chart review.

METHODS

We conducted a retrospective study of 3881 randomly selected internal medicine hospital admissions from six acute care hospitals in Toronto and Mississauga, Ontario, Canada. Delirium status was determined using ICD-10-CA codes from hospital administrative data and through a previously validated chart review method. Baseline sociodemographic and clinical characteristics, processes of care and outcomes were compared across those without delirium in hospital and those with delirium as determined by administrative data and chart review.

RESULTS

Delirium was identified in 6.3% of admissions by ICD-10-CA codes compared to 25.7% by chart review. Using chart review as the reference standard, ICD-10-CA codes for delirium had sensitivity 24.1% (95%CI: 21.5-26.8%), specificity 99.8% (95%CI: 99.5-99.9%), positive predictive value 97.6% (95%CI: 94.6-98.9%), and negative predictive value 79.2% (95%CI: 78.6-79.7%). Age over 80, male gender, and Charlson comorbidity index greater than 2 were associated with misclassification of delirium. Inpatient mortality and median costs of care were greater in patients determined to have delirium by ICD-10-CA codes (5.8% greater mortality, 95% CI: 2.0-9.5 and $6824 greater cost, 95%CI: 4713-9264) and by chart review (11.9% greater mortality, 95%CI: 9.5-14.2% and $4967 greater cost, 95%CI: 4415-5701), compared to patients without delirium.

CONCLUSIONS

Administrative data are specific but highly insensitive, missing most cases of delirium in hospital. Mortality and costs of care were greater for both the delirium cases that were detected and missed by administrative data. Better methods of routinely measuring delirium in hospital are needed.

A machine-learning model to predict postoperative delirium following knee arthroplasty using electronic health records

BACKGROUND

Postoperative delirium is a challenging complication due to its adverse outcome such as long hospital stay. The aims of this study were: 1) to identify preoperative risk factors of postoperative delirium following knee arthroplasty, and 2) to develop a machine-learning prediction model.

METHOD

A total of 3,980 patients from two hospitals were included in this study. The model was developed and trained with 1,931 patients from one hospital and externally validated with 2,049 patients from another hospital. Twenty preoperative variables were collected using electronic hospital records. Feature selection was conducted using the sequential feature selection (SFS). Extreme Gradient Boosting algorithm (XGBoost) model as a machine-learning classifier was applied to predict delirium. A tenfold-stratified area under the curve (AUC) served as the metric for variable selection and internal validation.

RESULTS

The incidence rate of delirium was 4.9% (n = 196). The following seven key predictors of postoperative delirium were selected: age, serum albumin, number of hypnotics and sedatives drugs taken preoperatively, total number of drugs (any kinds of oral medication) taken preoperatively, neurologic disorders, depression, and fall-down risk (all p < 0.05). The predictive performance of our model was good for the developmental cohort (AUC: 0.80, 95% CI: 0.77-0.84). It was also good for the external validation cohort (AUC: 0.82, 95% CI: 0.80-0.83). Our model can be accessed at https://safetka.connecteve.com .

CONCLUSIONS

A web-based predictive model for delirium after knee arthroplasty was developed using a machine-learning algorithm featuring seven preoperative variables. This model can be used only with information that can be obtained from pre-operative electronic hospital records. Thus, this model could be used to predict delirium before surgery and may assist physician's effort on delirium prevention.

The frequency and quality of delirium documentation in discharge summaries

BACKGROUND

The National Institute for Health and Care Excellence recommends documenting all delirium episodes in the discharge summary using the term "delirium". Previous studies demonstrate poor delirium documentation rates in discharge summaries and no studies have assessed delirium documentation quality. The aim of this study was to determine the frequency and quality of delirium documentation in discharge summaries and explore differences between medical and surgical services.

METHODS

This was a multi-center retrospective chart review. We included 110 patients aged ≥ 65 years identified to have delirium during their hospitalization using the Chart-based Delirium Identification Instrument (CHART-DEL). We assessed the frequency of any delirium documentation in discharge summaries, and more specifically, for the term "delirium". We evaluated the quality of delirium discharge documentation using the Joint Commission on Accreditation of Healthcare Organization's framework for quality discharge summaries. Comparisons were made between medical and surgical services. Secondary outcomes included assessing factors influencing the frequency of "delirium" being documented in the discharge summary.

RESULTS

We identified 110 patients with sufficient chart documentation to identify delirium and 80.9 % of patients had delirium documented in their discharge summary ("delirium" or other acceptable term). The specific term "delirium" was reported in 63.6 % of all delirious patients and more often by surgical than medical specialties (76.5 % vs. 52.5 %, p = 0.02). Documentation quality was significantly lower by surgical specialties in reporting delirium as a diagnosis (23.5 % vs. 57.6 %, p < 0.001), documenting delirium workup (23.4 % vs. 57.6 %, p = 0.001), etiology (43.3 % vs. 70.4 %, p = 0.03), treatment (36.7 % vs. 66.7 %, p = 0.02), medication changes (44.4 % vs. 100 %, p = 0.002) and follow-up (36.4 % vs. 88.2 %, p = 0.01).

CONCLUSIONS

The frequency of delirium documentation is higher than previously reported but remains subpar. Medical services document delirium with higher quality, but surgical specialties document the term "delirium" more frequently. The documentation of delirium in discharge summaries must improve to meet quality standards.

Relationship Between Pain, Opioid Treatment, and Delirium in Older Emergency Department Patients

OBJECTIVES

Emergency department (ED) stay and its associated conditions (immobility, inadequate hydration and nutrition, lack of stimulation) increase the risk of delirium in older patients. Poorly controlled pain and paradoxically opioid pain treatment have also been identified as triggers for delirium. The aim of this study was to assess the relationship between pain, opioid treatment, and delirium in older ED patients.

METHODS

A multicenter prospective cohort study was conducted in four hospitals across the province of Québec (Canada). Patients aged ≥ 65 years old, waiting for hospital admission between March and July 2015, who were nondelirious upon ED arrival, who were independent or semi-independent in their daily living activities, and who had an ED stay of at least 8 hours were included. Delirium assessments were conducted twice a day during the patient's entire ED stay and their first 24 hours on the hospital ward using the Confusion Assessment Method. Pain intensity was evaluated using a visual analog scale (VAS = 0-100) during the initial interview, and all opioid treatments were documented.

RESULTS

A total of 338 patients were included; 51% were female, and mean (±SD) age was 77 (±8) years. Forty-one patients (12%) experienced delirium during their hospital stay occurring within a mean (±SD) delay of 47 (±19) hours after ED admission. Among patients with pain intensity ≥ 65 from VAS (0-100), 26% experienced delirium compared to 11% for patients with pain < 65 (p < 0.01), and no significant association was found between opioid consumption and delirium (p = 0.31). Logistic regression controlling for confounding factors showed that patients with pain intensity ≥ 65 are 3.3 (95% confidence interval = 1.4 to 7.9) times more likely to develop delirium than patients who had pain intensity of <65.

CONCLUSIONS

Severe pain, not opioids, is associated with the development of delirium during ED stay. Adequate pain control during the hospital stay may contribute to a decrease in delirium episodes.