Discharge Destinations of Delirious Patients: Findings from a Prospective Cohort Study of 27,026 Patients From a Large Health Care System

Requesting That Delirium Achieve Parity With Acute Encephalopathy in the MS-DRG System

Since 2007, the Medicare Severity Diagnosis Related Groups classification system has favored billing codes for acute encephalopathy over delirium codes in determining hospital reimbursement and several quality-of-care value metrics, despite broad overlap between these sets of diagnostic codes. Toxic and metabolic encephalopathy codes are designated as major complication or comorbidity, whereas causally specified delirium codes are designated as complication or comorbidity and thus associated with a lower reimbursement and lesser impact on value metrics. The authors led a submission to the U.S. Centers for Medicare and Medicaid Services requesting that causally specified delirium be designated major complication or comorbidity alongside toxic and metabolic encephalopathy. Delirium warrants reclassification because it satisfies U.S. Centers for Medicare and Medicaid Services' guiding principles for re-evaluating Medicare Severity Diagnosis Related Group severity levels. Delirium: (1) has a bidirectional relationship with the permanent condition of dementia (major neurocognitive disorder per DSM-5-TR), (2) indexes vulnerability across populations, (3) impacts healthcare systems across levels of care, (4) complicates postoperative recovery, (5) consigns patients to higher levels of care, (6) impedes patient engagement in care, (7) has several recent treatment guidelines, (8) often indicates neuronal/brain injury, and (9) represents a common expression of terminal illness. The proposal's impact was explored using the 2019 National Inpatient Sample, which suggested that increasing delirium's complexity designation would lead to an upcoding of less than 1% of eligible discharges. Parity for delirium is essential to enhancing awareness of delirium's clinical and economic costs. Appreciating delirium's impact would encourage delirium prevention and screening efforts, thereby mitigating its dire outcomes for patients, families, and healthcare systems.

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.

Delirium in trauma ICUs: a review of incidence, risk factors, outcomes, and management

PURPOSE OF REVIEW

This article reviews the impact and importance of delirium on patients admitted to the ICU after trauma, including the latest work on prevention and treatment of this condition. As the population ages, the incidence of geriatric trauma will continue to increase with a concomitant rise in the patient and healthcare costs of delirium in this population.

RECENT FINDINGS

Recent studies have further defined the risk factors for delirium in the trauma ICU patient population, as well as better demonstrated the poor outcomes associated with the diagnosis of delirium in these patients. Recent trials and meta-analysis offer some new evidence for the use of dexmedetomidine and quetiapine as preferred agents for prevention and treatment of delirium and add music interventions as a promising part of nonpharmacologic bundles.

SUMMARY

Trauma patients requiring admission to the ICU are at significant risk of developing delirium, an acute neuropsychiatric disorder associated with increased healthcare costs and worse outcomes including increased mortality. Ideal methods for prevention and treatment of delirium are not well established, especially in this population, but recent research helps to clarify optimal prevention and treatment strategies.

Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study

Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relying only on daily collected nursing data for delirium screening could expand the populations covered by such screening programs. Here, we present the results of the development and validation of a series of machine-learning based delirium prediction models. For this purpose, we used data of all patients 18 years or older which were hospitalized for more than a day between January 1, 2014, and December 31, 2018, at a single tertiary teaching hospital in Zurich, Switzerland. A total of 48,840 patients met inclusion criteria. 18,873 (38.6%) were excluded due to missing data. Mean age (SD) of the included 29,967 patients was 71.1 (12.2) years and 12,231 (40.8%) were women. Delirium was assessed with the Delirium Observation Scale (DOS) with a total score of 3 or greater indicating that a patient is at risk for delirium. Additional measures included structured data collected for nursing process planning and demographic characteristics. The performance of the machine learning models was assessed using the area under the receiver operating characteristic curve (AUC). The training set consisted of 21,147 patients (mean age 71.1 (12.1) years; 8,630 (40.8%) women|) including 233,024 observations with 16,167 (6.9%) positive DOS screens. The test set comprised 8,820 patients (median age 71.1 (12.4) years; 3,601 (40.8%) women) with 91,026 observations with 5,445 (6.0%) positive DOS screens. Overall, the gradient boosting machine model performed best with an AUC of 0.933 (95% CI, 0.929 - 0.936). In conclusion, machine learning models based only on structured nursing data can reliably predict patients at risk for delirium in an acute care setting. Prediction models, using existing data collection processes, could reduce the resources required for delirium screening procedures in clinical practice.

Predisposing and precipitating risk factors for delirium in gastroenterology and hepatology: Subgroup analysis of 718 patients from a hospital-wide prospective cohort study

BACKGROUND AND AIMS

Delirium is the most common acute neuropsychiatric syndrome in hospitalized patients. Higher age and cognitive impairment are known predisposing risk factors in general hospital populations. However, the interrelation with precipitating gastrointestinal (GI) and hepato-pancreato-biliary (HPB) diseases remains to be determined.

PATIENTS AND METHODS

Prospective 1-year hospital-wide cohort study in 29'278 adults, subgroup analysis in 718 patients hospitalized with GI/HPB disease. Delirium based on routine admission screening and a DSM-5 based construct. Regression analyses used to evaluate clinical characteristics of delirious patients.

RESULTS

Delirium was detected in 24.8% (178/718). Age in delirious patients (median 62 years [IQR 21]) was not different to non-delirious (median 60 years [IQR 22]), p = 0.45). Dementia was the strongest predisposing factor for delirium (OR 66.16 [6.31-693.83], p < 0.001). Functional impairment, and at most, immobility increased odds for delirium (OR 7.78 [3.84-15.77], p < 0.001). Patients with delirium had higher in-hospital mortality rates (18%; OR 39.23 [11.85-129.93], p < 0.001). From GI and HPB conditions, cirrhosis predisposed to delirium (OR 2.11 [1.11-4.03], p = 0.023), while acute renal failure (OR 4.45 [1.61-12.26], p = 0.004) and liver disease (OR 2.22 [1.12-4.42], p = 0.023) were precipitators. Total costs were higher in patients with delirium (USD 30003 vs. 10977; p < 0.001).

CONCLUSION

Delirium in GI- and HPB-disease was not associated with higher age per se, but with cognitive and functional impairment. Delirium needs to be considered in younger adults with acute renal failure and/or liver disease. Clinicians should be aware about individual risk profiles, apply preventive and supportive strategies early, which may improve outcomes and lower costs.