Identifying patients with delirium based on unstructured clinical notes. (Preprint)

Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records

COVID-19 has a range of complications, from no symptoms to severe pneumonia. It can also affect multiple organs including the nervous system. COVID-19 affects the brain, leading to neurological symptoms such as delirium. Delirium, a sudden change in consciousness, can increase the risk of death and prolong the hospital stay. However, research on delirium prediction in patients with COVID-19 is insufficient. This study aimed to identify new risk factors that could predict the onset of delirium in patients with COVID-19 using machine learning (ML) applied to nursing records. This retrospective cohort study used natural language processing and ML to develop a model for classifying the nursing records of patients with delirium. We extracted the features of each word from the model and grouped similar words. To evaluate the usefulness of word groups in predicting the occurrence of delirium in patients with COVID-19, we analyzed the temporal changes in the frequency of occurrence of these word groups before and after the onset of delirium. Moreover, the sensitivity, specificity, and odds ratios were calculated. We identified (1) elimination-related behaviors and conditions and (2) abnormal patient behavior and conditions as risk factors for delirium. Group 1 had the highest sensitivity (0.603), whereas group 2 had the highest specificity and odds ratio (0.938 and 6.903, respectively). These results suggest that these parameters may be useful in predicting delirium in these patients. The risk factors for COVID-19-associated delirium identified in this study were more specific but less sensitive than the ICDSC (Intensive Care Delirium Screening Checklist) and CAM-ICU (Confusion Assessment Method for the Intensive Care Unit). However, they are superior to the ICDSC and CAM-ICU because they can predict delirium without medical staff and at no cost.

Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study

BACKGROUND

Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium.

OBJECTIVE

The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. In this study, we will develop models for predicting hospital-induced delirium. In order to accomplish this objective, we will create a computable phenotype for our outcome (hospital-induced delirium), design an expert-based traditional logistic regression model, leverage machine learning techniques to generate a model using structured data, and use machine learning and natural language processing to produce an integrated model with components from both structured data and text data.

METHODS

This study will explore text-based data, such as nursing notes, to improve the predictive capability of prognostic models for hospital-induced delirium. By using supervised and unsupervised text mining in addition to structured data, we will examine multiple types of information in electronic health record data to predict medical-surgical patient risk of developing delirium. Development and validation will be compliant to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

RESULTS

Work on this project will take place through March 2024. For this study, we will use data from approximately 332,230 encounters that occurred between January 2012 to May 2021. Findings from this project will be disseminated at scientific conferences and in peer-reviewed journals.

CONCLUSIONS

Success in this study will yield a durable, high-performing research-data infrastructure that will process, extract, and analyze clinical text data in near real time. This model has the potential to be integrated into the electronic health record and provide point-of-care decision support to prevent harm and improve quality of care.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48521.

Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes

BACKGROUND

Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients.

METHODS

We obtained electronic clinical notes, demographic information, outcomes, and treatment data from three medical-surgical ICUs. Using NLP, we screened for behavioural disturbance phenotypes based on words suggestive of an agitated state, a non-agitated state, or a combination of both.

RESULTS

We studied 2931 patients. Of these, 225 (7.7%) were NLP-Dx-BD positive for the agitated phenotype, 544 (18.6%) for the non-agitated phenotype and 667 (22.7%) for the combined phenotype. Patients with these phenotypes carried multiple clinical baseline differences. On time-dependent multivariable analysis to compensate for immortal time bias and after adjustment for key outcome predictors, agitated phenotype patients were more likely to receive antipsychotic medications (odds ratio [OR] 1.84, 1.35-2.51, p < 0.001) compared to non-agitated phenotype patients but not compared to combined phenotype patients (OR 1.27, 0.86-1.89, p = 0.229). Moreover, agitated phenotype patients were more likely to die than other phenotypes patients (OR 1.57, 1.10-2.25, p = 0.012 vs non-agitated phenotype; OR 4.61, 2.14-9.90, p < 0.001 vs. combined phenotype). This association was strongest in patients receiving mechanical ventilation when compared with the combined phenotype (OR 7.03, 2.07-23.79, p = 0.002). A similar increased risk was also seen for patients with the non-agitated phenotype compared with the combined phenotype (OR 6.10, 1.80-20.64, p = 0.004).

CONCLUSIONS

NLP-Dx-BD screening enabled identification of three behavioural disturbance phenotypes with different characteristics, prevalence, trajectory, treatment, and outcome. Such phenotype identification appears relevant to prognostication and trial design.

Nighttime dexmedetomidine for delirium prevention in non-mechanically ventilated patients after cardiac surgery (MINDDS): A single-centre, parallel-arm, randomised, placebo-controlled superiority trial

BACKGROUND

The delirium-sparing effect of nighttime dexmedetomidine has not been studied after surgery. We hypothesised that a nighttime dose of dexmedetomidine would reduce the incidence of postoperative delirium as compared to placebo.

METHODS

This single-centre, parallel-arm, randomised, placebo-controlled superiority trial evaluated whether a short nighttime dose of intravenous dexmedetomidine (1 μg/kg over 40 min) would reduce the incidence of postoperative delirium in patients 60 years of age or older undergoing elective cardiac surgery with cardiopulmonary bypass. Patients were randomised to receive dexmedetomidine or placebo in a 1:1 ratio. The primary outcome was delirium on postoperative day one. Secondary outcomes included delirium within three days of surgery, 30-, 90-, and 180-day abbreviated Montreal Cognitive Assessment scores, Patient Reported Outcome Measures Information System quality of life scores, and all-cause mortality. The study was registered as NCT02856594 on ClinicalTrials.gov on August 5, 2016, before the enrolment of any participants.

FINDINGS

Of 469 patients that underwent randomisation to placebo (n = 235) or dexmedetomidine (n = 234), 75 met a prespecified drop criterion before the study intervention. Thus, 394 participants (188 dexmedetomidine; 206 placebo) were analysed in the modified intention-to-treat cohort (median age 69 [IQR 64, 74] years; 73.1% male [n = 288]; 26·9% female [n = 106]). Postoperative delirium status on day one was missing for 30 (7.6%) patients. Among those in whom it could be assessed, the primary outcome occurred in 5 of 175 patients (2.9%) in the dexmedetomidine group and 16 of 189 patients (8.5%) in the placebo group (OR 0.32, 95% CI: 0.10-0.83; P = 0.029). A non-significant but higher proportion of participants experienced delirium within three days postoperatively in the placebo group (25/177; 14.1%) compared to the dexmedetomidine group (14/160; 8.8%; OR 0.58; 95% CI, 0.28-1.15). No significant differences between groups were observed in secondary outcomes or safety.

INTERPRETATION

Our findings suggested that in elderly cardiac surgery patients with a low baseline risk of postoperative delirium and extubated within 12 h of ICU admission, a short nighttime dose of dexmedetomidine decreased the incidence of delirium on postoperative day one. Although non-statistically significant, our findings also suggested a clinical meaningful difference in the three-day incidence of postoperative delirium.

FUNDING

National Institute on Aging (R01AG053582).