Mechanical Ventilation in Older Adults With Dementia: Opportunities to Promote Goal-Concordant Care.

CONTEXT: Recent studies show increasing use of mechanical ventilation among people living with dementia. There are concerns that this trend may not be driven by patient preferences. OBJECTIVES: To better understand decision-making regarding mechanical ventilation in people living with dementia. METHODS: This was an electronic health record-based retrospective cohort study of older adults with dementia (n = 295) hospitalized at one of two teaching hospitals between 2015 and 2019 who were supported with mechanical ventilation (n = 191) or died without mechanical ventilation (n = 104). Multivariable logistic regression was used to examine associations between patient characteristics and mechanical ventilation use. RESULTS: The median age was 78 years (IQR 71-86), 41% were female, 28% resided in a nursing home, and 58% had clinical markers of advanced dementia (dehydration, weight loss, mobility limitations, or pressure ulcers). Among patients supported with mechanical ventilation, 70% were intubated within 24 hours of presentation, including 31% intubated before hospital arrival. Younger age, higher illness acuity, and absence of a treatment-limiting Physician Orders for Life-Sustaining Treatment document were associated with mechanical ventilation use; nursing home residence and clinical markers of advanced dementia were not. Most patients (89%) had a documented goals of care discussion (GOCD) during hospitalization. CONCLUSION: Future efforts to promote goal-concordant care surrounding mechanical ventilation use for people living with dementia should involve identifying barriers to goal-concordant care in pre-hospital settings, assessing the timeliness of in-hospital GOCD, and developing strategies for in-the-moment crisis communication across settings.

Predictors of Documented Goals-of-Care Discussion for Hospitalized Patients With Chronic Illness.

CONTEXT: Goals-of-care discussions are important for patient-centered care among hospitalized patients with serious illness. However, there are little data on the occurrence, predictors, and timing of these discussions. OBJECTIVES: To examine the occurrence, predictors, and timing of electronic health record (EHR)-documented goals-of-care discussions for hospitalized patients. METHODS: This retrospective cohort study used natural language processing (NLP) to examine EHR-documented goals-of-care discussions for adults with chronic life-limiting illness or age ≥80 hospitalized 2015-2019. The primary outcome was NLP-identified documentation of a goals-of-care discussion during the index hospitalization. We used multivariable logistic regression to evaluate associations with baseline characteristics. RESULTS: Of 16,262 consecutive, eligible patients without missing data, 5,918 (36.4%) had a documented goals-of-care discussion during hospitalization; approximately 57% of these discussions occurred within 24 hours of admission. In multivariable analysis, documented goals-of-care discussions were more common for women (OR=1.26, 95%CI 1.18-1.36), older patients (OR=1.04 per year, 95%CI 1.03-1.04), and patients with more comorbidities (OR=1.11 per Deyo-Charlson point, 95%CI 1.10-1.13), cancer (OR=1.88, 95%CI 1.72-2.06), dementia (OR=2.60, 95%CI 2.29-2.94), higher acute illness severity (OR=1.12 per National Early Warning Score point, 95%CI 1.11-1.14), or prior advance care planning documents (OR=1.18, 95%CI 1.08-1.30). Documentation of these discussions was less common for racially or ethnically minoritized patients (OR=0.823, 95%CI 0.75-0.90). CONCLUSION: Among hospitalized patients with serious illness, documented goals-of-care discussions identified by NLP were more common among patients with older age and increased burden of acute or chronic illness, and less common among racially or ethnically minoritized patients. This suggests important disparities in goals-of-care discussions.

Mixed-methods evaluation of three natural language processing modeling approaches for measuring documented goals-of-care discussions in the electronic health record.

CONTEXT: Documented goals-of-care discussions are an important quality metric for patients with serious illness. Natural language processing (NLP) is a promising approach for identifying goals-of-care discussions in the electronic health record (EHR). OBJECTIVES: To compare three NLP modeling approaches for identifying EHR documentation of goals-of-care discussions and generate hypotheses about differences in performance. METHODS: We conducted a mixed-methods study to evaluate performance and misclassification for three NLP featurization approaches modeled with regularized logistic regression: bag-of-words (BOW), rule-based, and a hybrid approach. From a prospective cohort of 150 patients hospitalized with serious illness over 2018 to 2020, we collected 4391 inpatient EHR notes; 99 (2.3%) contained documented goals-of-care discussions. We used leave-one-out cross-validation to estimate performance by comparing pooled NLP predictions to human abstractors with receiver-operating-characteristic (ROC) and precision-recall (PR) analyses. We qualitatively examined a purposive sample of 70 NLP-misclassified notes using content analysis to identify linguistic features that allowed us to generate hypotheses underpinning misclassification. RESULTS: All three modeling approaches discriminated between notes with and without goals-of-care discussions (AUCROC: BOW, 0.907; rule-based, 0.948; hybrid, 0.965). Precision and recall were only moderate (precision at 70% recall: BOW, 16.2%; rule-based, 50.4%; hybrid, 49.3%; AUCPR: BOW, 0.505; rule-based, 0.579; hybrid, 0.599). Qualitative analysis revealed patterns underlying performance differences between BOW and rule-based approaches. CONCLUSION: NLP holds promise for identifying EHR-documented goals-of-care discussions. However, the rarity of goals-of-care content in EHR data limits performance. Our findings highlight opportunities to optimize NLP modeling approaches, and support further exploration of different NLP approaches to identify goals-of-care discussions.