CONTEXT: Palliative care (PC) benefits critically ill patients but remains underutilized. Important to developing interventions to overcome barriers to PC in the ICU and address PC needs of ICU patients is to understand how, when, and for which patients PC is provided in the ICU. OBJECTIVES: Compare characteristics of specialty PC consultations in the ICU to those on medical-surgical wards. METHODS: Retrospective analysis of national Palliative Care Quality Network data for hospitalized patients receiving specialty PC consultation January 1, 2013 to December 31, 2019 in ICU or medical-surgical setting. 98 inpatient PC teams in 16 states contributed data. Measures and outcomes included patient characteristics, consultation features, process metrics and patient outcomes. Mixed effects multivariable logistic regression was used to compare ICU and medical-surgical units. RESULTS: Of 102,597 patients 63,082 were in medical-surgical units and 39,515 ICU. ICU patients were younger and more likely to have non-cancer diagnoses (all P < 0.001). While fewer ICU patients were able to report symptoms, most patients in both groups reported improved symptoms. ICU patients were more likely to have consultation requests for GOC, comfort care, and withdrawal of interventions and less likely for pain and/or symptoms (OR-all P < 0.001). ICU patients were less often discharged alive. CONCLUSION: ICU patients receiving PC consultation are more likely to have non-cancer diagnoses and less likely able to communicate. Although symptom management and GOC are standard parts of ICU care, specialty PC in the ICU is often engaged for these issues and results in improved symptoms, suggesting routine interventions and consultation targeting these needs could improve care.

Background

Older adults are increasingly admitted to the ICU, and those with disabilities, dementia, frailty, and multimorbidity are vulnerable to adverse outcomes. Little is known about how pre-existing geriatric conditions have changed over time.

Research question

How have changes in disability, dementia, frailty, and multimorbidity in older adults admitted to the ICU changed from 1998 through 2015?

Study design and methods

Medicare-linked Health and Retirement Survey (HRS) data identifying patients 65 years of age and older admitted to an ICU between 1998 and 2015. ICU admission was the unit of analysis. Year of ICU admission was the exposure. Disability, dementia, frailty, and multimorbidity were identified based on responses to HRS surveys before ICU admission. Disability represented the need for assistance with ≥ 1 activity of daily living. Dementia used cognitive and functional measures. Frailty included deficits in ≥ 2 domains (physical, nutritive, cognitive, or sensory function). Multimorbidity represented ≥ 3 self-reported chronic diseases. Time trends in geriatric conditions were modeled as a function of year of ICU admission and were adjusted for age, sex, race or ethnicity, and proxy interview status.

Results

Across 6,084 ICU patients, age at admission increased from 77.6 years (95% CI, 76.7-78.4 years) in 1998 to 78.7 years (95% CI, 77.5-79.8 years) in 2015 (P < .001 for trend). The adjusted proportion of ICU admissions with pre-existing disability rose from 15.5% (95% CI, 12.1%-18.8%) in 1998 to 24.0% (95% CI, 18.5%-29.6%) in 2015 (P = .001). Rates of dementia did not change significantly (P = .21). Frailty increased from 36.6% (95% CI, 30.9%-42.3%) in 1998 to 45.0% (95% CI, 39.7%-50.2%) in 2015 (P = .04); multimorbidity rose from 54.4% (95% CI, 49.2%-59.7%) in 1998 to 71.8% (95% CI, 66.3%-77.2%) in 2015 (P < .001).

Interpretation

Rates of pre-existing disability, frailty, and multimorbidity in older adults admitted to ICUs increased over time. Geriatric principles need to be deeply integrated into the ICU setting.

Drug overdoses are a national and global epidemic. However, while overdoses are inextricably linked to social, demographic, and geographical determinants, geospatial patterns of drug-related admissions and overdoses at the neighborhood level remain poorly studied. The objective of this paper is to investigate spatial distributions of patients admitted for drug-related admissions and overdoses from a large, urban, tertiary care center using electronic health record data. Additionally, these spatial distributions were adjusted for a validated socioeconomic index called the Area Deprivation Index (ADI). We showed spatial heterogeneity in patients admitted for opioid, amphetamine, and psychostimulant-related diagnoses and overdoses. While ADI was associated with drug-related admissions, it did not correct for spatial variations and could not account alone for this spatial heterogeneity.

OBJECTIVES: To describe the utilization of early ketamine use among patients mechanically ventilated for COVID-19, and examine associations with in-hospital mortality and other clinical outcomes. DESIGN: Retrospective cohort study. SETTING: Six hundred ten hospitals contributing data to the Premier Healthcare Database between April 2020 and June 2021. PATIENTS: Adults with COVID-19 and greater than or equal to 2 consecutive days of mechanical ventilation within 5 days of hospitalization. INTERVENTION: The exposures were early ketamine use initiated within 2 days of intubation and continued for greater than 1 day. MEASUREMENTS: Primary was hospital mortality. Secondary outcomes included length of stay (LOS) in the hospital and ICUs, ventilator days, vasopressor days, renal replacement therapy (RRT), and total hospital cost. The propensity score matching analysis was used to adjust for confounders. MAIN RESULTS: Among 42,954 patients, 1,423 (3.3%) were exposed to early ketamine use. After propensity score matching including 1,390 patients in each group, recipients of ketamine infusions were associated with higher hospital mortality (52.5% vs. 45.9%, risk ratio: 1.14, [1.06-1.23]), longer median ICU stay (13 vs. 12 d, mean ratio [MR]: 1.15 [1.08-1.23]), and longer ventilator days (12 vs. 11 d, MR: 1.19 [1.12-1.27]). There were no associations for hospital LOS (17 [10-27] vs. 17 [9-28], MR: 1.05 [0.99-1.12]), vasopressor days (4 vs. 4, MR: 1.04 [0.95-1.14]), and RRT (22.9% vs. 21.7%, RR: 1.05 [0.92-1.21]). Total hospital cost was higher (median $72,481 vs. $65,584, MR: 1.11 [1.05-1.19]). CONCLUSIONS: In a diverse sample of U.S. hospitals, about one in 30 patients mechanically ventilated with COVID-19 received ketamine infusions. Early ketamine may have an association with higher hospital mortality, increased total cost, ICU stay, and ventilator days, but no associations for hospital LOS, vasopressor days, and RRT. However, confounding by the severity of illness might occur due to higher extracorporeal membrane oxygenation and RRT use in the ketamine group. Further randomized trials are needed to better understand the role of ketamine infusions in the management of critically ill patients.

BACKGROUND: Language in nonmedical data sets is known to transmit human-like biases when used in natural language processing (NLP) algorithms that can reinforce disparities. It is unclear if NLP algorithms of medical notes could lead to similar transmissions of biases. RESEARCH QUESTION: Can we identify implicit bias in clinical notes, and are biases stable across time and geography? STUDY DESIGN AND METHODS: To determine whether different racial and ethnic descriptors are similar contextually to stigmatizing language in ICU notes and whether these relationships are stable across time and geography, we identified notes on critically ill adults admitted to the University of California, San Francisco (UCSF), from 2012 through 2022 and to Beth Israel Deaconess Hospital (BIDMC) from 2001 through 2012. Because word meaning is derived largely from context, we trained unsupervised word-embedding algorithms to measure the similarity (cosine similarity) quantitatively of the context between a racial or ethnic descriptor (eg, African-American) and a stigmatizing target word (eg, nonco-operative) or group of words (violence, passivity, noncompliance, nonadherence). RESULTS: In UCSF notes, Black descriptors were less likely to be similar contextually to violent words compared with White descriptors. Contrastingly, in BIDMC notes, Black descriptors were more likely to be similar contextually to violent words compared with White descriptors. The UCSF data set also showed that Black descriptors were more similar contextually to passivity and noncompliance words compared with Latinx descriptors. INTERPRETATION: Implicit bias is identifiable in ICU notes. Racial and ethnic group descriptors carry different contextual relationships to stigmatizing words, depending on when and where notes were written. Because NLP models seem able to transmit implicit bias from training data, use of NLP algorithms in clinical prediction could reinforce disparities. Active debiasing strategies may be necessary to achieve algorithmic fairness when using language models in clinical research.

OBJECTIVES: To develop proof-of-concept algorithms using alternative approaches to capture provider sentiment in ICU notes. DESIGN: Retrospective observational cohort study. SETTING: The Multiparameter Intelligent Monitoring of Intensive Care III (MIMIC-III) and the University of California, San Francisco (UCSF) deidentified notes databases. PATIENTS: Adult (≥18 yr old) patients admitted to the ICU. MEASUREMENTS AND MAIN RESULTS: We developed two sentiment models: 1) a keywords-based approach using a consensus-based clinical sentiment lexicon comprised of 72 positive and 103 negative phrases, including negations and 2) a Decoding-enhanced Bidirectional Encoder Representations from Transformers with disentangled attention-v3-based deep learning model (keywords-independent) trained on clinical sentiment labels. We applied the models to 198,944 notes across 52,997 ICU admissions in the MIMIC-III database. Analyses were replicated on an external sample of patients admitted to a UCSF ICU from 2018 to 2019. We also labeled sentiment in 1,493 note fragments and compared the predictive accuracy of our tools to three popular sentiment classifiers. Clinical sentiment terms were found in 99% of patient visits across 88% of notes. Our two sentiment tools were substantially more predictive (Spearman correlations of 0.62-0.84, p values < 0.00001) of labeled sentiment compared with general language algorithms (0.28-0.46). CONCLUSION: Our exploratory healthcare-specific sentiment models can more accurately detect positivity and negativity in clinical notes compared with general sentiment tools not designed for clinical usage.