The Natural History of Changes in Preferences for Life‐Sustaining Treatments and Implications for Inpatient Mortality in Younger and Older Hospitalized Adults

Ys, Kim; Escobar, Gabriel J.; Halpern, Scott D.; Greene, J; Kipnis, Patricia; Liu, Vincent

doi:10.1111/jgs.14048

Cited by 36 publications

(21 citation statements)

References 55 publications

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“…We also estimated the association between organ dysfunction and hospital mortality with multivariable logistic regression models adjusting for age, gender, predicted hospital mortality, acute severity of illness (Laboratory and Acute Physiology Score, version 2; LAPS2), composite comorbid disease burden (Comorbidity Point Score, version 2; COPS2), intensive care unit utilization, and full code status. (24, 25, 27–29) To reduce residual confounding, we also adjusted these models for covariates quantifying illness severity including the maximum SOFA subscore values for each organ besides the specific organ of interest during the entire hospitalization (since patients could have multiple types of organ dysfunction within the same hospitalization).…”

Section: Methodsmentioning

confidence: 99%

The Impact of Acute Organ Dysfunction on Long-Term Survival in Sepsis*

Schuler

Wulf

et al. 2018

Critical Care Medicine

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109

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Section: Methodsmentioning

confidence: 99%

The Impact of Acute Organ Dysfunction on Long-Term Survival in Sepsis*

Schuler

Wulf

et al. 2018

Critical Care Medicine

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109

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“…In order to determine the duration of invasive mechanical ventilation, we used a validated set of algorithms to identify the start and stop times for ventilation based on flowsheet data (26). Documented limits on life-sustaining therapies were based on orders for ‘code status’ in the EMR at the time of hospital admission, rather than specifically during or at the end of patients’ ICU stays (27). …”

Section: Methodsmentioning

confidence: 99%

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach*

Vranas

Jopling

Sweeney

et al. 2017

Critical Care Medicine

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Objective Identifying subgroups of intensive care unit (ICU) patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients’ shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts. Design We performed clustering analysis using data from patients’ hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population. Setting Kaiser Permanente Northern California (KPNC), a healthcare delivery system serving 3.9 million members. Patients ICU patients aged ≥ 18 years with an ICU admission between January 1, 2012 and December 31, 2012 at one of 21 KPNC hospitals. Interventions none. Measurements and Main Results We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics, and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically-recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables. Conclusions A machine learning approach revealed important differences between empirically-derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients’ shared needs.

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“…At present, available electronic data are limited (primarily, orders such as "do not resuscitate"). 9 However, this EMR domain is gradually expanding. 10,11 Entities such as the National Institutes of Health could develop sophisticated and rapid questionnaires around patient preferences that are similar to those developed for the Patient Reported Outcomes Measurement Information System.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Escobar

Dellinger

2016

Journal of Hospital Medicine

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Patients who deteriorate outside the intensive care unit (ICU) are known to have elevated mortality and morbidity. Rapid response teams (RRTs) were developed to address such deterioration. It has not been possible to establish that RRTs employing manual detection methods have definitively improved hospital outcomes. Because of this, automated early detection systems based on data from modern electronic medical records have been developed. This article attempts to establish a conceptual framework for early detection, prevention, and mitigation of critical illness in hospitalized patients outside the ICU. Taking a step back from the now voluminous clinical and statistical literature on early warning systems, this article focuses on definitional issues (What is early detection? What does it aim to detect, and how?). Particular attention is given to how early detection systems interface with patients who are near the end of life. Also addressed are problems of how one quantifies benefit, which includes consideration of downsides such as potential harms of early detection. Journal of Hospital Medicine 2016;11:S5–S10. © 2016 Society of Hospital Medicine

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The Natural History of Changes in Preferences for Life‐Sustaining Treatments and Implications for Inpatient Mortality in Younger and Older Hospitalized Adults

Cited by 36 publications

References 55 publications

The Impact of Acute Organ Dysfunction on Long-Term Survival in Sepsis*

The Impact of Acute Organ Dysfunction on Long-Term Survival in Sepsis*

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach*

Early detection, prevention, and mitigation of critical illness outside intensive care settings

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