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Crit Care Explor ; 5(11): e0996, 2023 Nov.
Article in English | MEDLINE | ID: mdl-38304704

ABSTRACT

OBJECTIVES: To evaluate the association of race with proportion of time in deep sedation among mechanically ventilated adults. DESIGN: Retrospective cohort study from October 2017 to December 2019. SETTING: Five hospitals within a single health system. PATIENTS: Adult patients who identified race as Black or White who were mechanically ventilated for greater than or equal to 24 hours in one of 12 medical, surgical, cardiovascular, cardiothoracic, or mixed ICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The exposure was White compared with Black race. The primary outcome was the proportion of time in deep sedation during the first 48 hours of mechanical ventilation, defined as Richmond Agitation-Sedation Scale values of -3 to -5. For the primary analysis, we performed mixed-effects linear regression models including ICU as a random effect, and adjusting for age, sex, English as preferred language, body mass index, Elixhauser comorbidity index, Laboratory-based Acute Physiology Score, Version 2, ICU admission source, admission for a major surgical procedure, and the presence of septic shock. Of the 3337 included patients, 1242 (37%) identified as Black, 1367 (41%) were female, and 1002 (30%) were admitted to a medical ICU. Black patients spent 48% of the first 48 hours of mechanical ventilation in deep sedation, compared with 43% among White patients in unadjusted analysis. After risk adjustment, Black race was significantly associated with more time in early deep sedation (mean difference, 5%; 95% CI, 2-7%; p < 0.01). CONCLUSIONS: There are disparities in sedation during the first 48 hours of mechanical ventilation between Black and White patients across a diverse set of ICUs. Future work is needed to determine the clinical significance of these findings, given the known poorer outcomes for patients who experience early deep sedation.

3.
Crit Care Med ; 48(12): 1897-1898, 2020 12.
Article in English | MEDLINE | ID: mdl-33255107
5.
Crit Care Med ; 47(11): 1591-1598, 2019 11.
Article in English | MEDLINE | ID: mdl-31464767

ABSTRACT

OBJECTIVES: As ICUs are increasingly a site of end-of-life care, many have adopted end-of-life care resources. We sought to determine the association of such resources with outcomes of ICU patients. DESIGN: Retrospective cohort study. SETTING: Pennsylvania ICUs. PATIENTS: Medicare fee-for-service beneficiaries. INTERVENTIONS: Availability of any of one hospital-based resource (palliative care consultants) or four ICU-based resources (protocol for withdrawal of life-sustaining therapy, triggers for automated palliative care consultation, protocol for family meetings, and palliative care clinicians embedded in ICU rounds). MEASUREMENTS AND MAIN RESULTS: In mixed-effects regression analyses, admission to a hospital with end-of-life resources was not associated with mortality, length of stay, or treatment intensity (mechanical ventilation, hemodialysis, tracheostomy, gastrostomy, artificial nutrition, or cardiopulmonary resuscitation); however, it was associated with a higher likelihood of discharge to hospice (odds ratio, 1.58; 95% CI, 1.11-2.24), an effect that was driven by ICU-based resources (odds ratio, 1.37; 95% CI, 1.04-1.81) rather than hospital-based resources (odds ratio, 1.19; 95% CI, 0.83-1.71). Instrumental variable analysis using differential distance (defined as the additional travel distance beyond the hospital closest to a patient's home needed to reach a hospital with end-of-life resources) demonstrated that among those for whom differential distance would influence receipt of end-of-life resources, admission to a hospital with such resources was not associated with any outcome. CONCLUSIONS: ICU-based end-of-life care resources do not appear to change mortality but are associated with increased hospice utilization. Given that this finding was not confirmed by the instrumental variable analysis, future studies should attempt to verify this finding, and identify specific resources or processes of care that impact the care of ICU patients at the end of life.


Subject(s)
Health Services Accessibility , Intensive Care Units/organization & administration , Palliative Care , Adolescent , Adult , Aged , Clinical Protocols , Cohort Studies , Female , Hospices/statistics & numerical data , Hospital Mortality , Humans , Male , Middle Aged , Patient Discharge , Pennsylvania/epidemiology , Referral and Consultation , Retrospective Studies , Withholding Treatment , Young Adult
6.
Crit Care Med ; 45(8): e758-e762, 2017 Aug.
Article in English | MEDLINE | ID: mdl-28441234

ABSTRACT

OBJECTIVES: Describe the operating characteristics of a proposed set of revenue center codes to correctly identify ICU stays among hospitalized patients. DESIGN: Retrospective cohort study. We report the operating characteristics of all ICU-related revenue center codes for intensive and coronary care, excluding nursery, intermediate, and incremental care, to identify ICU stays. We use a classification and regression tree model to further refine identification of ICU stays using administrative data. The gold standard for classifying ICU admission was an electronic patient location tracking system. SETTING: The University of Pennsylvania Health System in Philadelphia, PA, United States. PATIENTS: All adult inpatient hospital admissions between July 1, 2013, and June 30, 2015. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 127,680 hospital admissions, the proposed combination of revenue center codes had 94.6% sensitivity (95% CI, 94.3-94.9%) and 96.1% specificity (95% CI, 96.0-96.3%) for correctly identifying hospital admissions with an ICU stay. The classification and regression tree algorithm had 92.3% sensitivity (95% CI, 91.6-93.1%) and 97.4% specificity (95% CI, 97.2-97.6%), with an overall improved accuracy (χ = 398; p < 0.001). CONCLUSIONS: Use of the proposed combination of revenue center codes has excellent sensitivity and specificity for identifying true ICU admission. A classification and regression tree algorithm with additional administrative variables offers further improvements to accuracy.


Subject(s)
Clinical Coding/methods , Hospital Administration/statistics & numerical data , Intensive Care Units/statistics & numerical data , Patient Admission/statistics & numerical data , Adult , Aged , Algorithms , Clinical Coding/standards , Female , Hospital Administration/standards , Hospital Charges/statistics & numerical data , Hospital Departments/economics , Hospital Departments/statistics & numerical data , Humans , Male , Middle Aged , Radio Frequency Identification Device , Retrospective Studies , Sensitivity and Specificity , Socioeconomic Factors , United States
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