Patient Risk Factor Profiles Associated With the Timing of Goals-of-Care Consultation Before Death: A Classification and Regression Tree Analysis

2020 ◽  
Vol 37 (10) ◽  
pp. 767-778
Author(s):  
Lauren T. Starr ◽  
Connie M. Ulrich ◽  
Paul Junker ◽  
Liming Huang ◽  
Nina R. O’Connor ◽  
...  

Background: Early palliative care consultation (“PCC”) to discuss goals-of-care benefits seriously ill patients. Risk factor profiles associated with the timing of conversations in hospitals, where late conversations most likely occur, are needed. Objective: To identify risk factor patient profiles associated with PCC timing before death. Methods: Secondary analysis of an observational study was conducted at an urban, academic medical center. Patients aged 18 years and older admitted to the medical center, who had PCC, and died July 1, 2014 to October 31, 2016, were included. Patients admitted for childbirth or rehabilitationand patients whose date of death was unknown were excluded. Classification and Regression Tree modeling was employed using demographic and clinical variables. Results: Of 1141 patients, 54% had PCC “close to death” (0-14 days before death); 26% had PCC 15 to 60 days before death; 21% had PCC >60 days before death (median 13 days before death). Variables associated with receiving PCC close to death included being Hispanic or “Other” race/ethnicity intensive care patients with extreme illness severity (85%), with age <46 or >75 increasing this probability (98%). Intensive care patients with extreme illness severity were also likely to receive PCC close to death (64%) as were 50% of intensive care patients with less than extreme illness severity. Conclusions: A majority of patients received PCC close to death. A complex set of variable interactions were associated with PCC timing. A systematic process for engaging patients with PCC earlier in the care continuum, and in intensive care regardless of illness severity, is needed.

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 831-832
Author(s):  
Lauren Starr ◽  
Connie Ulrich ◽  
Paul Junker ◽  
Liming Huang ◽  
Nina O’Connor ◽  
...  

Abstract Early palliative care consultation to discuss goals-of-care (“PCC”) benefits seriously ill patients. To identify risk factor profiles associated with inpatient PCC timing before death, we conducted a secondary analysis of seriously ill adults who had PCC at a high-acuity hospital and died 2014-2016. Of 1,141 patients, 54% had PCC “close to death” (0-14 days before death); 26% had PCC 15-60 days before death; 21% had PCC &gt;60 days before death (median 13 days). Classification and Regression Tree modeling showed Hispanic or “Other” race/ethnicity intensive care patients with extreme illness severity (85%) were most likely to have PCC close to death, with age &lt;46 or &gt;75 increasing probability (98%). Among age groups, the highest proportion of patients with PCC close to death was &gt;75 years. Complex variable interactions associated with PCC timing suggests we need a systematic process for initiating PCC earlier and effective primary palliative training for providers across settings.


2021 ◽  
Vol 21 (3) ◽  
pp. 1083-1092
Author(s):  
Sevinç Dağıstanlı ◽  
Süleyman Sönmez ◽  
Murat Ünsel ◽  
Emre Bozdağ ◽  
Ali Kocataş ◽  
...  

Background/aim: The present study aimed to create a decision tree for the identification of clinical, laboratory and radio- logical data of individuals with COVID-19 diagnosis or suspicion of Covid-19 in the Intensive Care Units of a Training and Research Hospital of the Ministry of Health on the European side of the city of Istanbul. Materials and methods: The present study, which had a retrospective and sectional design, covered all the 97 patients treated with Covid-19 diagnosis or suspicion of COVID-19 in the intensive care unit between 12 March and 30 April 2020. In all cases who had symptoms admitted to the COVID-19 clinic, nasal swab samples were taken and thoracic CT was per- formed when considered necessary by the physician, radiological findings were interpreted, clinical and laboratory data were included to create the decision tree. Results: A total of 61 (21 women, 40 men) of the cases included in the study died, and 36 were discharged with a cure from the intensive care process. By using the decision tree algorithm created in this study, dead cases will be predicted at a rate of 95%, and those who survive will be predicted at a rate of 81%. The overall accuracy rate of the model was found at 90%. Conclusions: There were no differences in terms of gender between dead and live patients. Those who died were older, had lower MON, MPV, and had higher D-Dimer values than those who survived. Keywords: Survival algorithm; COVID-19 intensive care patients; CRT method.


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