The American journal of hospice & palliative care · 2018
Development of a Risk Tool to Support Discussions of Care for Older Adults Admitted to the ICU With Pneumonia.
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Satchidanand Nikhil, Servoss Timothy J, Singh Ranjit, Bosinski Angela M, Tirpak Penny, Horton Linda L, Naughton Bruce J
Аннотация
BACKGROUND: Early, data-driven discussion surrounding palliative care can improve care delivery and patient experience.
OBJECTIVE: To develop a 30-day mortality prediction tool for older patients in intensive care unit (ICU) with pneumonia that will initiate palliative care earlier in hospital course.
DESIGN: Retrospective Electronic Health Record (EHR) review.
SETTING: Four urban and suburban hospitals in a Western New York hospital system.
PARTICIPANTS: A total of 1237 consecutive patients (>75 years) admitted to the ICU with pneumonia from July 2011 to December 2014.
MEASUREMENTS: Data abstracted included demographics, insurance type, comorbidities, and clinical factors. Thirty-day mortality was also determined. Logistic regression identified predictors of 30-day mortality. Area under the receiver operating curve (ROC) was calculated to quantify the degree to which the model accurately classified participants. Using the coordinates of the ROC, a predicted probability was identified to indicate high risk.
RESULTS: A total of 1237 patients were included with 30-day mortality data available for 100% of patients. The mortality rate equaled 14.3%. Age >85 years, having active cancer, Congestive Heart Failure (CHF), Chronic Obstructive Pulmonary Disease (COPD), sepsis, and being on a vasopressor all predicted mortality. Using the derived index, with a predicted probability of mortality >0.146 as a cutoff, sensitivity equaled 70.6% and specificity equaled 65.6%. The area under the ROC was 0.735.
CONCLUSION: Our risk tool can help care teams make more informed decisions among care options by identifying a patient group for whom a careful review of goals of care is indicated both during and after hospitalization. External validation and further refinement of the index with a larger sample will improve prognostic value.
OBJECTIVE: To develop a 30-day mortality prediction tool for older patients in intensive care unit (ICU) with pneumonia that will initiate palliative care earlier in hospital course.
DESIGN: Retrospective Electronic Health Record (EHR) review.
SETTING: Four urban and suburban hospitals in a Western New York hospital system.
PARTICIPANTS: A total of 1237 consecutive patients (>75 years) admitted to the ICU with pneumonia from July 2011 to December 2014.
MEASUREMENTS: Data abstracted included demographics, insurance type, comorbidities, and clinical factors. Thirty-day mortality was also determined. Logistic regression identified predictors of 30-day mortality. Area under the receiver operating curve (ROC) was calculated to quantify the degree to which the model accurately classified participants. Using the coordinates of the ROC, a predicted probability was identified to indicate high risk.
RESULTS: A total of 1237 patients were included with 30-day mortality data available for 100% of patients. The mortality rate equaled 14.3%. Age >85 years, having active cancer, Congestive Heart Failure (CHF), Chronic Obstructive Pulmonary Disease (COPD), sepsis, and being on a vasopressor all predicted mortality. Using the derived index, with a predicted probability of mortality >0.146 as a cutoff, sensitivity equaled 70.6% and specificity equaled 65.6%. The area under the ROC was 0.735.
CONCLUSION: Our risk tool can help care teams make more informed decisions among care options by identifying a patient group for whom a careful review of goals of care is indicated both during and after hospitalization. External validation and further refinement of the index with a larger sample will improve prognostic value.
Предметные рубрики MeSH
Age FactorsAgedAged, 80 and overComorbidityElectronic Health RecordsFemaleHealth Status IndicatorsHumansIntensive Care UnitsMaleMental Status and Dementia TestsNew YorkPalliative CarePneumoniaPrognosisROC CurveRetrospective StudiesRisk AssessmentSeverity of Illness IndexSex FactorsSocioeconomic FactorsVital Signs