Evaluating the need for palliative care and predicting its mortality play important roles in the emergency department.
Aim:
We developed a screening model for predicting 1-year mortality.
Design:
A retrospective cohort study was conducted to identify risk factors associated with 1-year mortality. Our risk scores based on these significant risk factors were then developed. Its predictive validity performance was evaluated using area under receiving operating characteristic analysis and leave-one-out cross-validation.
Setting and participants:
Patients aged 15 years or older were enrolled from June 2015 to May 2016 in the emergency department.
Results:
We identified five independent risk factors, each of which was assigned a number of points proportional to its estimated regression coefficient: age (0.05 points per year), qSOFA ⩾ 2 (1), Cancer (4), Eastern Cooperative Oncology Group Performance Status score ⩾ 2 (2), and Do-Not-Resuscitate status (3). The sensitivity, specificity, positive predictive value, and negative predictive value of our screening tool given the cutoff larger than 3 points were 0.99 (0.98–0.99), 0.31 (0.29–0.32), 0.26 (0.24–0.27), and 0.99 (0.98–1.00), respectively. Those with screening scores larger than 9 points corresponding to 64.0% (60.0–67.9%) of 1-year mortality were prioritized for consultation and communication. The area under the receiving operating characteristic curves for the point system was 0.84 (0.83–0.85) for the cross-validation model.
Conclusions:
A-qCPR risk scores provide a good screening tool for assessing patient prognosis. Routine screening for end-of-life using this tool plays an important role in early and efficient physician-patient communications regarding hospice and palliative needs in the emergency department.
LambaSMosenthalAC.Hospice and palliative medicine: a novel subspecialty of emergency medicine. J Emerg Med2012; 43: 849–853.
2.
QuestTEMarcoCADerseAR.Hospice and palliative medicine: new subspecialty, new opportunities. Ann Emerg Med2009; 54: 94–102.
3.
GrudzenCRStoneSCMorrisonRS.The palliative care model for emergency department patients with advanced illness. J Palliat Med2011; 14: 945–950.
4.
GrudzenCRStoneSCMohantySA, et al. “I want to be taking my own last breath”: patients’ reflections on illness when presenting to the emergency department at the end of life. J Palliat Med2011; 14: 293–296.
5.
GrudzenCRRichardsonLDMorrisonM, et al. Palliative care needs of seriously ill, older adults presenting to the emergency department. Acad Emerg Med2010; 17: 1253–1257.
6.
LambaSDeSandrePLToddKH, et al. Integration of palliative care into emergency medicine: the Improving Palliative Care in Emergency Medicine (IPAL-EM) collaboration. J Emerg Med2014; 46: 264–270.
7.
QuestTEAsplinBRCairnsCB, et al. Research priorities for palliative and end-of-life care in the emergency setting. Acad Emerg Med2011; 18: e70–e76.
8.
SmithAKFisherJSchonbergMA, et al. Am I doing the right thing? Provider perspectives on improving palliative care in the emergency department. Ann Emerg Med2009; 54: 86–93.
9.
RosenbergMLambaSMisraS.Palliative medicine and geriatric emergency care: challenges, opportunities, and basic principles. Clin Geriatr Med2013; 29: 1–29.
10.
SeowHBrazilKSussmanJ, et al. Impact of community based, specialist palliative care teams on hospitalisations and emergency department visits late in life and hospital deaths: a pooled analysis. BMJ2014; 348: g3496.
11.
CooperEHutchinsonASheikhZ, et al. Palliative care in the emergency department: a systematic literature qualitative review and thematic synthesis. Palliat Med2018; 32: 1443–1454.
12.
NeubergerJGuthrieCAaronovitchD. More care, less pathway: a review of the Liverpool Care Pathway. Department of Health, U.K., 2013.
13.
GlarePVirikKJonesM, et al. A systematic review of physicians’ survival predictions in terminally ill cancer patients. BMJ2003; 327: 195–198.
14.
ChowEHarthTHrubyG, et al. How accurate are physicians’ clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients? A systematic review. Clin Oncol (R Coll Radiol)2001; 13: 209–218.
15.
ZhouMHoldenLLaoN, et al. Accuracy of clinicians’ prediction of survival and prognostic factors indicative of survival: a systematic review. Hong Kong J Radiol2013; 16: 168–182.
16.
CoventryPAGrandeGERichardsDA, et al. Prediction of appropriate timing of palliative care for older adults with non-malignant life-threatening disease: a systematic review. Age Ageing2005; 34: 218–227.
17.
StromKBoeJ.Quality assessment and predictors of survival in long-term domiciliary oxygen therapy. The Swedish Society of Chest Medicine. Eur Respir J1991; 4: 50–58.
18.
ChamogeorgakisTToumpoulisITomosP, et al. External validation of the modified Thoracoscore in a new thoracic surgery program: prediction of in-hospital mortality. Interact Cardiovasc Thorac Surg2009; 9: 463–466.
19.
St JulienJBAldrichMCShengS, et al. Obesity increases operating room time for lobectomy in the society of thoracic surgeons database. Ann Thorac Surg2012; 94: 1841–1847.
20.
GlarePSinclairCDowningM, et al. Predicting survival in patients with advanced disease. Eur J Cancer2008; 44: 1146–1156.
21.
SoaresMCarusoPSilvaE, et al. Characteristics and outcomes of patients with cancer requiring admission to intensive care units: a prospective multicenter study. Crit Care Med2010; 38: 9–15.
22.
RosolemMMRabelloLSLisboaT, et al. Critically ill patients with cancer and sepsis: clinical course and prognostic factors. J Crit Care2012; 27: 301–307.
23.
ChristodoulouCRizosMGalaniE, et al. Performance status (PS): a simple predictor of short-term outcome of cancer patients with solid tumors admitted to the intensive care unit (ICU). Anticancer Res2007; 27: 2945–2948.
24.
AzoulayEMokartDPeneF, et al. Outcomes of critically ill patients with hematologic malignancies: prospective multicenter data from France and Belgium—a groupe de recherche respiratoire en reanimation onco-hematologique study. J Clin Oncol2013; 31: 2810–2818.
25.
LoprinziCLLaurieJAWieandHS, et al. Prospective evaluation of prognostic variables from patient-completed questionnaires. North Central Cancer Treatment Group. J Clin Oncol1994; 12: 601–607.
26.
BuccheriGFerrignoDTamburiniM.Karnofsky and ECOG performance status scoring in lung cancer: a prospective, longitudinal study of 536 patients from a single institution. Eur J Cancer1996; 32A: 1135–1141.
27.
CorreaGTBandeiraGACavalcantiBG, et al. Analysis of ECOG performance status in head and neck squamous cell carcinoma patients: association with sociodemographical and clinical factors, and overall survival. Support Care Cancer2012; 20: 2679–2685.
28.
DewysWDBeggCLavinPT, et al. Prognostic effect of weight loss prior to chemotherapy in cancer patients. Eastern Cooperative Oncology Group. Am J Med1980; 69: 491–497.
29.
RosenthalMAGebskiVJKeffordRF, et al. Prediction of life-expectancy in hospice patients: identification of novel prognostic factors. Palliat Med1993; 7: 199–204.
30.
BoumendilAMauryEReinhardI, et al. Prognosis of patients aged 80 years and over admitted in medical intensive care unit. Intensive Care Med2004; 30: 647–654.
31.
RochAWiramusSPaulyV, et al. Long-term outcome in medical patients aged 80 or over following admission to an intensive care unit. Crit Care2011; 15: R36.
32.
SacanellaEPerez-CastejonJMNicolasJM, et al. Mortality in healthy elderly patients after ICU admission. Intensive Care Med2009; 35: 550–555.
33.
ParkCMKohYJeonK, et al. Impact of Eastern Cooperative Oncology Group Performance Status on hospital mortality in critically ill patients. J Crit Care2014; 29: 409–413.
34.
SimmonsCPLMcMillanDCMcWilliamsK, et al. Prognostic tools in patients with advanced cancer: a systematic review. J Pain Symptom Manage2017; 53: 962–970.
35.
WhiteNKupeliNVickerstaffV, et al. How accurate is the ‘Surprise Question’ at identifying patients at the end of life? A systematic review and meta-analysis. BMC Med2017; 15: 139.
36.
AfsharKFeichtnerABoydK, et al. Systematic development and adjustment of the German version of the Supportive and Palliative Care Indicators Tool (SPICT-DE). BMC Palliat Care2018; 17: 27.
37.
HighetGCrawfordDMurraySA, et al. Development and evaluation of the Supportive and Palliative Care Indicators Tool (SPICT): a mixed-methods study. BMJ Support Palliat Care2014; 4: 285–290.
38.
NgCJYenZSTsaiJC, et al. Validation of the Taiwan triage and acuity scale: a new computerised five-level triage system. Emerg Med J2011; 28: 1026–1031.
39.
WangSSHuangCMFengRC, et al. Validation of a concise screening tool for the identification of palliative care needs among inpatients: a prospective study in hospital setting. J Formos Med Assoc2019; 118: 883–890.
40.
MolinaroAMSimonRPfeifferRM.Prediction error estimation: a comparison of resampling methods. Bioinformatics2005; 21: 3301–3307.
41.
ChengHGarrickDJFernandoRL.Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction. J Anim Sci Biotechnol2017; 8: 38.
42.
KaltonGKasprzykD.Imputing for missing survey responses. Am Stat Assoc Proc Sec Surv Res Methods1982; 22: 31.
43.
FreundYLemachattiNKrastinovaE, et al. Prognostic accuracy of Sepsis-3 criteria for in-hospital mortality among patients with suspected infection presenting to the emergency department. JAMA2017; 317: 301–308.
44.
KwakHSuhGJKimT, et al. Prognostic performance of Emergency Severity Index (ESI) combined with qSOFA score. Am J Emerg Med2018; 36(10): 1784–1788.
45.
HuangCTChuangYCTsaiYJ, et al. High mortality in severe sepsis and septic shock patients with do-not-resuscitate orders in East Asia. PLoS One2016; 11: e0159501.
46.
DeanDMartinezMSNewgardCD.Variability in early do not attempt resuscitation orders among patients with serious traumatic brain injury. Acad Emerg Med2015; 22: 54–60.
47.
ScarboroughJEPappasTNBennettKM, et al. Failure-to-pursue rescue: explaining excess mortality in elderly emergency general surgical patients with preexisting “do-not-resuscitate” orders. Ann Surg2012; 256: 453–461.
48.
AzizHBrancoBCBraunJ, et al. The influence of do-not-resuscitate status on the outcomes of patients undergoing emergency vascular operations. J Vasc Surg2015; 61: 1538–1542.
49.
RichardsonDKZiveDDayaM, et al. The impact of early do not resuscitate (DNR) orders on patient care and outcomes following resuscitation from out of hospital cardiac arrest. Resuscitation2013; 84: 483–487.
50.
ChengYHWangJJWuKH, et al. Do-not-resuscitate orders and related factors among family surrogates of patients in the emergency department. Support Care Cancer2016; 24: 1999–2006.
51.
CareyIShoulsSBristoweK, et al. Improving care for patients whose recovery is uncertain. The AMBER care bundle: design and implementation. BMJ Support Palliat Care2015; 5: 405–411.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
