Terminally Ill Cancer Patients' Concordance Between Preferred Life-Sustaining Treatment States in Their Last Six Months of Life and Received Life-Sustaining Treatment States in Their Last Month: An Observational Study

Wen, Fur‐Hsing; Chen, Jen‐Shi; Su, Po‐Jung; Chang, Wen‐Cheng; Hsieh, Chia-Hsun; Hou, Ming‐Mo; Chou, Wen‐Chi; Tang, Siew Tzuh

doi:10.1016/j.jpainsymman.2018.07.003

Cited by 17 publications

(19 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We analyzed data from a longitudinal study on the quality of death and dying in Taiwanese cancer patients. 15 The stability of patients’ state-specific life-sustaining-treatment preferences 16 and concordance between states of preferred and received life-sustaining treatments (hereafter referred to as preferred–received life-sustaining-treatment-state concordance) at end of life 17 have been reported. Adult terminally ill cancer patients were recruited by convenience from a medical center in northern Taiwan in 2009–2012 and followed until December 2015.…”

Section: Methodsmentioning

confidence: 99%

“…We previously identified 17 distinct life-sustaining-treatment-preference states in patients’ last 6 months and life-sustaining treatment states received before death by a latent transition model with the hidden Markov modeling. 19 Latent transition modeling assigned patients to a finite number of mutually exclusive probabilistic life-sustaining-treatment-preference states and life-sustaining-treatment states received in the last month by common characteristics that discriminate among patients in each state.…”

Section: Methodsmentioning

confidence: 99%

“…Based on each state’s initial prevalence and transition probability, we identified four life-sustaining-treatment-preference states estimated just before death: life-sustaining preferring, comfort preferring (uniformly rejected all life-sustaining treatments), uncertain (uniformly uncertain about all life-sustaining treatments), and nutrition preferring but rejecting other life-sustaining treatments (Appendix 2). 17 States of life-sustaining treatments received in patients’ last month were identified as generally received life-sustaining treatments, life-sustaining treatments uniformly withheld, selectively received life-sustaining treatments, and received intravenous nutrition only (Appendix 2). 17 Detailed procedures have been described for determining the optimal number of preferred and received life-sustaining-treatment states.…”

Section: Methodsmentioning

confidence: 99%

“…17 Detailed procedures have been described for determining the optimal number of preferred and received life-sustaining-treatment states. 16,17…”

Section: Methodsmentioning

confidence: 99%

“…Latent transition modeling identified four life-sustaining-treatment-preference states: life-sustaining preferring (uniformly preferred all life-sustaining treatments), comfort preferring (uniformly rejected all life-sustaining treatments), uncertain (uniformly uncertain about all life-sustaining treatments), and nutrition preferring but rejecting other life-sustaining treatments. 17 Patients comprising the three “uniform-response” states (states 1–3) uniformly preferred, rejected, or were uncertain about all life-sustaining treatments (Life-sustaining treatments) examined. State 4, favoring intravenous nutrition support but rejecting other treatments, represents patients who preferred intravenous nutrition support but rejected CPR, ICU care, cardiac massage, intubation with mechanical ventilation, and nasogastric tube feeding.…”

Section: Interview Questions Regarding Preferences For Life-sustaininmentioning

confidence: 99%

See 4 more Smart Citations

Accurate prognostic awareness and preference states influence the concordance between terminally ill cancer patients’ states of preferred and received life-sustaining treatments in the last 6 months of life

et al. 2019

Self Cite

View full text Add to dashboard Cite

Background: Factors facilitating/hindering concordance between preferred and received life-sustaining treatments may be distorted if preferences and predictors are measured long before death. Aim: To examine factors facilitating/hindering concordance between cancer patients’ preferred and received life-sustaining-treatment states in their last 6 months. Design: Longitudinal, observational design. Setting/participants: States of preferred and received life-sustaining treatments (cardio-pulmonary resuscitation, intensive care unit care, cardiac massage, intubation with mechanical ventilation, intravenous nutritional support, and nasogastric tube feeding) were examined in 218 Taiwanese cancer patients by a latent transition model with hidden Markov modeling. Multivariate logistic regression modeling was used to examine factors facilitating/hindering concordance between preferred and received life-sustaining-treatment states. Results: Concordance between preferred and received life-sustaining-treatment states was poor (40.8%, kappa value (95% confidence interval): 0.05 [–0.03, 0.14]). Patients who accurately understood their prognosis and preferred comfort care were significantly more likely to receive preferred life-sustaining treatments before death than those who did not know their prognosis but wanted to know, those who were uniformly uncertain about what life-sustaining treatments they preferred to receive, and those who preferred nutritional support but declined other life-sustaining treatments. Patient age, physician–patient end-of-life-care discussions, symptom distress, and functional dependence were not associated with concordance between preferred and received life-sustaining-treatment states. Conclusion: Prognostic awareness and preferred states of life-sustaining treatments were significantly associated with concordance between preferred and received life-sustaining-treatment states. Personalized interventions should be developed to cultivate terminally ill cancer patients’ accurate prognostic awareness, allowing them to formulate realistic life-sustaining-treatment preferences and facilitating their receiving value-concordant end-of-life care.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…17 Detailed procedures have been described for determining the optimal number of preferred and received life-sustaining-treatment states. 16,17…”

Section: Methodsmentioning

confidence: 99%

Section: Interview Questions Regarding Preferences For Life-sustaininmentioning

confidence: 99%

See 3 more Smart Citations

Accurate prognostic awareness and preference states influence the concordance between terminally ill cancer patients’ states of preferred and received life-sustaining treatments in the last 6 months of life

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

Clinician- and Patient-Directed Communication Strategies for Patients With Cancer at High Mortality Risk

Takvorian,

Gabriel,

Wileyto

et al. 2024

JAMA Netw Open

View full text Add to dashboard Cite

ImportanceSerious illness conversations (SICs) that elicit patients’ values, goals, and care preferences reduce anxiety and depression and improve quality of life, but occur infrequently for patients with cancer. Behavioral economic implementation strategies (nudges) directed at clinicians and/or patients may increase SIC completion.ObjectiveTo test the independent and combined effects of clinician and patient nudges on SIC completion.Design, Setting, and ParticipantsA 2 × 2 factorial, cluster randomized trial was conducted from September 7, 2021, to March 11, 2022, at oncology clinics across 4 hospitals and 6 community sites within a large academic health system in Pennsylvania and New Jersey among 163 medical and gynecologic oncology clinicians and 4450 patients with cancer at high risk of mortality (≥10% risk of 180-day mortality).InterventionsClinician clusters and patients were independently randomized to receive usual care vs nudges, resulting in 4 arms: (1) active control, operating for 2 years prior to trial start, consisting of clinician text message reminders to complete SICs for patients at high mortality risk; (2) clinician nudge only, consisting of active control plus weekly peer comparisons of clinician-level SIC completion rates; (3) patient nudge only, consisting of active control plus a preclinic electronic communication designed to prime patients for SICs; and (4) combined clinician and patient nudges.Main Outcomes and MeasuresThe primary outcome was a documented SIC in the electronic health record within 6 months of a participant’s first clinic visit after randomization. Analysis was performed on an intent-to-treat basis at the patient level.ResultsThe study accrued 4450 patients (median age, 67 years [IQR, 59-75 years]; 2352 women [52.9%]) seen by 163 clinicians, randomized to active control (n = 1004), clinician nudge (n = 1179), patient nudge (n = 997), or combined nudges (n = 1270). Overall patient-level rates of 6-month SIC completion were 11.2% for the active control arm (112 of 1004), 11.5% for the clinician nudge arm (136 of 1179), 11.5% for the patient nudge arm (115 of 997), and 14.1% for the combined nudge arm (179 of 1270). Compared with active control, the combined nudges were associated with an increase in SIC rates (ratio of hazard ratios [rHR], 1.55 [95% CI, 1.00-2.40]; P = .049), whereas the clinician nudge (HR, 0.95 [95% CI, 0.64-1.41; P = .79) and patient nudge (HR, 0.99 [95% CI, 0.73-1.33]; P = .93) were not.Conclusions and RelevanceIn this cluster randomized trial, nudges combining clinician peer comparisons with patient priming questionnaires were associated with a marginal increase in documented SICs compared with an active control. Combining clinician- and patient-directed nudges may help to promote SICs in routine cancer care.Trial RegistrationClinicalTrials.gov Identifier: NCT04867850

show abstract

Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer

et al. 2020

View full text Add to dashboard Cite

IMPORTANCE Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Interventions that increase the rate of SICs between oncology clinicians and patients may improve goal-concordant care and patient outcomes. OBJECTIVE To determine the effect of a clinician-directed intervention integrating machine learning mortality predictions with behavioral nudges on motivating clinician-patient SICs. DESIGN, SETTING, AND PARTICIPANTS This stepped-wedge cluster randomized clinical trial was conducted across 20 weeks (from June 17 to November 1, 2019) at 9 medical oncology clinics (8 subspecialty oncology and 1 general oncology clinics) within a large academic health system in Pennsylvania. Clinicians at the 2 smallest subspecialty clinics were grouped together, resulting in 8 clinic groups randomly assigned to the 4 intervention wedge periods. Included participants in the intention-to-treat analyses were 78 oncology clinicians who received SIC training and their patients (N = 14 607) who had an outpatient oncology encounter during the study period. INTERVENTIONS (1) Weekly emails to oncology clinicians with SIC performance feedback and peer comparisons; (2) a list of up to 6 high-risk patients (Ն10% predicted risk of 180-day mortality) scheduled for the next week, estimated using a validated machine learning algorithm; and (3) opt-out text message prompts to clinicians on the patient's appointment day to consider an SIC. Clinicians in the control group received usual care consisting of weekly emails with cumulative SIC performance. MAIN OUTCOMES AND MEASURES Percentage of patient encounters with an SIC in the intervention group vs the usual care (control) group. RESULTS The sample consisted of 78 clinicians and 14 607 patients. The mean (SD) age of patients was 61.9 (14.2) years, 53.7% were female, and 70.4% were White. For all encounters, SICs were conducted among 1.3% in the control group and 4.6% in the intervention group, a significant difference (adjusted difference in percentage points, 3.3; 95% CI, 2.3-4.5; P < .001). Among 4124 high-risk patient encounters, SICs were conducted among 3.6% in the control group and 15.2% in the intervention group, a significant difference (adjusted difference in percentage points, 11.6; 95% CI, 8.2-12.5; P < .001). CONCLUSIONS AND RELEVANCE In this stepped-wedge cluster randomized clinical trial, an intervention that delivered machine learning mortality predictions with behavioral nudges to oncology clinicians significantly increased the rate of SICs among all patients and among patients with high mortality risk who were targeted by the intervention. Behavioral nudges combined with machine learning mortality predictions can positively influence clinician behavior and may be applied more broadly to improve care near the end of life.

show abstract

Terminally Ill Cancer Patients' Concordance Between Preferred Life-Sustaining Treatment States in Their Last Six Months of Life and Received Life-Sustaining Treatment States in Their Last Month: An Observational Study

Cited by 17 publications

References 42 publications

Accurate prognostic awareness and preference states influence the concordance between terminally ill cancer patients’ states of preferred and received life-sustaining treatments in the last 6 months of life

Accurate prognostic awareness and preference states influence the concordance between terminally ill cancer patients’ states of preferred and received life-sustaining treatments in the last 6 months of life

Clinician- and Patient-Directed Communication Strategies for Patients With Cancer at High Mortality Risk

Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer

Contact Info

Product

Resources

About