In recent years, the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Adult Cancer Pain have undergone substantial revisions focusing on the appropriate and safe prescription of opioid analgesics, optimization of nonopioid analgesics and adjuvant medications, and integration of nonpharmacologic methods of cancer pain management. This selection highlights some of these changes, covering topics on management of adult cancer pain including pharmacologic interventions, nonpharmacologic interventions, and treatment of specific cancer pain syndromes. The complete version of the NCCN Guidelines for Adult Cancer Pain addresses additional aspects of this topic, including pathophysiologic classification of cancer pain syndromes, comprehensive pain assessment, management of pain crisis, ongoing care for cancer pain, pain in cancer survivors, and specialty consultations.

Purpose: To summarize evidence on conservative, nondialytic management of end-stage renal disease regarding 1) prognosis and 2) symptom burden and quality of life (QOL). Methods: Medline, Cinahl, and Cochrane were searched for records indexed prior to March 1, 2011. Bibliographies of articles and abstracts from recent meetings were reviewed. Authors and nephrologists were contacted to identify additional studies. Articles were reviewed by two authors and selected if they described stage 5 chronic kidney disease (CKD) patients managed without dialysis, including one or more of the following outcomes: prognosis, symptoms, or QOL. Levels of evidence ratings were assigned using the SORT (Strength of Recommendation Taxonomy) system. Data was abstracted independently by two authors for descriptive analysis. Results: Thirteen studies were included. In studies of prognosis, conservative management resulted in median survival of at least six months (range 6.3 to 23.4 months). Findings are mixed as to whether dialysis prolongs survival in the elderly versus conservative, nondialytic management. Any survival benefit from dialysis decreases with comorbidities, especially ischemic heart disease. Patients managed conservatively report a high symptom burden, underscoring the need for concurrent palliative care. Additional head-to-head studies are needed to compare the symptoms of age-matched dialysis patients, but preliminary studies suggest that QOL is similar. Conclusions: Conservative management is an important alternative to discuss when counseling patients and families about dialysis. Unlike withdrawal of dialysis in which imminent death is expected, patients who decline dialysis initiation can live for months to years with appropriate supportive care.

Key PointsQuestionCan machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?FindingsIn this cohort study of 26 525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.MeaningIn this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.

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.

IMPORTANCEPatients with acute myeloid leukemia (AML) receiving intensive chemotherapy experience substantial decline in their quality of life (QOL) and mood during their hospitalization for induction chemotherapy and often receive aggressive care at the end of life (EOL). However, the role of specialty palliative care for improving the QOL and care for this population is currently unknown.OBJECTIVE To assess the effect of integrated palliative and oncology care (IPC) on patient-reported and EOL outcomes in patients with AML. DESIGN, SETTING, AND PARTICIPANTSWe conducted a multisite randomized clinical trial of IPC (n = 86) vs usual care (UC) (n = 74) for patients with AML undergoing intensive chemotherapy. Data were collected from January 2017 through July 2019 at 4 tertiary care academic hospitals in the United States.INTERVENTIONS Patients assigned to IPC were seen by palliative care clinicians at least twice per week during their initial and subsequent hospitalizations. MAIN OUTCOMES AND MEASURESPatients completed the 44-item Functional Assessment of Cancer Therapy-Leukemia scale (score range, 0-176) to assess QOL; the 14-item Hospital Anxiety and Depression Scale (HADS), with subscales assessing symptoms of anxiety and depression (score range, 0-21); and the PTSD Checklist-Civilian version to assess posttraumatic stress disorder (PTSD) symptoms (score range, 17-85) at baseline and weeks 2, 4, 12, and 24. The primary end point was QOL at week 2. We used analysis of covariance adjusting and mixed linear effect models to evaluate patient-reported outcomes. We used Fisher exact test to compare patient-reported discussion of EOL care preferences and receipt of chemotherapy in the last 30 days of life. RESULTSOf 235 eligible patients, 160 (68.1%) were enrolled; of the 160 participants, the median (range) age was 64.4 (19.7-80.1) years, and 64 (40.0%) were women. Compared with those receiving UC, IPC participants reported better QOL (adjusted mean score, 107.59 vs 116.45; P = .04), and lower depression (adjusted mean score, 7.20 vs 5.68; P = .02), anxiety (adjusted mean score, 5.94 vs 4.53; P = .02), and PTSD symptoms (adjusted mean score, 31.69 vs 27.79; P = .01) at week 2. Intervention effects were sustained to week 24 for QOL (β, 2.35; 95% CI, 0.02-4.68; P = .048), depression (β, −0.42; 95% CI, −0.82 to −0.02; P = .04), anxiety (β, −0.38; 95% CI, −0.75 to −0.01; P = .04), and PTSD symptoms (β, −1.43; 95% CI, −2.34 to −0.54; P = .002). Among patients who died, those receiving IPC were more likely than those receiving UC to report discussing EOL care preferences (21 of 28 [75.0%] vs 12 of 30 [40.0%]; P = .01) and less likely to receive chemotherapy near EOL (15 of 43 [34.9%] vs 27 of 41 [65.9%]; P = .01). CONCLUSIONS AND RELEVANCEIn this randomized clinical trial of patients with AML, IPC led to substantial improvements in QOL, psychological distress, and EOL care. Palliative care should be considered a new standard of care for patients with AML.