Background and objectives: Healthcare providers communicate the risks and benefits of treatments using frequencies, percentages, or proportions. However, many patients lack the numerical skills needed to interpret this information accurately to make informed choices.Design, setting, participants, and measurements: We assessed numeracy, the capacity to use and comprehend numbers, in a prospective cohort study of 187 patients with stage 4 to 5 chronic kidney disease or ESRD. Patients completed a three-item numeracy test and were assessed for global mental status, cognitive function, type of hemodialysis access, and kidney transplant use. We examined the association of numeracy with healthcare use and other cognitive and sociodemographic variables.Results: Over 50% of patients answered one or fewer numeracy questions correctly. Although African Americans (P ‫؍‬ 0.0001), women (P ‫؍‬ 0.05), and the unemployed (P ‫؍‬ 0.0004) demonstrated lower numeracy skills, numeracy deficits were prevalent in every subgroup. In analyses adjusted for demographics and length of follow-up, higher numeracy was significantly associated with receipt of a transplant or active waiting list status. Numeracy was not associated with dialysis modality or hemodialysis vascular access.Conclusion: Similar to prior findings in the general population, these findings indicate that poor numeracy skills are very common in patients with advanced chronic kidney disease and end-stage renal disease. Additional research is needed to further explore whether poor numeracy is a barrier to receipt of a kidney transplant. Clinicians caring for patients with kidney disease should consider using tools to enhance communication and overcome limited numeracy skills.

PURPOSE: In December 2016, 49% of patients admitted to inpatient oncology services at University of Pittsburgh Medical Center Shadyside Hospital had cardiopulmonary resuscitation (CPR) status discussion documentation before discharge. The aim of this project was to improve the rate of CPR status conversations. METHODS: During Plan-Do-Study-Act (PDSA) cycle 1, a stakeholder workgroup was formed in January 2017 by oncology faculty, fellows, nurses, advance practice providers (APPs), medicine housestaff, and palliative care faculty. All oncology clinicians and inpatient team members were reminded weekly to discuss and document CPR status preferences. APPs received training on efficient and effective CPR status assessment from palliative care faculty. Oncology leadership received monthly e-mail updates of CPR status documentation rates and endorsed CPR status best practice guidelines. For PDSA cycle 2, patient charts without CPR status documentation in March 2018 were reviewed, and themes were shared with oncology leadership and reviewed with APPs. RESULTS: After PDSA cycle 1, CPR status assessment rates increased from 49% to greater than 80%. In 2017, more than 1,500 more CPR status discussions were documented than in 2016. The percentage of patients discharged with “comfort measures only” or “do not resuscitate” orders increased from 14.2% (95% CI, 9.5% to 19.0%) to 19.8% (95% CI, 15.6% to 24.0%). For PDSA cycle 2, charts of 60 patients without CPR assessment were reviewed. Of these, 52% were admitted overnight by nocturnists and 48% by daytime APPs. Fifty-five percent of patients (n = 33 of 60) had metastatic disease. CPR status was documented on previous admissions for 53% of patients (n = 31 of 60) in the past 12 months. Fifteen percent (n = 11 of 60) were admitted for scheduled inpatient chemotherapy. CONCLUSION: A multipronged approach significantly increased CPR status assessments. More patients transitioned to comfort measures only or do not resuscitate when their preferences were clearly assessed and documented.

25 Background: The “Surprise Question” — Would I be surprised if this patient died in the next 12 months? — was developed to help clinicians predict when patients are nearing the end of life. Limited data has shown that the “Surprise Question” is modestly predictive of mortality (CMAJ 2017 Apr 3;189(13):E484-E493), though its performance seems to be superior among cancer patients (Palliat Med 2014 Jul;28(7):959-964). Via Oncology Pathways, a platform used by UPMC Hillman Cancer Center and other institutions nationwide to guide treatment decisions, asks physicians the “Surprise Question” when a new treatment plan is implemented for patients with metastatic cancer. We assessed the “Surprise Question’s” ability to predict survival among Hillman Cancer Center patients with select stage IV diagnoses. Methods: We queried the UPMC Hillman Cancer Center Registry Information and Reporting Services for cases of colorectal, non-small cell lung, prostate, pancreatic, and breast cancer with clinic visits between 1/1/2016 and 12/31/2017 and residence in Allegheny County, the primary referral base for the UPMC Hillman Cancer Center network’s flagship facility. Results: The “Surprise Question” was completed for 1,584 patients with metastatic disease of the 5,330 patients that were screened. “No” was the response for 891 patients (56.3%). Mortality at 12 months for patients for whom the answer to the “Surprise Question” was “no” was 63.1%, compared to 32.5% for patients for whom the answer was “yes” (P < 0.0001). The sensitivity of the “Surprise Question” was 71.4% (95% CI 69.0 – 73.8%), and the specificity was 58.7% (95% CI 56.3 – 61.0%). The positive predictive value was 63.1% (95% CI 60.9 – 65.2%) and negative predictive value 67.5% (64.8% – 70.2%). Finally, the positive likelihood ratio was 1.73 (95% CI 1.58 – 1.89) and negative likelihood ratio 0.49 (0.43 – 0.55). Conclusions: While a “no” response to the “Surprise Question” for UPMC oncology patients with select stage IV diagnoses was more likely to predict 12-month mortality than a “yes” response, the “Surprise Question” was only modestly predictive of 12-month mortality. Future work will focus on determining if there are patient populations for whom the “Surprise Question” is more predictive and assessing the ability of the “Surprise Question” to predict other clinical outcomes, such as ED visits and hospitalizations.

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When used appropriately, HM allows researchers to specify and test hypotheses that would not otherwise be possible, and avoid incorrect conclusions from nested data.