Racial Disparities in Pulse Oximeter Device Inaccuracy and Estimated Clinical Impact on COVID-19 Treatment Course.

Arterial blood oxygen saturation as measured by pulse oximetry (peripheral oxygen saturation (SpO2)) may be differentially less accurate for people with darker skin pigmentation, which could potentially affect the course of coronavirus disease 2019 (COVID-19) treatment. We analyzed pulse oximeter accuracy and its association with COVID-19 treatment outcomes using electronic health record data from Sutter Health, a large, mixed-payer, integrated health-care delivery system in Northern California. We analyzed 2 cohorts: 1) 43,753 non-Hispanic White (NHW) or non-Hispanic Black/African-American (NHB) adults with concurrent arterial blood gas oxygen saturation/SpO2 measurements taken between January 2020 and February 2021; and 2) 8,735 adults who went to a hospital emergency department with COVID-19 between July 2020 and February 2021. Pulse oximetry systematically overestimated blood oxygenation by 1% more in NHB individuals than in NHW individuals. For people with COVID-19, this was associated with lower admission probability (-3.1 percentage points), dexamethasone treatment (-3.1 percentage points), and supplemental oxygen treatment (-4.5 percentage points), as well as increased time to treatment: 37.2 minutes before dexamethasone initiation and 278.5 minutes before initiation of supplemental oxygen. These results call for additional investigation of pulse oximeters and suggest that current guidelines for development, testing, and calibration of these devices should be revisited, investigated, and revised.

Predicting need for advanced illness or palliative care in a primary care population using electronic health record data.

Timely outreach to individuals in an advanced stage of illness offers opportunities to exercise decision control over health care. Predictive models built using Electronic health record (EHR) data are being explored as a way to anticipate such need with enough lead time for patient engagement. Prior studies have focused on hospitalized patients, who typically have more data available for predicting care needs. It is unclear if prediction driven outreach is feasible in the primary care setting. In this study, we apply predictive modeling to the primary care population of a large, regional health system and systematically examine the impact of technical choices, such as requiring a minimum number of health care encounters (data density requirements) and aggregating diagnosis codes using Clinical Classifications Software (CCS) groupings to reduce dimensionality, on model performance in terms of discrimination and positive predictive value. We assembled a cohort of 349,667 primary care patients between 65 and 90 years of age who sought care from Sutter Health between July 1, 2011 and June 30, 2014, of whom 2.1% died during the study period. EHR data comprising demographics, encounters, orders, and diagnoses for each patient from a 12 month observation window prior to the point when a prediction is made were extracted. L1 regularized logistic regression and gradient boosted tree models were fit to training data and tuned by cross validation. Model performance in predicting one year mortality was assessed using held-out test patients. Our experiments systematically varied three factors: model type, diagnosis coding, and data density requirements. We found substantial, consistent benefit from using gradient boosting vs logistic regression (mean AUROC over all other technical choices of 84.8% vs 80.7% respectively). There was no benefit from aggregation of ICD codes into CCS code groups (mean AUROC over all other technical choices of 82.9% vs 82.6% respectively). Likewise increasing data density requirements did not affect discrimination (mean AUROC over other technical choices ranged from 82.5% to 83%). We also examine model performance as a function of lead time, which is the interval between death and when a prediction was made. In subgroup analysis by lead time, mean AUROC over all other choices ranged from 87.9% for patients who died within 0 to 3 months to 83.6% for those who died 9 to 12 months after prediction time.

Impact of home-based, patient-centered support for people with advanced illness in an open health system: A retrospective claims analysis of health expenditures, utilization, and quality of care at end of life.

BACKGROUND: Home-based care coordination and support programs for people with advanced illness work alongside usual care to promote personal care goals, which usually include a preference for home-based end-of-life care. More research is needed to confirm the efficacy of these programs, especially when disseminated on a large scale. Advanced Illness Management is one such program, implemented within a large open health system in northern California, USA. AIM: To evaluate the impact of Advanced Illness Management on end-of-life resource utilization, cost of care, and care quality, as indicators of program success in supporting patient care goals. DESIGN: A retrospective-matched observational study analyzing medical claims in the final 3 months of life. SETTING/PARTICIPANTS: Medicare fee-for-service 2010-2014 decedents in northern California, USA. RESULTS: Final month total expenditures for Advanced Illness Management enrollees ( N = 1352) were reduced by US$4824 (US$3379, US$6268) and inpatient payments by US$6127 (US$4874, US$7682). Enrollees also experienced 150 fewer hospitalizations/1000 (101, 198) and 1361 fewer hospital days/1000 (998, 1725). The percentage of hospice enrollees increased by 17.9 percentage points (14.7, 21.0), hospital deaths decreased by 8.2 percentage points (5.5, 10.8), and intensive care unit deaths decreased by 7.1 percentage points (5.2, 8.9). End-of-life chemotherapy use and non-inpatient expenditures in months 2 and 3 prior to death did not differ significantly from the control group. CONCLUSION: Advanced Illness Management has a positive impact on inpatient utilization, cost of care, hospice enrollment, and site of death. This suggests that home-based support programs for people with advanced illness can be successful on a large scale in supporting personal end-of-life care choices.