Using Machine Learning to Leverage Biomarker Change and Predict Colorectal Cancer Recurrence.

PURPOSE: The risk of colorectal cancer (CRC) recurrence after primary treatment varies across individuals and over time. Using patients' most up-to-date information, including carcinoembryonic antigen (CEA) biomarker profiles, to predict risk could improve personalized decision making. METHODS: We used electronic health record data from an integrated health system on a cohort of patients diagnosed with American Joint Committee on Cancer stage I-III CRC between 2008 and 2013 (N = 3,970) and monitored until recurrence or end of follow-up. We addressed missingness in recurrence outcomes and longitudinal CEA measures, and engineered CEA features using current and past biomarker values for inclusion in a risk prediction model. We used a discrete time Superlearner model to evaluate various algorithms for predicting recurrence. We evaluated the time-varying discrimination and calibration of the algorithms and assessed the role of individual predictors. RESULTS: Recurrence was documented in 448 (11.3%) patients. XGBoost with depth = 1 (XGB-D1) predicted recurrence substantially better than all other algorithms at all time points, with AUC ranging from 0.87 (95% CI, 0.86 to 0.88) at 6 months to 0.94 (95% CI, 0.92 to 0.96) at 54 months. The only variable used by XGB-D1 was 6-month change in log CEA. Predicted 1-year risk of recurrence was nearly zero for patients whose log CEA did not increase in the last 6 months, between 12.2% and 34.1% for patients whose log CEA increased between 0.10 and 0.40, and 43.6% for those with a log CEA increase >0.40. Compared with XGB, penalized regression approaches (lasso, ridge, and elastic net) performed poorly, with AUCs ranging from 0.58 to 0.69. CONCLUSION: A flexible, machine learning approach that incorporated longitudinal CEA information yielded a simple and high-performing model for predicting recurrence on the basis of 6-month change in log CEA.

Factors associated with lung cancer risk factor documentation.

Objectives: To identify factors associated with the minimum necessary information to determine an individual's eligibility for lung cancer screening (ie, sufficient risk factor documentation) and to characterize clinic-level variability in documentation. Study Design: Cross-sectional observational study using electronic health record data from an academic health system in 2019. Methods: We calculated the relative risk of sufficient lung cancer risk factor documentation by patient-, provider-, and system-level variables using Poisson regression models, clustering by clinic. We compared unadjusted, risk-adjusted, and reliability-adjusted proportions of patients with sufficient smoking documentation across 31 clinics using logistic regression models and 2-level hierarchical logit models to estimate reliability-adjusted proportions across clinics. Results: Among 20,632 individuals, 60% had sufficient risk factor documentation to determine screening eligibility. Patient-level factors inversely associated with risk factor documentation included Black race (relative risk [RR], 0.70; 95% CI, 0.60-0.81), non-English preferred language (RR, 0.60; 95% CI, 0.49-0.74), Medicaid insurance (RR, 0.64; 95% CI, 0.57-0.71), and nonactivated patient portal (RR, 0.85; 95% CI, 0.80-0.90). Documentation varied across clinics. The reliability-adjusted intraclass correlation coefficient decreased from 11.0% (95% CI, 6.9%-17.1%) to 5.3% (95% CI, 3.2%-8.6%), adjusting for covariates. Conclusions: We found a low rate of sufficient lung cancer risk factor documentation and associations of risk factor documentation based on patient-level factors such as race, insurance status, language, and patient portal activation. Risk factor documentation rates varied across clinics, and only approximately half the variation was explained by factors in our analysis.

Racial and Ethnic Bias in Risk Prediction Models for Colorectal Cancer Recurrence When Race and Ethnicity Are Omitted as Predictors.

Importance: Including race and ethnicity as a predictor in clinical risk prediction algorithms has received increased scrutiny, but there continues to be a lack of empirical studies addressing whether simply omitting race and ethnicity from the algorithms will ultimately affect decision-making for patients of minoritized racial and ethnic groups. Objective: To examine whether including race and ethnicity as a predictor in a colorectal cancer recurrence risk algorithm is associated with racial bias, defined as racial and ethnic differences in model accuracy that could potentially lead to unequal treatment. Design, Setting, and Participants: This retrospective prognostic study was conducted using data from a large integrated health care system in Southern California for patients with colorectal cancer who received primary treatment between 2008 and 2013 and follow-up until December 31, 2018. Data were analyzed from January 2021 to June 2022. Main Outcomes and Measures: Four Cox proportional hazards regression prediction models were fitted to predict time from surveillance start to cancer recurrence: (1) a race-neutral model that explicitly excluded race and ethnicity as a predictor, (2) a race-sensitive model that included race and ethnicity, (3) a model with 2-way interactions between clinical predictors and race and ethnicity, and (4) separate models by race and ethnicity. Algorithmic fairness was assessed using model calibration, discriminative ability, false-positive and false-negative rates, positive predictive value (PPV), and negative predictive value (NPV). Results: The study cohort included 4230 patients (mean [SD] age, 65.3 [12.5] years; 2034 [48.1%] female; 490 [11.6%] Asian, Hawaiian, or Pacific Islander; 554 [13.1%] Black or African American; 937 [22.1%] Hispanic; and 2249 [53.1%] non-Hispanic White). The race-neutral model had worse calibration, NPV, and false-negative rates among racial and ethnic minority subgroups than non-Hispanic White individuals (eg, false-negative rate for Hispanic patients: 12.0% [95% CI, 6.0%-18.6%]; for non-Hispanic White patients: 3.1% [95% CI, 0.8%-6.2%]). Adding race and ethnicity as a predictor improved algorithmic fairness in calibration slope, discriminative ability, PPV, and false-negative rates (eg, false-negative rate for Hispanic patients: 9.2% [95% CI, 3.9%-14.9%]; for non-Hispanic White patients: 7.9% [95% CI, 4.3%-11.9%]). Inclusion of race interaction terms or using race-stratified models did not improve model fairness, likely due to small sample sizes in subgroups. Conclusions and Relevance: In this prognostic study of the racial bias in a cancer recurrence risk algorithm, removing race and ethnicity as a predictor worsened algorithmic fairness in multiple measures, which could lead to inappropriate care recommendations for patients who belong to minoritized racial and ethnic groups. Clinical algorithm development should include evaluation of fairness criteria to understand the potential consequences of removing race and ethnicity for health inequities.

Racial Disparities in the Ascertainment of Cancer Recurrence in Electronic Health Records.

PURPOSE: There is growing interest in using computable phenotypes or proxies to identify important clinical outcomes, such as cancer recurrence, in rich electronic health records data. However, the race/ethnicity-specific accuracies of these proxies remain unclear. We examined whether the accuracy of a proxy for colorectal cancer (CRC) recurrence differed by race/ethnicity and the possible mechanisms that drove the differences. METHODS: Using data from a large integrated health care system, we identified a stratified random sample of 282 Black/African American (AA), Hispanic, and non-Hispanic White (NHW) patients with CRC who received primary treatment. Patient 5-year recurrence status was estimated using a utilization-based proxy and evaluated against the true recurrence status obtained using detailed chart review and by race/ethnicity. We used covariate-adjusted probit regression models to estimate the associations between race/ethnicity and misclassification. RESULTS: The recurrence proxy had excellent overall accuracy (positive predictive value [PPV] 89.4%; negative predictive value 96.5%; mean difference in timing 1.96 months); however, accuracy varied by race/ethnicity. Compared with NHW patients, PPV was 14.9% lower (95% CI, 2.53 to 28.6) among Hispanic patients and 4.3% lower (95% CI, -4.8 to 14.8) among Black/AA patients. The proxy disproportionately inflated the 5-year recurrence incidence for Hispanic patients by 10.6% (95% CI, 4.2 to 18.2). Compared with NHW patients, proxy recurrences for Hispanic patients were almost three times as likely to have been misclassified as positive (adjusted risk ratio 2.91 [95% CI, 1.21 to 8.31]). Higher false positives among racial/ethnic minorities may be related to higher prevalence of noncancerous lung-related problems and substantial delays in primary treatment because of insufficient patient-provider communication and abnormal treatment patterns. CONCLUSION: Using a proxy with worse accuracy among racial/ethnic minority patients to estimate population health may misdirect resources and support erroneous conclusions around treatment benefit for these patients.

How Much Does the US Public Value Equity in Health? A Systematic Review.

OBJECTIVES: This systematic review aims to summarize and qualitatively assess published evaluations on the US public's preferences for health equity and their willingness to trade-off efficiency for equity. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension guidelines, we searched MEDLINE and Embase for relevant peer-reviewed publications on this topic before February 2021. We included English-language articles that solicited US preferences regarding efficiency-equity trade-offs and prioritizing healthcare resources based on socioeconomic status, race, disability, or burden of disease. Quantitative and qualitative data captured were decided a priori and iteratively adapted as themes emerged. RESULTS: Fourteen studies were found over a 25-year span. Only 4 focused on resource allocation across social groups. Three distinct notions of fairness were studied: equal distribution of resources, priority to the worse-off, and equal health achieved. We found modest support for equal distribution of resources and willingness to sacrifice efficiency for equity in the United States. Prioritizing the underserved was relatively less studied and received less support and was more preferred when resources were scarce, when allocating resources between social groups, or when participants were informed about the fundamental origins of health inequities. Equal health was the least studied, but received nontrivial support. CONCLUSIONS: The existing literature evaluating the US public's understanding and preferences toward equity was severely limited by the lack of rigorous quantitative studies and heterogeneous attribute selection and fairness definitions. High-quality studies that clearly define fairness, focus on social groups, and apply rigorous methods to quantify equity preferences are needed to integrate the public's value on equity into healthcare decisions.

Evaluation of a Multilevel Laboratory Stewardship Intervention Targeted to Cardiac and Thoracic Surgical Services at an Academic Medical Center.

CONTEXT.­: Unnecessary laboratory tests are ordered because of factors such as preselected orders on order sets, clinician habits, and trainee concerns. Excessive use of laboratory testing increases patient discomfort via unnecessary phlebotomy, contributes to iatrogenic anemia, increases risk of bloodstream infections, and increases the cost of care. OBJECTIVE.­: To address these concerns, we implemented a multilevel laboratory stewardship intervention to decrease unnecessary laboratory testing, measured by laboratory tests per day attributed to service, across 2 surgical divisions with high laboratory use. DESIGN.­: The multilevel intervention included 5 components: stakeholder engagement, provider education, computerized provider order entry modification, performance feedback, and culture change supported by leadership. The primary outcome of the study was laboratory tests ordered per patient-day. Secondary outcomes included the number of blood draws per patient-day, total lab-associated costs, length of stay, discharge to a nursing facility, 30-day readmissions, and deaths. A difference-in-differences analytic approach assessed the outcome measures in the intervention period, with other surgical services as controls. RESULTS.­: The primary outcome of laboratory tests per patient-day showed a significant decrease across both thoracic and cardiac surgery services, with between 1.5 and 2 fewer tests ordered per patient-day for both services and an estimated 20 000 fewer tests performed during the intervention period. Blood draws per patient-day were also significantly decreased on the thoracic surgery service but not for cardiac surgery. CONCLUSIONS.­: A multilevel laboratory stewardship intervention targeted to 2 surgical services resulted in a significant decrease in laboratory test use without negatively impacting length of stay, readmissions, or mortality.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1-Data From Wearable Devices.

OBJECTIVES: With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to wearable data in HEOR. METHODS: We identified studies published in PubMed between January 2016 and March 2021. Studies that included at least 1 HEOR-related Medical Subject Headings term, applied an ML, and used wearable data were eligible for inclusion. Two reviewers abstracted information including ML application types and data on which ML was applied and analyzed them using descriptive analyses. RESULTS: A total of 148 studies were identified from PubMed, among which 32 studies met the inclusion criteria. There has been an increase over time in the number of ML studies using wearable data. ML has been more frequently used for monitoring events in real time (78%) than to predict future events (22%). There has been a wide range of outcomes examined, ranging from general physical or mental health (24%) to more disease-specific outcomes (eg, disease incidence [19%] and progression [13%]) and treatment-related outcomes (eg, treatment adherence [9%] and outcomes [9%]). Data for ML models were more often derived from wearable devices with specific medical purposes (60%) than those without (40%). CONCLUSION: There has been a wide range of applications of ML to wearable data. Both medical and nonmedical wearable devices have been used as a data source, showing the potential for providing rich data for ML studies in HEOR.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables.

OBJECTIVES: Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS: We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS: We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS: The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

Association Between a Population Health Intervention and Hypertension Control.

INTRODUCTION: As part of the Centers for Medicare and Medicaid Innovation Practice Transformation Network, an integrated healthcare system implemented a multimodal, population health-based hypertension clinical pathway program (HCPP) focused on hypertension management. AIM: To determine whether the HCPP was associated with changes in hypertension control or process-of-care measures and whether associations varied for sites serving higher versus lower proportions of historically underserved patients. SETTING: An integrated academic health system encompassing 5 clinic networks and 85 primary and specialty care sites. PROGRAM DESCRIPTION: The HCPP was implemented at some sites (adopters) but not others (non-adopters) and had four components: (1) stakeholder engagement; (2) clinical staff retraining; (3) electronic health record-based prompts; and (4) performance monitoring and feedback. Program goals were to encourage clinical teams to increase the frequency of follow up visits and adopt standardized approaches to blood pressure (BP) measurements and antihypertensive medication regimen advancement defined as adding or titrating existing medication. PROGRAM EVALUATION: This quasi-experimental study used 2017-2019 data from 63,497 patients with hypertension and multivariable difference-in-differences analyses to evaluate changes in outcomes at 19 adopter versus 39 non-adopter sites before and after HCPP implementation. Adoption was associated with 3.5 times differentially greater odds of a BP reassessment (OR 3.5, 95% CI 3.3-3.8), 11% differentially greater odds of BP control (BP<140/90 mmHg) (OR 1.11, 95% CI 1.07-1.15), and 12% differentially greater odds of having non-severely elevated BP (systolic BP < 155 mmHg) (OR 1.12, 95% CI 1.05-1.19). HCPP adoption was not associated with differential changes in 90-day follow-up BP measurement. Adoption was associated with 23% differentially greater odds of appropriate medication advancement (OR 1.23, 95% CI 1.04-1.46). A similar pattern was observed when limiting comparisons to sites caring for a higher proportion of historically underserved populations. DISCUSSION: A multimodal population health approach to transforming hypertension care was associated with improved BP outcomes.

Risk of Adverse Financial Events in Patients With Cancer: Evidence From a Novel Linkage Between Cancer Registry and Credit Records.

PURPOSE: Although financial toxicity is a growing cancer survivorship issue, no studies have used credit data to estimate the relative risk of financial hardship in patients with cancer versus individuals without cancer. We conducted a population-based retrospective matched cohort study using credit reports to investigate the impact of a cancer diagnosis on the risk of adverse financial events (AFEs). METHODS: Western Washington SEER cancer registry (cases) and voter registry (controls) records from 2013 to 2018 were linked to quarterly credit records from TransUnion. Controls were age-, sex-, and zip code-matched to cancer cases and assigned an index date corresponding to the case's diagnosis date. Cases and controls experiencing past-due credit card payments and any of the following AFEs at 24 months from diagnosis or index were compared, using two-sample z tests: third-party collections, charge-offs, tax liens, delinquent mortgage payments, foreclosures, and repossessions. Multivariate logistic regression models were used to evaluate the association of cancer diagnosis with AFEs and past-due credit payments. RESULTS: A total of 190,722 individuals (63,574 cases and 127,148 controls, mean age 66 years) were included. AFEs (4.3% v 2.4%, P < .0001) and past-due credit payments (2.6% v 1.9%, P < .0001) were more common in cases than in controls. After adjusting for age, sex, average baseline credit line, area deprivation index, and index/diagnosis year, patients with cancer had a higher risk of AFEs (odds ratio 1.71; 95% CI, 1.61 to 1.81; P < .0001) and past-due credit payments (odds ratio 1.28; 95% CI, 1.19 to 1.37; P < .0001) than controls. CONCLUSION: Patients with cancer were at significantly increased risk of experiencing AFEs and past-due credit card payments relative to controls. Studies are needed to investigate the impact of these events on treatment decisions, quality of life, and clinical outcomes.

What's in a Number? Assessing the Burden of Diverticular Disease.

MINI-ABSTRACT: In this prospective observational cohort of patients with a history of diverticulitis, we assessed the correlation between the diverticulitis quality of life survey (DVQOL) and other patient-reported expressions of disease measures including work and activity impairment, and contentment with gastrointestinal-related health. Then, we assessed whether the DVQOL is better correlated with these measures than diverticulitis episode count. Our study results showed that the DVQOL has a stronger correlation with other disease measures than diverticulitis episode count, and our findings support the broader use of the DVQOL in assessing the burden of diverticulitis and monitoring response to management.

The health and economic impact of constructing temporary field hospitals to meet the COVID-19 pandemic surge: Wuhan Leishenshan Hospital in China as a case study.

BACKGROUND: In response to the COVID-19 pandemic, two new temporary hospitals were constructed in record time in Wuhan, China, to help combat the fast-spreading virus in February 2020. Using the experience of one of the hospitals as a case study, we discuss the health and economic implications of this response strategy and its potential application in other countries. METHODS: This retrospective observational study analyzed health resource utilization and clinical outcomes data for 2011 inpatients diagnosed with COVID-19 and admitted to Leishenshan Hospital during its 67 days of operation from February 8th to April 14th, 2020. We used a top-down costing approach to estimate the total cost of treating patients at the Leishenshan Hospital, including capital cost for hospital construction, health personnel costs, and direct health care costs. We used a multivariate generalized linear model to examine risk factors associated with in-hospital deaths. RESULTS: During the 67 days of hospital operation, 19 medical teams comprising of 933 doctors and 2312 nurses were gradually transferred to Leishenshan Hospital from across China. Of the 2011 admissions, 4.5% used intensive care and 2.0% used ventilators. Overall median length of stay was 19 days, and 21 days for patients in the intensive care unit (ICU). The case fatality rate (CFR) was 2.3% overall, 41.8% in the ICU, and 0.4% in general ward (GW). CFRs were 55% and 50% among patients using non-invasive and invasive ventilators, respectively. The mean total cost and direct health care cost were CNY806 997 (US$114 793) and CNY16 087 (US$2288), respectively. Patients admitted to the ICU had much higher direct health care costs, on average, compared to those in the GW (CNY150 415 vs CNY9720, or US$21 396 vs US$1383). The mean direct health care cost per patient with severe or critical diseases was more than five times higher than those with mild or moderate diseases (CNY45 191 vs CNY8838, or US$6428 vs US$1257). Older age, having comorbidities, and critical disease were associated with higher risks of death from COVID-19. Lower health worker to patient ratio (<2.6) was not associated with in-hospital death. CONCLUSION: An adequate health workforce were mobilized and deployed to a new temporary hospital. The Leishenshan Hospital increased access to care during the surge in COVID-19 infections, facilitated timely treatment, and transferred COVID-19 patients between GWs and ICUs within the hospital, all of which are potential contributors to lowering the CFR. Patients in the ICU experienced a much higher CFR and a greater burden of health care cost than those in GW. Our results have important implications for other countries interested in constructing temporary emergency hospitals, such as the need for adequate infrastructure capacities and financial support, centralized strategies to mobilize health workforce and to provide respiratory protective devices, and improvement in access to health care.

Establishing Clinically Significant Patient-reported Outcomes for Diverticular Disease.

BACKGROUND: Diverticular disease can undermine health-related quality of life. The diverticulitis quality of life (DV-QOL) instrument was designed and validated to measure patient-reported burden of diverticular disease. However, values reflecting meaningful improvement (i.e., minimal clinically important difference [MCID]) and the patient acceptable symptom state (PASS) have yet to be established. We sought to establish the MCID and PASS of the DV-QOL and describe the characteristics of those with DV-QOL above the PASS threshold. MATERIALS AND METHODS: We performed a prospective cohort study of adults with diverticular disease from seven centers in Washington and California (2016-2018). Patients were surveyed at baseline, then quarterly up to 30 mo. To determine the MCID and PASS for DV-QOL, we applied various previously established distribution- and anchor-based approaches and compared the resulting values. RESULTS: The study included 177 patients (mean age 57 y, 43% women). A PASS threshold of 3.2/10 distinguished between those with and without health-related quality of life-impacting diverticulitis with acceptable accuracy (area under the curve 0.76). A change of 2.2 points in the DV-QOL was the most appropriate MCID: above the distribution-based MCIDs and corresponding to patient perception of importance of change (AUC 0.70). Patients with DV-QOL ≥ PASS were more often men, younger, had Medicaid, had more serious episodes of diverticulitis, and had an occupational degree or high-school education or less. CONCLUSIONS: Our study is the first to define MCID and PASS for DV-QOL. These thresholds are critical for measuring the impact of diverticular disease and the evaluation of treatment effectiveness.

Cause-specific mortality of type B aortic dissection and assessment of competing risks of mortality.

OBJECTIVE: Natural history studies of type B aortic dissection (TBAD) commonly report all-cause mortality. Our aim was to determine cause-specific mortality in TBAD and to evaluate the clinical characteristics associated with aorta-related and nonaorta-related mortality. METHODS: Clinical and administrative records were reviewed for patients with acute TBAD between 1995 and 2017. Demographics, comorbidities, presentation, and initial imaging findings were abstracted. Cause of death was ascertained through a multimodality approach using electronic health records, obituaries, social media, Social Security Death Index, and state mortality records. Causes of death were classified as aorta related, nonaorta related, or unknown. A Fine-Gray multivariate competing risk regression model for subdistribution hazard ratio was employed to analyze the association of clinical characteristics with aorta-related and nonaorta-related mortality. RESULTS: A total of 275 individuals met inclusion criteria (61.1 ± 13.7 years, 70.9% male, 68% white). Mean survival after discharge was 6.3 ± 4.7 years. Completeness of follow-up Clark C index was 0.87. All-cause mortality was 50.2% (n = 138; mean age, 70.1 ± 14.6 years) including an in-hospital mortality of 8.4%. Cause-specific mortality was aorta related, nonaorta related, and unknown in 51%, 43%, and 6%, respectively. Compared with patients with nonaorta-related mortality, patients with aorta-related mortality were younger at acute TBAD (69.5 ± 11.2 years vs 61.6 ± 15.5 years; P = .001), underwent more descending thoracic aortic repairs (19.4% vs 45.8%; P = .002), and had a shorter survival duration (5.7 ± 3.9 vs 3.4 ± 4.5 years; P = .002). There was clear variation in cause of death by each decade of life, with higher aorta-related mortality among those younger than 50 years and older than 70 years and a stepwise increase in nonaorta-related mortality with each increasing decade (P < .001). All-cause mortality at 1 year, 3 years, and 10 years was 15%, 24%, and 57%, respectively. After accounting for competing risks, the cumulative incidence of aorta-related mortality at 1 year, 3 years, and 10 years was 8.9%, 16.5%, and 27.2%, respectively, and that of nonaorta-related mortality was 2.7%, 7.2%, and 29%, respectively. A maximum descending thoracic aortic diameter >4 cm was associated with an increase in hazard of aorta-related mortality by 84% (subdistribution hazard ratio, 1.84; 95% confidence interval, 1.03-3.28) on multivariate competing risk regression analysis. CONCLUSIONS: TBAD is associated with high 10-year mortality. Those at risk for aorta-related mortality have a clinical phenotype different from that of individuals at risk for nonaorta-related mortality. This information is important for building risk prediction models that account for competing mortality risks and to direct optimal and individualized surgical and medical management of TBAD.

Transforming Population-Based Depression Care: a Quality Improvement Initiative Using Remote, Centralized Care Management.

INTRODUCTION: With the growing prevalence of value-based contracts, health systems are incentivized to consider population approaches to service delivery, particularly for chronic conditions like depression. To this end, UW Medicine implemented the Depression-Population Approach to Health (PATH) program in primary care (PC) as part of a system-wide Center for Medicare and Medicaid Innovation (CMMI) quality improvement (QI) initiative. AIM: To examine the feasibility of a pilot PATH program and its impact on clinical and process-of-care outcomes. SETTING: A large, diverse, geographically disparate academic health system in Western Washington State including 28 PC clinics across five networks. PROGRAM DESCRIPTION: The PATH program was a population-level, centralized, measurement-based care intervention that utilized a clinician to provide remote monitoring of treatment progress via chart review and facilitate patient engagement when appropriate. The primary goals of the program were to improve care engagement and increase follow-up PHQ-9 assessments for patients with depression and elevated initial PHQ-9 scores. PROGRAM EVALUATION: We employed a prospective, observational study design, including commercially insured adult patients with new depression diagnoses and elevated initial PHQ-9 scores. The pilot intervention group, consisting of accountable care network (ACN) self-enrollees (N = 262), was compared with a similar commercially insured cohort (N = 2527) using difference-in-differences analyses adjusted for patient comorbidities, initial PHQ-9 score, and time trends. The PATH program was associated with three times the odds of PHQ-9 follow-up (OR 3.28, 95% CI 1.79-5.99), twice the odds of a follow-up PC clinic visit (OR 1.74, 95% CI 0.99-3.08), and twice the odds of treatment response, defined as reduction in PHQ-9 score by ≥ 50% (OR 2.02, 95% CI 0.97-4.21). DISCUSSION: Our results demonstrate that a centralized, remote care management initiative is both feasible and effective for large academic health systems aiming to improve depression outcome ascertainment, treatment engagement, and clinical care.

Appropriate Management of Vertebral Fragility Fractures: Development of a Pathway Based on a Vertebral Compression Fracture Registry.

BACKGROUND: The BenchMarket Medical (BMM) Vertebral Compression Fracture (VCF) Registry, now known as Talosix, is a collaborative effort between Talosix (the authorized registry vendor), Noridian Healthcare Solutions, and clinicians to gather outcomes evidence for cement augmentation treatments in patients with acute painful osteoporotic VCFs. The VCF Registry was designed to provide outcomes evidence to inform the Medicare payer's "coverage with evidence development" decision to authorize reimbursement for cement augmentation treatments. OBJECTIVES: The purpose of this article was to present a pathway for appropriate use of vertebral augmentation based on the findings of the VCF Registry. STUDY DESIGN: Prospective observational data, including patient characteristics, diagnosis, process of care, and patient-reported outcomes (PROs) for pain and function, were collected from patients undergoing cement augmentation treatment. The PROs were collected at baseline, 1, 3, and 6 months following the procedure. SETTING: The VCF Registry is a national ongoing registry with no specified end time or designated sample size. METHODS: Primary outcomes were pain improvement measured using the Numeric Rating Scale and function improvement, measured using the Roland Morris Disability Questionnaire (RMDQ). Secondary outcomes included cement leakage, new neurologic deficits, adverse events, readmissions, and death. RESULTS: The VCF Registry delivered outcomes data to support Noridian's "coverage with evidence development" decision. A total of 732 patients were included in this study. Registry outcomes confirmed postmarket evidence of highly significant pain relief with mean pain score improvement of 6.5/10 points at 6 months. Function also improved significantly with mean RMDQ score change of 11.4/24 points 6 months after surgery. Results also showed the safety and reliability of cement augmentation. LIMITATIONS: The nature of the registry data is that it contains nonrandomized, nonplacebo controlled data and should not be perceived as such. The real-world setting and the large number of patients within the dataset should increase the external validity of the findings. CONCLUSIONS: Cement augmentation treatments of patients with acute painful VCFs reliably results in highly significant benefits of pain decrease and functional improvement for this Medicare population. KEY WORDS: Vertebral compression fractures, osteoporosis, kyphoplasty, back pain, registry.

Hospital and Surgeon Variation in Patient-reported Functional Outcomes After Lumbar Spine Fusion: A Statewide Evaluation.

STUDY DESIGN: Statewide retrospective cohort study using prospectively collected data from the Spine Care and Outcomes Assessment Program, capturing ∼75% of the state's spine fusion procedures. OBJECTIVE: The aim of this study was to estimate the variation in patient-reported outcomes (PROs) 1 year after elective lumbar fusion surgery across surgeons and hospitals; and to discuss the potential impact of guiding patient selection using a PRO prediction tool. SUMMARY OF BACKGROUND DATA: Despite an increasing interest in incorporating PROs as part of the move toward value-based payment and to improve quality, limited evidence exists on how PROs vary across hospitals and surgeons, a key aspect of using these metrics for quality profiling. METHODS: We examined patient-reported functional improvement (≥15-point reduction in the Oswestry Disability Index [ODI]) and minimal disability (reaching ≤22 on the ODI) 1 year after surgery in 17 hospitals and 58 surgeons between 2012 and 2017. Outcomes were risk-adjusted for patient characteristics with multiple logistic regressions and reliability-adjusted using hierarchical models. RESULTS: Of the 737 patients who underwent lumbar fusion (mean [SD] age, 63 [12] years; 60% female; 84% had stenosis; 70% had spondylolisthesis), 58.7% achieved functional improvement and 42.5% reached minimal disability status at 1 year. After adjusting for patient factors, there was little variation between hospitals and surgeons (maximum interclass correlation was 3.5%), and this variation became statistically insignificant after further reliability adjustment. Avoiding operation on patients with <50% chance of functional improvement may reduce current surgical volume by 63%. CONCLUSION: Variations in PROs across hospitals and surgeons were mainly driven by differences in patient populations undergoing lumbar fusion, suggesting that PROs may not be useful indicators of hospital or surgeon quality. Careful patient selection using validated prediction tools may decrease differences in outcomes across hospitals and providers and improve overall quality, but would significantly reduce surgical volumes. LEVEL OF EVIDENCE: 3.