The Association of Racial Residential Segregation and Survival After Out-of-Hospital Cardiac Arrest in the United States.

Background: Residential segregation has been identified as drivers of disparities in health outcomes, but further work is needed to understand this association with clinical outcomes for out-of-hospital cardiac arrest (OHCA). We utilized Cardiac Arrest Registry to Enhance Survival (CARES) dataset to examine if there are differences in survival to discharge and survival with good neurological outcome, as well as likelihood of bystander CPR, using validated measures of racial, ethnic, and economic segregation. Methods: We conducted a retrospective observational study using data from the Cardiac Arrest Registry to Enhance Survival (CARES) dataset to examine associations among adult OHCA patients. The primary predictor was the Index of Concentration at the Extremes (ICE), a validated measure that includes race, ethnicity, and income across three measures at the census tract level. The primary outcomes were survival to discharge and survival with good neurological status. A multivariable modified Poisson regression modeling approach with random effects at the EMS agency and hospital level was utilized. Results: We identified 626,264 OHCA patients during the study period. The mean age was 62 years old (SD 17.2 years), and 35.7% (n =223,839) of the patients were female. In multivariable models, we observed an increased likelihood of survival to discharge and survival with good neurological outcome for those patients residing in predominately White population census tracts and higher income census tracts as compared to lower income Black and Hispanic/Latinx population census tracts (RR 1.24, CI 1.20-1.28) and a 32% increased likelihood of receiving bystander CPR in higher income census tracts as compared to reference (RR 1.32, CI 1.30-1.34). Conclusions: In this study examining the association of measures of residential segregation and OHCA outcomes, there was an increased likelihood of survival to discharge, survival with good neurological status, and likelihood of receiving B-CPR for those patients residing in predominately White population and higher income census tracts when compared to predominately Black and/or Hispanic Latinx populations and lower income census tracts. This research suggests that areas impacted by residential and economic segregation are important targets for both public policy interventions as well as addressing disparities in care across the chain of survival for OHCA.

Association of Racial Residential Segregation With Long-Term Outcomes and Readmissions After Out-of-Hospital Cardiac Arrest Among Medicare Beneficiaries.

Background The national impact of racial residential segregation on out-of-hospital cardiac arrest outcomes after initial resuscitation remains poorly understood. We sought to characterize the association between measures of racial and economic residential segregation at the ZIP code level and long-term survival and readmissions after out-of-hospital cardiac arrest among Medicare beneficiaries. Methods and Results In this retrospective cohort study, using Medicare claims data, our primary predictor was the index of concentration at the extremes, a measure of racial and economic segregation. The primary outcomes were death up to 3 years and readmissions. We estimated hazard ratios (HRs) across all 3 types of index of concentration at the extremes measures for each outcome while adjusting for beneficiary demographics, treating hospital characteristics, and index hospital procedures. In fully adjusted models for long-term survival, we found a decreased hazard of death and risk of readmission for beneficiaries residing in the more segregated White communities  and higher-income ZIP codes compared with the more segregated Black communities and lower-income ZIP codes across all 3 indices of concentration at the extremes measures (race: HR, 0.87 [95% CI, 0.81-0.93]; income: HR, 0.75 [95% CI, 0.69-0.78]; and race+income: HR, 0.77 [95% CI, 0.72-0.82]). Conclusions We found a decreased hazard of death and risk for readmission for those residing in the more segregated White communities  and higher-income ZIP codes compared with the more segregated Black communities and lower-income ZIP codes when using validated measures of racial and economic segregation. Although causal pathways and mechanisms remain unclear, disparities in outcomes after out-of-hospital cardiac arrest are associated with the structural components of race and wealth and persist up to 3 years after discharge.

Using medicare claims to estimate risk-adjusted performance of Pennsylvania trauma centers.

Trauma centers use registry data to benchmark performance using a standardized risk adjustment model. Our objective was to utilize national claims to develop a risk adjustment model applicable across all hospitals, regardless of designation or registry participation. Patients from 2013-14 Pennsylvania Trauma Outcomes Study (PTOS) registry data were probabilistically matched to Medicare claims using demographic and injury characteristics. Pairwise comparisons established facility linkages and matching was then repeated within facilities to link records. Registry models were estimated using GLM and compared with five claims-based LASSO models: demographics, clinical characteristics, diagnosis codes, procedures codes, and combined demographics/clinical characteristics. Area under the curve and correlation with registry model probability of death were calculated for each linked and out-of-sample cohort. From 29 facilities, a cohort comprising 16,418 patients were linked between datasets. Patients were similarly distributed: median age 82 (PTOS IQR: 74-87 vs. Medicare IQR: 75-88); non-white 6.2% (PTOS) vs. 5.8% (Medicare). The registry model AUC was 0.86 (0.84-0.87). Diagnosis and procedure codes models performed poorest. The demographics/clinical characteristics model achieved an AUC = 0.84 (0.83-0.86) and Spearman = 0.62 with registry data. Claims data can be leveraged to create models that accurately measure the performance of hospitals that treat trauma patients.

Clofarabine and Busulfan Myeloablative Conditioning in Allogeneic Hematopoietic Cell Transplantation for Patients With Active Myeloid Malignancies.

Patients with refractory or relapsed and refractory myeloid malignancies have a poor prognosis. Allogeneic hematopoietic cell transplantation (HCT) with myeloablative conditioning (MAC) in patients with active, chemotherapy-refractory myeloid disease is historically associated with high rates of relapse and nonrelapse mortality (NRM). A MAC regimen combining clofarabine with busulfan (Clo/Bu4) has been reported to exhibit antileukemic activity with acceptable toxicity in patients age ≤70 years. Here we describe the clinical outcomes of a real-world population of patients with active myeloid malignancies undergoing allogeneic HCT with Clo/Bu4 MAC. In a single-center retrospective descriptive analysis, we identified patients who underwent HCT for myeloid malignancies not in remission using Clo/Bu4 MAC between 2012 and 2020. We report event-free survival (EFS) and overall survival (OS), cumulative incidences of relapse and NRM, and the incidence and severity of acute and chronic graft-versus-host disease (GVHD). We identified 69 patients with a median age of 60 years (range, 22 to 70 years). Most patients had relapsed/refractory or primary refractory acute myelogenous leukemia (AML; n = 55) or refractory myelodysplastic syndrome (MDS; n = 12); 1 patient had chronic myelogenous leukemia, and 1 patient had a blastic plasmacytoid dendritic cell neoplasm. Fifty patients (72.5%) had complete remission at day 100 post-transplantation. Two-year EFS and OS were 30% (95% confidence interval [CI], 20% to 44%) and 40% (95% CI, 29% to 54%), respectively. Patients with AML had a 2-year EFS and OS of 28% (95% CI, 18% to 44%) and 38% (95% CI, 27% to 54%), respectively; those with MDS had a 2-year EFS and OS of 47% (95% CI, 25% to 88%) and 56% (95% CI, 33% to 94%), respectively. The cumulative incidence of relapse at 2 years was 39% (95% CI, 27% to 51%) for all patients, including 45% (95% CI, 31% to 58%) in the patients with AML and 18% (95% CI, 2% to 45%) in those with MDS. NRM at 2 years was 31% (95% CI, 20% to 42%), including 27% (95% CI, 15% to 39%) in patients with AML and 35% (95% CI, 10% to 63%) in those with MDS. The total incidence of acute GVHD (aGVHD) of any severity was 80%, and the incidence of grade III-IV aGVHD was 22%. In patients who achieved remission, those who required systemic immunosuppression for aGVHD (58%) had poorer 2-year EFS (29% versus 54%; P = .05) and 2-year OS (39% versus 70%; P = .04) compared to those who did not. The 2-year cumulative incidence of chronic GVHD was 44% (95% CI, 28% to 58%). Clo/Bu4 MAC followed by allogeneic HCT for patients with active myeloid malignancies is an effective transplantation strategy for patients up to age 70, particularly those with advanced MDS. The high incidence of and poor outcomes associated with aGVHD highlight the importance of optimizing preventative strategies.

Survival After Out-of-Hospital Cardiac Arrest: The Role of Racial Residential Segregation.

Racial and racialized economic residential segregation has been empirically associated with outcomes across multiple health conditions but not yet explored in relation to out-of-hospital cardiac arrest (OHCA). We sought to examine if measures of racial and economic residential segregation are associated with differences in survival to discharge after OHCA for Black and White Medicare beneficiaries. Utilizing age-eligible Medicare fee-for-service claims data from 2013 to 2015, we identified OHCA claims and determined survival to discharge. The primary predictor, residential segregation, was calculated using the index of concentration at the extremes (ICE) for the beneficiary residential ZIP code. Multilevel modified Poisson regression models were used to determine the association of OHCA outcomes and ZIP code level ICE measures. In total, 194,263 OHCA cases were identified among beneficiaries residing in 75% of US ZIP codes. Black beneficiaries exhibited 12.1% survival to discharge, compared with 12.5% of White beneficiaries. In fully adjusted models of the three ICE measures accounting for differences in treating hospital characteristics, there was as high as a 28% (RR 1.28, CI 1.23-1.26) higher relative likelihood of survival to discharge in the most segregated White ZIP codes (Q5) as compared to the most segregated Black ZIP codes (Q1). Racial residential segregation is independently associated with disparities in OHCA outcomes; among Medicare beneficiaries who generated a claim after suffering an OHCA, ICE measures of racial segregation are associated with a lower likelihood of survival to discharge for those living in the most segregated Black and lower income quintiles compared to higher quintiles.

Machine learning to predict in-hospital mortality among patients with severe obesity: Proof of concept study.

Objectives: Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in-hospital mortality among this population. Methods: Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m2) was analyzed. Data was retrieved from five hospitals from the Mount Sinai health system, New York. The study time frame was between January 2011 and December 2019. Data was used to train a gradient-boosting machine learning model to identify in-hospital mortality. The model was trained and evaluated based on the data from four hospitals and externally validated on held-out data from the fifth hospital. Results: A total of 14,078 hospital admissions of inpatients with severe obesity were included. The in-hospital mortality rate was 297/14,078 (2.1%). In univariate analysis, albumin (area under the curve [AUC] = 0.77), blood urea nitrogen (AUC = 0.76), acuity level (AUC = 0.73), lactate (AUC = 0.72), and chief complaint (AUC = 0.72) were the best single predictors. For Youden's index, the model had a sensitivity of 0.77 (95% CI: 0.67-0.86) with a false positive rate of 1:9. Conclusion: A machine learning model trained on clinical measures provides proof of concept performance in predicting mortality in patients with severe obesity. This implies that such models may help to adopt specific decision support tools for this population.

Developing a measure of overall intensity of injury care: A latent class analysis.

BACKGROUND: While injury is a leading cause of death and debility in older adults, the relationship between intensity of care and trauma remains unknown. The focus of this analysis is to measure the overall intensity of care delivered to injured older adults during hospitalization. METHODS: We used Centers for Medicare and Medicaid Services Medicare fee-for-service claims data (2013-2014), to identify emergency department-based claims for moderate and severe blunt trauma in age-eligible beneficiaries. Medical procedures associated with care intensity were identified using a modified Delphi method. A latent class model was estimated using the identified procedures, intensive care unit length of stay, demographics, and injury characteristics. Clinical phenotypes for each class were explored. RESULTS: A total of 683,398 cases were classified as low- (73%), moderate- (23%), and high-intensity care (4%). Greater age and reduced injury severity were indicators of lower intensity, while males, non-Whites, and nonfall mechanisms were more common with high intensity. Intubation/mechanical ventilation was an indicator of high intensity and often occurred with at least one other procedure or an extended intensive care unit stay. CONCLUSION: This work demonstrates that, although heterogeneous, care for blunt trauma can be evaluated using a single novel measure. LEVEL OF EVIDENCE: For prognostic/epidemiological studies, level III.

The Short and the Long of it: Timing of Mortality for Older Adults in a State Trauma System.

BACKGROUND: The impact of injury extends beyond the hospital stay, but trauma center performance metrics typically focus on in-hospital mortality. We compared risk adjusted rates of in-hospital and long-term mortality among Pennsylvania trauma centers. We hypothesized that centers with low rates of in-hospital mortality would also have low rates of long-term mortality. METHODS: We identified injured patients (age ≥ 65) admitted to Pennsylvania trauma centers in 2013 and 2014 using the Pennsylvania Trauma Outcomes Study, a robust, state-wide trauma registry. We matched trauma registry records to Medicare claims from the y 2013 to 2015. Matching variables included admission date and patient demographics including date of birth, zip, sex, and race and/or ethnicity. Outcomes examined were inpatient, 30-day, and 1-y mortality. Multivariable logistic regression models including presenting physiology, comorbidities, injury characteristics, and demographics were developed to calculate expected mortality rates for each trauma center at each time point. Trauma center performance was assessed using observed-to-expected ratios and ranking for in-hospital, 30-day, and 1-y mortality. RESULTS: Of the 15,451 patients treated at 28 centers, 8.1% died before discharge or were discharged to hospice. Another 3.4% died within 30 d, and another 14.7% died within 1 y of injury. Of patients who survived hospitalization but died within 30 d, 92.5% were injured due to fall, and 75.0% sustained head injuries. Survival at 1 y was higher in patients discharged home (88.4%), compared to those discharged to a skilled nursing facility or long-term acute care hospital (72.7% and 52.6%, respectively). Three centers were identified as outliers (two low and one high) for in-hospital mortality, none of which were outliers when the horizon was stretched to 30 d from injury. At 30 d, two different low and two different high outliers were found. CONCLUSION: Nearly one-in-three injured older adults who die within 30 d of injury dies after hospital discharge. Hospital rankings for in-hospital mortality correlate poorly with long-term outcomes. These findings underscore the importance of looking beyond survival to discharge for quality improvement and benchmarking.

Inpatient hospital performance is associated with post-discharge sepsis mortality.

BACKGROUND: Post-discharge deaths are common in patients hospitalized for sepsis, but the drivers of post-discharge deaths are unclear. The objective of this study was to test the hypothesis that hospitals with high risk-adjusted inpatient sepsis mortality also have high post-discharge mortality, readmissions, and discharge to nursing homes. METHODS: Retrospective cohort study of age-qualifying Medicare beneficiaries with sepsis hospitalization between January 2013 and December 2014. Hospital survivors were followed for 180-days post-discharge, and mortality, readmissions, and new admission to skilled nursing facility were measured. Inpatient hospital-specific sepsis risk-adjusted mortality ratio (observed: expected) was the primary exposure. RESULTS: A total of 830,721 patients in the cohort were hospitalized for sepsis, with inpatient mortality of 20% and 90-day mortality of 48%. Higher hospital-specific sepsis risk-adjusted mortality was associated with increased 90-day post-discharge mortality (aOR 1.03 per each 0.1 increase in hospital inpatient O:E ratio, 95% CI 1.03-1.04). Higher inpatient risk adjusted mortality was also associated with increased probability of being discharged to a nursing facility (aOR 1.03, 95% CI 1.02-1.03) and 90-day readmissions (aOR 1.03, 95% CI 1.02-1.03). CONCLUSIONS: Hospitals with the highest risk-adjusted sepsis inpatient mortality also have higher post-discharge mortality and increased readmissions, suggesting that post-discharge complications are a modifiable risk that may be affected during inpatient care. Future work will seek to elucidate inpatient and healthcare practices that can reduce sepsis post-discharge complications.

Qualitative study of system-level factors related to genomic implementation.

PURPOSE: Research on genomic medicine integration has focused on applications at the individual level, with less attention paid to implementation within clinical settings. Therefore, we conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify system-level factors that played a role in implementation of genomic medicine within Implementing GeNomics In PracTicE (IGNITE) Network projects. METHODS: Up to four study personnel, including principal investigators and study coordinators from each of six IGNITE projects, were interviewed using a semistructured interview guide that asked interviewees to describe study site(s), progress at each site, and factors facilitating or impeding project implementation. Interviews were coded following CFIR inner-setting constructs. RESULTS: Key barriers included (1) limitations in integrating genomic data and clinical decision support tools into electronic health records, (2) physician reluctance toward genomic research participation and clinical implementation due to a limited evidence base, (3) inadequate reimbursement for genomic medicine, (4) communication among and between investigators and clinicians, and (5) lack of clinical and leadership engagement. CONCLUSION: Implementation of genomic medicine is hindered by several system-level barriers to both research and practice. Addressing these barriers may serve as important facilitators for studying and implementing genomics in practice.

Modifiable Failures in the Colorectal Cancer Screening Process and Their Association With Risk of Death.

BACKGROUND & AIMS: Colorectal cancer (CRC) deaths occur when patients do not receive screening or have inadequate follow-up of abnormal results or when the screening test fails. We have few data on the contribution of each to CRC-associated deaths or factors associated with these events. METHODS: We performed a retrospective cohort study of patients in the Kaiser Permanente Northern and Southern California systems (55-90 years old) who died of CRC from 2006 through 2012 and had ≥5 years of enrollment before diagnosis. We compared data from patients with those from a matched cohort of cancer-free patients in the same system. Receipt, results, indications, and follow-up of CRC tests in the 10-year period before diagnosis were obtained from electronic databases and chart audits. RESULTS: Of 1750 CRC deaths, 75.9% (n = 1328) occurred in patients who were not up to date in screening and 24.1% (n = 422) occurred in patients who were up to date. Failure to screen was associated with fewer visits to primary care physicians. Of 3486 cancer-free patients, 44.6% were up to date in their screening. Patients who were up to date in their screening had a lower risk of CRC death (odds ratio, 0.38; 95% confidence interval, 0.33-0.44). Failure to screen, or failure to screen at appropriate intervals, occurred in a 67.8% of patients who died of CRC vs 53.2% of cancer-free patients; failure to follow-up on abnormal results occurred in 8.1% of patients who died of CRC vs 2.2% of cancer-free patients. CRC death was associated with higher odds of failure to screen or failure to screen at appropriate intervals (odds ratio, 2.40; 95% confidence interval, 2.07-2.77) and failure to follow-up on abnormal results (odds ratio, 7.26; 95% confidence interval, 5.26-10.03). CONCLUSIONS: Being up to date on screening substantially decreases the risk of CRC death. In 2 health care systems with high rates of screening, most people who died of CRC had failures in the screening process that could be rectified, such as failure to follow-up on abnormal findings; these significantly increased the risk for CRC death.

Genetic Biomarker Prevalence Is Similar in Fecal Immunochemical Test Positive and Negative Colorectal Cancer Tissue.

BACKGROUND: Fecal immunochemical test (FIT) screening detects most asymptomatic colorectal cancers. Combining FIT screening with stool-based genetic biomarkers increases sensitivity for cancer, but whether DNA biomarkers (biomarkers) differ for cancers detected versus missed by FIT screening has not been evaluated in a community-based population. AIMS: To evaluate tissue biomarkers among Kaiser Permanente Northern California patients diagnosed with colorectal cancer within 2 years after FIT screening. METHODS: FIT-negative and FIT-positive colorectal cancer patients 50-77 years of age were matched on age, sex, and cancer stage. Adequate DNA was isolated from paraffin-embedded specimens in 210 FIT-negative and 211 FIT-positive patients. Quantitative allele-specific real-time target and signal amplification assays were performed for 7 K-ras mutations and 10 aberrantly methylated DNA biomarkers (NDRG4, BMP3, SFMBT2_895, SFMBT2_896, SFMBT2_897, CHST2_7890, PDGFD, VAV3, DTX1, CHST2_7889). RESULTS: One or more biomarkers were found in 414 of 421 CRCs (98.3%). Biomarker expression was not associated with FIT status, with the exception of higher SFMBT2_897 expression in FIT-negative (194 of 210; 92.4%) than in FIT-positive cancers (180 of 211; 85.3%; p = 0.02). There were no consistent differences in biomarker expression by FIT status within age, sex, stage, and cancer location subgroups. CONCLUSIONS: The biomarkers of a currently in-use multi-target stool DNA test (K-ras, NDRG4, and BMP3) and eight newly characterized methylated biomarkers were commonly expressed in tumor tissue specimens, independent of FIT result. Additional study using stool-based testing with these new biomarkers will allow assessment of sensitivity, specificity, and clinical utility.

Observational methods to assess the effectiveness of screening colonoscopy in reducing right colon cancer mortality risk: SCOLAR.

AIMS: Screening colonoscopy's effectiveness in reducing risk of death from right colon cancers remains unclear. Methodological challenges of existing observational studies addressing this issue motivated the design of 'Effectiveness of Screening for Colorectal Cancer in Average-Risk Adults (SCOLAR)'. METHODS: SCOLAR is a nested case-control study based on two large integrated health systems. This affords access to a large, well-defined historical cohort linked to integrated data on cancer outcomes, patient eligibility, test indications and important confounders. RESULTS: We found electronic data adequate for excluding ineligible patients (except family history), but not the detailed information needed for test indication assignment. CONCLUSION: The lessons of SCOLAR's design and implementation may be useful for future studies seeking to evaluate the effectiveness of screening tests in community settings.