Demographic, social, economic and geographic factors associated with long-term outcomes in a cohort of cardiac arrest survivors

Coppler, Patrick J; Elmer, Jonathan; Rittenberger, Jon C.; Callaway, Clifton W.; Wallace, David J.

doi:10.1016/j.resuscitation.2018.04.032

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…Because this model controlled for confounding and included comorbidity data, we were able to analyze the potential effects of tracheostomy timing and technique while controlling for patient social factors, which have been shown to play an important role in patient outcomes. [28][29][30] In this study, having followed patients over a period of maximum 3 years following discharge, it is important to note that on observation of the Kaplan-Meier curves we see the majority of mortalities concentrated within the first 2 months. Thus, management efforts and followup planning should be targeted toward the immediate postdischarge period, regardless of patient demographic and tracheostomy timing and technique.…”

Section: Discussionmentioning

confidence: 88%

Effect of Patient Demographics and Tracheostomy Timing and Technique on Patient Survival

et al. 2020

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Objectives: The ideal timing and technique of tracheostomy vary among patients and may impact outcomes. We aim to examine the association between tracheostomy timing, placement technique, and patient demographics on survival.Study Design: Retrospective cohort study. Methods: A retrospective review was performed for all patients who underwent tracheostomy in 2016 and 2017 at one urban academic tertiary-care hospital. Kaplan-Meier curves were created based on combinations of tracheostomy timing and technique (early percutaneous, early non-percutaneous, late percutaneous, and late non-percutaneous). Cox proportional hazard models were used to determine multivariable effects of timing, technique, and other demographic factors. Primary outcome measures were tracheostomy-related mortality and overall survival. Secondary outcomes were in-hospital, 30-day, and 90-day mortality.Results: Our study included 523 patients. There were six tracheostomy-related deaths, with hemorrhage and tracheoesophageal fistula being the most common causes. Tracheostomy timing and technique combinations were not associated with differences in all-cause mortality or survival following discharge. Cox proportional hazard models showed that Charlson Comorbidity Index (CCI) and unknown partner status were associated with a decrease in survival (P < .01 and P = .05, respectively). Additionally, patient age, gender, race, CCI, and body mass index were not independently associated with changes in survival.Conclusion: Late and non-percutaneous tracheostomies were associated with more tracheostomy-related deaths, but timing and technique were not associated with differences in patient survival. Multiple regression analysis showed that increased patient comorbidities, measured via CCI, and unknown partner status were independently associated with decreased survival. Proceduralists should discuss timing, technique, and patient social factors together with the medical care team when constructing plans for postdischarge management.

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Section: Discussionmentioning

confidence: 88%

Effect of Patient Demographics and Tracheostomy Timing and Technique on Patient Survival

et al. 2020

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“…The MA group may also have had a higher rate of non-shockable rhythm due to more severe noncardiac comorbidities. Eid et al and Coppler et al reported that a high comorbidity burden and non-shockable rhythm show a positive association with short-and long-term mortality [22,23]. Such different pre-existing conditions can induce the results of a lower rate of defibrillation, given amiodarone, and provided CAG due to chronic non-cardiac diseases.…”

Section: Plos Onementioning

confidence: 99%

“…Health insurance service systems and the clinical characteristics of a population may affect the impact of insurance types on short-and long-term outcomes for patients with cardiac arrest. Insurance coverage may also have a financial impact on short-term hospital costs for acute care and long-term hospital costs for chronic care [1,23]. However, nation-wide studies to examine the influence of health insurance types on short-and long-term outcomes and costs rarely include patients with cardiac arrest.…”

Section: Introductionmentioning

confidence: 99%

Impact of insurance type on outcomes in cardiac arrest patients from 2004 to 2015: A nation-wide population-based study

et al. 2021

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Objectives There do not appear to be many studies which have examined the socio-economic burden and medical factors influencing the mortality and hospital costs incurred by patients with cardiac arrest in South Korea. We analyzed the differences in characteristics, medical factors, mortality, and costs between patients with national health insurance and those on a medical aid program. Methods We selected patients (≥20 years old) who experienced their first episode of cardiac arrest from 2004 to 2015 using data from the National Health Insurance Service database. We analyzed demographic characteristics, insurance type, urbanization of residential area, comorbidities, treatments, hospital costs, and mortality within 30 days and one year for each group. A multiple regression analysis was used to identify an association between insurance type and outcomes. Results Among the 487,442 patients with cardiac arrest, the medical aid group (13.3% of the total) had a higher proportion of females, rural residents, and patients treated in low-level hospitals. The patients in the medical aid group also reported a higher rate of non-shockable conditions; a high Charlson Comorbidity Index; and pre-existing comorbidities, such as hypertension, diabetes mellitus, and renal failure with a lower rate of providing a coronary angiography. The national health insurance group reported a lower one-year mortality rate (91.2%), compared to the medical aid group (94%), and a negative association with one-year mortality (Adjusted OR 0.74, 95% CI 0.71–0.76). While there was no significant difference in short-term costs between the two groups, the medical aid group reported lower long-term costs, despite a higher rate of readmission. Conclusions Medical aid coverage was an associated factor for one-year mortality, and may be the result of an insufficient delivery of long-term services as reflected by the lower long-term costs and higher readmission rates. There were differences of characteristics, comorbidities, medical and hospital factors and treatments in two groups. These differences in medical and hospital factors may display discrepancies by type of insurance in the delivery of services, especially in chronic healthcare services.

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“…Healthcare disparities in the prehospital and in-hospital management of patients with symptomatic coronary artery disease (CAD) based on gender, race, distance from hospital, and the system providing care are well described in literature (Barnato et al, 2005;Coppler et al, 2018;Davies & Rier, 2018;Ladapopo et al, 2017;Meadows et al, 2011;Nguyen et al, 2008). Such disparities have become even more pronounced during the COVID-19 pandemic (Rashid et al, 2021).…”

mentioning

confidence: 99%

“…Such disparities have become even more pronounced during the COVID-19 pandemic (Rashid et al, 2021). While most studies have focused on disparities attributable to non-modifiable demographic factors such as sex and race (Davies & Rier, 2018;Meadows et al, 2011); other studies have explored the role of more dynamic social determinants of health (SDOH), such as distance to a hospital and access to a primary care provider (Coppler et al, 2018;Schultz et al, 2018). However, while SDOH are complex, they are often represented solely by socioeconomic indicators, such as income and education.…”

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confidence: 99%

Your neighborhood matters: A machine‐learning approach to the geospatial and social determinants of health in 9‐1‐1 activated chest pain

Faramand

Alrawashdeh

Helman

et al. 2021

Research in Nursing & Health

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Healthcare disparities in the initial management of patients with acute coronary syndrome (ACS) exist. Yet, the complexity of interactions between demographic, social, economic, and geospatial determinants of health hinders incorporating such predictors in existing risk stratification models. We sought to explore a machine-learning-based approach to study the complex interactions between the geospatial and social determinants of health to explain disparities in ACS likelihood in an urban community.This study identified consecutive patients transported by Pittsburgh emergency medical service for a chief complaint of chest pain or ACS-equivalent symptoms. We extracted demographics, clinical data, and location coordinates from electronic health records. Median income was based on US census data by zip code. A random forest (RF) classifier and a regularized logistic regression model were used to identify the most important predictors of ACS likelihood. Our final sample included 2400 patients (age 59 ± 17 years, 47% Females, 41% Blacks, 15.8% adjudicated ACS). In our RF model (area under the receiver operating characteristic curve of 0.71 ± 0.03) age, prior revascularization, income, distance from hospital, and residential neighborhood were the most important predictors of ACS likelihood. In regularized regression (akaike information criterion = 1843, bayesian information criterion = 1912, χ 2 = 193, df = 10, p < 0.001), residential neighborhood remained a significant and independent predictor of ACS likelihood. Findings from our study suggest that residential neighborhood constitutes an upstream factor to explain the observed healthcare disparity in ACS risk prediction, independent from known demographic, social, and economic determinants of health, which can inform future work on ACS prevention, in-hospital care, and patient discharge.

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Demographic, social, economic and geographic factors associated with long-term outcomes in a cohort of cardiac arrest survivors

Abstract: There are persistent long-term outcome differences in cardiac arrest survival based on sex, income, and geographic access acute care.

Cited by 9 publications

References 37 publications

Effect of Patient Demographics and Tracheostomy Timing and Technique on Patient Survival

Effect of Patient Demographics and Tracheostomy Timing and Technique on Patient Survival

Impact of insurance type on outcomes in cardiac arrest patients from 2004 to 2015: A nation-wide population-based study

Your neighborhood matters: A machine‐learning approach to the geospatial and social determinants of health in 9‐1‐1 activated chest pain

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