Association of socioeconomic status in the incidence, quality-of-care metrics, and outcomes for patients with cardiogenic shock in a pre-hospital setting

Abstract

Aims The relationship between lower socioeconomic status(SES) and poor cardiovascular outcomes is well-described, however there exists a paucity of data exploring this association in cardiogenic shock(CS). This study aimed to investigate whether any disparities exist between SES and the incidence, quality-of-care or outcomes of CS patients attended by emergency medical services(EMS). Methods and results This population-based cohort study included consecutive patients transported by EMS with CS between January 1st 2015 and June 30th 2019 in Victoria, Australia. Data were collected from individually linked ambulance, hospital and mortality datasets. Patients were stratified into SES quintiles using national census data produced by the Australia Bureau of Statistics. A total of 2 628 patients were attended by EMS for CS. The age-standardized incidence of CS amongst all patients was 11.8(95% confidence interval [CI], 11.4–12.3) per 100 000 person-years, with a stepwise increase from the highest to lowest SES quintile(lowest quintile 17.0 vs. highest quintile 9.7 per 100 000 person-years, p-trend < 0.001). Patients in lower SES quintiles were less likely to attend metropolitan hospitals and more likely to be received by inner regional and remote centers without revascularization capabilities. A greater proportion of the lower SES groups presented with CS due to non-ST elevation myocardial infarction(NSTEMI) or unstable angina pectoris(UAP), and overall were less likely to undergo coronary angiography. Multivariable analysis demonstrated an increased 30-day all-cause mortality rate in the lowest 3 SES quintiles when compared to the highest quintile. Conclusion This population-based study demonstrated discrepancies between SES status in the incidence, care-metrics and mortality rates of patients presenting to EMS with CS. These findings outline the challenges in equitable healthcare delivery within this cohort.

Healthcare and economic cost burden of emergency medical services treated non-traumatic shock using a population-based cohort in Victoria, Australia

“…Participant demographics, pre-existing comorbid conditions, prehospital observations and paramedic-initiated interventions were obtained from ambulance ePCR data. Socioeconomic status was determined by the Index of Relative Scio-Economic Disadvantage Score (IRSD), a validated measure that ranks individual postcodes into deciles of relative disadvantage 13. The score is derived from Census data that includes household income, education level, employment status, occupation, housing ownership and non-English-speaking background 14.…”

“…The score is derived from Census data that includes household income, education level, employment status, occupation, housing ownership and non-English-speaking background 14. For this analysis, we divided the IRSD into quintiles 13. Geographical remoteness was determined using the residential area postcode of each event via the Accessibility and Remoteness Index of Australia—a geographical accessibility index that divides Australia into five classes of remoteness (‘major city’, ‘inner regional’, ‘outer regional’, ‘remote’ and ‘very remote’) to reflect relative access to services 15.…”

“…Socioeconomic status was determined by the Index of Relative Scio-Economic Disadvantage Score (IRSD), a validated measure that ranks individual postcodes into deciles of relative disadvantage. 13 The score is derived from Census data that includes household income, education level, employment status, occupation, housing ownership and non-English-speaking background. 14 For this analysis, we divided the IRSD into quintiles.…”

“…14 For this analysis, we divided the IRSD into quintiles. 13 Geographical remoteness was determined using the residential area postcode of each event via the Accessibility and Remoteness Index of Australia—a geographical accessibility index that divides Australia into five classes of remoteness (‘major city’, ‘inner regional’, ‘outer regional’, ‘remote’ and ‘very remote’) to reflect relative access to services. 15 Due to low numbers of patients from ‘remote’ or ‘very remote’ regions, these groups were combined with the ‘outer regional’ group for this study.…”

Karakteristik Penderita Syok Kardiogenik Tahun 2022

“…Syok kardiogenik ditandai dengan nadi lemah, penurunan tekanan rerata arteri (MAP) <65 mmHg, peningkatan LVEDP (>18mmHg), dan penurunan curah jantung (CO <3,2 L/menit). Syok kardiogenik disebabkan oleh sindrom koroner akut dan komplikasi mekanik yang ditimbulkannya (seperti ruptur dinding ventrikel, ruptur septum interventrikular (IVS), dan ruptur chordae), kelainan katup jantung, dan gagal jantung yang berat pada gangguan miokard lainnya (3).…”