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Record W3188522932 · doi:10.1002/ejhf.2320

Cardiogenic shock centres for optimal care coordination and improving outcomes in cardiogenic shock

2021· letter· en· W3188522932 on OpenAlexaff
Ovidiu Chioncel, Marco Metra

Bibliographic record

VenueEuropean Journal of Heart Failure · 2021
Typeletter
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsCardiogenic shockMedicineShock (circulatory)Heart failureCardiologyInternal medicineIntensive care medicineMyocardial infarction

Abstract

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This article refers to ‘Impact of hospital transfer to hubs on outcomes of cardiogenic shock in the real world’ by D.Y. Lu et al., published in this issue on pages 1927–1937. Cardiogenic shock (CS) is a complex multifactorial clinical syndrome, developing as a continuum, and progressing from the initial insult to the subsequent occurrence of organ failure and death.1 Despite advanced management, including aetiological treatment and mechanical circulatory support (MCS), CS represents the most severe manifestation of acute heart failure2 with in-hospital mortality varying between 30–50%, depending on the underlying aetiology.1, 3-5 CS management remains challenging and substantial investments in research and development have not yielded proof of efficacy and safety for most of the therapies tested. Evidence from randomized trials is limited, mostly because small numbers of patients are recruited, with only approximately 2000 patients being randomized in CS trials.4 In addition, blinding is often not possible, and the primary endpoints often differ among the studies. Furthermore, designing new outcome trials in CS remains particularly challenging in this critical and very costly scenario in cardiology. In these conditions, gaps in evidence are extensive in terms of disposition decisions, use of haemodynamic monitoring, and timely deployment of interventions.1, 3-5 As a result of limited evidence base and inequal distribution of facilities there is a wide heterogeneity of patterns of care between the hospitals treating CS, which may delay timely deployment of appropriate resources or may delay the patient transfer to an experienced centre for haemodynamic support or definitive intervention. Recently, new approaches to care of CS patients have focused on mechanisms beyond medical therapies per se, implying that CS management should consider appropriate organization of the health care services in order to facilitate optimal care coordination and to minimize time delay.6 There is growing interest for developing CS centres and CS teams using a standardized multidisciplinary team-based approach in the management of CS. The role of CS teams is to facilitate timely diagnosis, appropriate use of invasive haemodynamics, revascularization strategies and implementation of MCS or other advanced therapies6, 7 (Figure 1, Table 1). In this issue of the Journal, Lu et al.8 presented the results of a post-hoc analysis aimed to describe baseline characteristics, treatment pattern and in-hospital outcomes of patients with CS stratified by type of hospital admission and transfer status. The authors used data from the Nationwide Readmissions Database (NRD) from 2010–2014 and identified a total of 412 667 hospitalizations for CS. Centres receiving any interhospital transfers for CS were classified as ‘CS hubs’, those without transfers were classified as ‘CS spokes’. Also, hospitals were classified as hubs or spokes based on annual procedural volumes for CS-related procedures. Using NRD variables/ICD-9-CM coding and administrative queries to ensure fidelity of data pertaining to hub-to-spoke transfers, authors studied three cohorts of patients: direct admission to spoke hospital (Cohort A), direct admission to hub (Cohort B) and interhospital transfer from spoke to hub (Cohort C). The primary outcome was in-hospital mortality. Secondary outcomes included length of stay, cost and 30-day readmission rate. Of hospitals treating CS, 70.6% were classified as spoke and 29.4% were classified as hub. ‘CS hubs’ were larger, teaching hospitals and more likely to be in the highest tertiles of procedural volume, including percutaneous coronary intervention (PCI), and coronary artery bypass graft (CABG), right heart catheterization (RHC) diagnosis, MCS and more advanced therapies, such as left ventricular assist device (LVAD) and transplant. Of a total number of 412 667 CS admissions, 31.7% were direct admissions to a spoke hospital (Cohort A), 61.4% were direct admissions to a hub hospital (Cohort B) and 7.0% of hospitalizations involved interhospital transfer from spoke to a hub hospital (Cohort C). Overall, in-hospital mortality was 41.6%, being higher at spoke hospitals (47.8%) and lower at hub hospitals, both for directly admitted (39.3%, P < 0.01) and transferred patients (33.4%, P < 0.01). Despite lower mortality, CS patients directly admitted or transferred to hub hospital had longer lengths of stay, higher rates of procedural-related complications such as major bleeding, stroke, vascular complications and acute kidney injury (AKI), and finally higher costs and higher 30-day readmissions.8 In multivariable analysis, direct admission to a hub hospital [odds ratio (OR) 0.86, 95% confidence interval (CI) 0.84–0.89] and interhospital transfer to a hub hospital (OR 0.72, 95% CI 0.69–0.76), revascularization with PCI or CABG and use of RHC were independently associated with significantly lower mortality. In contrast, MCS use and other index admission characteristics, such as acute myocardial infarction (AMI), cardiac arrest, mechanical ventilation, and AKI requiring dialysis, were associated with significantly higher mortality. The authors should be congratulated for this research, which is the first to study the impact of admission and transfer status on in-hospital mortality in CS. One of the major strengths of the present study was the use of a large, real-world sample of CS hospitalizations to assess the impact of transfer hubs on a national level. The authors reported a significantly lower in-hospital mortality for CS patients treated at hub hospitals, for both directly admitted and transferred patients, and this represents the most important finding in present study. Characteristics of the CS hub centres, mentioned in the manuscript, certainly contribute to this result, since hospital volume9, 10 and cardiovascular procedural volumes11, 12 have been consistently associated with improved clinical outcomes in CS in several studies. Also, the survival benefit reported for transferred patients has a strong component of ‘patient selection’, that may actually represent a critical component of CS transfer decision-making. Despite of a higher baseline comorbidity index, patients who are selected for transfer by CS multidisciplinary teams may still be deemed more ‘salvageable’, as suggested by the significantly younger age of transferred patients. In this sense, the role of the CS team is to efficiently triage ‘selected patients’ to an appropriate care location in order to facilitate specific interventions tailored to the aetiology and pathophysiology of CS (Figure 1). Transferring patients who are too sick or out of the window of benefit may be futile, while careful selection for transfer of targeted patients who may be potentially candidates for advanced therapies, may contribute to better outcomes. However, since the reason for transfer has not been investigated in the study, ‘patient selection’, as a strong component of the mortality benefit, remains only hypothesis-generating. A lower length of stay at spoke hospitals should be cautiously interpretated. In a large European registry,13 50% of CS deaths occurred in the first day of admission and this earlier mortality rate may explain the overall lower length of stay at spoke hospitals. In addition, very complex CS patients are transferred away from spoke hospitals to hubs for their longer stays. Interesting, only 24.7% of CS hospitalizations were associated with AMI, demonstrating once again changing of the epidemiological landscape of CS, with a decline in the prevalence of AMI over the past two decades, in parallel with an increase of CS of other aetiologies.1, 14, 15 A lower mortality was observed in AMI-CS patients when directly admitted or transferred to hub hospitals, a finding only partially explained by the protective effect related to PCI and especially CABG. In AMI-CS, the association between lower mortality and direct admission or transfer to hub hospitals remains significant even after adjusting for revascularization procedures, suggesting that, beyond procedural resources, a combination of appropriate ‘patient selection’ and medical expertise may have a potential role. To note, the magnitude of the improved mortality in AMI-CS was lower when compared with CS of other aetiologies, despite more frequent utilization of MCS in the AMI-CS subgroup. Right heart catheterization was independently associated with a survival benefit (OR 0.61) regardless of disposition decision. Although clinical trials have not shown a survival benefit for the empiric use of RHC in an all-comer population, in two large retrospective studies the use of RHC for haemodynamic monitoring in CS was associated with improved survival over time which may reflect better selection of patients or better use of information to guide therapies.16, 17 To note, all recent CS team protocols include obligatory RHC with subsequent assessment of the robust haemodynamic markers, such as cardiac power output and pulmonary artery pulsatility index.7 The survival benefit of RHC is in contrast with utilization of MCS which was associated with higher in-hospital mortality, and this result is not surprising since MCS is addressed to the most severe CS patients, and it was beyond the scope of the study to primarily analyse the independent relationship between use of MCS and mortality. The appropriate MCS device for a given CS stage is of utmost importance to maximize the survival benefit while minimizing the risks.1, 4 MCS devices should be tailored to patient profile to offer the highest chance of haemodynamic augmentation, and this process can be facilitated by the CS teams, selecting the right patient for the right device. Also, given the high vascular and bleeding complications and substantial costs associated with MCS, its judicious use to carefully selected CS patients is critical.1, 3-5 The inference of in-hospital mortality with organization of care and disposition decisions within regionalized system of care CS network is only partially explained by facilities and expertise of CS hub centres, and may be due to many confounders not investigated in the present study. Individual patient transfer criteria and timing, type and intensity of drug therapy (in particular type and dose of inotropes and vasopressors), organ function biomarkers, haemodynamic profile, and the exact timing of procedures in relation to admission and transfer, are several limitations of the current research that diminish the understanding of transfer decision-making and patient selection.8 However, in spite of limitations, the results of the current research suggest a clear signal of survival benefit for these critically ill patients when referred to a CS network with appropriate organization in terms of facilities, staff allocation, expertise and standardized protocols. Frequent training and quality improvement should be incorporated into CS teams to sustain adequate clinical and procedural proficiency.6, 8 Although, an accepted threshold for cost-effectiveness has not been ‘politically specified’, the extensive resources needed to maintain CS teams require constant institutional administrative support, sufficient funding and staffing, and continuous documentation by cost-effective analysis.6 Also, further research, collecting the full diversity of post-discharge outcomes,18 including change in quality of life, or referrals to palliative care, is obviously needed. A standardized CS team-based multidisciplinary care in the context of a network of regionalized care system may serve as a roadmap for future large-scale studies evaluating care pathways aimed to deliver quality improvement initiatives in order to improve outcomes in patients with CS. Conflict of interest: none declared.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.209
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations7
Published2021
Admission routes1
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