Transferring Patients with Intracerebral Hemorrhage Does Not Increase In-Hospital Mortality

Vahidy, Farhaan S; Nguyen, Claude; Albright, Karen C.; Boehme, Amelia K; Mir, Osman; Sands, Kara; Savitz, Sean I

doi:10.1371/journal.pone.0159174

Cited by 10 publications

(5 citation statements)

References 28 publications

(15 reference statements)

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“…In patients with intracerebral hemorrhage, Vahidy et al observed no difference in functional outcomes and mortality between direct-admit and transferred patients. 30 Naval and Carhuapoma also saw no differences in in-hospital mortality rates between the patients with intracerebral hemorrhage who were directly admitted or transferred. 31 Holland et al showed that the mortality rate in transferred patients with spinal cord injury decreased in their study period, while the frequency of surgical management before transfer increased.…”

Section: In-hospital Mortalitymentioning

confidence: 87%

Interhospital transfer status for spinal metastasis patients in the United States is associated with more severe clinical presentations and higher rates of inpatient complications

Ramos

Park²,

McCray³

et al. 2021

Neurosurgical Focus

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OBJECTIVE In patients with metastatic spinal disease (MSD), interhospital transfer can potentially impact clinical outcomes as the possible benefits of transferring a patient to a higher level of care must be weighed against the negative effects associated with potential delays in treatment. While the association of clinical outcomes and transfer status has been examined in other specialties, the relationship between transfer status, complications, and risk of mortality in patients with MSD has yet to be explored. The purpose of this study was to examine the impact of transfer status on in-hospital mortality and clinical outcomes in patients diagnosed with MSD. METHODS The National (Nationwide) Inpatient Sample (NIS) database was retrospectively queried for adult patients diagnosed with vertebral pathological fracture and/or spinal cord compression in the setting of metastatic disease between 2012 and 2014. Demographics, baseline characteristics (e.g., metastatic spinal cord compression [MSCC] and paralysis), comorbidities, type of intervention, and relevant patient outcomes were controlled in a multivariable logistic regression model to analyze the association of transfer status with patient outcomes. RESULTS Within the 10,360 patients meeting the inclusion and exclusion criteria, higher rates of MSCC (50.2% vs 35.9%, p < 0.001) and paralysis (17.3% vs 8.4%, p < 0.001) were observed in patients transferred between hospitals compared to those directly admitted. In univariable analysis, a higher percentage of transferred patients underwent surgical intervention (p < 0.001) when compared with directly admitted patients. After controlling for significant covariates and surgical intervention, transferred patients were more likely to develop in-hospital complications (OR 1.34, 95% CI 1.18–1.52, p < 0.001), experience prolonged length of stay (OR 1.33, 95% CI 1.16–1.52, p < 0.001), and have a discharge disposition other than home (OR 1.70, 95% CI 1.46–1.98, p < 0.001), with no significant difference in inpatient mortality rates. CONCLUSIONS Patients with MSD who were transferred between hospitals demonstrated more severe clinical presentations and higher rates of inpatient complications compared to directly admitted patients, despite demonstrating no difference in in-hospital mortality rates.

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Section: In-hospital Mortalitymentioning

confidence: 87%

Interhospital transfer status for spinal metastasis patients in the United States is associated with more severe clinical presentations and higher rates of inpatient complications

Ramos

Park²,

McCray³

et al. 2021

Neurosurgical Focus

View full text Add to dashboard Cite

show abstract

“…Illness severity, time-sensitive variables, and detailed information related to the transfer may be effect modifiers, and may have contributed to the outcomes of patients examined; due to limitations of the database, they could not be modelled, and should be investigated in future studies. The patient’s neurological status and intracranial haemorrhage as the reason for transfer, for example, have an effect on mortality but are not modeled here (11). Sixth, subgroup analysis would have been helpful to assess heterogeneity of treatment effect; however, relevant variables such as timing of the transfer and differences between the sending and receiving hospitals were not available in the database.…”

Section: Discussionmentioning

confidence: 99%

“…The assumption has been that IHT of critically ill patients will result in improved outcomes, and that the risks and costs of transportation are outweighed by the benefits (1–3). The risk-benefit ratio appears to strongly favor transfer for some conditions, such as acute coronary syndrome, stroke, and trauma (4–11), but the ratio is less clear (and often remains to be studied) for many others (12, 13). IHT has been shown to cause delays in administering timely treatments for sepsis, and there is recent concern that IHT could be a vector for nosocomial pathogens(14, 15).…”

Section: Introductionmentioning

confidence: 99%

Outcomes of Ventilated Patients With Sepsis Who Undergo Interhospital Transfer: A Nationwide Linked Analysis*

Rush

Tyler

Stone

et al. 2018

Critical Care Medicine

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“…Therefore, it would be difficult to implement any of these ML algorithms at other institutions where the PACS software and CT imaging protocols for ICH patients may have subtle differences. Many critically ill ICH patients at larger academic centers are often transferred from smaller outside hospitals where the initial head CT was performed (Vahidy et al, 2016). A successful algorithm is one that can be implemented at both tertiary centers and smaller hospitals with differing software.…”

Section: Limitations and Future Directions For ML Algorithms In Cereb...mentioning

confidence: 99%

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Gilotra,

Swarna,

Mani

et al. 2023

Front. Hum. Neurosci.

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IntroductionCerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms.MethodsWe review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice.ResultsThe incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.ConclusionThe implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

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Transferring Patients with Intracerebral Hemorrhage Does Not Increase In-Hospital Mortality

Cited by 10 publications

References 28 publications

Interhospital transfer status for spinal metastasis patients in the United States is associated with more severe clinical presentations and higher rates of inpatient complications

Interhospital transfer status for spinal metastasis patients in the United States is associated with more severe clinical presentations and higher rates of inpatient complications

Outcomes of Ventilated Patients With Sepsis Who Undergo Interhospital Transfer: A Nationwide Linked Analysis*

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

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