Prehospital stroke-scale machine-learning model predicts the need for surgical intervention

A Multi-Parametric Approach for Characterising Cerebral Haemodynamics in Acute Ischaemic and Haemorrhagic Stroke

BACKGROUND AND PURPOSE

Early differentiation between acute ischaemic (AIS) and haemorrhagic stroke (ICH), based on cerebral and peripheral hemodynamic parameters, would be advantageous to allow for pre-hospital interventions. In this preliminary study, we explored the potential of multiple parameters, including dynamic cerebral autoregulation, for phenotyping and differentiating each stroke sub-type.

METHODS

Eighty patients were included with clinical stroke syndromes confirmed by computed tomography within 48 h of symptom onset. Continuous recordings of bilateral cerebral blood velocity (transcranial Doppler ultrasound), end-tidal CO2 (capnography), electrocardiogram (ECG), and arterial blood pressure (ABP, Finometer) were used to derive 67 cerebral and peripheral parameters.

RESULTS

A total of 68 patients with AIS (mean age 66.8 ± SD 12.4 years) and 12 patients with ICH (67.8 ± 16.2 years) were included. The median ± SD NIHSS of the cohort was 5 ± 4.6. Statistically significant differences between AIS and ICH were observed for (i) an autoregulation index (ARI) that was higher in the unaffected hemisphere (UH) for ICH compared to AIS (5.9 ± 1.7 vs. 4.9 ± 1.8 p = 0.07); (ii) coherence function for both hemispheres in different frequency bands (AH, p < 0.01; UH p < 0.02); (iii) a baroreceptor sensitivity (BRS) for the low-frequency (LF) bands that was higher for AIS (6.7 ± 4.2 vs. 4.10 ± 2.13 ms/mmHg, p = 0.04) compared to ICH, and that the mean gain of the BRS in the LF range was higher in the AIS than in the ICH (5.8 ± 5.3 vs. 2.7 ± 1.8 ms/mmHg, p = 0.0005); (iv) Systolic and diastolic velocities of the affected hemisphere (AH) that were significantly higher in ICH than in AIS (82.5 ± 28.09 vs. 61.9 ± 18.9 cm/s), systolic velocity (p = 0.002), and diastolic velocity (p = 0.05).

CONCLUSION

Further multivariate modelling might improve the ability of multiple parameters to discriminate between AIS and ICH and warrants future prospective studies of ultra-early classification (<4 h post symptom onset) of stroke sub-types.

Physiological Variability during Prehospital Stroke Care: Which Monitoring and Interventions Are Used?

Prehospital care is a fundamental component of stroke care that predominantly focuses on shortening the time between diagnosis and reaching definitive stroke management. With growing evidence of the physiological parameters affecting long-term patient outcomes, prehospital clinicians need to consider the balance between rapid transfer and increased physiological-parameter monitoring and intervention. This systematic review explores the existing literature on prehospital physiological monitoring and intervention to modify these parameters in stroke patients. The systematic review was registered on PROSPERO (CRD42022308991) and conducted across four databases with citation cascading. Based on the identified inclusion and exclusion criteria, 19 studies were retained for this review. The studies were classified into two themes: physiological-monitoring intervention and pharmacological-therapy intervention. A total of 14 included studies explored prehospital physiological monitoring. Elevated blood pressure was associated with increased hematoma volume in intracerebral hemorrhage and, in some reports, with increased rates of early neurological deterioration and prehospital neurological deterioration. A reduction in prehospital heart rate variability was associated with unfavorable clinical outcomes. Further, five of the included records investigated the delivery of pharmacological therapy in the prehospital environment for patients presenting with acute stroke. BP-lowering interventions were successfully demonstrated through three trials; however, evidence of their benefit to clinical outcomes is limited. Two studies investigating the use of oxygen and magnesium sulfate as neuroprotective agents did not demonstrate an improvement in patient's outcomes. This systematic review highlights the absence of continuous physiological parameter monitoring, investigates fundamental physiological parameters, and provides recommendations for future work, with the aim of improving stroke patient outcomes.

Priorities for developing stroke care in Ireland from the perspectives of stroke survivors, family carers and professionals involved in stroke care: A mixed methods study

INTRODUCTION

Increasing numbers of people are living with stroke, due to population ageing and improved survival, leading to a need for evidence to inform future policy decision-making. This study aimed to engage with stakeholders in Ireland to identify priorities for stroke services development.

METHODS

A sequential mixed methods design was used. Phase 1 (qualitative) was exploratory, involving initial priority gathering via an online qualitative survey and interviews, with stroke survivors, family/main carers, and professionals working in stroke care. Framework analysis was used to generate a long-list of improvements to stroke services. Phase 2 involved a quantitative survey, where stakeholders selected five priority improvements from the long-list. Results were discussed in a stakeholder meeting.

RESULTS

In-depth interviews were completed with 18 survivors, 13 carers and 8 professionals, while 80 professionals took part in a qualitative survey (phase 1). Priority areas of care were identified and a long-list of 45 priority improvements was generated. In phase 2, 34 survivors, 19 family carers and 42 professionals completed a survey. The highest priority improvements (selected by >20% of respondents) were access to specialist neuro-rehabilitation, ongoing support for life after stroke, recruitment/retention of specialist staff, improved information and support for health system navigation, and access to specialist acute care. Stroke survivors/carers prioritised exploring ways to improve access for strokes with atypical presentation, while professionals prioritised specialist inpatient rehabilitation and early supported discharge. Neither group prioritised stroke prevention. Based on discussions in the stakeholder meeting (n = 12), it was decided that support for mental health should also be included as a priority.

DISCUSSION

The development of stroke services benefits from exploring the priorities of those receiving and delivering stroke care. Findings emphasise the need for equitable access to high quality adequately-staffed services, particularly post-discharge, that are easy to navigate, with good communication, and effective information provision.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.