A Qualitative Inquiry Into Patient Reported Factors That Influence Time From Stroke Symptom Onset to Hospitalization

Development of an Acute Stroke Care Seeking Framework

ABSTRACT

BACKGROUND: A multitude of variables influence an individual's decision to seek care in emergency situations. By recognizing these variables and their impact on the timeline of an individual seeking care for a stroke, nurses have an opportunity to positively affect the outcomes of stroke within the community. The purpose of this narrative review was to develop a research framework describing the variables involved in care seeking during an acute stroke. METHODS: Using a theory synthesis methodology that included variable identification and the establishment of relationships between variables based on existing literature, a framework describing variables relevant to acute stroke care seeking behavior was developed. RESULTS: Fourteen recently published studies reported significant variables related to seeking emergency medical care during the hyperacute phase of a stroke. Eight variables were identified and characterized as either promoters or distractors. Promoters led an individual to seek acute stroke care earlier, such as perceived symptom severity, stroke knowledge, and the presence of others. Distractors led an individual to delay seeking acute stroke care and resulted in later hospital arrival times, such as a lack of social network or resources, comorbid conditions, and incongruity with the local health system. CONCLUSION: Although individual decision making is exceedingly complex and varies by individual and situation, the developed acute stroke care seeking framework may provide a basis on which to develop stroke awareness programs and interventions targeted at individuals at risk for delayed acute stroke care.

Symptoms at stroke onset as described by patients: a qualitative study

BACKGROUND

Stroke is a common and severe disease that requires prompt care. Symptom expressions as one-sided weakness and speech difficulties are common and included in public stroke campaigns. For some patients stroke can present with subtle and less common symptoms, difficult to interpret. The symptom severity assessed by the National Institutes of Health Stroke Scale has decreased, and symptoms at onset may have changed. Therefore, we aimed to investigate how patients describe their symptoms at the onset of a first-time stroke.

METHODS

The study used a qualitative descriptive design and conventional content analysis. Data were collected through recorded interviews with 27 patients aged 18 years and older hospitalised with a first-time stroke between October 2018 and April 2020. Data were analysed on a manifest level.

RESULTS

Symptoms at stroke onset were presented in two themes: Altered Reality and Discomfort and Changed Body Functions and described in five categories. Various types of symptoms were found. All symptoms were perceived as sudden, persistent, and never experienced before and this appear as a "red thread" in the result. Regardless of symptom expressions, no specific symptom was described as more severe than another.

CONCLUSIONS

Stroke symptoms were described with a variety of expressions. Many described complex symptoms not typical of stroke, which can make it difficult to recognise the symptoms as a stroke and delay medical care. Public stroke campaigns should emphasize the importance of seeking medical care at the slightest suspicion of stroke and could be designed to help achieve this.

Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point

BACKGROUND AND PURPOSE

Stroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medical Dispatcher (EMD) and in Denmark approximately 50% of stroke patients arrive at the hospital within the time-to-treatment. An automatic speech recognition software (ASR) can increase the recognition of Out-of-Hospital cardiac arrest (OHCA) at the EMS by 16%. This research aims to analyse the potential impact an ASR could have on stroke recognition at the EMS Copenhagen and the related treatment.

METHODS

Stroke patient data (n = 9049) from the years 2016-2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristics such as stroke type, sex, age, weekday, time of day, year, EMS number contacted, and treatment. The possible increase in stroke detection through an ASR and the effect on stroke treatment was calculated based on the impact of an existing ASR to detect OHCA from CORTI AI.

RESULTS

The Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation between stroke detection and treatment and thrombolysis. While the association analysis showed a moderate correlation between stroke detection and treatment the correlation to the other treatment options was weak or very weak. A potential increase in stroke detection to 61.19% with an ASR and hence an increase of thrombolysis by 5% in stroke patients calling within time-to-treatment was predicted.

CONCLUSIONS

An ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevant for females, younger stroke patients, calls received through the 1813-Medical Helpline, and on weekends.

TRIAL REGISTRATION

This study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122).

Patient-reported factors associated with early arrival for stroke treatment

OBJECTIVE

Timely evaluation and initiation of treatment is the key for improving stroke outcomes, although minimizing the time from symptom onset to the first contact with healthcare professionals remains a challenge. We aimed to identify patient-related factors associated with early hospital arrival.

MATERIALS AND METHODS

In this cross-sectional survey, we included patients with stroke or transient ischemic attack admitted directly to one of two noncomprehensive stroke units or transferred to the units from comprehensive stroke centers in the Capital Region of Denmark. Patient-reported factors associated with early hospital arrival were analyzed using multivariable logistic regression analysis adjusted for age, sex, education, living arrangement, brain location of the stroke, stroke severity, patient-perceived symptom severity, history of prior stroke, stroke risk factors, and knowledge of stroke symptoms.

RESULTS

In total, 479 patients with acute stroke were included (median age 74 (25th-75th percentile, 64-80), 40% women), of whom 46.4% arrived within 180 min of symptom onset. Factors associated with early hospital arrival were patients or bystanders choosing emergency medical service (EMS) for the first contact with a medical professional (adjusted odds ratio (OR), 3.41; 95% confidence interval, CI [1.57, 7.35]) or the patient's perceived symptom severity above the median score of 25 on a 100-point verbal scale (adjusted OR, 2.44; 95% CI [1.57, 3.82]). Living alone reduced the likelihood of early arrival (adjusted OR, 0.53; 95% CI [0.33, 0.86]).

CONCLUSIONS

Only when patients perceived symptoms as severe or when EMS was selected as the first contact, early arrival for stroke treatment was ensured.