Characterisation of young stroke presentations, pathways of care, and support for ‘invisible’ difficulties: a retrospective clinical audit study
Michaela Grech
A * , Toni Withiel B , Marlena Klaic C , Caroline A. Fisher B , Leonie Simpson B and Dana Wong A
A
B
C
Abstract
Young stroke survivors are likely to be discharged home from acute hospital care without rehabilitation more quickly than older survivors, but it is not clear why. File-audit studies capturing real-world clinical practice are lacking for this cohort. We aimed to compare characteristics and care pathways of young and older survivors and describe stroke presentations and predictors of pathways of care in young survivors (≤45 years), including a focus on care received for ‘invisible’ (cognitive, psychological) difficulties.
A retrospective audit of 847 medical records (67 young stroke survivors, mean age = 36 years; 780 older patients, mean age = 70 years) was completed for stroke survivors admitted to an Australian tertiary hospital. Stroke characteristics and presence of cognitive difficulties (identified through clinician opinion or cognitive screening) were used to predict length of stay and discharge destination in young stroke survivors.
There were no differences in length of stay between young and older survivors, however, young stroke survivors were more likely to be discharged home without rehabilitation (though this may be due to milder strokes observed in young stroke survivors). For young stroke survivors, stroke severity and age predicted discharge destination, while cognitive difficulties predicted longer length of stay. While almost all young survivors were offered occupational therapy and physiotherapy, none received psychological input (clinical, health or neuropsychology).
Cognitive and psychological needs of young stroke survivors may remain largely unmet by a service model designed for older people. Findings can inform service development or models of care, such as the new Australian Young Stroke Service designed to better meet the needs of young survivors.
Keywords: cognitive difficulties, invisible difficulties, length of stay, pathways of care, rehabilitation, stroke, working age, young stroke survivors.
Introduction
Although stroke is historically considered a disease of older age, approximately one-quarter of all strokes occur in younger, working-aged adults (Marini et al. 2011; Andrew et al. 2014; Lannin et al. 2017). Stroke is also increasing in young people (Deloitte Access Economics 2020). Compared with older individuals, young survivors often present with more unusual aetiologies, such as burst arteriovenous malformations, with unknown or non-atherosclerotic (inflammatory) causes (Maaijwee et al. 2014; Maino et al. 2015). Although the severity of stroke symptoms tends to be milder in younger survivors on acute presentation (Lutski et al. 2017), they still experience long-term social challenges, including loss or change of professional, family and community roles (Teasell et al. 2000; Röding et al. 2003). As a result, young survivors often require specialised treatment and rehabilitation that recognises their psychosocial comorbidities. However, younger survivors experience no or fractured rehabilitation, follow-up and support for these psychosocial challenges (Wolfenden and Grace 2015). This is despite the potentially long period of productive years in which these challenges will have an impact on their participation in meaningful life roles such as work and parenting (Jacobs et al. 2002; Smajlović 2015).
Past research has also shown that young survivors are commonly discharged home quickly, with fewer bed-stay days than older patients (Al-Jadid and Robert 2010). Variables such as age, stroke severity and cognition are associated with quicker discharge in some studies but not others, and it is not fully understood why younger survivors are discharged quicker than older survivors (Diamond et al. 1996; Tooth et al. 2005; Paker et al. 2010; Mutai et al. 2012).
Predictors of length of stay and discharge destination
Length of stay is defined as the number of days a patient is admitted in the hospital and has been used to measure clinical outcomes given it is associated with short-term prognosis post-stroke (Lin et al. 2009). Younger survivors tend to spend less time in acute wards and in rehabilitation given their milder presentations (Teasell et al. 2000) and show a quicker recovery, potentially due to better brain compensation mechanisms and neuronal plasticity (Park and Bischof 2013; Ghasemian-Shirvan et al. 2020). Previous findings have demonstrated that lower levels of disability pre-stroke are also associated with better recovery outcomes, including shorter length of stay (Kwok et al. 2012); however, this has not been explored for other outcomes, such as discharge destination, for younger survivors. Including measures of stroke severity in stroke trials has been found to significantly improve models that predict mortality outcomes (Fonarow et al. 2012). Given that variables such as stroke severity and treatment type (such as thrombolysis – breaking down clots with intravenous drugs – or surgical clot retrieval) are known to influence recovery, it is expected that these factors might also influence younger patients’ length of stay and discharge destination (Chiong et al. 2014; Emberson et al. 2014; Goyal et al. 2016; Downer et al. 2023). However, these factors have not been fully explored in younger cohorts.
Young survivors have been found to have reduced cognitive function compared with age-matched healthy controls (Waje-Andreassen et al. 2013). However, research has found that cognitive ability at discharge does not predict whether younger patients are discharged to rehabilitation facilities, rather than to home (Dutrieux et al. 2016). This raises questions about whether cognitive ability is overlooked when determining the most appropriate care pathways for young stroke patients (Mutai et al. 2012).
Provision of care
Young survivors report experiencing frustration at being treated in a rehabilitation model designed for older adults (Lawrence 2010; Röding et al. 2003) and dissatisfaction with a lack of ongoing support (Sadler et al. 2014). Since most individuals who experience a stroke are older, where the risk of physical disability is higher (Furlan et al. 2021), rehabilitation tends to focus on physical outcomes (Varona et al. 2004; Graham et al. 2011). However, younger survivors seldom report difficulties with mobility, and instead report greater unmet social, cognitive and psychological needs relative to their older counterparts, suggesting that current rehabilitation approaches do not adequately address the factors that are most important to younger patients (Kersten et al. 2002). For example, cognitive outcomes of young stroke survivors are often overlooked (Varona et al. 2004; Graham et al. 2011), which may affect recovery given post-stroke cognitive impairment is associated with poorer activity and participation outcomes (Stolwyk et al. 2021). Neuropsychological assessment following stroke can assist with identifying and characterising cognitive difficulties to inform personalised rehabilitation programs (Fabrizio et al. 2020). A lack of identification of cognitive impairment early in acute care prevents survivors from accessing neuropsychological rehabilitation later, which can negatively impact participation in meaningful life roles such as work (Withiel et al. 2019; Wong et al. 2021). Given these consequences, access to neuropsychological assessment examining a range of cognitive areas is needed. Younger survivors commonly experience emotional challenges adjusting to new limitations post-stroke, impacting their social participation (Edwards et al. 2017; Kapoor et al. 2019). Access to clinical or health psychology is crucial for providing support in emotional adjustment (Waje-Andreassen et al. 2013; Maaijwee et al. 2016), though the consistency of these services remains uncertain.
Separate recommendations for stroke management specific to young adults are lacking, with young and older patients receiving similar rehabilitation approaches. Tailored guidelines for treating and managing young stroke may help to address their specific needs and challenges. However, a deeper understanding of the current characteristics and pathways of care for young stroke patients is needed as a precursor to establishing such guidelines and service models. While a recent national audit has been conducted on the effectiveness of stroke care (Stroke Foundation 2021), to our knowledge, there are no existing audit studies focusing specifically on young stroke survivors in Australia.
The current study
This file audit study aimed to (1) compare the stroke characteristics and pathways of care for young and older survivors at a tertiary dedicated stroke treatment facility in Melbourne; (2) characterise stroke presentations and the provision of care within the young stroke cohort – in particular care provided for cognitive and psychosocial challenges, given their prevalence and impact; and (3) explore predictors of length of stay and discharge destination within the young stroke cohort. While Aim 1 compares stroke characteristics and care pathways between the younger and older groups, Aims 2 and 3 focus specifically on the younger cohort, addressing the paucity of research in this group, to inform future service delivery. Regarding Aim 1, we hypothesised that (H1) compared to older patients, young patients would present with milder stroke severity and a higher rate of unusual stroke aetiology, and (H2) have shorter lengths of stay and be more likely to be discharged home. Regarding Aim 2, it was predicted that (H3) young patients would predominantly receive physical allied-health interventions with limited provision of psychological, cognitive and social interventions (including neuropsychological assessments and clinical/health psychological therapy). For Aim 3, we hypothesised that (H4) length of stay and discharge destination in young patients would be predicted by age, stroke severity, pre-morbid functioning, acute treatment type and presence of cognitive difficulties.
Method
Design and setting
A retrospective file audit was conducted using a research database provided by the hospital and existing medical records. The database included demographic, stroke information, level of functional independence, length of stay in an acute ward and discharge destination. The file audit took place at a comprehensive stroke care unit, at a large tertiary trauma hospital in Melbourne, Australia. The local environment was a 20-bed acute stroke care unit which provides acute care to approximately 900 stroke patients across Victoria, Australia, each year. The unit uses blanket referrals to occupational therapy and physiotherapy, where all patients are automatically screened for rehabilitation. There are no dedicated psychologists in the unit, however, referrals to clinical psychologists and neuropsychologists within the hospital are made on a case-by-case basis. The sample also included patients who were managed in an acute neurology ward and intensive care unit (ICU). The study was approved by the health service’s Human Research Ethics Committee (MH-A2019011), and permission was obtained to use information in the dataset for the purposes of this study.
Participants
Participants were patients admitted to the hospital with a primary diagnosis of stroke between 1 January 2018 and 31 December 2018. This included patients who presented with first stroke or had experienced previous strokes. Consistent with previous research, patients who were 45 years and under were classified as ‘young’, while those older than 45 were classified as ‘older’ stroke patients (Griffiths and Sturm 2011; Marini et al. 2001). Patients below 18 years old or with transient ischaemic attack were excluded.
Data collection
During the acute presentation, a series of measures were routinely collected from all patients (both young and older) with new onset stroke. These measures were provided in the research database. Demographic and lifestyle characteristics of the patients were collected via the database.
Stroke severity was evaluated using the National Institute of Health Stroke Scale (NIHSS; Brott et al. 1989), where scores range from 0 to 42 (higher scores quantify greater severity).
Pre-morbid functioning was evaluated using the modified Rankin Scale (mRS; Farrell et al.1991), which measures levels of functional independence. Scores range from 0 (no symptoms) to 5 (severe disability, requiring continuous care). A separate category of 6 (deceased) is sometimes used when examining post-stroke functioning, however, given the mRS was not used for this purpose and there were no patients identified to have died prior to their admission, this category was not used.
Stroke location and whether acute management with intra-arterial therapy (catheter-based treatments to restore blood flow after ischaemic stroke, such as mechanical clot disruption) occurred were collated for patients. To classify stroke subtypes, the Oxford stroke classification scale was recorded. This categorises stroke into four subtypes according to symptoms: total anterior circulation infarct, partial anterior circulation infarct, posterior circulation infarct or lacunar infarct (Bamford et al. 1991). Aetiological subtype of stroke was also recorded using the Trial of Organin Acute Stroke Treatment (TOAST) classification system, whereby aetiological subtypes are classified as large-artery atherosclerosis, cardio-embolism, small-vessel occlusion, other determined aetiology or undetermined aetiology (Adams et al. 1993).
Length of stay was captured for all participants via the research database. Due to highly skewed data, length of stay was dichotomised by the auditor into short or long stay. A cut point of 5 days was set, which was based on the median value.
Finally, discharge destination was recorded as home with rehabilitation, home without rehabilitation, inpatient rehabilitation facility and other acute hospital. A fifth classification of death was also included to capture those who died before leaving hospital.
To better inform understanding of stroke presentations and pathways of care for young patients specifically, several additional lifestyle and demographic characteristics were extracted from the medical records of young stroke patients only, including pre-morbid employment, marital status and stroke risk factors. These variables were extracted by a single auditor (student researcher M. G.). The auditor reviewed the clinical records of all young patients twice to maximise reliability. The additional variables extracted for the young stroke patient group could not be extracted for older patients as the time estimated to extract these data was well beyond what was feasible for the project timeline. The study was also conducted prior to the roll-out of the hospital’s electronic medical record. As such, extraction of all file data needed to occur in a manual ‘by-hand’ method, of paper-based medical records.
Acute treatment type was extracted and classified as pharmacological (such as with aspirin, thrombolysis and antiplatelet drugs), surgical (such as through decompression surgery or mechanical clot retrieval) and comfort measures (strategies used to address patient discomfort, such as dressing and repositioning the patient, particularly when active treatment has stopped).
Records indicating the administration of cognitive screening tools was extracted and patients were classified as either ‘impaired’ or ‘not impaired’. Patients with scores below 80/100 on the Neuropsychiatry Unit Cognitive Assessment Tool (NUCOG; Walterfang et al. 2003), and below 25 on the Montreal Cognitive Assessment (MoCA; Nasreddine et al. 2005) and Mini-Mental State Examination (MMSE; Folstein et al. 1975) were classified as ‘impaired’. Informal commentary or mention of cognitive difficulties by treating clinicians was also recorded (such as ‘difficulty sustaining attention’, ‘difficulties with short-term memory’ or ‘unable to follow 2–3 step commands’). This included patients who also presented with aphasia (though the presence of aphasia in and of itself, in the absence of any other cognitive difficulties, was not classified as cognitive dysfunction). Cognitive assessments were coded by the auditor as either formal (i.e. based on the use of valid screening tools outlined above) or informal (i.e. based on clinician observation of cognitive difficulties) and recorded dichotomously (yes/no). Although formal assessments provided a quantitative indication of the presence (or absence) of cognitive impairment, as they were not used consistently on the ward, presence of cognitive difficulties was judged based on either formal assessment or informal mention.
Other data extracted from young survivors’ medical records were inpatient intervention type and frequency (total number of therapy contacts), classified as occupational therapy, physiotherapy, social work, speech pathology, music therapy, clinical psychology or neuropsychological assessment, and the number of outpatient referrals for allied health input, including referral for neuropsychological assessment.
Length of stay and discharge destination were collected via the research database. Predictors of length of stay and discharge destination included age (in years), stroke severity, premorbid functioning and acute treatment type.
Statistical analyses
An alpha value of 0.05 indicated statistical significance across all analyses. Demographic and stroke characteristics for patients were descriptively analysed.
For Aim 1, a series of independent samples t tests were used to explore differences in patient characteristics between young and older survivors on continuous outcome variables that were normally distributed. For categorical variables, the Chi-square test for independence was used when the assumption of expected cell frequencies was satisfied. Logistic regression analysis examined whether discharge destination was predicted by age grouping (young versus older).
For Aim 2, descriptive statistics were used to characterise stroke presentations and provision of care within young stroke survivors.
For Aim 3, logistic regression was planned for length of stay with five predictor variables (age, stroke severity, functional independence, acute treatment type and presence of cognitive difficulties based on either formal assessment or informal mention). As only 38 patients were assessed on their stroke severity, stroke severity was entered as the single predictor for length of stay (as the assumption for minimal cell frequencies would have been violated and the sample size too small to run the analysis with multiple predictors; Tabachnick and Fidell 2013). A test of the full binary logistic model with the remaining four predictors, including age (in years), functional independence (mRS scores), acute treatment type and presence of cognitive difficulties, was also conducted. Given the lack of well-established predictors of length of stay in this population, predictor variables were simultaneously entered into the regression model (Tabachnick and Fidell 2013). Pharmacological treatment (yes/no) was taken as the measure of treatment type given it was the most applicable across both ischaemic and haemorrhagic strokes. Finally, multinomial logistic regression was also planned for discharge destination with five predictor variables (age, stroke severity, pre-morbid functional independence, acute treatment type and presence of cognitive difficulties). However, the sample size needed to predict multiple discharge destinations was larger compared to the analysis predicting length of stay (which had only two levels). Therefore, the inclusion of mRS scores for premorbid independence, treatment type and the presence of a noted cognitive impairment violated the assumptions for logistical regression analyses, in which the expected frequency for each outcome should be five or more for at least 80% of cases. These three variables were excluded from this analysis. Stroke severity and age (in years) were, therefore, entered into the model predicting discharge destination, as including only these two variables did not violate statistical assumptions.
A summary of the study aims, hypotheses and the variables and measures relevant to each hypothesis is presented in Table 1.
| Aims and hypotheses | Variable | Variable type | Measure | |
|---|---|---|---|---|
| Aim 1: compare stroke characteristics and pathways of care in young and older survivors | Pre-morbid and current functional independence | Continuous | Modified Rankin Scale (mRS), scores range from 0 (no symptoms) to 6 (deceased) | |
| H1: younger patients will present with milder strokes and a higher rate of more unusual stroke aetiology | Stroke severity | Continuous | NIHSS, scores range from 0 to 42. | |
| Stroke aetiology | Categorical | Stroke aetiology is classified as either: large-artery atherosclerosis, cardio-embolism, small-vessel occlusion, other determined aetiology or undetermined aetiology | ||
| H2: younger patients will have shorter lengths of stay and will be more likely to be discharged home than to other destinations | Length of stay | Categorical | Short <4 days Long >5 days | |
| Discharge destinations | Categorical | Home with rehabilitation, home without rehabilitation, inpatient rehabilitation facility, other acute hospital and death | ||
| Aim 2: characterise stroke presentations and provision of care in younger survivors – in particular for cognitive and psychosocial challenges | ||||
| H3: younger patients will predominantly receive physical interventions with limited provision of psychological, cognitive and social interventions | Acute treatment type | Categorical | Pharmacological, surgical or comfort measures | |
| Informal cognitive screen | Categorical | Yes/no | ||
| Formal cognitive screen (e.g. NUCOG, MoCA, MMSE) | Categorical | Yes/no | ||
| Informal mention of cognitive difficulties | Categorical | Yes/no | ||
| Inpatient intervention type | Categorical | Occupational therapy, physiotherapy, social work, speech pathology, music therapy, clinical psychology or neuropsychological assessment | ||
| Outpatient referrals | Categorical | Occupational therapy, physiotherapy, speech pathology, clinical psychology or neuropsychological assessment | ||
| Aim 3: explore predictors of length of stay and discharge destination within the group of young stroke survivors | ||||
| H4: length of stay and discharge destination in young patients will be predicted by age, stroke severity, pre-morbid functioning, acute treatment type and presence of cognitive difficulties | Length of stay | Categorical | (see above) | |
| Discharge destination | Categorical | (see above) | ||
| Age | Continuous | Age in years | ||
| Stroke severity | Continuous | (see above) | ||
| Pre-morbid functioning | Continuous | Modified Rankin Scale (mRS), scores range from 0 to 6 | ||
| Acute treatment type | Categorical | Pharmacological, surgical and comfort measures | ||
| Informal mention of cognitive difficulties | Categorical | Yes/no | ||
Results
Cases were excluded if they were missing data from any of the predictor variables (N = 28), leaving a total of 847 stroke admissions, of which 67 (7.91%) were classified as young. Importantly, all 28 patients with missing predictor variables were not assessed or treated on a stroke care unit, and none were offered referrals to post-discharge rehabilitation.
Sample characteristics
Descriptive statistics for demographic characteristics of the sample, by age grouping (young vs older), are presented in Table 2. Consistent with proportions of stroke in Australia, patients were mostly older males (Australian Institute of Health and Welfare 2016). Unexpectedly, younger patients had significantly lower pre-stroke functioning scores. The magnitude of the differences in the means (mean difference = −0.44, 95% CI: −0.65 to −0.23) was medium (η2 = 0.10). For the total sample, 51.24% had a short length of stay (≤4 days), with the remaining 48.76% having a long length of stay (≥5 days).
| Characteristic | Total (N = 847) | Young (N = 67) | Older (N = 780) | Test statistic | |
|---|---|---|---|---|---|
| Age in years | 69.86 (14.97) | 36.30 (8.10) | 72.74 (11.51) | t = 25.37* | |
| Gender | |||||
| Male | 474 (56.00%) | 32 (47.76%) | 442 (56.67%) | χ2 = 1.99 | |
| Female | 373 (44.00%) | 35 (52.24%) | 338 (43.33%) | ||
| Country of birth | |||||
| Australian born | 394 (46.52%) | 36 (53.73%) | 358 (45.90%) | χ2 = 1.89 | |
| Premorbid functioning | |||||
| Premorbid mRS | 0.60 (1.23) | 0.19 (0.76) | 0.63 (1.26) | t = −4.14* | |
Scores for premorbid mRS range from 0 (no symptoms) to 5 (severe disability). Data is presented as mean (standard deviation) or absolute frequency (percentage).
*P < 0.001.
Consistent with the broader stroke population, ischaemic stroke was the most frequent stroke type (Table 3; Bindawas et al. 2017). Haemorrhagic stroke occurred significantly more often in younger patients. For the TOAST classification groupings, post hoc chi-square analyses revealed cardioembolic stroke aetiology was similar across both age groups, χ2 (1, n = 847) = 0.72, P = 0.39, φ = −0.03. However, younger patients had a significantly higher proportion of ‘other’ aetiology, χ2 (1, n = 847) = 34.09, P < 0.001, φ = 0.21. There were no significant differences in stroke location among young and older patients. Younger patients obtained lower NIHSS scores, and this effect was small (eta squared = 0.02). In terms of pathways of care, there was a small but significant association between age groups and intraarterial intervention, whereby receiving intraarterial intervention in acute care was more common in older patients (Table 4). Young and older patients did not significantly differ in length of stay or other pathways of care.
| Characteristic | Young (N = 67) | Older (N = 780) | Test statistic | |
|---|---|---|---|---|
| Stroke type | ||||
| Ischaemic | 44 (65.67%) | 654 (83.85%) | χ 2 = 15.41* | |
| Haemorrhagic | 22 (32.84%) | 124 (15.90%) | ||
| Ischaemic & haemorrhagic | 1 (1.49%) | 2 (0.26%) | ||
| Stroke location | ||||
| Left hemisphere | 25 (37.31%) | 354 (45.38%) | χ 2 = 7.52 | |
| Right hemisphere | 24 (35.82%) | 311 (39.87%) | ||
| Bilateral | 8 (11.94%) | 50 (6.41%) | ||
| Brainstem | 7 (10.45%) | 39 (5.00%) | ||
| Other/undetermined | 3 (4.47%) | 26 (3.33%) | ||
| Oxford classification | ||||
| TACI | 6 (13.33%) | 156 (23.78%) | χ 2 = 25.89* | |
| PACI | 16 (35.56%) | 285 (43.45%) | ||
| POCI | 20 (44.44%) | 131 (19.97%) | ||
| LACI | 2 (4.44%) | 67 (10.21%) | ||
| Unknown | 1 (2.22%) | 17 (2.49%) | ||
| TOAST subtype | ||||
| Cardioembolic | 16 (35.55%) | 231 (35.21%) | χ 2 = 57.57* | |
| Small vessel occlusion | 1 (2.22%) | 61 (9.30%) | ||
| Large artery occlusion | 3 (6.67%) | 91 (13.87%) | ||
| Other aetiology | 13 (28.89%) | 25 (3.81%) | ||
| Undetermined | 12 (26.67%) | 248 (37.80%) | ||
| NIHSS stroke severity score | 7.26 (7.12) | 10.27 (8.14) | t = −2.22* | |
TACI, total anterior circulation infarct; PACI, partial anterior circulation infarct; POCI, posterior circulation infarct; LACI, lacunar infarct. Data is presented as mean (standard deviation) or absolute frequency (percentage).
*P < 0.05.
| Young | Older | |||||||
|---|---|---|---|---|---|---|---|---|
| Care received | N | Frequency (%) | Frequency (%) | χ 2 | P | OR | φ | |
| Managed in an SCU | 847 | 62 (92.54) | 667 (85.51) | 0.02 | 0.89 | 0.31 | 0.55 | |
| Short length of stay (≤4 days) | 847 | 27 (40.30) | 407 (52.12) | 3.03 | 0.08 | 3.50 | 0.06 | |
| IA Intervention | 847 | 11 (16.42) | 239 (30.64) | 5.33 | 0.02* | 6.66 | 0.01 | |
| IPR | 847 | 13 (19.40) | 151 (19.36) | 0.00 | 1.00 | 0.00 | 0.99 |
OR, odds ratio; SCU, stroke care unit; IA, intraarterial; IPR, inpatient rehabilitation.
*P < 0.05 (two-tailed).
Table 5 summarises the proportion of young and older patients who were discharged to each destination. As there were no young patients discharged to nursing homes, palliative care or geriatric care, or transferred to a separate medical unit, these destinations were not included in the model. The logistic regression model predicting discharge destination from age grouping was statistically significant, χ2 (4, N = 695) = 15.34, P = 0.004, indicating that the model was able to distinguish between discharge destinations. There was also a significant association between age and discharge destination, wherein young age was associated with increased relative odds of being discharged home without rehabilitation. The model explained between 2.18% (Cox and Snell R2) and 2.29% (Nagelkerke R2) of the variance in discharge destination. Classification accuracy was fairly low, however, with an overall success rate of 33.67%.
| Destination | Young | Older | B | s.e. | Wald χ2 | P | OR | 95% CI | |
|---|---|---|---|---|---|---|---|---|---|
| Frequency (%) | Frequency (%) | ||||||||
| Home with rehab | 11 (16.42) | 91 (14.49) | 1.38 | 0.79 | 3.10 | 0.08 | 3.99 | 0.86–18.60 | |
| Home without rehab | 24 (35.82) | 110 (17.52) | 1.97 | 0.75 | 6.89 | 0.01* | 7.20 | 1.65–31.45 | |
| Inpatient rehab | 13 (19.40) | 151 (24.04) | 1.04 | 0.77 | 1.82 | 0.18 | 2.84 | 0.62–12.95 | |
| Other hospital | 17 (25.37) | 210 (43.33) | 0.98 | 0.76 | 1.67 | 0.20 | 2.67 | 0.60–11.8 | |
| Constant (death) | 2 (2.99) | 66 (8.46) | – | – | – | – | – | – |
OR, odds ratio; CI, confidence interval; rehab, rehabilitation. The reference category was death.
*P < 0.05.
Given younger patients had significantly lower NIHSS scores than older survivors, the impact of stroke severity on analyses was further explored by removing patients from the analyses (pairwise) who had more severe strokes (as indicated by NIHSS scores ≥15, which corresponded to moderate to severe stroke; Kogan et al. 2020). When patients with more severe strokes were removed from the analyses, there was no longer a significant association between age grouping and type of treatment received (χ2 (1, n = 378) = 0.69, P = 0.41, φ = −0.05) and age grouping no longer predicted discharge destination (χ2 (4, N = 378) = 5.64, P = 0.23).
Within the group of young stroke patients (N = 67), most were premorbidly physically independent and employed pre-stroke (Table 6).
| Characteristic | N | Absolute frequency (%) | |
|---|---|---|---|
| Physically independent | 67 | 65 (97.01%) | |
| Employment | 60 | ||
| Not employed | 13 (19.40%) | ||
| Employed | 41 (61.9%) | ||
| Student | 6 (8.96%) | ||
| Cognitive impairment | 66 | 1 (1.49%) | |
| Marital status | 67 | ||
| Single | 27 (40.30%) | ||
| In a relationship | 5 (7.46%) | ||
| De-facto | 9 (13.43%) | ||
| Married | 26 (38.81%) | ||
| Risk factors | |||
| Smoking | 63 | 18 (28.57%) | |
| Alcohol use | 63 | 7 (10.94%) | |
| Illicit drug use | 65 | 9 (13.85%) | |
| Previous stroke | 67 | 9 (13.43%) | |
The N for some characteristics were not equivalent to the total sample size (N = 67) due to some patients not being assessed on these characteristics during acute hospital admission. ‘De-facto’ included patients who were in a relationship and living together, but not married.
Most young patients were informally screened for cognitive difficulties with approximately one-third functionally noted by allied health professionals to have cognitive difficulties. Notably, all young patients whose formal cognitive screen indicated cognitive impairment were also informally noted by allied health professions to have cognitive difficulties. However, referral for comprehensive neuropsychological assessment was not made for any young stroke survivor patients during their acute stay and was uncommonly completed for outpatients within this service (Table 7). Physical interventions, including occupational therapy and physiotherapy, were the most common intervention type. No patients received psychological intervention (clinical, health or neuropsychology). Almost half of the young patients were referred to outpatient rehabilitation services, of which most were referred to occupational or physiotherapy. In the 67 young patients, a total of 109 consultations were offered after discharge. Almost two-thirds of patients were referred to medical services, which included consultations with a neurologist, cardiologist or ophthalmologist. Of those who were offered outpatient referrals, 74 (67.89%) consultations were attended by the patients.
| Stroke management | Total N | Frequency (%) | |
|---|---|---|---|
| Acute treatment type | 67 | ||
| Pharmacological | 46 (68.67%) | ||
| Surgical | 20 (29.85%) | ||
| Comfort measures | 1 (1.49%) | ||
| Informal cognitive screen conducted | 67 | 54 (81.82%) | |
| Formal cognitive screen conducted | 67 | 10 (14.93%) | |
| Informal mention of cognitive difficulties | 60A | 22 (32.83%) | |
| Inpatient intervention type | 67 | ||
| Occupational therapy | 62 (92.54%) | ||
| Physiotherapy | 62 (92.54%) | ||
| Social work | 26 (38.81%) | ||
| Speech pathology | 35 (52.24%) | ||
| Music therapy | 9 (13.43%) | ||
| Clinical Psychology | 0 (0.00%) | ||
| Neuropsychological Assessment | 0 (0.00%) | ||
| Outpatient referrals | 67 | ||
| Occupational therapy | 10 (14.93%) | ||
| Physiotherapy | 9 (13.43%) | ||
| Speech pathology | 4 (5.97%) | ||
| Clinical psychology | 0 (0.00%) | ||
| Neuropsychological assessment | 3 (4.48%) | ||
| Other | 4 (5.97%) | ||
| Outpatient medical consultations | 67 | 41 (61.19%) |
The binary logistic model predicting length of stay (short versus long) from stroke severity as a single criterion variable was not significant χ2 (1, N = 38) = 2.62, P = 0.11, indicating that stroke severity was not able to distinguish between long and short length of stay within the group of young survivors.
A test of the full binary logistic model with the remaining four predictors, namely, age (in years), functional independence (mRS scores), acute treatment type and presence of cognitive difficulties, was statistically significant, χ2 (4, N = 57) = 10.16, P = 0.04. This indicated that the predictors, as a set, distinguished between long and short length of stay. The model explained between 16.29% (Cox and Snell R2) and 22.10% (Nagelkerke R2) of the variance in length of stay. There were 56% of young patients with a short stay correctly classified, and 82% of long stay patients correctly classified, for an overall success rate of 71%. The presence of cognitive difficulties made a significant individual contribution to the model (Table 8). Patients with cognitive difficulties were four times more likely to stay in hospital longer, after controlling for all other factors in the model.
| Predictor | Short stay | Long stay | B | s.e. | Wald χ2 | d.f. | P | OR | 95% CI | |
|---|---|---|---|---|---|---|---|---|---|---|
| Descriptive statistics | Descriptive statistics | |||||||||
| Age (years) | 34.37 (8.10) | 37.60 (7.94) | 0.04 | 0.04 | 1.28 | 1 | 0.26 | 1.04 | 0.97–1.12 | |
| Premorbid mRS | 0.37 (1.12) | 0.06 (0.23) | −0.65 | 0.78 | 0.69 | 1 | 0.41 | 0.52 | 0.11–2.42 | |
| Pharmacological treatment | 21 (80.77%) | 25 (62.50%) | −0.89 | 0.72 | 1.54 | 1 | 0.22 | 0.41 | 0.10–1.68 | |
| Cognitive difficulty | 4 (17.39%) | 18 (48.65%) | 1.43 | 0.69 | 4.25 | 1 | 0.04 | 4.19 | 1.07–16.37 | |
| Constant | −0.84 | 1.39 | 0.37 | 1 | 0.54 | 0.43 |
Descriptive statistics are displayed as mean (standard deviation) or frequency (percentage). OR, odds ratio; CI, confidence interval.
Discussion
This study aimed to retrospectively compare stroke characteristics and pathways of care between young and older survivors, as well as explore predictors of care in young survivors. As hypothesised, stroke presentations and aetiologies differed between young and older patients. While young patients’ strokes were less severe, there were no differences in length of stay between young and older patients. Younger patients were more likely to be discharged home without rehabilitation supports, probably because their strokes were generally less severe. Only 15% of young survivorss received formal cognitive screening and no comprehensive neuropsychological assessments of younger survivors were undertaken in an acute setting, despite many being informally identified to have cognitive difficulties. Additionally, there were no clinical or health psychology interventions offered during acute care, highlighting the need for better utilisation of psychology services in acute settings. It was also hypothesised that length of stay would be predicted by age, stroke severity, premorbid functioning and treatment type within young survivors; however, only cognitive impairment predicted length of stay for this group.
Consistent with prior research, we found a larger proportion of ischaemic stroke with ‘other’ determined aetiology among younger compared to older patients (Barlas et al. 2013; Smajlović et al. 2013), indicating that care pathways for these patients need to be appropriately tailored (for example, comprehensive diagnostic investigations). While this audit was conducted pre-pandemic, there is also some evidence to suggest a link between COVID-19 and a cluster of strokes among younger people (Oxley et al. 2020), further emphasising the importance of appropriate evaluation and care.
The lower stroke severity for younger patients in this study was consistent with previous literature (Kato et al. 2015; Subha et al. 2015; Lutski et al. 2017). While there were no differences in length of stay between these cohorts, this was surprising, given past research has reported shorter length of stay in younger patients (Saposnik et al. 2009; Al-Jadid and Robert 2010). The overall short length of stay across both cohorts in our study may be attributed, at least in part, to the specialist nature of stroke-specific services within the hospital, including clot retrieval services.
The Australian and New Zealand Clinical Guidelines for Stroke Management include practice points suggesting that all patients be screened for cognitive deficits prior to discharge from acute care, and a consensus recommendation that positive screens should be referred for comprehensive neuropsychological assessment (Stroke Foundation 2023, accessed October 2023). Our findings indicate that, at the time of data-base collation (prior to the publication of the clinical guidelines), these best-practice points were not being implemented. Although the current guidelines referenced here were published after the data was collected, the 2018 guidelines were similar in terms of access and referrals to psychology. While around one-third of patients in our study were informally noted to have cognitive difficulties, less than half of those underwent a brief cognitive screen, and none received a neuropsychological assessment during acute care. Additionally, less than 5% of younger survivors were referred for a neuropsychological assessment upon discharge. While it is less common for stroke patients to be seen by a neuropsychologist during the acute phase compared to community or rehabilitation centres, given the high proportion of cognitive difficulties identified, we expected a greater number of referrals to neuropsychology to have been made. Instead, inpatients were rarely offered access to neuropsychological services, compared to the readily accessible occupational therapy and physiotherapy services. This discrepancy may reflect an issue with the service-model and the response of health services. Another possibility is that practice points and consensus recommendations may be a lower priority in the health service compared to stronger recommendations for physical rehabilitation. Given the unmet cognitive and psychological needs in this study, there were likely multiple missed opportunities for connecting younger survivors with milder impairments to appropriate hospital and community supports.
Cognitive impairment was identified as an important and significant predictor of longer length of stay in young patients, which aligns with past research (Tooth et al. 2005; Paker et al. 2010). However, given the low classification accuracy of short compared to longer length of stay in the regression model, there may have been patients who should have stayed longer but were discharged early (Dixon et al. 2007). Nevertheless, cognition is clearly an important factor in predicting functional and participation outcomes for young patients (Stolwyk et al. 2021). Given there are effective cognitive rehabilitation programs post-stroke (Withiel et al. 2019), all patients should have been screened for cognitive difficulties and referred to appropriate cognitive rehabilitation if needed.
Adding to the most recent national stroke audit finding that one-third of stroke services do not have access to psychologists (Stroke Foundation 2021), our results revealed that no young patient was offered referrals to either clinical or health psychology during their acute stay. Psychosocial challenges, such as difficulties returning to work, financial pressure, low self-worth and depressed mood, are among the highest unmet needs in young stroke patients (Morris 2011; Lannin et al. 2017; Keating et al. 2021). This suggests that, like cognitive difficulties, early indicators of psychological problems are not followed up with appropriate referrals to clinical services who could assist.
Taken together, our data shows the missed potential to proactively address and support cognitive and psychosocial impacts in this young cohort. In addition, medical and other rehabilitation (i.e. occupational therapy, physiotherapy and speech pathology) appointments were provided consistently after discharge, yet one-third of appointments were not attended. While it is possible that patients may not be attending appointments due to minimal or no long-term stroke consequences, it is also possible non-attendance reflects a mismatch between services offered and patients’ needs. To address these shortfalls, findings from this research have contributed to the development of a young brain-injury clinic at the hospital, which aims to meet the cognitive and mood-related needs of young people after they experience a stroke. These findings may also inform models of service provision within the new Australian Young Stroke Service currently being established (see www.youngstrokeservice.org.au).
The main strength of this research was the utilisation of a retrospective clinical record audit as a method of data collection. This can be a more accurate way of capturing actual clinical practice than through prospective studies, as clinicians may alter their practice when they know it is being evaluated. This audit, therefore, served to explore real-world clinical practice and stroke outcomes. Having a single auditor, who was not a clinician at the service, ensured consistency and minimal bias at the point at which the data was collected (Wu and Ashton 1997).
Several limitations should be noted. As this study was restricted to a population of stroke patients from one metropolitan tertiary teaching hospital, generalisations to other institutions and non-teaching hospitals are limited. In addition, the sample of young surviviros was relatively small (n = 67), which may affect the robustness of our findings. Therefore, to gauge a better understanding of stroke care across Australia, further auditing is required at a multi-centre level. It is nevertheless possible that the service model utilised at the hospital in this study is similar to others used across the state and nationally.
Another limitation is that patients with multiple strokes were included in the audit. Patients with multiple strokes present differently to those with first-time stroke. Acute symptoms tend to be more severe, and greater levels of physical and cognitive disability are experienced (Elwan et al. 2021). In addition, the risk of experiencing multiple strokes is greater in older individuals (Feng et al. 2010). As such, including cases of multiple strokes may have confounded the impact of age in this study, whereby differences in stroke characteristics and pathways may have been due to the presence of multiple strokes.
It is also worth noting that some records were incomplete and stroke severity was inconsistently measured by clinicians. Results may have, therefore, been biased by missing data, as only patients with sufficient data across predictor variables were analysed. Additionally, some of the variables were not collected for older patients, so the number of direct comparisons between younger and older cohorts was limited. The current results are limited to a population of young stroke survivors whose information was appropriately documented. However, this documentation indicates ‘invisible’ cognitive and psychological difficulties are being missed and left untreated. Finally, although this retrospective study explored real-world clinical practice within the hospital, care provided outside of this health service was not included in data collection and analysis for this study.
Clinical implications
Our study highlights the lag between the Stroke Foundation guidelines and the response of health services. Strategies should be developed to facilitate implementation of stroke guidelines, such as by adequately resourcing the workforce.
There is a need for much better availability and utilisation of neuropsychological assessment services. Patients assessed as having cognitive deficits should then be referred to appropriate and effective cognitive rehabilitation.
Increased identification of psychological support needs and proactive referrals to appropriate services (such as clinical, health or neuropsychology) will ensure these needs can be addressed early and potentially prevent further harms (Dewey and Bernhardt 2007; Kneebone 2016; Fang et al. 2017).
Conclusions
To our knowledge, this is the first clinical audit study to characterise care pathways of younger stroke survivors in Australia, including acute and post-acute care and predictors of length of stay and discharge destination. This study highlights that young stroke patients present with medical, cognitive and support needs that were not consistently met by acute and post-acute care pathways. Younger patients presented with more unusual stroke aetiologies that were less severe than their older counterparts, and therefore they were often discharged home without rehabilitation. The care received by young patients during an acute stay seldom addressed their identified cognitive difficulties and potential psychosocial needs. Future research should prospectively explore the views of stroke patients and their carers on the care they receive, and develop tailored pathways of care addressing the cognitive and psychosocial needs of younger stroke survivors.
Data availability
The data that support this study cannot be publicly shared due to ethical or privacy reasons.
Declaration of funding
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Ethics standard
The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
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