Validated tools for diagnosis of cerebrovascular disease exist for stroke, and the original motivation for their derivation was earlier transport of patients to hospital from the community i.e. to aid paramedics in their recognition of stroke. Stroke recognition tools are centred on physical signs with key features from the history which identify stroke mimics. The absence
36 of physical signs in TIA (by definition) contributes to the difficulty in deriving simple
diagnostic tools.
The Cincinnati Prehospital Stroke Scale (CPSS) is a three item scale scoring arm drift, facial droop and speech clarity and in a small sample of paramedics and ED support workers it showed good reproducibility (206). However, a prospective assessment of its use via a one hour training session in a 12 month before and after study showed no effect on recognition of stroke or time spent ‘on scene’ (207).
The Melbourne Ambulance Stroke Screen (MASS) involves initially testing for facial droop, hand grip (rather than the arm drift of CPSS) and speech disturbance (208). If any of these features are present, then the rule out features of age <45 years, history of epilepsy, and blood glucose levels (for hypoglycaemia) are assessed as well as the then thrombolysis ineligibility criterion of being bedridden or chair-bound. The MASS improved paramedic sensitivity in detecting stroke (78% to 94%) and reduced time to medical review in the ED by notification of potential stroke before arrival. This improvement was sustained three years after city wide implementation in terms of sensitivity of diagnosis and specificity (209). In this study, CPSS was calculated retrospectively from case notes and both MASS and CPSS had high NPVs around 95% with PPVs of 56% for CPSS and 65% for MASS.
The Los Angeles Prehospital Stroke Screen (LAPSS) starts with the rule out features of history of epilepsy, age <45, abnormal blood glucose together with the thrombolysis ineligibility features of being bedridden or chairbound and symptom duration of 24 hours or more. If patients ‘pass’ this test then three motor tests are used – unilateral facial droop, arm weakness and grip strength (i.e. no speech component compared with the CPSS and
MASS) (210). The LAPSS was tested retrospectively using clinical data for patients entered into acute stroke trials with a high sensitivity for stroke detection of 93% (210). A prospective validation of LAPSS used by US paramedics on 206 eligible emergency transfers to hospital showed a PPV of 86% and an NPV of 98% for a diagnosis of stroke (211).
The Face, Arm, Speech Test (FAST) uses the presence of unilateral facial weakness or arm weakness or speech disturbance as a stroke recognition tool (212). It differs from the CPSS as the examiner assesses speech by observation rather than asking the patient to repeat a sentence. It was validated by comparing paramedics assessment of the three clinical features with those of an admitting stroke physician in one UK hospital (213). 78% of admitted stroke or TIA patients had FAST signs and the strongest level of agreement between paramedics and stroke physicians was for arm weakness (Cohen’s kappa of 0.8) and slightly weaker agreement for the presence of speech disturbance (kappa = 0.7) (213). Consecutive suspected stroke referrals from paramedics using FAST as a recognition tool
37 were compared with ED and GP referrals using clinical judgement, and this demonstrated similar PPVs for stroke across the three referral routes (214). The FAST recognition tool has a clear bias towards anterior circulation stroke detection as evidenced by fewer posterior circulation strokes referred from the paramedics compared with ED and GPs (214).
A tool designed to improve the referral of patients with stroke from ED to acute stroke teams rather than from paramedics assessing patients in the community uses a combination of history, clinical features and blood glucose. The Recognition of Stroke in the Emergency Room (ROSIER) scale starts with checking blood glucose and correcting hypoglycaemia if present, then runs through negative predictors for stroke – loss of consciousness/syncope, ictal activity (which score -1 if present, 0 if absent). Positive features of stroke score 1 point each – face, arm or leg weakness, speech deficit and visual field defect (215). A prospective validation phase of the ROSIER scale showed a 93% sensitivity and 83 % specificity for stroke diagnosis with PPV of 90% and NPV of 88% (215). These measures of diagnostic performance were higher than for FAST, CPSS and LAPSS which were also calculated in the same cohort of patients. In a high prevalence setting (consecutive admissions to an acute stroke unit), nurses using the ROSIER scale had an equivalent sensitivity and positive predictive value as doctors using clinical acumen for stroke diagnosis (216). A small
validation study in an external setting showed a high PPV (94%) for stroke diagnosis (217). Other studies have examined predictors for stroke or TIA diagnosis rather than deriving a scale. One study of ED presentations with dizziness, vertigo or postural imbalance found that positive predictors of TIA or stroke were higher age and male sex and negative predictors were isolated symptoms and non-hispanic or white ethnicity (218), although the methods were weak as there was no follow up and diagnosis was from record review. Distinguishing TIA from non-TIA in the ED is as difficult as in primary care with a similar 50% prevalence of true TIA in consecutive cases with suspected TIA (219).
However, predictors of true TIA in the ED population may be different to primary care
attenders, as a study deriving a logistic regression model for TIA diagnosis from ED referrals found that significant discriminators were gradual onset symptoms, non-specific complaints and a history of prior transient neurological symptoms resulting in an area under the receiver operating characteristic curve of 0.79 (220). The ABCD2 score did not provide any
discriminating utility in the study.
Features that predict stroke in a cohort of admitted patients in a UK study were absence of a history of dementia, no signs of systemic illness, any focal neurological sign, an exact time of onset, abnormal vascular findings, clinical features sufficient for an Oxford Community Stroke Project classification (total or partial anterior circulation, lacunar, posterior circulation),
38 a deficit sufficient for a National Institute of Health Stroke score >1 and neurological signs consistent with a unilateral cerebral lesion (221). Given the degree of expertise required to elicit these features, it would not be possible to use them to derive a scale for a generalist or non-clinician.
The only specific tool for TIA diagnosis in the literature was derived using multivariable logistic regression from routinely recorded secondary care clinical notes (222). Given that by definition a patient with TIA has non-persisting symptoms, the tool is based on the clinical history. A score weighted using the beta coefficients of the regression model consisted of negative points for headache, loss of consciousness/syncope and ictal features, positive points for diplopia, past history of stroke/TIA, unilateral face/limb weakness, speech disturbance and increasing age. Internal validation performed with a 2:1 cost ratio used for misclassifying TIA as non-TIA was used to derive an optimal cut point for clinical usage (222).
The ABCD2 score, although designed for prognostic use (24), may have a role in TIA diagnosis. It has a higher score in patients with an ED physician diagnosis of TIA who are deemed to have had a true TIA by case record review from a neurologist, compared to those patients deemed not to have had a TIA (223). A hospital clinician calculated ABCD2 score has been used to distinguish between true TIA and non-TIA in patients referred by GPs and ED physicians to TIA clinics. One study reported an area under the receiver operator
characteristic curve of 0.75 (224) and another of 0.68 (177) for TIA diagnosis using ABCD2. Other potential tools include blood tests and machine learning algorithms. A search for novel biomarkers using mass spectrometry, by examining patterns caused by combinations of proteins in serum and comparing these in patients with stroke and controls has shown a 77.5% sensitivity and 72% specificity in a small number of matched patients (225). The diagnosis of TIA using neural networks which take an alternative prediction methodology using existing history and examination factors to generate a learning network of associations has not been validated in external cohorts (226).