Prognostic value of the ABCD² clinical prediction rule: a systematic review and meta-analysis.

OBJECTIVE: The purpose of this systematic review with meta-analysis is to determine the predictive value of the ABCD ²at 7 and 90 days across three strata of risk. Background. The risk of stroke after transient ischaemic attack (TIA) is significant. The ABCD ²clinical prediction rule is designed to predict early risk of stroke after TIA. A number of independent validation studies have been conducted since the rule was derived. METHODS: A systematic literature search was conducted to identify studies that validated the ABCD². The derived rule was used as a predictive model and applied to subsequent validation studies. Comparisons were made between observed and predicted number of strokes stratified by risk group: low (0-3 points), moderate (4-5 points) and high (6-7 points). Pooled results are presented as risk ratios (RRs) with 95% confidence intervals (CIs), in terms of over-prediction (RR > 1) or under-prediction (RR < 1) of stroke at 7 and 90 days. RESULTS: We include 16 validation studies. Fourteen studies report 7-day stroke risk (n = 6282, 388 strokes). The ABCD² rule correctly predicts occurrence of stroke at 7 days across all three risk strata: low [RR 0.86, 95% CI (0.47-1.58), I² = 16%], moderate [RR 0.99, 95% CI (0.67-1.47), I² = 68%] and high [RR 0.84, 95% CI (0.6-1.19), I² = 46%]. Eleven studies report 90-day stroke risk (n = 6304). There is a non-significant trend towards over-prediction of stroke in all risk categories at 90 days. There are 426 strokes observed in contrast to a predicted 626 strokes. As the trichotomized ABCD² score increases, the risk of stroke increases (P < 0.01). There is no evidence of publication bias in these studies (P > 0.05). CONCLUSION: The ABCD² is a useful CPR, particularly in relation to 7-day risk of stroke.

Using acoustics to estimate inspiratory flow rate and drug removed from a dry powder inhaler.

Morbidity and mortality rates of chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) are rising. There is a strong requirement for more effective management of these chronic diseases. Dry powder inhalers (DPIs) are one kind of devices currently employed to deliver medication aimed at controlling asthma and COPD symptoms. Despite their proven effectiveness when used correctly, some patients are unable to reach the inspiratory flow rate required to remove medication from the breath actuated devices and as a result, the medication does not reach the airways. This study employs an acoustic recording device, attached to a common DPI to record the audio signals of simulated inhalations. A rotameter was used to measure the flow rate through the inhaler while a milligram weighing scale was used to measure the amount of drug removed from each simulated inhalation. It was found that a strong correlation existed (R(2)>0.96) when average power, median amplitude, root mean square and mean absolute deviation were used to predict peak inspiratory flow rate. At a flow of 30 L/Min (mean absolute deviation=0.0049), it was found that 77% of the total emitted dose was removed from the inhaler. Results indicate that acoustic measurements may be used in the prediction of inspiratory flow rate and quantity of medication removed from an inhaler.

A method of estimating inspiratory flow rate and volume from an inhaler using acoustic measurements.

Inhalers are devices employed to deliver medication to the airways in the treatment of respiratory diseases such as asthma and chronic obstructive pulmonary disease. A dry powder inhaler (DPI) is a breath actuated inhaler that delivers medication in dry powder form. When used correctly, DPIs improve patients' clinical outcomes. However, some patients are unable to reach the peak inspiratory flow rate (PIFR) necessary to fully extract the medication. Presently clinicians have no reliable method of objectively measuring PIFR in inhalers. In this study, we propose a novel method of estimating PIFR and also the inspiratory capacity (IC) of patients' inhalations from a commonly used DPI, using acoustic measurements. With a recording device, the acoustic signal of 15 healthy subjects using a DPI over a range of varying PIFR and IC values was obtained. Temporal and spectral signal analysis revealed that the inhalation signal contains sufficient information that can be employed to estimate PIFR and IC. It was found that the average power (Pave) in the frequency band 300-600 Hz had the strongest correlation with PIFR (R(2) = 0.9079), while the power in the same frequency band was also highly correlated with IC (R(2) = 0.9245). This study has several clinical implications as it demonstrates the feasibility of using acoustics to objectively monitor inhaler use.