A System for Estimating Drug Delivery from a Dry Powder Inhaler by Analysis of Acoustic Recordings of Time-Stamped Inhaler Events

Inhaled medications are the mainstay of therapy in the treatment of chronic respiratory diseases like asthma and COPD because they allow delivery of the active ingredient directly to the site of action. Poor adherence to inhaled controller medications has been estimated to account for up to 60% of asthma-related hospitalizations and increased rates of 30- and 60- day hospital readmissions in patients with COPD. Numerous electronic monitoring devices have been developed over the last four decades to monitor temporal non-adherence; however, many of these devices do not monitor all or most aspects of inhaler technique. Currently used methods for monitoring inhaler technique, including subjective checklists, are suboptimal. There is a need to study the frequency of temporal and technique non-adherence in the Irish population and to investigate the impact of dosing and technique errors on drug delivery. Moreover, a comprehensive system of tracking the date and time of inhaler use, as well as the presence or absence of technique errors, on a daily basis is essential to not only an epidemiological understanding of inhaler use but to tailoring of inhaler training and clinical care plans to individual patients. This thesis describes the use of the INCATM device, a novel acoustic monitor, which provides longitudinal data on the date and time of inhaler use, as well as data on inhaler technique. Studies showed that inhalation flow rate, exhalation into the inhaler mouthpiece prior to inhalation, breath-hold duration and missed doses had a significant effect on delivered dose. Data on both temporal and technique adherence were combined in an algorithm, which provided a single measure of overall adherence, called “actual adherence”. The dose counter rate correlated poorly with INCATM derived adherence rates, highlighting the need to incorporate technologies, like the INCATM device, into clinical trials and patient care

Investigating the relationship between peak inspiratory flow rate and volume of inhalation from a Diskus™ Inhaler and baseline spirometric parameters: a cross-sectional study

Drug delivery from a Dry Powder Inhaler (DPI) is dependent on the peak inspiratory flow rate (PIFR) generated. Currently available methods for estimating PIFR from most DPIs are limited and mainly rely on subjective assessment. We aim to show that spirometric and Diskus™ PIFR and Inspiratory Vital Capacity (IVC) are related to the underlying respiratory condition and that spirometric PIFR can be used to assess whether Diskus™ PIFR will be adequate when using this DPI. Healthy volunteers and patients with asthma, COPD, neuromuscular disease and non-respiratory disorders were recruited (n = 85). Demographics and baseline lung function by spirometry were recorded. Flow and volume readings were taken while patients used a Diskus™ DPI, housed in an airtight container connected to a spirometer. T-tests were performed to compare mean spirometric and Diskus™ PIFR/ IVC between groups. Stepwise regression analysis of Diskus™ PIFR versus spirometric PIFR, spirometric IVC, age, gender, condition, BMI, FEV1 and FVC was performed. The Diskus™ PIFR for the COPD and Neuromuscular Disease group was more than 10 L/min lower than the Healthy or Asthma groups (p \u3c 0.05). The mean spirometric and Diskus™ IVC of the Healthy group was significantly (\u3e0.75 L) higher than the mean for the other three groups (p \u3c 0.05). Diskus™ PIFR was moderately correlated with spirometric PIFR and age (Adjusted R2 = 0.58, p \u3c 0.0001). PIFR generated using a Diskus™ DPI is dependent on the underlying disease and age. A spirometric PIFR of less than 196 L/min should prompt further investigation into the suitability of a patient for a Diskus™ DPI, with possible consideration of alternate devices

A protocol for a randomised clinical trial of the effect of providing feedback on inhaler technique and adherence from an electronic device in patients with poorly controlled severe asthma

ntroduction In clinical practice, it is difficult to distinguish between patients with refractory asthma from those with poorly controlled asthma, where symptoms persist due to poor adherence, inadequate inhaler technique or comorbid diseases. We designed an audio recording device which, when attached to an inhaler, objectively identifies the time and technique of inhaler use, thereby assessing both aspects of adherence. This study will test the hypothesis that feedback on these two aspects of adherence when passed on to patients improves adherence and helps clinicians distinguish refractory from difficult-to-control asthma. Methods This is a single, blind, prospective, randomised, clinical trial performed at 5 research centres. Patients with partially controlled or uncontrolled severe asthma who have also had at least one severe asthma exacerbation in the prior year are eligible to participate. The effect of two types of nurse-delivered education interventions to promote adherence and inhaler technique will be assessed. The active group will receive feedback on their inhaler technique and adherence from the new device over a 3-month period. The control group will also receive training in inhaler technique and strategies to promote adherence, but no feedback from the device. The primary outcome is the difference in actual adherence, a measure that incorporates time and technique of inhaler use between groups at the end of the third month. Secondary outcomes include the number of patients who remain refractory despite good adherence, and differences in the components of adherence after the intervention. Data will be analysed on an intention-to-treat and a per-protocol basis. The sample size is 220 subjects (110 in each group), and loss to follow-up is estimated at 10% which will allow results to show a 10% difference (0.8 power) in adherence between group means with a type I error probability of 0.05. Trial registration number NCT01529697; Pre-results

A Method to Calculate Adherence to Inhaled Therapy That Reflects the Changes in Clinical Features of Asthma.

Rationale Currently studies on adherence to inhaled medications report Average Adherence over time. This measure does not account for variations in the interval between doses nor for errors in inhaler use. Objectives We investigated whether adherence calculated as a single Area Under the concentration-time Curve (AUC) measure, incorporating the interval between doses and inhaler technique, was more reflective of patient outcomes than current methods of assessing adherence. Methods We attached a digital audio device (INCATM) to a dry powder inhaler. This recorded when the inhaler was used and analysis of the audio data indicated if the inhaler had been used correctly. These aspects of inhaler use were combined to calculate adherence over time, as an AUC measure. Over a 3 month period a cohort of asthma patients were studied. Adherence to a twice-daily inhaler preventer therapy using this device and clinical measures were assessed. Measurements and Results Recordings from 239 patients with severe asthma were analysed. Average Adherence, based on the dose counter was 84.4%, whereas the ratio of expected to observed accumulated AUC, Actual Adherence, was 61.8% (

A protocol for a randomised clinical trial of the effect of providing feedback on inhaler technique and adherence from an electronic device in patients with poorly controlled severe asthma.

INTRODUCTION: In clinical practice, it is difficult to distinguish between patients with refractory asthma from those with poorly controlled asthma, where symptoms persist due to poor adherence, inadequate inhaler technique or comorbid diseases. We designed an audio recording device which, when attached to an inhaler, objectively identifies the time and technique of inhaler use, thereby assessing both aspects of adherence. This study will test the hypothesis that feedback on these two aspects of adherence when passed on to patients improves adherence and helps clinicians distinguish refractory from difficult-to-control asthma. METHODS: This is a single, blind, prospective, randomised, clinical trial performed at 5 research centres. Patients with partially controlled or uncontrolled severe asthma who have also had at least one severe asthma exacerbation in the prior year are eligible to participate. The effect of two types of nurse-delivered education interventions to promote adherence and inhaler technique will be assessed. The active group will receive feedback on their inhaler technique and adherence from the new device over a 3-month period. The control group will also receive training in inhaler technique and strategies to promote adherence, but no feedback from the device. The primary outcome is the difference in actual adherence, a measure that incorporates time and technique of inhaler use between groups at the end of the third month. Secondary outcomes include the number of patients who remain refractory despite good adherence, and differences in the components of adherence after the intervention. Data will be analysed on an intention-to-treat and a per-protocol basis. The sample size is 220 subjects (110 in each group), and loss to follow-up is estimated at 10% which will allow results to show a 10% difference (0.8 power) in adherence between group means with a type I error probability of 0.05. TRIAL REGISTRATION NUMBER: NCT01529697; Pre-results

Objective Assessment of Adherence to Inhalers by COPD Patients.

RATIONALE: Objective adherence to inhaled therapy by patients with COPD has not been reported. OBJECTIVES: The aim of this study was to objectively quantify adherence to preventer DiskusTM inhaler therapy by patients with COPD with an electronic audio recording device (INCATM). METHODS: This was a prospective observational study. On discharge from hospital patients were given a salmeterol/fluticasone inhaler with an INCATM device attached. Analysis of this audio quantified the frequency and proficiency of inhaler use. MEASUREMENTS AND MAIN RESULTS: COPD patients (n=265) were recruited. The mean age 71 years, mean Forced Expiratory Volume in 1-second 1.3 Litres, and 80% had evidence of mild/moderate cognitive impairment. By combining time of use, interval between doses and critical technique errors, thus incorporating both intentional and unintentional non-adherence, a measure \u22Actual Adherence\u22 was calculated. Mean Actual Adherence was 22.9% of that expected if the doses were taken correctly and on time. Seven percent had an Actual Adherence\u3e80%. Hierarchical clustering found three equally sized well-separated clusters corresponding to distinct patterns: Cluster 1 (34%) had low inhaler use and high error rates, Cluster 2 (31%) had high inhaler use and high error rates, and Cluster 3 (30%) had overall good adherence. Lung function and co-morbidities were predictive of poor technique, while age and cognition with poor lung function distinguished those with poor adherence and frequent errors in technique. CONCLUSION: These data may inform clinicians both in understanding why a prescribed inhaler is not effective and to devise strategies to promote adherence in COPD

A Hazard-Aware Metric for Ordinal Multi-Class Classification in Pathology

Artificial Intelligence (AI) for decision support and diagnosis in pathology could provide immense value to society, improving patient outcomes and alleviating workload demands on pathologists. However, this potential cannot be realized until sufficient methods for testing and evaluation of such AI systems are developed and adopted. We present a novel metric for evaluation of multi-class classification algorithms for pathology, Error Severity Index (ESI), to address the needs of pathologists and pathology lab managers in evaluating AI systems

Artificial Intelligence Enhances Diagnostic Flow Cytometry Workflow in the Detection of Minimal Residual Disease of Chronic Lymphocytic Leukemia

Flow cytometric (FC) immunophenotyping is critical but time-consuming in diagnosing minimal residual disease (MRD). We evaluated whether human-in-the-loop artificial intelligence (AI) could improve the efficiency of clinical laboratories in detecting MRD in chronic lymphocytic leukemia (CLL). We developed deep neural networks (DNN) that were trained on a 10-color CLL MRD panel from treated CLL patients, including DNN trained on the full cohort of 202 patients (F-DNN) and DNN trained on 138 patients with low-event cases (MRD < 1000 events) (L-DNN). A hybrid DNN approach was utilized, with F-DNN and L-DNN applied sequentially to cases. “Ground truth” classification of CLL MRD was confirmed by expert analysis. The hybrid DNN approach demonstrated an overall accuracy of 97.1% (95% CI: 84.7–99.9%) in an independent cohort of 34 unknown samples. When CLL cells were reported as a percentage of total white blood cells, there was excellent correlation between the DNN and expert analysis [r > 0.999; Passing–Bablok slope = 0.997 (95% CI: 0.988–0.999) and intercept = 0.001 (95% CI: 0.000–0.001)]. Gating time was dramatically reduced to 12 s/case by DNN from 15 min/case by the manual process. The proposed DNN demonstrated high accuracy in CLL MRD detection and significantly improved workflow efficiency. Additional clinical validation is needed before it can be fully integrated into the existing clinical laboratory practice