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 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

Dual T-cell constant β chain (TRBC)1 and TRBC2 staining for the identification of T-cell neoplasms by flow cytometry

Abstract The diagnosis of leukemic T-cell malignancies is often challenging, due to overlapping features with reactive T-cells and limitations of currently available T-cell clonality assays. Recently developed therapeutic antibodies specific for the mutually exclusive T-cell receptor constant β chain (TRBC)1 and TRBC2 isoforms provide a unique opportunity to assess for TRBC-restriction as a surrogate of clonality in the flow cytometric analysis of T-cell neoplasms. To demonstrate the diagnostic utility of this approach, we studied 164 clinical specimens with (60) or without (104) T-cell neoplasia, in addition to 39 blood samples from healthy donors. Dual TRBC1 and TRBC2 expression was studied within a comprehensive T-cell panel, in a fashion similar to the routine evaluation of kappa and lambda immunoglobulin light chains for the detection of clonal B-cells. Polytypic TRBC expression was demonstrated on total, CD4+ and CD8+ T-cells from all healthy donors; and by intracellular staining on benign T-cell precursors. All neoplastic T-cells were TRBC-restricted, except for 8 cases (13%) lacking TRBC expression. T-cell clones of uncertain significance were identified in 17 samples without T-cell malignancy (13%) and accounted for smaller subsets than neoplastic clones (median: 4.7 vs. 69% of lymphocytes, p < 0.0001). Single staining for TRBC1 produced spurious TRBC1-dim subsets in 24 clinical specimens (15%), all of which resolved with dual TRBC1/2 staining. Assessment of TRBC restriction by flow cytometry provides a rapid diagnostic method to detect clonal T-cells, and to accurately determine the targetable TRBC isoform expressed by T-cell malignancies

Rate of D-alloimmunization in trauma does not depend on the number of RhD-positive units transfused: The BEST collaborative study

BACKGROUND: Evidence indicates the life-saving benefits of early blood product transfusion in severe trauma resuscitation. Many of these products will be RhD-positive, so understanding the D-alloimmunization rate is important. METHODS: This was a multicenter, retrospective study whereby injured RhD-negative patients between 18-50 years of age who received at least one unit of RhD-positive red blood cells (RBC) or low titer group O whole blood (LTOWB) during their resuscitation between 1 January, 2010 through 31 December, 2019 were identified. If an antibody detection test was performed ≥14 days after the index RhD-positive transfusion then basic demographic information was collected, including whether the patient became D-alloimmunized. The overall D-alloimmunization rate, and the rate stratified by the number of units transfused, were calculated. RESULTS: Data were collected from nine institutions. Five institutions reported fewer than 10 eligible patients each and were excluded. From the remaining four institutions, all from the USA, there were 235 eligible patients; 77 (random effects estimate: 32.7%; 95% CI: 19.1-50.1%) became D-alloimmunized. Three of the institutions reported D-alloimmunization rates ≥38.6%, while the remaining institution\u27s rate was 12.2%. In both random and fixed-effects models, the rate of D-alloimmunization was not significantly different between those who received one RhD-positive unit and those who received multiple RhD-positive units. CONCLUSION: In this large, multicenter study of injured patients, the overall rate of D-alloimmunization fell within the range previously reported. The rate of D-alloimmunization did not increase as the number of transfused RhD-positive units increased. These data can help to inform RhD type selection decisions