A smart shirt can accurately measure tidal volumes during various tasks of daily living (Preprint)

2021 ◽  
Author(s):  
Denise Mannee ◽  
Frans de Jongh ◽  
Hanneke van Helvoort
Keyword(s):  
Daily Living ◽  
Healthy Subjects ◽  
A Priori ◽  
Trial Registration ◽  
Altman Analysis ◽  
Lung Volumes ◽  
Registration System ◽  
Bland Altman Analysis ◽  
Dutch Trial ◽  
System P

BACKGROUND The Hexoskin smart shirt (HX) is a shirt measuring continuously and objectively and could be a potential telemonitoring system. OBJECTIVE The main focus is to determine the accuracy of the calibrated HX to measure tidal volumes (TV) in comparison to spirometry (SPIRO) in various tasks. METHODS TV of fifteen healthy subjects were measured in 7 tasks with SPIRO and HX. These tasks were performed in two sessions, between sessions all equipment was removed. A one-time spirometer-based calibration per task was determined in session 1 and applied to the corresponding task in both sessions. Bland-Altman analysis was used to determine the agreement between TV measured with HX and SPIRO. A priori we determined the bias had to be less than ±5% with LOA less than ±15%. Lung volumes were measured and should have LOA less than ±0.150 L. RESULTS In the first session, all tasks had a median bias within the criteria (±0.6%). In the second session, biases were ±8.9%, only two tasks met the criteria. In both sessions, LOA were within criteria in six out of seven tasks (±14.7%). LOA of lung volumes were > 0.150 L. CONCLUSIONS HX is able to correctly measure TV in healthy subjects in various tasks. However, after reapplication of the equipment, calibration factors cannot be reused to obtain results within the determined boundaries. CLINICALTRIAL NTR7130 (Dutch trial registration system)

scholarly journals Comparison of IOL Power Calculated by Preoperative Biometry versus Intraoperative Wavefront Aberrometry in Thai Cataract Patients.

2020 ◽  
Author(s):  
Bharkbhum Khambhiphant ◽  
Sribenjapanon Thanyaporn
Keyword(s):  
Trial Registration ◽  
Altman Analysis ◽  
Clinical Trial Registration ◽  
Bland Altman Analysis ◽  
Multifocal Iol ◽  
Registration Number ◽  
Wavefront Aberrometry ◽  
Iol Power ◽  
The One ◽  
Cataract Patients

Abstract Background: To find agreement between the calculated intraocular lens (IOL) power from using the SRK/T based preoperative biometry and the intraoperative wavefront aberrometry (ORA®) in Thai cataract patients, and to compare the accuracy of each method with the postoperative refraction results.Methods: Eyes that underwent cataract surgery with monofocal or multifocal IOL implantation were enrolled in this prospective study. All eye biometry was measured preoperatively and the ORA intraoperatively. The SRK/T suggested IOL from the preoperative biometry was chosen in all cases. The suggested power and the estimated refraction (EST) from both devices were collected. Bland Altman analysis was used to find the agreement between them. The predicted EST of implanted IOL from both devices were compared with the one-month postoperative SE. Results: The study comprised 97 eyes (79 patients). Of these, 38 eyes (39.2%) had the same suggested IOL power, 36 eyes (37.1%) were within ±0.5D, 20 eyes (20.6%) were within ±1.0D and 3 eyes were beyond ±1.0D. Bland-Altman analysis found the mean difference between IOL power calculated from both devices was 0.39 with LoA of -0.54 to 1.31. The correlation was 98.50% (95%CI 98%- 99.10%). In the same suggested IOL power group, the median difference of EST by preoperative biometry and ORA compared with one-month postoperative SE were -0.08 (95%CI: -0.08, 1.11), and -0.14 (95%CI: -0.88, 1.2), respectively. Conclusions:The ORA and preoperative biometry results were in concordance with each other. The result of preoperative biometry was more accurate than ORA in this study. Trial Registration: The thai clinical trial registration number: TCTR20171005001Registration Date: October 3rd, 2017First Enrollment: November 10th, 2017

scholarly journals Test–Retest Reliability of a Conventional Gait Model for Registering Joint Angles during Initial Contact and Toe-Off in Healthy Subjects

2021 ◽  
Vol 18 (3) ◽  
pp. 1343
Author(s):  
Francisco Molina-Rueda ◽  
Pilar Fernández-González ◽  
Alicia Cuesta-Gómez ◽  
Aikaterini Koutsou ◽  
María Carratalá-Tejada ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Intraclass Correlation ◽  
Minimal Detectable Change ◽  
Initial Contact ◽  
Altman Analysis ◽  
Joint Angles ◽  
Retest Reliability ◽  
Bland Altman Analysis ◽  
Motion System ◽  
Test Retest Reliability

The aim of this study was to evaluate the test–retest reliability of a conventional gait model (CGM), the Plug-in Gait model, to calculate the angles of the hip, knee, and ankle during initial contact (IC) and toe-off (TO). Gait analysis was performed using the Vicon Motion System® (Oxford Metrics, Oxford, UK). The study group consisted of 50 healthy subjects. To evaluate the test–retest reliability, the intraclass correlation coefficient (ICC), the standard error of measurement (SEM), the minimal detectable change (MDC), and the Bland–Altman analysis with 95% limits of agreement were calculated. The ICC for the joint angles of the hip, knee, and ankle was higher than 0.80. However, the ankle angle at IC had an ICC lower than 0.80. The SEM was <5° for all parameters. The MDC was large (>5°) for the hip angle at IC. The Bland–Altman analysis indicated that the magnitude of divergence was between ±5° and ±9° at IC and around ±7° at TO. In conclusion, the ICC for the plug-in gait model was good for the hip, knee, and ankle angles during IC and TO. The plots revealed a disagreement between measurements that should be considered in patients’ clinical assessments.

scholarly journals Comparison of IOL Power Calculated by Preoperative Biometry versus Intraoperative Wavefront Aberrometry in Thai Cataract Patients.

2020 ◽  
Author(s):  
Bharkbhum Khambhiphant ◽  
Sribenjapanon Thanyaporn
Keyword(s):  
Trial Registration ◽  
Altman Analysis ◽  
Clinical Trial Registration ◽  
Bland Altman Analysis ◽  
Multifocal Iol ◽  
Registration Number ◽  
Wavefront Aberrometry ◽  
Iol Power ◽  
The One ◽  
Cataract Patients

Abstract Background: To find agreement between the calculated intraocular lens (IOL) power from using the SRK/T based preoperative biometry and the intraoperative wavefront aberrometry (ORA®) in Thai cataract patients, and to compare the accuracy of each method with the postoperative refraction results.Methods: Eyes that underwent cataract surgery with monofocal or multifocal IOL implantation were enrolled in this prospective study. All eye biometry was measured preoperatively and the ORA intraoperatively. The SRK/T suggested IOL from the preoperative biometry was chosen in all cases. The suggested power and the estimated refraction (EST) from both devices were collected. Bland Altman analysis was used to find the agreement between them. The predicted EST of implanted IOL from both devices were compared with the one-month postoperative SE. Results: The study comprised 97 eyes (79 patients). Of these, 38 eyes (39.2%) had the same suggested IOL power, 36 eyes (37.1%) were within ±0.5D, 20 eyes (20.6%) were within ±1.0D and 3 eyes were beyond ±1.0D. Bland-Altman analysis found the mean difference between IOL power calculated from both devices was 0.39 with LoA of -0.54 to 1.31. The correlation was 98.50% (95%CI 98%- 99.10%). In the same suggested IOL power group, the median difference of EST by preoperative biometry and ORA compared with one-month postoperative SE were -0.08 (95%CI: -0.08, 1.11), and -0.14 (95%CI: -0.88, 1.2), respectively. Conclusions:The ORA and preoperative biometry results were in concordance with each other. The result of preoperative biometry was more accurate than ORA in this study. Trial Registration: The thai clinical trial registration number: TCTR20171005001Registration Date: October 3rd, 2017First Enrollment: November 10th, 2017

scholarly journals Reporting Standards for a Bland–Altman Agreement Analysis: A Review of Methodological Reviews

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 334 ◽  
Author(s):  
Oke Gerke
Keyword(s):  
A Priori ◽  
Visual Assessment ◽  
Epidemiological Survey ◽  
Altman Analysis ◽  
Limits Of Agreement ◽  
Quality Of Reporting ◽  
Local Study ◽  
Bland Altman Analysis ◽  
Homogeneity Assumption ◽  
Pubmed Search

The Bland–Altman Limits of Agreement is a popular and widespread means of analyzing the agreement of two methods, instruments, or raters in quantitative outcomes. An agreement analysis could be reported as a stand-alone research article but it is more often conducted as a minor quality assurance project in a subgroup of patients, as a part of a larger diagnostic accuracy study, clinical trial, or epidemiological survey. Consequently, such an analysis is often limited to brief descriptions in the main report. Therefore, in several medical fields, it has been recommended to report specific items related to the Bland–Altman analysis. The present study aimed to identify the most comprehensive and appropriate list of items for such an analysis. Seven proposals were identified from a MEDLINE/PubMed search, three of which were derived by reviewing anesthesia journals. Broad consensus was seen for the a priori establishment of acceptability benchmarks, estimation of repeatability of measurements, description of the data structure, visual assessment of the normality and homogeneity assumption, and plotting and numerically reporting both bias and the Bland–Altman Limits of Agreement, including respective 95% confidence intervals. Abu-Arafeh et al. provided the most comprehensive and prudent list, identifying 13 key items for reporting (Br. J. Anaesth. 2016, 117, 569–575). An exemplification with interrater data from a local study accentuated the straightforwardness of transparent reporting of the Bland–Altman analysis. The 13 key items should be applied by researchers, journal editors, and reviewers in the future, to increase the quality of reporting Bland–Altman agreement analyses.

scholarly journals Comparison of IOL Power Calculated by Preoperative Biometry versus Intraoperative Wavefront Aberrometry in Thai Cataract Patients.

2020 ◽  
Author(s):  
Bharkbhum Khambhiphant ◽  
Sribenjapanon Thanyaporn
Keyword(s):  
Trial Registration ◽  
Altman Analysis ◽  
Clinical Trial Registration ◽  
Bland Altman Analysis ◽  
Multifocal Iol ◽  
Registration Number ◽  
Wavefront Aberrometry ◽  
Iol Power ◽  
The One ◽  
Cataract Patients

Abstract Background : To find agreement between the calculated intraocular lens (IOL) power from using the SRK/T based preoperative biometry and the intraoperative wavefront aberrometry (ORA ® ) in Thai cataract patients, and to compare the accuracy of each method with the postoperative refraction results. Methods : Eyes that underwent cataract surgery with monofocal or multifocal IOL implantation were enrolled in this prospective study. All eye biometry was measured preoperatively and the ORA intraoperatively. The SRK/T suggested IOL from the preoperative biometry was chosen in all cases. The suggested power and the estimated refraction (EST) from both devices were collected. Bland Altman analysis was used to find the agreement between them. The predicted EST of implanted IOL from both devices were compared with the one-month postoperative SE. Results : The study comprised 97 eyes (79 patients). Of these, 38 eyes (39.2%) had the same suggested IOL power, 36 eyes (37.1%) were within ±0.5D, 20 eyes (20.6%) were within ±1.0D and 3 eyes were beyond ±1.0D. Bland-Altman analysis found the mean difference between IOL power calculated from both devices was 0.39 with LoA of -0.54 to 1.31. The correlation was 98.50% (95%CI 98%- 99.10%). In the same suggested IOL power group, the median difference of EST by preoperative biometry and ORA compared with one-month postoperative SE were -0.08 (95%CI: -0.08, 1.11), and -0.14 (95%CI: -0.88, 1.2), respectively. Conclusions :The ORA and preoperative biometry results were in concordance with each other. The result of preoperative biometry was more accurate than ORA in this study. Trial Registration: The clinical trial registration number: TCTR20171005001 Registration Date October 3 rd , 2017 First Enrollment November 1 st , 2017

Reply: Bland-Altman analysis for pupillometer comparison

2010 ◽  
Vol 36 (10) ◽  
pp. 1803-1804
Author(s):  
Magdalena Scheffel ◽  
Christoph Kuehne ◽  
Thomas Kohnen
Keyword(s):  
Altman Analysis ◽  
Bland Altman Analysis

scholarly journals Population-based input function for TSPO quantification and kinetic modeling with [11C]-DPA-713

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mercy I. Akerele ◽  
Sara A. Zein ◽  
Sneha Pandya ◽  
Anastasia Nikolopoulou ◽  
Susan A. Gauthier ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Kinetic Analysis ◽  
Kinetic Modeling ◽  
Input Function ◽  
Brain Regions ◽  
Population Based ◽  
Patient Specific ◽  
Systematic Bias ◽  
Altman Analysis ◽  
Bland Altman Analysis

Abstract Introduction Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF. Materials and methods Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected, (b) area under AIF curve (AUC), and (c) Weightsubject×AUC. The variability in the VT measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed. Results Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is −10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ± 38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%). Conclusions PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.

scholarly journals Evaluation of automated microvascular flow analysis software AVA 4: a validation study

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Christian S. Guay ◽  
Mariam Khebir ◽  
T. Shiva Shahiri ◽  
Ariana Szilagyi ◽  
Erin Elizabeth Cole ◽  
...  
Keyword(s):  
General Anesthesia ◽  
Validation Study ◽  
Validation Studies ◽  
Gold Standard ◽  
Flow Analysis ◽  
Automated Analysis ◽  
Altman Analysis ◽  
Analysis Software ◽  
Bland Altman Analysis ◽  
Microvascular Flow

Abstract Background Real-time automated analysis of videos of the microvasculature is an essential step in the development of research protocols and clinical algorithms that incorporate point-of-care microvascular analysis. In response to the call for validation studies of available automated analysis software by the European Society of Intensive Care Medicine, and building on a previous validation study in sheep, we report the first human validation study of AVA 4. Methods Two retrospective perioperative datasets of human microcirculation videos (P1 and P2) and one prospective healthy volunteer dataset (V1) were used in this validation study. Video quality was assessed using the modified Microcirculation Image Quality Selection (MIQS) score. Videos were initially analyzed with (1) AVA software 3.2 by two experienced investigators using the gold standard semi-automated method, followed by an analysis with (2) AVA automated software 4.1. Microvascular variables measured were perfused vessel density (PVD), total vessel density (TVD), and proportion of perfused vessels (PPV). Bland–Altman analysis and intraclass correlation coefficients (ICC) were used to measure agreement between the two methods. Each method’s ability to discriminate between microcirculatory states before and after induction of general anesthesia was assessed using paired t-tests. Results Fifty-two videos from P1, 128 videos from P2 and 26 videos from V1 met inclusion criteria for analysis. Correlational analysis and Bland–Altman analysis revealed poor agreement and no correlation between AVA 4.1 and AVA 3.2. Following the induction of general anesthesia, TVD and PVD measured using AVA 3.2 increased significantly for P1 (p < 0.05) and P2 (p < 0.05). However, these changes could not be replicated with the data generated by AVA 4.1. Conclusions AVA 4.1 is not a suitable tool for research or clinical purposes at this time. Future validation studies of automated microvascular flow analysis software should aim to measure the new software’s agreement with the gold standard, its ability to discriminate between clinical states and the quality thresholds at which its performance becomes unacceptable.

scholarly journals Comparison of IOP Measurement by Goldmann Applanation Tonometer, ICare Rebound Tonometer, and Tono-Pen in Keratoconus Patients after MyoRing Implantation

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Mahmoud Rateb ◽  
Mahmoud Abdel-Radi ◽  
Zeiad Eldaly ◽  
Mohamed Nagy Elmohamady ◽  
Asaad Noor El Din
Keyword(s):  
Central Corneal Thickness ◽  
Pearson Correlation ◽  
Corneal Thickness ◽  
Altman Analysis ◽  
Bland Altman Analysis ◽  
Rebound Tonometer ◽  
Goldmann Applanation Tonometer ◽  
Significant Difference ◽  
Applanation Tonometer ◽  
Icare Rebound Tonometer

Purpose. To evaluate the different IOP readings by Goldmann applanation tonometer (GAT), ICare rebound tonometer, and Tono-Pen in keratoconus patients after MyoRing implantation. To assess the influence of central corneal thickness (CCT) and thinnest corneal location (TCL) on IOP measurements by different tonometers. Setting. Prospective observational study was conducted in two private centers in Egypt from February 2015 to November 2016. Methods. Seventeen eyes of 10 patients suffering from keratoconus and who underwent MyoRing implantation were recruited. All subjects underwent GAT, ICare, and Tono-Pen IOP measurements in random order. Central corneal thickness and thinnest corneal location were assessed by Pentacam. Difference in mean in IOP readings was assessed by T-test. Correlation between each pair of devices was evaluated by Pearson correlation coefficient. The Bland–Altman analysis was used to assess intertonometer agreement. Results. Seventeen eyes (10 patients) were evaluated. The mean IOP reading was 13.9 ± 3.68, 12.41 ± 2.87, and 14.29 ± 1.31 mmHg in GAT, ICare, and Tono-Pen group, respectively. There was a significant difference between IOP readings by GAT/ICare and Tono-Pen/ICare (p value: 0.032 and 0.002, respectively) with no significant difference between GAT/Tono-Pen (p value: 0.554). Mean difference in IOP measurements between GAT/ICare was 1.49 ± 2.61 mmHg, Tono-Pen/ICare was 1.89 ± 2.15 mmHg, and GAT/Tono-Pen was −0.39 ± 2.59 mmHg. There was no significant correlation between the difference in IOP readings among any pair of devices and CCC or TCL. The Bland–Altman analysis showed a reasonable agreement between any pair of tonometers.

Nonlinear increases in diffusing capacity during exercise by seated and supine subjects

1981 ◽  
Vol 51 (4) ◽  
pp. 858-863 ◽  
Author(s):  
D. L. Stokes ◽  
N. R. MacIntyre ◽  
J. A. Nadel
Keyword(s):  
Carbon Monoxide ◽  
Oxygen Consumption ◽  
Healthy Subjects ◽  
Vital Capacity ◽  
Maximal Exercise ◽  
Sitting Position ◽  
Diffusing Capacity ◽  
Heavy Exercise ◽  
Lung Volumes ◽  
Rate Of Increase

To study the effects of exercise on pulmonary diffusing capacity, we measured the lungs' diffusing capacity for carbon monoxide (DLCO) during exhalation from 30 to 45% exhaled vital capacity in eight healthy subjects at rest and during exercise while both sitting and supine. We found that DLCO at these lung volumes in resting subjects was 26.3 +/- 3.2% (mean +/- SE) higher in the supine than in the sitting position (P less than 0.001). We also found that, in both positions, DLCO at these lung volumes increased significantly (P less than 0.001) with increasing exercise and approached similar values at maximal exercise. The pattern of increase in DLCO with an increase in oxygen consumption in both positions was curvilinear in that the rate of increase in DLCO during mild exercise was greater than the rate of increase in DLCO during heavy exercise (P = 0.02). Furthermore, in the supine position during exercise, it appeared that DLCO reached a physiological maximum.

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