Model-informed vancomycin precision dosing by population pharmacokinetics combined with machine learning algorithms.

The aims of this study are the following: To examine whether field tests predict cardiorespiratory fitness in people with coronary heart disease (CHD) and to determine if heart rate (HR) agreement between the first ventilatory threshold (VT1) and field tests is sufficient for prescribing exercise intensity. Participants randomly completed field tests and a cardiopulmonary exercise test (CPET). Linear regression models were developed to predict VT1. Agreement between predicted and measured peak oxygen consumption (V̇O2peak) as well as field test terminal HR and HR at VT1 (VT1HR) was assessed using Pearson correlations, Bland-Altman analyses, mean absolute percentage error (MAPE), Lin's concordance correlation coefficient (CCC), and standard error of estimate (SEE). Agreement between predicted and measured V̇O2peak was modest (Pearson's r = 0.27-0.77; Lin's CCC = 0.132-0.735; MAPE = 16.1-30.1%; SEE = 4.7-6.8 mL·kg-1·min-1). Agreement between field test terminal HR and VT1HR was moderate (Pearson's r = 0.50-0.67; Lin's CCC = 0.36-0.68; MAPE = 8.9-13.7%; SEE = 11.9-18.7 bpm; Bland-Altman 95%LOA = -3.5 to 13.7 bpm). Field tests demonstrated variable accuracy for predicting V̇O2peak, with none meeting predefined agreement criteria. Regression models indicate field tests can estimate VT1; however, levels of HR agreement indicate CPET is necessary for prescribing exercise intensity.

Validation of lower limb segmental volumetry with hand-held, self-positioning three-dimensional laser scanner against water displacement.

Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. Children (n = 82, 61% male, 43.9% black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. Three-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's concordance correlation coefficient (CCC), mean absolute error (MAE), and mean absolute percent error (MAPE) assessed agreement with ECG estimated heart rate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD = 14.2) beats/minute and 20.4% (SD = 18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD = 9.9) beats/minute and 7.3% (SD = 10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e. hardware, software, etc.) of the GT9X's poor performance.

The objective of this cross-sectional, diagnostic accuracy study was to validate a human blood glucose meter (Contour Next One Meter, Ascensia Diabetes Care) for accuracy and precision when measuring blood glucose, and for diagnostic accuracy for hypoglycemic status in dairy calves using whole blood and blood plasma. A total of 49 male dairy calves [body weight (BW): 46.3 ± 0.8 kg] had jugular catheters placed within 75 min after birth. Thereafter, blood was withdrawn from the catheter at specific time points (−10, 10, 20, 30, 45, 60, 90, 120, 180, 240, 360, 480, and 600 min) relative to the first and second colostrum feedings (2 h 15 min and 14 h 5 min postnatal; feeding rate: 7% of BW wt/wt). The reference standard method for plasma glucose concentration was determined colorimetrically and in duplicate using the glucose oxidase-peroxidase reaction. Data were assessed for agreement between the glucose meter and the reference standard using Lin's concordance correlation coefficients (CCC), coefficients of determination (precision), and Bland-Altman plots. In addition, a mixed linear regression model was built using the reference method as the outcome, with the glucose meter and repeated measures of time as the explanatory variables and calf as a random effect. The sensitivity (Se), specificity (Sp), and area under the curve (AUC) for the glucose meter using calf whole-blood and plasma were calculated at a threshold of <4.44 mmol/L to determine hypoglycemia. The precision (CCC = 0.95, R2 = 0.93) and accuracy (AUC = 0.98) of the glucose meter were very high when used on 1,303 blood plasma samples. Youden's index revealed a threshold of <4.45 mmol/L for the glucose meter when used with plasma, leading to Se of 94.2% and Sp of 91.9%, with 92.5% of samples being correctly classified, suggesting high diagnostic accuracy. When using whole blood, precision (CCC = 0.85 and R2 = 0.73) and accuracy (AUC = 0.92) were high when used on 476 samples. Youden's index revealed a threshold of <4.95 mmol/L for the glucose meter when used with whole calf blood, leading to Se of 95.6% and Sp of 80.3%, with 84.7% of samples being correctly classified, suggesting high diagnostic accuracy for use on farm. In summary, this glucose meter was validated for measuring calf blood glucose using both plasma and whole blood. This meter can measure glycemic status in calves and may be useful for clinical and on-farm use to make intervention decisions.

To evaluate continuous and non-invasive sweat-inferred blood lactate monitoring for determining the exercise intensity at the second metabolic threshold (MT2) and maximal lactate steady state (MLSS), compared to capillary blood lactate and gas exchange analysis. 17 physically active individuals (11 males and 6 females) and 19 endurance athletes (14 males and 5 females) completed a maximal graded exercise test (GXT) to quantify oxygen uptake (VO2), percentage of peak oxygen uptake (%VO2peak) and power output (watts) at MT2, using sweat-inferred blood lactate and capillary blood lactate for the second lactate threshold (LT2), and gas exchange analysis for the second ventilatory threshold (VT2). Participants also completed a MLSS test to quantify the power output (watts) at MLSS. Between-methods agreement was assessed using linear mixed models with mean differences (MD), mean absolute error (MAE), mean absolute percentage error (MAPE), Lin's concordance correlation coefficient (CCC) and Bland-Altman analysis. Compared with VT2, LT2 derived from sweat-inferred blood lactate showed no significant differences when using the Log-Exp-Mod-Dmax method, yielding the strongest agreement across VO2 (MD = 0.3 mL·min -1·kg -1, p = 0.999; MAE = 2.1 mL·min -1·kg -1, MAPE = 5.2%), %VO2peak (MD = 0.4%, p = 0.999; MAE = 4.1%, MAPE = 5.2%), and power output (MD = 1.9W, p = 0.999; MAE = 9.1W, MAPE = 5.0%). Direct comparison of capillary blood and sweat-inferred blood lactate using the Log-Exp-Mod-Dmax method showed high agreement for VO2 (MD = 0.2 mL·min -1·kg -1, p = 0.999; MAE = 2.4 mL·min -1·kg -1, MAPE = 6.4%), %VO2peak (MD = -0.1%, p = 0.999; MAE = 4.9%, MAPE = 6.4%), and power output (MD = 0.0W, p = 0.999; MAE = 10.6W, MAPE = 6.1%). Capillary blood and sweat-inferred blood lactate also showed excellent agreement for MLSS-associated power output (MD = -2.0 W, p = 0.089; MAE = 3.1 W, MAPE = 2.5%). Continuous sweat-inferred blood lactate can be used to determine MT2 and MLSS intensities, providing an alternative to capillary blood lactate.

(1) Background: Consumer smartwatches may be a helpful tool to screen for atrial fibrillation (AF). However, validation studies on older stroke patients remain scarce. The aim of this pilot study from RCT NCT05565781 was to validate the resting heart rate (HR) measurement and the irregular rhythm notification (IRN) feature in stroke patients in sinus rhythm (SR) and AF. (2) Methods: Resting clinical HR measurements (every 5 min) were assessed using continuous bedside ECG monitoring (CEM) and the Fitbit Charge 5 (FC5). IRNs were gathered after at least 4 h of CEM. Lin's concordance correlation coefficient (CCC), Bland-Altman analysis, and mean absolute percentage error (MAPE) were used for agreement and accuracy assessment. (3) Results: In all, 526 individual pairs of measurements were obtained from 70 stroke patients-age 79.4 years (SD ± 10.2), 63% females, BMI 26.3 (IQ 22.2-30.5), and NIHSS score 8 (IQR 1.5-20). The agreement between the FC5 and CEM was good (CCC 0.791) when evaluating paired HR measurements in SR. Meanwhile, the FC5 provided weak agreement (CCC 0.211) and low accuracy (MAPE 16.48%) when compared to CEM recordings in AF. Regarding the accuracy of the IRN feature, analysis found a low sensitivity (34%) and high specificity (100%) for detecting AF. (4) Conclusion: The FC5 was accurate at assessing the HR during SR, but the accuracy during AF was poor. In contrast, the IRN feature was acceptable for guiding decisions regarding AF screening in stroke patients.

BackgroundAutomatic noninvasive oscillometric blood pressure (NIBP) devices measure mean arterial pressure (MAP); systolic and diastolic blood pressure (SBP, DBP) are algorithmically derived from MAP. The most invalid NIBP measure is SBP, yet stroke practitioners use it to manage blood pressure (BP) in accordance with thrombolysis guidelines. We determined agreement between SBP, DBP, and MAP measured manually and by NIBP in patients treated with alteplase.MethodsA multisite prospective observational study of NIBP and manual BP agreement was conducted in patients treated with alteplase immediately after bolus and infusion initiation using methods established in guidelines for the assessment of device agreement. Dual auscultatory stethoscopes were used by 2 investigators to ensure agreement with each manual BP variable and MAP was calculated using the standard formula for manual BP measures. Data were analyzed using Bland–Altman analyses and Lin concordance correlation coefficient.ResultsA total of 7 hospitals participated, collecting 5 sets of manual/NIBP BPs in 95 patients treated with alteplase (475 paired measures). Range in limits of agreement were SBP: −28.91 to 21.41 mmHg with Lin's concordance correlation coefficient 0.8; DBP: −21.0 to 19.0 mmHg with Lin's concordance correlation coefficient 0.69; and MAP: −27.5 to 16.5 mmHg with Lin's concordance correlation coefficient 0.7. There was no difference in device agreement by BP device manufacturer brand. Differences in SBP, DBP, and MAP between NIBP and manual sphygmomanometry failed to reach guideline recommendations requiring 80% of measures to fall within a 5 mmHg difference and 95% of measures to fall within a 10 mmHg difference.ConclusionNIBP devices produce significantly different BP measures then manual sphygmomanometry auscultated BP. Because NIBP devices rely on the MAP and do not directly measure SBP and DBP, definition of what constitutes safe MAP boundaries in patients treated with alteplase should be determined when automatic BP measurement is used in clinical practice.

As wearable technology becomes increasingly popular and sophisticated, independent validation is needed to determine its accuracy and potential applications. Therefore, the purpose of this study was to evaluate the accuracy (validity) of VO2max estimates and blood oxygen saturation measured via pulse oximetry using the Garmin fēnix 6 with a general population participant pool. We recruited apparently healthy individuals (both active and sedentary) for VO2max (n = 19) and pulse oximetry testing (n = 22). VO2max was assessed through a graded exercise test and an outdoor run, comparing results from the Garmin fēnix 6 to a criterion measurement obtained from a metabolic system. Pulse oximetry involved comparing fēnix 6 readings under normoxic and hypoxic conditions against a medical-grade pulse oximeter. Data analysis included descriptive statistics, error analysis, correlation analysis, equivalence testing, and bias assessment, with the validation criteria set at a concordance correlation coefficient (CCC) > 0.7 and a mean absolute percentage error (MAPE) < 10%. The Garmin fēnix 6 provided accurate VO2max estimates, closely aligning with the 15 s and 30 s averaged laboratory data (MAPE for 30 s avg = 7.05%; Lin's concordance correlation coefficient for 30 s avg = 0.73). However, it failed to accurately measure blood oxygen saturation (BOS) under any condition or combined analysis (MAPE for combined conditions BOS = 4.29%; Lin's concordance correlation coefficient for combined conditions BOS = 0.10). While the Garmin fēnix 6 shows promise for estimating the VO2max, reflecting its utility for both individuals and researchers, it falls short in accurately measuring BOS, limiting its application for monitoring acclimatization and managing pulmonary diseases. This research underscores the importance of validating wearable technology to leverage its full potential in enhancing personal health and advancing public health research.

ObjectiveThis study aims to assess the suitability of Fitbit devices for real-time physical activity (PA) and sedentary behaviour (SB) monitoring in the context of just-in-time adaptive interventions (JITAIs) and event-based ecological momentary assessment (EMA) studies.MethodsThirty-seven adults (18–65 years) and 32 older adults (65+) from Belgium and the Czech Republic wore four devices simultaneously for 3 days: two Fitbit models on the wrist, an ActiGraph GT3X+ at the hip and an ActivPAL at the thigh. Accuracy measures included mean (absolute) error and mean (absolute) percentage error. Concurrent validity was assessed using Lin's concordance correlation coefficient and Bland–Altman analyses. Fitbit's sensitivity and specificity for detecting stepping events across different thresholds and durations were calculated compared to ActiGraph, while ROC curve analyses identified optimal Fitbit thresholds for detecting sedentary events according to ActivPAL.ResultsFitbits demonstrated validity in measuring steps on a short time scale compared to ActiGraph. Except for stepping above 120 steps/min in older adults, both Fitbit models detected stepping bouts in adults and older adults with sensitivities and specificities exceeding 87% and 97%, respectively. Optimal cut-off values for identifying prolonged sitting bouts achieved sensitivities and specificities greater than 93% and 89%, respectively.ConclusionsThis study provides practical insights into using Fitbit devices in JITAIs and event-based EMA studies among adults and older adults. Fitbits’ reasonable accuracy in detecting short bouts of stepping and SB makes them suitable for triggering JITAI prompts or EMA questionnaires following a PA or SB event of interest.

Negative dietary cation-anion difference (DCAD) diets have been implemented to combat hypocalcemia, a common peripartal disease in dairy cows; however, the extent of compensatory metabolic acidosis necessary and the subsequent effects on performance are still debated. Additionally, there is a need for an inexpensive, accurate method to measure urine pH on farm during the prepartum period to assess the extent of metabolic acidosis achieved by negative DCAD diets. Therefore, this experiment was conducted to determine the accuracy of Fisher pH sticks (pHF; ThermoFisher Scientific) and pHion balance test strips (pHI; pHion Balance) compared with a portable pH meter (pHP; Accumet AP115, ThermoFisher Scientific) in measuring urine pH (UpH) and the effect of UpH on pre- and postpartum dry matter intake (DMI), milk, and milk composition yields. Cows consumed a total mixed ration with a DCAD of −118 mEq/kg for 4 wk prepartum and 397 mEq/kg for 4 wk postpartum. Prepartum UpH measurements (n = 75) for each cow were averaged and used to classify cows in terms of urine pH as low (UpH ≤5.54; mean ± standard deviation; 5.44 ± 0.07), medium (UpH >5.54 and ≤5.90; 5.67 ± 0.09), or high (UpH >5.90; 6.42 ± 0.36). Cows were milked twice a day, and milk samples were taken on d 7 ± 1.3, 14 ± 1.4, and 28 ± 1.1 relative to calving. Milk yield and DMI were recorded daily and averaged weekly. Bland-Altman plots and Lin's concordance correlation coefficient (CCC) were used to assess the agreement between pHP and pHF or pHI (n = 375). Receiver operating characteristic curves were used to determine the threshold with pHF and pHI that best discriminated between UpH >5.75 and ≤5.75 compared with pHP, and area under the curve (AUC) was used to assess the accuracy. At the UpH threshold of 5.75 for pHF and pHI, the sensitivity, specificity, and AUC were 89.5 and 87.4, 99.1 and 97.0, and 0.94 and 0.92, respectively. The CCC was 0.93 for pHF and pHI, indicating near-perfect agreement with pHP. The UpH did not affect pre- or postpartum DMI. There was a tendency for a UpH × week interaction for milk yield, in which milk yields were less for cows in the low and medium groups. In conclusion, pHI and pHF are accurate measurements for UpH, and UpH did not affect DMI; however, when UpH was low or medium, milk yield was decreased at wk 1 postpartum.

This study aimed to evaluate the agreement between three body composition devices and dual-energy X-ray absorptiometry (DXA) in assessing segmental body composition among healthy Iranian adults. This comparative study recruited 62 healthy adults (33 men and 29 women) from Tehran, Iran, using a convenience sampling approach. Socio-demographic data were collected, and segmental body composition was assessed using several devices, including the InBody 770, Anea Bioelectrical Impedance Analysis (BIA), Tanita BC-418, and DXA. Agreement between DXA and the BIA devices was evaluated using Lin's concordance correlation coefficient. Additionally, Bland-Altman plots and mean percentage error were applied to assess the consistency between the two methods. The Anea, InBody 770, and Tanita BC-418 devices showed strong correlation with DXA for all segmental measurements (r≥0.74, p<0.05). Moderate agreement (concordance correlation coefficient (CCC): 0.90 ≤ CCC < 0.95) with the DXA method was found for some segments using the Anea (trunk fat mass, arms fat-free mass, legs fat-free mass, and trunk fat-free mass) and the InBody 770 (trunk lean body mass and trunk fat mass) devices. The estimation of legs fat mass was the least accurate across all BIA devices. Furthermore, subgroup analyses showed that the BIA devices provided more precise results in men and in individuals with a Body Mass Index (BMI) <25.00 kg/m2. The Anea BIA and InBody 770 devices show relatively acceptable validity for segmental body composition assessment. More research is needed to confirm these findings.

Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr. Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC). Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95. The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.

Evaluation of the agreement and clinical utility of anthropometric equation-estimated appendicular skeletal muscle mass compared to dual-energy X-ray absorptiometry in patients with type 2 diabetes mellitus.

To validate MRI for the quantification of the femoral neck version (FNV) using posterior lesser trochanteric Line (PLTL) and to compare reliability of the PLTL to the epicondylar and retrocondylar measurements. A retrospective review of 3T MRI scans performed for femoroacetabular impingement (FAI). Two musculoskeletal radiologists performed the measurements. MRI measurements of the PLTL were compared to CT using Bland Altman, Lin's concordance, and Lin's correlation coefficients. Interobserver reliability was determined using Bland Altman, Lin's concordance, and Lin's correlation coefficients. Intraobserver reliability was determined using Lin's concordance and Lin's correlation coefficients. Forty-five patients (90 lower extremities) met the inclusion criteria. Ages ranged from 20 to 41years, with a mean of 31.5years. There were 22 females and 23 males. Lin's concordance correlation coefficient for MRI and CT measurements of PLTL was substantial: 0.96 (95% CI: 0.94-0.98). PLTL Lin's correlation coefficient was 0.825 (95% CI 0.732-0.918) and indicated good interobserver reliability. Epicondylar and retrocondylar methods Lin's correlation coefficients demonstrated moderate interobserver reliability at 0.601 (0.415-0.786) and 0.632 (0.456-0.807), respectively. There was moderate 95% confidence interval overlap between the PLTL and the other measurements. Bland-Altman plots for each measurement were similar and demonstrated no bias. There was excellent intraobserver reliability (> 0.900) with significant 95% confidence interval overlap. MRI measurements of the PLTL are comparable to CT. The PLTL has good reliability between readers for the quantification of FNV using MRI, which could help avoid unnecessary radiation exposure using CT and reduce MRI scan time.

The quick and the dead