Accuracy of the CNAP™ monitor, a noninvasive continuous blood pressure device, in providing beat-to-beat blood pressure readings in pediatric patients weighing 20-40 kilograms

AbstractThe pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

Download Full-text

Serum Periostin as a Potential Biomarker in Pediatric Patients with Primary Hypertension

Journal of Clinical Medicine ◽

10.3390/jcm10102138 ◽

2021 ◽

Vol 10 (10) ◽

pp. 2138

Author(s):

Michał Szyszka ◽

Piotr Skrzypczyk ◽

Anna Stelmaszczyk-Emmel ◽

Małgorzata Pańczyk-Tomaszewska

Keyword(s):

Blood Pressure ◽

Body Mass Index ◽

Uric Acid ◽

Body Mass ◽

Pediatric Patients ◽

Univariate Analysis ◽

Primary Hypertension ◽

Potential Biomarker ◽

Arterial Damage ◽

Serum Periostin

Experimental studies suggest that periostin is involved in tissue repair and remodeling. The study aimed to evaluate serum periostin concentration as potential biomarker in pediatric patients with primary hypertension (PH). We measured serum periostin, blood pressure, arterial damage, biochemical, and clinical data in 50 children with PH and 20 age-matched healthy controls. In univariate analysis, children with PH had significantly lower serum periostin compared to healthy peers (35.42 ± 10.43 vs. 42.16 ± 12.82 [ng/mL], p = 0.038). In the entire group of 70 children serum periostin concentration correlated negatively with peripheral, central, and ambulatory blood pressure, as well as with aortic pulse wave velocity (aPWV). In multivariate analysis, periostin level significantly correlated with age (β = −0.614, [95% confidence interval (CI), −0.831–−0.398]), uric acid (β = 0.328, [95%CI, 0.124–0.533]), body mass index (BMI) Z-score (β = −0.293, [95%CI, −0.492–−0.095]), high-density lipoprotein (HDL)-cholesterol (β = 0.235, [95%CI, 0.054–0.416]), and triglycerides (β = −0.198, [95%CI, −0.394–−0.002]). Neither the presence of hypertension nor blood pressure and aPWV influenced periostin level. To conclude, the role of serum periostin as a biomarker of elevated blood pressure and arterial damage in pediatric patients with primary hypertension is yet to be unmasked. Age, body mass index, uric acid, and lipid concentrations are key factors influencing periostin level in pediatric patients.

Download Full-text

Screening for asymptomatic diabetes and metabolic comorbidities in pediatric patients during therapy for acute lymphoblastic leukemia

Journal of Pediatric Endocrinology and Metabolism ◽

10.1515/jpem-2020-0457 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Valerie Larouche ◽

Caroline Bellavance ◽

Pauline Tibout ◽

Sebastien Bergeron ◽

David Simonyan ◽

...

Keyword(s):

Blood Pressure ◽

Acute Lymphoblastic Leukemia ◽

Pediatric Patients ◽

Lymphoblastic Leukemia ◽

Fasting Blood Glucose ◽

Metabolic Disturbances ◽

Metabolic Complications ◽

Glucose Levels ◽

Oncology Unit ◽

Hba1c Levels

Abstract Objectives Chronic metabolic disturbances related to cancer treatment are well reported among survivors of pediatric acute lymphoblastic leukemia (ALL). However, few studies have investigated the incidence of these complications during the phase of chemotherapy. We evaluated the incidence of acute metabolic complications occurring during therapy in our cohort of patients diagnosed with ALL. Methods A prospective study involving 50 ALL pediatric patients diagnosed and treated between 2012 and 2016 in our oncology unit. We collected weight, blood pressure, fasting plasma glucose and hemoglobin A1C (HBA1c) levels during the two years of therapy. Results Obesity and overweight occurred in 43 and 25%, respectively among patients and have been reached at 12 months of chemotherapy. About 26% of the patients developed high blood pressure and 14% experienced hyperglycemias without meeting diabetes criteria. There was a significant decrease of HBA1c levels between the beginning and the end of therapy (p<0.0001). Conclusions Increase of body mass index in our ALL pediatric patients occurred during the first months of therapy and plateaued after a year of treatment. We should target this population for early obesity prevention. HbA1c levels measured during therapy did not reveal diabetes criteria. Hence, fasting blood glucose levels are sufficient to monitor ALL pediatric patients’ glycemia.

Download Full-text

Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation

Sensors ◽

10.3390/s21092952 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2952

Author(s):

Latifa Nabila Harfiya ◽

Ching-Chun Chang ◽

Yung-Hui Li

Keyword(s):

Blood Pressure ◽

Absolute Error ◽

Estimation Methods ◽

British Hypertension Society ◽

Arterial Blood ◽

Proposed Model ◽

Pressure Estimation ◽

Effective Signal ◽

Blood Pressure Estimation ◽

Continuous Blood Pressure

Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based BP estimation methods are susceptible to noise and only provides systolic blood pressure (SBP) and diastolic blood pressure (DBP) prediction. Here, instead of estimating a discrete value, we focus on different perspectives to estimate the whole waveform of BP. We propose a novel deep learning model to learn how to perform signal-to-signal translation from PPG to arterial blood pressure (ABP). Furthermore, using a raw PPG signal only as the input, the output of the proposed model is a continuous ABP signal. Based on the translated ABP signal, we extract the SBP and DBP values accordingly to ease the comparative evaluation. Our prediction results achieve average absolute error under 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. These results fulfill the standard from Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) with grade A. From the results, we believe that our model is applicable and potentially boosts the accuracy of an effective signal-to-signal continuous blood pressure estimation.

Download Full-text