scholarly journals Comparison of the Accuracy of Noninvasive Hemoglobin Monitoring by Spectrophotometry (SpHb) and HemoCue® with Automated Laboratory Hemoglobin Measurement

2011 ◽  
Vol 115 (3) ◽  
pp. 548-554 ◽  
Author(s):  
Lionel Lamhaut ◽  
Roxana Apriotesei ◽  
Xavier Combes ◽  
Marc Lejay ◽  
Pierre Carli ◽  
...  
Keyword(s):  
Clinical Study ◽  
Linear Regression ◽  
Point Of Care ◽  
Reference Method ◽  
Hemoglobin Concentration ◽  
Automated Analysis ◽  
Laboratory Measurement ◽  
Altman Analysis ◽  
Simultaneous Measurements ◽  
Hemoglobin Measurement

Background The reference method for hemoglobin concentration measurement remains automated analysis in the laboratory. Although point-of-care devices such as the HemoCue® 201+ (HemoCue, Ängelholm, Sweden) provide immediate hemoglobin values, a noninvasive, spectrophotometry-based technology (Radical-7®; Masimo Corp., Irvine, CA) that provides continuous online hemoglobin (SpHb) measurements has been introduced. This clinical study aimed to test the hypothesis that SpHb monitoring was equivalent to that of HemoCue® (the automated hemoglobin measurement in the laboratory taken as a reference method) during acute surgical hemorrhage. Methods Blood for laboratory analysis was sampled after induction of anesthesia, during surgery according to the requirements of the anesthesiologist, and finally after the transfer of the patient to the recovery room. When each blood sample was taken, capillary samples were obtained for analysis with HemoCue®. SpHb monitoring was performed continuously during surgery. Using the automated hemoglobin measurement in the laboratory as a reference method, the authors tested the hypothesis that SpHb monitoring is equivalent to that of HemoCue®. The agreement between two methods was evaluated by linear regression and Bland and Altman analysis. Results Eighty-five simultaneous measurements from SpHb, HemoCue®, and the laboratory were obtained from 44 patients. Bland and Altman comparison of SpHb and HemoCue® with the laboratory measurement showed, respectively, bias of -0.02 ± 1.39 g · dl(-1) and -0.17 ± 1.05 g · dl(-1), and a precision of 1.11 ± 0.83 g · dl(-1) and 0.67 ± 0.83 g · dl(-1). Considering an acceptable difference of ± 1.0 g · dl(-1) with the laboratory measurement, the percentage of outliers was significantly higher for SpHb than for HemoCue® (46% vs. 16%, P < 0.05). Conclusions Taking automated laboratory hemoglobin measurement as a reference, the study shows that SpHb monitoring with Radical-7® gives lower readings than does the HemoCue® for assessment of hemoglobin concentration during hemorrhagic surgery.

NIR-based sensing system for non-Invasive detection of Hemoglobin for point-of-care applications

2021 ◽  
Vol 17 ◽  
Author(s):  
Yogesh Kumar ◽  
Ayush Dogra ◽  
Vikash Shaw ◽  
Ajeet Kaushik ◽  
Sanjeev Kumar
Keyword(s):  
Linear Regression ◽  
Point Of Care ◽  
Principal Component ◽  
Hemoglobin Concentration ◽  
Multivariate Linear Regression ◽  
Bulbar Conjunctiva ◽  
Clinical Practices ◽  
Non Invasive ◽  
Sensing System ◽  
Sliding Mechanism

Background: Hemoglobin is essential biomolecule for the transportation of oxygen therefore; its assessment is also obligatory very frequently in innumerable clinical practices. Traditional invasive techniques have concomitant shortcomings e.g. time delay, onset of infections and discomfort, which necessitates a non-invasive hemoglobin estimating solution to get rid of these constraints in health informatics. Currently various techniques are underway in allied domain and scanty products are also feasible in the market but due to low satisfaction rate, invasive solutions are still assumed as gold standard. Recently introduced technologies are effectively evolved as optical spectroscopy and digital photographic concepts on different sensing spots e.g. fingertip, palpebral conjunctiva, bulbar conjunctiva and fingernail. Productive sensors utilize more than eight wavelengths to compute hemoglobin concentration and four wavelengths to display only Hb-index (trending of hemoglobin) either in disposable adhesive or reusable clip type sensor’s configuration. Objective: This study aims an optimistic optical spectroscopic technique to measure hemoglobin concentration and conditional usability of non-invasive blood parameters’ diagnostics at point-of-care. Methods: Two distinguishable light emitting sources (810nm & 1300nm) are utilized at isosbestic points with single photodetector (800-1700nm). With this purpose, reusable finger probe assembly is facilitated in transmittance mode based on newly offered sliding mechanism to block ambient light. Results: Investigation with proposed design presents correlation coefficients between reference hemoglobin and every individual feature, multivariate linear regression model for highly correlated independent features. Moreover, principal component analytical model with multivariate linear regression offers mean bias of 0.036 & -0.316 g/dL, precision of 0.878 & 0.838 and limits of agreement from -1.685 to 1.758 g/dL & -1.790 to 1.474 g/dL for 18 & 21 principle components respectively. Conclusion: The encouraging readouts emphasize favorable precision therefore proposed sensing system is amenable to assess hemoglobin in settings with limited resources and strengthening future routes for point of care applications.

scholarly journals Performance of StatSensor Point-of-Care Device for Measuring Creatinine in Patients With Chronic Kidney Disease and Postkidney Transplantation

2020 ◽  
Vol 7 ◽  
pp. 205435812097071
Author(s):  
Melissa Nataatmadja ◽  
Angela W. S. Fung ◽  
Beryl Jacobson ◽  
Jack Ferera ◽  
Eva Bernstein ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Whole Blood ◽  
Point Of Care ◽  
Reference Method ◽  
Plasma Creatinine ◽  
Altman Analysis ◽  
Bland Altman Analysis ◽  
Deming Regression ◽  
Ckd Stage

Background: The StatSensor is a point-of-care device which measures creatinine in capillary whole blood. Previous studies reported an underestimation of the creatinine measurements at high creatinine concentrations and were performed in the prestandardization era for creatinine. Objective: This accuracy-based study evaluates the use of this device in kidney-transplanted patients and those with chronic kidney disease (CKD). Design: Cross-sectional diagnostic accuracy study. Setting: Nephrology outpatient clinic in an urban tertiary center. Participants: Adults with CKD or a functioning kidney transplant. Measurements: Duplicate StatSensor creatinine measurements were performed on capillary whole blood samples collected by direct fingerstick and SAFE-T-FILL collection device. Results were compared with simultaneous venous blood sampling for serum and plasma creatinine measured by an enzymatic method on the Roche Integra 400 mainframe analyzer with traceability to the ID-GC-MS (isotope dilution gas chromatography mass spectrometry) reference method. Methods: Deming regression, Pearson correlation coefficient, and Bland-Altman analysis were used to assess accuracy and comparability between capillary whole blood measured by StatSensor and plasma creatinine measured by routine analyzer with traceability to the reference method. Estimated glomerular filtration (eGFR) rates were calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and concordance with Kidney Disease Improving Global Outcomes (KDIGO) CKD stage classification was evaluated. Results: There were 60 participants (mean age = 61.9 ± 15.0 years, 55% men, 33% transplant, mean plasma creatinine = 137 ± 59 µmol/L). Bland-Altman analysis indicated a positive mean bias of 12.7 µmol/L between StatSensor fingerstick creatinine measurement and plasma creatinine. Comparison of eGFR (CKD-EPI) calculated from the StatSensor fingerstick creatinine versus plasma creatinine showed misclassification across all KDIGO CKD stages. Postanalytical correction of the bias did not improve misclassifications. The use of mean of duplicate StatSensor creatinine results did not improve performance compared with the use of singlet results. Limitations: Single center, limited participant numbers. Conclusions: The results of our study suggest that the limiting characteristics of the StatSensor device are not only bias, but also imprecision. The level of imprecision observed may influence clinical decision-making and limit the usefulness of StatSensor as a CKD screening tool. If choosing to utilize it for either screening for or monitoring CKD, it is essential that clinicians understand the limitations of point-of-care devices and apply this knowledge to test interpretation.

scholarly journals Comparison of noninvasive and invasive point-of-care testing methods with reference method for hemoglobin measurement

2017 ◽  
Vol 32 (3) ◽  
pp. e22309 ◽  
Author(s):  
Gamze Avcioglu ◽  
Cemil Nural ◽  
Fatma Meriç Yilmaz ◽  
Pervin Baran ◽  
Özcan Erel ◽  
...  
Keyword(s):  
Point Of Care ◽  
Reference Method ◽  
Point Of Care Testing ◽  
Testing Methods ◽  
Hemoglobin Measurement

A New Detection Chamber Design on Centrifugal Microfluidic Platform to Measure Hemoglobin of Whole Blood

2021 ◽  
pp. 247263032098545
Author(s):  
Ehsan Mahmodi Arjmand ◽  
Maryam Saadatmand ◽  
Manouchehr Eghbal ◽  
Mohammad Reza Bakhtiari ◽  
Sima Mehraji
Keyword(s):  
Driving Forces ◽  
Focal Point ◽  
Point Of Care ◽  
Hemoglobin Concentration ◽  
Turnaround Time ◽  
Air Bubbles ◽  
Final Solution ◽  
Blood Samples ◽  
Detection Chamber ◽  
Hemoglobin Measurement

Undoubtedly, microfluidics has been a focal point of interdisciplinary science during the last two decades, resulting in many developments in this area. Centrifugal microfluidic platforms have good potential for use in point-of-care devices because they take advantage of some intrinsic forces, most notably centrifugal force, which obviates the need to any external driving forces. Herein, we introduce a newly designed detection chamber for use on microfluidic discs that can be employed as an absorbance readout step in cases where the final solution has a very low viscosity and surface tension. In such situations, our chamber easily eliminates the air bubbles from the final solution without any interruption. One microfluidic disc for measuring the hemoglobin concentration was designed and constructed to verify the correct functioning of this detection chamber. This disc measured the hemoglobin concentration of the blood samples via the HiCN method. Then, the hemoglobin concentration of 11 blood samples was quantified and compared with the clinic’s data using the hemoglobin measurement disc, which included four hemoglobin measurement sets, and each set contained two inlets for the blood sample and the reagent, one two-part mixing chamber, and one bubble-free detection chamber. The measured values of the disc had good linearity and conformity compared with the clinic’s data, and there were no air bubbles in the detection step. In this study, the standard deviation and the turnaround time were ± 0.51 g/dL and 68 s, respectively.

scholarly journals Comparison of Four Commercially Available Point-of-Care Tests to Detect Antibodies against Canine Parvovirus in Dogs

Viruses ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Michèle Bergmann ◽  
Mike Holzheu ◽  
Yury Zablotski ◽  
Stephanie Speck ◽  
Uwe Truyen ◽  
...  
Keyword(s):  
Test Performance ◽  
Point Of Care ◽  
Reference Method ◽  
Canine Parvovirus ◽  
P Value ◽  
Important Indicator ◽  
Specificity And Sensitivity ◽  
Specific Pathogen ◽  
The One ◽  
Point Of Care Tests

Measuring antibodies to evaluate dogs´ immunity against canine parvovirus (CPV) is useful to avoid unnecessary re-vaccinations. The study aimed to evaluate the quality and practicability of four point-of-care (POC) tests for detection of anti-CPV antibodies. The sera of 198 client-owned and 43 specific pathogen-free (SPF) dogs were included; virus neutralization was the reference method. Specificity, sensitivity, positive and negative predictive value (PPV and NPV), and overall accuracy (OA) were calculated. Specificity was considered to be the most important indicator for POC test performance. Differences between specificity and sensitivity of POC tests in the sera of all dogs were determined by McNemar, agreement by Cohen´s kappa. Prevalence of anti-CPV antibodies in all dogs was 80% (192/241); in the subgroup of client-owned dogs, it was 97% (192/198); and in the subgroup of SPF dogs, it was 0% (0/43). FASTest® and CanTiCheck® were easiest to perform. Specificity was highest in the CanTiCheck® (overall dogs, 98%; client-owned dogs, 83%; SPF dogs, 100%) and the TiterCHEK® (overall dogs, 96%; client-owned dogs, 67%; SPF dogs, 100%); no significant differences in specificity were observed between the ImmunoComb®, the TiterCHEK®, and the CanTiCheck®. Sensitivity was highest in the FASTest® (overall dogs, 95%; client-owned dogs, 95%) and the CanTiCheck® (overall dogs, 80%; client-owned dogs, 80%); sensitivity of the FASTest® was significantly higher compared to the one of the other three tests (McNemars p-value in each comparison: <0.001). CanTiCheck® would be the POC test of choice when considering specificity and practicability. However, differences in the number of false positive results between CanTiCheck®, TiterCHEK®, and ImmunoComb® were minimal.

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.

Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency

2021 ◽  
pp. 000370282110345
Author(s):  
Tatu Rojalin ◽  
Dexter Antonio ◽  
Ambarish Kulkarni ◽  
Randy P. Carney
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Domain Knowledge ◽  
Collection Efficiency ◽  
Point Of Care ◽  
Automated Analysis ◽  
Downstream Processing ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Expert User

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

scholarly journals DIFFERENCES IN CHANGES OF HEMOGLOBIN BETWEEN 6-12 HOURS AND 12-14 HOURS AFTER TRANSFUSION

2018 ◽  
Vol 24 (2) ◽  
pp. 108
Author(s):  
Rosita Linda ◽  
Devita Ninda
Keyword(s):  
Blood Transfusion ◽  
Secondary Data ◽  
Hemoglobin Concentration ◽  
Fresh Frozen Plasma ◽  
Blood Components ◽  
Blood Products ◽  
Fresh Frozen ◽  
Frozen Plasma ◽  
Hemoglobin Measurement ◽  
Baseline Hemoglobin

Each year more than 41,000 blood donations are needed every day and 30 million blood components are transfused. Blood products that can be transfused include Packed Red Cells (PRC), Whole Blood (WB), Thrombocyte Concentrate (TC), Fresh Frozen Plasma (FFP). Monitoring Hemoglobin (Hb) after transfusion is essential for assessing the success of a transfusion. The time factor after transfusion for Hemoglobin (Hb) examination needs to be established, analyze to judge the success of a blood transfusion which is performed. The aim of this study was to analyze the differences in changes of hemoglobin between 6-12 hours, and 12-24 hours after-transfusion. This study was retrospective observational using secondary data. The subjects were patients who received PRC, and WBC transfusion. At 6-12, and 12-24 hours after-transfusion, hemoglobin, RBC, and hematocrit were measured. Then the data were analyzed by unpaired t-test. The collected data included the results of the Hb pre-transfusion, 6-12, and 12-24 hours after-transfusion. The subjects of this study were 98 people. The administration of transfusion increased by 10-30% in hemoglobin concentration at 6-12 hours after-transfusion. While at 12-24 hours after-transfusion, hemoglobin after-transfusion increased 15-37% from the baseline. Hemoglobin values were not different at any of the defined after-transfusion times (p = 0.76 (p>0.05)). Hemoglobin values were not different at 6-12 hours, and 12-24 hours after-transfusion.    Keywords: Hemoglobin, measurement, after-transfusion 

scholarly journals Point-of-care microvolume cytometer measures platelet counts with high accuracy from capillary blood

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256423
Author(s):  
William M. Dickerson ◽  
Rebecca Yu ◽  
Helena U. Westergren ◽  
Jonathan Paraskos ◽  
Philipp Schatz ◽  
...  
Keyword(s):  
Point Of Care ◽  
Venous Blood ◽  
Reference Method ◽  
Time Data ◽  
Platelet Recovery ◽  
Blood Samples ◽  
Impedance Analyzer ◽  
Platelet Counts ◽  
Platelet Antibodies ◽  
Care Assessment

Background Point-of-care (PoC) testing of platelet count (PLT) provides real-time data for rapid decision making. The goal of this study is to evaluate the accuracy and precision of platelet counting using a new microvolume (8 μL), absolute counting, 1.5 kg cytometry-based blood analyzer, the rHEALTH ONE (rHEALTH) in comparison with the International Society of Laboratory Hematology (ISLH) platelet method, which uses a cytometer and an impedance analyzer. Methods Inclusion eligibility were healthy adults (M/F) ages 18–80 for donation of fingerprick and venous blood samples. Samples were from a random N = 31 volunteers from a single U.S. site. Samples were serially diluted to test thrombocytopenic ranges. Interfering substances and conditions were tested, including RBC fragments, platelet fragments, cholesterol, triglycerides, lipids, anti-platelet antibodies, and temperature. Results The concordance between the rHEALTH and ISLH methods had a slope = 1.030 and R2 = 0.9684. The rHEALTH method showed a correlation between capillary and venous blood samples (slope = 0.9514 and R2 = 0.9684). Certain interferents changed platelet recovery: RBC fragments and anti-platelet antibodies with the ISLH method; platelet fragments and anti-platelet antibodies on the rHEALTH; and RBC fragments, platelets fragments, triglycerides and LDL on the clinical impedance analyzer. The rHEALTH’s precision ranged from 3.1–8.0%, and the ISLH from 1.0–10.5%. Conclusions The rHEALTH method provides similar results with the reference method and good correlation between adult capillary and venous blood samples. This demonstrates the ability of the rHEALTH to provide point-of-care assessment of normal and thrombocytopenic platelet counts from fingerprick blood with high precision and limited interferences.

scholarly journals Evidence for the use of the Alere Afinion™ AS100 for measuring the levels of C-reactive protein in an elderly South African population

2020 ◽  
pp. 71-76
Author(s):  
I Mpofana ◽  
M Nyirenda ◽  
N Abbai
Keyword(s):  
Linear Regression Analysis ◽  
Reference Method ◽  
Altman Analysis ◽  
Concordance Correlation ◽  
Excellent Correlation ◽  
C Reactive Protein ◽  
Serum Samples ◽  
Cvd Risk ◽  
Bland Altman Analysis ◽  
Reactive Protein

Introduction: This study evaluated the performance of the Alere Afinion™ AS100 analyser for the measurement of C-reactive protein (CRP) levels in a population of older adults from South Africa. Methods: This study was a sub-study of the Sexual Health, HIV infection and comorbidity with non-communicable diseases among Older Persons (SHIOP) study. The median age of SHIOP participants was 61 years (interquartile range 12). Serum samples collected through SHIOP were used to measure CRP levels on the Alere Afinion™ AS100 (Point-of-care) and ABX Pentra 400 (reference method), respectively. Bland–Altman analysis and Lin’s concordance correlation coefficients were used to assess the agreement between the two analysers. Results: A total of 183 serum samples were tested in the study. The Alere Afinion™ AS100 median values for CRP were 9.5 mg/L and 11.5 mg/L in women and men respectively (p = 0.275). The ABX Pentra 400 median levels were lower with 5.6 mg/L and 3.6 mg/L for women and men (p = 0.027), respectively. Bland–Altman analysis and linear regression analysis showed an excellent correlation between the Pentra and Afinion analysers, with a Lin’s concordance correlation coefficient of 0.971. The Alere Afinion™ AS100 was able to correctly classify > 90% (165/183) of the CRP results when compared to the ABX Pentra 400. Conclusion: This study showed that the Alere Afinion™ AS100 had an excellent correlation with a standard laboratory method. However, the Afinion™ AS100 did not correlate well at elevated CRP levels. This may not be clinically significant since the cut-points for CVD risk are at much lower levels.

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