Improved non-invasive total haemoglobin measurements after in-vivo adjustment

Noninvasive hemoglobin monitoring for maintaining hemoglobin concentration within the target range during major noncardiac surgery: A randomized controlled trial

STUDY OBJECTIVE

The effect of noninvasive CO-oximetry hemoglobin (SpHb) monitoring on the clinical outcomes of patients undergoing surgery remains unclear. This trial aimed to evaluate whether SpHb monitoring helps maintain hemoglobin levels within a predefined target range during major noncardiac surgeries with a potential risk of intraoperative hemorrhage.

DESIGN

A single-center, prospective, randomized controlled trial.

SETTING

University hospital.

PATIENTS

One hundred and thirty patients undergoing elective noncardiac surgery with a potential risk of hemorrhage.

INTERVENTIONS

Patients were randomly allocated to undergo either SpHb-guided management (SpHb group) or usual care (control group).

MEASUREMENTS

The primary outcome was the rate of deviation of the total hemoglobin concentration (determined from laboratory testing) from a pre-specified target range (8-14 g/dL). This was defined as the number of laboratory tests revealing such deviations divided by the total number of laboratory tests performed during the surgery.

MAIN RESULTS

The primary outcome occurred significantly less frequently in the SpHb group as compared to that in the control group (15/555 [2.7%]) vs. 68/598 [11.4%]; relative risk, 0.24; 95% confidence interval, 0.13-0.41; P < 0.001). Fewer point-of-care blood tests were performed in the SpHb group than in the control group (median [interquartile range], 2 [1-4] vs. 4 [2-5]; P < 0.001). There were no significant intergroup differences in the number of patients who received red blood cell transfusions during surgery (SpHb vs. control, 33.8% vs. 46.2%; P = 0.201). The incidence of unnecessary red blood cell preparation (>2 units) was lower in the SpHb group than in the control group (3.1% vs. 16.9%; P = 0.024).

CONCLUSIONS

Compared with routine care, SpHb-guided management resulted in significantly lower rates of hemoglobin deviation outside the target range intraoperatively in patients undergoing major noncardiac surgeries with a potential risk of hemorrhage.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov (identifier: NCT03816514).

Postoperative Noninvasive Hemoglobin Monitoring Is Useful to Prevent Unnoticed Postoperative Anemia and Inappropriate Blood Transfusion in Patients Undergoing Total Hip or Knee Arthroplasty: A Randomized Controlled Trial

Introduction

Postoperative nadir hemoglobin (Hb) is related to a longer length of stay for geriatric patients undergoing orthopedic surgery. We investigated whether postoperative pulse Hb (SpHb) measurement is useful for avoiding anemia and inappropriate blood transfusion after total hip arthroplasty and total knee arthroplasty.

Material and Methods

This prospective randomized controlled study included 150 patients randomly assigned to receive blood transfusion, either guided by SpHb monitoring (SpHb group) or based on the surgeons’ experience (control group). The target laboratory Hb value was set to >8 g/dL at postoperative day 1 (POD1). The primary endpoints were the product of total time and degree of SpHb <8 g/dL (area under SpHb 8 g/dL) during the period up to POD1 and the incidence of laboratory Hb <8 g/dL at POD1. The secondary endpoints were the amount of blood transfusion and inappropriate blood transfusion, which was defined as allogeneic blood transfusion unnecessary in a case of SpHb >12 g/dL or delayed transfusion in a case of SpHb <8 g/dL.

Results

The area under SpHb 8 g/dL was 37.6 ± 44.1 g/dL-min (5 patients) in the control group and none in the SpHb group (P = .0281). There was 1 patient with Hb <8 g/dL at POD1 in the control group. There was no difference in laboratory Hb levels and the amount of blood transfusion. Forty-one patients (19 in the control group and 22 in the SpHb group) received an allogeneic blood transfusion. Among these patients, 7 in the control group and none in the SpHb group received inappropriate blood transfusion (P = .0022).

Discussion

The SpHb monitoring could reduce unnoticed anemia, which may prevent complications and be useful in avoiding unnecessary and excessive blood transfusion.

Conclusion

Postoperative SpHb monitoring decreased the incidence of transient, unnoticed anemia during the period up to POD1 and inappropriate blood transfusion.

Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations

BACKGROUND

There is worldwide demand for an affordable hemoglobin measurement solution, which is a particularly urgent need in developing countries. The smartphone, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Consideration of a smartphone-based hemoglobin measurement tool is compelling because of the possibilities for an affordable, portable, and reliable point-of-care tool by leveraging the camera capacity, computing power, and lighting sources of the smartphone. However, several smartphone-based hemoglobin measurement techniques have encountered significant challenges with respect to data collection methods, sensor selection, signal analysis processes, and machine-learning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device.

OBJECTIVE

In this study, we analyzed existing invasive, minimally invasive, and noninvasive approaches for blood hemoglobin level measurement with the goal of recommending data collection techniques, signal extraction processes, feature calculation strategies, theoretical foundation, and machine-learning algorithms for developing a noninvasive hemoglobin level estimation point-of-care tool using a smartphone.

METHODS

We explored research papers related to invasive, minimally invasive, and noninvasive hemoglobin level measurement processes. We investigated the challenges and opportunities of each technique. We compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, machine-learning algorithms, and prediction models to calculate hemoglobin levels. This analysis was then used to recommend realistic approaches to build a smartphone-based point-of-care tool for hemoglobin measurement in a noninvasive manner.

RESULTS

The fingertip area is one of the best data collection sites from the body, followed by the lower eye conjunctival area. Near-infrared (NIR) light-emitting diode (LED) light with wavelengths of 850 nm, 940 nm, and 1070 nm were identified as potential light sources to receive a hemoglobin response from living tissue. PPG signals from fingertip videos, captured under various light sources, can provide critical physiological clues. The features of PPG signals captured under 1070 nm and 850 nm NIR LED are considered to be the best signal combinations following a dual-wavelength theoretical foundation. For error metrics presentation, we recommend the mean absolute percentage error, mean squared error, correlation coefficient, and Bland-Altman plot.

CONCLUSIONS

We addressed the challenges of developing an affordable, portable, and reliable point-of-care tool for hemoglobin measurement using a smartphone. Leveraging the smartphone's camera capacity, computing power, and lighting sources, we define specific recommendations for practical point-of-care solution development. We further provide recommendations to resolve several long-standing research questions, including how to capture a signal using a smartphone camera, select the best body site for signal collection, and overcome noise issues in the smartphone-captured signal. We also describe the process of extracting a signal's features after capturing the signal based on fundamental theory. The list of machine-learning algorithms provided will be useful for processing PPG features. These recommendations should be valuable for future investigators seeking to build a reliable and affordable hemoglobin prediction model using a smartphone.

Assessment of pulse co-oximetry technology after in vivo adjustment in anaesthetized dogs

OBJECTIVE

To compare values of haemoglobin concentration (SpHb), arterial haemoglobin saturation (SpO₂) and calculated arterial oxygen content (SpOC), measured noninvasively with a pulse co-oximeter before and after in vivo adjustment (via calibration of the device using a measured haemoglobin concentration) with those measured invasively using a spectrophotometric-based blood gas analyser in anaesthetized dogs.

STUDY DESIGN

Prospective observational clinical study.

ANIMALS

A group of 39 adult dogs.

METHODS

In all dogs after standard instrumentation, the dorsal metatarsal artery was catheterised for blood sampling, and a pulse co-oximeter probe was applied to the tongue for noninvasive measurements. Paired data for SpHb, SpO₂ and SpOC from the pulse co-oximeter and haemoglobin arterial oxygen saturation (SaO₂) and arterial oxygen content (CaO₂) from the blood gas analyser were obtained before and after in vivo adjustment. Bland-Altman analysis for repeated measurements was used to evaluate the bias, precision and agreement between the pulse co-oximeter and the blood gas analyser. Data are presented as mean differences and 95% limits of agreement (LoA).

RESULTS

A total of 39 data pairs were obtained before in vivo adjustment. The mean invasively measured haemoglobin-SpHb difference was -2.7 g dL^-1 with LoA of -4.9 to -0.5 g dL^-1. After in vivo adjustment, 104 data pairs were obtained. The mean invasively measured haemoglobin-SpHb difference was -0.2 g dL^-1 with LoA of -1.1 to 0.6 g dL^-1. The mean SaO₂-SpO₂ difference was 0.86% with LoA of -0.8% to 2.5% and that between CaO₂-SpOC was 0.66 mL dL^-1 with LoA of -2.59 to 3.91 mL dL^-1.

CONCLUSIONS

Before in vivo adjustment, pulse co-oximeter derived values overestimated the spectrophotometric-based blood gas analyser haemoglobin and CaO₂ values. After in vivo adjustment, the accuracy, precision and LoA markedly improved. Therefore, in vivo adjustment is recommended when using this device to monitor SpHb in anaesthetised dogs.

Comparison of invasive and noninvasive blood hemoglobin measurement in the operating room: a systematic review and meta-analysis

Noninvasive hemoglobin (Hb)-monitoring devices are new inventions in pulse oximeter systems that show hemoglobin levels continuously. The aim of this systematic review and meta-analysis was to evaluate the accuracy and precision of noninvasive versus standard central laboratory Hb measurements in the operating room. We systematically searched multiple databases. Then, for the quality assessment of studies, we modified QUADAS-2 in the Revman 5.3 software. The GRADE approach was used to measure the quality of evidence (Grading of Recommendations Assessment, Development, and Evaluation). Data were analyzed using the meta-analysis method (random effect model) using STATA 11 software. A total of 28 studies on 2000 participants were included in the meta-analysis. Meta-analysis results of mean differences between noninvasive and the central laboratory Hb measurements in overall pooled random effects were - 0.27 (95% LoA (0.44, - 0.10); P value < 0.05). According to this meta-analysis, noninvasive hemoglobin measurement has acceptable accuracy in comparison with the standard invasive method.

Noninvasive Continuous Hemoglobin Monitoring in Combat Casualties: A Pilot Study

OBJECTIVE

To describe the accuracy and precision of noninvasive hemoglobin measurement (SpHb) compared with laboratory or point-of-care Hb, and SpHb ability to trend in seriously injured casualties.

METHODS

Observational study in a convenience sample of combat casualties undergoing resuscitation at two US military trauma hospitals in Afghanistan. SpHb was obtained using the Masimo Rainbow SET (Probe Rev E/Radical-7 Pulse CO-Oximeter v 7.6.2.1). Clinically indicated Hb was analyzed with a Coulter or iStat and compared with simultaneous SpHb values.

RESULTS

Twenty-three patients were studied (ISS 20 ± 9.8; age 29 ± 9 years; male 97%; 100% intubated). Primary injury cause: improvised explosive device (67%) or gunshot (17%). There were 49 SpHb-Hb pairs (median 2 per subject). Bias: 0.3 ± 1.6 g/dL (95% LOA -2.4, 3.4 g/dL). The SpHb-Hb difference < ± 1 g/dL in 37% of pairs. Eighty-six percent of pairs changed in a similar direction. Using an absolute change in Hb of >1 g/dL, a concurrent absolute change in SpHb of >1 g/dL had a sensitivity: 61%, specificity 85%, positive predictive value: 80%, and a negative predictive value: 69%. The SpHb signal was present in 4643 of 6137 min monitored (76%).

CONCLUSIONS

This was the first study to describe continuous SpHb in seriously injured combat casualties. Using a threshold of 1 g/dL previously specified in the literature, continuous SpHb is not precise enough to serve as sole transfusion trigger in trauma patients. Further research is needed to determine if it is useful for trending Hb changes or as an early indicator of deterioration in combat casualties.

Meta-analytic estimation of measurement variability and assessment of its impact on decision-making: the case of perioperative haemoglobin concentration monitoring

Background

As a part of a larger Health Technology Assessment (HTA), the measurement error of a device used to monitor the hemoglobin concentration of a patient undergoing surgery, as well as its decision consequences, were to be estimated from published data.

Methods

A Bayesian hierarchical model of measurement error, allowing the meta-analytic estimation of both central and dispersion parameters (under the assumption of normality of measurement errors) is proposed and applied to published data; the resulting potential decision errors are deduced from this estimation. The same method is used to assess the impact of an initial calibration.

Results

The posterior distributions are summarized as mean ± sd (credible interval). The fitted model exhibits a modest mean expected error (0.24 ± 0.73 (−1.23 1.59) g/dL) and a large variability (mean absolute expected error 1.18 ± 0.92 (0.05 3.36) g/dL). The initial calibration modifies the bias (−0.20 ± 0.87 (−1.99 1.49) g/dL), but the variability remains almost as large (mean absolute expected error 1.05 ± 0.87 (0.04 3.21) g/dL). This entails a potential decision error (“false positive” or “false negative”) for about one patient out of seven.

Conclusions

The proposed hierarchical model allows the estimation of the variability from published aggregates, and allows the modeling of the consequences of this variability in terms of decision errors. For the device under assessment, these potential decision errors are clinically problematic.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0107-5) contains supplementary material, which is available to authorized users.