Association between venous blood lactate levels and differences in quantitative capillary refill time

Recent Advances in Bedside Device-Based Early Detection of Sepsis

Early detection of sepsis is challenging to achieve with current diagnostic methods, leading to expenditures of $27 billion annually in the United States with significant associated mortality. Various scoring systems have been proposed such as the sequential organ failure assessment (SOFA) and systemic inflammatory response syndrome (SIRS) criteria for identification of sepsis, but their sensitivities range from 60% to 70% when used in the emergency department triage. Other methods for the recognition of sepsis may rely on laboratory work, in addition to vitals monitoring, and are often outpaced by the development of sepsis. Automated alerts have not shown any reduction in mortality thus far. New technology may fill a critical gap in the early detection of sepsis. The ideal bedside screening device for would demonstrate rapid time to result, high portability, and high sensitivity to not miss cases, but also reasonable specificity to prevent provider fatigue from excessive false alerts. Non-invasive end-organ perfusion devices analyzing lactate and capillary refill time (CRT) tend to perform well in speed and portability, but may be less sensitive. Biomarker devices demonstrate a wider array of performance metrics. Those analyzing a single biomarker tend to be more sensitive but are less specific to the diagnosis of sepsis than technologies that assess multiple biomarkers, which in turn have lower sensitivity. Additionally, biomarker devices are generally invasive requiring blood samples, which may or may not be feasible in all patients especially when serial draws are needed. Sepsis is a complex disease process and most likely will require a combination of improved technology in addition to vital signs and high-risk patient history for better recognition. This review examines recent advances in the device-based early detection of sepsis between 2017 and 2020 with emphasis on bedside diagnostics, divided into markers of perfusion and biomarkers commonly implicated in sepsis.

Advances in the Approaches Using Peripheral Perfusion for Monitoring Hemodynamic Status

Measures of peripheral perfusion can be used to assess the hemodynamic status of critically ill patients. By monitoring peripheral perfusion status, clinicians can promptly initiate life-saving therapy and reduce the likelihood of shock-associated death. Historically, abnormal perfusion has been indicated by the observation of pale, cold, and clammy skin with increased capillary refill time. The utility of these assessments has been debated given that clinicians may vary in their clinical interpretation of body temperature and refill time. Considering these constraints, current sepsis bundles suggest the need to revise resuscitation guidelines. New technologies have been developed to calculate capillary refill time in the hopes of identifying a new gold standard for clinical care. These devices measure either light reflected at the surface of the fingertip (reflected light), or light transmitted through the inside of the fingertip (transmitted light). These new technologies may enable clinicians to monitor peripheral perfusion status more accurately and may increase the potential for ubiquitous hemodynamic monitoring across different clinical settings. This review will summarize the different methods available for peripheral perfusion monitoring and will discuss the advantages and disadvantages of each approach.

Supervised Machine Learning Applied to Automate Flash and Prolonged Capillary Refill Detection by Pulse Oximetry

Objective

Develop an automated approach to detect flash (<1.0 s) or prolonged (>2.0 s) capillary refill time (CRT) that correlates with clinician judgment by applying several supervised machine learning (ML) techniques to pulse oximeter plethysmography data.

Materials and Methods

Data was collected in the Pediatric Intensive Care Unit (ICU), Cardiac ICU, Progressive Care Unit, and Operating Suites in a large academic children’s hospital. Ninety-nine children and 30 adults were enrolled in testing and validation cohorts, respectively. Patients had 5 paired CRT measurements by a modified pulse oximeter device and a clinician, generating 485 waveform pairs for model training. Supervised ML models using gradient boosting (XGBoost), logistic regression (LR), and support vector machines (SVMs) were developed to detect flash (<1 s) or prolonged CRT (≥2 s) using clinician CRT assessment as the reference standard. Models were compared using Area Under the Receiver Operating Curve (AUC) and precision-recall curve (positive predictive value vs. sensitivity) analysis. The best performing model was externally validated with 90 measurement pairs from adult patients. Feature importance analysis was performed to identify key waveform characteristics.

Results

For flash CRT, XGBoost had a greater mean AUC (0.79, 95% CI 0.75–0.83) than logistic regression (0.77, 0.71–0.82) and SVM (0.72, 0.67–0.76) models. For prolonged CRT, XGBoost had a greater mean AUC (0.77, 0.72–0.82) than logistic regression (0.73, 0.68–0.78) and SVM (0.75, 0.70–0.79) models. Pairwise testing showed statistically significant improved performance comparing XGBoost and SVM; all other pairwise model comparisons did not reach statistical significance. XGBoost showed good external validation with AUC of 0.88. Feature importance analysis of XGBoost identified distinct key waveform characteristics for flash and prolonged CRT, respectively.

Conclusion

Novel application of supervised ML to pulse oximeter waveforms yielded multiple effective models to identify flash and prolonged CRT, using clinician judgment as the reference standard.

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Supervised machine learning applied to pulse oximeter waveform features predicts flash or prolonged capillary refill.

Pulse oximetry-based capillary refilling evaluation predicts postoperative outcomes in liver transplantation: a prospective observational cohort study

Background

Capillary refill time (CRT) is a non-invasive technique to evaluate tissue perfusion, and quantitative CRT (Q-CRT) adapted to pulse oximetry was developed with patients with sepsis and compared to blood lactate and sepsis scores. In post liver transplantation, large amounts of fluid administration are necessary for maintaining tissue perfusion to grafted liver against intravascular hypovolemia. This study aimed to evaluate whether Q-CRT can predict poor outcomes by detecting peripheral tissue perfusion abnormality in patients with liver transplantations who were treated with massive fluid administration.

Methods

In this single-center prospective cohort study, we enrolled adult patients with liver transplantations between June 2018 and July 2019. Measurement of Q-CRT was conducted at intensive care units (ICU) admission and postoperative day 1 (POD1).

Results

A total of 33 patients with liver transplantations were enrolled. Significant correlations of Q-CRT and ΔA_b, a tissue oxygen delivery parameter calculated by pulse oximetry data, at ICU admission with the postoperative outcomes such as length of ICU and hospital stay and total amount of ascitic fluid discharge were observed. Quantitative CRT and ΔA_b at ICU admission were significantly associated with these postoperative outcomes, even after adjusting preoperative and operative factors (MELD score and bleeding volume, respectively). However, quantitative CRT and ΔA_b at POD1 and changes from ICU admission to POD1 failed to show significant associations.

Conclusions

Q-CRT values were significantly associated with postoperative outcomes in liver transplantation. Although the mechanisms of this association need to be clarified further, Q-CRT may enable identification of high-risk patients that need intensive postoperative managements.

Quantitative capillary refill time predicts sepsis in patients with suspected infection in the emergency department: an observational study

Background

Outcomes in emergent patients with suspected infection depend on how quickly clinicians evaluate the patients and start treatment. This study was performed to compare the predictive ability of the quantitative capillary refill time (Q-CRT) as a new rapid index versus the quick sequential organ failure assessment (qSOFA) score and the systemic inflammatory response syndrome (SIRS) score for sepsis screening in the emergency department.

Methods

This was a multicenter, observational, retrospective study of adult patients with suspected infection. The area under the curve (AUC) of receiver operating characteristic curve analyses and multivariate analyses were used to explore associations of the Q-CRT with the qSOFA score, SIRS score, and lactate concentration.

Results

Of the 75 enrolled patients, 48 had sepsis. The AUC, sensitivity, and specificity of Q-CRT were 0.74, 58%, and 81%, respectively; those for the qSOFA score were 0.83, 66%, and 100%, respectively; those for the SIRS score were 0.61, 81%, and 40%, respectively, for SIRS score; and those for the lactate concentration were 0.76, 72%, and 81%, respectively. We found no statistically significant differences in the AUC between the scores. We then combined the Q-CRT and qSOFA score (Q-CRT/qSOFA combination) for sepsis screening. The AUC, sensitivity, and specificity of Q-CRT/qSOFA combination were 0.82, 83%, and 81%, respectively.

Conclusions

In this study, Q-CRT/qSOFA combination had better sensitivity than the qSOFA score alone and better specificity than the SIRS score alone. There was no significant difference in accuracy between Q-CRT/qSOFA combination and the qSOFA score or lactate concentration. The ability of the Q-CRT to predict sepsis may be similar to that of the qSOFA score or serum lactate concentration; therefore, measurement of the Q-CRT may be an alternative for invasive measurement of the blood lactate concentration in evaluating patients with suspected sepsis.

Electronic supplementary material

The online version of this article (10.1186/s40560-019-0382-4) contains supplementary material, which is available to authorized users.