Monitoring in the intensive care

The mental workload of ICU nurses performing human-machine tasks and associated factors: A cross-sectional questionnaire survey

AIMS

To assess the level of mental workload (MWL) of intensive care unit (ICU) nurses in performing different human-machine tasks and examine the predictors of the MWL.

DESIGN

A cross-sectional questionnaire study.

METHODS

Between January and February 2021, data were collected from ICU nurses (n = 427) at nine tertiary hospitals selected from five (east, west, south, north, central) regions in China through an electronic questionnaire, including sociodemographic questions, the National Aeronautics and Space Administration Task Load Index, General Self-Efficacy Scale, Difficulty-assessing Index System of Nursing Operation Technique, and System Usability Scale. Descriptive statistics, t-tests, one-way ANOVA and multiple linear regression models were used.

RESULTS

ICU nurses experienced a medium level of MWL (score 52.04 on a scale of 0-100) while performing human-machine tasks. ICU nurses' MWL was notably higher in conducting first aid and life support tasks (using defibrillators or ventilators). Predictors of MWL were task difficulty, system usability, professional title, age, self-efficacy, ICU category, and willingness to study emerging technology actively. Task difficulty and system usability were the strongest predictors of nearly all typical tasks.

CONCLUSION

ICU nurses experience a medium MWL while performing human-machine tasks, but higher mental, temporal, and effort are perceived compared to physical demands. The MWL varied significantly across different human-machine tasks, among which are significantly higher: first aid and life support and information-based human-machine tasks. Task difficulty and system availability are decisive predictors of MWL.

IMPACT

This is the first study to investigate the level of MWL of ICU nurses performing different representative human-machine tasks and to explore its predictors, which provides a reference for future research. These findings suggest that healthcare organizations should pay attention to the MWL of ICU nurses and develop customized management strategies based on task characteristics to maintain a moderate level of MWL, thus enabling ICU nurses to perform human-machine tasks better.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Tele-monitoring system for intensive care ventilators in isolation rooms

The COVID-19 pandemic and discovery of new mutant strains have a devastating impact worldwide. Patients with severe COVID-19 require various equipment, such as ventilators, infusion pumps, and patient monitors, and a dedicated medical team to operate and monitor the equipment in isolated intensive care units (ICUs). Medical staff must wear personal protective equipment to reduce the risk of infection. This study proposes a tele-monitoring system for isolation ICUs to assist in the monitoring of COVID-19 patients. The tele-monitoring system consists of three parts: medical-device panel image processing, transmission, and tele-monitoring. This system can monitor the ventilator screen with obstacles, receive and store data, and provide real-time monitoring and data analysis. The proposed tele-monitoring system is compared with previous studies, and the image combination algorithm for reconstruction is evaluated using structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The system achieves an SSIM score of 0.948 in the left side and a PSNR of 23.414 dB in the right side with no obstacles. It also reduces blind spots, with an SSIM score of 0.901 and a PSNR score of 18.13 dB. The proposed tele-monitoring system is compatible with both wired and wireless communication, making it accessible in various situations. It uses camera and performs live data monitoring, and the two monitoring systems complement each other. The system also includes a comprehensive database and an analysis tool, allowing medical staff to collect and analyze data on ventilator use, providing them a quick, at-a-glance view of the patient's condition. With the implementation of this system, patient outcomes may be improved and the burden on medical professionals may be reduced during the COVID-19 pandemic-like situations.

Human-centered visualization technologies for patient monitoring are the future: a narrative review

Medical technology innovation has improved patient monitoring in perioperative and intensive care medicine and continuous improvement in the technology is now a central focus in this field. Because data density increases with the number of parameters captured by patient-monitoring devices, its interpretation has become more challenging. Therefore, it is necessary to support clinicians in managing information overload while improving their awareness and understanding about the patient's health status. Patient monitoring has almost exclusively operated on the single-sensor-single-indicator principle-a technology-centered way of presenting data in which specific parameters are measured and displayed individually as separate numbers and waves. An alternative is user-centered medical visualization technology, which integrates multiple pieces of information (e.g., vital signs), derived from multiple sensors into a single indicator-an avatar-based visualization-that is a meaningful representation of the real-world situation. Data are presented as changing shapes, colors, and animation frequencies, which can be perceived, integrated, and interpreted much more efficiently than other formats (e.g., numbers). The beneficial effects of these technologies have been confirmed in computer-based simulation studies; visualization technologies improved clinicians' situation awareness by helping them effectively perceive and verbalize the underlying medical issue, while improving diagnostic confidence and reducing workload. This review presents an overview of the scientific results and the evidence for the validity of these technologies.

The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study

Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to identify patient characteristics that affect the ICU alarm rate with the goal of proposing a straightforward solution that can easily be implemented in ICUs. Alarm logs from eight adult ICUs of a tertiary care university-hospital in Berlin, Germany were retrospectively collected between September 2019 and March 2021. Adult patients admitted to the ICU with at least 24 h of continuous alarm logs were included in the study. The sum of alarms per patient per day was calculated. The median was 119. A total of 26,890 observations from 3205 patients were included. 23 variables were extracted from patients' electronic health records (EHR) and a multivariable logistic regression was performed to evaluate the association of patient characteristics and alarm rates. Invasive blood pressure monitoring (adjusted odds ratio (aOR) 4.68, 95%CI 4.15-5.29, p < 0.001), invasive mechanical ventilation (aOR 1.24, 95%CI 1.16-1.32, p < 0.001), heart failure (aOR 1.26, 95%CI 1.19-1.35, p < 0.001), chronic renal failure (aOR 1.18, 95%CI 1.10-1.27, p < 0.001), hypertension (aOR 1.19, 95%CI 1.13-1.26, p < 0.001), high RASS (aOR 1.22, 95%CI 1.18-1.25, p < 0.001) and scheduled surgical admission (aOR 1.22, 95%CI 1.13-1.32, p < 0.001) were significantly associated with a high alarm rate. Our study suggests that patient-specific alarm management should be integrated in the clinical routine of ICUs. To reduce the overall alarm load, particular attention regarding alarm management should be paid to patients with invasive blood pressure monitoring, invasive mechanical ventilation, heart failure, chronic renal failure, hypertension, high RASS or scheduled surgical admission since they are more likely to have a high contribution to noise pollution, alarm fatigue and hence compromised patient safety in ICUs.

CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring

Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.

Admission and Mortality Patterns in Intensive Care Delivery at Enugu State University of Science and Technology Teaching Hospital: A Three-Year Retrospective Study

Background

The intensive care unit (ICU) provides critical care to high-risk patients to prevent morbidity and mortality. This requires closer monitoring and better management than the care provided to patients in normal admission wards and non-critical care units. Mortality rates in ICUs in developing countries are remarkably high compared to rates in more developed countries. Evaluating outcomes of treatment is a way to improve the quality of care. Therefore, this study was conducted to review the pattern of admission and outcome in the ICU of Enugu State University of Science and Technology Teaching Hospital (ESUT-TH).

Methodology

This study was a three-year retrospective, descriptive review of all patients admitted to the ICU of ESUT-TH between January 1, 2019, and December 21, 2021. Data were collected from admissions and discharge registers of the ICU ward. Data were analyzed and expressed as frequencies and percentages. Categorical parameters were compared using the chi-squared test, and the significance level was set at p < 0.05.

Results

A total of 179 patients were admitted in the three-year period. Of them, 49.2% were postoperative patients while 21.2% were admitted from the accident and emergency unit. There were a total of 74 (41.3%) medical cases and 81 (45.3%) surgical cases, and the rest were unspecified. Among surgical cases, 19% were from the general surgery department followed by obstetrics and gynecology (18.4%) and neurosurgery (16.8%). Cerebrovascular accidents and traumatic brain injury were the most common specific diagnoses recorded among ICU admitted patients. The most common reason for admission was close monitoring of high-risk patients. The mortality rate during the studied period was 34.1%, and this was significantly associated with patient age and type of illness at presentation (p < 0.05). Stratified by year of admission, the highest rate of mortality was noted in the year 2020 (46.7%).

Conclusion

There is a high level of mortality among ICU admissions in our center. This calls for the improvement of intensive care delivery in the healthcare facility, including training and retraining of manpower and provision of essential facilities for high-quality healthcare delivery.

Joint Modeling of Repeated Measurements of Different Biomarkers Predicts Mortality in COVID-19 Patients in the Intensive Care Unit

Introduction:

Predicting disease severity is important for treatment decisions in patients with COVID-19 in the intensive care unit (ICU). Different biomarkers have been investigated in COVID-19 as predictor of mortality, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and soluble urokinase-type plasminogen activator receptor (suPAR). Using repeated measurements in a prediction model may result in a more accurate risk prediction than the use of single point measurements. The goal of this study is to investigate the predictive value of trends in repeated measurements of CRP, PCT, IL-6, and suPAR on mortality in patients admitted to the ICU with COVID-19.

Methods:

This was a retrospective single center cohort study. Patients were included if they tested positive for SARS-CoV-2 by PCR test and if IL-6, PCT, suPAR was measured during any of the ICU admission days. There were no exclusion criteria for this study. We used joint models to predict ICU-mortality. This analysis was done using the framework of joint models for longitudinal and survival data. The reported hazard ratios express the relative change in the risk of death resulting from a doubling or 20% increase of the biomarker’s value in a day compared to no change in the same period.

Results:

A total of 107 patients were included, of which 26 died during ICU admission. Adjusted for sex and age, a doubling in the next day in either levels of PCT, IL-6, and suPAR were significantly predictive of in-hospital mortality with HRs of 1.523 (1.012-6.540), 75.25 (1.116-6247), and 24.45 (1.696-1057) respectively. With a 20% increase in biomarker value in a subsequent day, the HR of PCT, IL-6, and suPAR were 1.117 (1.03-1.639), 3.116 (1.029-9.963), and 2.319 (1.149-6.243) respectively.

Conclusion:

Joint models for the analysis of repeated measurements of PCT, suPAR, and IL-6 are a useful method for predicting mortality in COVID-19 patients in the ICU. Patients with an increasing trend of biomarker levels in consecutive days are at increased risk for mortality.

Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients.

Smart alarms towards optimizing patient ventilation in intensive care: the driving pressure case

OBJECTIVE

Alarms are a substantial part of clinical practice, warning clinicians of patient complications. In this paper, we focus on alarms in the intensive care unit and especially on the use of machine learning techniques for the creation of alarms for the ventilator support of patients. The aim is to study a method to enable timely interventions for intubated patients and prevent complications induced by high driving pressure (ΔP) and lung strain during mechanical ventilation.

APPROACH

The relation between the ΔP and the total set of the ventilator parameters was examined and resulted in a predictive model with bimodal implementation for the short-term prediction of the ΔP level (high/low). The proposed method includes two sub-models for the prediction of future ΔP level based on the current level being high or low, named cH and cL, respectively. Based on this method, for both sub-models, an alarm will be triggered when the predicted ΔP level is considered to be high. In this vein, three classifiers (the random forest, linear support vector machine, and kernel support vector machine methods) were tested for each sub-model. To adjust the highly unbalanced classes, four different sampling methods were considered: downsampling, upsampling, synthetic minority over-sampling technique (SMOTE) sampling, and random over-sampling examples (ROSE) sampling.

MAIN RESULTS

For the cL sub-model the combination of linear support vector machine with SMOTE sampling showed the best performance, resulting in accuracy of 93%, while the cH sub-model reached the best performance, with accuracy of 73%, with kernel support vector machine combined with the downsampling method.

SIGNIFICANCE

The results are positive in terms of the generation of new alarms in mechanical ventilation. The technical and organizational possibility of integrating data from multiple modalities is expected to further advance this line of work.

Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning

Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring in the Intensive Care Unit (ICU). As an exemplary prevalent condition, we characterized delirious patients and their environment. We used wearable sensors, light and sound sensors, and a camera to collect data on patients and their environment. We analyzed collected data to detect and recognize patient's face, their postures, facial action units and expressions, head pose variation, extremity movements, sound pressure levels, light intensity level, and visitation frequency. We found that facial expressions, functional status entailing extremity movement and postures, and environmental factors including the visitation frequency, light and sound pressure levels at night were significantly different between the delirious and non-delirious patients. Our results showed that granular and autonomous monitoring of critically ill patients and their environment is feasible using a noninvasive system, and we demonstrated its potential for characterizing critical care patients and environmental factors.

Clinical Requirements of Future Patient Monitoring in the Intensive Care Unit: Qualitative Study

Background

In the intensive care unit (ICU), continuous patient monitoring is essential to detect critical changes in patients’ health statuses and to guide therapy. The implementation of digital health technologies for patient monitoring may further improve patient safety. However, most monitoring devices today are still based on technologies from the 1970s.

Objective

The aim of this study was to evaluate statements by ICU staff on the current patient monitoring systems and their expectations for future technological developments in order to investigate clinical requirements and barriers to the implementation of future patient monitoring.

Methods

This prospective study was conducted at three intensive care units of a German university hospital. Guideline-based interviews with ICU staff—5 physicians, 6 nurses, and 4 respiratory therapists—were recorded, transcribed, and analyzed using the grounded theory approach.

Results

Evaluating the current monitoring system, ICU staff put high emphasis on usability factors such as intuitiveness and visualization. Trend analysis was rarely used; inadequate alarm management as well as the entanglement of monitoring cables were rated as potential patient safety issues. For a future system, the importance of high usability was again emphasized; wireless, noninvasive, and interoperable monitoring sensors were desired; mobile phones for remote patient monitoring and alarm management optimization were needed; and clinical decision support systems based on artificial intelligence were considered useful. Among perceived barriers to implementation of novel technology were lack of trust, fear of losing clinical skills, fear of increasing workload, and lack of awareness of available digital technologies.

Conclusions

This qualitative study on patient monitoring involves core statements from ICU staff. To promote a rapid and sustainable implementation of digital health solutions in the ICU, all health care stakeholders must focus more on user-derived findings. Results on alarm management or mobile devices may be used to prepare ICU staff to use novel technology, to reduce alarm fatigue, to improve medical device usability, and to advance interoperability standards in intensive care medicine. For digital transformation in health care, increasing the trust and awareness of ICU staff in digital health technology may be an essential prerequisite.

Trial Registration

ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173 (Archived by WebCite at http://www.webcitation.org/77T1HwOzk)

Non-Invasive Tissue Oximetry-An Integral Puzzle Piece

Non-invasive tissue oximetry is a monitoring method for continuous assessment of tissue oxygenation, which may aid in detection of hemodynamic instability and otherwise unnoticed hypoxia. Numerous studies focused on using non-invasive tissue oximetry intraoperatively, proposing its predictive value in relation to clinical outcome. Tissue oximetry may be part of standard monitoring practice for brain monitoring during cardiac surgery in many clinical centers; however, the monitoring method can be deployed in numerous clinical settings. This succinct overview aims to determine the role of non-invasive tissue oximetry in current clinical practice.

The Combined Use of Cardiac Output and Intracranial Pressure Monitoring to Maintain Optimal Cerebral Perfusion Pressure and Minimize Complications for Severe Traumatic Brain Injury

Objective

To show the effect of dual monitoring including cardiac output (CO) and intracranial pressure (ICP) monitoring for severe traumatic brain injury (TBI) patiens. We hypothesized that meticulous treatment using dual monitoring is effective to sustain maintain minimal intensive care unit (ICU) complications and maintain optimal ICP and cerebral perfusion pressure (CPP) for severe TBI patiens.

Methods

We included severe TBI, below Glasgow Coma Scale (GCS) 8 and head abbreviation injury scale (AIS) >4 and performed decompressive craniectomy at trauma ICU of our hospital. We collected the demographic data, head AIS, injury severity score (ISS), initial GCS, ICU stay, sedation duration, fluid therapy related complications, Glasgow Outcome Scale (GOS) at 3 months and variable parameters of ICP and CO monitor.

Results

Thirty patients with severe TBI were initially selected. Thirteen patients were excluded because 10 patients had fixed pupillary reflexes and 3 patients had uncontrolled ICP due to severe brain edema. Overall 17 patients had head AIS 5 except 2 patients and 10 patients (58.8%) had multiple traumas as mean ISS 29.1. Overall complication rate of the patients was 64.7%. Among the parameters of CO monitoring, high stroke volume variation is associated with fluid therapy related complications (p=0.043) and low cardiac contractibility is associated with these complications (p=0.009) statistically.

Conclusion

Combined use of CO and ICP monitors in severe TBI patients who could be necessary to decompressive craniectomy and postoperative sedation is good alternative methods to maintain an adequate ICP and CPP and reduce fluid therapy related complications during postoperative ICU care.

[Changes in blood oxygen metabolism indices and their clinical significance in children with septic shock]

The key to the treatment of septic shock is to provide adequate oxygen supply and improve tissue perfusion. Lactate and central venous oxygen saturation (ScvO₂) are commonly used as the indices of oxygen metabolism, but tissue hypoxia may still exist even when lactate and ScvO₂ are within the normal range. Arteriovenous difference in carbon dioxide partial pressure (CO₂ gap) can accurately reflect oxygen delivery when ScvO₂ is in the normal range. This article reviews the advantages and shortages of lactate, lactate clearance rate, ScvO₂, and CO₂ gap in evaluating tissue hypoxia, in order to provide a reference for treatment and severity evaluation of septic shock.

Effects of blood transfusion on oxygen extraction ratio and central venous saturation in children after cardiac surgery

BACKGROUND

Red blood cell transfusion is common in critically ill children after cardiac surgery. Since the threshold for hemoglobin (Hb) transfusion need is not well defined, the threshold Hb level at which dependent critical oxygen uptake-to-delivery (VO2-DO2) status compensation is uncertain.

OBJECTIVES

To assess the effects of blood transfusion on the oxygen extraction ratio (O2ER) and central venous oxygen saturation (ScvO2) to identify a critical O2ER value that could help us determine the critical need for blood transfusion.

DESIGN

Prospective, observational cohort study.

SETTING

Cardiac Surgical Intensive Care Unit at Prince Sultan Cardiac Center in Qassim, Saudi Arabia.

PATIENTS AND METHODS

Between January 2013 and December 2015, we included all children with cardiac disease who underwent surgery and needed a blood transfusion. Demographic and laboratory data with physiological parameters before and 1 and 6 hours after transfusion were recorded and O2ER before and 6 hours after transfusion was computed. Cases were divided into two groups based on O2ER: Patients with increased O2ER (O2ER > 40%) and normal patients without increased O2ER (O2ER < =40%) before transfusion.

MAIN OUTCOME MEASURE(S)

Changes in O2ER and ScvO2 following blood transfusion.

RESULTS

Of 103 patients who had blood transfusion, 75 cases had normal O2ER before transfusion while 28 cases had increased O2ER before transfusion. Following blood transfusion, O2ER and ScvO2 improved in the group that had increased O2ER before transfusion, but not in the group that had normal O2ER before transfusion.

CONCLUSIONS

The clinical and hemodynamic indicators O2ER and ScvO2 may be considered as markers that can indicate a need for blood transfusion.

LIMITATIONS

The limitation of this study is the small number of patients that had increased O2ER before transfusion. There were few available variables to assess oxygen consumption.

Near-Infrared Spectroscopy: The New Must Have Tool in the Intensive Care Unit?

Standard hemodynamic monitoring such as blood pressure and pulse oximetry may only provide a crude estimation of organ perfusion in the critical care setting. Near-infrared spectroscopy (NIRS) is based on the same principle as a pulse oximeter and allows continuous noninvasive monitoring of hemoglobin oxygenation and deoxygenation and thus tissue saturation "StO2" This review aims to provide an overview of NIRS technology principles and discuss its current clinical use in the critical care setting. The study selection was performed using the PubMed database to find studies that investigated the use of NIRS in both the critical care setting and in the intensive care unit. Currently, NIRS in the critical care setting is predominantly being used for infants and neonates. A number of studies in the past decade have shown promising results for the use of NIRS in surgical/trauma intensive care units during shock management as a prognostic tool and in guiding resuscitation. It is evident that over the past 2 decades, NIRS has gone from being a laboratory fascination to an actively employed clinical tool. Even though the benefit of routine use of this technology to achieve better outcomes is still questionable, the fact that NIRS is a low-cost, noninvasive monitoring modality improves the attractiveness of the technology. However, more research may be warranted before recommending its routine use in the critical care setting.

Patterns of central venous oxygen saturation, lactate and veno-arterial CO2 difference in patients with septic shock

BACKGROUND AND AIMS

Tissue hypoperfusion is reflected by metabolic parameters such as lactate, central venous oxygen saturation (ScvO2) and the veno-arterial CO2 (vaCO2) difference. We studied the relation of these parameters over time and with outcome in patients with severe septic shock.

MATERIALS AND METHODS

In this single-center, prospective observational cohort study, adult patients (≥18 years) with circulatory shock were included. Echocardiography and simultaneous arterial and venous blood gases were done on enrolment (0 h) and at 24, 48 and 72 h. The partial pressure of CO2, lactate and ScvO2 were recorded from the central venous blood samples. The vaCO2 was calculated as the difference in CO2 between paired venous and arterial blood gas samples.

RESULTS

Of the 104 patients with circulatory shock, 79 patients (44 males) with septic shock aged 49.8 (standard deviation ± 14.6) years and with sequential organ failure assessment (SOFA) score of 11.0 ± 3.4 were included. 71 patients (89.9%) were ventilated (11.4 ± 12.3 ventilator-free days). The duration of hospitalization was 16.6 ± 12.8 days and hospital mortality 50.6%. Lactate significantly decreased over time with a greater decrement in survivors than nonsurvivors (-0.35 vs. -0.10, P < 0.001). For every l/min increase in cardiac output, vaCO2 decreased by 0.34 mmHg (P = 0.006). There was no association between ScvO2 and mortality (P = 0.930). 0 h SOFA and vaCO2 ≤6 mmHg were strongly associated (P = 0.005, P = 0.018, respectively) with higher odds of mortality. However, this association was evident only in those with ScvO2 >70% and not in ScvO2 ≤70%.

CONCLUSION

In septic shock, vaCO2 ≤6 mmHg is independently associated with mortality, particularly in those with normalized ScvO2 consistent with metabolic microcirculatory abnormalities in these patients.

Reductions in red blood cell 2,3-diphosphoglycerate concentration during continuous renal replacment therapy

BACKGROUND AND OBJECTIVES

Hypophosphatemia is a frequent complication during continuous renal replacement therapy (CRRT), a dialytic technique used to treat AKI in critically ill patients. This study sought to confirm that phosphate depletion during CRRT may decrease red blood cell (RBC) concentration of 2,3-diphosphoglycerate (2,3-DPG), a crucial allosteric effector of hemoglobin's (Hgb's) affinity for oxygen, thereby leading to impaired oxygen delivery to peripheral tissues.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Phosphate mass balance studies were performed in 20 patients with severe AKI through collection of CRRT effluent. RBC concentrations of 2,3-DPG, venous blood gas pH, and oxygen partial pressure required for 50% hemoglobin saturation (P50) were measured at CRRT initiation and days 2, 4, and 7. Similar measurements were obtained on days 0 and 2 in a reference group of 10 postsurgical patients, most of whom did not have AKI. Associations of 2,3-DPG with laboratory parameters and clinical outcomes were examined using mixed-effects and Cox regression models.

RESULTS

Mean 2,3-DPG levels decreased from a mean (±SD) of 13.4±3.4 µmol/g Hgb to 11.0±3.1 µmol/g Hgb after 2 days of CRRT (P<0.001). Mean hemoglobin saturation P50 levels decreased from 29.7±4.4 mmHg to 26.7±4.0 mmHg (P<0.001). No significant change was seen in the reference group. 2,3-DPG levels after 2 days of CRRT were not significantly lower than those in the reference group on day 2. Among patients receiving CRRT, 2,3-DPG decreased by 0.53 µmol/g Hgb per 1 g phosphate removed (95% confidence interval 0.38 to 0.68 µmol/g Hgb; P<0.001). Greater reductions in 2,3-DPG were associated with higher risk for death (hazard ratio, 1.43; 95% confidence interval, 1.09 to 1.88; P=0.01).

CONCLUSIONS

CRRT-induced phosphate depletion is associated with measurable reductions in RBC 2,3-DPG concentration and a shift in the O2:Hgb affinity curve even in the absence of overt hypophosphatemia. 2,3-DPG reductions may be associated with higher risk for in-hospital death and represent a potentially avoidable complication of CRRT.