The use of venous blood gas in assessing arterial acid-base and oxygenation status - an analysis of aggregated data from multiple studies evaluating the venous to arterial conversion (v-TAC) method.

BACKGROUND: Several methods exist to reduce the number of arterial blood gases (ABGs). One method, Roche v-TAC, has been evaluated in different patient groups. This paper aggregates data from these studies, in different patient categories using common analysis criteria. RESEARCH DESIGN AND METHODS: We included studies evaluating v-TAC based on paired arterial and peripheral venous blood samples. Bland-Altman analysis compared measured and calculated arterial values of pH, PCO2, and PO2. Subgroup analyses were performed for normal, chronic hypercapnia and chronic base excess, acute hyper- and hypocapnia, and acute and chronic base deficits. RESULTS: 811 samples from 12 studies were included. Bias and limits of agreement for measured and calculated values: pH 0.001 (-0.029 to 0.031), PCO2 -0.08 (-0.65 to 0.49) kPa, and PO2 0.04 (-1.71 to 1.78) kPa, with similar values for all sub-group analyses. CONCLUSION: These data suggest that v-TAC analysis may have a role in replacing ABGs, avoiding arterial puncture. Substantial data exist in patients with chronic hypercapnia and chronic base excess, acute hyper- and hypocapnia, and in patients with relatively normal acid-base status, with similar bias and precision across groups and across study data. Limited data exist for patients with acute and chronic base deficits.

A comparison of predictors for mortality and bacteraemia in patients suspected of infection.

BACKGROUND: Stratification by clinical scores of patients suspected of infection can be used to support decisions on treatment and diagnostic workup. Seven clinical scores, SepsisFinder (SF), National Early Warning Score (NEWS), Sequential Orgen Failure Assessment (SOFA), Mortality in Emergency Department Sepsis (MEDS), quick SOFA (qSOFA), Shapiro Decision Rule (SDR) and Systemic Inflammatory Response Syndrome (SIRS), were evaluated for their ability to predict 30-day mortality and bacteraemia and for their ability to identify a low risk group, where blood culture may not be cost-effective and a high risk group where direct-from-blood PCR (dfbPCR) may be cost effective. METHODS: Retrospective data from two Danish and an Israeli hospital with a total of 1816 patients were used to calculate the seven scores. RESULTS: SF had higher Area Under the Receiver Operating curve than the clinical scores for prediction of mortality and bacteraemia, significantly so for MEDS, qSOFA and SIRS. For mortality predictions SF also had significantly higher area under the curve than SDR. In a low risk group identified by SF, consisting of 33% of the patients only 1.7% had bacteraemia and mortality was 4.2%, giving a cost of € 1976 for one positive result by blood culture. This was higher than the cost of € 502 of one positive dfbPCR from a high risk group consisting of 10% of the patients, where 25.3% had bacteraemia and mortality was 24.2%. CONCLUSION: This may motivate a health economic study of whether resources spent on low risk blood cultures might be better spent on high risk dfbPCR.

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Usability study of a new tool for nutritional and glycemic management in adult intensive care: Glucosafe 2.

The new decision support tool Glucosafe 2 (GS2) is based on a mathematical model of glucose and insulin dynamics, designed to assist caregivers in blood glucose control and nutrition. This study aims to assess end-user acceptance and usability of this bedside decision support tool in an adult intensive care setting. Caregivers were first trained and then invited to trial GS2 prototype on bedside computers. Data for qualitative analysis were collected through semi-structured interviews from twenty users after minimum three trial days. Most caregivers (70%) rated GS2 as convenient and believed it would help improving adherence to current guidelines (85%). Moreover, most nurses (80%) believed that GS2 would be timesaving. Nurses' risk perceptions and manual data entry emerged as central barriers to use GS2 in routine practice. Issues emerged from the caregivers were compiled into a list of 12 modifications of the GS2 prototype to increase end-user acceptance and usability. This usability study showed that GS2 was considered by ICU caregivers as helpful in daily clinical practice, allowing time-saving and better standardization of ICU patient's care. Important issues were raised by the users with implications for the development and deployment of GS2. Integrating the technology into existing IT infrastructure may facilitate caregivers' acceptance. Further clinical studies of the performance and potential health outcomes are warranted.

Clinical- vs. model-based selection of patients suspected of sepsis for direct-from-blood rapid diagnostics in the emergency department: a retrospective study.

Selecting high-risk patients may improve the cost-effectiveness of rapid diagnostics. Our objective was to assess whether model-based selection or clinical selection is better for selecting high-risk patients with a high rate of bacteremia and/or DNAemia. This study involved a model-based, retrospective selection of patients from a cohort from which clinicians selected high-risk patients for rapid direct-from-blood diagnostic testing. Patients were included if they were suspected of sepsis and had blood cultures ordered at the emergency department. Patients were selected by the model by adding those with the highest probability of bacteremia until the number of high-risk patients selected by clinicians was reached. The primary outcome was bacteremia rate. Secondary outcomes were DNAemia rate, and 30-day mortality. Data were collected for 1395 blood cultures. Following exclusion, 1142 patients were included in the analysis. In each high-risk group, 220/1142 were selected, where 55 were selected both by clinicians and the model. For the remaining 165 in each group, the model selected for a higher bacteremia rate (74/165, 44.8% vs. 45/165, 27.3%, p = 0.001), and a higher 30-day mortality (49/165, 29.7% vs. 19/165, 11.5%, p = 0.00004) than the clinically selected group. The model outperformed clinicians in selecting patients with a high rate of bacteremia. Using such a model for risk stratification may contribute towards closing the gap in cost between rapid and culture-based diagnostics.

Automatic learning of mortality in a CPN model of the systemic inflammatory response syndrome.

The aim of this paper is to apply machine learning as a method to refine a manually constructed CPN for the assessment of the severity of the systemic inflammatory response syndrome (SIRS).The goal of tuning the CPN is to create a scoring system that uses only objective data, compares favourably with other severity-scoring systems and differentiates between sepsis and non-infectious SIRS. The resulting model, the Learned-Age (LA) -Sepsis CPN has good discriminatory ability for the prediction of 30-day mortality with an area under the ROC curve of 0.79. This result compares well to existing scoring systems. The LA-Sepsis CPN also has a modest ability to discriminate between sepsis and non-infectious SIRS.

Predicting Antibiotic Resistance in Urinary Tract Infection Patients with Prior Urine Cultures.

To improve antibiotic prescribing, we sought to establish the probability of a resistant organism in urine culture given a previous resistant culture in a setting endemic for multidrug-resistant (MDR) organisms. We performed a retrospective analysis of inpatients with paired positive urine cultures. We focused on ciprofloxacin-resistant (cipro(r)) Gram-negative bacteria, extended-spectrum-beta-lactamase (ESBL)-producing Enterobacteriaceae, carbapenem-resistant Enterobacteriaceae (CRE), and carbapenem-resistant nonfermenters (CRNF). Comparisons were made between the frequency of each resistance phenotype following a previous culture with the same phenotype and the overall frequency of that phenotype, and odds ratios (ORs) were calculated. We performed a regression to assess the effects of other variables on the likelihood of a repeat resistant culture. A total of 4,409 patients (52.5% women; median age, 70 years) with 19,546 paired positive urine cultures were analyzed. The frequencies of cipro(r) bacteria, ESBL-producing Enterobacteriaceae, CRE, and CRNF among all cultures were 47.7%, 30.6%, 1.7%, and 2.6%, respectively. ORs for repeated resistance phenotypes were 1.87, 3.19, 48.25, and 19.02 for cipro(r) bacteria, ESBL-producing Enterobacteriaceae, CRE, and CRNF, respectively (P < 0.001 for all). At 1 month, the frequencies of repeated resistance phenotypes were 77.4%, 66.4%, 57.1%, and 33.3% for cipro(r) bacteria, ESBL-producing Enterobacteriaceae, CRE, and CRNF, respectively. Increasing time between cultures and the presence of an intervening nonresistant culture significantly reduced the chances of a repeat resistant culture. Associations were statistically significant over the duration of follow-up (60 months) for CRE and for up to 6 months for all other pathogens. Knowledge of microbiology results in the six preceding months may assist with antibiotic stewardship and improve the appropriateness of empirical treatment for urinary tract infections (UTIs).

Energy expenditure in critically ill patients estimated by population-based equations, indirect calorimetry and CO2-based indirect calorimetry.

BACKGROUND: Indirect calorimetry (IC) is the reference method for measurement of energy expenditure (EE) in mechanically ventilated critically ill patients. When IC is unavailable, EE can be calculated by predictive equations or by VCO2-based calorimetry. This study compares the bias, quality and accuracy of these methods. METHODS: EE was determined by IC over a 30-min period in patients from a mixed medical/postsurgical intensive care unit and compared to seven predictive equations and to VCO2-based calorimetry. The bias was described by the mean difference between predicted EE and IC, the quality by the root mean square error (RMSE) of the difference and the accuracy by the number of patients with estimates within 10 % of IC. Errors of VCO2-based calorimetry due to choice of respiratory quotient (RQ) were determined by a sensitivity analysis, and errors due to fluctuations in ventilation were explored by a qualitative analysis. RESULTS: In 18 patients (mean age 61 ± 17 years, five women), EE averaged 2347 kcal/day. All predictive equations were accurate in less than 50 % of the patients with an RMSE ≥ 15 %. VCO2-based calorimetry was accurate in 89 % of patients, significantly better than all predictive equations, and remained better for any choice of RQ within published range (0.76-0.89). Errors due to fluctuations in ventilation are about equal in IC and VCO2-based calorimetry, and filtering reduced these errors. CONCLUSIONS: This study confirmed the inaccuracy of predictive equations and established VCO2-based calorimetry as a more accurate alternative. Both IC and VCO2-based calorimetry are sensitive to fluctuations in respiration.

An in silico method to identify computer-based protocols worthy of clinical study: An insulin infusion protocol use case.

OBJECTIVE: Develop an efficient non-clinical method for identifying promising computer-based protocols for clinical study. An in silico comparison can provide information that informs the decision to proceed to a clinical trial. The authors compared two existing computer-based insulin infusion protocols: eProtocol-insulin from Utah, USA, and Glucosafe from Denmark. MATERIALS AND METHODS: The authors used eProtocol-insulin to manage intensive care unit (ICU) hyperglycemia with intravenous (IV) insulin from 2004 to 2010. Recommendations accepted by the bedside clinicians directly link the subsequent blood glucose values to eProtocol-insulin recommendations and provide a unique clinical database. The authors retrospectively compared in silico 18,984 eProtocol-insulin continuous IV insulin infusion rate recommendations from 408 ICU patients with those of Glucosafe, the candidate computer-based protocol. The subsequent blood glucose measurement value (low, on target, high) was used to identify if the insulin recommendation was too high, on target, or too low. RESULTS: Glucosafe consistently provided more favorable continuous IV insulin infusion rate recommendations than eProtocol-insulin for on target (64% of comparisons), low (80% of comparisons), or high (70% of comparisons) blood glucose. Aggregated eProtocol-insulin and Glucosafe continuous IV insulin infusion rates were clinically similar though statistically significantly different (Wilcoxon signed rank test P = .01). In contrast, when stratified by low, on target, or high subsequent blood glucose measurement, insulin infusion rates from eProtocol-insulin and Glucosafe were statistically significantly different (Wilcoxon signed rank test, P < .001), and clinically different. DISCUSSION: This in silico comparison appears to be an efficient nonclinical method for identifying promising computer-based protocols. CONCLUSION: Preclinical in silico comparison analytical framework allows rapid and inexpensive identification of computer-based protocol care strategies that justify expensive and burdensome clinical trials.

Interpretative reading of the antibiogram--a semi-naïve Bayesian approach.

BACKGROUND: An antibiogram (ABG) gives the results of in vitro susceptibility tests performed on a pathogen isolated from a culture of a sample taken from blood or other tissues. The institutional cross-ABG consists of the conditional probability of susceptibility for pairs of antimicrobials. This paper explores how interpretative reading of the isolate ABG can be used to replace and improve the prior probabilities stored in the institutional ABG. Probabilities were calculated by both a naïve and semi-naïve Bayesian approaches, both using the ABG for the given isolate and institutional ABGs and cross-ABGs. METHODS AND MATERIAL: We assessed an isolate database from an Israeli university hospital with ABGs from 3347 clinically significant blood isolates, where on average 19 antimicrobials were tested for susceptibility, out of 31 antimicrobials in regular use for patient treatment. For each of 14 pathogens or groups of pathogens in the database the average (prior) probability of susceptibility (also called the institutional ABG) and the institutional cross-ABG were calculated. For each isolate, the normalized Brier distance was used as a measure of the distance between susceptibility test results from the isolate ABG and respectively prior probabilities and posteriori probabilities of susceptibility. We used a 5-fold cross-validation to evaluate the performance of different approaches to predict posterior susceptibilities. RESULTS: The normalized Brier distance between the prior probabilities and the susceptibility test results for all isolates in the database was reduced from 37.7% to 28.2% by the naïve Bayes method. The smallest normalized Brier distance of 25.3% was obtained with the semi-naïve min2max2 method, which uses the two smallest significant odds ratios and the two largest significant odds ratios expressing respectively cross-resistance and cross-susceptibility, calculated from the cross-ABG. CONCLUSION: A practical method for predicting probability for antimicrobial susceptibility could be developed based on a semi-naïve Bayesian approach using statistical data on cross-susceptibilities and cross-resistances. The reduction in Brier distance from 37.7% to 25.3%, indicates a significant advantage to the proposed min2max2 method (p<10(99)).

Model-based measurement of gas exchange in healthy subjects using ALPE essential--influence of age, posture and gender.

The ALPE Essential device for model-based measurement of pulmonary gas exchange status may be a useful alternative to current methods for diagnosing, monitoring and evaluating treatment related to pulmonary gas exchange. In this study, shunt and ventilation/perfusion mismatch were measured with ALPE Essential in 106 healthy subjects with the aim of investigating the influence of age, posture and gender on gas exchange parameters and evaluating the test-retest reliability of the measurements. Age and gender did not have statistically significant influence on gas exchange parameters, although there was a tendency for poorer matching of ventilation and perfusion with age. Posture was shown to be important when measuring gas exchange parameters. Absolute measurement reliability was acceptable with future studies in patients being necessary for accurate evaluation of relative reliability.

Clinical refinement of the automatic lung parameter estimator (ALPE).

The automatic lung parameter estimator (ALPE) method was developed in 2002 for bedside estimation of pulmonary gas exchange using step changes in inspired oxygen fraction (FIO2). Since then a number of studies have been conducted indicating the potential for clinical application and necessitating systems evolution to match clinical application. This paper describes and evaluates the evolution of the ALPE method from a research implementation (ALPE1) to two commercial implementations (ALPE2 and ALPE3). A need for dedicated implementations of the ALPE method was identified: one for spontaneously breathing (non-mechanically ventilated) patients (ALPE2) and one for mechanically ventilated patients (ALPE3). For these two implementations, design issues relating to usability and automation are described including the mixing of gasses to achieve FIO2 levels, and the automatic selection of FIO2. For ALPE2, these improvements are evaluated against patients studied using the system. The major result is the evolution of the ALPE method into two dedicated implementations, namely ALPE2 and ALPE3. For ALPE2, the usability and automation of FIO2 selection has been evaluated in spontaneously breathing patients showing that variability of gas delivery is 0.3 % (standard deviation) in 1,332 breaths from 20 patients. Also for ALPE2, the automated FIO2 selection method was successfully applied in 287 patient cases, taking 7.2 ± 2.4 min and was shown to be safe with only one patient having SpO2 < 86 % when the clinician disabled the alarms. The ALPE method has evolved into two practical, usable systems targeted at clinical application, namely ALPE2 for spontaneously breathing patients and ALPE3 for mechanically ventilated patients. These systems may promote the exploration of the use of more detailed descriptions of pulmonary gas exchange in clinical practice.

Decision support for optimized blood glucose control and nutrition in a neurotrauma intensive care unit: preliminary results of clinical advice and prediction accuracy of the Glucosafe system.

Assessment of glycemic control with model-based decision support ("Glucosafe") in neurotrauma intensive care patients in an ongoing randomized controlled trial with a blood glucose (BG) target of 5-8 mmol/L. Assessment of BG prediction accuracy of the model and assessment of the effect that two potential model extensions would have on prediction accuracy in this trial. In the intervention group insulin infusion rates and nutrition are varied based on Glucosafe's decision support. In the control group, the caloric target is 25-30 kcal/kg per day and insulin is regulated according to department rules. BG concentrations, insulin infusion rates, and feed rates are compared from the data of 12 consecutive patients. BG measurements are predicted retrospectively and the mean relative prediction error is calculated using (1) the current model from the trial, (2) the current model modified by using a BG-dependent variable endogenous insulin appearance rate, (3) the current model modified by a patient-specific carbohydrate absorption factor. BG control was improved by Glucosafe. 76 % of BG measurements in Glucosafe patients were in the 5-8 mmol/L band (Controls: 51 %). BG means (log-normal) ± SD were 7.0 ± 1.19 mmol/L in Glucosafe patients compared to 8.0 ± 1.24 mmol/L in controls (P = 0.05). Mean caloric intake was 93.5 ± 15 % of resting energy expenditure in Glucosafe patients (Controls: 129 ± 29 %). The BG-dependent variable insulin appearance rate had no measurable effect on prediction accuracy. The patient-specific carbohydrate absorption factor improved prediction accuracy significantly (P = 0.001). Glucosafe advice reduces hyperglycemia in neurotrauma intensive care patients. Further parameterization can improve model prediction accuracy.

Mathematical arterialization of venous blood in emergency medicine patients.

OBJECTIVES: Arterial punctures represent a painful and unpleasant experience. Acid-base and oxygenation status can be assessed from peripheral venous blood, but agreement with arterial values is not always clinically acceptable. This study evaluates a method for mathematically transforming peripheral venous values into arterial values in emergency medicine patients. METHODS: Paired arterial and peripheral venous samples were analysed in groups A (47 patients) and B (101 patients), corresponding to the clinical need for arterial blood sampling (A) and without (B). Venous values were input into the mathematical arterialization method and the values of arterial pH, PCO2 and PO2 were calculated and compared with the measured values. RESULTS: The calculated and measured arterial pH and PCO2 values correlated well with the correlation coefficients (r ) of group A, pH 0.94, PCO2 0.97; group B, pH 0.87, PCO2 0.83; and Bland-Altman limits of agreement well within the limits of acceptable laboratory and clinical performance. The calculated values of arterial PO2 followed a set of predefined rules relating calculated and measured PO2 levels in all cases. The method represents an improvement on the use of venous blood alone where the correlation coefficients were as follows: group A, pH 0.85, PCO2 0.88; group B, pH 0.79, PCO2 0.59; and limits of agreement for PCO2 at the border of (group A) or beyond (group B) acceptable clinical limits. CONCLUSION: Application of the mathematical arterialization method may reduce the pain associated with assessment of acid-base and oxygenation status, maximize the information obtained from peripheral venous blood and allow venous measurements to be presented as more commonly interpreted arterial values.

Retrospective evaluation of a decision support system for controlled mechanical ventilation.

Management of mechanical ventilation in intensive care patients is complicated by conflicting clinical goals. Decision support systems (DSS) may support clinicians in finding the correct balance. The objective of this study was to evaluate a computerized model-based DSS for its advice on inspired oxygen fraction, tidal volume and respiratory frequency. The DSS was retrospectively evaluated in 16 intensive care patient cases, with physiological models fitted to the retrospective data and then used to simulate patient response to changes in therapy. Sensitivity of the DSS's advice to variations in cardiac output (CO) was evaluated. Compared to the baseline ventilator settings set as part of routine clinical care, the system suggested lower tidal volumes and inspired oxygen fraction, but higher frequency, with all suggestions and the model simulated outcome comparing well with the respiratory goals of the Acute Respiratory Distress Syndrome Network from 2000. Changes in advice with CO variation of about 20% were negligible except in cases of high oxygen consumption. Results suggest that the DSS provides clinically relevant and rational advice on therapy in agreement with current 'best practice', and that the advice is robust to variation in CO.

Variability of preference toward mechanical ventilator settings: a model-based behavioral analysis.

PURPOSE: The purpose of this study was to evaluate Danish clinicians' opinions toward ventilator settings using standardized model-simulated patients. The models ensured that all clinicians received identical presentations of data and anticipated responses to changes in patient state, enabling opinions on the same patient cases to be obtained from different clinicians. MATERIALS AND METHODS: Ten Danish intensive care clinicians' and a computerized decision support system each provided suggestions for respiratory frequency (f), tidal volume (Vt) and insoired oxygen fraction (FiO2) in the same 10 model-simulated patient cases. The 110 suggestions were then evaluated by the 10 clinicians in a ranking and classification procedure. RESULTS: Clinicians' preferences toward ventilator settings (Fio(2), Vt, and f) and the resulting simulated values of arterial oxygen saturation, peak inspiratory pressure, and pH were significantly different (P < .005). The results of the classification showed that clinicians generally had poor opinion of the advice provided by other clinicians and the decision support system, considering this advice to be unacceptable in 33% of cases and good only in 21%. The ranking procedure also showed that clinicians did not agree on the best and worst advice. CONCLUSION: The present study shows significant difference in opinion on appropriate settings of f, Vt, and Fio(2) in the same computerized decision support system model-simulated patient cases.

Tidal breathing model describing end-tidal, alveolar, arterial and mixed venous CO2 and O2.

Thermodilution is the current standard for determination of cardiac output. The method is invasive and constitutes a risk for the patient. As an alternative CO2 rebreathing allows non-invasive cardiac output estimation using Ficks principle. The method relies on estimation of arterial CO2 partial pressure from end-tidal CO2 pressure and estimation of mixed venous CO2 partial pressure from end-tidal CO2 during rebreathing. Presumably the oxygenation of blood in the lung capillaries increases lung capillary CO2 pressure due to the Haldane effect, which during rebreathing may result in overestimation of the mixed venous CO2 pressure. However, the Haldane effect is not discussed in the current literature describing cardiac output estimation using CO2 rebreathing. The purpose of this study is to construct and verify a compartmental tidal breathing lung model to investigate the physiological mechanisms that influence the CO2 rebreathing technique. The model simulations show agreement with previous studies describing end-tidal to arterial differences in CO2 pressure and rebreathing with high and low O2 fractions in the rebreathing bag. In conclusion the simulations show that caution has to be taken when using end-tidal measurements to estimate CO2 pressures, especially during rebreathing where the Haldane effect causes mixed venous CO2 partial pressure to be substantially overestimated.