Metrology in medicine: from measurements to decision, with specific reference to anesthesia and intensive care.

Metrology is the science of measurements. Although of critical importance in medicine and especially in critical care, frequent confusion in terms and definitions impact either interphysician communications or understanding of manufacturers' and engineers' instructions and limitations when using devices. In this review, we first list the terms defined by the International Bureau of Weights and Measures regarding quantities and units, measurements, devices for measurement, properties of measuring devices, and measurement standards. The traditional tools for assessing the most important measurement quality criteria are also reviewed with clinical examples for diagnosis, alarm, and titration purposes, as well as for assessing the uncertainty of reference methods.

Intensive care unit alarms--how many do we need?

OBJECTIVE: To validate cardiovascular alarms in critically ill patients in an experimental setting by generating a database of physiologic data and clinical alarm annotations, and report the current rate of alarms and their clinical validity. Currently, monitoring of physiologic parameters in critically ill patients is performed by alarm systems with high sensitivity, but low specificity. As a consequence, a multitude of alarms with potentially negative impact on the quality of care is generated. DESIGN: Prospective, observational, clinical study. SETTING: Medical intensive care unit of a university hospital. DATA SOURCE: Data from different medical intensive care unit patients were collected between January 2006 and May 2007. MEASUREMENTS AND MAIN RESULTS: Physiologic data at 1-sec intervals, monitor alarms, and alarm settings were extracted from the surveillance network. Video recordings were annotated with respect to alarm relevance and technical validity by an experienced physician. During 982 hrs of observation, 5934 alarms were annotated, corresponding to six alarms per hour. About 40% of all alarms did not correctly describe the patient condition and were classified as technically false; 68% of those were caused by manipulation. Only 885 (15%) of all alarms were considered clinically relevant. Most of the generated alarms were threshold alarms (70%) and were related to arterial blood pressure (45%). CONCLUSION: This study used a new approach of off-line, video-based physician annotations, showing that even with modern monitoring systems most alarms are not clinically relevant. As the majority of alarms are simple threshold alarms, statistical methods may be suitable to help reduce the number of false-positive alarms. Our study is also intended to develop a reference database of annotated monitoring alarms for further application to alarm algorithm research.

Comparison of a two-lead, computerized, resting ECG signal analysis device, the MultiFunction-CardioGram or MCG (a.k.a. 3DMP), to quantitative coronary angiography for the detection of relevant coronary artery stenosis (>70%) - a meta-analysis of all published trials performed and analyzed in the US.

BACKGROUND: Accurate, non-invasive diagnosis of, and screening for, coronary artery disease (CAD) and restenosis after coronary revascularization has been a challenge due to either low sensitivity/specificity or relevant morbidity associated with current diagnostic modalities. METHODS: To assess sensitivity and specificity of a new computerized, multiphase, resting electrocardiogram analysis device (MultiFunction-CardioGram(sm) or MCG a.k.a. 3DMP) for the detection of relevant coronary stenosis (>70%), a meta-analysis of three published prospective trials performed in the US on patient data collected using the US manufactured device and analyzed using the US-based software and New York data analysis center from patients in the US, Germany, and Asia was completed. A total of 1076 patients from the three trials (US - 136; Germany - 751; Asia - 189) (average age 62 +/- 11.5, 65 for women, 60 for men) scheduled for coronary angiography, were included in the analysis. Patients enrolled in the trials may or may not have had prior angiography and/or coronary intervention. Angiographic results in all studies were classified for hemodynamically relevant stenosis (> 70%) by two US based angiographers independently. RESULTS: Hemodynamically relevant stenosis was diagnosed in 467 patients (43.4%). The device, after performing a frequency-domain, computational analysis of the resting ECG leads and computer-database comparison, calculated a coronary ischemia "severity" score from 0 to 20 for each patient. The severity score was significantly higher for patients with relevant coronary stenosis (5.4 +/- 1.8 vs. 1.7 +/- 2.1). The study device (using a cut-off score for relevant stenosis of 4.0) correctly classified 941 of the 1076 patients with or without relevant stenosis (sensitivity-91.2%; specificity-84.6%; NPV 0.942, PPV 0.777). Adjusted positive and negative predictive values (PPV and NPV) were 81.9% and 92.6%, respectively (ROC AUC = 0.881 [95% CI: 0.860-0.903]). Subgroup analysis showed no significant influence of sex, age, race/nationality, previous revascularization procedures, resting ECG morphology, or participating center on the device's diagnostic performance. CONCLUSIONS: The new computerized, multiphase, resting ECG analysis device (MultiFunction-CardioGram(sm)) has been shown in this meta-analysis to safely and accurately identify patients with relevant coronary stenosis (>70%) with high sensitivity and specificity and high negative predictive value. Its potential use in the evaluation of symptomatic patients suspected to suffer from coronary disease/ischemia is discussed.

EIT monitors valid and robust regional ventilation distribution in pathologic ventilation states in porcine study using differential DualEnergy-CT (ΔDECT).

It is crucial to precisely monitor ventilation and correctly diagnose ventilation-related pathological states for averting lung collapse and lung failure in Intensive Care Unit (ICU) patients. Although Electrical Impedance Tomography (EIT) may deliver this information continuously and non-invasively at bedside, to date there are no studies that systematically compare EIT and Dual Energy CT (DECT) during inspiration and expiration (ΔDECT) regarding varying physiological and ICU-typical pathological conditions such as atelectasis. This study aims to prove the accuracy of EIT through quantitative identification and monitoring of pathological ventilation conditions on a four-quadrant basis using ΔDECT. In a cohort of 13 pigs, this study investigated systematic changes in tidal volume (TV) and positive end-expiratory pressure (PEEP) under physiological ventilation conditions. Pathological ventilation conditions were established experimentally by single-lung ventilation and pulmonary saline lavage. Spirometric data were compared to voxel-based entire lung ΔDECT, and EIT intensities were compared to ΔDECT of a 12-cm slab of the lung around the EIT belt, the so called ΔDECTBelt. To validate ΔDECT data with spirometry, a Pearson's correlation coefficient of 0.92 was found for 234 ventilation conditions. Comparing EIT intensity with ΔDECT(Belt), the correlation r = 0.84 was found. Normalized cross-correlation function (NCCF) between scaled global impedance (EIT) waveforms and global volume ventilator curves was r = 0.99 ± 0.003. The EIT technique correctly identified the ventilated lung in all cases of single-lung ventilation. In the four-quadrant based evaluation, which assesses the difference between end-expiratory lung volume (ΔEELV) and the corresponding parameter in EIT, i.e. the end-expiratory lung impedance (ΔEELI), the Pearson's correlation coefficient of 0.94 was found. The respective Pearson's correlation coefficients implies good to excellent concurrence between global and regional EIT ventilation data validated by ventilator spirometry and DECT imaging. By providing real-time images of the lung, EIT is a promising, EIT is a promising, clinically robust tool for bedside assessment of regional ventilation distribution and changes of end-expiratory lung volume.

Computerized 2-lead resting ECG analysis for the detection of relevant coronary artery stenosis in comparison with angiographic findings.

To assess the sensitivity and specificity of a new computer-enhanced resting electrocardiographic analysis device for the detection of coronary stenosis, 189 patients (aged 61.3+/-12.9 years, 57 women) scheduled for coronary angiography from 4 Asian centers were included in an observational study. Angiographic results were independently classified for hemodynamically relevant stenosis by 2 angiographers. The device calculated a severity score from 0 to 20. The score was significantly higher for patients with coronary stenosis (5.4+/-1.8 vs 1.7+/-2.1). The study device (cutoff 4.0) identified 73 of 77 patients with stenosis (sensitivity 94.8%, specificity 86.6%). Adjusted positive and negative predictive values were 78.4% and 97.1%, respectively (receiver operating characteristic area under the curve, 0.914 [95% confidence interval, 0.868-0.961]). Subgroup analysis showed no significant influence of sex, age, previous revascularization procedures, or participating center. The new computer-enhanced, resting electrocardiographic analysis device appears to identify patients with relevant coronary stenosis with high sensitivity and specificity.

Computerized two-lead resting ECG analysis for the detection of coronary artery stenosis after coronary revascularization.

BACKGROUND: Resting electrocardiogram (ECG) shows limited sensitivity and specificity for the detection of coronary artery disease (CAD), where patients with a history of coronary revascularization may pose special challenges. Several methods exist to enhance sensitivity and specificity of resting ECG for diagnosis of CAD, but such methods are not better than a specialist's judgement. We compared a new computer-enhanced, resting ECG analysis device, 3DMP, to coronary angiography to evaluate the device's accuracy in detecting hemodynamically relevant CAD. METHODS: A convenience sample of 172 patients with a history of coronary revascularization scheduled for coronary angiography was evaluated with 3DMP before coronary angiography. 3DMP's sensitivity and specificity in detecting hemodynamically relevant coronary stenosis as diagnosed with coronary angiography were calculated as well as odds ratios for the 3DMP severity score and coronary artery disease risk factors. RESULTS: The 3DMP system accurately identified 50 of 55 patients as having hemodynamically relevant stenosis (sensitivity 90.9%, specificity 88.0%). Positive and negative predictive values for the identification of coronary stenosis as diagnosed in coronary angiograms were 62.7% and 97.8% respectively. Risk and demographic factors in a logistic regression model had a markedly lower predictive power for the presence of coronary stenosis in these patients than did 3DMP severity score (odds ratio 2.04 [0.74-5.62] vs. 73.57 [25.10-215.68]). A logistic regression combining severity score with risk and demographic factors did not add significantly to the prediction quality (odds ratio 80.00 [27.03-236.79]). CONCLUSIONS: 3DMP's computer-based, mathematically derived analysis of resting two-lead ECG data provides detection of hemodynamically relevant CAD in patients with a history of coronary revascularization with high sensitivity and specificity that appears to be at least as good as those reported for other resting and/or stress ECG methods currently used in clinical practice.

Computerized two-lead resting ECG analysis for the detection of coronary artery stenosis.

BACKGROUND: Resting electrocardiogram (ECG) shows limited sensitivity and specificity for the detection of coronary artery disease (CAD). Several methods exist to enhance sensitivity and specificity of resting ECG for diagnosis of CAD, but such methods are not better than a specialist's judgement. We compared a new computer-enhanced, resting ECG analysis device, 3DMP, to coronary angiography to evaluate the device's accuracy in detecting hemodynamically relevant CAD. METHODS: A convenience sample of 423 patients without prior coronary revascularization was evaluated with 3DMP before coronary angiography. 3DMP's sensitivity and specificity in detecting hemodynamically relevant coronary stenosis as diagnosed with coronary angiography were calculated as well as odds ratios for the 3DMP severity score and coronary artery disease risk factors. RESULTS: 3DMP identified 179 of 201 patients with hemodynamically relevant stenosis (sensitivity 89.1%, specificity 81.1%). The positive and negative predictive values for identification of coronary stenosis as diagnosed in coronary angiograms were 79% and 90% respectively. CAD risk factors in a logistic regression model had markedly lower predictive power for the presence of coronary stenosis in patients than did 3DMP severity score (odds ratio 3.35 [2.24-5.01] vs. 34.87 [20.00-60.79]). Logistic regression combining severity score with risk factors did not add significantly to the prediction quality (odds ratio 36.73 [20.92-64.51]). CONCLUSIONS: 3DMP's computer-based, mathematically derived analysis of resting two-lead ECG data provides detection of hemodynamically relevant CAD with high sensitivity and specificity that appears to be at least as good as those reported for other resting and/or stress ECG methods currently used in clinical practice.

Rotational bed therapy to prevent and treat respiratory complications: a review and meta-analysis.

BACKGROUND: Immobility is associated with complications involving many body systems. OBJECTIVE: To review the effect of rotational therapy (use of therapeutic surfaces that turn on their longitudinal axes) on prevention and/or treatment of respiratory complications in critically ill patients. METHODS: Published articles evaluating prophylaxis and/or treatment were reviewed. Prospective randomized controlled trials were assessed for quality and included in meta-analyses. RESULTS: A literature search yielded 15 nonrandomized, uncontrolled, or retrospective studies. Twenty prospective randomized controlled trials on rotational therapy were published between 1987 and 2004. Various types of beds were studied, but few details on the rotational parameters were reported. The usual control was manual turning of patients by nurses every 2 hours. One animal investigation and 12 clinical trials addressed the effectiveness of rotational therapy in preventing respiratory complications. Significant benefits were reported in the animal study and 4 of the trials. Significant benefits to patients were reported in 2 of another 4 studies focused on treatment of established complications. Researchers have examined the effects of rotational therapy on mucus transport, intrapulmonary shunt, hemodynamic effects, urine output, and intracranial pressure. Little convincing evidence is available, however, on the most effective rotation parameters (eg, degree, pause time, and amount of time per day). Meta-analysis suggests that rotational therapy decreases the incidence of pneumonia but has no effect on duration of mechanical ventilation, number of days in intensive care, or hospital mortality. CONCLUSIONS: Rotational therapy may be useful for preventing and treating respiratory complications in selected critically ill patients receiving mechanical ventilation.

Alarm algorithms in critical care monitoring.

The alarms of medical devices are a matter of concern in critical and perioperative care. The frequent false alarms not only are a nuisance for patients and caregivers but can also compromise patient safety and effectiveness of care. The development of alarm systems has lagged behind the technological advances of medical devices over the last 20 years. From a clinical perspective, major improvements of alarm algorithms are urgently needed. We give an overview of the current clinical situation and the underlying problems and discuss different methods from statistics and computational science and their potential for clinical application.

Diagnosis, prevention and treatment of accidental and perioperative hypothermia.

Accidental hypothermia and its variant, perioperative hypothermia, is a rather common clinical phenomenon in patients. This is surprising because the negative effects on clinical outcomes are well described and effective patient-warming devices are available today. The aim of this paper is to describe the physiologic background of accidental and perioperative hypothermia, the clinical relevance and existing prophylaxis and treatment options. Patient warming techniques will be discussed in detail. Remaining technical and clinical challenges and the need for further research will be addressed. We will present existing guidelines and standards and analyse the impact of accidental and perioperative hypothermia on cost effectiveness.

Temperature measurement.

For many decades the measurement of body core temperature has been ubiquitously established in medical and non-medical applications, e.g., in hospitals, occupational medicine, sports medicine, military and other settings. However, there are still numerous challenges, such as the precise definition of the body core temperature, establishing the clinical importance of the measured temperature and the lack of a reliable, non-invasive and fast measurement method for body core temperature. After an introduction to the topic, the medical aspects from a user point of view are presented, i.e., the needs for temperature measurements, as well as possible measurement sites and clinical specifications and needs are highlighted. Subsequently, technical methods are presented which are used for temperature measurement. The analysis of the technical methods is divided into two sections: the first deals with the standard methods, which are currently used and the second describes methods, which are currently under development. Although temperature measurement appears very easy and is very common in daily use, it has many constraints, which are considered later. The need for further research is deduced from the above-mentioned sections and is finally followed by the conclusions section.

Medical device alarms.

The high number of false positive alarms has long been known to be a serious problem in critical care medicine - yet it remains unresolved. At the same time, threats to patient safety due to missing or suppressed alarms are being reported. The purpose of this paper is to present results from a workshop titled "Too many alarms? Too few alarms?" organized by the Section Patient Monitoring and the Workgroup Alarms of the German Association of Biomedical Engineering of the Association for Electrical, Electronic and Information Technologies. The current situation regarding alarms and their problems in intensive care, such as lack of clinical relevance, alarm fatigue, workload increases due to clinically irrelevant alarms, usability problems in alarm systems, problems with manuals and training, and missing alarms due to operator error are outlined, followed by a discussion of solutions and strategies to improve the current situation. Finally, the need for more research and development, focusing on signal quality considerations, networking of medical devices at the bedside, diagnostic alarms and predictive warnings, usability of alarm systems, education of healthcare providers, creation of annotated clinical databases for testing, standardization efforts, and patient monitoring in the regular ward, are called for.

Collection of annotated data in a clinical validation study for alarm algorithms in intensive care--a methodologic framework.

INTRODUCTION: Monitoring of physiologic parameters in critically ill patients is currently performed by threshold alarm systems with high sensitivity but low specificity. As a consequence, a multitude of alarms are generated, leading to an impaired clinical value of these alarms due to reduced alertness of the intensive care unit (ICU) staff. To evaluate a new alarm procedure, we currently generate a database of physiologic data and clinical alarm annotations. METHODS: Data collection is taking place at a 12-bed medical ICU. Patients with monitoring of at least heart rate, invasive arterial blood pressure, and oxygen saturation are included in the study. Numerical physiologic data at 1-second intervals, monitor alarms, and alarm settings are extracted from the surveillance network. Bedside video recordings are performed with network surveillance cameras. RESULTS: Based on the extracted data and the video recordings, alarms are clinically annotated by an experienced physician. The alarms are categorized according to their technical validity and clinical relevance by a taxonomy system that can be broadly applicable. Preliminary results showed that only 17% of the alarms were classified as relevant, and 44% were technically false. DISCUSSION: The presented system for collecting real-time bedside monitoring data in conjunction with video-assisted annotations of clinically relevant events is the first allowing the assessment of 24-hour periods and reduces the bias usually created by bedside observers in comparable studies. It constitutes the basis for the development and evaluation of "smart" alarm algorithms, which may help to reduce the number of alarms at the ICU, thereby improving patient safety.

Smart alarms from medical devices in the OR and ICU.

Alarms in medical devices are a matter of concern in critical and perioperative care. The high rate of false alarms is not only a nuisance for patients and caregivers, but can also compromise patient safety and effectiveness of care. The development of alarm systems has lagged behind the technological advances of medical devices over the last 20 years. From a clinical perspective, major improvements in alarm algorithms are urgently needed. This review gives an overview of the current clinical situation and the underlying problems, and discusses different methods from statistics and computational science and their potential for clinical application. Some examples of the application of new alarm algorithms to clinical data are presented.

Detecting relationships between physiological variables using graphical models.

In intensive care physiological variables of the critically ill are measured and recorded in short time intervals. The proper extraction and interpretation of the information contained in this flood of information can hardly be done by experience alone. Intelligent alarm systems are needed to provide suitable bedside decision support. So far there is no commonly accepted standard for detecting the actual clinical state from the patient record. We use the statistical methodology of graphical models based on partial correlations for detecting time-varying relationships between physiological variables. Graphical models provide information on the relationships among physiological variables that is helpful e.g. for variable selection. Separate analyses for different pathophysiological states show that distinct clinical states are characterized by distinct partial correlation structures. Hence, this technique can provide new insights into physiological mechanisms.

Graphical models for multivariate time series from intensive care monitoring.

Nowadays physicians are confronted with high-dimensional data generated by clinical information systems. The proper extraction and interpretation of the information contained in such massive data sets, which are often observed with high sampling frequencies, can hardly be done by experience only. This yields new perspectives of data recording and also sets a new challenge for statistical methodology. Recently graphical models have been developed for analysing the partial correlations between the components of multivariate time series. We apply this technique to the haemodynamic system of critically ill patients monitored in intensive care. In this way we can appraise the practical value of the new procedure by re-identifying known associations within the haemodynamic system. From separate analyses for different pathophysiological states we can even conclude that distinct clinical states are characterized by distinct partial correlation structures. Hence, this technique seems useful for automatic statistical analysis of high-dimensional physiological time series and it can provide new insights into physiological mechanisms. Moreover, we can use it to achieve an adequate dimension reduction of the variables needed for online monitoring at the bedside.