Intelligence-based medicine

Despite seven hundred thousand new medical references last year, the relationship between a given set of medical features and specific pathophysiology, treatment, and criteria of improvement is often weak. Moreover, the generalization of evidences obtained in specific settings may lead to under-treat or to over-treat a significant proportion of patients. We expose an application of the cybernetic loop, based on traditional medical steps: nosology, semeiology, pathophysiology, therapy and on the four transitions between these steps. This approach leads to formulate eight basic questions evaluating the steps in terms of reproducibility and the transitions in terms of predictivity. We detail two practical applications: 1) the evaluation of a medical decision (implantation of an internal cardioverter-defibrillator) and 2) the evaluation of a specific study (EPHESUS). Using this loop allows to determine clearly when evidence is lacking and/or to which extend an evidence really increases the medical knowledge or just creates a market

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

A New Ratio for Protocol Categorization

The present review describes and validates a new ratio “S” created for matching predictability and balance between TP and TN. Validity of S was studied in a three-step process as follows: (i) S was applied to the data of a past study predicting cardiac output response to fluid bolus from response to passive leg raise (PLR); (ii) S was comparatively analyzed with traditional ratios by modeling different 2 * 2 contingency tables in 1000 hypothetical patients; (iii) precision of S was compared with other ratios by computing random fluctuations in the same patients. In comparison to other ratios, S performs better in predicting the cardiac response to fluid bolus and supports more directly the clinical conclusions. When the proportion of false responses is high, S is close to the coefficient correlation (CC). When the proportion of true responses is high, S is the unique ratio that identifies the categorization that balances the proportion of TP and TN. The precision of S is close to that of CC. In conclusion, S should be considered for creating categories from quantitative variables; especially when matching predictability with balance between TP and TN is a concern

Comparison of monitoring performance of Bioreactance vs. pulse contour during lung recruitment maneuvers

INTRODUCTION: This study was designed to test the hypothesis of equivalence in cardiac output (CO) and stroke volume (SV) monitoring capabilities of two devices: non invasive transthoracic bioreactance (NICOM), and a pulse contour analysis (PICCO PC) coupled to transpulmonary thermodilution (PICCO TD). METHODS: We included consecutive patients of a single ICU following cardiac surgery. Continuous minute-by-minute hemodynamic variables obtained from NICOM and PICCO PC were recorded and compared in 20 patients at baseline, during a lung recruitment maneuver (20 cmH(2)O of PEEP) and following withdrawal of PEEP. PICCO TD measurements were also determined. We evaluated the accuracy of these two technologies at baseline using PICCO TD as reference and we estimated the precision by the fluctuation around the mean value (2SD/mean). Then, we assessed time response, amplitude response and reliability for detecting expected decreases when PEEP was applied. Type I and type II errors were analyzed. RESULTS: CO values (PICCO TD) ranged from 1.6 to 8.0 L.min(-1). At baseline, CO values were comparable for NICOM, PICCO PC and PICCO TD: 5.0 ± 1.2, 4.7 ± 1.4 and 4.6 ± 1.3 L.min.(-1), respectively (NS). Limits of agreements with PICCO TD were 1.52 L.min.(-1 )for NICOM and 1.77 L.min.(-1 )for PICCO PC, NS. The 95% statistical power gives an equivalence with a threshold of 0.52 L.min.(-1 )for NICOM vs. PICCO PC. The CO precision was 6 ± 3% and 6 ± 5% for NICOM and PICCO PC, respectively, NS. When PEEP was applied, CO was reduced by 33 ± 12%, 31 ± 14% and 32 ± 13%, for NICOM, PICCO PC and PICCO TD, respectively (NS). Time response was 3.2 ± 0.7 minute for NICOM vs. 2 ± 0.5 minute for PICCO PC (NS). SV results were comparable to those for CO. CONCLUSIONS: Although limited to 20 patients, this study has enough power to show comparable CO and SV monitoring capabilities of Bioreactance and pulse contour analysis calibrated by transpulmonary thermodilution

Metrology part 1:definition of quality criteria

Any measurement is always afflicted with some degree of uncertainty. A correct understanding of the different types of uncertainty, their naming, and their definition is of crucial importance for an appropriate use of measuring instruments. However, in perioperative and intensive care medicine, the metrological requirements for measuring instruments are poorly defined and often used spuriously. The correct use of metrological terms is also of crucial importance in validation studies. The European Union published a new directive on medical devices, mentioning that in the case of devices with a measuring function, the notified body is involved in all aspects relating to the conformity of the device with the metrological requirements. It is therefore the task of the scientific societies to establish the standards in their area of expertise. Adopting the same understandings and definitions among clinicians and scientists is obviously the first step. In this metrologic review (part 1), we list and explain the most important terms defined by the International Bureau of Weights and Measures regarding quantities and units, properties of measurements, devices for measurement, properties of measuring devices, and measurement standards, with specific examples from perioperative and intensive care medicine

Metrology part 2:Procedures for the validation of major measurement quality criteria and measuring instrument properties

A measurement is always afflicted with some degree of uncertainty. A correct understanding of the different types of uncertainty, their naming, and their definition is of crucial importance for an appropriate use of the measuring instruments. However, in perioperative and intensive care medicine, the metrological requirements for measuring instruments are poorly defined and often used spuriously. The correct use of metrological terms is also of crucial importance in validation studies. The European Union published a new directive on medical devices, mentioning that in the case of devices with a measuring function, the notified body is involved in all aspects relating to the conformity of the device with the metrological requirements. It is therefore the task of scientific societies to establish the standards in their area of expertise. After adopting the same understandings and definitions (part 1), the different procedures for the validation of major quality criteria of measuring devices must be consensually established. In this metrologic review (part 2), we review the terms and definitions of validation, some basic processes leading to the display of an indication from a physiologic signal, and procedures for the validation of measuring instrument properties, with specific focus on perioperative and intensive care medicine including appropriate examples

The Ockham’s razor for estimating the needs of ICU beds during a pandemic

Abstract Background It is possible to monitor an epidemic evolution by plotting the number of patients, or its log-transform, as a function of time. However, these representations do not allow quick identifications of significant changes in the outbreak; a key information for estimating the needs for hospital and ICU beds, for decision-making, and resource allocation. Moreover, an epidemic is characterised by a heterogeneous evolution that depends on many unpredictable factors, coming from the virus itself or from its ecosystem. Simulations are very complex and based on hypotheses that are impossible to certify a priori, since each outbreak is different and has specific characteristics. A validation phase is necessary that may delay the usefulness of these tools. We tested a simpler method for monitoring the epidemic and rapidly predicting the peak. Results We present here a simple and easy-to-draw figure by plotting the daily rate of change in the number of patients as a function of time. This allows: (1) to rapidly identify the changes in the infection growth, (2) to extrapolate the regression lines for predicting the peaks, and (3) to use simple statistical models for identifying the significant inflexions and deriving the uncertainties. This figure predicted confidently the peak epidemic of the three waves in France. Conclusion Plotting the daily rate of change in the number of patients as a function of time is a simple tool for monitoring an epidemic growth, allowing to quickly identify significant changes and to help in predicting the peak of the infection, with its confidence interval

Toward Intelligent Hemodynamic Monitoring: A Functional Approach

Technology is now available to allow a complete haemodynamic analysis; however this is only used in a small proportion of patients and seems to occur when the medical staff have the time and inclination. As a result of this, significant delays occur between an event, its diagnosis and therefore, any treatment required. We can speculate that we should be able to collect enough real time information to make a complete, real time, haemodynamic diagnosis in all critically ill patients. This article advocates for “intelligent haemodynamic monitoring”. Following the steps of a functional analysis, we answered six basic questions. (1) What is the actual best theoretical model for describing haemodynamic disorders? (2) What are the needed and necessary input/output data for describing this model? (3) What are the specific quality criteria and tolerances for collecting each input variable? (4) Based on these criteria, what are the validated available technologies for monitoring each input variable, continuously, real time, and if possible non-invasively? (5) How can we integrate all the needed reliably monitored input variables into the same system for continuously describing the global haemodynamic model? (6) Is it possible to implement this global model into intelligent programs that are able to differentiate clinically relevant changes as opposed to artificial changes and to display intelligent messages and/or diagnoses

Non-Invasive Monitoring of Cardiac Output in Critical Care Medicine

Critically ill patients require close hemodynamic monitoring to titrate treatment on a regular basis. It allows administering fluid with parsimony and adjusting inotropes and vasoactive drugs when necessary. Although invasive monitoring is considered as the reference method, non-invasive monitoring presents the obvious advantage of being associated with fewer complications, at the expanse of accuracy, precision, and step-response change. A great many methods and devices are now used over the world, and this article focuses on several of them, providing with a brief review of related underlying physical principles and validation articles analysis. Reviewed methods include electrical bioimpedance and bioreactance, respiratory-derived cardiac output (CO) monitoring technique, pulse wave transit time, ultrasound CO monitoring, multimodal algorithmic estimation, and inductance thoracocardiography. Quality criteria with which devices were reviewed included: accuracy (closeness of agreement between a measurement value and a true value of the measured), precision (closeness of agreement between replicate measurements on the same or similar objects under specified conditions), and step response change (delay between physiological change and its indication). Our conclusion is that the offer of non-invasive monitoring has improved in the past few years, even though further developments are needed to provide clinicians with sufficiently accurate devices for routine use, as alternative to invasive monitoring devices