Artificial Intelligence and Machine Learning in Patient Blood Management: A Scoping Review.

Machine learning (ML) and artificial intelligence (AI) are widely used in many different fields of modern medicine. This narrative review gives, in the first part, a brief overview of the methods of ML and AI used in patient blood management (PBM) and, in the second part, aims at describing which fields have been analyzed using these methods so far. A total of 442 articles were identified by a literature search, and 47 of them were judged as qualified articles that applied ML and AI techniques in PBM. We assembled the eligible articles to provide insights into the areas of application, quality measures of these studies, and treatment outcomes that can pave the way for further adoption of this promising technology and its possible use in routine clinical decision making. The topics that have been investigated most often were the prediction of transfusion (30%), bleeding (28%), and laboratory studies (15%). Although in the last 3 years a constantly increasing number of questions of ML in PBM have been investigated, there is a vast scientific potential for further application of ML and AI in other fields of PBM.

A comparative study on adequate anesthesia depth: clinical judgement and the Narcotrend® measurement.

PURPOSE: To compare the clinical judgement of electroencephalogram (EEG)-naïve anesthesiologists with an EEG-based measurement of anesthetic depth (AD) using the Narcotrend® monitor. METHODS: In this prospective cohort study including 600 patients, AD during stable anesthesia was assessed by clinical judgement of the attending, EEG-blinded anesthesiologist (using a scale staging the AD as mid-adequate, adequate but fairly deep, or adequate but fairly light) and by simultaneously recorded Narcotrend measurements. RESULTS: In 42% of patients (n = 250), the anesthesiologist's clinical judgement was in agreement with anesthetic levels as measured by the Narcotrend monitor. In 46% of patients (n = 274), the anesthesiologist's judgement and the Narcotrend monitor differed by one AD level (minor discordance). Major discordance was observed in 76 (13%) measurements (judged deeper than measured, n = 29 [5%]; judged lighter than measured, n = 47 [8%]). In 7% of patients (n = 44), the Narcotrend index was outside the limits of adequate AD (too deep, n = 28 [5%]; too superficial, n = 16 [3%]). The overall level of agreement between the anesthesiologist's judgement and the Narcotrend monitor was not statistically significant (Cohen's kappa, -0.039; P = 0.17). Using a random forests algorithm, age, mean blood pressure, the American Society of Anesthesiologists classification, body mass index, and frailty were the variables with the highest relative feature importance to predict the level of agreement. CONCLUSION: These results suggest that clinical judgement of AD during stable anesthesia was not in agreement with EEG-based assessment of anesthetic depth in 58% of cases. Nevertheless, this finding could be influenced by the lack of validated scales to clinically judge AD. TRIAL REGISTRATION: www.clinicaltrials.gov (NCT02766894); registered 10 May, 2016.

RéSUMé: OBJECTIF: Notre objectif était de comparer le jugement clinique d'anesthésiologistes n'ayant pas accès à un électroencéphalogramme (EEG) à une mesure de la profondeur anesthésique (PA) fondée sur l'EEG utilisant le moniteur Narcotrend®. MéTHODE: Dans cette étude de cohorte prospective de 600 patients, la PA a été évaluée pendant la phase de maintien stable de l'anesthésie selon le jugement clinique de l'anesthésiologiste traitant, qui n'avait pas accès à l'EEG (sur une échelle évaluant la PA comme étant adéquate, adéquate mais relativement profonde ou adéquate mais relativement légère) et par des mesures simultanément enregistrées par le Narcotrend. RéSULTATS: Chez 42 % des patients (n = 250), le jugement clinique de l'anesthésiologiste concordait aux niveaux anesthésiques tels que mesurés par le moniteur Narcotrend. Chez 46 % des patients (n = 274), le jugement de l'anesthésiologiste et le moniteur Narcotrend différaient d'un niveau de PA (discordance mineure). Une discordance majeure a été observée dans 76 (13 %) mesures (jugées plus profondes que mesurées, n = 29 [5 %], jugées plus légères que mesurées, n = 47 [8 %]). Chez 7 % des patients (n = 44), l'indice Narcotrend était situé au-delà des limites d'une PA adéquate (trop profond, n = 28 [5 %]; trop superficiel, n = 16 [3 %]). Le niveau global de concordance entre le jugement de l'anesthésiologiste et le moniteur Narcotrend n'était pas significatif d'un point de vue statistique (kappa de Cohen, -0,039; P = 0,17). En se fondant sur un algorithme de forêt d'arbres décisionnels (random forests algorithm), l'âge, la tension artérielle moyenne, la classification selon l'American Society of Anesthesiologists, l'indice de masse corporelle et l'index de fragilité ont été identifiés comme les variables ayant la plus grande importance relative pour prédire le niveau de concordance. CONCLUSION: Ces résultats suggèrent que, dans 58 % des cas, le jugement clinique de la PA ne concordait pas à l'évaluation par EEG de la profondeur anesthésique pendant une phase de maintien stable de l'anesthésie. Toutefois, ces résultats pourraient être influencés par l'absence d'échelles validées pour juger la PA d'un point de vue clinique. ENREGISTREMENT DE L'éTUDE: www.clinicaltrials.gov (NCT02766894); enregistrée le 10 mai 2016.