Studies on the assessment of the adequacy of anesthesia

Several hypnosis monitoring systems based on the processed electroencephalogram (EEG) have been developed for use during general anesthesia. The assessment of the analgesic component (antinociception) of general anesthesia is an emerging field of research. This study investigated the interaction of hypnosis and antinociception, the association of several physiological variables with the degree of intraoperative nociception, and aspects of EEG Bispectral Index Scale (BIS) monitoring during general anesthesia. In addition, EEG features and heart rate (HR) responses during desflurane and sevoflurane anesthesia were compared. A propofol bolus of 0.7 mg/kg was more effective than an alfentanil bolus of 0.5 mg in preventing the recurrence of movement responses during uterine dilatation and curettage (D C) after a propofol-alfentanil induction, combined with nitrous oxide (N2O). HR and several HR variability-, frontal electromyography (fEMG)-, pulse plethysmography (PPG)-, and EEG-derived variables were associated with surgery-induced movement responses. Movers were discriminated from non-movers mostly by the post-stimulus values per se or normalized with respect to the pre-stimulus values. In logistic regression analysis, the best classification performance was achieved with the combination of normalized fEMG power and HR during D C (overall accuracy 81%, sensitivity 53%, specificity 95%), and with the combination of normalized fEMG-related response entropy, electrocardiography (ECG) R-to-R interval (RRI), and PPG dicrotic notch amplitude during sevoflurane anesthesia (overall accuracy 96%, sensitivity 90%, specificity 100%). ECG electrode impedances after alcohol swab skin pretreatment alone were higher than impedances of designated EEG electrodes. The BIS values registered with ECG electrodes were higher than those registered simultaneously with EEG electrodes. No significant difference in the time to home-readiness after isoflurane-N2O or sevoflurane-N2O anesthesia was found, when the administration of the volatile agent was guided by BIS monitoring. All other early and intermediate recovery parameters were also similar. Transient epileptiform EEG activity was detected in eight of 15 sevoflurane patients during a rapid increase in the inspired volatile concentration, and in none of the 16 desflurane patients. The observed transient EEG changes did not adversely affect the recovery of the patients. Following the rapid increase in the inhaled desflurane concentration, HR increased transiently, reaching its maximum in two minutes. In the sevoflurane group, the increase was slower and more subtle. In conclusion, desflurane may be a safer volatile agent than sevoflurane in patients with a lowered seizure threshold. The tachycardia induced by a rapid increase in the inspired desflurane concentration may present a risk for patients with heart disease. Designated EEG electrodes may be superior to ECG electrodes in EEG BIS monitoring. When the administration of isoflurane or sevoflurane is adjusted to maintain BIS values at 50-60 in healthy ambulatory surgery patients, the speed and quality of recovery are similar after both isoflurane-N2O and sevoflurane-N2O anesthesia. When anesthesia is maintained by the inhalation of N2O and bolus doses of propofol and alfentanil in healthy unparalyzed patients, movement responses may be best avoided by ensuring a relatively deep hypnotic level with propofol. HR/RRI, fEMG, and PPG dicrotic notch amplitude are potential indicators of nociception during anesthesia, but their performance needs to be validated in future studies. Combining information from different sources may improve the discrimination of the level of nociception.Useita aivosähkökäyrään (EEG) perustuvia unen syvyyden valvontamenetelmiä on kehitetty yleisanestesian aikana käytettäväksi. Kudosvaurion aiheuttamia ärsykkeitä vaimentavan yleisanestesian osan arviointimenetelmien tutkimus on alkamassa. Tässä väitöskirjatyössä tutkittiin unen syvyyden ja kudosvaurion aiheuttamien ärsykkeiden vaimennuksen vuorovaikutusta, useiden elimistön toimintaa kuvaavien muuttujien ja kudosvaurion aiheuttaman ärsytyksen suhdetta sekä EEG:n bispektraali-indeksin (BIS) käyttöä yleisanestesian valvonnassa. Lisäksi verrattiin desfluraani- ja sevofluraanianestesiaan liittyviä EEG-muutoksia ja sykevasteita. Kun anestesia oli aloitettu propofolilla, alfentaniililla ja typpioksiduulilla (N2O), propofoliannos 0.7 mg/kg esti liikevasteiden toistumista kohdun kaavinnan aikana tehokkaammin kuin alfentaniiliannos 0.5 mg. Sydämen syke ja useat sykevaihtelusta, otsalihastoiminnasta, pulssiaallon mittauksesta ja EEG:sta johdetut muuttujat olivat yhteydessä leikkauksen aiheuttamiin liikevasteisiin. Pääsääntöisesti ärsykkeen jälkeen mitatut arvot sellaisenaan tai suhteutettuna ärsykettä edeltäviin arvoihin (normalisoituna) erottelivat liikkuvat potilaat liikkumattomista potilaista. Paras luokittelu saavutettiin kohdun kaavinnan aikana yhdistämällä tieto normalisoidusta otsalihastoiminnasta ja sykkeestä (herkkyys 53%, tarkkuus 95%), ja sevofluraanianestesian aikana yhdistämällä tieto normalisoidusta (otsalihastoimintaan liittyvästä) vaste-entropiasta, sykkeestä ja pulssiaallon muodosta (herkkyys 90%, tarkkuus 100%). Kun iho valmisteltiin vain pyyhkäisemällä alkoholilla, sydänsähkökäyrän (EKG) mittaukseen tarkoitettujen elektrodien sähköinen vastus oli suurempi kuin EEG:n mittaukseen tarkoitettujen elektrodien. EKG-elektrodeilla mitatut BIS-arvot olivat korkeampia kuin samanaikaisesti EEG-elektrodeilla mitatut BIS-arvot. Kun höyrystyviä anestesia-aineita annosteltiin BIS-arvojen mukaan, toipuminen oli yhtä nopeaa isofluraani-N2O-anestesian jälkeen kuin sevofluraani-N2O-anestesian jälkeen. Kun höyrystyvän anestesia-aineen pitoisuutta nostettiin nopeasti viiden minuutin ajaksi, ohimenevää epileptistä aivosähkötoimintaa havaittiin kahdeksalla viidestätoista sevofluraanipotilaasta, mutta ei yhdelläkään kuudestatoista desfluraanipotilaasta. Havaitut ohimenevät EEG-muutokset eivät vaikuttaneet haitallisesti potilaiden toipumiseen. Desfluraanipitoisuuden nopean noston jälkeen syke kiihtyi ohimenevästi ja saavutti huippunsa kahdessa minuutissa. Sevofluraaniryhmässä sykenousu oli hitaampi ja lievempi. Tulosten perusteella voidaan todeta, että desfluraani saattaa olla sevofluraania turvallisempi anestesia-aine potilailla joiden kouristuskynnys on alentunut. Nopeaan desfluraanipitoisuuden nostoon liittyvä sykenousu voi olla haitallinen sydänsairaille potilaille. EEG-elektrodit ovat BIS-mittauksessa parempia kuin halvemmat EKG-elektrodit. Kun höyrystyvä anestesia-aine annostellaan siten että BIS-arvo pysyy välillä 50-60, toipuminen on samankaltainen sekä isofluraani-N2O- että sevofluraani-N2O-anestesian jälkeen terveillä päiväkirurgisilla potilailla. Kun anestesiaa ylläpidetään N2O:lla sekä propofoli- ja alfentaniiliannoksilla terveillä potilailla, joiden lihastoimintaa ei ole lamattu, liikevasteita estetään mahdollisesti parhaiten pitämällä uni verrattain syvänä propofolin avulla. Sydämen syke, otsalihastoiminta ja pulssiaallon muoto saattavat soveltua kudosvaurion aiheut-taman ärsytyksen valvontaan anestesian aikana, mutta lisätutkimuksia aiheesta tarvitaan. Kudosvaurion aiheuttaman ärsytyksen arviointia voidaan mahdollisesti tarkentaa yhdistämällä tietoa useista elimistön toimintaa kuvaavista muuttujista

Clasificación de la profundidad anestésica en función del procesamiento digital de señales de los sistemas nervioso central y autónomo

194 páginasLa anestesia desempeña un papel fundamental en la práctica clínica, siendo esencial en procedimientos quirúrgicos. Corresponde a un proceso progresivo y reversible inducido por fármacos, en el que se procura un estado de pérdida de conciencia, analgesia e inmovilidad del paciente. El monitoreo de la profundidad anestésica del paciente, así como los mecanismos fisiológicos que subyacen este fenómeno constituyen una dinámica área de investigación. Por lo anterior este trabajo apunta a resolver la pregunta: ¿Es posible clasificar los estados de profundidad anestésica, al evaluar en conjunto la actividad de los sistemas nervioso central y autónomo, en el paciente quirúrgico durante la utilización de anestesia total intravenosa? Inicialmente, los fundamentos de la técnica anestésica junto a los modelos de farmacocinética y farmacodinamia, y la relación con la variabilidad de los índices de entropía de Datex-Ohmeda (Entropía Estado y Entropía de Respuesta) fueron explorados mediante la implementación de un estudio clínico cruzado aleatorizado. Este estudio fue publicado en una revista científica revisada por pares (Anexo 1). El análisis estadístico de este estudio consideró pruebas paramétricas (Entropía de Estado: p=0.64, T=0.54; Entropía de Respuesta: p=0.84, T=0.41) y no paramétricas (Entropía de Estado: p=0.57; Entropía de Respuesta: p=0.77,) para comparar el efecto de los modelos. Los resultados no evidenciaron diferencias estadísticamente significativas (p> 0.05 en todas las comparaciones). Sin embargo, el modelo propuesto por Marsh mostró marcados valores atípicos asociados a la inducción, estos valores y otros parámetros farmacocineticos sugieren una ligera superioridad del modelo de Schnider.Doctorado en BiocienciasDoctor en Biociencia

Analysis of consciousness for complete locked-in syndrome patients

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL)

Separator fluid volume requirements in multi-infusion settings

INTRODUCTION. Intravenous (IV) therapy is a widely used method for the administration of medication in hospitals worldwide. ICU and surgical patients in particular often require multiple IV catheters due to incompatibility of certain drugs and the high complexity of medical therapy. This increases discomfort by painful invasive procedures, the risk of infections and costs of medication and disposable considerably. When different drugs are administered through the same lumen, it is common ICU practice to flush with a neutral fluid between the administration of two incompatible drugs in order to optimally use infusion lumens. An important constraint for delivering multiple incompatible drugs is the volume of separator fluid that is sufficient to safely separate them. OBJECTIVES. In this pilot study we investigated whether the choice of separator fluid, solvent, or administration rate affects the separator volume required in a typical ICU infusion setting. METHODS. A standard ICU IV line (2m, 2ml, 1mm internal diameter) was filled with methylene blue (40 mg/l) solution and flushed using an infusion pump with separator fluid. Independent variables were solvent for methylene blue (NaCl 0.9% vs. glucose 5%), separator fluid (NaCl 0.9% vs. glucose 5%), and administration rate (50, 100, or 200 ml/h). Samples were collected using a fraction collector until <2% of the original drug concentration remained and were analyzed using spectrophotometry. RESULTS. We did not find a significant effect of administration rate on separator fluid volume. However, NaCl/G5% (solvent/separator fluid) required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). Also, G5%/G5% required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). The significant decrease in required flushing volume might be due to differences in the viscosity of the solutions. However, mean differences were small and were most likely caused by human interactions with the fluid collection setup. The average required flushing volume is 3.7 ml. CONCLUSIONS. The choice of separator fluid, solvent or administration rate had no impact on the required flushing volume in the experiment. Future research should take IV line length, diameter, volume and also drug solution volumes into account in order to provide a full account of variables affecting the required separator fluid volume

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.

INTRODUCTION. Unplanned readmission to intensive care is highly undesirable in that it contributes to increased variance in care, disruption, difficulty in resource allocation and may increase length of stay and mortality particularly if subject to delays. Unlike the ICU admission from the ward, readmission prediction has received relatively little attention, perhaps in part because at the point of ICU discharge, full physiological information is systematically available to the clinician and so it is expected that readmission should be largely due to unpredictable factors. However it may be that there are multidimensional trends that are difficult for the clinician to perceive that may nevertheless be predictive of readmission. OBJECTIVES. We investigated whether machine learning (ML) techniques could be used to improve on the simple published SWIFT score [1] for the prediction of unplanned readmission to ICU within 48 hours. METHODS. We extracted systolic BP, pulse pressure, heart and respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate, white cell count, platelet count, pH, FiO2, and total Glasgow Coma Score from ICU stays of over 2000 adult patients from our hospital electronic patient record system. We trained our own custom multidimensional / time-sensitive algorithmic ML system to predict failed discharges defined as either readmission or unexpected death within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our system by transfer learning (TL) with 44,000 additional cases from the MIMIC III database. RESULTS. The SWIFT score performed relatively poorly with an AUROC of around 0.6 which our ML system trained on local data was also able to match. However when augmented with an additional dataset by TL, the AUROC for the ML system improved statistically and clinically significantly to over 0.7. CONCLUSIONS. Machine learning is able to improve on predictors based on simple multiple logistic regression. Thus there is likely to be information in the trends and in combinations of variables. A disadvantage with this technique is that ML approaches require large amounts of data for training. However, ML approaches can be improved by TL. Basing prediction models on locally derived data augmented by TL is a potentially novel approach to generating tools that customised to the institution yet can exploit the potential power of ML algorithms. REFERENCES [1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008;36(3):676–82. Grant Acknowledgement This work was internally funded