Background and objectives Currently, no evidence-based criteria exist for decision making in the post anesthesia care unit (PACU). This could be valuable for the allocation of postoperative patients to the appropriate level of care and beneficial for patient outcomes such as unanticipated intensive care unit (ICU) admissions. The aim is to assess whether the inclusion of intra- and postoperative factors improves the prediction of postoperative patient deterioration and unanticipated ICU admissions. Methods A retrospective observational cohort study was performed between January 2013 and December 2017 in a tertiary Dutch hospital. All patients undergoing surgery in the study period were selected. Cardiothoracic surgeries, obstetric surgeries, catheterization lab procedures, electroconvulsive therapy, day care procedures, intravenous line interventions and patients under the age of 18 years were excluded. The primary outcome was unanticipated ICU admission. Results An unanticipated ICU admission complicated the recovery of 223 (0.9%) patients. These patients had higher hospital mortality rates (13.9% versus 0.2%, p<0.001). Multivariable analysis resulted in predictors of unanticipated ICU admissions consisting of age, body mass index, general anesthesia in combination with epidural anesthesia, preoperative score, diabetes, administration of vasopressors, erythrocytes, duration of surgery and post anesthesia care unit stay, and vital parameters such as heart rate and oxygen saturation. The receiver operating characteristic curve of this model resulted in an area under the curve of 0.86 (95% CI 0.83–0.88). Conclusions The prediction of unanticipated ICU admissions from electronic medical record data improved when the intra- and early postoperative factors were combined with preoperative patient factors. This emphasizes the need for clinical decision support tools in post anesthesia care units with regard to postoperative patient allocation.
Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
There is a clinical need for monitoring inspiratory effort to prevent lung- and diaphragm injury in patients who receive supportive mechanical ventilation in an Intensive Care Unit. Different pressure-based techniques are available to estimate this inspiratory effort at the bedside, but the accuracy of their effort estimation is uncertain since they are all based on a simplified linear model of the respiratory system, which omits gas compressibility of air, and the viscoelasticity and nonlinearities of the respiratory system. The aim of this in-silico study was to provide an overview of the pressure-based estimation techniques and to evaluate their accuracy using a more sophisticated model of the respiratory system and ventilator. The influence of the following parameters on the accuracy of the pressure-based estimation techniques was evaluated using the in-silico model: 1) the patient's respiratory mechanics 2) PEEP and the inspiratory pressure of the ventilator 3) gas compressibility of air 4) viscoelasticity of the respiratory system 5) the strength of the inspiratory effort. The best-performing technique in terms of accuracy was the whole breath occlusion. The average error and maximum error were the lowest for all patient archetypes. We found that the error was related to the expansion of gas in the breathing set and lungs and respiratory compliance. However, concerns exist that other factors not included in the model, such as a changed muscle-force relation during an occlusion, might influence the true accuracy. The estimation techniques based on the esophageal pressure showed an error related to the viscoelastic element in the model which leads to a higher error than the occlusion. The error of the esophageal pressure-based techniques is therefore highly dependent on the pathology of the patient and the settings of the ventilator and might change over time while a patient recovers or becomes more ill.
Low-resolution thermal cameras have already been used in the detection of respiratory flow. However, microbolometer technology has a high production cost compared to thermopile arrays. In this work, the feasibility of using a thermopile array to detect respiratory flow has been investigated in multiple settings. To prove the concept, we tested the detector on six healthy subjects. Our method automatically selects the region-of-interest by discriminating between sensor elements that output noise and flow-induced signals. The thermopile array yielded an average root mean squared error of 1.59 b r e a t h s p e r m i n u t e . Parameters such as distance, breathing rate, orientation, and oral or nasal breathing resulted in being fundamental in the detection of respiratory flow. The paper provides the proof-of-concept that low-cost thermopile-arrays can be used to monitor respiratory flow in a lab setting and without the need for facial landmark detection. Further development could provide a more attractive alternative for the earlier bolometer-based proposals.
Although mechanical ventilation is a lifesaving intervention in the ICU, it has harmful side-effects, such as barotrauma and volutrauma. These harms can occur due to asynchronies. Asynchronies are defined as a mismatch between the ventilator timing and patient respiratory effort. Automatic detection of these asynchronies, and subsequent feedback, would improve lung ventilation and reduce the probability of lung damage. Neural networks to detect asynchronies provide a promising new approach but require large annotated data sets, which are difficult to obtain and require complex monitoring of inspiratory effort. In this work, we propose a model-based approach to generate a synthetic data set for machine learning and educational use by extending an existing lung model with a first-order ventilator model. The physiological nature of the derived lung model allows adaptation to various disease archetypes, resulting in a diverse data set. We generated a synthetic data set using 9 different patient archetypes, which are derived from measurements in the literature. The model and synthetic data quality have been verified by comparison with clinical data, review by a clinical expert, and an artificial intelligence model that was trained on experimental data. The evaluation showed it was possible to generate patient-ventilator waveforms including asynchronies that have the most important features of experimental patient-ventilator waveforms.
Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data.
Abstract Background Five percent of pre-menopausal women experience abnormal uterine bleeding. Endometrial ablation (EA) is one of the treatment options for this common problem. However, this technique shows a decrease in patient satisfaction and treatment efficacy on the long term. Study objective To develop a prediction model to predict surgical re-intervention (for example re-ablation or hysterectomy) within 2 years after endometrial ablation (EA) by using machine learning (ML). The performance of the developed prediction model was compared with a previously published multivariate logistic regression model (LR). Design This retrospective cohort study, with a minimal follow-up time of 2 years, included 446 pre-menopausal women (18+) that underwent an EA for complaints of heavy menstrual bleeding. The performance of the ML and the LR model was compared using the area under the receiving operating characteristic (ROC) curve. Results We found out that the ML model (AUC of 0.65 (95% CI 0.56–0.74)) is not superior compared to the LR model (AUC of 0.71 (95% CI 0.64–0.78)) in predicting the outcome of surgical re-intervention within 2 years after EA. Based on the ML model, dysmenorrhea and duration of menstruation have the highest impact on the surgical re-intervention rate. Conclusion Although machine learning techniques are gaining popularity in development of clinical prediction tools, this study shows that ML is not necessarily superior to the traditional statistical LR techniques. Both techniques should be considered when developing a clinical prediction model. Both models can identify the clinical predictors to surgical re-intervention and contribute to the shared decision-making process in the clinical practice.
Mechanical ventilation is an important life-saving intervention on the ICU. Lung-protective ventilation techniques such as pressure support ventilation (PSV) are used frequently in the ICU. However, asynchronies, poor patient-ventilator interactions during PSV, are shown to be harmful and are linked with increased lung injury and mortality. There is a need for automatic detection and classification of asynchronies for clinical studies, algorithm development and for real time clinical decision support for smart ventilation technologies. So far, reasonable results of detection of asynchronies have been obtained, but classification is still a challenge. In this work, we generate training and classification waveforms for our machine learning study using a patient-ventilator simulation model. From these models the flow, pressure and volume waveforms can be created for different types of parameter settings. Note that the type of asynchrony and timing of the patient effort are known.
The gold standard in medical research to estimate the causal effect of a treatment is the Randomized Controlled Trial (RCT), but in many cases these are not feasible due to ethical, financial or practical issues. Observational studies are an alternative, but can easily lead to doubtful results, because of unbalanced selection bias and confounding. Moreover, RCTs often only apply to a specific subgroup and cannot readily be extrapolated. In response, we present Rod of Asclepius (RoA), a novel visual analytics method that integrates modern techniques designed for identification of causal effects and effect size estimation with subgroup analysis. The result is an interactive display designed to combine exploratory analysis with a robust set of techniques, including causal do-calculus, propensity score weighting, and effect estimation. It enables analysts to conduct observational studies in an exploratory, yet robust way. This is demonstrated by means of a use case involving patients undergoing surgery, for which we collaborated closely with clinical researchers.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
