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Improving mixed-integer temporal modeling by generating synthetic data using conditional generative adversarial networks: A case study of fluid overload prediction in the intensive care unit
... In order to predict fluid overload in critical care patients, the development of an ensemble machine learning model has been proposed [10]. This novel approach integrates both original and synthetically generated datasets. ...
... Data generation approaches Statistical-based generation [8,9] Machine learning-based generation [10][11][12][13][14][15][16][17][18][19][20][21][22][23] Data characteristics Numerical data generation [24] Categorical data generation [25] Temporal data generation [26,27] Privacy preservation [28][29][30][31][32] ...
The rapid advancement of data generation techniques has spurred innovation across multiple domains. This comprehensive review delves into the realm of data generation methodologies, with a keen focus on statistical and machine learning-based approaches. Notably, novel strategies like the divide-and-conquer (DC) approach and cutting-edge models such as GANBLR have emerged to tackle a spectrum of challenges, spanning from preserving intricate data relationships to enhancing interpretability. Furthermore, the integration of generative adversarial networks (GANs) has sparked a revolution in data generation across sectors like healthcare, cybersecurity, and retail. This review meticulously examines how these techniques mitigate issues such as class imbalance, data scarcity, and privacy concerns. Through a meticulous analysis of evaluation metrics and diverse applications, it underscores the efficacy and potential of synthetic data in refining predictive models and decision-making software. Concluding with insights into prospective research trajectories and the evolving role of synthetic data in propelling machine learning and data-driven solutions across disciplines, this work provides a holistic understanding of the transformative power of contemporary data generation methodologies.
... One of the studies aimed at SDG through existing medical datasets for improving the prediction of fluid consumed by patients in ICUs. In this study, four machine learning algorithms were developed and trained using both original and synthetic datasets, leading to enhanced model performance [28]. For example, the study addresses the challenges associated with applying machine learning to medical and cancer research by using the SMOTE algorithm for SDG. ...
There has been a growth of collaborative robots in Industry 5.0 due to the research in automation involving human-centric workplace design. It has had a substantial impact on industrial processes; however, physical exertion in human workers is still an issue, requiring solutions that combine technological innovation with human-centric development. By analysing real-world data, machine learning (ML) models can detect physical fatigue. However, sensor-based data collection is frequently used, which is often expensive and constrained. To overcome this gap, synthetic data generation (SDG) uses methods such as tabular generative adversarial networks (GANs) to produce statistically realistic datasets that improve machine learning model training while providing scalability and cost-effectiveness. This study presents an innovative approach utilising conditional GAN with auxiliary conditioning to generate synthetic datasets with essential features for detecting human physical fatigue in industrial scenarios. This approach allows us to enhance the SDG process by effectively handling the heterogeneous and imbalanced nature of human fatigue data, which includes tabular, categorical, and time-series data points. These generated datasets will be used to train specialised ML models, such as ensemble models, to learn from the original dataset from the extracted feature and then identify signs of physical fatigue. The trained ML model will undergo rigorous testing using authentic, real-world data to evaluate its sensitivity and specificity in recognising how closely generated data match with actual human physical fatigue within industrial settings. This research aims to provide researchers with an innovative method to tackle data-driven ML challenges of data scarcity and further enhance ML technology’s efficiency through training on SD. This study not only provides an approach to create complex realistic datasets but also helps in bridging the gap of Industry 5.0 data challenges for the purpose of innovations and worker well-being by improving detection capabilities.
... The main goals of this study include assessing the effectiveness of the novel adversarial ensemble learning mechanism in ICU patient mortality prediction and evaluating the performance sensitivity of different ensemble models to adversarial attacks [9]. We evaluate this ability via real-world ICU data without losing our ability to increase the accuracy of the predictions and instead fight against malevolent attempts to alter the input data, which in turn would promote the safe development of AI-assisted clinical applications. ...
... [119], [120] use CTGAN to generate synthetic data to augment an imbalanced dataset of medication use in critically ill patients. It helps balance the minority class to improve predictive modeling performance. ...
Machine learning algorithms are used in diverse domains, many of which face significant challenges due to data imbalance. Studies have explored various approaches to address the issue, like data preprocessing, cost-sensitive learning, and ensemble methods. Generative Adversarial Networks (GANs) showed immense potential as a data preprocessing technique that generates good quality synthetic data. This study employs a systematic mapping methodology to analyze 3041 papers on GAN-based sampling techniques for imbalanced data sourced from four digital libraries. A filtering process identified 100 key studies spanning domains such as healthcare, finance, and cybersecurity. Through comprehensive quantitative analysis, this research introduces three categorization mappings as application domains, GAN techniques, and GAN variants used to handle the imbalanced nature of the data. GAN-based over-sampling emerges as an effective preprocessing method. Advanced architectures and tailored frameworks helped GANs to improve further in the case of data imbalance. GAN variants like vanilla GAN, CTGAN, and CGAN show great adaptability in structured imbalanced data cases. Interest in GANs for imbalanced data has grown tremendously, touching a peak in recent years, with journals and conferences playing crucial roles in transmitting foundational theories and practical applications. While with these advances, none of the reviewed studies explicitly explore hybridized GAN frameworks with diffusion models or reinforcement learning techniques. This gap leads to a future research idea develop innovative approaches for effectively handling data imbalance.
... For instance, one of the studies focuses on enhancing fluid overload prediction in intensive care units (ICUs) by integrating synthetic data with the existing medication dataset. Four ML algorithms were devised and trained on both the original and synthetically generated datasets, resulting in improved model performance (Rafiei et al., 2024). An additional study seeks to address challenges prevalent in applying machine learning to medical and cancer research, arising from issues such as data scarcity and privacy concerns. ...
Collaborative robots, or cobots, are one of the Industry 4.0 technologies that have and continue to change many industrial procedures. However, amid this technological advancement, the persisting physical strain on human workers remains a significant concern. Even with the advent of cobots aimed at alleviating burdensome tasks, certain physical jobs continue to induce fatigue in human workers. Addressing this challenge necessitates the development of robust solutions that combine technological innovation with human-centric considerations. One critical aspect in mitigating physical fatigue in human workers involves the application of Machine Learning (ML) models. These models heavily depend on data obtained from real-world situations that accurately represent the complexities of physical strain. However, this kind of data is frequently limited and costly to gather using sensors, which hinders the development of an effective ML model. This scarcity underscores the need for alternative approaches, with Synthetic Data Generation (SDG) emerging as a viable solution to this problem. The production of synthetic data offers a new approach to address the lack of relevant data needed to train machine learning algorithms. By employing techniques like Tabular Generative Adversarial Networks (GANs), synthetic datasets can be created, simulating realistic human physical fatigue detection features. Tabular GANs have, for example, been shown to be effective in creating synthetic data that closely resembles the statistical characteristics and patterns of real-world datasets. Furthermore, tabular GANs present a scalable and affordable response to the problem of data scarcity. The research reported here presents a novel approach centred on employing the Tabular GAN methodology to create synthetic datasets encompassing key features pertinent to the detection of human physical fatigue. The results of this study are expected to contribute substantially to creating robust solutions to alleviate physical strain and enhance human workers' overall well-being in industrial settings. The goal is to create datasets that accurately represent the complexities found in real-world scenarios where physical fatigue notably influences human performance. These synthetically generated datasets will serve as the foundation for training specialized ML models designed explicitly for detecting the development of human physical fatigue. The trained ML model will undergo rigorous testing and validation using a substantial repository of authentic real-world data. The model's accuracy and reliability in detecting human physical fatigue will be assessed through this evaluation process. The ultimate objective is to achieve a level of accuracy that demonstrates the model's proficiency in identifying and predicting the onset of physical fatigue in human workers within industrial settings. This research endeavours to bridge the gap between Industry 4.0 innovations and human well-being by leveraging synthetic data generation techniques to enhance the accuracy and efficiency of ML models in detecting human physical fatigue.
Background: Blood transfusions, crucial in managing anemia and coagulopathy in intensive care unit (ICU) settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have primarily targeted a particular patient demographic with unique medical conditions and focused on a single type of blood transfusion. This study aims to develop an advanced machine learning-based model to predict the probability of transfusion necessity over the next 24 h for a diverse range of non-traumatic ICU patients. Methods: We conducted a retrospective cohort study on 72,072 non-traumatic adult ICU patients admitted to a high-volume US metropolitan academic hospital between 2016 and 2020. We developed a meta-learner and various machine learning models to serve as predictors, training them annually with 4-year data and evaluating on the fifth, unseen year, iteratively over 5 years. Results: The experimental results revealed that the meta-model surpasses the other models in different development scenarios. It achieved notable performance metrics, including an area under the receiver operating characteristic curve of 0.97, an accuracy rate of 0.93, and an F1 score of 0.89 in the best scenario. Conclusion: This study pioneers the use of machine learning models for predicting the likelihood of blood transfusion receipt in a diverse cohort of critically ill patients. The findings of this evaluation confirm that our model not only effectively predicts transfusion reception but also identifies key biomarkers for making transfusion decisions.
Collaboration in providing threat intelligence and disseminating information enables cyber security professionals to embrace digital security most successfully, whose risks are ever-changing. This article dwells on the capacity of machine intelligence to change information security by categorising indicators of compromise (IOC) and threat actors, then highlights the limits of traditional methods. Among Artificial intelligence tools such as generative adversarial networks (GANs) and Variational autoencoders (VAEs), which are the key innovators, one can create synthetic or fake threat data that emulates real attack scenarios in the past. This allows cyber-related risks to be analysed differently from before. In addition, this feature enables secure stakeholder collaborations. It is also meant mainly for factual data that protects private information but allows the exchange of helpful information. It is clear from the fact that showcasing real-world examples demonstrates Al's automation through cybersecurity detection.
As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model’s capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization. These unique characteristics position meta-learning as a suitable choice for developing influential solutions in various healthcare contexts, where the available data is often insufficient, and the data collection methodologies are different. This survey discusses meta-learning broad applications in the healthcare domain to provide insight into how and where it can address critical healthcare challenges. We first describe the theoretical foundations and pivotal methods of meta-learning. We then divide the employed meta-learning approaches in the healthcare domain into two main categories of multi/single-task learning and many/few-shot learning and survey the studies. Finally, we highlight the current challenges in meta-learning research, discuss the potential solutions, and provide future perspectives on meta-learning in healthcare.
Objective
Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts.
Materials and Methods
A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles.
Results
Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online.
Conclusion
The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.
Elderly hypertensive patients diagnosed with transient ischemic attack (TIA) are at a heightened risk for developing acute ischemic stroke (AIS). This underscores the critical need for effective risk prediction and identification of predictive factors. In our study, we utilized patient data from peripheral blood tests and clinical profiles within hospital information systems. These patients were followed for a three-year period to document incident AIS. Our cohort of 11,056 individuals was randomly divided into training, validation, and testing sets in a 5:2:3 ratio. We developed an XGBoost model, developed using selected indicators, provides an effective and non-invasive method for predicting the risk of AIS in elderly hypertensive patients diagnosed with TIA. Impressively, this model achieved a balanced accuracy of 0.9022, a recall of 0.8688, and a PR-AUC of 0.9315. Notably, our model effectively encapsulates essential data variations involving mixed nonlinear interactions, providing competitive performance against more complex models that incorporate a wider range of variables. Further, we conducted an in-depth analysis of the importance and sensitivity of each selected indicator and their interactions. This research equips clinicians with the necessary tools for more precise identification of high-risk individuals, thereby paving the way for more effective stroke prevention and management strategies.
Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48–72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.
Background
Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed ‘pharmacophenotypes’) correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality).
Methods
This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate.
Results
A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay ( p < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6.
Conclusion
The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.
Background
Trauma is one of the most critical public health issues worldwide, leading to death and disability and influencing all age groups. Therefore, there is great interest in models for predicting mortality in trauma patients admitted to the ICU. The main objective of the present study is to develop and evaluate SMOTE-based machine-learning tools for predicting hospital mortality in trauma patients with imbalanced data.
Methods
This retrospective cohort study was conducted on 126 trauma patients admitted to an intensive care unit at Besat hospital in Hamadan Province, western Iran, from March 2020 to March 2021. Data were extracted from the medical information records of patients. According to the imbalanced property of the data, SMOTE techniques, namely SMOTE, Borderline-SMOTE1, Borderline-SMOTE2, SMOTE-NC, and SVM-SMOTE, were used for primary preprocessing. Then, the Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) methods were used to predict patients' hospital mortality with traumatic injuries. The performance of the methods used was evaluated by sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), accuracy, Area Under the Curve (AUC), Geometric Mean (G-means), F1 score, and P-value of McNemar's test.
Results
Of the 126 patients admitted to an ICU, 117 (92.9%) survived and 9 (7.1%) died. The mean follow-up time from the date of trauma to the date of outcome was 3.98 ± 4.65 days. The performance of ML algorithms is not good with imbalanced data, whereas the performance of SMOTE-based ML algorithms is significantly improved. The mean area under the ROC curve (AUC) of all SMOTE-based models was more than 91%. F1-score and G-means before balancing the dataset were below 70% for all ML models except ANN. In contrast, F1-score and G-means for the balanced datasets reached more than 90% for all SMOTE-based models. Among all SMOTE-based ML methods, RF and ANN based on SMOTE and XGBoost based on SMOTE-NC achieved the highest value for all evaluation criteria.
Conclusions
This study has shown that SMOTE-based ML algorithms better predict outcomes in traumatic injuries than ML algorithms. They have the potential to assist ICU physicians in making clinical decisions.
Data are central to research, public health, and in developing health information technology (IT) systems. Nevertheless, access to most data in health care is tightly controlled, which may limit innovation, development, and efficient implementation of new research, products, services, or systems. Using synthetic data is one of the many innovative ways that can allow organizations to share datasets with broader users. However, only a limited set of literature is available that explores its potentials and applications in health care. In this review paper, we examined existing literature to bridge the gap and highlight the utility of synthetic data in health care. We searched PubMed, Scopus, and Google Scholar to identify peer-reviewed articles, conference papers, reports, and thesis/dissertations articles related to the generation and use of synthetic datasets in health care. The review identified seven use cases of synthetic data in health care: a) simulation and prediction research, b) hypothesis, methods, and algorithm testing, c) epidemiology/public health research, d) health IT development, e) education and training, f) public release of datasets, and g) linking data. The review also identified readily and publicly accessible health care datasets, databases, and sandboxes containing synthetic data with varying degrees of utility for research, education, and software development. The review provided evidence that synthetic data are helpful in different aspects of health care and research. While the original real data remains the preferred choice, synthetic data hold possibilities in bridging data access gaps in research and evidence-based policymaking.
In this paper, we devise a novel method involving deep neural networks (DNNs) that improves the early prediction of sepsis for patients admitted to the intensive care units (ICUs). It is assumed that the patient data sets are dramatically corrupted by missing information, which negatively impacts the detection of the onset of sepsis. We propose a generative learning framework to estimate the missing information in data. Our model involves Conditional Generative Adversarial Networks (GANs) utilizing Long Short-Term Memory (LSTM) networks as the generator and discriminator when conditioned on class labels. A deep LSTM network is also employed for prediction purposes. The prediction network is trained with an output of the conditional GAN and evaluated on an unseen test set to investigate the performance of the proposed model. Here, we show that the proposed framework not only identifies long-term temporal dependencies but also exploits the missing patterns. We present the performance results and compare them to other well-known techniques. For the 4-hour, 8-hour, and 12-hour prediction of sepsis, the proposed method attains area under the receiver operating characteristic (AUROC) of 94.49%, 93.74%, and 94.01%, respectively. It is shown here that the improvement in imputation and prediction promises a highly effective method that can offer early detection of sepsis in high-risk patients.
Introduction
De-resuscitation practices in septic patients with heart failure (HF) are not well characterized. This study aimed to determine if diuretic initiation within 48 hours of intensive care unit (ICU) admission was associated with a positive fluid balance and patient outcomes.
Methods
This single-center, retrospective cohort study included adult patients with an established diagnosis of HF admitted to the ICU with sepsis or septic shock. The primary outcome was the incidence of positive fluid balance in patients receiving early (<48 hours) versus late (>48 hours) initiation of diuresis. Secondary outcomes included hospital mortality, ventilator-free days, and hospital and ICU length of stay. Continuous variables were assessed using independent t-test or Mann-Whitney U, while categorical variables were evaluated using the Pearson Chi-squared test.
Results
A total of 101 patients were included. Positive fluid balance was significantly reduced at 72 hours (−139 mL vs 4370 mL, P < .001). The duration of mechanical ventilation (4 vs 5 days, P = .129), ventilator-free days (22 vs 18.5 days, P = .129), and in-hospital mortality (28 (38%) vs 12 (43%), P = .821) were similar between groups. In a subgroup analysis excluding patients not receiving renal replacement therap (RRT) (n = 76), early diuretics was associated with lower incidence of mechanical ventilation (41 [73.2%] vs 20 (100%), P = .01) and reduced duration of mechanical ventilation (4 vs 8 days, P = .018).
Conclusions
Diuretic use within 48 hours of ICU admission in septic patients with HF resulted in less incidence of positive fluid balance. Early diuresis in this unique patient population warrants further investigation.
Background:
Days before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression.
Methods:
Data were collected from patients at an ambulatory surgery center. The primary outcome measurement was determined to have a value of 1 (versus 0) if they met both criteria: (1) surgery ends by 5 pm and (2) patient is discharged from the recovery room by 7 pm. We developed models to determine if a procedure would meet both criteria if it were scheduled at 1 pm, 2 pm, 3 pm, or 4 pm. We implemented regression, random forest, balanced random forest, balanced bagging, neural network, and support vector classifier, and included the following features: surgery, surgeon, service line, American Society of Anesthesiologists score, age, sex, weight, and scheduled case duration. We evaluated model performance with Synthetic Minority Oversampling Technique (SMOTE). We compared the following performance metrics: F1 score, area under the receiver operating characteristic curve (AUC), specificity, sensitivity, precision, recall, and Matthews correlation coefficient.
Results:
Among 13,447 surgical procedures, the median total perioperative time (actual case duration and PACU length stay) was 165 minutes. When SMOTE was not used, when predicting whether surgery will end by 5 pm and patient will be discharged by 7 pm, the average F1 scores were best with random forest, balanced bagging, and balanced random forest classifiers. When SMOTE was used, these models had improved F1 scores compared to no SMOTE. The balanced bagging classifier performed best with F1 score of 0.78, 0.80, 0.82, and 0.82 when predicting our outcome if cases were to start at 1 pm, 2 pm, 3 pm, or 4 pm, respectively.
Conclusions:
We demonstrated improvement in predicting the outcome at a range of start times when using ensemble learning versus regression techniques. Machine learning may be adapted by operating room management to allow for a better determination whether an add-on case at an outpatient surgery center could be appropriately booked.
Background:
The detrimental impact of fluid overload (FO) on intensive care unit (ICU) morbidity and mortality is well known. However, research to identify subgroups of patients particularly prone to fluid overload is scarce. The aim of this cohort study was to derive "FO phenotypes" in the critically ill by using machine learning techniques.
Methods:
Retrospective single center study including adult intensive care patients with a length of stay of ≥3 days and sufficient data to compute FO. Data was analyzed by multivariable logistic regression, fast and frugal trees (FFT), classification decision trees (DT), and a random forest (RF) model.
Results:
Out of 1772 included patients, 387 (21.8%) met the FO definition. The random forest model had the highest area under the curve (AUC) (0.84, 95% CI 0.79-0.86), followed by multivariable logistic regression (0.81, 95% CI 0.77-0.86), FFT (0.75, 95% CI 0.69-0.79) and DT (0.73, 95% CI 0.68-0.78) to predict FO. The most important predictors identified in all models were lactate and bicarbonate at admission and postsurgical ICU admission. Sepsis/septic shock was identified as a risk factor in the MV and RF analysis.
Conclusion:
The FO phenotypes consist of patients admitted after surgery or with sepsis/septic shock with high lactate and low bicarbonate.
The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars. Few-shot learning is an open problem: current computer vision systems struggle to categorize objects they have seen only rarely during training, and collecting a sufficient number of training examples of rare events is often challenging and expensive, and sometimes outright impossible. We explore in depth an approach to this problem: complementing the few available training images with ad-hoc simulated data. Our testbed is animal species classification, which has a real-world long-tailed distribution. We analyze the effect of different axes of variation in simulation, such as pose, lighting, model, and simulation method, and we prescribe best practices for efficiently incorporating simulated data for real-world performance gain. Our experiments reveal that synthetic data can considerably reduce error rates for classes that are rare, that as the amount of simulated data is increased, accuracy on the target class improves, and that high variation of simulated data provides maximum performance gain.
Objective:
Administration of diuretics has been shown to assist fluid management and improve clinical outcomes in the critically ill post-shock resolution. Current guidelines have not yet included standardization or guidance for diuretic-based de-resuscitation in critically ill patients. This study aimed to evaluate the impact of a multi-disciplinary protocol for diuresis-guided de-resuscitation in the critically ill.
Methods:
This was a pre-post single-center pilot study within the medical intensive care unit (ICU) of a large academic medical center. Adult patients admitted to the Medical ICU receiving mechanical ventilation with either (1) clinical signs of volume overload via chest radiography or physical exam or (2) any cumulative fluid balance ≥ 0 mL since hospital admission were eligible for inclusion. Patients received diuresis per clinician discretion for a 2-year period (historical control) followed by a diuresis protocol for 1 year (intervention). Patients within the intervention group were matched in a 1:3 ratio with those from the historical cohort who met the study inclusion and exclusion criteria.
Results:
A total of 364 patients were included, 91 in the protocol group and 273 receiving standard care. Protocolized diuresis was associated with a significant decrease in 72-h post-shock cumulative fluid balance [median, IQR - 2257 (- 5676-920) mL vs 265 (- 2283-3025) mL; p < 0.0001]. In-hospital mortality in the intervention group was lower compared to the historical group (5.5% vs 16.1%; p = 0.008) and higher ICU-free days (p = 0.03). However, no statistically significant difference was found in ventilator-free days, and increased rates of hypernatremia and hypokalemia were demonstrated.
Conclusions:
This study showed that a protocol for diuresis for de-resuscitation can significantly improve 72-h post-shock fluid balance with potential benefit on clinical outcomes.
Intravenous fluids (IVFs) are the most common drugs administered in the intensive care unit. Despite the ubiquitous use, IVFs are not benign and carry significant risks associated with under- or overadministration. Hypovolemia is associated with decreased organ perfusion, ischemia, and multi-organ failure. Hypervolemia and volume overload are associated with organ dysfunction, delayed liberation from mechanical ventilation, and increased mortality. Despite appropriate provision of IVF, adverse drug effects such as electrolyte abnormalities and acid–base disturbances may occur. The management of volume status in critically ill patients is both dynamic and tenuous, a process that requires frequent monitoring and high clinical acumen. Because patient-specific considerations for fluid therapy evolve across the continuum of critical illness, a standard approach to the assessment of fluid needs and prescription of IVF therapy is necessary. We propose the principle of “fluid stewardship,” guided by 4 rights of medication safety: right patient, right drug, right route, and right dose. The successful implementation of fluid stewardship will aid pharmacists in making decisions regarding IVF therapy to optimize hemodynamic management and improve patient outcomes. Additionally, we highlight several areas of focus for future research, guided by the 4 rights construct of fluid stewardship.
Abstract ‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things. Biomedical research also generates a significant portion of big data relevant to public healthcare. This data requires proper management and analysis in order to derive meaningful information. Otherwise, seeking solution by analyzing big data quickly becomes comparable to finding a needle in the haystack. There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with appropriate infrastructure to systematically generate and analyze big data. An efficient management, analysis, and interpretation of big data can change the game by opening new avenues for modern healthcare. That is exactly why various industries, including the healthcare industry, are taking vigorous steps to convert this potential into better services and financial advantages. With a strong integration of biomedical and healthcare data, modern healthcare organizations can possibly revolutionize the medical therapies and personalized medicine.
Background and objectives
Excess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volume-responsive (VR) and volume-unresponsive (VU) AKI.
Methods
AKI patients with urine output < 0.5 ml/kg/h for the first 6 h after ICU admission and fluid intake > 5 l in the following 6 h in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-III)) were considered. Patients who received diuretics and renal replacement on day 1 were excluded. Two predictive models, using either machine learning extreme gradient boosting (XGBoost) or logistic regression, were developed to predict urine output > 0.65 ml/kg/h during 18 h succeeding the initial 6 h for assessing oliguria. Established models were assessed by using out-of-sample validation. The whole sample was split into training and testing samples by the ratio of 3:1.
Main results
Of the 6682 patients included in the analysis, 2456 (36.8%) patients were volume responsive with an increase in urine output after receiving > 5 l fluid. Urinary creatinine, blood urea nitrogen (BUN), age, and albumin were the important predictors of VR. The machine learning XGBoost model outperformed the traditional logistic regression model in differentiating between the VR and VU groups (AU-ROC, 0.860; 95% CI, 0.842 to 0.878 vs. 0.728; 95% CI 0.703 to 0.753, respectively).
Conclusions
The XGBoost model was able to differentiate between patients who would and would not respond to fluid intake in urine output better than a traditional logistic regression model. This result suggests that machine learning techniques have the potential to improve the development and validation of predictive modeling in critical care research.
In patients with septic shock, the administration of fluids during initial hemodynamic resuscitation remains a major therapeutic challenge. We are faced with many open questions regarding the type, dose and timing of intravenous fluid administration. There are only four major indications for intravenous fluid administration: aside from resuscitation, intravenous fluids have many other uses including maintenance and replacement of total body water and electrolytes, as carriers for medications and for parenteral nutrition. In this paradigm-shifting review, we discuss different fluid management strategies including early adequate goal-directed fluid management, late conservative fluid management and late goal-directed fluid removal. In addition, we expand on the concept of the "four D's" of fluid therapy, namely drug, dosing, duration and de-escalation. During the treatment of patients with septic shock, four phases of fluid therapy should be considered in order to provide answers to four basic questions. These four phases are the resuscitation phase, the optimization phase, the stabilization phase and the evacuation phase. The four questions are "When to start intravenous fluids?", "When to stop intravenous fluids?", "When to start de-resuscitation or active fluid removal?" and finally "When to stop de-resuscitation?" In analogy to the way we handle antibiotics in critically ill patients, it is time for fluid stewardship.
Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.
Background
MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. ResultsBy comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. Conclusions
The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.
Background
Fluid overload is frequently found in acute kidney injury patients in critical care units. Recent studies have shown the relationship of fluid overload with adverse outcomes; hence, manage and optimization of fluid balance becomes a central component of the management of critically ill patients.
Discussion
In critically ill patients, in order to restore cardiac output, systemic blood pressure and renal perfusion an adequate fluid resuscitation is essential. Achieving an appropriate level of volume management requires knowledge of the underlying pathophysiology, evaluation of volume status, and selection of appropriate solution for volume repletion, and maintenance and modulation of the tissue perfusion. Numerous recent studies have established a correlation between fluid overload and mortality in critically ill patients. Fluid overload recognition and assessment requires an accurate documentation of intakes and outputs; yet, there is a wide difference in how it is evaluated, reviewed and utilized. Accurate volume status evaluation is essential for appropriate therapy since errors of volume evaluation can result in either in lack of essential treatment or unnecessary fluid administration, and both scenarios are associated with increased mortality. There are several methods to evaluate fluid status; however, most of the tests currently used are fairly inaccurate. Diuretics, especially loop diuretics, remain a valid therapeutic alternative. Fluid overload refractory to medical therapy requires the application of extracorporeal therapies.
In critically ill patients, fluid overload is related to increased mortality and also lead to several complications like pulmonary edema, cardiac failure, delayed wound healing, tissue breakdown, and impaired bowel function. Therefore, the evaluation of volume status is crucial in the early management of critically ill patients. Diuretics are frequently used as an initial therapy; however, due to their limited effectiveness the use of continuous renal replacement techniques are often required for fluid overload treatment. Successful fluid overload treatment depends on precise assessment of individual volume status, understanding the principles of fluid management with ultrafiltration, and clear treatment goals.
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply reusing the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability, and billing reasons, rather than for developing new algorithms. With increasing excitement surrounding “secondary use of medical records” and “Big Data” analytics, it is important to understand the limitations of current databases and what needs to change in order to enter an era of “precision medicine.” This review article covers many of the issues involved in the collection and preprocessing of critical care data. The three challenges in critical care are considered: compartmentalization, corruption, and complexity. A range of applications addressing these issues are covered, including the modernization of static acuity scoring; online patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data.
An approach to the construction of classifiers from imbalanced datasets is
described. A dataset is imbalanced if the classification categories are not
approximately equally represented. Often real-world data sets are predominately
composed of "normal" examples with only a small percentage of "abnormal" or
"interesting" examples. It is also the case that the cost of misclassifying an
abnormal (interesting) example as a normal example is often much higher than
the cost of the reverse error. Under-sampling of the majority (normal) class
has been proposed as a good means of increasing the sensitivity of a classifier
to the minority class. This paper shows that a combination of our method of
over-sampling the minority (abnormal) class and under-sampling the majority
(normal) class can achieve better classifier performance (in ROC space) than
only under-sampling the majority class. This paper also shows that a
combination of our method of over-sampling the minority class and
under-sampling the majority class can achieve better classifier performance (in
ROC space) than varying the loss ratios in Ripper or class priors in Naive
Bayes. Our method of over-sampling the minority class involves creating
synthetic minority class examples. Experiments are performed using C4.5, Ripper
and a Naive Bayes classifier. The method is evaluated using the area under the
Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Background: At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine Learning (ML) models trained with the available data, which is limited in quantity and poor in diversity, will often be biased and inaccurate. At the same time, ML algorithms designed to fight pandemics must have good performance and be developed in a time-sensitive manner. To tackle the challenges of limited data, and label scarcity in the available data, we propose generating conditional synthetic data, to be used alongside real data for developing robust ML models. Methods: We present a hybrid model consisting of a conditional generative flow and a classifier for conditional synthetic data generation. The classifier decouples the feature representation for the condition, which is fed to the flow to extract the local noise. We generate synthetic data by manipulating the local noise with fixed conditional feature representation. We also propose a semi-supervised approach to generate synthetic samples in the absence of labels for a majority of the available data. Results: We performed conditional synthetic generation for chest computed tomography (CT) scans corresponding to normal, COVID-19, and pneumonia afflicted patients. We show that our method significantly outperforms existing models both on qualitative and quantitative performance, and our semi-supervised approach can efficiently synthesize conditional samples under label scarcity. As an example of downstream use of synthetic data, we show improvement in COVID-19 detection from CT scans with conditional synthetic data augmentation.
Objectives:
Despite the established role of the critical care pharmacist on the ICU multiprofessional team, critical care pharmacist workloads are likely not optimized in the ICU. Medication regimen complexity (as measured by the Medication Regimen Complexity-ICU [MRC-ICU] scoring tool) has been proposed as a potential metric to optimize critical care pharmacist workload but has lacked robust external validation. The purpose of this study was to test the hypothesis that MRC-ICU is related to both patient outcomes and pharmacist interventions in a diverse ICU population.
Design:
This was a multicenter, observational cohort study.
Setting:
Twenty-eight ICUs in the United States.
Patients:
Adult ICU patients.
Interventions:
Critical care pharmacist interventions (quantity and type) on the medication regimens of critically ill patients over a 4-week period were prospectively captured. MRC-ICU and patient outcomes (i.e., mortality and length of stay [LOS]) were recorded retrospectively.
Measurements and main results:
A total of 3,908 patients at 28 centers were included. Following analysis of variance, MRC-ICU was significantly associated with mortality (odds ratio, 1.09; 95% CI, 1.08-1.11; p < 0.01), ICU LOS (β coefficient, 0.41; 95% CI, 00.37-0.45; p < 0.01), total pharmacist interventions (β coefficient, 0.07; 95% CI, 0.04-0.09; p < 0.01), and a composite intensity score of pharmacist interventions (β coefficient, 0.19; 95% CI, 0.11-0.28; p < 0.01). In multivariable regression analysis, increased patient: pharmacist ratio (indicating more patients per clinician) was significantly associated with increased ICU LOS (β coefficient, 0.02; 0.00-0.04; p = 0.02) and reduced quantity (β coefficient, -0.03; 95% CI, -0.04 to -0.02; p < 0.01) and intensity of interventions (β coefficient, -0.05; 95% CI, -0.09 to -0.01).
Conclusions:
Increased medication regimen complexity, defined by the MRC-ICU, is associated with increased mortality, LOS, intervention quantity, and intervention intensity. Further, these results suggest that increased pharmacist workload is associated with decreased care provided and worsened patient outcomes, which warrants further exploration into staffing models and patient outcomes.
Purpose
Intravenous fluids are the most commonly prescribed medication in the intensive care unit (ICU) and can have a negative impact on patient outcomes if not utilized properly. Fluid stewardship aims to heighten awareness and improve practice in fluid therapy. This report describes a practical construct for implementation of fluid stewardship services and characterizes the pharmacist’s role in fluid stewardship practice.
Summary
Fluid stewardship services were integrated into an adult medical ICU at a large community hospital. Data characterizing these services over a 2-year span are reported and categorized based on the 4 rights (right patient, right drug, right route, right dose) and the ROSE (rescue, optimization, stabilization, evacuation) model of fluid administration. The review encompassed 305 patients totaling 905 patient days for whom 2,597 pharmacist recommendations were made, 19% of which were related to fluid stewardship. This corresponded to an average of 1.52 fluid stewardship recommendations per patient. Within the construct of the 4 rights, 39% of recommendations were related to the right patient, 33% were related to the right route, 17% were related to the right drug, and 11% were related to the right dose. By the ROSE model, 1% of recommendations were related to the rescue phase, 3% were related to optimization, 79% were related to stabilization, and 17% were related to evacuation.
Conclusion
Implementation of fluid stewardship pharmacy services in a community hospital medical ICU is feasible. Integration of this practice contributed to 19% of pharmacy recommendations. The most common recommendations involved evaluation of the patient for the appropriateness of fluid therapy during the stabilization phase. The impact of fluid stewardship on patient outcomes needs to be explored.
The proliferation of synthetic data in artificial intelligence for medicine and healthcare raises concerns about the vulnerabilities of the software and the challenges of current policy.
What gets measured, gets improved. —Robert Sharma
Every critically ill patient requires care by a critical care pharmacist (CCP) for best possible outcomes. Indeed, these highly trained professionals generate benefit through direct patient care (eg, pharmacist-driven protocols, medication monitoring, etc), participation on the intensive care unit (ICU) interprofessional team (eg, pharmacotherapy recommendations, team education, etc), and leadership in the development and implementation of quality improvement initiatives.¹ However, clinical CCP services are not provided for all ICU patients, and CCP staffing models often vary substantially across ICUs in a given hospital and among ICUs in the United States.²⁻⁴ In this narrative review, we use a gap analysis approach to define current levels of clinical CCP services, identify barriers to reaching an optimal level of these services, and propose strategies focused on expanding clinical CCP services and justifying those that currently exist.
Current critical care pharmacy clinical services
The broad scope of beneficial activities performed by the CCP has been extensively reviewed and supported by a position statement from the American Society of Health-System Pharmacists (ASHP), the American College of Clinical Pharmacy (ACCP), and the Society of Critical Care Medicine (SCCM): the CCP is an essential member of the healthcare team for delivery of patient-centered care in the ICU.
Despite the frequent use of maintenance intravenous fluids (mIVF) in critically ill patients, limited guidance is available. Notably, fluid overload secondary to mIVF mismanagement is associated with significant adverse patient outcomes. The Four Rights (right drug, right dose, right duration, right patient) construct of fluid stewardship has been proposed for the safe evaluation and use of fluids. The purpose of this evidence-based review is to offer practical insights for the clinician regarding mIVF selection, dosing, and duration in line with the Four Rights of Fluid Stewardship.
Background
Critically ill patients are at increased risk for fluid overload, but objective prediction tools to guide clinical decision-making are lacking. The MRC-ICU scoring tool is an objective tool for measuring medication regimen complexity.
Objective
To evaluate the relationship between MRC-ICU score and fluid overload in critically ill patients.
Methods
In this multi-center, retrospective, observational study, the relationship between MRC-ICU and the risk of fluid overload was examined. Patient demographics, fluid balance at day 3 of ICU admission, MRC-ICU score at 24 hours, and clinical outcomes were collected from the medical record. The primary outcome was relationship between MRC-ICU and fluid overload. To analyze this, MRC-ICU scores were divided into tertiles (low, moderate, high), and binary logistic regression was performed. Linear regression was performed to determine variables associated with positive fluid balance.
Results
A total of 125 patients were included. The median MRC-ICU score at 24 hours of ICU admission for low, moderate, and high tertiles were 9, 15, and 21, respectively. For each point increase in MRC-ICU, a 13% increase in the likelihood of fluid overload was observed (OR 1.128, 95% CI 1.028-1.238, p = 0.011). The MRC-ICU score was positively associated with fluid balance at day 3 (β-coefficient 218.455, 95% CI 94.693-342.217, p = 0.001) when controlling for age, gender, and SOFA score.
Conclusions
Medication regimen complexity demonstrated a weakly positive correlation with fluid overload in critically ill patients. Future studies are necessary to establish the MRC-ICU as a predictor to identify patients at risk of fluid overload.
In data science, the ability to model the distribution of rows in tabular data and generate realistic synthetic data enables various important applications including data compression, data disclosure, and privacy-preserving machine learning. However, because tabular data usually contains a mix of discrete and continuous columns, building such a model is a non-trivial task. Continuous columns may have multiple modes, while discrete columns are sometimes imbalanced, making modeling difficult. To address this problem, I took two major steps. (1) I designed SDGym, a thorough benchmark, to compare existing models, identify different properties of tabular data and analyze how these properties challenge different models. Our experimental results show that statistical models, such as Bayesian networks, that are constrained to a fixed family of available distributions cannot model tabular data effectively, especially when both continuous and discrete columns are included. Recently proposed deep generative models are capable of modeling more sophisticated distributions, but cannot outperform Bayesian network models in practice, because the network structure and learning procedure are not optimized for tabular data which may contain non-Gaussian continuous columns and imbalanced discrete columns. (2) To address these problems, I designed CTGAN, which uses a conditional generative adversarial network to address the challenges in modeling tabular data. Because CTGAN uses reversible data transformations and is trained by re-sampling the data, it can address common challenges in synthetic data generation. I evaluated CTGAN on the benchmark and showed that it consistently and significantly outperforms existing statistical and deep learning models.
Introduction:
The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship between medication regimen complexity and patient outcomes.
Methods:
This study was a single-center, retrospective observational evaluation of 130 adults admitted to the medical ICU. The MRC-ICU score was utilized to improve the inpatient model's prediction accuracy. Three models were proposed: model I, demographic data without medication data; model II, demographic data and medication regimen complexity variables; and model III: demographic data and the MRC-ICU score. A total of 6 ML classifiers was developed: k-nearest neighbor (KNN), naïve Bayes (NB), random forest, support vector machine, neural network, and logistic classifier (LC). They were developed and tested using electronic health record data to predict inpatient mortality.
Results:
The results demonstrated that adding medication regimen complexity variables (model II) and the MRC-ICU score (model III) improved inpatient mortality prediction.. The LC outperformed the other classifiers (KNN and NB), with an overall accuracy of 83%, sensitivity (Se) of 87%, specificity of 67%, positive predictive value of 93%, and negative predictive value of 46%. The APACHE III score and the MRC-ICU score at the 24-hour interval were the 2 most important variables.
Conclusion and relevance:
Inclusion of the MRC-ICU score improved the prediction of patient outcomes on the previously established APACHE III score. This novel, proof-of-concept methodology shows promise for future application of the MRC-ICU scoring tool for patient outcome predictions.
Despite evidence highlighting harms of fluid overload, minimal guidance exists on counteraction via utilization of diuretics in the de-resuscitation phase. While diuretics have been shown to decrease net volume and improve clinical outcomes in the critically ill, a lack of standardization surrounding selection of diuretic regimen or monitoring of de-resuscitation exists. Current monitoring parameters of de-resuscitation often rely on clinical signs of fluid overload, end organ recovery and other biochemical surrogate markers which are often deemed unreliable. The majority of evidence suggests that achieving a net-negative fluid balance within 72 h after shock resolution may be of benefit; however, approaches to such goal are uncertain. Loop diuretics are a widely available type of diuretic for removal of volume in patients with sufficient kidney function, with the potential for adjunct diuretics in special circumstances. At present, administration of diuretics within the broad critically ill population fails to find uniformity and often efficacy. Given the lack of randomized controlled trials in this susceptible population, we aim to provide a thorough therapeutic understanding of diuretic pharmacotherapy which is necessary in order to achieve desired goal of fluid balance and improve overall outcomes.
Background
Clinical pharmacists are established members of the interprofessional patient care team, but limited guidance for the optimal utilization of pharmacy resources is available. Objective measurement of medication regimen complexity offers a novel process for evaluating pharmacist activity. The purpose of this study was to evaluate the relationship between medication regimen complexity, as measured by a novel medication regimen complexity scoring tool (MRC‐ICU), and both pharmacist interventions and drug‐drug interactions (DDIs).
Methods
This was a multi‐center, prospective, observational study. The electronic medical record was reviewed to collect patient demographics, patient outcomes, and MRC‐ICU and modified MRC‐ICU (mMRC‐ICU) score at 24, 48 hours, and at discharge. Pharmacist interventions were recorded during the patients' intensive care unit (ICU) stay. DDIs were also evaluated at 24, 48 hours, and at discharge. Spearman's rank‐order correlation was used to determine any correlation between the MRC‐ICU score at each time point and the number of pharmacist interventions and DDIs.
Results
A total of 153 patients were evaluated from both centers. The median MRC‐ICU at 24 hours was 11 (interquartile range [IQR] 7‐15). MRC‐ICU at 24 hours was correlated with interventions at 24 hours ( r s .439, P <.001). Furthermore, MRC‐ICU was correlated with total DDIs ( r s .4, P < .001). A modified version of the MRC‐ICU was also correlated with number of pharmacist interventions ( P < .001) and DDIs ( P < .001).
Conclusions
Medication regimen complexity showed a relationship with number of pharmacist interventions and number of DDIs.
Purpose
The purpose of this study was to develop and validate a novel medication regimen complexity–intensive care unit (MRC-ICU) scoring tool in critically ill patients and to correlate MRC with illness severity and patient outcomes.
Methods
This study was a single-center, retrospective observational chart review of adults admitted to the medical ICU (MICU) between November 2016 and June 2017. The primary aim was the development and internal validation of the MRC-ICU scoring tool. Secondary aims included external validation of the MRC-ICU and exploration of relationships between medication regimen complexity and patient outcomes. Exclusion criteria included a length of stay of less than 24 hours in the MICU, active transfer, or hospice orders at 24 hours. A total of 130 patient medication regimens were used to test, modify, and validate the MRC-ICU tool.
Results
The 39-line item medication regimen complexity scoring tool was validated both internally and externally. Convergent validity was confirmed with total medications (p < 0.0001). Score discriminant validity was confirmed by lack of association with age (p = 0.1039) or sex (p = 0.7829). The MRC-ICU score was significantly associated with ICU length of stay (p = 0.0166), ICU mortality (p = 0.0193), and patient acuity (p < 0.0001).
Conclusion
The MRC-ICU scoring tool was validated and found to correlate with length of stay, inpatient mortality, and patient acuity.
Audio Interview
Interview with Dr. Isaac Kohane on machine learning in medicine. (16:31)Download
In this view of the future of medicine, patient–provider interactions are informed and supported by massive amounts of data from interactions with similar patients. These data are collected and curated to provide the latest evidence-based assessment and recommendations.
Most critically ill patients experience external or internal fluid shifts and hemodynamic instability. In response to these changes, intravenous fluids are frequently administered. However, rapid losses of administered fluids from circulation and the indirect link between the short-lived plasma volume expansion and end points frequently result in transient responses to fluid therapy. Therefore, fluid overload is a common finding in intensive care units. The authors consider the evidence of harm associated with fluid overload and the physiologic processes that lead to fluid accumulation in critical illness. The authors then consider methods to prevent fluid accumulation and/or manage its resolution.
Williams, Jones, and Tukey (1999) showed that a sequential approach to controlling the false discovery rate in multiple comparisons, due to Benjamini and Hochberg (1995), yields much greater power than the widely used Bonferroni technique that limits the familywise Type I error rate. The Benjamini-Hochberg (B-H) procedure has since been adopted for use in reporting results from the National Assessment of Educational Progress (NAEP), as well as in other research applications. This short note illustrates that the B-H procedure is extremely simple to implement using widely available spreadsheet software. Given its easy implementation, it is feasible to include the B-H procedure in introductory instruction in inferential statistics, augmenting or replacing the Bonferroni technique.
Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided.
Visualizing data using t-SNE
- Van der Maaten