Exploiting big data for critical care research:

Chinese experts' consensus on the application of intensive care big data

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

Data-driven sudden cardiac arrest research in Europe: Experts' perspectives on ethical challenges and governance strategies

Background

Observational studies using large-scale databases and biobanks help improve prevention and treatment of sudden cardiac arrest (SCA) but the lack of guidance on data protection issues in this setting may harm patients' rights and the research enterprise itself. This qualitative study explored the ethical aspects of observational SCA research, as well as solutions.

Methods

European experts in SCA research, medical ethics and health law reflected on this topic through semi-structured interviews (N = 29) and a virtual roundtable conference (N = 18). The ESCAPE-NET project served as a discussion case. Findings were coded and thematically analysed.

Results

The first theme concerned the potential benefits and harms (at individual and group level) of observational data-based SCA studies and included the following sub-themes: societal value, scientific validity, data privacy, disclosure of genetic findings, stigma and discrimination, and medicalisation of sudden death. The second theme involved governance through 'privacy by design', 'privacy by policy' and associated regulation and oversight. Sub-themes were: de-identification of data, informed consent (broad and deferred), ethics review, and harmonisation.

Conclusions

Researchers and scientific societies should be aware that ethico-legal issues may arise during data-driven studies in SCA and other emergencies. These can be mitigated by combining technical data protection safeguards with appropriate informed consent policies and proportional ethics oversight. To ensure responsible conduct of data research in emergency medicine, we recommend the establishment of 'codes of conduct' which should be developed in interdisciplinary groups and together with patient representatives.

Validation of an Intensive Care Unit Data Mart for Research and Quality Improvement

Data derived from the electronic health record (EHR) is frequently extracted using undefined approaches that may affect the accuracy of collected variables. Further, efforts to assess data accuracy often suffer from limited collaboration between clinicians and data analysts who perform the extraction. In this manuscript, we describe the methodology behind creation of a structured, rigorously derived intensive care unit (ICU) data mart based on data automatically and routinely derived from the EHR. This ICU data mart includes high-quality data elements commonly used for quality improvement and research purposes. These data elements were identified by physicians working closely with data analysts to iteratively develop and refine algorithmic definitions for complex outcomes and risk factors. We contend that this methodology can be reproduced and applied across other institution or to other clinical domains to create high quality data marts, inclusive of complex outcomes data.

Applications of artificial intelligence and machine learning in heart failure

Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.

Structure-based screening of natural product libraries in search of potential antiviral drug-leads as first-line treatment to COVID-19 infection

The study focuses on identifying and screening natural products (NPs) based on their structural similarities with chemical drugs followed by their possible use in first-line treatment to COVID-19 infection. In the present study, the in-house natural product libraries, consisting of 26,311 structures, were screened against potential targets of SARS-CoV-2 based on their structural similarities with the prescribed chemical drugs. The comparison was based on molecular properties, 2 and 3-dimensional structural similarities, activity cliffs, and core fragments of NPs with chemical drugs. The screened NPs were evaluated for their therapeutic effects based on their predicted in-silico pharmacokinetic and pharmacodynamics properties, binding interactions with the appropriate targets, and structural stability of the bound complex using molecular dynamics simulations. The study yielded NPs with significant structural similarities to synthetic drugs currently used to treat COVID-19 infections. The study proposes the probable biological action of the selected NPs as Anti-retroviral protease inhibitors, RNA-dependent RNA polymerase inhibitors, and viral entry inhibitors.

Prediction of delirium using data mining: A systematic review

Delirium remains a significant cause of morbidity, mortality and economic burden to society. "Big data" refers to data of significantly large volume, obtained from a variety of resources, which is created and processed at high velocity. We conducted a systematic review and meta-analysis exploring whether big data could predict the incidence of delirium of patients in the inpatient setting. Medline, Embase, the Cochrane Library, Web of Science, CINAHL, clinicaltrials.gov, who.int and IEEE Xplore were searched using MeSH terms "big data", "data mining", "delirium" and "confusion" up to 30th September 2019. We included both randomised and observational studies. The primary outcome of interest was development of delirium and the secondary outcomes of interest were type of statistical methods used, variables included in the mining algorithms and clinically important outcomes such as mortality and length of hospital stay. The quality of studies was graded using the CHARMs checklist. Six retrospective single centre observational studies were included (n = 178,091), of which 17, 574 participants developed delirium. Studies were of generally of low to moderate quality. The most commonly studied method was random forest, followed by support vector machine and artificial neural networks. The model with best performance for delirium prediction was random forest, with area under receiver operating curve (AUROC) ranging from 0.78 to 0.91. Sensitivity ranged from 0.59 to 0.81 and specificity ranged from 0.73 to 0.92. Our systematic review suggests that machine-learning techniques can be utilised to predict delirium.

Evolutionary selectivity of amino acid is inspired from the enhanced structural stability and flexibility of the folded protein

AIM

The present study attempts to decipher the site-specific amino acid alterations at certain positions experiencing preferential selectivity and their effect on proteins' stability and flexibility. The study examines the selection preferences by considering pair-wise non-bonded interaction energies of adjacent and interacting amino acids present at the interacting site, along with their evolutionary history.

MATERIALS AND METHODS

For the study, variations in the interacting residues of spike protein (S-Protein) receptor-binding domain (RBD) of different coronaviruses were examined. The MD simulation trajectory analysis revealed that, though all the variants studied were structurally stable at their native and bound confirmations, the RBD of 2019-nCoV/SARS-CoV-2 was found to be more flexible and more dynamic. Furthermore, a noticeable change observed in the non-bonded interaction energies of the amino acids interacting with the receptor corroborated their selection at respective positions.

KEY FINDINGS

The conformational changes exerted by the altered amino acids could be the reason for a broader range of interacting receptors among the selected proteins.

SIGNIFICANCE

The results envisage a strong indication that the residue selection at certain positions is governed by a well-orchestrated feedback mechanism, which follows increased stability and flexibility in the folded structure compared to its evolutionary predecessor.

[Ethics of resuscitation and end of life decisions]

These European Resuscitation Council Ethics guidelines provide evidence-based recommendations for the ethical, routine practice of resuscitation and end-of-life care of adults and children. The guideline primarily focus on major ethical practice interventions (i.e. advance directives, advance care planning, and shared decision making), decision making regarding resuscitation, education, and research. These areas are tightly related to the application of the principles of bioethics in the practice of resuscitation and end-of-life care.

European Resuscitation Council Guidelines 2021: Ethics of resuscitation and end of life decisions

These European Resuscitation Council Ethics guidelines provide evidence-based recommendations for the ethical, routine practice of resuscitation and end-of-life care of adults and children. The guideline primarily focus on major ethical practice interventions (i.e. advance directives, advance care planning, and shared decision making), decision making regarding resuscitation, education, and research. These areas are tightly related to the application of the principles of bioethics in the practice of resuscitation and end-of-life care.

Big data and artificial intelligence in future patient management. How is it all started? Where are we at now? Quo tendimus?

Background

This article is focused on the understanding of the key points and their importance and impact on the future of early disease predictive models, accurate and fast diagnosis, patient management, optimise treatment, precision medicine, and allocation of resources through the applications of Big Data (BD) and Artificial Intelligence (AI) in healthcare.

Content

BD and AI processes include learning which is the acquisition of information and rules for using the information, reasoning which is using rules to reach approximate or definite conclusions and self-correction. This can help improve the detection of diseases, rare diseases, toxicity, identifying health system barriers causing under-diagnosis. BD combined with AI, Machine Learning (ML), computing and predictive-modelling, and combinatorics are used to interrogate structured and unstructured data computationally to reveal patterns, trends, potential correlations and relationships between disparate data sources and associations.

Summary

Diagnosis-assisted systems and wearable devices will be part and parcel not only of patient management but also in the prevention and early detection of diseases. Also, Big Data will have an impact on payers, devise makers and pharmaceutical companies. BD and AI, which is the simulation of human intelligence processes, are more diverse and their application in monitoring and diagnosis will only grow bigger, wider and smarter.

Outlook

BD connectivity and AI of diagnosis-assisted systems, wearable devices and smartphones are poised to transform patient and to change the traditional methods for patient management, especially in an era where is an explosion in medical data.

Exposure to surgery is associated with better long-term outcomes in patients admitted to Swedish intensive care units

BACKGROUND

Long-term outcomes of patients admitted to intensive care units (ICUs) after surgery are unknown. We investigated the long-term effects of surgical exposure prior to ICU admission.

METHODS

Registry-based cohort study. The adjusted effect of surgical exposure for mortality was examined using Cox regression. Secondary analysis with conditional logistic regression in a case-control subpopulation matched for age, gender, and Simplified Acute Physiology Score III (SAPS3) was also conducted.

RESULTS

72 242 adult patients (56.9% males, median age 66 years [IQR 50-76]), admitted to Swedish ICUs in 3-year (2012-2014) were followed for a median of 2026 days (IQR 1745-2293). Cardiovascular diseases (17.5%), respiratory diseases (15.8%), trauma (11.2%), and infections (11.4%) were the leading causes for ICU admission. Mortality at longest follow-up was 49.4%. Age; SAPS3; admissions due to malignancies, respiratory, cardiovascular and renal diseases; and transfer to another ICU were associated with increased mortality. Surgical exposure prior to ICU admission (adjusted hazard ratio [aHR] 0.90; 95% CI 0.87-0.94; P < .001), admissions from the operation theatre (aHR 0.94; CI 0.90-0.99; P = .022) or post-anaesthesia care unit (aHR 0.92; CI 0.87-0.97; P = .003) were associated with decreased mortality. Conditional logistic regression confirmed the association between surgical exposure and decreased mortality (adjusted odds ratio 0.82; CI 0.75-0.91; P < .001).

CONCLUSIONS

Long-term ICU mortality was associated with known risk factors such as age and SAPS3. Transfer to other ICUs also appeared to be a risk factor and requires further investigation. Prior surgical exposure was associated with better outcomes, a noteworthy observation given limited ICU admissions after surgery in Sweden.

ICU management based on big data

PURPOSE OF REVIEW

The availability of large datasets and computational power has prompted a revolution in Intensive Care. Data represent a great opportunity for clinical practice, benchmarking, and research. Machine learning algorithms can help predict events in a way the human brain can simply not process. This possibility comes with benefits and risks for the clinician, as finding associations does not mean proving causality.

RECENT FINDINGS

Current applications of Data Science still focus on data documentation and visualization, and on basic rules to identify critical lab values. Recently, algorithms have been put in place for prediction of outcomes such as length of stay, mortality, and development of complications. These results have begun being implemented for more efficient allocation of resources and in benchmarking processes, to allow identification of successful practices and margins for improvement. In parallel, machine learning models are increasingly being applied in research to expand medical knowledge.

SUMMARY

Data have always been part of the work of intensivists, but the current availability has not been completely exploited. The intensive care community has to embrace and guide the data science revolution in order to decline it in favor of patients' care.

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK

OBJECTIVE

The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.

DESIGN

We used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.

SETTING

Bristol Royal Infirmary general intensive care unit (GICU).

PATIENTS

Two cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III.

RESULTS

In both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability.

CONCLUSIONS

Our findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

Opioid-Related Harms: Simplistic Solutions to the Crisis Ineffective and Cause Collateral Damage

The narrative of the opioid crisis is that ill-informed and careless prescribing by physicians has led to increases in opioid-related harms including overdose deaths. Focusing on reducing the access to prescribed opioids without treating substance use disorder has led to increases in use of heroin and illicitly produced fentanyl. Overall prescribing of opioids has declined causing collateral damage to those who use opioids appropriately to reduce pain and improve function. The complexity of this issue requires a change in focus and broad changes in society's approach to substance abuse and mental health.

Ethical aspects of sudden cardiac arrest research using observational data: a narrative review

Sudden cardiac arrest (SCA) accounts for half of all cardiac deaths in Europe. In recent years, large-scale SCA registries have been set up to enable observational studies into risk factors and the effect of treatment approaches. The increasing scale and variety of data sources, coupled with the implementation of a new European data protection legal framework, causes researchers to struggle with how to handle these ‘big data’. Data protection in the SCA setting is especially complex since patients become at least temporarily incapacitated, and are thus unable to provide prospective informed consent, and because the majority of patients do not survive. A narrative review employing a systematic literature search was conducted to thematically analyse ethical aspects of non-interventional emergency medicine and critical care research. Although the identified issues may apply to a wider patient population, we describe them within the context of SCA research. Potential harms were found to include: privacy breaches, genetic discrimination and issues associated with the disclosure of individual findings, study design and application of research results. Measures proposed to mitigate harms were: alternative informed consent models including deferred or waived consent and data governance approaches promoting data security, responsible sharing and public engagement. The themes identified in this study may serve as a basis for a much-needed ethical framework regarding research with data from patients with acute and critical illness such as SCA.

The spectrum of ethical issues in a Learning Health Care System: a systematic qualitative review

Purpose

To determine systematically the spectrum of ethical issues that is raised for stakeholders in a 'Learning Health Care System' (LHCS).

Data sources

The systematic review was conducted in PubMed and Google Books between the years 2007 and 2015.

Study selection

The literature search retrieved 1258 publications. Each publication was independently screened by two reviewers for eligibility for inclusion. Ethical issues were defined as arising when a relevant normative principle is not adequately considered or two principles come into conflict.

Data extraction

A total of 65 publications were included in the final analysis and were analysed using an adapted version of qualitative content analysis. A coding frame was developed inductively from the data, only the highest-level categories were generated deductively for a life-cycle perspective.

Results of data synthesis

A total of 67 distinct ethical issues could be categorized under different phases of the LHCS life-cycle. An overarching theme that was repeatedly raised was the conflict between the current regulatory system and learning health care.

Conclusion

The implementation of a LHCS can help realize the ethical imperative to continuously improve the quality of health care. However, the implementation of a LHCS can also raise a number of important ethical issues itself. This review highlights the importance for health care leaders and policy makers to balance the need to protect and respect individual participants involved in learning health care activities with the social value of improving health care.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Clinical research in critical care. Difficulties and perspectives

In the field of Intensive Care Medicine, improved survival has resulted from better patient care, the early detection of clinical deterioration, and the prevention of iatrogenic complications, while research on new treatments has been followed by an overwhelming number of disappointments. The origins of these fiascos must be sought in the conjunction of methodological problems - common to other disciplines - and the particularities of critically ill patients. The present article discusses both aspects and suggests some options for progress.

Perspective on optimizing clinical trials in critical care: how to puzzle out recurrent failures

Background

Critical care is a complex field of medicine, especially because of its diversity and unpredictability. Mortality rates of the diseases are usually high and patients are critically ill, admitted in emergency, and often have several overlapping diseases. This makes research in critical care also complex because of patients’ conditions and because of the numerous ethical and regulatory requirements and increasing global competition. Many clinical trials in critical care have thus failed and almost no drug has yet been developed to treat intensive care unit (ICU) patients. Learning from the failures, clinical trials must now be optimized.

Main body

Several aspects can be improved, beginning with the design of studies that should take into account patients’ diversity in the ICU. At the site level, selection should reflect more accurately the potential of recruitment. Management of all players that can be involved in the research at a site level should be a priority. Moreover, training should be offered to all staff members, including the youngest. National and international networks are also part of the future as they create a collective synergy potentially improving the efficacy of sites. Finally, computerization is another area that must be further developed with the appropriate tools.

Conclusion

Clinical research in the ICU is thus a discipline in its own right that still requires tailored approaches. Changes have to be initiated by the investigators themselves as they know all the specificities of the field.