Big Data in Neonatal Health Care: Big Reach, Big Reward?

The Finnegan Score for Neonatal Opioid Withdrawal Revisited With Routine Electronic Data: Retrospective Study

Background

The severity of neonatal abstinence syndrome (NAS) may be assessed with the Finnegan score (FS). Since the FS is laborious and subjective, alternative ways of assessment may improve quality of care.

Objective

In this pilot study, we examined associations between the FS and routine monitoring data obtained from the electronic health record system.

Methods

The study included 205 neonates with NAS after intrauterine (n=23) or postnatal opioid exposure (n=182). Routine monitoring data were analyzed at 60±10 minutes (t-1) and 120±10 minutes (t-2) before each FS assessment. Within each time period, the mean for each variable was calculated. Readings were also normalized to individual baseline data for each patient and parameter. Mixed effects models were used to assess the effect of different variables.

Results

Plots of vital parameters against the FS showed heavily scattered data. When controlling for several variables, the best-performing mixed effects model displayed significant effects of individual baseline-controlled mean heart rate (estimate 0.04, 95% CI 0.02-0.07) and arterial blood pressure (estimate 0.05, 95% CI 0.01-0.08) at t-1 with a goodness of fit (R2m) of 0.11.

Conclusions

Routine electronic data can be extracted and analyzed for their correlation with FS data. Mixed effects models show small but significant effects after normalizing vital parameters to individual baselines.

Artificial intelligence in the NICU to predict extubation success in prematurely born infants

OBJECTIVES

Mechanical ventilation in prematurely born infants, particularly if prolonged, can cause long term complications including bronchopulmonary dysplasia. Timely extubation then is essential, yet predicting its success remains challenging. Artificial intelligence (AI) may provide a potential solution.

CONTENT

A narrative review was undertaken to explore AI's role in predicting extubation success in prematurely born infants. Across the 11 studies analysed, the range of reported area under the receiver operator characteristic curve (AUC) for the selected prediction models was between 0.7 and 0.87. Only two studies implemented an external validation procedure. Comparison to the results of clinical predictors was made in two studies. One group reported a logistic regression model that outperformed clinical predictors on decision tree analysis, while another group reported clinical predictors outperformed their artificial neural network model (AUCs: ANN 0.68 vs. clinical predictors 0.86). Amongst the studies there was an heterogenous selection of variables for inclusion in prediction models, as well as variations in definitions of extubation failure.

SUMMARY

Although there is potential for AI to enhance extubation success, no model's performance has yet surpassed that of clinical predictors.

OUTLOOK

Future studies should incorporate external validation to increase the applicability of the models to clinical settings.

A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities

Bronchopulmonary dysplasia (BPD) is a complex, multifactorial lung disease affecting preterm neonates that can result in long-term pulmonary and non-pulmonary complications. Current therapies mainly focus on symptom management after the development of BPD, indicating a need for innovative approaches to predict and identify neonates who would benefit most from targeted or earlier interventions. Clinical informatics, a subfield of biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve patient outcomes. The application of clinical informatics to develop and enhance clinical therapies for BPD presents opportunities by leveraging electronic health record data, applying machine learning algorithms, and implementing clinical decision support systems. This review highlights the current barriers and the future potential of clinical informatics in identifying clinically relevant BPD phenotypes and developing clinical decision support tools to improve the management of extremely preterm neonates developing or with established BPD. However, the full potential of clinical informatics in advancing our understanding of BPD with the goal of improving patient outcomes cannot be achieved unless we address current challenges such as data collection, storage, privacy, and inherent data bias.

Bibliometric Analysis of Hotspots and Frontiers of Immunotherapy in Pancreatic Cancer

BACKGROUND

Pancreatic cancer is one of the most common malignant neoplasms with an increasing incidence, low rate of early diagnosis, and high degree of malignancy. In recent years, immunotherapy has made remarkable achievements in various cancer types including pancreatic cancer, due to the long-lasting antitumor responses elicited in the human body. Immunotherapy mainly relies on mobilizing the host's natural defense mechanisms to regulate the body state and exert anti-tumor effects. However, no bibliometric research about pancreatic cancer immunotherapy has been reported to date. This study aimed to assess research trends and offer possible new research directions in pancreatic cancer immunotherapy.

METHODS

The articles and reviews related to pancreatic cancer immunotherapy were collected from the Web of Science Core Collection. CiteSpace, VOSviewer, and an online platform, and were used to analyze co-authorship, citation, co-citation, and co-occurrence of terms retrieved from the literature highlighting the scientific advances in pancreatic cancer immunotherapy.

RESULTS

We collected 2475 publications and the number of articles was growing year by year. The United States had a strong presence worldwide with the most articles. The most contributing institution was Johns Hopkins University (103 papers). EM Jaffee was the most productive researcher with 43 papers, and L Zheng and RH Vonderheide ranked second and third, with 34 and 29 papers, respectively. All the keywords were grouped into four clusters: "immunotherapy", "clinical treatment study", "tumor immune cell expression", "tumor microenvironment". In the light of promising hotspots, keywords with recent citation bursts can be summarized into four aspects: immune microenvironment, adaptive immunotherapy, immunotherapy combinations, and molecular and gene therapy.

CONCLUSIONS

In recent decades, immunotherapy showed great promise for many cancer types, so various immunotherapy approaches have been introduced to treat pancreatic cancer. Understanding the mechanisms of immunosuppressive microenvironment, eliminating immune suppression and blocking immune checkpoints, and combining traditional treatments will be hotspots for future research.

Caffeine and Its Neuroprotective Role in Ischemic Events: A Mechanism Dependent on Adenosine Receptors

Ischemia is characterized by a transient, insufficient, or permanent interruption of blood flow to a tissue, which leads to an inadequate glucose and oxygen supply. The nervous tissue is highly active, and it closely depends on glucose and oxygen to satisfy its metabolic demand. Therefore, ischemic conditions promote cell death and lead to a secondary wave of cell damage that progressively spreads to the neighborhood areas, called penumbra. Brain ischemia is one of the main causes of deaths and summed with retinal ischemia comprises one of the principal reasons of disability. Although several studies have been performed to investigate the mechanisms of damage to find protective/preventive interventions, an effective treatment does not exist yet. Adenosine is a well-described neuromodulator in the central nervous system (CNS), and acts through four subtypes of G-protein-coupled receptors. Adenosine receptors, especially A1 and A2A receptors, are the main targets of caffeine in daily consumption doses. Accordingly, caffeine has been greatly studied in the context of CNS pathologies. In fact, adenosine system, as well as caffeine, is involved in neuroprotection effects in different pathological situations. Therefore, the present review focuses on the role of adenosine/caffeine in CNS, brain and retina, ischemic events.

Investigation of patients' access to EHR data via smart apps in Chinese Hospitals

Background

Given that China has encouraged EHR usage in hospitals for more than a decade, patients’ access to their own EHR data is still not as widely utilized as expected.

Methods

We cultivated a survey with four categories and field interviews of measures to identify whether hospitals have already released EHR data to patients, inpatients or outpatients, the top EHR release contents and the most popular release software.

Results

Of the 1344 responding hospitals from 30 provinces nationwide, 41.37% of hospitals have already released their EHR data to patients, of which 97.12% are through smart apps. More than 91% of hospitals use WeChat, and 32.37% of hospitals developed their own standalone apps or use vendors’ apps. A total of 54.63% were released to both outpatients and inpatients, and the top release contents were all objective. A rough estimation is made that releasing EHR data to patients via smart apps may save the hospital 15.9 million RMB per year and patients 9.4 million RMB altogether.

Conclusions

EHR data release is believed to bring both patient and hospital cost savings and efficiency gains but is still considered spontaneous and requires legal support and government regulation.

Brief introduction of medical database and data mining technology in big data era

Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.

Redefining the Use of Big Data in Urban Health for Increased Liveability in Smart Cities

Policy decisions and urban governance are being influenced by an emergence of data from internet of things (IoT), which forms the backbone of Smart Cities, giving rise to Big Data which is processed and analyzed by Artificial Intelligence models at speeds unknown to mankind decades ago. This is providing new ways of understanding how well cities perform, both in terms of economics as well as in health. However, even though cities have been increasingly digitalized, accelerated by the concept of Smart Cities, the exploration of urban health has been limited by the interpretation of sensor data from IoT devices, omitting the inclusion of data from human anatomy and the emergence of biological data in various forms. This paper advances the need for expanding the concept of Big Data beyond infrastructure to include that of urban health through human anatomy; thus, providing a more cohesive set of data, which can lead to a better knowledge as to the relationship of people with the city and how this pertains to the thematic of urban health. Coupling both data forms will be key in supplementing the contemporary notion of Big Data for the pursuit of more contextualized, resilient, and sustainable Smart Cities, rendering more liveable fabrics, as outlined in the Sustainable Development Goal (SDG) 11 and the New Urban Agenda.