Perioperative Data Science

Your Monthly AI Digest of the Latest in Anesthesiology Research

The following is an entirely automated, AI-generated summary of articles published and listed on PubMed in the last month. It has not been checked for correctness by a human. It should be used for entertainment and informational purposes only. Nothing here is a substitute for your best clinical judgment.

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Key Anesthesiology Insights:

1. Automatic Calculation of EQUAL Candida Score Using Machine Learning and Natural Language Processing: A study conducted in the Intensive Care Units of the University Hospital in Genoa, Italy, developed a pipeline that uses natural language processing and machine learning to automatically calculate the EQUAL Candida Score, which assesses the quality of candidemia management. The study found that the random forest classifier had the highest performance, reaching 82.35% accuracy in identifying the presence and removal of central venous catheters. This work represents a step towards real-time feedback on the quality of candidemia management, potentially leading to further improvements in patient health. PMID: 38848885
2. Pressure-Strain Product as a Surrogate for Left Ventricular Stroke Work Index in Brain Stem Death Donors: A study involving thirty-one female sheep found that the Pressure-Strain Product (PSP) could be used as a surrogate for catheter-based left ventricular stroke work index (LVSWI), reflecting myocardial mitochondrial function. The results showed that in brain stem death donor hearts, PSP had the best correlation with LVSWI among other echocardiographic parameters. This suggests that PSP could be used as a surrogate for catheter-based LVSWI, reflecting mitochondrial function. PMID: 38845111
3. Low-Dose Corticosteroids for Critically Ill Adults With Severe Pulmonary Infections: A review article discusses the importance of severe pulmonary infections as leading causes of death among adults worldwide. The article observes that these infections can lead to septic shock, acute respiratory distress syndrome (ARDS), or both, which have high mortality rates. The article mentions that low-dose corticosteroids have been found to reduce mortality in patients with severe COVID-19, community-acquired pneumonia, and Pneumocystis pneumonia. PMID: 38865154

Read more: Critical Care AI-Generated Literature Digest

In-Depth Analysis:

In a study titled “Parental perceptions and acceptance of silver diamine fluoride staining in Italy” PMID: 38853387, researchers evaluated parental acceptance of silver diamine fluoride (SDF) staining in pediatric dentistry in Italy. The study found that 65.4% of parents considered staining on posterior teeth esthetically “acceptable” or “somewhat acceptable,” while only 19.3% considered staining on anterior teeth acceptable. This difference was statistically significant. The level of acceptance increased as the difficulty the child would experience to receive conventional treatment increased. The study concludes that staining on posterior teeth is more acceptable to parents than staining on anterior teeth.

In another study titled “Analysis of fibrin networks using topological data analysis – a feasibility study” PMID: 38849447, researchers used topological data analysis (TDA) to assess plasma clot characteristics microscopically. The study found that both dilution and direct thrombin inhibition resulted in visual differences in plasma clot architecture, which could be quantified using TDA. The authors conclude that microscopic examination of plasma clots, coupled with TDA, offers a promising avenue for comprehensive characterization of clot microstructure, contributing to a deeper understanding of clot pathophysiology and refining the ability to assess clot characteristics.

These studies highlight the importance of understanding patient perceptions in treatment acceptance and the potential of novel analytical techniques in improving our understanding of disease processes.

Want more?

For more literature summaries and other ai tools, visit our department’s custom AI website.

ai.anes.uab.edu

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Stay Informed, Stay Ahead

Your Monthly AI Digest of the Latest in Anesthesiology Research

Your monthly AI Digest of the latest in anesthesiology research

The following is an entirely automated, AI-generated summary of articles published and listed on PubMed in the last month. It has not been checked for correctness by a human. It should be used for entertainment and informational purposes only. Nothing here is a substitute for your best clinical judgement.

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Key Anesthesiology Insights:

1. Preoperative albumin levels and length of hospital stay in non-cardiac surgery patients with pulmonary hypertension: A secondary retrospective analysis of 195 patients with pulmonary hypertension (PHTN) undergoing non-cardiac, non-obstetric surgery found that low levels of preoperative albumin were associated with an increased risk of prolonged hospital stay. This suggests that optimizing preoperative nutrition could potentially reduce the length of hospital stay in these patients. PMID: 38847677
2. Resistin as a biomarker for disease severity and survival in patients with pulmonary arterial hypertension: A study involving 1121 adults with pulmonary arterial hypertension (PAH) found that high resistin levels were associated with older age, shorter 6-min walk distance, reduced cardiac performance, and an increased risk of death. The study suggests that resistin plays an important role in the pathobiology of human PAH and represents a novel biomarker for prognostication. PMID: 38844967
3. Post-cardiac surgery transcriptomes of monocytes differ even at three months compared to baselines: A study of 13 patients admitted for elective cardiac surgery found that monocytes and T cells had distinct transcriptomes, with statistically significant differential expression of 558 T cell related genes. The authors concluded that the post-cardiac surgery transcriptomes of monocytes differ even at three months compared to baselines, which may reflect latent inflammation and persistent progression of tissue degenerative processes. PMID: 38851465

Read more: Cardiac AI-Generated Literature Digest

In-Depth Analysis:

In a study investigating the use of a novel non-invasive echocardiographic parameter, Pressure-Strain Product (PSP), as a potential surrogate for catheter-based left ventricular stroke work index (LVSWI) in an ovine model of brain stem death (BSD) donors, researchers found that PSP(circ) had the best correlation with LVSWI among other echocardiographic parameters. The study suggests that PSP(circ) could be used as a surrogate for catheter-based LVSWI, reflecting mitochondrial function. This finding could have significant implications for the assessment of cardiac function in BSD donors. PMID: 38845111

Another study aimed to assess whether the choice of anesthesia (locoregional anesthesia or general anesthesia) influences the rates of perioperative complications in patients with congestive heart failure (CHF) undergoing carotid endarterectomy (CEA). The study found that locoregional anesthesia was independently associated with lower rates of MI, acute HF, major cardiac complications, hemodynamic instability, cranial nerve injury, shunt use, and neuromonitoring device use compared to general anesthesia in symptomatic CHF patients. This suggests that CEA can be safely performed in patients with CHF, and utilizing locoregional anesthesia is associated with a decreased incidence of perioperative cardiac complications in patients with symptomatic heart failure undergoing CEA. PMID: 38851468

In a study investigating the pathological mechanisms of depression, researchers induced depression-like behavior in a rat model and conducted various behavioral tests to assess depressive-like behavior. The study identified five differentially expressed phosphorylated proteins (DEPPs) – Gys1, Nmt2, Lrp1, Bin1, and Atp1a1. These proteins were found to activate the synaptogenesis signaling pathway, induce mitochondrial dysfunction, and activate the phosphoinositide biosynthesis and degradation pathways. The study also confirmed the proteomic findings for Gys1, Nmt2, Lrp1, and Atp1a1 using qRT-PCR. Notably, inhibiting Nmt2 was found to alleviate depression-like behavior and neuronal apoptosis in the hippocampus of rats with chronic unpredictable mild stress (CUMS). The article concludes that these findings provide new insights into the molecular mechanisms of depression and suggest NMT2 as a potential target for depression treatment or diagnosis. PMID: 38852542

Want more?

For more literature summaries and other ai tools, visit our department’s custom AI website.

ai.anes.uab.edu

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We appreciate you reading this month’s AI Digest. Remember, these summaries are AI-generated and are intended for entertainment and informational purposes. Always rely on your clinical judgment and expertise.

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Stay Informed, Stay Ahead

Read these reflections from our first cohort of Perioperative Data Science summer interns.

Henry Spradlin:

The first thing I would like to do is thank Dr. Berkowitz for looking into the position, Dr. Melvin for offering me the position and helping me through my project, and Dr. Godwin for any other help I needed along the way. I quickly found one of the things I appreciated about this position was that everyone on the team is doing something that matters. During our daily check-in, Dr. Melvin and Dr. Godwin would mention the projects they were working on such as using AI with glucose monitoring or using image analysis on radiology images. When I initially met with Dr. Melvin, he asked me what I wanted to get out of this internship; I explained I wanted to improve my python skills. Machine learning is fascinating to me but as I am going into aerospace engineering, I figured a general knowledge of python would help me most in the future. Dr. Melvin started me right away with a project that improved my skills. The overarching goal of my project (in conjunction with Daniel Harrod) was to create a self-compiling database of past resident lectures for easy reference (nobody wants to watch 12 years of recordings for the two sentences of information they need). My part focused on taking a pre-transcribed lecture, summarizing that, and extracting the keywords. This fit my internship goal extremely well because it allowed me to practice writing a lot of more basic code engraining python syntax in my head as well as learning a few more advanced concepts. 

Andrew Glassford:

I spent my internship working on the Citation Count Prediction project, where the goal is to create an AI model that can read a scholarly paper’s title, abstract, and metadata, and then (accurately) predict how many future papers would cite this one. Dr. Godwin leads the CCP project, so I would technically be under his supervision, although Dr. Melvin was always ready to give advice when needed.

My first task was to recompile some of the test data that we were going to use. We wanted an extra datapoint, which meant altering and re-running the whole fetch-and-organize program. I was then tasked with finding a good model to read our data and make predictions. After experimenting with OpenAI’s GPT-3 and finding limited success, we opted for MATLAB’s text processing. After hybridizing NLP and MLP models built from scratch, we decided to replace the basic NLP with a pre-trained model called BERT. It produced the best results to date, with an overall accuracy of 65% and an AUC-ROC of 0.71. 

I learned a surprisingly broad array of skills, from using MATLAB to how to work on a research team. Apparently, there’s a difference between machine learning and deep learning, and just because it’s called “deep” doesn’t mean it’s actually better. I was exposed to a whole new world of business applications, like Azure DevOps and RocketChat. I even learned about and got certified to handle protected medical data! And, of course, no internship would be complete without some great mentors to patiently explain why exactly I was entirely wrong, several times over. My biggest takeaway from this internship probably sounds silly, but it’s true: just keep trying stuff. When an idea didn’t work, we didn’t try to brute force some haphazard nonsense; we backed up, analyzed our results, and moved on to the next option. Good work advice, and good relationship advice, too. Overall, an excellent enrichment experience.

Daniel Harrod:

Coming soon!

Our Department Chairman, Dr. Dan Berkowitz, recently gave a “State of the Department” address. We were honored and humbled by the number of mentions of the Data Science team. Here are the highlights of the Data Science activities reported on during the State of the Department address.

The Team

In October 2021, we co-recruited a data scientist with Radiology. Dr. Ryan C. Godwin joined us shortly before his daughter was born, meaning we had the pleasure of welcoming him to the department and his daughter to the world! We have also recruited three data science interns for summer 2022.

Accomplishments

Since January of 2021, our team has authored or co-authored seven published or accepted manuscripts.

1. Zaky A, Younan DS, Meers B, Davies J, Pereira S, Melvin RL, Kidd B, Morgan C, Tolwani A, Pittet JF. End-of-procedure volume responsiveness defined by the passive leg raise test is not associated with acute kidney injury after cardiopulmonary bypass. Journal of Cardiothoracic and Vascular Anesthesia. 2021 May 1;35(5):1299-306.
2. Mamidi TK, Tran-Nguyen TK, Melvin RL, Worthey EA. Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data. Frontiers in big Data. 2021 Jun 4;4:36.
3. Berenhaut KS, Moore KE, Melvin RL. A social perspective on perceived distances reveals deep community structure. Proceedings of the National Academy of Sciences. 2022 Jan 25;119(4).
4. Treacher AH, Garg P, Davenport E, Godwin R, Proskovec A, Bezerra LG, Murugesan G, Wagner B, Whitlow CT, Stitzel JD, Maldjian JA. MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks. NeuroImage. 2021 Nov 1;241:118402.
5. Melvin RL, Abella JR, Patel R, Hagood JM, Berkowitz DE, Mladinov D. Intraoperative utilisation of high-resolution data for cerebral autoregulation: a feasibility study. British journal of anaesthesia. 2022 Mar 1;128(3):e217-9.
6. Melvin RL, Broyles MG, Duggan EW, John S, Smith AD, Berkowitz DE. Artificial Intelligence in Perioperative Medicine: A Proposed Common Language with Applications to FDA-Approved Devices. Frontiers in Digital Health.:64.
7. Melvin RL, Barker SJ, Kiani J, Berkowitz E. Pro-Con Debate: Should Code Sharing Be Mandatory for Publication? Anesthesia and Analgesia. In press.

We have 7 more manuscripts in active preparation! Additionally, we submitted 6 external funding applications and are preparing 2 more. Our work has been the focus of 6 conference presentations, 2 webinars, and an Intel Spotlight video. We’ve also spent this year teaching via 5 grand rounds, invited lectures and colloquia and taught 1 class with a fully new curriculum designed by us!

Vision, values and goals

Reflecting on this last year, we took a moment to recenter on our vision, values and goals. Reflecting on these, we’ve set 3 goals for the coming years.

1. Integrate Data Science into all areas of the department, adding at least 1 operations and 1 education project.
2. Integrate into at least 1 hospital-wide initiative.
3. Publish at least 1 “precision” and 1 “prediction” paper per year.

Two publications from our data science faculty have recently been published in high-profile journals.

First, a feasibility study to determine the optimum blood pressure of cardiac patients during surgery appeared in British Journal of Anesthesia (BJA: impact factor 9.1, ranking it 2 out of 33 in Anesthesiology at the time of this writing) in December. An earlier blog post described the preliminary work for this article. Shortly after appearing on the BJA website, this work was the topic of an editorial discussing its importance — introducing a bulk, automated process for what was preciously a laborious clinical study procedure that had to be conducted one patient at a time. As described by its senior author, Dr. Domagoj Mladinov, “[this] study demonstrates feasibility of automatically calculating optimal arterial blood pressure based on cerebral autoregulation limits derived from cerebral oximetry during cardiac surgery.” The novelty of this methodology is echoed in the editorial by Hogue and colleagues, “What this report demonstrates is the feasibility of an operator-independent method for monitoring CBF [cerebral blood flow] autoregulation.” Additionally, this work from UAB Anesthesiology and Perioperative Medicine Faculty suggest a methodology for moving autoregulation out of the realm of retrospective studies and toward clinical interventions via a real-time, streaming data analysis platform being actively developed in our department in collaboration with Medical Informatics Corp (MIC, Houston, Texas).

Second, a novel algorithm for for extracting information on community structure from graphs (networks) is in press with Proceedings of the National Academy of Science (PNAS: impact factor 11.2), currently available online. This work — by our Principal Data Scientist in collaboration with his former M.A. thesis advisor at Wake Forest University — suggests a social framework for discussing and calculating the centrality of nodes (participants) in a network. This novel algorithm was cited (page 5) prior to publication by researchers seeking to use the algorithm efficiently on very large networks. As described by the authors, this work demonstrates “how meaningful community structure can be identified without additional inputs (e.g., number of clusters or neighborhood size), optimization criteria, iterative procedures, nor distributional assumptions.” That is, aside from the network itself, an investigator needs no further a priori information to extract the underlying community structure and detect highly central — or important — nodes.

In the spring semester of 2022, our Principal Data Scientist, Dr. Ryan L. Melvin (me), will be teaching INFO-403 Bioinformatics-II (Algorithms) here at UAB. The course serves as an introduction to several computational techniques and forms of algorithmic thinking. Computational topics include dynamic programming, optimization, hidden Markov models, graph algorithms, and unsupervised machine learning.

The course structure is modeled after one taught at Carnegie Mellon by one of the textbook’s authors, Phillip Compeau. An UAB-specific special edition of the online, interactive textbook will be the primary resource for content and assignments for those taking the course. Each chapter involves several assigned software/coding challenges that coach students through building famous bioinformatics algorithms from scratch. These assignments are programming-language-agnostic, so students can use whatever scripting or programming language is most comfortable for them. Though, all demos and instructor solutions will be presented in Python, since that is the language the instructor is most comfortable with. 🙂

In terms of in-class meetings, the course has a flipped structure. Each week, the course will meet for one 2.5-hour session. The session will be broken up into roughly five 30-minute segments with a break after the first two. The segments will be questions and troubleshooting/hints from the week’s assigned reading and software challenges, a hands-on activity that connects to a primary biology or algorithm concept from the week, and 3 rounds of discussion questions and graded student presentations.

The hands-on activities are pretty off-beat and will hopefully provide students with a fun, unique experience. For example, one week student groups will play a few rounds of the board game Pandemic. The next week, different groups will conduct a forensic investigation of the game and try to reconstruct the original board state (connecting to the biological concept of disease spread and the algorithmic concept of tree-based methods).

The primary content delivery method is an online, interactive textbook. For the outliers who successfully learn from lectures, recorded lectures are also available. Additionally, the vast majority of students’ final course grades come from interactions with the online textbook. The remaining portion comes from class participation.

For students at UAB taking this course, the online, interactive textbook must be purchased using a link to the UAB-specific special edition. The link is provided in the online syllabus in the course’s Canvas page.

It’s been a busy time in Perioperative Data Science.

Research and Publications

In just the last month, we received reviews on three papers and have already resubmitted two of those. Both have been accepted pending a minor revision — one in Anesthesia and Analgesia (A&A) and one in British Journal of Anesthesia (BJA). The A&A paper is an “Open Mind” article debating the pros and cons of having journal-level requirements for sharing code used in research. The BJA article discusses a retrospective study we performed to calculate personalized intraoperative blood pressure recommendations for patients using the Sickbay platform. Our third paper we received feedback on in the last month is also aimed at A&A and proposes a clinician-friendly taxonomy for classifying and understanding the utility of Machine Learning (ML) and Artificial Intelligence (AI) algorithms.

Also in the last month, we wrapped up two projects — one on predictors of renal failure and another on cost savings of a specialized perioperative service — and are currently writing manuscripts for them. Along with those, we also submitted an article to Journal of Clinical Anesthesia on predicting blood transfusion need.

Quality Improvement

Perioperative Data Science was asked to help out with a high-profile, institution-wide project to understand the impact of an Opioid Stewardship Program implemented a few years ago. Those results will be presented to hospital leadership soon and may inform decisions on next steps for the institutions Opioid Stewardship Program.

Additionally, Perioperative Data Science led a recent discussion on the next steps for a departmental hypotension prevention initiative. In this instance, we are predicting adverse patient outcomes related to hypotension, planning to convey those to providers so that they can be fully equipped with an understanding of the impact of blood pressure management on the patients under their care.

Operations

In this same time frame, Administrative team asked IT and Perioperative Data Science for assistance understanding the impacts of longer working days on department resources. I think it comes as no surprise that the continued pandemic has greatly impact every aspect of our work in the Department of Anesthesiology. And now we are attempting to quantify just how much the pandemic has stretched our resources. The results of this project are scheduled to be presented to Department leadership soon.

Education

We took on two projects with medical students over the last month. One of those is a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of AI/ML in Perioperative Medicine. The other is a document-processing project to automatically process reports from echocardiograms into a structured, tabular format that can be used in AI/ML algorithms. This project will also make this data more accessible to researchers, QI practitioners, and educators.

Recruiting

This last month we finalized the joint recruitment of a data scientist shared with Radiology. We’ll be announcing the details on this new hire’s start date. Stay tuned!

On August 9 and 10, I participated in the UAB AI Against Cancer Data Science Hackathon. My teammates and I applied recent advances in Computer Vision Artificial Intelligence applied to pathology slides (such as tissue samples of brain cancer). We were one of the three teams receiving the “main awards” of the hackathon. The three winning teams were selected on criteria similar to that of NIH grant-proposal review (see details at Hackathon 2021 – Cancer Bioinformatics and Data Science (C-BIDS) (ubrite.org)). You can view our showcase presentation of our work here on YouTube.

My teammates were Thi K. Tran-Nguyen and Tarun Karthik Kumar Mamidi, along with Liz Worthey and Rati Chkheidze (these two proposed the project focus). Together, our skills covered the gamut of data wrangling, model development, and medical relevance. As a result, we developed a proof of concept system for combining pathology slides and omics data in order to select cellular pathways based on omics data and have the AI highlight the participating cells on the slide (Figure below).

We set a particularly difficult challenge for ourselves, as we wanted to develop an unsupervised methodology. That is, we wanted an AI to accomplish this task without expert input or intervention. We did not provide examples of “right” answers for the computer to learn from! Overall, our short time in the hackathon proved feasibility for a method for connecting genotype (from omics data)