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Article: An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method.

Pal, Ravi / Rudas, Akos / Kim, Sungsoo / Chiang, Jeffrey N / Braney, Anna / Cannesson, Maxime

medRxiv : the preprint server for health sciences

2024

Abstract: Background and objective: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine ... ...

Abstract	Background and objective: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. Methods: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm with an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy PYTHON package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. Results: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP ( Conclusion: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.
Language	English
Publishing date	2024-03-07
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.05.24303735
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Antibiotic Switches in Urinary Tract Infection Are Associated With Atypical Symptoms and Emergent Care.

Khalfay, Nuha / Murray, Kristen / Shimabukuro, Julianna / Chiang, Jeffrey N / Ackerman, A Lenore

Urogynecology (Philadelphia, Pa.)

2024 Volume 30, Issue 3, Page(s) 256–263

Abstract: Importance: Given worsening global antibiotic resistance, antimicrobial stewardship aims to use the shortest effective duration of the most narrow-spectrum, effective antibiotic for patients with specific urinary symptoms and laboratory testing ... ...

Abstract	Importance: Given worsening global antibiotic resistance, antimicrobial stewardship aims to use the shortest effective duration of the most narrow-spectrum, effective antibiotic for patients with specific urinary symptoms and laboratory testing consistent with urinary tract infection (UTI). Inappropriate treatment and unnecessary antibiotic switching for UTIs harms patients in a multitude of ways. Objective: This study sought to analyze antibiotic treatment failures as measured by antibiotic switching for treatment of UTI in emergent and ambulatory care. Study design: For this retrospective cohort study, 908 encounters during July 2019 bearing a diagnostic code for UTI/cystitis in a single health care system were reviewed. Urinary and microbiological testing, symptoms endorsed at presentation, and treatments prescribed were extracted from the medical record. Results: Of 908 patients diagnosed with UTI, 64% of patients (579/908) received antibiotics, 86% of which were empiric. All patients evaluated in emergent care settings were prescribed antibiotics empirically in contrast to 71% of patients in ambulatory settings (P < 0.001). Of patients given antibiotics, 89 of 579 patients (15%, 10% of all 908 patients) were switched to alternative antibiotics within 28 days. Emergent care settings and positive urine cultures were significantly associated with increased antibiotic switching. Patients subjected to switching tended to have higher rates of presenting symptoms inconsistent with UTI. Conclusions: Empiric treatment, particularly in an emergent care setting, was frequently inappropriate and associated with increasing rates of antibiotic switching. Given the profound potential contribution to antibiotic resistance, these findings highlight the need for improved diagnostic and prescribing accuracy for UTI.
MeSH term(s)	Humans ; Anti-Bacterial Agents/therapeutic use ; Retrospective Studies ; Urinary Tract Infections/diagnosis ; Urinalysis ; Ambulatory Care
Chemical Substances	Anti-Bacterial Agents
Language	English
Publishing date	2024-03-07
Publishing country	United States
Document type	Journal Article
ISSN	2771-1897
ISSN (online)	2771-1897
DOI	10.1097/SPV.0000000000001464
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Article ; Online: Association of red blood cell distribution width with hospital admission and in-hospital mortality across all-cause adult emergency department visits.

Hong, Woo Suk / Rudas, Akos / Bell, Elijah J / Chiang, Jeffrey N

JAMIA open

2023 Volume 6, Issue 3, Page(s) ooad053

Abstract: Objectives: To test the association between the initial red blood cell distribution width (RDW) value in the emergency department (ED) and hospital admission and, among those admitted, in-hospital mortality.: Materials and methods: We perform a ... ...

Abstract	Objectives: To test the association between the initial red blood cell distribution width (RDW) value in the emergency department (ED) and hospital admission and, among those admitted, in-hospital mortality. Materials and methods: We perform a retrospective analysis of 210 930 adult ED visits with complete blood count results from March 2013 to February 2022. Primary outcomes were hospital admission and in-hospital mortality. Variables for each visit included demographics, comorbidities, vital signs, basic metabolic panel, complete blood count, and final diagnosis. The association of each outcome with the initial RDW value was calculated across 3 age groups (<45, 45-65, and >65) as well as across 374 diagnosis categories. Logistic regression (LR) and XGBoost models using all variables excluding final diagnoses were built to test whether RDW was a highly weighted and informative predictor for each outcome. Finally, simplified models using only age, sex, and vital signs were built to test whether RDW had additive predictive value. Results: Compared to that of discharged visits (mean [SD]: 13.8 [2.03]), RDW was significantly elevated in visits that resulted in admission (15.1 [2.72]) and, among admissions, those resulting in intensive care unit stay (15.3 [2.88]) and/or death (16.8 [3.25]). This relationship held across age groups as well as across various diagnosis categories. An RDW >16 achieved 90% specificity for hospital admission, while an RDW >18.5 achieved 90% specificity for in-hospital mortality. LR achieved a test area under the curve (AUC) of 0.77 (95% confidence interval [CI] 0.77-0.78) for hospital admission and 0.85 (95% CI 0.81-0.88) for in-hospital mortality, while XGBoost achieved a test AUC of 0.90 (95% CI 0.89-0.90) for hospital admission and 0.96 (95% CI 0.94-0.97) for in-hospital mortality. RDW had high scaled weights and information gain for both outcomes and had additive value in simplified models predicting hospital admission. Discussion: Elevated RDW, previously associated with mortality in myocardial infarction, pulmonary embolism, heart failure, sepsis, and COVID-19, is associated with hospital admission and in-hospital mortality across all-cause adult ED visits. Used alone, elevated RDW may be a specific, but not sensitive, test for both outcomes, with multivariate LR and XGBoost models showing significantly improved test characteristics. Conclusions: RDW, a component of the complete blood count panel routinely ordered as the initial workup for the undifferentiated patient, may be a generalizable biomarker for acuity in the ED.
Language	English
Publishing date	2023-07-13
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooad053
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Article ; Online: Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.

Jawadi, Zina / He, Rosemary / Srivastava, Pratyaksh K / Fonarow, Gregg C / Khalil, Suzan O / Krishnan, Srikanth / Eskin, Eleazar / Chiang, Jeffrey N / Nsair, Ali

ESC heart failure

2024

Abstract: Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre ( ... ...

Abstract	Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in-hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21 802 patients, of which 14 539 were in the training cohort and 7263 were in the validation cohort. The median age (25th-75th percentile) was 70 (58-82) for the entire cohort, 43.2% were female, and 6.7% experienced inpatient mortality. In the overall cohort, 7621 (35.0%) patients had heart failure with reduced ejection fraction (EF ≤ 40%), 1271 (5.8%) had heart failure with mildly reduced EF (EF 41-49%), and 12 910 (59.2%) had heart failure with preserved EF (EF ≥ 50%). Random forest models in the validation cohort demonstrated a c-statistic (95% confidence interval) of 0.96 (0.95-0.97), sensitivity (SN) of 87.3%, and specificity (SP) of 90.6% for the prediction of in-hospital mortality. Models for those with HFrEF demonstrated a c-statistic of 0.96 (0.94-0.98), SN 88.2%, and SP 91.0%, and those for patients with HFpEF showed a c-statistic of 0.95 (0.93-0.97), SN 87.4%, and SP 89.5% for predicting in-hospital mortality. The random forest model significantly outperformed logistic regression (c-statistic 0.87, SN 75.9%, and SP 86.9%), and current existing risk scores including the Acute Decompensated Heart Failure National Registry risk score (c-statistic of 0.70, SN 69%, and SP 62%), and the Get With the Guidelines-Heart Failure risk score (c-statistic 0.69, SN 67%, and SP 63%); P < 0.001 for comparison. Machine learning models built from commonly recorded patient information can accurately predict in-hospital mortality among patients hospitalized with a diagnosis of heart failure.
Language	English
Publishing date	2024-04-18
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2814355-3
ISSN	2055-5822 ; 2055-5822
ISSN (online)	2055-5822
ISSN	2055-5822
DOI	10.1002/ehf2.14796
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Article ; Online: Effect of statins on the age of onset of age-related macular degeneration.

Ganesh, Durga / Chiang, Jeffrey N / Corradetti, Giulia / Zaitlen, Noah / Halperin, Eran / Sadda, Srinivas R

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie

2023 Volume 261, Issue 8, Page(s) 2245–2255

Abstract: Background: This study evaluated the relationship between statin use and the age of onset of age-related macular degeneration (AMD).: Methods: Electronic Health Records from 52,840 patients evaluated at University of California Los Angeles (UCLA) ... ...

Abstract	Background: This study evaluated the relationship between statin use and the age of onset of age-related macular degeneration (AMD). Methods: Electronic Health Records from 52,840 patients evaluated at University of California Los Angeles (UCLA) Ophthalmology Clinics and 9,977 patients evaluated at University of California San Francisco (UCSF) Ophthalmology Clinics were screened. Survival analysis was performed using Cox proportional hazards regression models and visualized using Kaplan Meier survival curves, with the following covariates-sex, ethnicity, smoking history, fluoxetine use, obesity, diabetes mellitus, and hypertension. Results: 5,498 of 52,840 patients at UCLA were diagnosed with AMD. Statin use was associated with a later AMD onset (HR = 0.8823, p < 0.0001), while female sex (HR = 1.0852, p= 00,035), obesity (HR = 1.4555, p < 0.0001), and fluoxetine (HR = 1.3797, p= 0.0003) were associated with an earlier AMD onset. Non-hispanic black (HR = 0.5687, p < 0.0001) and hispanic ethnicities (HR = 0.8269, p= 0.0028) were associated with a later AMD onset. When stratifying for ethnicity, statins, fluoxetine, sex, and obesity were significant only within non-hispanic white subjects. Statin use was significant among patients with dry AMD (HR = 0.8410, p= 0.0001) but not wet AMD (0.9188, p= 0.0351). In the replication cohort, 526 of 9,977 patients at UCSF had AMD. Associations between statins (HR = 0.7643, p= 0.0033), non-hispanic black ethnicity (HR = 0.5043, p= 0.0035), and obesity (HR = 1.9602, p < 0.0001) on AMD onset were confirmed. Conclusions: In both cohorts, statin use and non-hispanic black ethnicity are associated with a later AMD onset, while obesity with an earlier AMD onset.
MeSH term(s)	Humans ; Female ; Hydroxymethylglutaryl-CoA Reductase Inhibitors ; Retrospective Studies ; Age of Onset ; Fluoxetine ; Risk Factors ; Macular Degeneration ; Obesity
Chemical Substances	Hydroxymethylglutaryl-CoA Reductase Inhibitors ; Fluoxetine (01K63SUP8D)
Language	English
Publishing date	2023-03-14
Publishing country	Germany
Document type	Journal Article
ZDB-ID	8435-9
ISSN	1435-702X ; 0721-832X
ISSN (online)	1435-702X
ISSN	0721-832X
DOI	10.1007/s00417-023-06017-0
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Article ; Online: Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers.

Rudas, Akos / Chiang, Jeffrey N / Corradetti, Giulia / Rakocz, Nadav / Avram, Oren / Halperin, Eran / Sadda, Srinivas R

PLOS digital health

2023 Volume 2, Issue 2, Page(s) e0000106

Abstract: Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative ... ...

Abstract	Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.
Language	English
Publishing date	2023-02-15
Publishing country	United States
Document type	Journal Article
ISSN	2767-3170
ISSN (online)	2767-3170
DOI	10.1371/journal.pdig.0000106
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Book ; Online: Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME)

Lee, Simon A. / Jain, Sujay / Chen, Alex / Biswas, Arabdha / Fang, Jennifer / Rudas, Akos / Chiang, Jeffrey N.

2024

Abstract: In this work, we introduce Multiple Embedding Model for EHR (MEME), an approach that views Electronic Health Records (EHR) as multimodal data. This approach incorporates "pseudo-notes", textual representations of tabular EHR concepts such as diagnoses ... ...

Abstract	In this work, we introduce Multiple Embedding Model for EHR (MEME), an approach that views Electronic Health Records (EHR) as multimodal data. This approach incorporates "pseudo-notes", textual representations of tabular EHR concepts such as diagnoses and medications, and allows us to effectively employ Large Language Models (LLMs) for EHR representation. This framework also adopts a multimodal approach, embedding each EHR modality separately. We demonstrate the effectiveness of MEME by applying it to several tasks within the Emergency Department across multiple hospital systems. Our findings show that MEME surpasses the performance of both single modality embedding methods and traditional machine learning approaches. However, we also observe notable limitations in generalizability across hospital institutions for all tested models.
Keywords	Computer Science - Computation and Language
Subject code	006
Publishing date	2024-01-31
Publishing country	us
Document type	Book ; Online
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Article ; Online: Distributed Code for Semantic Relations Predicts Neural Similarity during Analogical Reasoning.

Chiang, Jeffrey N / Peng, Yujia / Lu, Hongjing / Holyoak, Keith J / Monti, Martin M

Journal of cognitive neuroscience

2020 Volume 33, Issue 3, Page(s) 377–389

Abstract: The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coarsely coded as links in a semantic network or finely coded as distributed patterns over some ... ...

Abstract	The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coarsely coded as links in a semantic network or finely coded as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations are yet to be empirically established. Using sequential presentation of verbal analogies, we compared neural activities in making analogy judgments with predictions derived from alternative computational models of relational dissimilarity to adjudicate among rival accounts of how semantic relations are coded and compared in the brain. We found that a frontoparietal network encodes the three relation types included in the design. A computational model based on semantic relations coded as distributed representations over a pool of abstract relations predicted neural activities for individual relations within the left superior parietal cortex and for second-order comparisons of relations within a broader left-lateralized network.
MeSH term(s)	Brain Mapping ; Cognition ; Humans ; Parietal Lobe ; Problem Solving ; Semantics
Language	English
Publishing date	2020-08-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1007410-7
ISSN	1530-8898 ; 0898-929X ; 1096-8857
ISSN (online)	1530-8898
ISSN	0898-929X ; 1096-8857
DOI	10.1162/jocn_a_01620
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Article ; Online: Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.

Tajbakhsh, Nima / Jeyaseelan, Laura / Li, Qian / Chiang, Jeffrey N / Wu, Zhihao / Ding, Xiaowei

Medical image analysis

2020 Volume 63, Page(s) 101693

Abstract: The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, ...

Abstract	The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions. We hope this survey article increases the community awareness of the techniques that are available to handle imperfect medical image segmentation datasets.
MeSH term(s)	Deep Learning ; Diagnostic Imaging ; Humans ; Neural Networks, Computer
Language	English
Publishing date	2020-04-03
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2020.101693
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Article ; Online: Author Correction: Neural complexity is a common denominator of human consciousness across diverse regimes of cortical dynamics.

Frohlich, Joel / Chiang, Jeffrey N / Mediano, Pedro A M / Nespeca, Mark / Saravanapandian, Vidya / Toker, Daniel / Dell'Italia, John / Hipp, Joerg F / Jeste, Shafali S / Chu, Catherine J / Bird, Lynne M / Monti, Martin M

Communications biology

2023 Volume 6, Issue 1, Page(s) 41

Language	English
Publishing date	2023-01-13
Publishing country	England
Document type	Published Erratum
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-023-04460-7
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