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1. Machine learning-enabled screening for aortic stenosis with handheld ultrasound

   https://doi.org/10.1093/ehjimp/qyaf051  

   Karmiy, Samuel; Huang, Zhe; Velury, Divya; Mai, Eileen; Li, Jing; Dehn, Monica M; Balian, Dikran R; Ramsingh, Davinder; Martin, John; Kantrowitz, Jacob; et al (May 2025, European Heart Journal - Imaging Methods and Practice)

   Aims: Neural network classifiers can detect aortic stenosis (AS) using limited cardiac ultrasound images. While networks perform very well using cart-based imaging, they have never been tested or fine-tuned for use with focused cardiac ultrasound (FoCUS) acquisitions obtained on handheld ultrasound devices. Methods and results: Prospective study performed at Tufts Medical Center. All patients ≥65 years of age referred for clinically indicated transthoracic echocardigraphy (TTE) were eligible for inclusion. Parasternal long axis and parasternal short axis imaging was acquired using a commercially available handheld ultrasound device. Our cart-based AS classifier (trained on ∼10 000 images) was tested on FoCUS imaging from 160 patients. The median age was 74 (inter-quartile range 69–80) years, 50% of patients were women. Thirty patients (18.8%) had some degree of AS. The area under the received operator curve (AUROC) of the cart-based model for detecting AS was 0.87 (95% CI 0.75–0.99) on the FoCUS test set. Last-layer fine-tuning on handheld data established a classifier with AUROC of 0.94 (0.91–0.97). AUROC during temporal external validation was 0.97 (95% CI 0.89–1.0). When performance of the fine-tuned AS classifier was modelled on potential screening environments (2 and 10% AS prevalence), the positive predictive value ranged from 0.72 (0.69–0.76) to 0.88 (0.81–0.97) and negative predictive value ranged from 0.94 (0.94–0.94) to 0.99 (0.99–0.99) respectively. Conclusion: Our cart-based machine-learning model for AS showed a drop in performance when tested on handheld ultrasound imaging collected by sonographers. Fine-tuning the AS classifier improved performance and demonstrates potential as a novel approach to detecting AS through automated interpretation of handheld imaging. 

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2. Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning

   https://doi.org/10.1093/ehjacc/zuae037  

   Hu, Yuxuan; Lui, Albert; Goldstein, Mark; Sudarshan, Mukund; Tinsay, Andrea; Tsui, Cindy; Maidman, Samuel D; Medamana, John; Jethani, Neil; Puli, Aahlad; et al (March 2024, European Heart Journal: Acute Cardiovascular Care)

   Abstract AimsMyocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in the cardiac intensive care unit (ICU). Methods and resultsWe developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict the onset of cardiogenic shock. We prepared a cardiac ICU dataset using the Medical Information Mart for Intensive Care-III database by annotating with physician-adjudicated outcomes. This dataset which consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database which was also annotated with physician-adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792–0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717–0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top 10 predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, blood urea nitrogen, systolic blood pressure, serum chloride, serum sodium, and arterial blood pH. ConclusionThe novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for millions of patients who suffer from myocardial infarction and heart failure. 

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3. Evaluation of Sepsis Prediction Models before Onset of Treatment

   https://doi.org/10.1056/AIoa2300032  

   Kamran, Fahad; Tjandra, Donna; Heiler, Andrew; Virzi, Jessica; Singh, Karandeep; King, Jessie E; Valley, Thomas S; Wiens, Jenna (February 2024, NEJM AI)

   BACKGROUND Timely interventions, such as antibiotics and intravenous fluids, have been associated with reduced mortality in patients with sepsis. Artificial intelligence (AI) models that accurately predict risk of sepsis onset could speed the delivery of these interventions. Although sepsis models generally aim to predict its onset, clinicians might recognize and treat sepsis before the sepsis definition is met. Predictions occurring after sepsis is clinically recognized (i.e., after treatment begins) may be of limited utility. Researchers have not previously investigated the accuracy of sepsis risk predictions that are made before treatment begins. Thus, we evaluate the discriminative performance of AI sepsis predictions made throughout a hospitalization relative to the time of treatment. METHODS We used a large retrospective inpatient cohort from the University of Michigan’s academic medical center (2018–2020) to evaluate the Epic sepsis model (ESM). The ability of the model to predict sepsis, both before sepsis criteria are met and before indications of treatment plans for sepsis, was evaluated in terms of the area under the receiver operating characteristic curve (AUROC). Indicators of a treatment plan were identified through electronic data capture and included the receipt of antibiotics, fluids, blood culture, and/or lactate measurement. The definition of sepsis was a composite of the Centers for Disease Control and Prevention’s surveillance criteria and the severe sepsis and septic shock management bundle definition. RESULTS The study included 77,582 hospitalizations. Sepsis occurred in 3766 hospitalizations (4.9%). ESM achieved an AUROC of 0.62 (95% confidence interval [CI], 0.61 to 0.63) when including predictions before sepsis criteria were met and in some cases, after clinical recognition. When excluding predictions after clinical recognition, the AUROC dropped to 0.47 (95% CI, 0.46 to 0.48). CONCLUSIONS We evaluate a sepsis risk prediction model to measure its ability to predict sepsis before clinical recognition. Our work has important implications for future work in model development and evaluation, with the goal of maximizing the clinical utility of these models. (Funded by Cisco Research and others.) 

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4. Home-based blood pressure monitoring using the AHOMKA care model: a longitudinal single-group pilot study in Accra

   https://doi.org/10.46829/hsijournal.2024.12.6.2.960-968  

   Edwin, F; Tettey, M; Ansah, E; Tetteh, J; Swaray, S; Vidzro, E; Kpodonu, J; Tang, A; Koomson, V (December 2024, Health Sciences Investigations Journal)

   Background: Hypertension is a major risk factor for cardiovascular disease and requires long-term health treatment and ongoing monitoring to the extent that traditional management approaches may be limited in providing. Adopting appropriate digital tools like mobile health technology (mHealth) could be an effective strategy for improving the control and management of this public health burden. This pilot studyevaluated the feasibility of the AHOMKA care model at two tertiary hospitals in Ghana. Outcome measures were changes in systolic (SBP) and diastolic (DBP) blood pressure model acceptance by patients and health care providers.Objective: This study sought to assess the overall pattern of home blood pressure self-monitoring among participants from two teaching hospitals in southern Ghana, using mHealth.Methods: Participants attending two (2) cardiology clinics were recruited for this mixed-method pilot study over a period of eight (8) weeks. Following a longitudinal single-group approach, we conducted structured interviews at the baseline and end-line and used exports of the AHOMKA mHealth application, in-depth interviews and focus group discussions with patients and healthcare providers. Repeated measuresanalysis of variance was adopted to assess differences in SBP and DBP between baseline and end line.Results: This pilot study involved 27 participants with a mean of 50.4 ± 11.0 years-approximately 1:1 male-female participation. Mean SBP decreased by 11.6 mm Hg (95% CI = 15.0 to -8.2), from an average of 138.6 mmHg at baseline to 126.2 mmHg at endline. Average DBP was also significantly reduced by 3.0 mmHg (95% CI = -5.5 to -0.5), from an average of 87.0 mmHg at baseline to 83.0 mmHg at endline. Patients and healthcare providers were satisfied and optimistic about the AHOMKA care model.Conclusion: The encouraging trend in BP outcomes and high response rate from this pilot study provides evidence for further investigation involving the assessment of the effectiveness of the AHOMKA care model while culturally adapting the model to the Ghanaian context. In the spectrum of hypertension interventions, AHOMKA has the potential to ease the burden on the public health system 

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5. Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis

   https://doi.org/10.1186/s12933-022-01496-7  

   Edward, Justin A.; Josey, Kevin; Bahn, Gideon; Caplan, Liron; Reusch, Jane E.; Reaven, Peter; Ghosh, Debashis; Raghavan, Sridharan (December 2022, Cardiovascular Diabetology)

   Abstract Background Evidence to guide type 2 diabetes treatment individualization is limited. We evaluated heterogeneous treatment effects (HTE) of intensive glycemic control in type 2 diabetes patients on major adverse cardiovascular events (MACE) in the Action to Control Cardiovascular Risk in Diabetes Study (ACCORD) and the Veterans Affairs Diabetes Trial (VADT). Methods Causal forests machine learning analysis was performed using pooled individual data from two randomized trials (n = 12,042) to identify HTE of intensive versus standard glycemic control on MACE in patients with type 2 diabetes. We used variable prioritization from causal forests to build a summary decision tree and examined the risk difference of MACE between treatment arms in the resulting subgroups. Results A summary decision tree used five variables (hemoglobin glycation index, estimated glomerular filtration rate, fasting glucose, age, and body mass index) to define eight subgroups in which risk differences of MACE ranged from − 5.1% (95% CI − 8.7, − 1.5) to 3.1% (95% CI 0.2, 6.0) (negative values represent lower MACE associated with intensive glycemic control). Intensive glycemic control was associated with lower MACE in pooled study data in subgroups with low (− 4.2% [95% CI − 8.1, − 1.0]), intermediate (− 5.1% [95% CI − 8.7, − 1.5]), and high (− 4.3% [95% CI − 7.7, − 1.0]) MACE rates with consistent directions of effect in ACCORD and VADT alone. Conclusions This data-driven analysis provides evidence supporting the diabetes treatment guideline recommendation of intensive glucose lowering in diabetes patients with low cardiovascular risk and additionally suggests potential benefits of intensive glycemic control in some individuals at higher cardiovascular risk. 

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