Dataset Information

Development and clinical validation of a miniaturized finger probe for bedside hemodynamic monitoring.

ABSTRACT: Our aim is to develop a blood pressure (BP) measurement technology that could be integrated into a finger-worn pulse oximeter, eliminating the need for a brachial cuff. We present a miniature cuffless tonometric finger probe system that uses the oscillometric method to measure BP. Our approach uses a motorized press that is used to apply pressure to the fingertip to measure BP. We verified the functionality of the device in a clinical trial (n = 43) resulting in systolic and diastolic pressures ((mean ± SD) mmHg) of (-3.5 ± 8.4) mmHg and (-4.0 ± 4.4) mmHg, respectively. Comparison was made with manual auscultation (n = 26) and automated cuff oscillometry (n = 18). In addition to BP, we demonstrated the ability of the device to assess arterial stiffness (n = 18) and detect atrial fibrillation (n = 6). We were able to introduce a sufficiently small device that could be used for convenient ambulatory measurements with minimal discomfort.

SUBMITTER: Panula T

PROVIDER: S-EPMC10665806 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Development and clinical validation of a miniaturized finger probe for bedside hemodynamic monitoring.

Panula Tuukka T Sirkiä Jukka-Pekka JP Koivisto Tero T Pänkäälä Mikko M Niiranen Teemu T Kantola Ilkka I Kaisti Matti M

iScience 20231024 11

Our aim is to develop a blood pressure (BP) measurement technology that could be integrated into a finger-worn pulse oximeter, eliminating the need for a brachial cuff. We present a miniature cuffless tonometric finger probe system that uses the oscillometric method to measure BP. Our approach uses a motorized press that is used to apply pressure to the fingertip to measure BP. We verified the functionality of the device in a clinical trial (n = 43) resulting in systolic and diastolic pressures ...[more]

PMID: 38026187

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Project description:ObjectivesWe tested the hypothesis that routine monitoring data could describe a detailed and distinct pathophysiologic phenotype of impending hypoglycemia in adult ICU patients.DesignRetrospective analysis leading to model development and validation.SettingAll ICU admissions wherein patients received insulin therapy during a 4-year period at the University of Virginia Medical Center. Each ICU was equipped with continuous physiologic monitoring systems whose signals were archived in an electronic data warehouse along with the entire medical record.PatientsEleven thousand eight hundred forty-seven ICU patient admissions.InterventionsThe primary outcome was hypoglycemia, defined as any episode of blood glucose less than 70 mg/dL where 50% dextrose injection was administered within 1 hour. We used 61 physiologic markers (including vital signs, laboratory values, demographics, and continuous cardiorespiratory monitoring variables) to inform the model.Measurements and main resultsOur dataset consisted of 11,847 ICU patient admissions, 721 (6.1%) of which had one or more hypoglycemic episodes. Multivariable logistic regression analysis revealed a pathophysiologic signature of 41 independent variables that best characterized ICU hypoglycemia. The final model had a cross-validated area under the receiver operating characteristic curve of 0.83 (95% CI, 0.78-0.87) for prediction of impending ICU hypoglycemia. We externally validated the model in the Medical Information Mart for Intensive Care III critical care dataset, where it also demonstrated good performance with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77-0.81).ConclusionsWe used data from a large number of critically ill inpatients to develop and externally validate a predictive model of impending ICU hypoglycemia. Future steps include incorporating this model into a clinical decision support system and testing its effects in a multicenter randomized controlled clinical trial.

Project description:ImportanceMassive transfusion is essential to prevent complications during uncontrolled intraoperative hemorrhage. As massive transfusion requires time for blood product preparation and additional medical personnel for a team-based approach, early prediction of massive transfusion is crucial for appropriate management.ObjectiveTo evaluate a real-time prediction model for massive transfusion during surgery based on the incorporation of preoperative data and intraoperative hemodynamic monitoring data.Design, setting, and participantsThis prognostic study used data sets from patients who underwent surgery with invasive blood pressure monitoring at Seoul National University Hospital (SNUH) from 2016 to 2019 and Boramae Medical Center (BMC) from 2020 to 2021. SNUH represented the development and internal validation data sets (n = 17 986 patients), and BMC represented the external validation data sets (n = 494 patients). Data were analyzed from November 2020 to December 2021.ExposuresA deep learning-based real-time prediction model for massive transfusion.Main outcomes and measuresMassive transfusion was defined as a transfusion of 3 or more units of red blood cells over an hour. A preoperative prediction model for massive transfusion was developed using preoperative variables. Subsequently, a real-time prediction model using preoperative and intraoperative parameters was constructed to predict massive transfusion 10 minutes in advance. A prediction model, the massive transfusion index, calculated the risk of massive transfusion in real time.ResultsAmong 17 986 patients at SNUH (mean [SD] age, 58.65 [14.81] years; 9036 [50.2%] female), 416 patients (2.3%) underwent massive transfusion during the operation (mean [SD] duration of operation, 170.99 [105.03] minutes). The real-time prediction model constructed with the use of preoperative and intraoperative parameters significantly outperformed the preoperative prediction model (area under the receiver characteristic curve [AUROC], 0.972; 95% CI, 0.968-0.976 vs AUROC, 0.824; 95% CI, 0.813-0.834 in the SNUH internal validation data set; P < .001). Patients with the highest massive transfusion index (ie, >90th percentile) had a 47.5-fold increased risk for a massive transfusion compared with those with a lower massive transfusion index (ie, <80th percentile). The real-time prediction model also showed excellent performance in the external validation data set (AUROC of 0.943 [95% CI, 0.919-0.961] in BMC).Conclusions and relevanceThe findings of this prognostic study suggest that the real-time prediction model for massive transfusion showed high accuracy of prediction performance, enabling early intervention for high-risk patients. It suggests strong confidence in artificial intelligence-assisted clinical decision support systems in the operating field.

Project description:The introduction of imatinib, an oral tyrosine kinase inhibitor, as first-line standard therapy in patients with unresectable, metastatic, or recurrent gastro-intestinal stromal tumor (GIST), strongly improved their treatment outcomes. However, therapeutic drug monitoring (TDM) is recommended for this drug due to the large inter-individual variability in plasma concentration when standard dose is administered. A Cmin higher than 760 ng/mL was associated with a longer progression free survival. Thus, a LC-MS/MS method has been developed and fully validated to quantify imatinib and its active metabolite, norimatinib, in finger-prick dried blood spot (DBS). The influence of hematocrit, sample homogeneity, and spot size and the correlation between finger-prick and venous DBS measurements were also assessed. The method showed a good linearity (R2 > 0,996) between 50-7500 ng/mL for imatinib and 10-1500 ng/mL for norimatinib. Analytes were extracted from DBS samples by simply adding to 3 mm-discs 150 μL of acidified methanol containing IMA-D8. The collected extract was then injected on a LC Nexera system in-house configured for the on-line cleanup, coupled with an API-4000 QT. The chromatographic separation was conducted on a Synergi Fusion-RP column (4 μm, 2x50 mm) while the trapping column was a POROS R1/20 (20 μm, 2x30 mm). The total run time was 8.5 min. DBSs stored at room temperature in plastic envelopes containing a silica-gel drying bag were stable up to 16 months. The proposed method was applied to 67 clinical samples, showing a good correlation between patients' finger-prick DBS and plasma concentrations, measured by the reference LC-MS/MS method, internally validated. Imatinib and norimatinib concentrations found in finger-prick DBS were adjusted by hematocrit or by an experimental correction factor to estimate the corresponding plasma measurements. At the best of our knowledge, the proposed LC-MS/MS method is the first analytical assay to measure imatinib and norimatinib in DBS samples.

Dataset Information

Development and clinical validation of a miniaturized finger probe for bedside hemodynamic monitoring.

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Development and clinical validation of a miniaturized finger probe for bedside hemodynamic monitoring.

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