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Record W4323782468 · doi:10.4103/aer.aer_119_22

Comparison of hemodynamic stability with continuous noninvasive blood pressure monitoring and intermittent oscillometric blood pressure monitoring in hospitalized patients: A systematic review and meta-analysis

2023· review· en· W4323782468 on OpenAlexaff
Yamini Subramani, Manikandan Rajarathinam, KarinVan Veldhoven, Nikhil Taneja, Jill Querney, Nida Fatima, Mahesh Nagappa

Bibliographic record

VenueAnesthesia Essays and Researches · 2023
Typereview
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsSt Joseph's Health CareCanadian Medical Protective AssociationLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineHemodynamicsBlood pressureCardiologyMeta-analysisInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Background: Continuous noninvasive monitoring (NIM) of arterial blood pressure (BP) is a noninvasive technique, which can display real-time BP and therefore faster in identifying hemodynamic changes than intermittent oscillometric BP monitoring in patients. Objectives: The objective of this study was to assess the advantages of continuous NIM of arterial BP compared to standard intermittent oscillometric BP monitoring on the hemodynamic stability in hospital clinical care settings. The primary objective of this systematic review was to compare the incidence of hypotension and hypertension with both the above monitoring techniques. Design: This was a systematic review and meta-analysis. Methods: The randomized trials and observational studies that compared the hemodynamic outcomes with continuous NIM and intermittent oscillometric BP monitoring in all types of hospital settings were searched from multiple databases. The data were extracted for synthesis from the eligible studies and the results were summarized. The RoB 2 and Risk of Bias in Non-randomized Studies-of Interventions tools were used to assess the risk of bias for randomized trials and observational studies, respectively. A meta-analysis was performed using a random effects model for the incidence of hypotension and certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluations approach. A qualitative review was done for all the other outcomes. Results: Six randomized controlled trials (RCTs) and six observational studies were included. All the studies used a finger cuff BP monitoring device for continuous BP monitoring. The meta-analysis for the incidence of hypotension was done with data from 6 RCTs in a total of 686 patients. The pooled odds ratio from the random effects model was 0.25 (0.10, 0.62), P = 0.003, showing a significant reduction in the incidence of hypotension with continuous noninvasive BP (NIBP) monitoring compared to intermittent oscillometric NIBP monitoring. The incidence of hypertension was similar in the two groups except during emergence from anesthesia. The duration of hypotension and BP outside of normal range in the intraoperative period was reduced to less than half with continuous NIBP monitoring when compared to the intermittent oscillometric NIBP monitoring. The time to detect hypotension was at least 1.5 min earlier with the continuous NIBP monitoring versus intermittent oscillometric NIBP monitoring that was cycled every 3 min during cesarean sections (P < 0.001) and 8 min earlier in ED patients in whom NIBP monitoring was cycled every 15 min. Conclusion: There is moderate quality of evidence to suggest that the incidence of hypotension with continuous NIBP monitoring is less versus intermittent oscillometric BP monitoring based on the data from surgical patients. The duration of hemodynamic instability and time to detect hypotension may be shorter with continuous NIBP monitoring indicating a potential for improving quality of patient care and safety in appropriate clinical settings. The findings in this review thereby warrant further research with studies of large size and better methodological quality in diverse hospital settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0110.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.097
GPT teacher head0.377
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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