Evidence map›Paper›PMID 34372809›Full record

ArticleBMC nephrology2021

Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure.

Sheetal Chaudhuri, Hao Han, Caitlin Monaghan, John Larkin, Peter Waguespack, Brian Shulman, Zuwen Kuang, Srikanth Bellamkonda, Jane Brzozowski, Jeffrey Hymes and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC nephrology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.1field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2023
    Article
  5. Artificial intelligence and digital health for volume maintenance in hemodialysis patients.Hemodialysis international. International Symposium on Home Hemodialysis · 2022
    Review
  6. Article
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 4 institutions in 2 countries.

Sheetal ChaudhuriFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA. sheetal.chaudhuri@fmc-na.com.ORCID 0000-0002-8217-9520
Hao HanFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Caitlin MonaghanFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
John LarkinFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Peter WaguespackFresenius Medical Care North America, Waltham, MA, USA.
Brian ShulmanFresenius Medical Care North America, Waltham, MA, USA.
Zuwen KuangFresenius Medical Care North America, Waltham, MA, USA.
Srikanth BellamkondaFresenius Medical Care North America, Waltham, MA, USA.
Jane BrzozowskiFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Jeffrey HymesFresenius Medical Care North America, Waltham, MA, USA.
Mike BlackFresenius Medical Care North America, Waltham, MA, USA.
Peter KotankoRenal Research Institute, New York, NY, USA.
Jeroen P KoomanMaastricht University Medical Center, Maastricht, The Netherlands.
Franklin W MadduxFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Len UsvyatFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Fresenius Medical Care (United States) · USMaastricht University · NLMaastricht University Medical Centre · NLRenal Research Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ultrafiltration rate which in turn has a positive effect on intradialytic symptoms. It has been clinically challenging to identify changes RBV in real time to proactively intervene and reduce potential negative consequences of volume depletion. Leveraging advanced technologies to process large volumes of dialysis and machine data in real time and developing prediction models using machine learning (ML) is critical in identifying these signals.

methodWe conducted a proof-of-concept analysis to retrospectively assess near real-time dialysis treatment data from in-center patients in six clinics using Optical Sensing Device (OSD), during December 2018 to August 2019. The goal of this analysis was to use real-time OSD data to predict if a patient's relative blood volume (RBV) decreases at a rate of at least - 6.5 % per hour within the next 15 min during a dialysis treatment, based on 10-second windows of data in the previous 15 min. A dashboard application was constructed to demonstrate how reporting structures may be developed to alert clinicians in real time of at-risk cases. Data was derived from three sources: (1) OSDs, (2) hemodialysis machines, and (3) patient electronic health records.

resultsTreatment data from 616 in-center dialysis patients in the six clinics was curated into a big data store and fed into a Machine Learning (ML) model developed and deployed within the cloud. The threshold for classifying observations as positive or negative was set at 0.08. Precision for the model at this threshold was 0.33 and recall was 0.94. The area under the receiver operating curve (AUROC) for the ML model was 0.89 using test data.

conclusionsThe findings from our proof-of concept analysis demonstrate the design of a cloud-based framework that can be used for making real-time predictions of events during dialysis treatments. Making real-time predictions has the potential to assist clinicians at the point of care during hemodialysis.

Indexed as

Body Fluid CompartmentsHypotensionKidney Failure, ChronicMachine LearningMuscle CrampRenal DialysisVomitingBlood VolumeCloud ComputingEarly DiagnosisFemaleHumansMaleMiddle AgedPrognosisProof of Concept StudyEnd Stage Kidney DiseaseMachine LearningReal-Time Prediction

Identifiers

PMID34372809
PMCPMC8351092
OpenAlexW3188563795

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.