Evidence map›Paper›PMID 35372844›Full record

ArticleCritical care explorations2022

Methods for Phenotyping Adult Patients in Sepsis and Septic Shock: A Scoping Review.

Han Li, Asena Markal, Jeremy A Balch, Tyler J Loftus, Philip A Efron, Tezcan Ozrazgat-Baslanti, Azra Bihorac

Abstract readScoping Review
In one paragraph

Article in Critical care explorations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Article
  6. SepsisJournal of medical microbiology · 2025
    Review
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
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

7 authors.

Han LiDivision of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida College of Medicine, Gainesville, FL.
Asena MarkalUniversity of Florida College of Medicine, Gainesville, FL.
Jeremy A BalchDepartment of Surgery, University of Florida College of Medicine, Gainesville, FL.
Tyler J LoftusDepartment of Surgery, University of Florida College of Medicine, Gainesville, FL.
Philip A EfronDepartment of Surgery, University of Florida College of Medicine, Gainesville, FL.
Tezcan Ozrazgat-BaslantiDivision of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida College of Medicine, Gainesville, FL.
Azra BihoracDivision of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida College of Medicine, Gainesville, FL.

Funding

University of Florida Older Americans Independence Center (OAIC)P30AG028740 · NIA · UNIVERSITY OF FLORIDA · PI Karyn A Esser, Todd Manini · 2007 to 2026
$22.8M
Integrating data, algorithms and clinical reasoning for surgical risk assessmentR01GM110240 · NIGMS · UNIVERSITY OF FLORIDA · PI BIHORAC, AZRA, RASHIDI, PARISA · 2016 to 2025
$5.1M
(MEnD-AKI) Multicenter Implementation of an Electronic Decision Support System for Drug-associated AKIR01DK121730 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BIHORAC, AZRA, KANE-GILL, SANDRA L · 2021 to 2025
$3.2M
ADAPT: Autonomous Delirium Monitoring and Adaptive PreventionR01NS120924 · NINDS · UNIVERSITY OF FLORIDA · PI BIHORAC, AZRA, RASHIDI, PARISA · 2021 to 2025
$2.9M
Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-makingR01EB029699 · NIBIB · UNIVERSITY OF FLORIDA · PI BIHORAC, AZRA, RASHIDI, PARISA · 2021 to 2024
$2.4M
STUDENTS IN HEALTH PROFESSIONAL SCHOOLST35HL007489 · NHLBI · UNIVERSITY OF FLORIDA · PI Scott A Berceli, Robert Maile · 1985 to 2026
$2.4M
The circadian clock protein BMAL and post-translational regulation of ENaC in the kidneyR01DK123078 · NIDDK · UNIVERSITY OF FLORIDA · PI ALLI, ABDEL AYUBE · 2020 to 2024
$1.7M
Aligning Patient Acuity with Intensity of Care after SurgeryK23GM140268 · NIGMS · UNIVERSITY OF FLORIDA · PI LOFTUS, TYLER J · 2020 to 2023
$619k
Development and Validation of Computational Algorithms to Assess Kidney Health in Electronic Health RecordsK01DK120784 · NIDDK · UNIVERSITY OF FLORIDA · PI OZRAZGAT BASLANTI, TEZCAN · 2020 to 2023
$580k
Autonomous Pain Recognition in Non-Verbal and Critically Ill PatientsR21EB027344 · NIBIB · UNIVERSITY OF FLORIDA · PI RASHIDI, PARISA · 2019 to 2022
$577k
NHLBI NIH HHS T35 HL007489NIA NIH HHS P30 AG028740NIBIB NIH HHS R01 EB029699NIBIB NIH HHS R21 EB027344NIDDK NIH HHS K01 DK120784NIDDK NIH HHS R01 DK121730NIDDK NIH HHS R01 DK123078NIGMS NIH HHS K23 GM140268NIGMS NIH HHS R01 GM110240NINDS NIH HHS R01 NS120924
6 · The paper itself

Abstract

Despite its heterogeneous phenotypes, sepsis or life-threatening dysfunction in response to infection is often treated empirically. Identifying patient subgroups with unique pathophysiology and treatment response is critical to the advancement of sepsis care. However, phenotyping methods and results are as heterogeneous as the disease itself. This scoping review evaluates the prognostic capabilities and treatment implications of adult sepsis and septic shock phenotyping methods. DATA SOURCES: Medline and Embase. STUDY SELECTION: We included clinical studies that described sepsis or septic shock and used any clustering method to identify sepsis phenotypes. We excluded conference abstracts, literature reviews, comments, letters to the editor, and in vitro studies. We assessed study quality using a validated risk of bias tool for observational cohort and cross-sectional studies. DATA EXTRACTION: We extracted population, methodology, validation, and phenotyping characteristics from 17 studies. DATA SYNTHESIS: Sepsis phenotyping methods most frequently grouped patients based on the degree of inflammatory response and coagulopathy using clinical, nongenomic variables. Five articles clustered patients based on genomic or transcriptomic data. Seven articles generated patient subgroups with differential response to sepsis treatments. Cluster clinical characteristics and their associations with mortality and treatment response were heterogeneous across studies, and validity was evaluated in nine of 17 articles, hindering pooled analysis of results and derivation of universal truths regarding sepsis phenotypes, their prognostic capabilities, and their associations with treatment response.

conclusionsSepsis phenotyping methods can identify high-risk patients and those with high probability of responding well to targeted treatments. Research quality was fair, but achieving generalizability and clinical impact of sepsis phenotyping will require external validation and direct comparison with alternative approaches.

Indexed as

cluster analysesinfectionsmachine learningrisk assessmentsepsisseptic shock

Identifiers

PMID35372844
PMCPMC8970078

What OpenQuestion holds

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Registered trials

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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.