Evidence map›Paper›PMID 40160217›Full record

ArticleVascular health and risk management2025

Real-World Analysis of Clinical Characteristics, Treatment Outcomes, and the Novel Predictive Model for Patients with Thrombotic Thrombocytopenic Purpura (TTP) and TTP-Like Syndrome.

Mengya Lv, Xing Hu, Lijun Zhu, Hui Xu, Erling Chen, Na Zhao, Juan Tong, Changcheng Zheng

Abstract readComparative Study
In one paragraph

Article in Vascular health and risk management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

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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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Mengya LvDepartment of Hematology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, People's Republic of China.
Xing HuDepartment of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China.
Lijun ZhuDepartment of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China.
Hui XuDepartment of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China.ORCID 0000-0001-8499-6551
Erling ChenDepartment of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China.
Na ZhaoDepartment of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China.
Juan TongDepartment of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China.ORCID 0000-0002-1567-3625
Changcheng ZhengDepartment of Hematology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, People's Republic of China.ORCID 0000-0002-1976-6415

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: It is crucial to differentiate critically ill patients exhibiting thrombocytopenia and hemolytic anemia alongside organ damage to enable rapid identification of thrombotic thrombocytopenic purpura (TTP) and TTP-like syndrome, which allows for targeted emergency interventions such as plasma exchange. Patients and Methods: This study retrospectively analyzed clinical data from patients with TTP and TTP-like syndrome to further elucidate the potential differences between these conditions. We also established a new predictive model to facilitate early identification and differentiation between TTP and TTP-like syndrome. A new predictive model for diagnosing TTP was developed using five key indicators: reticulocyte percentage, platelet count, schistocyte percentage, LDH/ULN, and indirect bilirubin. The performance of this new model was compared with the traditional PLASMIC score by evaluating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Thirty-five patients were diagnosed with TTP and 42 were diagnosed with TTP-like syndrome. TTP is most commonly associated with autoimmune diseases (n=13, 37.14%), while TTP-like syndrome frequently arises from infections (n=23, 54.76%). The ADAMTS13 activity was significantly lower in the TTP group than in the TTP-like syndrome group (Mean 8.30% vs 46.12%). TTP-like syndrome patients had significantly higher levels of inflammatory markers. The new predictive model was developed for TTP with a predictive ability of 96.9%. Overall, 16 patients (20.77%) died, including 3 (8.57%) in the TTP group and 13 (30.95%) in the TTP-like syndrome group. Kaplan-Meier survival analysis showed significant differences in survival between TTP and TTP-like syndrome patients, with a 180-day overall survival (OS) rate of 90.6% vs 60.9% (p=0.009); and plasma exchange improved 180-day OS rate in the TTP group compared to the TTP-like syndrome group (90.6% vs 65.6%) (p=0.054). Conclusion: This study demonstrates that TTP and TTP-like syndrome are two distinct types of diseases. The new predictive model has shown good efficacy in distinguishing TTP and TTP-like syndrome. Plasma exchange significantly improves survival in TTP patients; however, its effect on TTP-like syndrome is minimal.

Indexed as

Decision Support TechniquesPlasma ExchangePurpura, Thrombotic ThrombocytopenicADAMTS13 ProteinAdultAgedBiomarkersDiagnosis, DifferentialFemaleHumansMaleMiddle AgedPlatelet CountPredictive Value of TestsRetrospective StudiesSyndromeADAMTS13 ProteinADAMTS13 protein, humanBiomarkersplasma exchangesystemic inflammatory responsethrombotic thrombocytopenic purpuraTTPTTP-like syndromeTTP predictive model

Identifiers

PMID40160217
PMCPMC11952049

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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.