Evidence map›Paper›PMID 34256743›Full record

ArticleBMC pulmonary medicine2021

Longitudinal trajectories of pneumonia lesions and lymphocyte counts associated with disease severity among convalescent COVID-19 patients: a group-based multi-trajectory analysis.

Nannan Shi, Chao Huang, Qi Zhang, Chunzi Shi, Fengjun Liu, Fengxiang Song, Qinguo Hou, Jie Shen, Fei Shan, Xiaoming Su and 4 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pulmonary medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06538441 (Dynamic Pattern of Etiology, Immunoinflammatory Factors and Their Association With Prognosis of Severe Pneumonia), which is not on this map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

NCT06538441 active not recruitingnot on this mapstarted 2024, after this paper: background citation

Dynamic Pattern of Etiology, Immunoinflammatory Factors and Their Association With Prognosis of Severe Pneumonia: a Longitudinal Observational Study

TypeobservationalSponsorShanghai General Hospital, ChinaRan2024 to 2025Enrolled600ConditionsSevere Pneumonia
3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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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

14 authors.

Nannan Shi *Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
Chao Huang *Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, 200051, China.
Qi ZhangShanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, 200051, China. qizhang.fdu@gmail.com.
Chunzi ShiDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
Fengjun LiuDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
Fengxiang SongDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
Qinguo HouDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
Jie ShenDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
Fei ShanDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
Xiaoming SuInstitute of Healthcare Research, Yizhi, Shanghai, China.
Cheng LiuShanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, 200051, China.
Zhiyong ZhangFudan University, Shanghai, China.
Lei ShiShanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, 200051, China.
Yuxin ShiDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China. shiyx828288@163.com.

Funding

Science and Technology Commission of Shanghai Municipality No. 20441900600
6 · The paper itself

Abstract

backgroundTo explore the long-term trajectories considering pneumonia volumes and lymphocyte counts with individual data in COVID-19.

methodsA cohort of 257 convalescent COVID-19 patients (131 male and 126 females) were included. Group-based multi-trajectory modelling was applied to identify different trajectories in terms of pneumonia lesion percentage and lymphocyte counts covering the time from onset to post-discharge follow-ups. We studied the basic characteristics and disease severity associated with the trajectories.

resultsWe characterised four distinct trajectory subgroups. (1) Group 1 (13.9%), pneumonia increased until a peak lesion percentage of 1.9% (IQR 0.7-4.4) before absorption. The slightly decreased lymphocyte rapidly recovered to the top half of the normal range. (2) Group 2 (44.7%), the peak lesion percentage was 7.2% (IQR 3.2-12.7). The abnormal lymphocyte count restored to normal soon. (3) Group 3 (26.0%), the peak lesion percentage reached 14.2% (IQR 8.5-19.8). The lymphocytes continuously dropped to 0.75 × 10

conclusionsOur findings provide new insights to understand the heterogeneous natural courses of COVID-19 patients and the associations of distinct trajectories with disease severity, which is essential to improve the early risk assessment, patient monitoring, and follow-up schedule.

Indexed as

ConvalescenceCOVID-19AdultFemaleHumansLymphocyte CountMaleMiddle AgedPneumonia, ViralRetrospective StudiesSeverity of Illness IndexClinical courseCOVID-19Group-based multi-trajectory modellingLymphocytePneumonia

Identifiers

PMID34256743
PMCPMC8276845

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