Evidence map›Paper›PMID 42143248›Full record

ArticleBMC infectious diseases2026

Comorbidity profiles and their impact on treatment outcomes in elderly patients with pulmonary tuberculosis: a single-center retrospective cohort study.

Yinping Feng, Zunjing Zhang, Jing Guo, Shuirong Luo, Ying Zhang, Zhongda Liu

Abstract read
In one paragraph

Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Yinping FengDepartment of Tuberculosis, Lishui tuberculosis Clinical Medical Research Center, Lishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang University of Traditional Chinese Medicine, Lishui, 323000, China.
Zunjing ZhangDepartment of Tuberculosis, Lishui tuberculosis Clinical Medical Research Center, Lishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang University of Traditional Chinese Medicine, Lishui, 323000, China.
Jing GuoDepartment of Tuberculosis, Lishui tuberculosis Clinical Medical Research Center, Lishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang University of Traditional Chinese Medicine, Lishui, 323000, China.
Shuirong LuoDepartment of Tuberculosis, Lishui tuberculosis Clinical Medical Research Center, Lishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang University of Traditional Chinese Medicine, Lishui, 323000, China.
Ying ZhangState Key Laboratory of Infectious Diseases Diagnosis and Treatment, National Infectious Disease Clinical Research Center, and Collaborative Innovation Center for Infectious Disease Diagnosis and Treatment, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310003, China.
Zhongda Liu *Department of Tuberculosis, Lishui tuberculosis Clinical Medical Research Center, Lishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang University of Traditional Chinese Medicine, Lishui, 323000, China. liu_zd0578@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to characterize comorbidity distribution patterns and assess their clinical impact on therapeutic effectiveness in elderly pulmonary tuberculosis (PTB) patients, establishing an evidence base for risk-stratified clinical decision-making.

methodsA retrospective cohort study was conducted among 1,340 hospitalized PTB patients (aged ≥ 60 years) from January 2020 to January 2024. Demographic characteristics, comorbidity data, and treatment outcomes were extracted from electronic medical records. Patients were categorized into treatment success (n = 1,105) and adverse outcome (n = 235) groups according to WHO criteria. Propensity score matching (PSM) was employed to balance baseline confounders, followed by multivariate logistic regression to evaluate associations between comorbidities and adverse outcomes.

resultsComorbidities were prevalent in 81.64% (1,094/1,340) of patients, with stratification as follows: single comorbidity (30.67%, 411), dyad (26.64%, 357), triad (18.13%, 243), and complex multimorbidity (≥4 conditions: 6.19%, 83). The most frequent comorbidities were chronic heart diseases (31.12%), chronic lung diseases (27.84%), and hypertension (26.49%), followed by diabetes mellitus(20.30%) and psychiatric disorders (15.07%). The adverse outcome rate was 17.54% (235/1,340), comprising 98 treatment failures (7.31%), 89 deaths (6.64%), and 48 treatment terminations (3.58%).Multivariate analysis identified the following independent risk factors: diabetes mellitus(adjusted odds ratio [aOR]=2.73, 95% confidence interval [CI]:1.91-3.90), chronic kidney diseases (aOR = 6.31, 95% CI:4.00-9.96), active malignancy (aOR = 3.27, 95% CI:2.07-5.14), and multimorbidity (≥3 comorbidities; aOR = 1.71, 95% CI:1.20-2.46) (all p < 0.05).

conclusionsElderly PTB patients exhibit a high comorbidity burden. Diabetes mellitus, chronic kidney diseases, active malignancy, and multimorbidity significantly increase the risk of adverse treatment outcomes. These findings underscore the necessity for multidisciplinary collaborative management models and early comorbidity screening to optimize clinical interventions. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Antitubercular AgentsTuberculosis, PulmonaryAgedAged, 80 and overComorbidityFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsTreatment OutcomeAntitubercular AgentsComorbidityElderlyMultimorbidityPropensity score matchingPulmonary tuberculosisRisk factors

Identifiers

PMID42143248
PMCPMC13360883

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.