Abstract
Background
This 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.
Methods
A 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.
Results
Comorbidities 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).
Conclusions
Elderly 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.
Keywords: Pulmonary tuberculosis, Elderly, Comorbidity, Multimorbidity, Propensity score matching, Risk factors
Introduction
Tuberculosis (TB) remains a formidable global public health challenge. According to the 2024 World Health Organization (WHO) Global Tuberculosis Report, it is the world’s most lethal single infectious disease, causing an estimated 1.25 million deaths in 2023 [1]. The epidemiological landscape of TB is increasingly characterized by its disproportionate burden on elderly populations. As one of the 30 high-TB-burden countries, China faces escalating challenges in TB control, driven by rapid population aging, the rising prevalence of chronic diseases, and the spread of drug-resistant strains [2]. Elderly patients exhibit a dual burden of high incidence and high treatment failure rates, largely attributable to immunosenescence—an age-associated decline in immune function that impairs the response to Mycobacterium tuberculosis [3, 4]. The clinical management of PTB in older adults is further complicated by a high burden of comorbidity. The coexistence of multiple chronic conditions, or multimorbidity, is a hallmark of aging and profoundly exacerbates the complexity of TB care. Chronic comorbidities can disrupt TB prognosis through multifaceted mechanisms, including the creation of immune-suppressive microenvironments, metabolic dysregulation, altered pharmacokinetics of anti-TB drugs, and diminished multiorgan functional reserves [5, 6]. A meta-analysis underscored this risk, revealing that elderly TB patients with two or more comorbidities face a 1.8-fold increased risk of treatment failure and a 2.3-fold higher all-cause mortality [7]. Common risk factors such as diabetes, chronic kidney diseases, and malignancies are of particular concern due to their synergistic negative impact on TB outcomes.Despite this severe and growing disease burden, systematic research focusing on comprehensive comorbidity profiles in elderly PTB patients within the Chinese context remains scarce.Moreover, there is a paucity of studies that holistically examine multimorbidity patterns—rather than isolated comorbidities—specifically among hospitalized elderly PTB patients in China. Such patients often present with greater disease severity and a higher burden of concurrent chronic conditions, yet the clustering patterns of these comorbidities and their collective impact on treatment outcomes remain poorly characterized. Many existing studies have adopted a narrow focus, investigating single comorbidities in isolation rather than evaluating the entire spectrum of concurrent diseases and their clustering patterns. This fragmented approach limits our understanding of the collective impact of multimorbidity and hinders the development of integrated, patient-centered care strategies.To address this critical knowledge gap, we conducted a retrospective cohort study among hospitalized elderly PTB patients at a single center. The primary aim of this study was to systematically characterize the distribution patterns of comorbidities and to rigorously assess their impact on therapeutic effectiveness in elderly patients with PTB. By employing robust methodological tools such as propensity score matching to control for confounders and multivariate logistic regression to identify independent risk factors, this study seeks to establish an evidence base for risk-stratified clinical decision-making. The primary aim of this study was to systematically characterize the distribution patterns of comorbidities and to assess their impact on treatment outcomes in a cohort of hospitalized elderly PTB patients. Our specific objectives were: (1) to describe the prevalence and clustering of comorbidities; (2) to identify independent comorbidity-related risk factors for adverse treatment outcomes using propensity score matching and multivariate logistic regression.Our findings are intended to provide critical insights for optimizing clinical interventions and advancing towards the goals of the WHO’s End TB Strategy in this vulnerable population.
Materials and methods
Study design and participants
This retrospective cohort study consecutively enrolled elderly patients (aged ≥ 60 years) diagnosed with PTB who were hospitalized in the Department of Tuberculosis between January 2020 and January 2024. Clinical data, including demographics (age, sex, residence), physiological indices (body mass index [BMI]), behavioral characteristics (smoking history), comorbidities (ten predefined categories: chronic heart diseases, diabetes, etc.), and treatment outcomes, were extracted from electronic medical records. This study was conducted with approval from the Ethics Committee of Lishui Hospital of Traditional Chinese Medicine (approval number: KY-2022005). Due to the retrospective design and the use of anonymized data, the requirement for written informed consent was waived by the ethics committee.Only the first TB episode per patient was included. Repeat admissions for the same TB episode were excluded from the analysis to avoid duplication.
Research procedures
Inclusion and exclusion criteria
Inclusion Criteria:1)Confirmed diagnosis of pulmonary tuberculosis according to WHO guidelines; 2) Age 60 years or older; 3) Completion of a standard anti-tuberculosis treatment regimen or availability of documented reasons for treatment discontinuation (e.g., adverse drug reactions, death);4) Availability of complete clinical records.Exclusion Criteria:1)Insufficient clinical data;2) Interruption of treatment due to non-medical factors, including: Loss to follow-up;Transfer to another hospital without subsequent tracking;Voluntary withdrawal from the study.
Additional Notes:Treatment outcomes were assessed based on hospital records and follow-up data until treatment completion or the occurrence of a documented adverse event. Patients who died during hospitalization or discontinued treatment due to medical reasons were categorized as having an adverse outcome.The exclusion of patients lost to follow-up or transferred may introduce selection bias, which is acknowledged as a study limitation in the Discussion section.
The diagnosis of PTB was established according to WHO guidelines [8], and required confirmation by at least one of the following criteria:
Microbiological confirmation: a positive sputum smear for acid-fast bacilli and/or a positive culture for Mycobacterium tuberculosis;
Molecular confirmation: a positive GeneXpert MTB/RIF assay result;
Clinical-radiological diagnosis: presence of radiological features suggestive of active PTB on chest X-ray or CT, combined with clinical assessment and a decision by a treating physician to initiate a full course of anti-tuberculosis therapy, followed by observed radiographic improvement upon treatment.
Data collection and management
Structured data were extracted from the EMR system. Demographic variables collected included age (categorized as 60–69, 70–79, or ≥80 years), sex, residence (rural or urban), and BMI (grouped as <18.5 kg/m2, 18.5–24 kg/m2, or >24 kg/m2). Smoking history was defined as positive for a cumulative exposure of 10 pack-years or more. Comorbidities were assessed based on predefined categories, diagnoses were based on physician documentation supported by ICD-10 codes and/or relevant clinical or laboratory criteria,which included hypertension, diabetes mellitus, chronic gastrointestinal diseases, chronic lung diseases, chronic heart diseases, neurological diseases, chronic kidney diseases, active malignancy, rheumatic diseases, and psychiatric disorders.Active malignancy: Patients receiving any form of active cancer therapy (e.g., chemotherapy, radiotherapy, immunotherapy, or targeted therapy) within the 6 months prior to TB diagnosis or during anti-TB treatment; Patients with documented metastatic or recurrent disease; Patients with untreated cancer or those under palliative care with the primary goal of symptom control.Cancers considered in remission for more than 5 years without any signs of recurrence were not classified as active.Chronic kidney diseases was defined as eGFR < 60 mL/min/1.73 m2 or documented diagnosis. Mental disorders included depression, anxiety, or schizophrenia diagnosed by a psychiatrist or documented use of psychotropic medications.Chronic lung diseases (including chronic obstructive pulmonary disease [COPD], bronchiectasis, and silicosis, diagnosed based on clinical history, pulmonary function tests, and imaging findings).
The variable ‘≥3 comorbidities’ (used to define multimorbidity) was operationalized as the presence of three or more distinct conditions from the following list of ten predefined chronic disease categories: hypertension, diabetes mellitus, chronic gastrointestinal diseases, chronic lung diseases, chronic heart diseases, neurological diseases, chronic kidney diseases, active malignancy, rheumatic diseases, and psychiatric disorders. Each category was counted independently, regardless of potential pathophysiological overlap. For example, a patient with hypertension, chronic heart diseases, and diabetes mellitus was counted as having three comorbidities.”
Treatment outcomes were assessed from the initiation of anti-TB treatment until either the completion of the standard course (typically 6–9 months) or the occurrence of a documented adverse event (death, treatment failure, or termination). Follow-up information was primarily obtained from electronic medical records for the duration of hospitalization. For patients discharged before treatment completion, outcome data were supplemented by reviewing outpatient clinic records or through structured telephone interviews to ascertain treatment completion status. This comprehensive follow-up approach ensured that outcomes were assessed over the entire treatment period, not solely during the hospital stay.
Outcome classification
Treatment outcomes were categorized in accordance with the WHO Tuberculosis Operational Handbook [9]. A “treatment success” was defined as either cure or treatment completion. Conversely, “adverse outcomes” encompassed treatment failure, death, or termination of treatment.
To enhance methodological transparency, “treatment termination” was specifically defined as the premature discontinuation of the prescribed anti-tuberculosis therapy due to medical reasons. These reasons included: (1) severe adverse drug reactions (e.g., hepatotoxicity, severe rash, or gastrointestinal intolerance unresponsive to management); (2) rapid clinical deterioration of the patient’s overall condition, making continuation of therapy untenable; (3) a decision to transition the patient to palliative care in the context of a terminal illness (e.g., advanced malignancy); or (4) a physician-led decision based on a comprehensive risk-benefit assessment indicating that continuing treatment would cause more harm than benefit.
Statistical methods
All statistical analyses were performed using SPSS 26.0 software (version 26.0; IBM Corp).
Categorical data were expressed as frequency and percentage (%), with between-group differences assessed by chi-square (χ2) test.Continuous variables violating normality assumptions were summarized as median (M) with interquartile range (IQR; P25, P75), and between-group comparisons were conducted using the Mann-Whitney U test.Comorbidity patterns were visualized through UpSet plots generated by the R package UpSetR.Propensity score matching (PSM) was implemented via the SPSS extension tool to balance baseline characteristics between groups.Propensity scores were estimated using logistic regression with the following covariates: age, sex, BMI, smoking status, residential status, and extrapulmonary TB. A 1:1 nearest-neighbor matching algorithm without replacement and a caliper width of 0.2 standard deviations was applied. Balance was assessed using standardized mean differences (SMD < 0.1 considered balanced).To evaluate the impact of comorbidities on unfavorable treatment outcomes in elderly PTB patients, multivariate logistic regression analysis was performed.Robust standard errors were used to account for matched pairs in the regression models.Statistical significance was defined as a two-tailed p-value < 0.05.Variance inflation factors were examined to assess potential multicollinearity between multimorbidity (≥3comorbidities) and individual comorbidities included in the regression models.Collinearity between multimorbidity (≥3 comorbidities) and individual comorbidities was assessed using variance inflation factors (VIF); all VIFs were < 2, indicating no serious multicollinearity. Therefore, both multimorbidity and individual comorbidities were retained in the same model to evaluate their independent effects.Important TB severity indicators such as smear status, cavitation, and nutritional markers were not available for matching or adjustment; this is acknowledged as a limitation.
Propensity scores were estimated using logistic regression with covariates selected a priori as known confounders and consistently documented in the electronic medical records: age, sex, BMI, smoking status, residential status, and extrapulmonary tuberculosis. Important TB severity markers (e.g., sputum smear status, cavitation, nutritional biomarkers) were not available for matching or adjustment, which may leave residual confounding. This limitation is addressed in the Discussion.
Results
Patient enrollment
A total of 1,465 elderly patients hospitalized for PTB were enrolled in this study. Among them, 39 were excluded due to incomplete clinical data, 36 were lost to follow-up, 26 were transferred to other hospitals without further tracking, and 24 voluntarily withdrew from the study. Consequently, 1,340 patients were included in the final analysis. Based on the treatment outcomes, the cohort was categorized into two groups: 1,105 cases with successful treatment and 235 cases with unfavorable outcomes. Among the 235 adverse outcomes, there were 98 cases (7.31% of total cohort) of treatment failure, 89 deaths (6.64%), and 48 cases (3.58%) of treatment termination due to medical reasons. After propensity score matching, the 211 matched pairs included 85 treatment failures (20.14% of matched sample), 78 deaths (18.48%), and 48 terminations (11.37%).PSM was subsequently performed, yielding 211 well-balanced matched pairs. A detailed enrollment flowchart is presented in Fig. 1.
Fig. 1.
Flowchart of patient enrollment and propensity score matching. Of 1,465 elderly PTB patients initially enrolled, 1,340 were included after excluding those with incomplete data (n = 39), loss to follow-up (n = 36), transfer (n = 26), or voluntary withdrawal (n = 24). Among the included patients, 1,105 had treatment success and 235 had adverse outcomes. Propensity score matching (1:1 nearest-neighbor, caliper = 0.2 SD) adjusted for age, sex, BMI, smoking, residence, and extrapulmonary TB, yielding 211 well-balanced pairs
Figur 1. Flowchart of patient selection and propensity score matching. Numbers indicate patients at each stage; reasons for exclusion are shown. PSM generated 211 matched pairs.
General characteristics of elderly pulmonary tuberculosis patients
Among the 1,340 enrolled patients, 326 (24.33%) were female and 1,014 (75.67%) were male. Age distribution analysis revealed that 593 patients (44.25%) were aged 60–69 years, 438 (32.69%) were aged 70–79 years, and 309 (23.06%) were aged ≥ 80 years. A history of smoking was reported in 427 (31.87%) patients. The BMI distribution demonstrated that 440 patients (32.84%) had a BMI < 18.5 kg/m2, 789 (58.88%) fell within the 18.5–24 kg/m2 range, and 111 (8.28%) exceeded 24 kg/m2. Residential status indicated that 745 patients (55.60%) resided in rural areas, and 595 (44.40%) resided in urban regions. Extrapulmonary tuberculosis was present in 100 patients (7.46%), whereas comorbidities were documented in 1,094 patients (81.64%). The detailed demographic and clinical characteristics are summarized in Table 1. This gender distribution aligns with reported sex disparities among hospitalized elderly TB patients in China, potentially reflecting higher exposure risks, delayed healthcare-seeking behavior in males, and/or greater disease severity leading to hospitalization.
Table 1.
General characteristics of elderly pulmonary tuberculosis patients
| Variables | Numbers（n） | Proportion (%) |
|---|---|---|
| Gender | ||
| Female | 326 | 24.33 |
| Male | 1014 | 75.67 |
| Age（years） | ||
| 60–69 | 593 | 44.25 |
| 70–79 | 438 | 32.69 |
| ≥80 | 309 | 23.06 |
| Place of residence | ||
| Rural | 745 | 55.60 |
| Urban | 595 | 44.40 |
| Smoking | 427 | 31.87 |
| No | 913 | 68.13 |
| Yes | 427 | 31.87 |
| BMI(kg/m2) | ||
| <18.5 | 440 | 32.84 |
| 18.5–24 | 789 | 58.88 |
| >24 | 111 | 8.28 |
| Extrapulmonary Tuberculosis | ||
| No | 1240 | 92.54 |
| Yes | 100 | 7.46 |
| Comorbidity | ||
| No | 246 | 18.36 |
| Yes | 1094 | 81.64 |
Extrapulmonary TB was defined as TB involving organs other than the lungs (e.g., lymph nodes, pleura, or genitourinary system)
Comorbidity distribution among elderly pulmonary tuberculosis patients
Among the 1,340 patients, comorbidities were identified in 1,094 cases (81.64%). The distribution of comorbidity burden revealed that 411 patients (30.67%) had one comorbidity, 357 (26.64%) had two comorbidities, 243 (18.13%) had three comorbidities, and 83 (6.19%) with ≥4 comorbidities. The most prevalent comorbidities included chronic heart diseases (417 cases, 31.12%), chronic lung diseases (373 cases, 27.84%), hypertension (355 cases, 26.49%), diabetes mellitus (272 cases, 20.30%), psychiatric disorders (202 cases, 15.07%), chronic gastrointestinal diseases (163 cases, 12.16%), neurological diseases (136 cases, 10.15%), active malignancy (115 cases, 8.58%), chronic kidney diseases (112 cases, 8.36%), and rheumatic diseases (51 cases, 3.81%). Details are presented in Table 2.
Table 2.
Comorbidity distribution among elderly pulmonary tuberculosis patients
| Variables | Numbers（n） | Proportion (%) |
|---|---|---|
| Diabetes mellitus | 272 | 20.30 |
| Hypertension | 355 | 26.49 |
| Chronic gastrointestinal diseases | 163 | 12.16 |
| Chronic lung diseases | 373 | 27.84 |
| Chronic heart diseases | 417 | 31.12 |
| Neurological diseases | 136 | 10.15 |
| Chronic kidney diseases | 112 | 8.36 |
| Active malignancy | 115 | 8.58 |
| Rheumatic diseases | 51 | 3.81 |
| Psychiatric disorders | 202 | 15.07 |
| Number of comorbidities | ||
| 0 | 246 | 18.36 |
| 1 | 411 | 30.67 |
| 2 | 357 | 26.64 |
| 3 | 243 | 18.13 |
| ≥4 | 83 | 6.19 |
Chronic gastrointestinal diseases include peptic ulcer disease, chronic hepatitis and cirrhosis.Neuropsychiatric disorders encompass depression, anxiety and dementia diagnoses.Comorbidity counts reflect coexisting conditions at TB diagnosis.Percentages calculated relative to total cohort (N = 1,340)
Comorbidity patterns in elderly pulmonary tuberculosis patients
Among patients with a single comorbidity (n = 411), the most prevalent conditions were chronic heart diseases (99 cases, 24.09%), followed by chronic lung diseases (95 cases, 23.11%) and hypertension (49 cases, 11.92%). For patients with two comorbidities (n = 357), the most frequent combination was chronic heart and lung diseases (51 cases, 14.29%), followed by chronic heart diseases with hypertension (26 cases, 7.28%). The full comorbidity clustering patterns are shown in Fig. 2.
Fig. 2.
UpSet plot of comorbidity patterns in elderly pulmonary tuberculosis patients (n = 1,340). This figure visualizes the frequency and co-occurrence of the ten predefined comorbidities. Left horizontal bars show the total number of patients with each individual comorbidity (e.g., chronic heart diseases: n = 417). Lower matrix (dots connected by lines): each column represents a unique combination of comorbidities (intersection). A filled dot indicates the presence of a specific comorbidity in that combination. Vertical bars (intersection Size): display the number of patients sharing each specific combination. Only the top 20 most frequent intersections are shown for clarity. For example, the tallest vertical bar corresponds to the combination of chronic heart diseases + chronic lung diseases (n = 51), followed by chronic heart diseases + hypertension (n = 26). This plot reveals that cardiovascular and respiratory comorbidities frequently cluster together, while more complex multimorbidity (≥3 conditions) is less common but still present. Active malignancy was defined as ongoing cancer treatment within 6 months prior to or during anti-TB therapy
Figure 2. UpSet plot of comorbidity patterns. The horizontal bars (Set Size) show the frequency of each individual comorbidity. The vertical bars (Intersection Size) represent the number of patients with overlapping comorbidities. Only the top 20 intersections are displayed for clarity.
Comparison of patient groups before and after propensity score matching
Among 1,340 elderly patients with PTB, 235 (17.54%) experienced unfavorable treatment outcomes. A 1:1 nearest-neighbor PSM was performed using the following covariates: age, sex, BMI, smoking status, residential status, and the presence of extrapulmonary tuberculosis. This process generated 211 matched pairs; during matching, 24 patients from the adverse outcome group and 894 from the treatment success group were discarded due to lack of suitable matches. After matching, all standardized mean differences were < 0.1, confirming adequate balance. Post-matching analysis demonstrated no significant differences in baseline characteristics between the groups (all p > 0.05), confirming balanced comparability. Detailed pre- and post-matching data are presented in Table 3.
Table 3.
Comparison of patient groups before and after propensity score matching
| Variables | Unmatched Cohort | Matched Cohort | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Treatment Success (n = 1105) |
Adverse Outcome (n = 235) |
Statistic | P | SMD | Treatment Success (n = 211) |
Adverse Outcome (n = 211) |
Statistic | P | SMD | |
| Age, M (Q1, Q3) | 70 (65, 78) | 73 (66, 80) | 2.758 | 0.006 | 0.22 | 72.00 (66, 81) | 73.00 (66, 81) | 0.175 | 0.861 | 0.02 |
| BMI, M (Q1, Q3) | 19.63 (17.90, 21.45) | 19.63 (17.64, 21.33) | 1.052 | 0.293 | 0.08 | 19.10 (17.22, 21.23) | 19.63 (17.64, 21.30) | 1.058 | 0.290 | 0.08 |
| Gender, n (%) | 0.488 | 0.485 | 0.03 | 0.230 | 0.632 | 0.02 | ||||
| Female | 273 (24.71) | 53 (22.55) | 42 (19.91) | 46 (21.80) | ||||||
| male | 832 (75.29) | 182 (77.45) | 169 (80.09) | 165 (78.20) | ||||||
| Smoking, n (%) | 3.489 | 0.062 | 0.15 | 1.800 | 0.180 | 0.10 | ||||
| No | 765 (69.23) | 148 (62.98) | 147 (69.67) | 134 (63.51) | ||||||
| Yes | 340 (30.77) | 87 (37.02) | 64 (30.33) | 77 (36.49) | ||||||
| Place of residence, n (%) | 0.147 | 0.701 | 0.01 | 0.010 | 0.922 | ＜0.01 | ||||
| Rural | 617 (55.84) | 128 (54.47) | 117 (55.45) | 118 (55.92) | ||||||
| Urban | 488 (44.16) | 107 (45.53) | 94 (44.55) | 93 (44.08) | ||||||
| Extrapulmonary TB, n (%) | 147.725 | <0.001 | 0.98 | 0.000 | 1.000 | 0.00 | ||||
| No | 1067 (96.56) | 173 (73.62) | 173 (81.99) | 173 (81.99) | ||||||
| Yes | 38 (3.44) | 62 (26.38) | 38 (18.01) | 38 (18.01) | ||||||
p-values derived from Mann-Whitney U tests for continuous variables and χ2 tests for categorical variables.BMI: Body mass index (kg/m2).Extrapulmonary TB diagnosis required bacteriological/histopathological confirmation.All standardized differences < 0.1 post-matching (balance threshold).Matching ratio 1:1 with nearest neighbor algorithm (caliper = 0.2 SD)
Univariate analysis of unfavorable treatment outcomes in matched elderly pulmonary tuberculosis patients
Following PSM to balance baseline covariates, univariate analysis revealed statistically significant associations (p < 0.05) between unfavorable treatment outcomes and the following factors: diabetes mellitus, chronic kidney diseases, active malignancy, and the presence of ≥ 3 comorbidities (all p < 0.05). The complete details of these associations are presented in Table 4.
Table 4.
Univariate analysis of unfavorable treatment outcomes in matched elderly pulmonary tuberculosis patients
| Variables | Total (n = 422) |
Treatment Success (n = 211) |
Adverse Outcome (n = 211) |
Statistic | P |
|---|---|---|---|---|---|
| Diabetes mellitus, n(%) | 28.39 | <0.001 | |||
| No | 305 (72.27) | 177 (83.89) | 128 (60.66) | ||
| Yes | 117 (27.73) | 34 (16.11) | 83 (39.34) | ||
| Hypertension, n(%) | 0.98 | 0.322 | |||
| No | 309 (73.22) | 159 (75.36) | 150 (71.09) | ||
| Yes | 113 (26.78) | 52 (24.64) | 61 (28.91) | ||
| Chronic gastrointestinal diseases, n(%) | 2.27 | 0.132 | |||
| No | 372 (88.15) | 191 (90.52) | 181 (85.78) | ||
| Yes | 50 (11.85) | 20 (9.48) | 30 (14.22) | ||
| Chronic lung diseases, n(%) | 1.43 | 0.232 | |||
| No | 305 (72.27) | 158 (74.88) | 147 (69.67) | ||
| Yes | 117 (27.73) | 53 (25.12) | 64 (30.33) | ||
| Chronic heart diseases, n(%) | 1.83 | 0.177 | |||
| No | 285 (67.54) | 149 (70.62) | 136 (64.45) | ||
| Yes | 137 (32.46) | 62 (29.38) | 75 (35.55) | ||
| Neurological diseases, n(%) | 2.54 | 0.111 | |||
| No | 378 (89.57) | 194 (91.94) | 184 (87.20) | ||
| Yes | 44 (10.43) | 17 (8.06) | 27 (12.80) | ||
| Chronic kidney diseases, n(%) | 33.35 | <0.001 | |||
| No | 360 (85.31) | 201 (95.26) | 159 (75.36) | ||
| Yes | 62 (14.69) | 10 (4.74) | 52 (24.64) | ||
| Active malignancy, n(%) | 12.63 | <0.001 | |||
| No | 370 (87.68) | 197 (93.36) | 173 (81.99) | ||
| Yes | 52 (12.32) | 14 (6.64) | 38 (18.01) | ||
| Rheumatic diseases, n(%) | 4.73 | 0.030 | |||
| No | 408 (96.68) | 208 (98.58) | 200 (94.79) | ||
| Yes | 14 (3.32) | 3 (1.42) | 11 (5.21) | ||
| Psychiatric disorders, n(%) | 0.30 | 0.582 | |||
| No | 360 (85.31) | 178 (84.36) | 182 (86.26) | ||
| Yes | 62 (14.69) | 33 (15.64) | 29 (13.74) | ||
| Number of comorbidities ≥ 3, n(%) | 52.76 | <0.001 | |||
| No | 284 (67.30) | 177 (83.89) | 107 (50.71) | ||
| Yes | 138 (32.70) | 34 (16.11) | 104 (49.29) |
Statistical significance threshold set at p < 0.05 (two-tailed).Active malignancy required histopathological confirmation within 6 months.Multimorbidity threshold (≥3 conditions) included all chronic diagnoses.Psychiatric disorders encompassed depression, anxiety, and schizophrenia
Multivariate logistic regression analysis of unfavorable treatment outcomes in elderly pulmonary tuberculosis patients
Variables showing statistical significance (p < 0.05) in the univariate analysis were included in the multivariate logistic regression model. The analysis identified the following independent risk factors for treatment failure: diabetes mellitus (OR = 2.73, 95% CI:1.91–3.90), chronic kidney diseases (OR = 6.31, 95% CI:4.00–9.96), active malignancy (OR = 3.27, 95%CI:2.07–5.14), and the presence of ≥ 3 comorbidities (OR = 1.71, 95% CI:1.20–2.46). A forest plot visualizing these associations is shown in Fig. 3.
Fig. 3.
Forest plot of multivariable logistic regression analysis for adverse treatment outcomes after propensity score matching. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) are shown for each comorbidity and for multimorbidity (≥3 comorbidities). The reference for each variable is absence of that condition. Independent risk factors for adverse outcomes were: diabetes mellitus (OR = 2.73, 95% CI:1.91–3.90), chronic kidney disease (OR = 6.31, 95% CI:4.00–9.96), active malignancy (OR = 3.27, 95% CI:2.07–5.14), and ≥3 comorbidities (OR = 1.71, 95% CI:1.20–2.46); all p < 0.05. The dashed vertical line indicates OR = 1. Squares represent point estimates; horizontal lines indicate 95% CIs
Figure 3. Forest plot of multivariable logistic regression analysis for adverse treatment outcomes after propensity score matching. Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for each comorbidity and for multimorbidity (≥3 comorbidities). The reference group is the absence of the respective condition.
Discussion
This retrospective cohort study of hospitalized elderly patients with PTB reveals a high comorbidity burden, with diabetes mellitus, chronic kidney diseases, active malignancy, and multimorbidity emerging as independent predictors of adverse treatment outcomes.
This finding is clinically significant given that Older adults face heightened susceptibility to both new TB infections and reactivation of latent tuberculosis infection (LTBI), positioning it as a critical reservoir for TB transmission [10]. As a high-risk group for PTB, elderly individuals exhibit an elevated transmission potential, diagnostic complexity, and therapeutic challenges. With the aging of the global population, TB in older adults has emerged as a pressing clinical and public health priority [11, 12]. In China, TB incidence rates increase progressively with age, with individuals aged 70–74 years reporting 2–3 times higher rates than those aged 20–24 years [13]. This trend mirrors global patterns, where TB disproportionately affects older populations in countries such as the United States, the United Kingdom, Japan, and other East and Southeast Asian nations [14, 15]. Clinical management of TB in older adults is further complicated by age-related immune senescence, high rates of chronic comorbidities, and atypical presentations, including lower sputum smear positivity rates, delayed diagnosis, increased risk of treatment-related adverse drug reactions, suboptimal therapeutic responses, and higher mortality rates [16–19]. Mechanistically, aging compromises mucosal barriers, impairs microbial clearance, and attenuates cellular immune responses to Mycobacterium tuberculosis, synergistically elevating TB susceptibility [20]. These challenges underscore the urgent need for age-specific research to optimize diagnostic and therapeutic strategies.
The coexistence of comorbidities such as HIV, diabetes mellitus, and chronic kidney diseases is strongly associated with unfavorable TB treatment outcomes [21–23]. Historically, TB care in resource-limited settings has been constrained by insufficient healthcare capacity and socioeconomic disparities, where efforts have traditionally prioritized single-disease management while overlooking the identification and coordinated care of comorbidities [24]. According to the WHO, approximately 9.9 million individuals will develop pulmonary TB globally in 2023, with 1.514 million deaths attributed to TB-related complications. Poorly managed comorbidities, including malignancies, liver cirrhosis, and renal insufficiency, significantly may contribute to TB mortality [25]. A WHO survey across 48 low- and middle-income countries revealed that 68.8% of TB patients had at least one non-communicable comorbidity [26]. Regional studies further highlight this burden; In South Africa, 26.9% of TB patients in public primary care settings exhibited comorbid non-communicable diseases, with a higher prevalence among elderly populations [27]. In India, over 50% of patients with PTB reported multimorbidity, which was dominated by depression, diabetes, gastrointestinal disorders, and hypertension [28]. A multinational analysis of 27 high TB burden countries identified HIV, diabetes,depression, and substance use disorders (tobacco/alcohol) as the most prevalent comorbidities [29].
In our cohort, the comorbidity burden was substantial. The most frequent conditions were cardiovascular diseases (chronic heart diseases and hypertension), chronic lung diseases, and diabetes mellitus, followed by psychiatric and other chronic disorders. This pattern aligns with the global aging trend, where older adults face elevated risks of non-communicable multimorbidity [30, 31]. Importantly, several specific comorbidities are known to amplify TB risk through well-defined mechanisms. For instance, a recent systematic review reported a 1.5-fold increased risk of major adverse cardiac events in individuals with a history of TB [32]. Chronic lung diseases—including COPD, bronchiectasis, and silicosis—were frequently observed in our patients. Patients with COPD have a two- to three-fold higher TB incidence than the general population, as chronic airway inflammation and structural damage create a permissive microenvironment for M. tuberculosis colonization [33]. Even more strikingly, silicosis confers a > 20-fold higher TB risk due to silica-induced fibrotic remodeling and immune dysregulation, which accelerate disease progression and worsen outcomes [34–37]. Similarly, CKD is associated with a 50-fold increased risk of TB infection, driven by oxidative stress, malnutrition, vitamin D dysregulation, and impaired cell-mediated immunity [38, 39]. Moreover, renal insufficiency complicates TB treatment by altering drug pharmacokinetics and increasing toxicity risks [40]. The global diabetes epidemic further exacerbates TB burden: diabetes increases active TB risk through hyperglycemia-driven immune dysfunction, including impaired phagocytic activity and cytokine dysregulation [41, 42]. Mental health disorders—depression, anxiety, insomnia, and TB-related stigma—are also highly prevalent. Depression-associated elevations in pro-inflammatory cytokines (e.g., IL-6, TNF-α) suppress immune responses, facilitating TB progression, while anti-TB drugs (e.g., isoniazid) can induce neuropsychiatric side effects [43–45]. Clinically, depression correlates strongly with poor adherence, treatment failure, drug resistance, and elevated mortality [46, 47].
Multimorbidity was a critical clinical challenge in our cohort.A substantial proportion of patients presented with two or more concurrent conditions, and multivariable regression confirmed that diabetes, CKD, active malignancy, and the presence of three or more comorbidities were independently associated with adverse treatment outcomes, with CKD showing the strongest effect. The pathophysiological mechanisms underlying these associations are multifaceted. Diabetes induces dual impairment of innate and adaptive immunity, creating a permissive environment for M. tuberculosis proliferation [48]. In malignancy, inherent cell-mediated immune deficiency is synergistically exacerbated by chemotherapy-induced immunosuppression, accelerating disease progression [49, 50]. CKD patients face unique therapeutic challenges due to mandatory dose adjustments of nephrotoxic anti-TB drugs, which may lead to subtherapeutic concentrations and treatment failure [51, 52]. Of note, we observed that patients with three or more comorbidities experienced the poorest prognosis, suggesting a potential association between higher comorbidity burden and worse clinical outcomes. However, given that our analysis used a binary threshold (≥3 comorbidities) rather than a graded comorbidity count, these findings should be interpreted as an indication of a stepwise relationship rather than a formal dose-response effect.This likely stems from cumulative pathophysiological interactions that amplify disease severity, coupled with polypharmacy risks and altered pharmacokinetic profiles.
Several considerations regarding generalizability are warranted. Our study population consisted exclusively of hospitalized elderly TB patients, who typically present with more advanced disease and a higher comorbidity burden than community-dwelling or outpatient cohorts. Consequently, the prevalence estimates and effect sizes reported here may be higher than those in less severe populations, and caution is warranted when extrapolating these results to non-hospitalized elderly individuals with TB.
Conclusions
In conclusion, our study suggests that multimorbidity represents a critical clinical feature in elderly PTB, with a substantial proportion presenting with multiple concurrent conditions. Through PSM analysis adjusted for potential confounders, diabetes mellitus, chronic kidney diseases, active malignancy, and the presence of ≥ 3 comorbidities were identified as independent predictors of unfavorable therapeutic outcomes in this population. These findings underscore the necessity for systematic comorbidity screening and early multidisciplinary interventions in geriatric TB management, and the clinical implications of this study extend beyond the identification of risk factors. The dose-dependent relationship between comorbidity burden and treatment failure highlights the importance of personalized therapeutic strategies that account for polypharmacy risks and organ dysfunction in patients with multimorbidity. Although our matched cohort design strengthens causal inference by minimizing selection bias, certain limitations warrant consideration. The retrospective nature of data collection may introduce residual confounding, and regional healthcare disparities may affect generalizability. Future large-scale prospective studies with extended follow-up periods are warranted to validate these observations and to elucidate the pathophysiological interplay between specific comorbidity clusters and TB progression.
Several limitations of this study should be acknowledged. First, its retrospective design at a single center inherently carries risks of information bias and unmeasured confounding, limiting the generalizability of our findings to other healthcare settings. Second, the exclusion of patients lost to follow-up, transferred, or voluntarily withdrawn may introduce selection bias. Because comprehensive data for these patients were unavailable, we could not compare their baseline characteristics with those of the included cohort or conduct a worst-case sensitivity analysis. This limitation should be considered when interpreting our findings.Third, by including only hospitalized patients, our cohort likely represents a more severe spectrum of illness, introducing potential selection bias and potentially overestimating the prevalence of adverse outcomes and comorbidity burdens compared to the general community-dwelling elderly TB population.Fourth, despite PSM, residual confounding from unmeasured factors—particularly TB severity indicators—cannot be excluded. Such factors could influence both comorbidity burden and outcomes, and their omission may affect the observed associations. Fifth,while we predefined comorbidity categories, their identification relied on electronic medical records rather than prospective systematic screening, which may have led to under-ascertainment, particularly for less severe or asymptomatic conditions.Sixth, our multivariable model included both individual comorbidities and the binary variable “≥3 comorbidities” simultaneously. Although variance inflation factors were low (<2), indicating no severe multicollinearity, conceptual overlap between these predictors remains. Therefore, the independent effect of multimorbidity beyond that of its constituent conditions should be interpreted with caution, and our findings are best viewed as exploratory.Future prospective, multi-center studies incorporating more comprehensive comorbidity assessments and broader patient recruitment are warranted to validate and extend our findings.
Acknowledgements
We would like to acknowledge the hard and dedicated work of all the staff that implemented the intervention and evaluation components of the study.
Abbreviations
- BMI
Body mass index
- CI
Confidence interval
- COPD
Chronic obstructive pulmonary disease
- eGFR
Estimated glomerular filtration rate
- EMR
Electronic medical record
- HIV
Human immunodeficiency virus
- ICD-10
International Classification of Diseases, Tenth Revision
- IL-6
Interleukin-6
- IQR
Interquartile range
- LTBI
Latent tuberculosis infection
- OR
Odds ratio
- PSM
Propensity score matching
- PTB
Pulmonary tuberculosis
- SD
Standard deviation
- SMD
Standardized mean difference
- TB
Tuberculosis
- TNF-α
Tumor necrosis factor-alpha
- WHO
World Health Organization
Authors’ contributions
Yinping Feng and Zunjing Zhang contributed equally to this work and share first authorship. Yinping Feng, Zunjing Zhang, and Jing Guo were involved in data curation and formal analysis. Shuirong Luo contributed to methodology and validation. Ying Zhang provided resources and supervision. Zhongda Liu conceptualized the study, supervised the project, and wrote the original draft. All authors reviewed and approved the final manuscript.
Funding
Zhejiang Provincial Administration of Traditional Chinese Medicine Co built Science and Technology Plan Project(No.GZY-ZJ-KJ-23096).
Data availability
All data generated or analysed during this study are included in this article.Further enquiries can be directed to the corresponding author.
Declarations
Ethics approval and consent to participate
I confirm that I have read the Editorial Policy pages. This study was conducted in accordance with the Declaration of Helsinki and with approval from the Ethics Committee of Lishui Hospital of Traditional Chinese Medicine (approval number: KY-2022005). Due to the retrospective design and the use of anonymized data, the requirement for written informed consent was waived by the ethics committee.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zhongda Liu contributed equally to this study.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated or analysed during this study are included in this article.Further enquiries can be directed to the corresponding author.