Non-tuberculous mycobacteria (NTM) refer to mycobacteria other than members of the Mycobacterium tuberculosis complex and Mycobacterium leprae (1). NTM are classified into rapidly growing mycobacteria (RGM) and slow-growing mycobacteria (SGM) (2). Further categorization includes specific complexes or groups, such as the Mycobacterium avium complex (MAC), Mycobacterium abscessus complex (MABC), and Mycobacterium simiae complex (MSC) (3).

NTM are opportunistic pathogens that can cause lesions in various tissues and organs, collectively referred to as NTM disease. Among these, NTM pulmonary disease (NTM-PD) is the most common and is increasing in incidence worldwide (4). In some countries, NTM infection rates currently exceed those of the M. tuberculosis complex (5). Therefore, efficient isolation and detection of NTM are essential. In the majority of hospitals, particularly primary care facilities, NTM detection relies on conventional mycobacterial culture. Due to the slow growth of NTM, it often takes several days or weeks for laboratories to issue reports, resulting in prolonged turnaround time (TAT). This delay poses significant challenges for the timely diagnosis and treatment of NTM-related diseases. TAT, defined as the interval from specimen receipt to report delivery (6), is a widely used metric for assessing laboratory efficiency (7, 8). However, few studies have explored TAT specifically for NTM.

Previous studies have shown that the time to positive (TTP) in mycobacterial culture is negatively correlated with acid-fast bacilli (AFB) smear grades and colony numbers (9). In addition, TTP has the potential to be a predictive biomarker for M. avium pulmonary disease (10). Unlike TTP, TAT encompasses the entire process from specimen receipt to report issuance, providing a more comprehensive metric. Despite its significance, the impact of various NTM species and host factors on TAT remains poorly understood, prompting this study to investigate these relationships.

Advancements in technology have identified more than 200 types of NTM, but the primary pathogens of NTM disease include MAC, MABC, and Mycobacterium kansasii (13, 14). MABC, which is part of the RGM, typically grows within 3–5 days, whereas MAC, a member of the SGM, requires 1 week or more to grow. Our results align with these observations, with a median TAT of 7 days for MABC and 10 days for MAC, which is consistent with the TTP values reported by Uwamino et al. (15). These findings suggest that TAT reflects strain growth rates, providing useful diagnostic insights.

MABC includes the subspecies MAB, M. massiliense, and Mycobacterium bolletii, which differ in drug susceptibility, making subspecies identification crucial for treatment (15). Our data indicate that MAB grows faster than M. massiliense does, allowing TAT to distinguish between these subspecies—a feature not previously emphasized in the literature. Similarly, TAT can differentiate Mycobacterium chimaera from other MAC subspecies, despite an earlier report suggesting no differences in TTP among MAC members (16). However, the previous study involved smaller sample sizes, potentially introducing bias. Our findings, supported by a larger dataset, highlight the potential of TAT for distinguishing these strains.

MSC, a large complex within the NTM, contains approximately 20 strains (17), which exhibit varying growth requirements (18–20). Despite being SGM, the calculated TAT for MSC was significantly longer than that for MAC. This discrepancy may stem from the limited number of MSC cases (n = 6) in our study, which included only Mycobacterium lentiflavum and Mycobacterium paraense, potentially biasing the results. Another explanation could be the rarity of MSC-associated NTM-PD (21), which complicates isolation and prolongs TAT.

In our study, TAT was significantly correlated with NTM-PD and bronchiectasis. Patients with NTM-PD require at least two positive sputum samples or one positive lavage sample for diagnosis (22). This diagnostic criterion implies a greater bacterial load in the respiratory tract, leading to shorter TTPs, higher AFB smear grades, and consequently shorter TATs. Our data confirmed a strong correlation between TAT and AFB smear grade, and the acid-fast smear result was identified as the key independent factor influencing TAT. Notably, acid-fast bacilli (AFB) smear results were categorized into six grades in the correlation analysis (Table 3). In the generalized multiple linear regression model, however, the AFB smear results were dichotomized into negative and positive (few to 4+) to facilitate clinical interpretation and ensure model stability (Table 4). Although bronchiectasis is not a common susceptibility factor for tuberculosis (13), it is closely associated with NTM disease (23, 24). Conversely, despite previous reports linking NTM disease to underlying conditions such as tuberculosis, chronic obstructive pulmonary disease, and autoimmune diseases (25), our data did not suggest an association between these conditions and the TAT. We hypothesize that the bacterial load in samples has a more direct impact on TAT than the underlying conditions.

According to the American Thoracic Society and the Infectious Diseases Society of America, NTM-PD diagnosis requires a combination of clinical, imaging, and microbiological findings (22, 26). The imaging criteria included nodules or cavitations in the lungs (26). Our data suggest that lung cavitation affects the TAT more than pulmonary nodules do, which may be due to the lack of distinction between nodule size and quantity in our analysis. Additionally, the median TAT in the population with pulmonary cavities was 8 days in our study, which contrasts with the previously reported TTP of 12 days in a smaller cohort (n = 12) (10), suggesting that our larger sample size (n = 55) provides more reliable estimates.

The TTP for the BACTEC MGIT 960 system, which uses an oxygen-quenching fluorescence sensor and a specific algorithm to detect culture positivity (27), has been shown to correlate negatively with AFB smear grade (9, 10), and pretreatment TTP correlates negatively with treatment response (28). Our findings further revealed that TAT correlates with bacterial load, suggesting its potential as a biomarker for therapeutic efficacy, although further research is needed.

Notably, given the limited number of TAT studies in the NTM, we only compared TAT with the TTP in previous literature. The TAT in this study was reduced by 2–3 days to calculate the corresponding TTP. However, there were a small number of cases (n = 6) in which we sequenced the specimens before issuing the report due to unclear smear morphology or findings that were inconsistent with historical results. Therefore, the TAT for these samples was prolonged. In addition, compared with the TTP, the TAT is easily accessible and can be obtained directly from the report sheet (12). The Chinese Basic Technical Standards for Clinical Microbiology Testing (WS/T805-2022) mandates that the laboratory is obligated to report the TAT of the sample. Thus, our exploration of TAT provides valuable insights for the clinical management of NTM.

As an indicator reflecting laboratory efficiency (6, 12), we recommend that each laboratory using the BACTEC MGIT 960 System establishes species-specific TAT benchmarks based on its own circumstances. For instance, the TAT benchmark for RGM could be set at 7–10 days and that for MAC at 10–14 days. When the actual TAT exceeds the benchmark, the laboratory can promptly investigate potential issues (e.g., sample quality, instrument status, or operational errors) to improve testing efficiency.

There are several limitations in this study, despite the meaningful findings. First, this was a retrospective study conducted in a single hospital, so the results might vary in other regions due to heterogeneity in species composition. Second, the sample size of RGM is significantly smaller than that of SGM, which constitutes an inherent limitation of the present research. Third, TAT in this study does not include species identification because species identification is not routinely performed in our clinical laboratory. In the future, the collection of RGM strains from multicenter collaboration will further verify the generalizability and robustness of our conclusions.

In summary, the TAT of NTM varies significantly by species. Patients with NTM-PD, bronchiectasis, or lung cavitation exhibit shorter TATs. Additionally, the AFB smear result was identified as the only independent factor significantly associated with TAT in the generalized linear model. These findings can be helpful in optimizing the laboratory detection workflow of mycobacteria and reasonably predicting the detection cycle. They may also provide support for clinical decision-making among physicians.