Respiratory Muscle Strength and Mortality in Patients with COPD: A Systematic Review and Meta-Analysis

Introduction

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality globally, ranking among the top three causes of death.¹ In addition to airflow limitation, patients experience progressive functional impairment, reduced quality of life, and high healthcare utilization.¹ Exercise-induced dyspnea and exercise intolerance partially reflect respiratory muscle dysfunction.² The diaphragm, which is the primary inspiratory muscle responsible for approximately 60–80% of inspiratory work,³ exhibits reduced exercise capacity and muscle strength in COPD and is closely associated with dynamic hyperinflation.⁴ Therefore, respiratory muscle function possesses clear validity as a clinically meaningful concept that influences symptoms and activities of daily living.

Measuring respiratory muscle strength is one of the methods for evaluating respiratory muscle function. Standardized guidelines for respiratory muscle assessment have evolved (American Thoracic Society (ATS)/European Respiratory Society (ERS) statements in 2002 and updated in 2019),^5,6 and clinical research has accumulated over more than 20 years. A recent retrospective cohort study (n=79; predominantly severe–very severe COPD) followed for 42 months identified maximum inspiratory pressure: maximal inspiratory strength (MIP) ≤55 cmH2O and maximal expiratory strength (MEP) ≤80 cmH2O as mortality risk cut-offs, with significant separation of Kaplan–Meier curves and confirmation in multivariable Cox models.⁷ Notably, adding MIP/MEP to forced expiratory volume in 1 second (FEV₁), peak oxygen consumption (V˙O₂peak), and 6-minute walking distance (6MWD) improved mortality risk discrimination, underscoring the complementary prognostic value of respiratory muscle strength beyond spirometry and exercise indices. Nevertheless, it remains uncertain whether respiratory muscle strength independently predicts survival more broadly. Despite detailed ATS/ERS recommendations on how to perform respiratory muscle testing, clinically standardized, reproducible protocols have not been established in practice or consistently implemented in research. Key aspects, including patient selection, timing (stable state vs post-exacerbation), maneuver quality control, and unit handling, remain variable across studies. Moreover, actionable prognostic thresholds have not been validated or adopted, and confounder adjustment is inconsistent, leaving the independent prognostic role of respiratory muscle strength uncertain.

Accordingly, we conducted a systematic review and meta-analysis in patients with COPD to quantify the association between baseline respiratory muscle strength and all-cause mortality, using narrative synthesis for indices or outcomes lacking compatible data. We also critically examined sources of inconsistency and outlined standards to support clinical implementation. We aimed to: (1) summarize current measurement methods and key sources of variability for respiratory muscle strength testing; (2) assess and quantify prognostic evidence, focusing on all-cause mortality in patients with COPD, while explaining observed heterogeneity; and (3) derive practical standards and research priorities to enable clinical implementation.

Methods

Protocol and Reporting

The systematic review and meta-analysis were reported and conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement.⁸ We prespecified eligibility, search strategy, and extraction/analysis procedures according to PRISMA 2020 guidelines (Figure 1).

Figure 1 PRISMA 2020 study selection flow. Flow diagram of record identification, screening, eligibility assessment, and inclusion, prepared in accordance with PRISMA 2020. The numbers in each box indicate the records identified and excluded and the studies included in the qualitative and quantitative syntheses. *Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases/registers). **If automation tools were used, indicate how many records were excluded by a human and automation tools. Abbreviations: PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Eligibility Criteria

We included studies involving adults with COPD. Index tests included respiratory muscle strength/flow measures (MIP, MEP, PImax/PEmax, sniff nasal inspiratory pressure (SNIP), peak inspiratory flow rate (PIFR), peak expiratory flow rate (PEFR), and cough peak flow (CPF)); diaphragm ultrasound was excluded as an index but considered in the background. The outcome was all-cause mortality, and eligible designs included observational cohort studies. We included baseline-prognosis analyses of trials (published from January 1, 1990 to September 30, 2025 in English/Japanese). Full operational criteria and edge-case rules are detailed in Supplementary Methods. We excluded letters, editorials, congress abstracts, case reports, reviews, cross-sectional studies, and animal studies.

Information Sources and Search Strategy

Two reviewers conducted an online search in parallel and independently. PubMed/MEDLINE, CENTRAL, and Web of Science were searched from January 1, 1990 to September 30, 2025 using controlled vocabulary and free-text terms (“Chronic Obstructive Pulmonary Disease” OR COPD OR “chronic obstructive pulmonary disease” OR emphysema) AND ((“maximal inspiratory pressure” OR MIP OR “maximal expiratory pressure” OR MEP OR “Sniff Nasal Inspiratory Pressure” OR SNIP OR “Peak Inspiratory Flow Rate” OR PIFR OR “Peak Expiratory Flow Rate” OR PEFR OR “Cough Peak Flow” OR CPF) AND (mortality OR survival OR prognosis OR “end of life” OR “overall mortality” OR “hazard ratio” OR “12 month” OR “1 year” OR “long-term” OR “long term”), with no device-brand filter. Complete strategies are presented in Supplementary Table S1.

Study Selection

Two reviewers conducted the study selection in parallel and independently. A third, more experienced reviewer resolved any conflicts concerning the inclusion of a study after a group discussion where the perspectives of both initial reviewers were considered. The study selection was conducted in stages: initially based on title evaluation, then abstract, and later by full-text analysis, following the predefined eligibility criteria.

Data Extraction

We used a standardized form to capture study design, setting/country, sample size, population characteristics (age, male, and % pred. FEV₁), index definition (unit, posture, trials, and repeatability), outcomes and follow-up, effect estimates (hazard ratio (HR)/odds ratio (OR)/risk ratio (RR) with 95% confidence interval (95% CI), adjustment covariates, and risk-of-bias items, as summarized in Table 1.

Table 1 Study Characteristics and Effect Estimates

Statistical Analysis and Data Synthesis

All statistical analyses were performed using EZR v.1.41 (Saitama Medical Center, Jichi Medical University, Saitama, Japan). When two or more studies reported comparable time-to-event estimates for the same index–outcome pair, we pooled HRs using an inverse-variance random-effects model. Between-study heterogeneity was quantified using I², τ², and the Cochran Q test (p-value). All estimates are presented with 95% CI, and results are displayed using forest plots. Where required, effect measures were harmonized to a common metric before synthesis, and units for respiratory pressure indices were standardized to cmH₂O.

Risk of Bias (RoB) Assessment

Cohort studies were assessed using the Newcastle–Ottawa Scale, while prognostic studies were further evaluated with the Quality In Prognosis Studies domains. For randomized controlled trials involving baseline-prognosis analyses, RoB-2 was used for selection/measurement domains. A summary of RoB is presented alongside effect estimates in Table 1.

Synthesis

When two or more studies reported comparable effect metrics for the same index–outcome pair, we conducted random-effects meta-analysis, reporting the pooled effect with 95% CI, between-study heterogeneity (I², τ²). Otherwise, results were narratively synthesized based on the index (MIP/MEP/SNIP/PIFR/PEFR) and mortality outcome. PRISMA flow diagram and forest plots are provided. Studies that could not be meta-analyzed because of incompatible outcomes, effect metrics, or insufficient data were synthesized narratively.

Results

Study Selection and Characteristics

As detailed in the selection flow diagram (Figure 1), the initial search yielded 259 results. After removing duplicate records and ineligible studies, 12 remained and were subjected to full-text review. Of these, six were included. The studies included outpatient COPD cohorts with variable severity and follow-up (12–60 months), predominantly reporting all-cause mortality. Extracted variables and adjustment sets are summarized in Table 1.

Quantitative Synthesis (Mortality)

Two cohorts reported comparable effect metrics for inspiratory strength and all-cause mortality, permitting quantitative pooling. The random-effects model indicated that lower inspiratory strength was associated with higher mortality (pooled HR 0.97 [95% CI 0.95–0.99]), with moderate heterogeneity (I² 58%, τ² = 0.0002, p = 0.12), consistent with between-study differences (Figure 2).

Figure 2 Association between inspiratory muscle strength and all-cause mortality in COPD: forest plot. Forest plot displaying study-specific hazard ratios (HR) with 95% confidence intervals (CI) for the association between inspiratory muscle strength and all-cause mortality. Square markers are proportional to inverse-variance weights; diamonds denote pooled estimates. The vertical solid line indicates the line of no effect (HR=1). The random-effects model yielded a pooled HR of 0.97 (95% CI, 0.95–0.99), with heterogeneity I2=58%, τ2=0.0002, and Cochran’s Q p=0.12. Abbreviations: CI, confidence interval; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; MIP, maximal inspiratory pressure.

Other Indices/Outcomes (Narrative)

Several studies were not eligible for quantitative pooling and are therefore summarized narratively in Table 1; their findings informed the qualitative interpretation but were not part of the sensitivity analyses. For MEP, results were directionally inconsistent across studies, with a prognostic signal weaker than that of MIP. SNIP showed univariate associations, which were often attenuated after adjustment. For PIFR, values <60 L/min were repeatedly associated with exacerbations and near-term readmission in stable/discharge cohorts (implementation relevance for inhaler choice). For PEFR, lower values aligned with worse inpatient or exacerbation outcomes in selected settings. A literature search identified no studies evaluating CPF as a prognostic marker in COPD. To aid interpretation and implementation, a concise, index-by-outcome summary of pros and cons is provided in Table 2. Furthermore, this table collates key measurement features (posture, trials, and repeatability), typical clinical use-cases, and the principal strengths/limitations of each index alongside a qualitative appraisal of evidence consistency.

Table 2 Respiratory Muscle and Flow Indices (“Index at a Glance”): What Each Measures, Feasibility/Learning Curve, Protocol Cues, and Clinical Use Cases

RoB

Overall, RoB was predominantly moderate, primarily due to confounding and measurement variability. Many studies lacked simultaneous adjustment for key covariates or relied on univariable models without a prespecified selection strategy, limiting internal validity. Protocols for MIP/SNIP/MEP/PIFR/CPF varied in posture, trial number, repeatability criteria, and units (mmHg vs cmH₂O); device details and PIFR device context were frequently unclear, likely increasing heterogeneity. Domain-level judgments are summarized in Table 1. For data synthesis, we harmonized units to cmH₂O and prioritized adjusted estimates.

Sensitivity and Small-Study Considerations

Qualitative sensitivities yielded consistent conclusions. Restricting adjusted HR to cohorts with clear measurement protocols (posture and repeatability) did not alter the direction of the observed effects. Given the small number of pooled studies, we did not perform small-study bias tests or funnel-plot assessments, which are unreliable with <10 studies.

Discussion

In this systematic review with meta-analysis, lower inspiratory muscle strength, which is primarily MIP, was consistently associated with higher mortality independent of airflow limitation. The pooled estimate favored a protective effect of greater inspiratory strength, although moderate heterogeneity was observed. Lower MIP was associated with higher all-cause mortality in patients with COPD in our quantitative synthesis; the direction of effect was consistent, although heterogeneity in measurement and adjustment warrants caution in magnitude interpretation. These findings refine prior single-center signals and align with contemporary task-force guidance,⁶ emphasizing standardized respiratory muscle testing and interpretation. However, only a few studies reported MEP or other respiratory muscle strength indicators as outcomes, making a statistical approach impossible. Through a narrative review, we could present the usefulness of these other indicators as currently reported prognostic predictors for patients with COPD. Finally, Figure 3 presents a conceptual causal framework integrating inspiratory muscle weakness, mediators, clinical outcomes, and potential interventions. In subsequent paragraphs, each pathway is described narratively to contextualize the meta-analysis findings.

Figure 3 Conceptual framework linking inspiratory muscle weakness to clinical outcomes and modifiable interventions. Schematic overview of proposed pathways from inspiratory muscle weakness (MIP ↓) to downstream consequences—including impaired cough mechanics (CPF ↓/MEP ↓), dynamic hyperinflation and ventilatory limitation (IC ↓), reduced exercise tolerance (VO2peak ↓), and heightened risk of exacerbation and mortality—mediated by skeletal-muscle dysfunction, nutritional status, and baseline pulmonary function. Potential intervention nodes include device matching using peak inspiratory flow and pulmonary rehabilitation augmented with inspiratory muscle training. Abbreviations: CPF, cough peak flow; IC, inspiratory capacity; IMT, inspiratory muscle training; MEP, maximal expiratory pressure; MIP, maximal inspiratory pressure; PIFR, peak inspiratory flow rate; PR, pulmonary rehabilitation; VO2peak, peak oxygen uptake.

Respiratory muscle strength in COPD is typically quantified by MIP, reflecting the coordinated force generation of the diaphragm, intercostal and scalene muscles, and accessory muscles—the sternocleidomastoid and parietal muscles—during maximal inspiration.⁹ MEP captures expiratory muscle performance driven by the abdominal wall and internal intercostals.^6,10 Beyond loss of mass or geometry, qualitative abnormalities—reduced force generation and decreased myosin heavy chain content per sarcomere—are evident in diaphragmatic fibers, which is the primary inspiratory muscle,¹¹ aligning with the “respiratory muscle dysfunction” node in Figure 3. This dysfunction, influenced by clinical confounders and sarcopenia, plausibly contributes to lower inspiratory muscle strength, which subsequently leads to hyperinflation, cough/clearance inefficiency, and the respiratory muscle metaboreflex.

In relation to acute exacerbations, available data, though still limited, support a link between respiratory muscle performance and exacerbation risk. An outpatient study reported a strong association between lower respiratory muscle strength and exacerbations in COPD.¹² While direct evidence for SNIP and exacerbations is lacking, several flow-based indices map onto the “exacerbations” pathway in our framework. Declines in PEFR precede both gradual- and sudden-onset events (beginning approximately 4 and 2 days prior, respectively).¹³ Suboptimal PIFR (<60 L/min) is associated with more frequent exacerbations and early re-exacerbations after discharge.^14–17 In patients with severe COPD, lower CPF characterizes frequent exacerbators, accompanied by lower PaO2.¹⁸ These observations align with the conceptual framework, linking inspiratory weakness to impaired secretion clearance and highlighting how inhaler technique and device selection influence exacerbation risk. Drug emission from dry-powder inhalers (DPIs) is strongly flow-dependent; patients should generate sufficient PIF through the device to ensure adequate de-aggregation and dispersion.^19,20 Measuring PIFR—for example, with an In-Check device matched to the target DPI’s internal resistance—helps determine device suitability.^19,21 As noted above, a pragmatic threshold of approximately 60 L/min is widely cited; values below this are linked to suboptimal aerosolization and have been proposed as a predictive therapeutic biomarker to guide device selection.^19–22 In practice, when PIFR is inadequate for a DPI, clinicians should consider less flow-dependent options (eg, pMDI + spacer, soft-mist inhaler, or nebulizer) and reinforce technique; contemporary guidance recommends reassessing inhaler technique and device suitability at each visit and after clinical changes.^1,23

Pulmonary rehabilitation is situated within the diagram as an intervention acting primarily on inspiratory strength and ventilatory mechanics. Across studies, respiratory muscle strength is a central rehabilitation outcome, with responders displaying higher baseline MIP and greater diaphragmatic excursion than non-responders.^24,25 Meta-analyses have indicated that rehabilitation improves both MIP and MEP, even when limited to aerobic training,²⁶ and the minimal clinically important difference in MIP has been estimated at 17.2 cmH2O.²⁷ Adding inspiratory muscle training (IMT) to standard programs augments gains in MIP and diaphragm excursion,^28,29 consistent with an effect pathway that strengthens inspiratory capacity and mitigates hyperinflation, thereby improving symptoms and functional performance. Trial data in lung-transplant candidates further underscore this pattern: pulmonary rehabilitation plus IMT yielded larger improvements in 6MWD and MIP than rehabilitation alone, with dyspnea improvement in both arms,³⁰ consistent with the intervention nodes depicted in Figure 3.

Sarcopenia is integrated into the framework as an upstream determinant of respiratory muscle dysfunction and strength. Age-related and disease-specific myocellular changes—including selective type II fiber loss, myosteatosis, and reduced type II satellite cells—are prominent.^9,31,32 Diaphragmatic and global respiratory muscle forces decline with aging and sarcopenia, with transdiaphragmatic pressure reduced in older adults and individuals with sarcopenia experiencing approximately 30% overall respiratory muscle strength.^33–35 Beyond age-related changes, COPD involves disease-specific fiber remodeling. In peripheral limb muscles, multiple biopsy studies have demonstrated a shift from oxidative type I fibers toward more glycolytic type II/IIx fibers, accompanied by reduced oxidative capacity and greater fatigability.^36–38 In contrast, the diaphragm shows endurance-like adaptation with an increased proportion of type I fibers and reduced type II content in severe COPD, although single-fiber studies also reveal intrinsic contractile impairments.^11,39,40 This divergence between limb and diaphragm helps explain why global respiratory muscle strength may decline even as diaphragmatic fatigue resistance increases, and it supports targeted interventions (eg, peripheral muscle reconditioning and IMT) tailored to distinct phenotypes.⁴¹

Emerging consensus defines respiratory sarcopenia as the coexistence of reduced respiratory muscle strength and low respiratory muscle mass, with ultrasound and computed tomography recommended for assessing respiratory muscle morphology (diaphragm thickness, thickening fraction, and excursion);⁹ however, cut-off values for low respiratory muscle mass remain insufficiently standardized.^42–44 In parallel, diaphragm ultrasound shows practical utilities. Its structural/functional measures are reproducible and help predict extubating/reintubation risk in critical illness, highlighting prognostic relevance.^42,45,46 Importantly, lower inspiratory muscle strength has been associated with increased COPD exacerbations, while low respiratory muscle area is linked to adverse outcomes, including mortality.^12,44–47 Beyond device matching, routine nutritional assessment is clinically pertinent because low nutritional status is associated with higher mortality in COPD.^48,49 Targeted nutritional supplementation can improve body weight, skeletal muscle mass, 6MWD, and health-related quality of life, and in malnourished patients with COPD, it may also enhance inspiratory/expiratory muscle strength.^50–52 When combined with exercise/pulmonary rehabilitation, nutrition support has further improved quadriceps strength and MIP. This pattern suggests a pragmatic, multimodal approach to mitigate respiratory and whole-body sarcopenia and potentially downstream readmission risk.^50,53–55 Contemporary guidance further recommends providing nutritional support in malnourished patients with COPD and reassessing their needs over time.²³

Exercise tolerance sits at the nexus of ventilatory limitation and muscle reflex biology in COPD. MIP correlates strongly with 6MWD,^56,57 and diaphragmatic function at baseline relates to gains in 6MWD,²⁵ echoing the path from inspiratory weakness to hyperinflation to functional capacity. The respiratory muscle metaboreflex provides a mechanistic bridge: fatigue of the respiratory muscles can trigger sympathetically mediated vasoconstriction that diverts blood flow from locomotor muscles,⁵⁸ a connection depicted from inspiratory weakness and dysfunction to the metaboreflex and then to reduced exercise tolerance. Quantitative assessments reinforce this chain: diaphragmatic excursion correlates with VO₂peak and with changes in inspiratory capacity during exercise.⁴ Furthermore, higher diaphragmatic domes on radiographs indicate less dynamic hyperinflation and greater VO₂ peak.⁵⁹ Accessory respiratory muscles contribute meaningfully; increased sternocleidomastoid muscle thickness and its inspiratory change correlate with exercise tolerance.⁶⁰ Respiratory muscle training studies align with these pathways: home-based expiratory muscle training improved endurance time in patients with COPD,⁶¹ and IMT within rehabilitation enhances inspiratory pressures and diaphragm excursion,²⁹ indicating modifiable links at the intervention nodes.

Finally, notwithstanding the consistent directional signal, these prognostic findings warrant cautious interpretation due to potential confounding, reverse causation, and measurement error. As summarized in Table 1, only a subset of cohorts reported multivariable models, and the covariate sets were frequently incomplete. Key factors, including body mass index/sarcopenia, smoking exposure, % predicted FEV₁ or severity strata, prior exacerbations or hospitalizations, cardiovascular comorbidity, and treatment intensity (pharmacotherapy and PR/IMT exposure), were seldom adjusted simultaneously; several reports provided univariable estimates only. Such incomplete control enables residual and unmeasured confounding to inflate or attenuate observed associations. Reverse causation is also plausible: low MIP may mark pre-terminal decline or advanced disease burden, rather than exerting a direct causal effect on survival. Additionally, non-uniform measurement protocols (posture, starting lung volume, number of trials, and repeatability thresholds), device and unit inconsistencies, and variable reporting of quality control likely introduced non-differential error. This may have biased estimates toward the null and contributed to the between-study heterogeneity evident in Figure 2. Finally, single-center sampling and incompletely characterized follow-up in several cohorts raise concerns about selection and attrition bias. To mitigate these issues in our synthesis, we prioritized adjusted estimates where available, harmonized units to cmH₂O, and summarized non-pooled studies narratively with domain-level risk-of-bias judgments reported alongside effect estimates.

Conclusion

Baseline inspiratory muscle strength, as measured by MIP, is consistently linked to all-cause mortality in patients with COPD. Our synthesis quantifies this association and explains heterogeneity due to protocol variability and residual confounding. For other indices, we clarify why signals diverge: weaker/inconsistent MEP, attenuation for SNIP, and the implementation relevance of PIFR. In practice, clinicians should measure and report MIP with a minimum reporting set, use PIFR (~60 L/min for DPIs) to match inhalers, and pair nutrition/protein support with pulmonary rehabilitation. Key priorities for enhancing clinical practice include standardizing test protocols and thresholds, establishing well-adjusted multicenter cohorts, and conducting pragmatic trials focused on MIP or SNIP screening, PIFR-guided device selection, and nutrition and rehabilitation bundles. Collectively, these steps aim to shift the field from description to actionable decision-support at the bedside.

Summary

Respiratory muscle strength indices are vital clinical tools in managing COPD. Their integration into daily practice can improve patient outcomes, and future multicenter studies are needed to develop standardized and reproducible methodologies for broader clinical implementation.