Abstract
Mechanical ventilation (MV) is a cornerstone of supportive care in intensive care units (ICUs), but prolonged ventilation is associated with adverse outcomes. Several pharmacologic agents with respiratory stimulants have been investigated to facilitate weaning and improve clinical outcomes; yet no comprehensive comparison across available agents exists. This network meta-analysis (NMA) aimed to compare and rank available interventions in adult patients receiving MV. A systematic search of PubMed, Web of Science, and Scopus (up to November 10, 2023) identified 15 randomized controlled trials (1,528 participants) evaluating ten respiratory stimulants in mechanically ventilated critically ill adults: Almitrine Bismesylate (AB), Doxofylline (DX), Progesterone (PRG), Acetazolamide (ACZT), Growth Hormone (GH), Oxandrolone (OXA), Nandrolone (NA), Caffeine (CAF), Donepezil (DPZ), and a multi-agent adjuvant therapeutic (AT) regimen containing anisodamine. Data were analyzed using a frequentist network meta-analysis with treatment rankings based on SUCRA values. Risk of bias was assessed using the modified Cochrane RoB 2 tool. No pharmacologic intervention significantly reduced hospital or ICU mortality, duration of mechanical ventilation, or time to successful weaning compared with placebo. According to SUCRA rankings, NA, OXA, and PRG had the highest probabilities of reducing hospital mortality, with NA also associated with shorter ICU and hospital stays. DPZ and PRG significantly shortened weaning duration, while GH showed the greatest reduction in mechanical ventilation duration. GH, PRG, and DPZ had the highest likelihood of successful weaning. Heterogeneity and inconsistency were generally low, except for the duration of mechanical ventilation (I² = 86.2%, p < 0.001). No pharmacologic intervention significantly reduced hospital mortality. However, agents such as NA, GH, and DPZ may help shorten ICU stay, reduce duration of mechanical ventilation, or improve weaning efficiency. These findings underscore the potential value of multi-agent adjuvant approaches and highlight the need for larger, high-quality trials to confirm their clinical benefits.
Trial registration: CRD42023454122 (18/10/2023).
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-025-29747-z.
Keywords: Mechanical ventilation, Respiratory stimulants, Network meta-analysis, Weaning, Intensive care
Subject terms: Diseases, Health care, Medical research
Introduction
While mechanical ventilation (MV) is a lifesaving intervention for patients with acute respiratory failure1, prolonged dependence on MV is associated with numerous complications, including ventilator-associated pneumonia, diaphragmatic atrophy, longer ICU stays, increased morbidity, mortality, and healthcare costs2,3. As a result, timely initiation of the weaning process is considered essential to minimize these risks and optimize patient outcomes4.
Weaning the patients off MV remains a complex clinical challenge5. Successful weaning requires careful assessment of both respiratory and neurological function and includes respiratory stimulant regimens, sedation management, and supportive care. In recent years, several studies have explored the potential role of pharmacological agents, particularly respiratory stimulants, in facilitating the weaning process. These agents aim to enhance respiratory center drive and support spontaneous breathing efforts6. Since both hypoactive and hyperactive respiratory drives can compromise the weaning process, the judicious use of respiratory stimulants may help maintain an optimal balance between respiratory load and neuromuscular capacity, ultimately contributing to successful extubation7.
In this network meta-analysis (NMA), respiratory stimulants were defined as medications whose direct or predefined purpose in randomized controlled trials (RCTs) was to improve alveolar ventilation, enhance respiratory drive, or reduce PaCO2/ hypoxemia via stimulation of central respiratory chemoreception, or medicine specifically used to reverse drug-induced respiratory depression (e.g., opioid-induced respiratory depression). Those interventions that improved respiratory function only through airway mechanics, anti-inflammatory properties, or secondary metabolic/ developmental effects were classified as adjunct/indirect group and were not considered primary respiratory stimulants by default, unless the RCT explicitly stated ventilatory enhancement or weaning facilitation as a main therapeutic target. Various medications have been proposed to stimulate breathing and facilitate the weaning process8–10. However, data in adult critical care setting remain limited and inconclusive. In supplementary Table 1, the details of pharmacological interventions with hypothesized respiratory stimulant or supportive effects were presented.
Recent advances have also focused on combination therapies that target multiple pathophysiological pathways of respiratory mechanics and ventilator dependence13. Despite these advances, the optimal pharmacological strategy to facilitate weaning remains unclear due to heterogeneous study designs, small sample sizes, and varying outcome measures.
Despite the clinical interest in these agents, there is no comprehensive study to systematically compare the efficacy of various respiratory stimulants in the context of weaning from MV. Therefore, in this NMA we adopted a broad working definition, including both direct stimulants and pharmacologic adjuvants that have been examined in RCTs for weaning facilitation. The objective of this systematic review and network meta-analysis is evaluation and comparison of the efficacy of these agents on respiratory outcomes and weaning success rate from MV among critically ill adult patients to identify the most effective pharmacologic strategies.
Method
Data sources and searches
The protocol of the current systematic review and NMA was registered in PROSPERO (CRD42023454122). We systematically searched PubMed, Web of Science, and Scopus for all parallel RCTs published through November 10, 2023. The search strategy included keywords and MeSH terms related to “respiratory system agents” OR “respiratory stimulants” AND “mechanical ventilation” AND (“mortality” OR “mechanical ventilation duration” OR “ICU stay”) (Appendix A). Automatic alerts were set up to identify new relevant studies after the last manual search; no additional RCTs meeting the inclusion criteria were identified.
Our initial list of respiratory stimulants was defined a priori based on previous literature, pharmacological rationale, and their hypothesized role in facilitating weaning from mechanical ventilation. During the systematic search, we also encountered additional agents investigated in eligible RCTs. These newly identified interventions were considered for inclusion if they met the predefined eligibility criteria. This approach ensured that study selection was not influenced by the availability of published results. A summary of the hypothesized mechanisms of action of all included respiratory stimulants is provided in Supplementary Table 1.
Eligibility criteria
We included randomized controlled trials (RCTs) conducted in adult critically ill patients receiving invasive mechanical ventilation. Eligible interventions were pharmacologic agents with direct or indirect evidence supporting facilitation of weaning from mechanical ventilation. Comparators could be placebo or standard care. Studies were required to report at least one outcome related to weaning success, weaning duration, duration of mechanical ventilation, ICU or hospital stay, ICU or hospital mortality, or ABG parameters.
Studies were excluded if they were not RCTs, involved non-critically ill populations, included pediatric patients, or did not explicitly assess weaning-related outcomes. This approach ensured that only studies directly relevant to pharmacologic facilitation of weaning were considered.
Study selection process
After removal of duplicates, a panel of two authors (A.S. and M.G.J.) independently evaluated the titles and abstracts of the articles and extracted data using a standardized form of Cochrane Data Collection for Randomized Controlled Trials. Any disagreements were resolved by consultation with a third author (A.S.A.). Another two authors (F.H. and S.M.H.) performed an updated literature review.
Data extraction
From eligible RCTs with at least two comparator arms, the following information was extracted: lead author, year of publication, country, baseline patient demographics and characteristics, intervention and comparator details, sample size, study duration, and main outcomes.
The primary outcomes included hospital mortality, ICU mortality, and the mean ± standard deviation (SD) of hospital length of stay, ICU length of stay, and duration of mechanical ventilation. Additionally, several secondary outcomes were analyzed based on data availability across the included studies, including the weaning success rate and weaning duration.
Considerations for network meta-analysis
Most included RCTs compared each intervention to placebo rather than to another active treatment. Consequently, estimates of comparative efficacy among respiratory stimulants rely predominantly on indirect comparisons. When direct comparisons between two active interventions were available, these were extracted and distinguished from indirect comparisons. Differences in baseline characteristics, control group outcomes, and study design across trials may influence indirect estimates.
Quality assessment
Two authors (F.S. and M.M.) independently judged the individual trials for potential risk of bias (low, unclear, or high) using a modified Cochrane Collaboration’s risk-of-bias assessment tool. The overall bias of a trial was assessed from five domains: selection, performance, detection, attrition, and reporting bias. Finally, the studies were assigned to low, unclear, or high risk of bias14. Any discrepancies between reviewers were resolved through discussion and consensus.
Statistical analysis
The primary outcomes included hospital mortality, ICU mortality, hospital and ICU lengths of stay, duration of mechanical ventilation, weaning duration, and weaning success, reported as mean ± SD or event rates, as appropriate. A frequentist network meta-analysis was performed using the netmeta package in R (version 4.4.3, 2025-02-28 ucrt). Network geometry plots were generated to illustrate the evidence connections among interventions.
Indirect and direct comparisons were evaluated under the transitivity and consistency assumptions. Network inconsistency was assessed globally before pooling estimates. Comparative effectiveness was summarized using network forest plots, and treatments were ranked according to surface under the cumulative ranking curve (SUCRA) values. A p-value < 0.05 was considered statistically significant.
Results
Study and patient characteristics
Our database search yielded 13,110 unique studies. Of them, 12338 studies were remained after removal of the duplicates. After screening titles and abstracts, 12,253 irrelevant studies were excluded. Screening of full texts resulted in the exclusion of articles that were not RCTs (n = 62); did not include an outcome of interest (n = 4); and include pediatrics (n = 4). Consequently, 15 clinical trials comparing ten treatments were included in the systematic review, of which 14 trials involving 1,519 participants were eligible for inclusion in the main NMA (Fig. 1).
Fig. 1.
PRISMA flow diagram.
The sample sizes of the individual RCTs ranged from 9 to 382 participants. Nearly 60%of the included studies were conducted in Europe and they were published during 1984–2023. The duration of follow-up ranged from 1 to 96 months. The mean age of the individuals ranged from 38.7 to 73.5 years, and half of the patients were male. Other characteristics of the trials are provided in the Table 1.
Table 1.
Baseline characteristics in each study.
| Lead author, year | Country | Main cause (s) of respiratory failure | Comparators | Sample size | Age, year (Mean ± SD) |
Duration (month) | Outcomes | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MV Duration1 | Weaning Duration1 | ICU Stay1 | Hospital stay1 | ICU Mortality2 | Hospital Mortality2 | Successful Weaning2 | ||||||||
| 1 | Castaing,1984 (15) | France | COPD | PLA: AB | 16 | – | – | – | – | – | – | – | – | – |
| 2 | Castaing,1986 (16) | France | COPD | PLA: AB | 18 |
65 ± 8.7 64.6 ± 6.4 |
– | – | – | – | – | – | 0: 1 | – |
| 3 | Poggi,1989 (17) | Italy | ARF and airflow obstruction | PLA: DX | 9 | − 54.8 ± 21.2 | – | – | – | – | – | – | – | – |
| 4 | Pichard,1996 (30) | Switzerland | Mixed ICU causes: sepsis, trauma, pneumonia, ARDS | PLA: GH | 20 | 64 ± 11 | 1 | – | – | – | – | – | – | 3: 3 |
| 5 | Takala,1999 (19) | Finland and other European countries | ARF | PLA: GH | 522 |
59.1 ± 15.1 60.5 ± 14.1 |
36 |
9.52 ± 9.41 13.3 ± 12.05 |
– |
11.44 ± 9.67 17.16 ± 16.61 |
26.6 ± 16.8 32.5 ± 22.5 |
51: 108 | – | |
| 6 | Bulger,2004 (20) | USA | Postoperative respiratory muscle weakness | PLA: OXA | 41 |
49 ± 16.3 45 ± 20.6 |
29 |
16.4 ± 6.8 21.7 ± 10.1 |
– |
19.5 ± 6.9 24.8 ± 12 |
34.0 ± 19.0 35.0 ± 14.0 |
5: 1 | – | |
| 7 | Golparvar,2005(12) | Iran | – | PLA: PRG | 53 |
42 ± 17 42.6 ± 19 |
– |
17.3 ± 4.0 16.9 ± 3.0 |
– | – | – | – | 2: 1 | – |
| 8 | Gulsvik,2013 (23) | Norway | Pulmonary disease with concurrent metabolic alkalosis | PLA: ACZT | 70 |
69.4 ± 9.4 73.5 ± 10.1 |
96 | – | – | – |
14.3 ± 9.3 14.6 ± 6.9 |
2: 2 | – | |
| 9 | Jia,2014 (13) | China | – | PLA: AT | 120 |
62.85 ± 18.12 66.25 ± 17.82 |
71 |
11.3 ± 5.3 8.6 ± 4.5 |
– |
19.5 ± 13.3 13.5 ± 7.4 |
– | – | – | – |
| 10 | Faisy,2016 (24) | France | COPD | PLA: ACZT | 382 |
69 69 |
32 |
6.79 ± 0.0 5.66 ± 0.0 |
0.91 ± 0.0 0.78 ± 0.0 |
11.7 ± 8.6 11.0 ± 8.2 |
26: 22 | 26: 22 | 127: 118 | |
| 11 | Cervera,2017 (25) | Spain | COPD or OHS | PLA: ACZT | 47 |
67 ± 11 67 ± 10 |
36 |
8.2 ± 5.6 6.8 ± 6.0 |
2.2 ± 2.6 1.9 ± 1.9 |
14.8 ± 13.8 11.8 ± 10.6 |
25.6 ± 16.7 21.5 ± 14.9 |
2: 4 | ||
| 12 | Alizade,2021 (26) | Iran | Non-pulmonary disorders | PLA: PRG: DPZ | 78 |
39.2 ± 21 45.5 ± 20.8 38.7 ± 22.1 |
7 |
7.35 ± 2.7 5.95 ± 1.9 5.2 ± 2.14 |
4.1 ± 1.9 2.7 ± 1.8 2.35 ± 1.49 |
18.1 ± 7.1 18.9 ± 9.7 14.7 ± 7.5 |
4: 5: 3 | – | 16: 23: 20 | |
| 13 | Shaker,2022 (27) | Egypt | Post abdominal surgery respiratory weakness | PLA: GH | 60 |
60.03 ± 10.49 58.23 ± 11.22 |
5 |
48.73 ± 10.66 33.20 ± 16.57 |
– | – | – | – | 7: 4 | 8: 20 |
| 14 | Anstey,2022 (28) | Australia | ICU-acquired weakness from Catabolism and immobility | PLA: ND | 22 |
62.7 ± 11.9 69.7 ± 9.6 |
20 |
6.9 ± 2.7 8.1 ± 5.3 |
340 ± 224.7 213 ± 113.9 |
22 ± 9.3 8.1 ± 5.3 |
34.0 ± 8.4 24.6 ± 6.8 |
2: 0 | 2: 0 | – |
| 15 | Shojaei,2023 (29) | Iran | – | PLA: CAF | 70 |
69.2 ± 15.0 72.51 ± 16.0 |
12 | – | – |
15.62 ± 1.31 15.33 ± 1.31 |
– | – | – | – |
PLA: Placebo; AB: Almitrine Bismesylate; DX: Doxofylline; PRG: Progesterone; ACZT: Acetazolamide; DPZ: Donepezil; CAF: Caffeine; AT: Adjuvant Treatment; GH: Growth Hormone; OXA: Oxandrolone; ND: Nandrolone; ARDS: Acute Respiratory Distress Syndrome; COPD: Chronic Obstructive Pulmonary Disease; OHS: Obesity Hypoventilation Syndrome; ARF: Acute Respiratory Failure.
1Data are based on mean ± SD (days).
2Data are based on N (%).
Risk of bias assessment
Of the 15 included studies, two had a low risk of bias, two had a high risk of bias, and the remaining 11 studies raised some concerns. Specifically, blinding of outcome assessment raised the most concern (Supplemental Table 2).
Given the limited number of publications, publication bias could not be assessed for any of the outcomes of interest.
Structure of NMA
As presented in Fig. 2, ten treatment regimens including AB, DX, PRG, ACZT, GH, OXA, NA, CAF, DPZ, and a AT modality containing anisodamine as pulmonary ventilation enhancer were compared in the current NMA without considering the dose. All regimens were directly compared to placebo (Table 2).
Fig. 2.
The Network Plots of Treatment Regimens. Nodes represent treatments. Edges show direct comparisons; numbers and line thickness indicate the number of studies.
Table 2.
Network meta-analysis estimates with 95% confidence intervals and GRADE assessments from each pairwise comparison in the network.
| Comparisons | Hospital Mortality | ICU Mortality | Hospital Stay | ICU Stay | Mechanical Ventilation Duration | Weaning Duration | Successful Weaning | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Effect Size, RR (CI) | Certainty | Effect Size, RR (CI) | Certainty | Effect Size, MD (CI) | Certainty | Effect Size, MD (CI) | Certainty | Effect Size, MD (CI) | Certainty | Effect Size, MD (CI) | Certainty | Effect Size, RR (CI) | Certainty | |
| PLA: AB | 0.33 (0.15, 7.19) | Low | – | – | – | – | – | – | – | – | – | – | – | – |
| PLA: PRG | 2.00 (0.19, 20.33) | Low | 0.86 (0.26, 2.85) | Low | – | – | − 0.8 (− 5.48, 3.88) | Low | 1.13 (0.01, 2.25) | Low | 1.40(0.37,2.42) | Low | 0.75 (0.54, 1.03) | Low |
| PLA: ACZT | 1.05 (0.64, 1.71) | Low | 1.14 (0.67, 1.94) | Low | 0.36 (− 3.16, 3.90) | Low | 0.82 (− 0.81, 2.46) | Low | 1.16 (− 0.01, 2.33) | Low | 0.13(0.12, 0.13) | Moderate | 1.04 (0.89, 1.21) | Low |
| PLA: AT | – | – | – | – | – | – | 6.00 (2.14, 9.85) | Low | 2.70 (0.94, 4.45) | Low | – | – | – | – |
| PLA: DPZ | – | – | 1.27 (0.32, 5.09) | Low | – | – | 3.40 (− 0.77, 7.57) | Low | 1.97 (0.65, 3.28) | Low | 1.75 (0.77, 2.72) | Low | 0.76 (0.55, 1.06) | Low |
| PLA: CAF | – | – | – | – | – | – | 0.29 (− 0.32, 0.90) | Low | – | – | – | – | – | – |
| PLA: GH | 0.69 (0.26, 1.84) | Low | – | – | − 5.85 (− 10.09, − 1.60) | Moderate | − 5.72 (− 8.67, − 2.76) | Moderate | 2.73 (− 2.78, 8.26) | Low | – | – | 0.52 (0.23, 1.17) | Very low |
| PLA: NA | 5.00 (0.26, 93.10) | Very low | 5.00 (0.26, 93.10) | Very low | 9.40 (3.01, 15.78) | Moderate | 13.90 (7.57, 20.22) | Moderate | 1.00 (− 1.03, 3.03) | Low | – | – | – | – |
| PLA: OXA | 3.91 (0.50, 30.59) | Low | – | – | − 1.00 (− 11.10, 9.10) | Low | − 5.30 (− 11.51, 0.91) | Low | − 5.30 (− 10.73, 0.13) | Low | – | – | 1.56 (1.00, 2.43) | Moderate |
| AB: PRG | 6.00 (0.12, 281.61) | Very low | – | – | – | – | – | – | – | – | – | – | – | – |
| AB: ACZT | 3.16 (0.14, 70.96) | Low | – | – | – | – | – | – | – | – | – | – | – | – |
| AB: AT | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| AB: DPZ | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| AB: CAF | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| AB: GH | 2.09 (0.06, 64.74) | Very low | – | – | – | – | – | – | – | – | – | – | – | – |
| AB: NA | 15.00 (0.21, 1042.22) | Very low | – | – | – | – | – | – | – | – | – | – | – | – |
| AB: OXA | 11.73 (0.29,473.18) | Very low | – | – | – | – | – | – | – | – | – | – | – | – |
| PRG: ACZT | 0.52 (0.04, 5.64) | Very low | 1.32 (0.35, 4.87) | Low | – | – | 1.62 (− 3.34, 6.59) | Very low | 0.02 (− 1.59, 1.64) | Very low | − 1.27(− 2.29,− 0.24) | Moderate | 1.38 (0.97, 1.96) | Low |
| PRG: AT | – | – | – | – | – | – | 6.80 (0.73, 12.86) | Very low | 1.56 (− 0.52, 3.64) | Very low | – | – | – | – |
| PRG: DPZ | – | – | 1.47 (0.39, 5.50) | Low | – | – | 4.20 (− 0.62, 9.02) | Low | 0.83 (− 0.28, 1.95) | Low | 0.35 (− 0.57,1.27) | Low | 1.01 (0.82, 1.25) | Low |
| PRG: CAF | – | – | – | – | – | – | 1.09 (− 3.63, 5.81) | Low | – | – | – | – | – | – |
| PRG: GH | 0.34 (0.02, 5.61) | Very low | – | – | – | – | − 4.92 (− 10.46, 0.62) | Low | 82 (− 5.42, 9.63) | Very low | – | – | 0.69 (0.22, 2.12) | Very low |
| PRG: NA | 2.50 (0.05, 104.42) | Very low | 5.76 (0.24, 135.69) | Very low | – | – | 14.70 (6.82, 22.57) | Moderate | − 0.13 (− 2.46, 2.18) | Low | – | – | – | – |
| PRG: OXA | 1.95 (0.08, 43.41) | Very low | – | – | – | – | − 4.50 (− 12.28, 3.28) | Low | − 6.43 (− 11.98, − 0.89) | Moderate | – | – | 2.07 (1.20, 3.57) | Moderate |
| ACZT: AT | – | – | – | – | – | – | 5.17 (0.98, 9.36) | Low | 1.53 (− 0.57, 3.65) | Low | – | – | – | – |
| ACZT: DPZ | – | – | 1.11 (0.25, 4.91) | Low | – | – | 2.57 (− 1.91, 7.06) | Low | 0.80 (− 0.95, 2.56) | Low | 1.62 (0.64, 2.59) | Low | 0.73 (0.51, 1.05) | Low |
| ACZT: CAF | – | – | – | – | – | – | − 0.53 (− 2.28, 1.21) | Very low | – | – | – | – | – | – |
| ACZT: GH | 0.75 (0.19, 2.99) | Low | – | – | − 6.21 (− 11.74, − 0.69) | Moderate | − 6.54 (− 9.92, − 3.16) | Moderate | 1.48 (− 5.85, 8.82) | Low | – | – | 0.49 (0.16, 1.48) | Very low |
| ACZT: NA | 4.73 (0.24, 91.87) | Very Low | 4.36 (0.22, 85.29) | Very Low | 9.03 (1.73, 16.32) | Moderate | 13.07 (6.53, 19.61) | Moderate | − 0.16 (− 2.51, 2.18) | Low | – | – | – | – |
| ACZT: OXA | 3.70 (0.44, 30.69) | Low | – | – | − 1.36 (− 12.07, 9.33) | Low | − 6.12 (− 12.55, 0.30) | Low | − 6.46 (− 12.02, − 0.90) | Moderate | – | – | 1.50 (0.94, 2.39) | Low |
| AT: DPZ | – | – | – | – | – | – | − 2.60 (− 8.28, 3.08) | Low | − 0.72 (− 2.92, 1.46) | Low | – | – | – | – |
| AT: CAF | – | – | – | – | – | – | − 5.71 (− 9.60, − 1.81) | Moderate | – | – | – | – | – | – |
| AT: GH | – | – | – | – | – | – | − 11.72 (− 16.57, − 6.86) | Moderate | 0.03 (− 8.59, 8.66) | Very Low | – | – | – | – |
| AT: NA | – | – | – | – | – | – | 7.90 (0.49, 15.30) | Low | − 1.69 (− 4.38, 0.99) | Low | – | – | – | – |
| AT: OXA | – | – | – | – | – | – | − 11.30 (− 18.61, − 3.98) | Moderate | − 8.00 (− 13.70, − 2.29) | Moderate | – | – | – | – |
| DPZ: CAF | – | – | – | – | – | – | − 3.11 (− 7.33, 1.11) | Very Low | – | – | – | – | – | – |
| DPZ: GH | – | – | – | – | – | – | − 9.12 (− 14.24, − 3.99) | Moderate | 0.83 (− 7.41, 9.07) | Very Low | – | – | 0.67 (0.21, 2.09) | Very low |
| DPZ: NA | – | – | 3.91 (0.15, 99.41) | Very low | – | – | 10.50 (2.91, 18.08) | Moderate | − 0.96 (− 3.39, 1.45) | Low | – | – | – | – |
| DPZ: OXA | – | – | – | – | – | – | − 8.70 (− 16.19, − 1.20) | Moderate | − 7.27 (− 12.85, − 1.68) | Moderate | – | – | 2.04 (1.17, 3.53) | Moderate |
| GH: NA | 7.14 (0.26, 193.43) | Very low | – | – | 15.25 (7.57, 22.92) | Moderate | 19.62 (12.63, 26.60) | Moderate | − 2.18 (− 9.63, 5.26) | Low | – | – | – | – |
| GH: OXA | 5.59 (0.43, 72.38) | Very low | – | – | 4.85 (− 6.11, 15.81) | Low | 0.42 (− 6.46, 7.30) | Low | − 8.30 (− 18.07, 2.00) | Low | – | – | 3.00 (0.93, 9.67) | Low |
| NA: OXA | 0.78 (0.02, 27.93) | Very low | – | – | − 10.40 (− 22.35, 1.55) | Low | − 19.20 (− 28.07, − 10.32) | Moderate | − 6.30 (− 12.10, − 0.50) | Moderate | – | – | – | – |
RR: Risk Ratio; MD: Mean Difference; PLA: Placebo; AB: Almitrine Bismesylate; DX: Doxofylline; PRG: Progesterone; ACZT: Acetazolamide; DPZ: Donepezil; CAF: Caffeine; AT: Adjuvant Treatment; GH: Growth Hormone; NA: Nandrolone; OXA: Oxandrolone.
Network meta-analysis results of intervention efficiency and ranking of treatment strategies
Among the 14 included randomized trials12,13,15,16,19,20,23–30, a total of 1519 participants across 11 arms were analyzed. Heterogeneity and inconsistency were formally assessed for all outcomes. No significant heterogeneity or inconsistency was observed for most outcomes, except for the duration of mechanical ventilation, which showed substantial heterogeneity (I² = 86.2% [69.8%; 93.7%], p < 0.001). Specifically, the heterogeneity estimates were as follows: hospital mortality, I² = 55.2% [0.0%; 85.2%], p = 0.082; ICU mortality, I² = 0%, p = NA; hospital length of stay, I² = 0%, p = 0.381; ICU length of stay, I² = 0%, p = 0.534; duration of mechanical ventilation, I² = 86.2% [69.8%; 93.7%], p < 0.001; weaning duration, I² = 0%, p = 0.799; and successful weaning, I² = 31.5%, p = 0.226.
As shown in Fig. 3, none of the interventions significantly reduced hospital mortality, ICU mortality, MV duration, or time to successful weaning when compared to PLA. According to SUCRA rankings, NA ranked first (SUCRA = 77.65), followed by OXA (SUCRA = 77.04) and PRG (SUCRA = 61.01) in terms of efficacy for reducing hospital mortality. SUCRA rankings for all other efficacy outcomes are provided in Supplementary Table 3.
Fig. 3.
Forest plots for efficacy Outcomes presenting risk ratios and 95% (CIs) comparted with placebo (reference).
NA showed the greatest effect among all interventions, being associated with shorter ICU stay (MD= -13.90; 95% CI -20.22 to -7.57) and hospital stay (MD = -9.40; 95% CI -15.78 to -3.01) (Fig. 3C and D). In the SUCRA ranking, the NA regimen had the highest probability for lowering ICU length of stay (SUCRA = 99.64) and hospital stay (SUCRA = 98.80).
Additionally, DPZ and PRG were associated with a significant reduction in weaning duration compared with PLA (Fig. 3F). The GH regimen demonstrated the greatest effect in shortening the duration of MV (SUCRA = 68.80), followed by AT (a combination of furosemide, enema, nitroglycerin, cedilanide, and anisodamine; SUCRA = 68.29), DPZ (SUCRA = 62.87), ACZT (SUCRA = 55.33), and PRG (SUCRA = 52.71).
Finally, GH, PRG, DPZ, ACZT, and OXA were associated with varying probabilities of successful weaning (Fig. 3G). According to the NMA SUCRA rankings, GH showed the highest likelihood of success (SUCRA = 86.42), followed by PRG (63.40), DPZ (61.94), PLA (37.94), ACZT (37.16), and OXA (13.14).
Discussion
This network meta-analysis provides a comprehensive comparison of various respiratory stimulant regimens aimed at facilitating weaning from mechanical ventilation in adult patients. While none of the interventions demonstrated a statistically significant reduction in hospital mortality compared to placebo, the SUCRA rankings suggested potential clinical benefits of NA in hospital and ICU mortality. It should be noted that the NA was associated with the highest likelihood of reducing ICU length of stay, after NA. This finding highlights the possible synergistic effects of diuretics and vasodilators in reducing fluid overload and improving respiratory mechanics. As a cholinergic antagonist, anisodamine has protective effect on respiratory function even in patients with traumatic acute lung injury31. Intravenous high dose of anisodamine was associated with shorter duration of MV with no significant adverse effects31. However, the heterogeneity of the AT components warrants caution in interpreting this finding.
In terms of successful weaning, GH, PRG, and DPZ ranked highest. GH may facilitate weaning from MV by enhancing diaphragmatic and peripheral muscle strength, thereby preventing critical illness–related muscle atrophy and improving ventilatory endurance32. However, given prior reports of adverse outcomes with systemic GH administration in critically ill patients, future studies should explore targeted or low-dose strategies to harness its muscle-preserving benefits safely19. Donepezil, a medicine usually prescribed for Alzheimer’s disease, can indirectly affect respiration by increasing acetylcholine levels in the brain. Subsequently, Donepezil improve ventilation by enhancing cholinergic neurotransmission33. By inhibiting carbonic anhydrase in the kidneys, Acetazolamide induces metabolic acidosis which consequently enhance ventilation and oxygenation and diminish carbon dioxide retention34.
The results of the current NMA showed that CAF was only evaluated for ICU length of stay, where it had a low SUCRA value and no significant effect. PRG demonstrated notable benefits for weaning duration (SUCRA = 0.7573) but had limited impact on mortality and successful weaning, and a relatively low probability for improving hospital or ICU stay. It should be noted that while caffeine is well-established in neonatal care for respiratory stimulation, its role in adult critical care remains under investigation and larger randomized trials are demanded29. Nevertheless, emerging evidence indicates that sex-related hormonal differences, particularly higher circulating progesterone levels, may influence respiratory drive and immune modulation in critically ill patients. Progesterone has been reported to improve ventilatory performance during partial-support MV, by acting as a respiratory stimulant with both central and peripheral effects12. Progesterone has been shown to exert anti-inflammatory effects by suppressing proinflammatory cytokines and protect against excessive inflammatory responses. Experimental and clinical data suggest that women of reproductive age, who have higher endogenous progesterone concentrations, demonstrate more favorable outcomes in septic shock and severe respiratory infections compared to age-matched men35–37. This may highlight progesterone as a potential candidate as a protective modulator in respiratory failure in critically ill patients. The results of the current NMA showed the favorable effect of progesterone on weaning duration.
Recent literature emphasizes the importance of individualized weaning strategies. For instance, a systematic review and network meta-analysis by Huang et al. highlighted the efficacy and safety of different mechanical ventilation strategies for patients with acute respiratory distress syndrome (ARDS), underscoring the need for tailored approaches based on patient-specific factors38.
Additionally, noninvasive respiratory support modalities, such as high-flow nasal oxygen (HFNO) and noninvasive ventilation (NIV), are also alternatives to conventional oxygen therapy in managing respiratory failure. A recent systematic review and NMA stated that continuous positive airway pressure (CPAP), high-flow nasal cannula (HFNC), and bilevel positive airway pressure (BiPAP) were more effective than conventional oxygen therapy in reducing extubation failure and treatment failure rates in infants and young children, suggesting potential benefits in adult populations as well29.
Sedation management is another critical factor influencing weaning outcomes. Recent findings indicate that light sedation or no sedation strategies, prioritizing analgesia before sedatives, along with paired spontaneous awakening and spontaneous breathing trials, promote ventilator weaning39. Combining pharmacologic stimulants with optimized sedation protocols may offer synergistic benefits, a concept that requires further prospective study. These insights suggest the multifaceted nature of weaning from mechanical ventilation, where pharmacologic interventions, sedation management, and noninvasive respiratory support strategies must be integrated to optimize patient outcomes.
Excessive respiratory drive can generate vigorous inspiratory efforts, leading to high transpulmonary pressures, alveolar overdistension, and patient self-inflicted lung injury (P-SILI)40. It may also increase left ventricular afterload, cause pulmonary edema, and contribute to diaphragm fatigue or injury41. In COVID-19 and ARDS studies, spontaneous breathing and excessive pleural pressure gradients have been associated with spontaneous air-leak syndromes, consistent with Macklin effect appearances on imaging42. In mechanically ventilated patients, unmonitored high respiratory drive may mask injurious pressures because ventilator airway pressure traces underestimate the additional pleural pressure swings imposed by patient effort43. To mitigate the adverse consequences of excessive respiratory drive, clinicians and future trial protocols should closely monitor inspiratory effort using esophageal or surrogate indices, optimize ventilator settings and interfaces with adequate positive end-expiratory pressure to enhance compliance, titrate ventilatory support and sedation to buffer extreme drive, consider proportional or neurally adjusted ventilatory modes when available, enforce strict lung-protective limits, implement temporary controlled ventilation or neuromuscular blockade in patients with refractory high effort, and include barotrauma/air-leak events as predefined safety endpoints40–43. These safeguards are essential to ensure that any potential benefits of stimulants are not counterbalanced by harm mediated through excessive respiratory stress. Unfortunately, none of the included studies in the current NMA reported barotrauma or air-leak outcomes as a safety concern, preventing a quantitative evaluation of this safety outcome. Future clinical trials of respiratory stimulants should predefine barotrauma and air-leak events as safety endpoints and report physiologic indices of respiratory drive.
While the clinical trials included in our NMA did not assess diaphragmatic function, growing evidence suggests that respiratory stimulants may influence diaphragmatic workload, atrophy, or injury. Augmenting neural drive could help maintain or enhance diaphragmatic contractile activity, mitigating disuse atrophy in patients with low respiratory effort. However, elevated inspiratory demand may increase diaphragm contractile workload and accelerate muscle fatigue or damage, especially when lung mechanics are unfavorable. Therefore, expert consensus underscores the importance of standardized diaphragm ultrasound metrics (e.g. thickness, thickening fraction) and highlights gaps in knowledge about force–effort relationships in critical care setting44. Meanwhile, a multicenter study of COVID-19 patients undergoing weaning found that early diaphragmatic thickening fraction (DTF) did not reliably predict weaning success, suggesting that enhanced contractility alone may not suffice to overcome mechanical or systemic limitations45. Future investigations on respiratory stimulants should prospectively incorporate serial assessments of diaphragm performance, as well as indices of inspiratory effort to determine whether respiratory stimulants facilitate diaphragmatic recruitment or contribute to overuse injury.
This NMA has several limitations that must be acknowledged. First, the number of included studies was relatively small, and a significant portion of included clinical trials had limited sample sizes, which may reduce the statistical power and influence the generalizability of our findings. Second, there was notable clinical and methodological heterogeneity among the included studies, including variability in patient populations, disease severity, ventilator settings, dosing regimens, and outcome definitions. These differences may have contributed to inconsistency in results and limit the comparability of studies. Furthermore, most studies had an unclear or high risk of bias, especially regarding the blindness of outcome assessments and allocation concealment, which may influence the validity of pooled estimates. It should be noted that publication bias assessment was not feasible due to the small number of studies, which raises the possibility that unpublished negative studies could alter the overall conclusions. Additionally, most trials focused on short-term outcomes such as ICU length of stay and duration of mechanical ventilation, with limited data on long-term and eventual clinical outcomes. Moreover, most included RCTs compared active treatments to placebo rather than head-to-head active comparators, so the network is largely informed by indirect evidence. While this introduces some uncertainty particularly due to variability among control groups in mortality and other outcomes, it also reflects a key methodological feature of network meta-analysis and enables the assessment of indirect comparisons among interventions even in the absence of direct head-to-head evidence. By assessing the transitivity and consistency, we reinforced the validity of the estimates. Future research should aim to overcome these limitations by conducting large-scale, multicenter randomized controlled trials with standardized protocols and robust methodological quality. Trials should prioritize consistent and clinically relevant outcome measures, including mortality, weaning success rate, long-term respiratory function, and health-related quality of life. Extended follow-up periods are essential to assess the sustained benefits and safety of respiratory stimulants after ICU discharge. Precision medicine approaches that consider patient-specific factors, such as underlying comorbidities, neurological status, and genetic predispositions, could optimize therapeutic efficacy and minimize adverse events.
Conclusion
The results of the current NMA suggested a multi-agent adjuvant therapeutic modality in improving weaning-related outcomes, particularly ICU length of stay and ventilation duration. However, the lack of robust mortality benefits with the evaluated interventions in adult population underscores the need for integrated, multimodal approaches combining pharmacologic and supportive strategies tailored to individual patient profiles to improve the eventual clinical outcomes of the patients and their survival.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors acknowledge the use of ChatGPT (https:// https://openai.com/index/chatgpt/) only to improve readability and language of the work. No assistance from AI was received to replace key authoring tasks such as producing scientific, pedagogic, or medical insights, drawing scientific conclusions, or providing clinical recommendations.
Author contributions
F.S. and A.S had full access to all of the data in the NMA and took responsibility for theintegrity of the data and the accuracy of the data analysis. Concept and design: A.S., F.S., and M.G.J. Acquisition, analysis, or interpretation of data: F.S., A.S.A., S.M.H., and F.H. Statistical analysis: M.M. and A.S.A. Supervision: F.S. and A.S.
Funding
None.
Data availability
The datasets used and/or analysed during the current NMA available from the corresponding author on reasonable request.
Declarations
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.
References
- 1.Dries, D. J. & Perry, J. F. Jr Mechanical Ventilation p. 577–595 (Basic Science and Clinical Evidence: Springer, 2008).
- 2.Eskandar, N. & Apostolakos, M. J. Weaning from mechanical ventilation. Crit. Care Clin.23 (2), 263–274 (2007). [DOI] [PubMed] [Google Scholar]
- 3.Needham, D. M. et al. Improving long-term outcomes after discharge from intensive care unit: report from a stakeholders’ conference. Critical care medicine. ;40(2):502-9. (2012). [DOI] [PubMed]
- 4.McLean, S. E., Jensen, L. A., Schroeder, D. G., Gibney, N. R. & Skjodt, N. M. Improving adherence to a mechanical ventilation weaning protocol for critically ill adults: outcomes after an implementation program. Am. J. Crit. Care. 15 (3), 299–309 (2006). [PubMed] [Google Scholar]
- 5.Boles, J-M. et al. Weaning from mechanical ventilation. Eur. Respir. J.29 (5), 1033–1056 (2007). [DOI] [PubMed] [Google Scholar]
- 6.Golder, F. J., Hewitt, M. M. & McLeod, J. F. Respiratory stimulant drugs in the post-operative setting. Respir. Physiol. Neurobiol.189 (2), 395–402 (2013). [DOI] [PubMed] [Google Scholar]
- 7.Chao, D. C. & Scheinhorn, D. J. Weaning from mechanical ventilation. Crit. Care Clin.14 (4), 799–817 (1998). [DOI] [PubMed] [Google Scholar]
- 8.Bancalari, E. & Claure, N. Strategies to accelerate weaning from respiratory support. Early Hum. Dev.89, S4–S6 (2013). [DOI] [PubMed] [Google Scholar]
- 9.Parnell, H., Quirke, G., Farmer, S., Adeyemo, S. & Varney, V. The successful treatment of hypercapnic respiratory failure with oral modafinil. Int. J. Chronic Obstr. Pulm. Dis. :413–419. (2014). [DOI] [PMC free article] [PubMed]
- 10.Kim, D. W. et al. Comparison of the recovery and respiratory effects of aminophylline and Doxapram following total intravenous anesthesia with Propofol and remifentanil. J. Clin. Anesth.25 (3), 173–176 (2013). [DOI] [PubMed] [Google Scholar]
- 11.Shao, X. M. & Feldman, J. L. Central cholinergic regulation of respiration: nicotinic receptors. Acta Pharmacol. Sin.30 (6), 761–770 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Golparvar, M., Ahmadi, F. & Saghaei, M. Effects of progesterone on the ventilatory performance in adult trauma patients during partial support mechanical ventilation. (2005).
- 13.Jia, L., Li, H., Bai, Y. & Zhu, X. Investigation of adjuvant treatment for difficult weaning from mechanical ventilation. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 26 (12), 849–854 (2014). [DOI] [PubMed] [Google Scholar]
- 14.Sterne, J. A. et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. Bmj ;366. (2019). [DOI] [PubMed]
- 15.Castaing, Y., Manier, G. & Guenard, H. Influence of almitrine dimesylate on gas exchange. Preliminary results in patients with chronic obstructive bronchopneumopathies under artificial ventilation. Presse Med.13 (34), 2078–2082 (1984). [PubMed] [Google Scholar]
- 16.Castaing, Y., Manier, G. & Guenard, H. Improvement in ventilation-perfusion relationships by almitrine in patients with chronic obstructive pulmonary disease during mechanical ventilation. Am. Rev. Respir. Dis.134 (5), 910–916 (1986). [DOI] [PubMed] [Google Scholar]
- 17.Poggi, R., Brandolese, R., Bernasconi, M., Manzin, E. & Rossi, A. Doxofylline and respiratory mechanics: Short-term effects in mechanically ventilated patients with airflow obstruction and respiratory failure. Chest96 (4), 772–778 (1989). [DOI] [PubMed] [Google Scholar]
- 18.Domenighetti, G., Suter, P. M., Schaller, M-D., Ritz, R. & Perret, C. Treatment with N-acetylcysteine during acute respiratory distress syndrome: a randomized, double-blind, placebo-controlled clinical study. J. Crit. Care. 12 (4), 177–182 (1997). [DOI] [PubMed] [Google Scholar]
- 19.Takala, J. et al. Increased mortality associated with growth hormone treatment in critically ill adults. N. Engl. J. Med.341 (11), 785–792 (1999). [DOI] [PubMed] [Google Scholar]
- 20.Bulger, E. M., Jurkovich, G. J., Farver, C. L., Klotz, P. & Maier, R. V. Oxandrolone does not improve outcome of ventilator dependent surgical patients. Ann. Surg.240 (3), 472–480 (2004). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Papazian, L. et al. Neuromuscular blockers in early acute respiratory distress syndrome. N. Engl. J. Med.363 (12), 1107–1116 (2010). [DOI] [PubMed] [Google Scholar]
- 22.Dessap, A. M. et al. Natriuretic peptide–driven fluid management during ventilator weaning: a randomized controlled trial. Am. J. Respir. Crit Care Med.186 (12), 1256–1263 (2012). [DOI] [PubMed] [Google Scholar]
- 23.Gulsvik, R. et al. Acetazolamide improves oxygenation in patients with respiratory failure and metabolic alkalosis. Clin. Respir. J.7 (4), 390–396 (2013). [DOI] [PubMed] [Google Scholar]
- 24.Faisy, C. et al. Effect of Acetazolamide vs placebo on duration of invasive mechanical ventilation among patients with chronic obstructive pulmonary disease: a randomized clinical trial. Jama315 (5), 480–488 (2016). [DOI] [PubMed] [Google Scholar]
- 25.Cervera, G. R. et al. Effects of early administration of Acetazolamide on the duration of mechanical ventilation in patients with chronic obstructive pulmonary disease or obesity-hypoventilation syndrome with metabolic alkalosis. A randomized trial. Pulm. Pharmacol. Ther.44, 30–37 (2017). [DOI] [PubMed] [Google Scholar]
- 26.Alizadeh, Z., Sahebnasagh, A., Hadadzadegan, N., Mohammadi, F. & Saghafi, F. Effects of donepezil and Medroxyprogesterone versus placebo on weaning in adult patients with Non-Pulmonary etiologies receiving invasive mechanical ventilation: A triple-blind randomized clinical trial. Front. Pharmacol.12, 735594 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Shaker, E. H. & Kamel, M. A. Role of Recombinant human growth hormone in weaning of prolonged ventilation after major abdominal cancer surgeries; open-label, placebo-controlled, randomized trial. Anaesth. Pain Intensive Care. 26 (2), 175–181 (2022). [Google Scholar]
- 28.Anstey, M. et al. Muscle growth and anabolism in intensive care survivors (GAINS 2.0): protocol for a multi-centre randomised; placebo controlled clinical trial of nandrolone in deconditioned adults recovering from critical illness. PLoS One. 20 (2), e0315170 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Shojaei, S. et al. Effect of oral caffeine on weaning from mechanical ventilation in intubated ICU patients. Tanaffos22 (2), 230 (2023). [PMC free article] [PubMed] [Google Scholar]
- 30.Pichard, C. et al. Lack of effects of Recombinant growth hormone on muscle function in patients requiring prolonged mechanical ventilation: a prospective, randomized, controlled study. Crit. Care Med.24 (3), 403–413 (1996). [DOI] [PubMed] [Google Scholar]
- 31.Guoshou, Z., Chengye, Z., Zhihui, L. & Jinlong, L. Effects of high dose of anisodamine on the respiratory function of patients with traumatic acute lung injury. Cell Biochem. Biophys.66 (2), 365–369 (2013). [DOI] [PubMed] [Google Scholar]
- 32.Knox, J. B., Wilmore, D. W., Demling, R. H., Sarraf, P. & Santos, A. A. Use of growth hormone for postoperative respiratory failure. Am. J. Surg.171 (6), 576–580 (1996). [DOI] [PubMed] [Google Scholar]
- 33.Li, Y. et al. The effect of donepezil on arousal threshold and apnea–hypopnea index. A randomized, double-blind, cross-over study. Annals Am. Thorac. Soc.13 (11), 2012–2018 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Heming, N., Urien, S. & Faisy, C. Acetazolamide: a second wind for a respiratory stimulant in the intensive care unit? Crit. Care. 16 (4), 318 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Zangrillo, A. et al. Sex-related mortality differences in young adult septic shock patients. Signa Vitae. 19 (1), 50–56 (2023). [Google Scholar]
- 36.Redaelli, M. B. et al. Novel coronavirus disease (COVID-19) in Italian patients: gender differences in presentation and severity. Saudi J. Med. Med. Sci.9 (1), 59–62 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Campanelli, F., Landoni, G., Cabrini, L. & Zangrillo, A. Gender differences in septic intensive care unit patients. Minerva Anestesiol.84 (4), 504–508 (2017). [DOI] [PubMed] [Google Scholar]
- 38.Huang, W. et al. Efficacy and safety of different mechanical ventilation strategies for patients with acute respiratory distress syndrome: systematic review and network meta-analysis. Intensive Care Res.3 (1), 50–60 (2023). [Google Scholar]
- 39.Vollbrecht, H. & Patel, B. K. Management of sedation during weaning from mechanical ventilation. Curr. Opin. Crit. Care. 31 (1), 78–85 (2025). [DOI] [PubMed] [Google Scholar]
- 40.Spinelli, E., Mauri, T., Beitler, J. R., Pesenti, A. & Brodie, D. Respiratory drive in the acute respiratory distress syndrome: pathophysiology, monitoring, and therapeutic interventions. Intensive Care Med.46 (4), 606–618 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Tonelli, R. et al. Assessing inspiratory drive and effort in critically ill patients at the bedside. Crit. Care. (London, England). 29 (1), 339 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Belletti, A. P. D., De Bonis, M., Landoni, G. & Zangrillo, A. The role of Macklin effect in management of ARDS: beyond spontaneous pneumomediastinum. Signa Vitae. 20 (11), 10–14 (2024). [Google Scholar]
- 43.Longhini, F. et al. Monitoring the patient-ventilator asynchrony during non-invasive ventilation. Front. Med.9, 1119924 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Haaksma, M. E. et al. EXpert consensus On Diaphragm UltraSonography in the critically ill (EXODUS): a Delphi consensus statement on the measurement of diaphragm ultrasound-derived parameters in a critical care setting. Critical care (London, England). ;26(1):99. (2022). [DOI] [PMC free article] [PubMed]
- 45.Vetrugno, L. et al. Diaphragm ultrasound evaluation during weaning from mechanical ventilation in COVID-19 patients: a pragmatic, cross-section, multicenter study. Respir. Res.23 (1), 210 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current NMA available from the corresponding author on reasonable request.