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Showing 133 to 137 of 137 entries
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Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials.

EBioMedicine

Sinha P, Spicer A, Delucchi KL, McAuley DF, Calfee CS, Churpek MM.
PMID: 34861492
EBioMedicine. 2021 Nov 30;74:103697. doi: 10.1016/j.ebiom.2021.103697. Epub 2021 Nov 30.

BACKGROUND: Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using...

Cite
Sinha P, Spicer A, Delucchi KL, et al. Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials. EBioMedicine. 2021;74:103697doi: 10.1016/j.ebiom.2021.103697.
Sinha, P., Spicer, A., Delucchi, K. L., McAuley, D. F., Calfee, C. S., & Churpek, M. M. (2021). Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials. EBioMedicine, 74103697. https://doi.org/10.1016/j.ebiom.2021.103697
Sinha, Pratik, et al. "Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials." EBioMedicine vol. 74 (2021): 103697. doi: https://doi.org/10.1016/j.ebiom.2021.103697
Sinha P, Spicer A, Delucchi KL, McAuley DF, Calfee CS, Churpek MM. Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials. EBioMedicine. 2021 Nov 30;74:103697. doi: 10.1016/j.ebiom.2021.103697. Epub 2021 Nov 30. PMID: 34861492.
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Transepithelial nasal potential difference in patients with, and at risk of acute respiratory distress syndrome.

Thorax

Mac Sweeney R, Reddy K, Davies JC, Parker M, Kelly B, Elborn JS, Conlon J, Verghis RM, Calfee CS, Matthay MA, Alton EWFW, McAuley DF.
PMID: 33888572
Thorax. 2021 Nov;76(11):1099-1107. doi: 10.1136/thoraxjnl-2020-215587. Epub 2021 Apr 22.

BACKGROUND: Impaired alveolar fluid clearance, determined in part by alveolar sodium transport, is associated with acute respiratory distress syndrome (ARDS). Nasal sodium transport may reflect alveolar transport. The primary objective of this prospective, observational study was to determine if...

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Mac Sweeney R, Reddy K, Davies JC, et al. Transepithelial nasal potential difference in patients with, and at risk of acute respiratory distress syndrome. Thorax. 2021;76(11):1099-1107doi: 10.1136/thoraxjnl-2020-215587.
Mac Sweeney, R., Reddy, K., Davies, J. C., Parker, M., Kelly, B., Elborn, J. S., Conlon, J., Verghis, R. M., Calfee, C. S., Matthay, M. A., Alton, E. W. F. W., & McAuley, D. F. (2021). Transepithelial nasal potential difference in patients with, and at risk of acute respiratory distress syndrome. Thorax, 76(11), 1099-1107. https://doi.org/10.1136/thoraxjnl-2020-215587
Mac Sweeney, Rob, et al. "Transepithelial nasal potential difference in patients with, and at risk of acute respiratory distress syndrome." Thorax vol. 76,11 (2021): 1099-1107. doi: https://doi.org/10.1136/thoraxjnl-2020-215587
Mac Sweeney R, Reddy K, Davies JC, Parker M, Kelly B, Elborn JS, Conlon J, Verghis RM, Calfee CS, Matthay MA, Alton EWFW, McAuley DF. Transepithelial nasal potential difference in patients with, and at risk of acute respiratory distress syndrome. Thorax. 2021 Nov;76(11):1099-1107. doi: 10.1136/thoraxjnl-2020-215587. Epub 2021 Apr 22. PMID: 33888572.
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Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study.

Thorax

Sinha P, Delucchi KL, Chen Y, Zhuo H, Abbott J, Wang C, Wickersham N, McNeil JB, Jauregui A, Ke S, Vessel K, Gomez A, Hendrickson CM, Kangelaris KN, Sarma A, Leligdowicz A, Liu KD, Matthay MA, Ware LB, Calfee CS.
PMID: 34253679
Thorax. 2022 Jan;77(1):13-21. doi: 10.1136/thoraxjnl-2021-217158. Epub 2021 Jul 12.

RATIONALE: Using latent class analysis (LCA), two subphenotypes of acute respiratory distress syndrome (ARDS) have consistently been identified in five randomised controlled trials (RCTs), with distinct biological characteristics, divergent outcomes and differential treatment responses to randomised interventions. Their existence...

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Sinha P, Delucchi KL, Chen Y, et al. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study. Thorax. 2021;77(1):13-21doi: 10.1136/thoraxjnl-2021-217158.
Sinha, P., Delucchi, K. L., Chen, Y., Zhuo, H., Abbott, J., Wang, C., Wickersham, N., McNeil, J. B., Jauregui, A., Ke, S., Vessel, K., Gomez, A., Hendrickson, C. M., Kangelaris, K. N., Sarma, A., Leligdowicz, A., Liu, K. D., Matthay, M. A., Ware, L. B., & Calfee, C. S. (2022). Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study. Thorax, 77(1), 13-21. https://doi.org/10.1136/thoraxjnl-2021-217158
Sinha, Pratik, et al. "Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study." Thorax vol. 77,1 (2022): 13-21. doi: https://doi.org/10.1136/thoraxjnl-2021-217158
Sinha P, Delucchi KL, Chen Y, Zhuo H, Abbott J, Wang C, Wickersham N, McNeil JB, Jauregui A, Ke S, Vessel K, Gomez A, Hendrickson CM, Kangelaris KN, Sarma A, Leligdowicz A, Liu KD, Matthay MA, Ware LB, Calfee CS. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study. Thorax. 2022 Jan;77(1):13-21. doi: 10.1136/thoraxjnl-2021-217158. Epub 2021 Jul 12. PMID: 34253679.
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Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials.

EBioMedicine

Sinha P, Spicer A, Delucchi KL, McAuley DF, Calfee CS, Churpek MM.
PMID: 34861492
EBioMedicine. 2021 Nov 30;74:103697. doi: 10.1016/j.ebiom.2021.103697. Epub 2021 Nov 30.

BACKGROUND: Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using...

Cite
Sinha P, Spicer A, Delucchi KL, et al. Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials. EBioMedicine. 2021;74:103697doi: 10.1016/j.ebiom.2021.103697.
Sinha, P., Spicer, A., Delucchi, K. L., McAuley, D. F., Calfee, C. S., & Churpek, M. M. (2021). Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials. EBioMedicine, 74103697. https://doi.org/10.1016/j.ebiom.2021.103697
Sinha, Pratik, et al. "Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials." EBioMedicine vol. 74 (2021): 103697. doi: https://doi.org/10.1016/j.ebiom.2021.103697
Sinha P, Spicer A, Delucchi KL, McAuley DF, Calfee CS, Churpek MM. Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials. EBioMedicine. 2021 Nov 30;74:103697. doi: 10.1016/j.ebiom.2021.103697. Epub 2021 Nov 30. PMID: 34861492.
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How Should We Measure Social Deprivation in Orthopaedic Patients?.

Clinical orthopaedics and related research

Cheng AL, McDuffie JV, Schuelke MJ, Calfee RP, Prather H, Colditz GA.
PMID: 34751675
Clin Orthop Relat Res. 2021 Nov 09; doi: 10.1097/CORR.0000000000002044. Epub 2021 Nov 09.

BACKGROUND: Social deprivation negatively affects a myriad of physical and behavioral health outcomes. Several measures of social deprivation exist, but it is unclear which measure is best suited to describe patients with orthopaedic conditions.QUESTIONS/PURPOSES: (1) Which measure of social...

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Cheng AL, McDuffie JV, Schuelke MJ, et al. How Should We Measure Social Deprivation in Orthopaedic Patients?. Clin Orthop Relat Res. 2021;doi: 10.1097/CORR.0000000000002044.
Cheng, A. L., McDuffie, J. V., Schuelke, M. J., Calfee, R. P., Prather, H., & Colditz, G. A. (2021). How Should We Measure Social Deprivation in Orthopaedic Patients?. Clinical orthopaedics and related research, . https://doi.org/10.1097/CORR.0000000000002044
Cheng, Abby L, et al. "How Should We Measure Social Deprivation in Orthopaedic Patients?." Clinical orthopaedics and related research vol. (2021). doi: https://doi.org/10.1097/CORR.0000000000002044
Cheng AL, McDuffie JV, Schuelke MJ, Calfee RP, Prather H, Colditz GA. How Should We Measure Social Deprivation in Orthopaedic Patients?. Clin Orthop Relat Res. 2021 Nov 09; doi: 10.1097/CORR.0000000000002044. Epub 2021 Nov 09. PMID: 34751675; PMCID: PMC8747613.
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Showing 133 to 137 of 137 entries