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  1. Article ; Online: Cutting the Gordian knot of heterogeneity

    Manoj V. Maddali / Pratik Sinha

    EBioMedicine, Vol 77, Iss , Pp 103884- (2022)

    Can integrated systems biology modelling rescue critical care syndromes?

    2022  

    Keywords Medicine ; R ; Medicine (General) ; R5-920
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Contact tracing

    Pratik Sinha / Alastair E Paterson

    EClinicalMedicine, Vol 24, Iss , Pp 100412- (2020)

    Can ‘Big tech’ come to the rescue, and if so, at what cost?

    2020  

    Keywords Medicine (General) ; R5-920
    Language English
    Publishing date 2020-07-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome

    Pratik Sinha / Alexandra Spicer / Kevin L Delucchi / Daniel F McAuley / Carolyn S Calfee / Matthew M Churpek

    EBioMedicine, Vol 74, Iss , Pp 103697- (2021)

    A secondary analysis of three randomised controlled trials

    2021  

    Abstract: 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 ... ...

    Abstract 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 clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected. Methods: Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable. Findings: No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms. Interpretation: Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying ...
    Keywords ARDS ; RCTs ; Clustering ; machine learning ; LCA ; Heterogeneity of treatment effect ; Medicine ; R ; Medicine (General) ; R5-920
    Subject code 006 ; 310
    Language English
    Publishing date 2021-12-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia

    Patrick G. Lyons / Sivasubramanium V. Bhavani / Aaloke Mody / Alice Bewley / Katherine Dittman / Aisling Doyle / Samuel L. Windham / Tej M. Patel / Bharat Neelam Raju / Matthew Keller / Matthew M. Churpek / Carolyn S. Calfee / Andrew P. Michelson / Thomas Kannampallil / Elvin H. Geng / Pratik Sinha

    EBioMedicine, Vol 85, Iss , Pp 104295- (2022)

    2022  

    Abstract: Summary: Background: A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS- ... ...

    Abstract Summary: Background: A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients. Methods: This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers. Findings: Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses. Interpretation: SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated. Funding: This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).
    Keywords Viral pneumonia ; SARS-CoV-2 ; Influenza ; Hospital outcomes ; Statistical modelling ; Medicine ; R ; Medicine (General) ; R5-920
    Subject code 310
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Defining phenotypes and treatment effect heterogeneity to inform acute respiratory distress syndrome and sepsis trials

    Manu Shankar-Hari / Shalini Santhakumaran / A Toby Prevost / Josie K Ward / Timothy Marshall / Claire Bradley / Carolyn S Calfee / Kevin L Delucchi / Pratik Sinha / Michael A Matthay / Jonathan Hackett / Cliona McDowell / John G Laffey / Anthony Gordon / Cecilia M O’Kane / Daniel F McAuley

    Efficacy and Mechanism Evaluation, Vol 8, Iss

    secondary analyses of three RCTs

    2021  Volume 10

    Abstract: Background: Sepsis and acute respiratory distress syndrome are two heterogeneous acute illnesses with high risk of death and for which there are many ‘statistically negative’ randomised controlled trials. We hypothesised that negative randomised ... ...

    Abstract Background: Sepsis and acute respiratory distress syndrome are two heterogeneous acute illnesses with high risk of death and for which there are many ‘statistically negative’ randomised controlled trials. We hypothesised that negative randomised controlled trials occur because of between-participant differences in response to treatment, illness manifestation (phenotype) and risk of outcomes (heterogeneity). Objectives: To assess (1) heterogeneity of treatment effect, which tests whether or not treatment effect varies with a patient’s pre-randomisation risk of outcome; and (2) whether or not subphenotypes explain the treatment response differences in sepsis and acute respiratory distress syndrome demonstrated in randomised controlled trials. Study population: We performed secondary analysis of two randomised controlled trials in patients with sepsis [i.e. the Vasopressin vs Noradrenaline as Initial Therapy in Septic Shock (VANISH) trial and the Levosimendan for the Prevention of Acute oRgan Dysfunction in Sepsis (LeoPARDS) trial] and one acute respiratory distress syndrome multicentre randomised controlled trial [i.e. the Hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in Acute lung injury to Reduce Pulmonary dysfunction (HARP-2) trial], conducted in the UK. The VANISH trial is a 2 × 2 factorial randomised controlled trial of vasopressin (Pressyn AR®; Ferring Pharmaceuticals, Saint-Prex, Switzerland) and hydrocortisone sodium phosphate (hereafter referred to as hydrocortisone) (EfcortesolTM; Amdipharm plc, St Helier, Jersey) compared with placebo. The LeoPARDS trial is a two-arm-parallel-group randomised controlled trial of levosimendan (Simdax®; Orion Pharma, Espoo, Finland) compared with placebo. The HARP-2 trial is a parallel-group randomised controlled trial of simvastatin compared with placebo. Methods: To test for heterogeneity of the effect on 28-day mortality of vasopressin, hydrocortisone and levosimendan in patients with sepsis and of simvastatin in patients with acute respiratory distress syndrome. We used the total Acute Physiology And Chronic Health Evaluation II (APACHE II) score as the baseline risk measurement, comparing treatment effects in patients with baseline APACHE II scores above (high) and below (low) the median using regression models with an interaction between treatment and baseline risk. To identify subphenotypes, we performed latent class analysis using only baseline clinical and biomarker data, and compared clinical outcomes across subphenotypes and treatment groups. Results: The odds of death in the highest APACHE II quartile compared with the lowest quartile ranged from 4.9 to 7.4, across the three trials. We did not observe heterogeneity of treatment effect for vasopressin, hydrocortisone and levosimendan. In the HARP-2 trial, simvastatin reduced mortality in the low-APACHE II group and increased mortality in the high-APACHE II group. In the VANISH trial, a two-subphenotype model provided the best fit for the data. Subphenotype 2 individuals had more inflammation and shorter survival. There were no treatment effect differences between the two subphenotypes. In the LeoPARDS trial, a three-subphenotype model provided the best fit for the data. Subphenotype 3 individuals had the greatest inflammation and lowest survival. There were no treatment effect differences between the three subphenotypes, although survival was lowest in the levosimendan group for all subphenotypes. In the HARP-2 trial, a two-subphenotype model provided the best fit for the data. The inflammatory subphenotype was associated with fewer ventilator-free days and higher 28-day mortality. Limitations: The lack of heterogeneity of treatment effect and any treatment effect differences between sepsis subphenotypes may be secondary to the lack of statistical power to detect such effects, if they truly exist. Conclusions: We highlight lack of heterogeneity of treatment effect in all three trial populations. We report three subphenotypes in sepsis and two subphenotypes in acute respiratory distress syndrome, with an inflammatory phenotype with greater risk of death as a consistent finding in both sepsis and acute respiratory distress syndrome. Future work: Our analysis highlights the need to identify key discriminant markers to characterise subphenotypes in sepsis and acute respiratory distress syndrome with an observational cohort study. Funding: This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and National Institute for Health Research (NIHR) partnership. This will be published in full in Efficacy and Mechanism Evaluation; Vol. 8, No. 10. See the NIHR Journals Library website for further project information.
    Keywords sepsis ; ards ; randomised controlled trials ; heterogeneity in treatment effect ; phenotypes ; latent class ; Medicine ; R
    Subject code 610
    Language English
    Publishing date 2021-07-01T00:00:00Z
    Publisher NIHR Journals Library
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Tracheal aspirate RNA sequencing identifies distinct immunological features of COVID-19 ARDS

    Aartik Sarma / Stephanie A. Christenson / Ashley Byrne / Eran Mick / Angela Oliveira Pisco / Catherine DeVoe / Thomas Deiss / Rajani Ghale / Beth Shoshana Zha / Alexandra Tsitsiklis / Alejandra Jauregui / Farzad Moazed / Angela M. Detweiler / Natasha Spottiswoode / Pratik Sinha / Norma Neff / Michelle Tan / Paula Hayakawa Serpa / Andrew Willmore /
    K. Mark Ansel / Jennifer G. Wilson / Aleksandra Leligdowicz / Emily R. Siegel / Marina Sirota / Joseph L. DeRisi / Michael A. Matthay / COMET Consortium / Carolyn M. Hendrickson / Kirsten N. Kangelaris / Matthew F. Krummel / Prescott G. Woodruff / David J. Erle / Carolyn S. Calfee / Charles R. Langelier

    Nature Communications, Vol 12, Iss 1, Pp 1-

    2021  Volume 10

    Abstract: Here, the authors perform transcriptional profiling on tracheal aspirates of adults requiring mechanical ventilation for SARS-CoV2-induced acute respiratory distress syndrome (ARDS) and identify a dysregulated host response predicted to predicted to be ... ...

    Abstract Here, the authors perform transcriptional profiling on tracheal aspirates of adults requiring mechanical ventilation for SARS-CoV2-induced acute respiratory distress syndrome (ARDS) and identify a dysregulated host response predicted to predicted to be potentially modulated by dexamethasone.
    Keywords Science ; Q
    Language English
    Publishing date 2021-08-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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