Heterogeneous treatment effects in molecular phenotypes of sepsis

Lead Investigator: Pratik Sinha, Washington University
Title of Proposal Research: Heterogeneous treatment effects in molecular phenotypes of sepsis
Vivli Data Request: 6104
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

In the U.S. alone, sepsis is estimated to result in 1.7 million hospital admissions and 250,000 deaths annually, making it one of the leading causes of death, and at approximately 25%, remains unacceptably high. Antibiotics aside, no biological pharmacotherapy has been shown to improve outcomes in sepsis clinical trials. In part, these failures to find a therapeutic benefit has been attributed to the vast heterogeneity of patients captured by the non-specific definition of sepsis. As an example, an 18-year-old person with viral meningitis and a 90-year-old with an abdominal abscess could both meet criteria for sepsis and be enrolled in the same trial. It is highly likely that the response to infection and the treatments trialled in a study would be vastly different in these two patients and illustrate why several therapies have failed in sepsis.

Increasingly, researchers are turning to the discipline of precision medicine, where through studies we aim to match the right patient to the right therapy. Our group are world leaders and experts in the field of precision medicine in critical care. In another heterogeneous critical care syndrome, called ARDS, we have consistently identified two distinct phenotypes (or subgroups) that have distinct biological characteristics and outcomes. More importantly, in analysis of three previously conducted trials, the phenotypes responded differently to the same therapies. In one group we observed benefit, whereas in another we observed harm. When the subgroups were analysed together as whole, however, the therapies were found to have no effect. These data suggest that many of previously discarded therapies may have a benefit in a select group of patients.

In this study, we hypothesize that the same subgroups found in acute respiratory distress syndrome are also found in sepsis. We will used previously developed advanced data science (machine learning) predictor models to identify these two phenotypes in the PROWESS-SHOCK study of patients with sepsis. We will further test whether the treatment trialled in this study is beneficial in one group and harmful in another. This study will have broader impact in the field of precision medicine in critical care and may well help identify a subgroup of patients in which this life-saving treatment may be of benefit to a group of patients. The collective expertise of my group will help answer this important question in a condition that is associate with an unacceptably high mortality.

Statistical Analysis Plan:

We will use the equivalent variables that are available at baseline in PROWESS-SHOCK to develop and validate bespoke clinical classifier models fin our observational sepsis cohorts. We will use gradient boosted machines to develop these models. We will then bespoke clinical classifier models to generate probabilities for class allocation in PROWESS-SHOCK. We will use these probabilities to assign the two phenotypes- > 0.5 as the hyper-inflammatory phenotype and < 0.5 as the hypo inflammatory. We will compare clinical characteristics, outcomes and differential treatments responses in the two phenotypes identified using the classifier models. To test HTE, we will create a logistic regression model with mortality at Days 28 and at Day 90 as outcome and treatment allocation, phenotypes, and the interaction term of treatment interaction and phenotype as predictor variables. We will repeat analysis using secondary outcomes as the dependent variable.

For the HTE algorithmic pipelines, we will use unsupervised clustering algorithms including K-means, partition around medoids, hierarchical and spectral clustering. For the supervised clustering we will use causal random forest and model based recursive partitioning. We will use consensus clustering to identify the most optimal number of clusters and we will use the above methods to detect heterogeneous treatment effect.

All analyses will be performed using RStudio with R plugin

Requested Studies:

A Randomized, Double-blind, Placebo-controlled, Multicenter, Phase 3 Study of Drotrecogin Alfa (Activated) Administered as a Continuous 96-hr Infusion to Adult Patients With Septic Shock
Data Contributor: Lilly
Study ID: NCT00604214
Sponsor ID: 11940

Public Disclosures:

Sinha, P., Kerchberger, V.E., Willmore, A., Chambers, J., Zhuo, H., Abbott, J., Jones, C., Wickersham, N., Wu, N., Neyton, L. and Langelier, C.R., 2023. Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials. The Lancet Respiratory Medicine. Doi:10.1016/S2213-2600(23)00237-0