Activated protein C (aPC) plays a pivotal role in modulating a severe inflammatory response and is thought to be beneficial for patients with sepsis. However, several meta-analyses of randomised controlled trials (RCTs) show that aPC is not significantly associated with improved survival in critically ill patients with sepsis. One suggestion is that these analyses simply ignored observational evidence. The present study aims to quantitatively demonstrate how observational data can alter the findings derived from synthesised evidence from RCTs by using a Bayesian approach.

RCTs and observational studies investigating the effect of aPC on mortality outcome in critically ill patients with sepsis will be included. The quality of included RCTs will be assessed by using the Delphi list. Publication bias will be quantitatively analysed by using the traditional Egger regression test and the Begg rank correlation test. Observational data will be used as the informative prior for the distribution of OR. A power transformation of the observational data likelihood will be considered. Observational evidence will be down-weighted by a power of α which takes values from 0 to 1. Trial sequential analysis will be performed to quantify the reliability of data in meta-analysis adjusting significance levels for sparse data and multiple testing on accumulating trials.

PROSPERO (CRD42014009562).

Sepsis is defined as systematic inflammatory response syndrome (SIRS) caused by infection.

Activated protein C (aPC) has pleiotropic biological effects and plays a pivotal role in modulating the severe inflammatory response which occurs in sepsis. Its biological effects include, but are not limited to, reduction of thrombin production by inactivating factors Va and VIII, and inhibition of IL-1, IL-6 and TNF-α production by monocytes.

Randomised controlled trials (RCT) are designed to test the biological efficacy of a particular treatment, while observational studies test the effectiveness of that treatment in the real world setting.

We will search electronic databases including the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EBSCO, EMBASE and ISI Web of Science from inception to January 2014. Our core search consists of terms related to aPC and sepsis (see

Search strategy performed in PubMed

Items | Search terms | Number of citations |
---|---|---|

1# | ((activated protein C[Title/Abstract]) OR xigris[Title/Abstract]) OR drotrecogin alfa[Title/Abstract] | 4460 |

2# | (sepsis[Title/Abstract]) OR septic shock[Title/Abstract] | 72 635 |

3# | (((mortality[Title/Abstract]) OR safety[Title/Abstract]) OR adverse events[Title/Abstract]) OR bleeding[Title/Abstract] | 875 580 |

1# AND 2# AND 3# | 531 |

We will include RCTs and OS for analysis. OS will include: (1) cohort studies using multivariable analysis with aPC treatment as one of the covariates; (2) cohort studies using propensity analysis; (3) case–control studies; (4) studies with both prospective and retrospective designs; and (5) all OS irrespective of their methodological design quality.

We will exclude studies that: (1) do not report mortality as an endpoint; (2) are a secondary analysis of a primary study whose data have been published elsewhere; and (3) only include a single arm so that no comparison can be made between different treatment strategies (eg, such as analysis of risk factors).

A custom-made form will be used to extract the following data from eligible studies: name of the first author, year of publication, sample size, illness severity scores (APACHE II, SOFA and SAPS), number of deaths in each arm, total number of participants in each arm, bleeding or haemorrhage events in each arm, OR of treatment versus non-treatment for mortality, the method used for covariate adjustment (propensity score analysis, logistic regression model) and the design of the OS (prospective vs retrospective). The adverse event of bleeding will be divided into two categories: major bleeding (terms consist of combinations of ‘massive’, ‘major’ and ‘bleeding’, ‘haemorrhage’) and any bleeding (terms consist of combinations of ‘minor’ and ‘bleeding’, ‘haemorrhage’). If only the risk ratio (RR) is reported, we will transform it into the OR by using standard formula (described elsewhere

Quality assessment of included RCTs will be performed by using the Delphi list, which consists of nine items: sequence generation, allocation concealment, baseline characteristics, eligibility criteria, blindness to outcome assessor, blindness to care provider, blindness to patient, use of point estimate and variability for outcome measures, and use of intention to treat analysis.

Quality assessment of randomised controlled trials using tools adapted from the Delphi list

Items | Explanation | Rating |
---|---|---|

Sequence generation | Is the method of sequence generation clearly reported? | Yes/no/unclear |

Allocation concealment | Is treatment allocation concealment (using an opaque envelope, central allocation) performed? | Yes/no/unclear |

Baseline characteristics | Are the groups similar at baseline regarding the most important prognostic factors? | Yes/no/unclear |

Eligibility criteria | Are eligibility criteria clearly specified? | Yes/no/unclear |

Blindness to outcome assessor | Is the outcome (mortality) assessor blinded? | Yes/no/unclear |

Blindness to care provider | Is the allocation unknown to the treating physician? | Yes/no/unclear |

Blindness to patient | Is the patient blinded? | Yes/no/unclear |

Point estimate and variability | Are the point estimate and variability reported for the outcome measure? | Yes/no/unclear |

Intention-to-treat | Does the analysis include intention to treat analysis? | Yes/no/unclear |

Quality assessment of included observational studies using the modified Newcastle–Ottawa scale

Selection | Representativeness of the exposed cohort | This item will be assigned a ‘⋆’ when all eligible patients with severe sepsis or septic shock are included in the analysis during the study period |

Selection of the non-exposed cohort | This item will be assigned a ‘⋆’ when all eligible patients without aPC treatment are included in the analysis during the study period | |

Ascertainment of exposure | This item will be assigned a ‘⋆’ when aPC administration is directly obtained from a medical chart, not from reporting by the patient | |

Outcome of interest is not present at the start of the study | This item will be assigned a ‘⋆’ when the subject is alive at the time of enrolment | |

Comparability | Comparability of cohorts on the basis of design or analysis | Baseline characteristics of aPC and control groups are comparable. Usually this can be found in |

Outcome | Assessment of outcome | This item will be assigned a ‘⋆’when mortality is assessed by the investigator, not by the report of the patient's family or next-of-kin |

Is follow-up long enough for outcome to occur? | Adequate follow-up is carried out during hospital stay, ICU stay or redefined study time | |

Adequacy of follow-up of the cohort | This item will be assigned a ‘⋆’ when the follow-up rate is >80% |

aPC, activated protein C.

Publication bias will be quantitatively analysed by using the traditional Egger regression test and Begg rank correlation test.

Sensitivity analysis will be performed by excluding studies with poor methodological design. Subgroup analysis will be performed to explore confounding factors such as shock versus non-shock, and the effect of aPC modified by disease severity. If there are enough studies with the same definition of mortality (n>5), subgroup analysis will be performed by different mortality definitions.

Three key components of Bayesian analysis are prior, likelihood and posterior. The quantity of interest in our study is the OR for mortality. Observational data are used as the informative prior for the distribution of OR. For studies using a logistic regression model for risk adjustment, we will extract adjusted OR and relevant 95% CI for analysis. For studies using propensity matched analysis, the OR from matched samples are calculated. Random effects meta-analysis will be performed to combine the results obtained from OS, by using a Bayesian approach._{up} and L_{lo} represent the upper and lower limits of the 95% credible interval. The precision is the reciprocal of SE.

The framework to incorporate observational data as informative prior is presented by Chen and Ibrahim._{0}. Furthermore, let P(θ) denote the prior distribution for θ before OS are incorporated. P(θ) is the initial prior distribution for θ. Given α, the power prior distribution of θ is defined as:_{0} is the hyperparameter for initial prior, and α is used to weight observational evidence relative to the likelihood of RCT evidence. The value of α controls the impact of observational evidence on P(θ|D_{0}, α). When evidence from RCTs is added to the model, a power transformation of the observational data likelihood is considered:

WinBUGS codes for performing random effects meta-analysis and meta-analysis incorporating observational data

Random effects meta-analysis | Informative prior with observational data | |
---|---|---|

Model† | model { | model { |

Data‡ | list(Y=c(-0.51083, -0.73397, -0.24846, -0.15082, -0.54473, -0.52763, -0.36817, -0.13926, -0.75502, -0.27444, -0.26136), | list(rt.dat=c(0,2,3,2,3), |

Initials§ | list( | list(d = c(0,0,0), |

†Contents following # are not syntax used for analysis, but are used to annotate corresponding codes.

‡Data are used for illustration purpose and are not obtained from the real analysis.

§Initial values are randomly generated and do not represent the actual values used in analysis.

The mean of prior distribution (the figure 0.33 in the expression is used for illustration purposes, and is not obtained from real analysis) is the natural log of the pooled OR (LOR) estimated from observational data. The pooled OR is estimated with a Bayesian approach with a random effects model. The code for the random effects meta-analysis is shown in

Convergence diagnostics will be explored by running two chains. Simulated values will be compared to identify when they become similar. History plots with different chains superimposed (in different colours) will help to determine convergence. Furthermore, we will use the Brooks–Gelman–Rubin diagnostic to test convergence. The procedure will produce three coloured lines (red, blue and green). Convergence is deemed to occur when the red line settles close to 1 and the blue and green lines converge together.

Trial sequential analysis (TSA) is performed to quantify the reliability of data in meta-analysis adjusting significance levels for sparse data and multiple testing on accumulating trials.

Statistical analysis will be performed by using WinBUGS (Imperial College and MRC, UK) and Stata V.12.0 (College Station, Texas, USA). TSA will be performed by using the software TSA V.0.9 Beta (Copenhagen Trial Unit, 2011).

Search results will be displayed in a flowchart. Pooled results from conventional meta-analysis techniques will be displayed in forest plots separately for RCTs and OS. Publication bias as shown in funnel plots will also be displayed, again separately for RCTs and OS. The results of TSA will be reported graphically. Random effects meta-analysis using a Bayesian approach will be used to pool summary effects for observational evidence and the results will be reported by using a caterpillar plot. Summary OR will also be plotted against different values of α to examine how observational evidence influences the summary effect. The Brooks–Gelman–Rubin plot will be used to display convergence diagnostics.

aPC was once the only approved drug for the treatment of sepsis. However, it was withdrawn from the market after the large clinical trial PROWESS-SHOCK failed to identify any beneficial effect in patients with sepsis. However, in the first place, aPC was approved for use in patients with sepsis because the PROWESS study demonstrated a significant beneficial effect, with the study being stopped early because of its efficacy.

When both RCTs and OS are available, common practice is to combine data by equally weighting these two types of studies. When evaluating protective ventilation for non-acute respiratory distress syndrome (ARDS) patients, Serpa Neto

None.

The study was approved by the ethics committee of Jinhua Municipal Central Hospital.

Not commissioned; externally peer reviewed.