MétaCan
Menu
← Back to cohort
Record W2913060591 · doi:10.1182/blood-2018-99-110622

White Blood Cell Count Trajectory and Mortality in Septic Shock: A Retrospective Cohort Study

2018· article· en· W2913060591 on OpenAlexaffabout
Emily Rimmer, Steve Doucette, Donald S. Houston, Brett L. Houston, Chantalle Menard, Murdoch Leeies, Salaheddin M. Mahmud, Anand Kumar, Allan Garland, Ryan Zarychanski

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsDalhousie UniversityUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsSeptic shockMedicineRetrospective cohort studyPopulationWhite blood cellShock (circulatory)TrajectoryCohortEmergency medicineCohort studyIntensive careSepsisInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Background: Septic shock is among the most common causes of admission to medical intensive care units (ICU) and is associated with mortality of 20-40%. The white blood cell count (WBC) at time of admission correlates with prognosis in septic shock but it is not known if the change in WBC over time (i.e. the WBC trajectory) impacts survival. Hypothesis: We hypothesized that the trajectory of the WBC count in septic shock can identify distinct clinical groups and be an independent predictor of 30-day mortality. Objectives: 1) To identify groups of patients with different WBC trajectories using group-based trajectory analysis; 2) To evaluate patient and illness factors associated with WBC count trajectories; and 3) To estimate the association of WBC trajectory with mortality in septic shock. Methods: We completed a retrospective cohort study of adult patients with septic shock admitted to an ICU in Winnipeg, Canada between 2006-2014. We used group-based trajectory analysis to analyze the trend of WBC over the first 7 days of ICU admission to group patients according to stastically similar trajectories. Group-based trajectory analysis is a statistical method that can be used to describe the pattern of a variable over time. Rather than pre-specifying groups within a population, or using methods to measure an average trajectory for the entire population, group-based trajectory analysis allows for different groups with different trajectories to emerge. We used the Bayesian Information Criterion (BIC) and clinical validity characteristics to select the optimal trajectory model. We developed a multinomial logistic regression model to evaluate the association of patient and illness factors with WBC trajectories. We constructed a multivariable Cox proportional hazard models adjusted for age, Acute Physiology and Chronic Health Evaluation (APACHE) II score, comorbidities, infection type and antibiotics to evaluate the association of WBC trajectory on 30-day mortality. Results: Our final study cohort comprised 917 patients with septic shock. The favoured model identified 7 distinct trajectories of WBC (Figure 1). We found that only baseline platelet count and sex were associated with WBC trajectory. The 30-day mortality of the entire cohort was 26.3%, and ranged from 23.1% in group 4 to 63% in group 5 (rising WBC trajectory). In a multivariable Cox proportional hazard model, group 5 was independently associated with an increased hazard of death (Hazard Ratio 3.48, 95% CI 1.92 to 6.35, p<0.01). Conclusions: We found seven unique and clinically relevant groups of patients with septic shock using trajectory analysis of the WBC count. Routine baseline characteristics are poor predictors of trajectory group assignment. The rising WBC trajectory is associated with an increased risk of death in septic shock. Further studies are required to fully describe the clinical characteristics and prognosis associated with distinct WBC trajectories and whether this information can inform level of care decisions and anticipated response to treatments. In the era of Big Data, trajectory analysis will be broadly applicable to the field of hematology where trends in blood counts or biomarkers of disease may provide valuable clinical or prognostic information. Examples could include analysis of the M-protein trajectory in Multiple Myeloma, and the trajectory of platelet counts in immune thrombocytopenia. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.303
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueBlood→Same topicSepsis Diagnosis and Treatment→French-language works237,207→