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Record W2891954376 · doi:10.1503/cmaj.171284

Susceptible period for cardiovascular complications in patients recovering from sepsis

2018· article· en· W2891954376 on OpenAlexvenueno aff
Chih‐Cheng Lai, Meng-tse Gabriel Lee, Wan-Chien Lee, Christin Chih-Ting Chao, Tzu‐Chun Hsu, Si-Huei Lee, Chien‐Chang Lee

Bibliographic record

VenueCanadian Medical Association Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
FundersNational Taiwan UniversityNational Taiwan University Hospital
KeywordsMedicineSepsisStroke (engine)Myocardial infarctionHazard ratioPopulationCohortConfidence intervalProportional hazards modelCohort studyRetrospective cohort studyInternal medicineEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Patients are at increased risk of cardiovascular complications while recovering from sepsis. We aimed to study the temporal change and susceptible periods for cardiovascular complications in patients recovering from sepsis by using a national database. METHODS: In this retrospective population-based cohort study, patients with sepsis were identified from the National Health Insurance Research Database in Taiwan. We estimated the risk of myocardial infarction (MI) and stroke following sepsis by comparing a sepsis cohort to a matched population and hospital control cohort. The primary outcome was first occurrence of MI or stroke requiring admission to hospital during the 180-day period following discharge from hospital after sepsis. To delineate the risk profile over time, we plotted the weekly risk of MI and stroke against time using the Cox proportional hazards model. We determined the susceptible period by fitting the 2 phases of time-dependent risk curves with free-knot splines, which highlights the turning point of the risk of MI and stroke after discharge from the hospital. RESULTS: We included 42 316 patients with sepsis; stroke developed in 831 of these patients and MI developed in 184 within 180 days of discharge from hospital. Compared with population controls, patients recovering from sepsis had the highest risk for MI or stroke in the first week after discharge (hazard ratio [HR] 4.78, 95% confidence interval [CI] 3.19 to 7.17; risk difference 0.0028, 95% CI 0.0021 to 0.0034), with the risk decreasing rapidly until the 28th day (HR 2.38, 95% CI 1.94 to 2.92; risk difference 0.0045, 95% CI 0.0035 to 0.0056) when the risk stabilized. In a repeated analysis comparing the sepsis cohort with the nonsepsis hospital control cohort, we found an attenuated but still marked elevated risk before day 36 after discharge (HR 1.32, 95% CI 1.15 to 1.52; risk difference 0.0026, 95% CI 0.0013 to 0.0039). The risk of MI or stroke was found to interact with age, with younger patients being associated with a higher risk than older patients (interaction p = 0.0004). INTERPRETATION: Compared with the general population with similar characteristics, patients recovering from sepsis had a markedly elevated risk of MI or stroke in the first 4 weeks after discharge from hospital. More close monitoring and pharmacologic prevention may be required for these patients at the specified time.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.284
Teacher spread0.253 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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