Dynamic Changes in Right Ventricular Function But Not Left Ventricular Function Are Predictive of Outcomes in Critically-ill Patients With Sepsis
Bibliographic record
Abstract
Abstract Rationale: Cardiac dysfunction in sepsis affects approximately 20% of admission. However, most studies are limited by point-prevalence estimates and/or to left ventricular (LV) dysfunction. Our objectives were to evaluate: 1) dynamic changes in global cardiac function from pre- to sepsis onset; 2) the prognostic value of cardiac phenotypes incorporating LV and right ventricular dysfunction (using tricuspid annular plane systolic excursion; TAPSE). Method: We analyzed a retrospective observational cohort of critically-ill adults admitted to a quaternary ICU with sepsis/septic shock (2010-2020). The pre-sepsis cohort (N=1,230) consisted of patients with echocardiogram within 72 hours of index admission and pre-sepsis echocardiogram performed in non-critical settings in the preceding 365 days, provided no significant cardiovascular events occurred in the interim. We used adjusted mixed effects models to evaluate for temporal changes in echocardiogram findings and in-hospital mortality. Given the importance of RV dysfunction in this analysis, we used driven cut-offs to derive distinct phenotypes (N=1,915) using during-sepsis echocardiogram findings (LV ejection fraction; LVEF and TAPSE). We used multivariable logistic regression models to evaluate the association of phenotypes with in-hospital mortality. Result: In the pre-sepsis cohort, 65% had septic shock, 29.8% chronic heart failure, and 47.7% were mechanically ventilated, with a mortality of 32%. Only 26.5% of patients had >10% change in their LVEF from baseline. Notably, most patients remained in the same LVEF categories during sepsis as pre-sepsis, with only 5.4% making transitions beyond one LVEF category (Figure A). In the mixed effects model, a change in TAPSE (-0.21(-0.28 – -0.15)), but not LVEF -0.46(-1.83-0.91)), was associated with in-hospital mortality. In the phenotype cohort, we identified five distinct phenotypes: phenotype I had normal LV/RV (54.3%), phenotype II had hyperdynamic LV (11.2%), phenotype III had Isolated LV dysfunction (8.1%), phenotype IV had isolated RV dysfunction (22.0%), and phenotype V had LV and RV dysfunction (4.3%). Phenotypes IV (OR 1.56, 95%CI:1.12-2.17) and V (OR: 2.27, 95%CI:1.41-3.63) were associated with higher in-hospital mortality compared to Phenotype I (Figure B). These findings were consistent in a sensitivity analysis only including septic shock patients. Conclusion: Most patients maintain their pre-sepsis LVEF during sepsis. Notably, decreased RV and not LV function were associated with death. During sepsis, a combination of LVEF and TAPSE can delineate distinct cardiovascular phenotypes with distinct outcomes, which may lead to more precision-based approaches for cardiovascular care in sepsis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".