Predicting preload responsiveness using simultaneous recordings of inferior and superior vena cavae diameters
Bibliographic record
Abstract
INTRODUCTION: Echocardiographic indices based on respiratory variations of superior and inferior vena cavae diameters (ΔSVC and ΔIVC, respectively) have been proposed as predictors of fluid responsiveness in mechanically ventilated patients, but they have never been compared simultaneously in the same patient sample. The aim of this study was to compare the predictive value of these echocardiographic indices when concomitantly recorded in mechanically ventilated septic patients. METHODS: Septic shock patients requiring hemodynamic monitoring were prospectively enrolled over a 1-year period in a mixed medical surgical ICU of a university teaching hospital (Toulouse, France). All patients were mechanically ventilated. Predictive indices were obtained by transesophageal and transthoracic echocardiography and were calculated as follows: (Dmax - Dmin)/Dmax for ΔSVC and (Dmax - Dmin)/Dmin for ΔIVC, where Dmax and Dmin are the maximal and minimal diameters of SVC and IVC. Measurements were performed at baseline and after a 7-ml/kg volume expansion using a plasma expander. Patients were separated into responders (increase in cardiac index ≥15%) and nonresponders (increase in cardiac index <15%). RESULTS: Among 44 included patients, 26 (59%) patients were responders (R). ΔSVC was significantly more accurate than ΔIVC in predicting fluid responsiveness. The areas under the receiver operating characteristic curves for ΔSVC and ΔIVC regarding assessment of fluid responsiveness were significantly different (0.74 (95% confidence interval (CI): 0.59 to 0.88) and 0.43 (95% CI: 0.25 to 0.61), respectively (P = 0.012)). No significant correlation between ΔSVC and ΔIVC was found (r = 0.005, P = 0.98). The best threshold values for discriminating R from NR was 29% for ΔSVC, with 54% sensitivity and 89% specificity, and 21% for ΔIVC, with 38% sensitivity and 61% specificity. CONCLUSIONS: ΔSVC was better than ΔIVC in predicting fluid responsiveness in our cohort. It is worth noting that the sensitivity and specificity values of ΔSVC and ΔIVC for predicting fluid responsiveness were lower than those reported in the literature, highlighting the limits of using these indices in a heterogeneous sample of medical and surgical septic patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".