High levels of mid-regional proadrenomedullin in ARDS COVID-19 patients: the experience of a single, Italian Center.
Bibliographic record
Abstract
OBJECTIVE: This study evaluated the ability of mid-regional proadrenomedullin (MR-proADM) to identify disease severity in Coronavirus disease 2019 (COVID-19) patients in comparison to conventional inflammatory biomarkers and clinical scores. PATIENTS AND METHODS: In an observational trial, COVID-19 acute respiratory distress syndrome (ARDS) patients were enrolled. MR-proADM, C-reactive protein (CRP), procalcitonin (PCT) and lactic acid (LA) were measured in all patients at admission (T0), at 24 hours (T1) and in the third (T3) and fifth day (T5) of hospitalization. The aims of this study were to determine the role of MR-proADM to detect patients with high risk of mortality and compare the prognostic value of MR-proADM with commonly used clinical scores (Sequential Organ Failure Assessment score - SOFA score, Acute Physiologic Assessment and Chronic Health Evaluation II score - APACHE II score, and Simplified Acute Physiological score II - SAPS II score). RESULTS: Twenty-one COVID-19 ARDS patients admitted to the Intermediate Care Unit (IMCU) were enrolled. The median MR-proADM values were 2.28, 2.41, 1.96 and 1.89 nmol/L at T0, T1, T3 and T5, respectively. The 30-day all-cause mortality rate was 52.4%. Mean MR-proADM T0 value was significantly higher in non-survivors compared with survivors (3.5 vs. 1.1 nmol/L, p < 0.05). No significant differences were found for the other inflammatory biomarkers. In terms of the area under the receiver-operating characteristic curve (AUC), MR-proADM showed a similar discriminatory power compared with APACHE II, SOFA and SAPS II score (0.81, 0.91, 0.70 and 0.78, respectively). The optimal MR-proADM cut-point cut-off point was 1.07 nmol/L, which corresponds to a sensitivity of 91% and a specificity of 71%. CONCLUSIONS: MR-proADM, in addition to the clinical scores, could be useful to predict outcome in COVID-19 ARDS patients.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".