Demographics, Care Patterns, and Outcomes of Patients Admitted to Cardiac Intensive Care Units
Notice bibliographique
Résumé
Importance: Single-center and claims-based studies have described substantial changes in the landscape of care in the cardiac intensive care unit (CICU). Professional societies have recommended research to guide evidence-based CICU redesigns. Objective: To characterize patients admitted to contemporary, advanced CICUs. Design, Setting, and Participants: This study established the Critical Care Cardiology Trials Network (CCCTN), an investigator-initiated multicenter network of 16 advanced, tertiary CICUs in the United States and Canada. For 2 months in each CICU, data for consecutive admissions were submitted to the central data coordinating center (TIMI Study Group). The data were collected and analyzed between September 2017 and 2018. Main Outcomes and Measures: Demographics, diagnoses, management, and outcomes. Results: Of 3049 participants, 1132 (37.1%) were women, 797 (31.4%) were individuals of color, and the median age was 65 years (25th and 75th percentiles, 55-75 years). Between September 2017 and September 2018, 3310 admissions were included, among which 2557 (77.3%) were for primary cardiac problems, 337 (10.2%) for postprocedural care, 253 (7.7%) for mixed general and cardiac problems, and 163 (4.9%) for overflow from general medical ICUs. When restricted to the initial 2 months of medical CICU admissions for each site, the primary analysis population included 3049 admissions with a high burden of noncardiovascular comorbidities. The top 2 CICU admission diagnoses were acute coronary syndrome (969 [31.8%]) and heart failure (567 [18.6%]); however, the proportion of acute coronary syndrome was highly variable across centers (15%-57%). The primary indications for CICU care included respiratory insufficiency (814 [26.7%]), shock (643 [21.1%]), unstable arrhythmia (521 [17.1%]), and cardiac arrest (265 [8.7%]). Advanced CICU therapies or monitoring were required for 1776 patients (58.2%), including intravenous vasoactive medications (1105 [36.2%]), invasive hemodynamic monitoring (938 [30.8%]), and mechanical ventilation (652 [21.4%]). The overall CICU mortality rate was 8.3% (95% CI, 7.3%-9.3%). The CICU indications that were associated with the highest mortality rates were cardiac arrest (101 [38.1%]), cardiogenic shock (140 [30.6%]), and the need for renal replacement therapy (51 [34.5%]). Notably, patients admitted solely for postprocedural observation or frequent monitoring had a mortality rate of 0.2% to 0.4%. Conclusions and Relevance: In a contemporary network of tertiary care CICUs, respiratory failure and shock predominated indications for admission and carried a poor prognosis. While patterns of practice varied considerably between centers, a substantial, low-risk population was identified. Multicenter collaborative networks, such as the CCCTN, could be used to help redesign cardiac critical care and to test new therapeutic strategies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».