Re: The burden of death following discharge after lobectomy
Notice bibliographique
Résumé
The current issue of the European Journal of Cardio-Thoracic Surgery publishes the remarkable work of Schneider et al. [1], which contributes to a deeper understanding of actual postoperative mortality—presently differentiated as in-hospital mortality (IHM) and 90-day post-discharge mortality (PDM)—in the framework of pulmonary lobectomy for lung cancer. For this purpose, data from patients who underwent lobectomy over a 7-year period (2005–11) were queried from an Ontario population-based database. Of 5389 patients who underwent lobectomy for non-small-cell lung carcinoma (NSCLC), the median length of stay was 6 (1–30) days. IHM (n = 73) was 1.4% (1.1–1.6%) whereas PDM (n = 101) was an additional 1.9% (1.6–2.3%) within 90-day post-lobectomy discharge. When searching for potential predictors of mortality, the following eight variables were found to be significant predictors of IHM: age [odds ratio (OR) = 1.5], myocardial infarction (OR = 3.6), congestive heart failure (OR = 5.8), chronic obstructive pulmonary disease (OR = 1.9), preoperative positive emission tomography (OR = 2.7), peptic ulcer disease (OR = 22.1), hemiplegia (OR = 15.8) and other primary cancer (OR = 0.5). In addition, logistic regression showed that length of hospital stay [hazard ration (HR) = 1.1], male gender (HR = 1.5), age (HR = 1.1) and metastatic cancer were potential predictors of 90-day PDM. The authors concluded that PDM represents a substantive under-reported burden of mortality due to lobectomy. Patient factors play a significant role in both IHM and PDM. Finally, they emphasize that more than half of post-lobectomy mortality occurs post-discharge and the annual rate remained unchanged while IHM decreased with time, suggesting that the improvement seen in mortality might be exclusive to the smaller IHM. The current article is the ‘mirror manuscript’ of another work published by the same team from the same Ontario database, on pneumonectomy [2]. In addition, numerous studies dealing with PDM after resection for lung cancer have been recently published [3–5], showing that this issue is gaining notoriety and concern within the surgical field. Schneider et al. [1] nicely demonstrated that patients undergoing lobectomy for NSCLC experience a greater risk of death after discharge from hospital compared with when they are admitted as in-patients. This raises legitimate questions as to the actual delivery of quality care and firstly, whether anything might have been done differently at the time of the discharge evaluation or immediately after discharge to prevent these deaths. As pointed out by McMillan et al. [3], a closer monitoring after discharge with earlier and repeated follow-up visits, better coordination with primary physicians and regular home visits by care providers in these high-risk patients could lead to earlier detection of problems and improvement outcomes, but there is no evidence yet to support this strategy in the literature. However, according to this stimulating article's results, we implicitly understand that a deep exploration of the post-discharge timeframe would need to be urgently undertaken to try to elucidate reasons for this elevated mortality. This might be the clue not only to more precisely assess already known predictive factors of mortality on which prevention and/or potential intervention can be done (targeted arrhythmia management, venous thrombo-embolism prophylaxis, enhance care of elderly population, for example), but also, going one step further, to shed light on a probable insufficiency of the healthcare system and try to reverse this weakness in a well-structured postoperative quality improvement care programme. For this purpose, the recommendation that national or international registries—such as the Society of Thoracic Surgeons or European Society of Thoracic Surgeons databases—consider the inclusion of 90-day mortality in their data collection to be of major importance. Indeed, it may well contribute in the near future to a powerful analysis of this critical period, by providing a more reliable and precise estimate of death rates and their potential predictors. In the meantime, Schneider et al. are to be congratulated on their investigations in this area. From the standpoint of medical care, their results will certainly prove to be most beneficial to the thoracic surgery community.
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 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 ».