Letter to Editor: Using Proper Methods to Identify Patients With Cirrhosis in Administrative Databases Is Crucial to Correctly Predict Outcomes
Notice bibliographique
Résumé
Potential conflict of interest: Dr. Swain advises and received grants from Gilead, Intercept, Allergan, and Novartis. He is on the speakers' bureau for Abbott. He received grants from CymaBay, GSK, and Genkyotex. To the Editor: We would like to congratulate Mumtaz et al.1 for their important study attempting to develop and validate a risk score to predict 30‐day hospital readmission in patients with decompensated cirrhosis using the US nationwide readmission database (NRD). Identifying patients with cirrhosis at high risk for readmission is critical for developing processes to effectively address this problem. However, flaws in patient identification challenge the utility of the risk score outlined in this study. Specifically, the authors failed to discuss their observed high readmission rates in the context of published data from the large multistate study by Tapper et al.2 The NRD consists of patient data from multiple state inpatient databases (SID). Tapper et al. used SID data from five large geographically diverse states to describe 12.9% to 24.2% 30‐day readmission rates in patients with 1‐3 cirrhosis decompensation features, respectively. In contrast, Mumtaz et al. report a baseline 30‐day readmission rate of 27%—an unexpected significant difference in readmission rates, given that the NRD is based on SID. This variation could be due to the case definition of decompensated cirrhosis. Specifically, the authors defined decompensated cirrhosis as the presence of cirrhosis plus any of the following: ascites, hepatic encephalopathy, variceal bleeding, or spontaneous bacterial peritonitis. However, in reviewing their coding, the International Classification of Disease Ninth Edition (ICD‐9) clinical modification codes 348.30, 348.39, and 780.97 were included, which describe general encephalopathy or altered mental status. The authors cite two studies to support the use of these codes. However, no study has in fact validated these codes in patients with cirrhosis. Similarly, the authors used nonvalidated codes for coagulopathy. There are important studies describing the validity of ICD‐9 codes to identify patients with cirrhosis.3 The use of well‐validated codes to identify patients with cirrhosis in administrative databases is critical for accuracy of conclusions made from using the data. We re‐extracted the cohort of patients with cirrhosis from the NRD 2013 (n = 14,325,172) using the authors' codes and methodology (cirrhosis plus at least one decompensation condition) and identified 107,317 patients with decompensated cirrhosis, compared with 175,761 patients used for the authors' analysis. Therefore, it is very likely that other nonvalidated decompensation features such as coagulopathy were included in their case definition. An additional concern is the inclusion of patients with cirrhosis who were readmitted for non‐cirrhosis‐related conditions, such as epilepsy or pneumonia, which would increase readmission rates.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».