C-Reactive Protein Monitoring Identifies Urinary Tract Infections in Ambulatory Kidney Transplant Recipients
Notice bibliographique
Résumé
Background: Urinary tract infections (UTI) are common in kidney transplant recipients (KTR). Although risk factors for UTI are well described, predicting symptomatic UTI with positive urine cultures in the first posttransplant year is challenging. Objective: Our clinic routinely monitors serum highly sensitive C-reactive protein (CRP) as part of posttransplant care. We sought to define the role of CRP in identifying symptomatic UTI in KTR. Design: Nested case control study Setting: A large adult single-organ kidney transplant center in Toronto, Canada. Patients: We identified a nested cohort of 78 KTR who experienced a symptomatic UTI with positive urine cultures (cases) and compared them to a cohort of 78 KTR controls matched by time elapsed posttransplant. Measurements: Patient demographics, urine cultures, CRP, and kidney function during the first posttransplant year. Methods: We identified a cohort of KTR transplanted between January 1, 2016, and December 31, 2019. A positive urine culture ordered only for clinical indication in the first posttransplant year identified KTR with a UTI defined >10 5 colony forming units/mL. UTI cases were matched 1:1 to non-UTI controls transplanted immediately preceding or succeeding the UTI case. Bivariate comparisons were performed by t test, Wilcoxon 2-sample test for continuous variables, chi-square, or Fisher’s exact test as appropriate, with clinically significant variables entered into multivariable logistic regression models to determine associations. Results: Older age, female sex, and the presence of a stent were each associated with a UTI. Immediately preceding UTI, eGFR ( P = .019), serum albumin ( P < .0001), and hemoglobin ( P = .002) were lower, while serum CRP ( P < .0001) and absolute neutrophils ( P = .03) were higher in cases than controls. However, in several multivariable models, only absolute CRP ( P = .001), change in CRP ( P = .005), female sex ( P < .0001), and ureteric stent ( P = .008) consistently predicted a UTI. Each 5 mg/dL change between the 2 preceding CRP values predicted a 15% increased likelihood of UTI, while each 1 mg/dL in absolute CRP concentration was associated with a 5% risk. Limitations: Retrospective case-control design, single-center, small sample size. Hospital inpatients and patients with other infections, acute inflammatory conditions, or rejection were excluded. Urine infections may more easily be detected when patients visit the clinic frequently. Conclusions: Routine ambulatory CRP monitoring in the first year may help identify subsequent symptomatic UTI in KTR, allow for the initiation of earlier therapy, and reduce patient morbidity. What was known before? UTI in KTR are common in the first posttransplant year. Antibiotic therapy is typically not initiated until the results of urine cultures become known. What this adds: The routine use of appropriate biomarkers such as CRP as part of a posttransplant monitoring strategy may allow clinicians to order urine cultures, help identify UTI earlier, and start therapy sooner, promoting patient well-being.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».