Updates on the Risk of Dalteparin Thromboprophylaxis Failure in Hospitalized Acutely Ill Medical Patients with Cancer (ARTIC STUDY)
Notice bibliographique
Résumé
Abstract Background Hospitalization is a significant risk factor for venous thromboembolism (VTE) with 25% of all VTE occurring in this setting. In patients with cancer this risk may be higher due to the inherent procoagulant state malignancy-induced, -cancer therapy and its complications. Current oncology guidelines suggest that hospitalized medical patients receive venous thromboprophylaxis with unfractionated heparin or low molecular weight heparin if their hospital stay is longer than 3 days. We sought to evaluate if patients with cancer hospitalized for management of an acute medical illness are at higher risk for failing standard anticoagulation prophylaxis with dalteparin compared to historical data. Methods This is a single-centre retrospective cohort study (London, Canada). We included adult patients; with any type of active cancer admitted for at least 3 days for treatment of an acute medical reason, who received prophylaxis with dalteparin during hospital stay. Acute medical illness was: failure to thrive; fever; need for cancer treatment as an inpatient; pain control; acute respiratory illness. The main study outcome was failure of VTE prophylaxis defined as symptomatic and objectively diagnosed pulmonary embolism (PE) or deep venous thrombosis (DVT) within 3 months of the most recent hospital discharge. Results Between January 2011 and December 2013 our hospital registered 4262 admissions of patients with cancer for treatment of an acute medical illness. 875 patients (1132 admissions) fulfilled our eligibility criteria. 681 (78%) patients were classified as having a single admission. Of those, 247 had previous but excluded admissions ("pseudo-single"), which leaves 434 patients with true single admissions. In total, there were 434 (49.5%) were males, mean age was 64.3 (SD= 13.5). Primary tumor sites were hematological (n=180); genitourinary (n= 170); lung (n=158); gastrointestinal (n=128) and others (n= 289).559 (70%) patients had stage III or IV. Reasons for admission were failure to thrive (n= 232; 26.6%); fever (n= 202; 23.3%); need for cancer treatment as inpatient (n= 154; 17.7%); pain (n=126; 14.5%); respiratory distress(n= 108; 11.6%) or pain (n= 53; 6.3%). Mean hospitalization days was 14.7 (±12). VTE occurred in 78 of 875 (8.9%) patients or 78 of 1132 admission (6.9%): 36(46%) DVT, 34 (43.5%) PE and 7 PE + DVT (8.9%). 34 of 78 (43.6%) VTE occurred within the first 14 days of admission. However, the overall risk for VTE appeared to be much more significant if the patient remained hospitalized for more than 14 days [RR=3.7 (95%CI= 1.99 - 4.71; p<0.001)]. 150 (15%) patients with single admissions had a concomitant diagnosis of cancer and VTE (within 30 days of admission). A univariate analysis suggested that having had multiple admissions (OR=0.3; 95%CI=0.17- 0.54; p=0.008); being man (OR= 1.69; 95%CI: 1.03 - 2.78; p=0.039); being admitted due to respiratory distress (OR=2.6; 95%CI: 0.9 - 6.8; p=0.052) or failure to thrive (2.52; 95%CI: 1.06 - 5.9; p=0.036) were potentially significant predictors of VTE risk. However, the logistic regression confirmed that the only significant risk factor is the number of admissions (Table). When we compared our results with the pooled data from the MEDENOX, PREVENT and ARTEMIS trials we found that the incidence of VTE in the cancer patients included in the trials was the same as ours [13 of 143 patients (8.8%)] and the incidence of VTE in those without cancer was 95 of 2139 (4.5%), significantly lower compared to the cancer cohort (p=0.0004). Conclusion Our study suggests that hospitalized patients with active cancer are at high risk for VTE prophylaxis failure (8.9%) and our results are in keeping with the literature. It appears that the most important risk factor for thromboprophylaxis failure is having a first admission as a cancer patient. New VTE prophylactic strategies for this population should be investigated in future prospective studies. Table 1. Logistic Regression of potential predictors of VTE risk in hospitalized cancer patients Variable Odds Ratio 95% CI p -value Male 1.05 0.64 - 1.74 0.8270 "Pseudo-single" admission* 0.53 0.28 - 0.97 0.9182 Multiple admission* 0.29 0.16 - 0.53 0.0029 Respiratory distress 1.19 0.53 - 2.68 0.7597 Pain 1.27 0.59 - 2.71 0.5079 Failure to thrive 1.02 0.46 - 2.24 0.6820 *Reference: single admission Disclosures Louzada: janssen: Consultancy, Other: advisory board and expert opinion; pfizer: Consultancy, Other: advisory board and expert opinion; Celegene: Consultancy, Other: advisory board and expert opinion. Kovacs:Pfizer: Honoraria, Research Funding; Bayer: Honoraria, Research Funding; LEO Pharma: Honoraria; Daiichi Sankyo Pharma: Research Funding.
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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 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».