James P Kahan

Hôtel-Dieu de Paris – Hôpitaux universitaires Paris Centre, Paris, Ile-de-France, France

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Publications (2)24.8 Total impact

  • Source
    Article: An electronic tool for venous thromboembolism prevention in medical and surgical patients.
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    ABSTRACT: Venous thromboembolism (VTE) is a complex disorder influenced by numerous risk factors, and occurs frequently in at-risk hospitalized patients. Because appropriate prevention with thromboprophylaxis is underused, we wanted to create an electronic tool to provide a simple risk assessment and suggest appropriate prophylaxis. To develop the risk matrix, iterative rating of odds ratios was performed for 60 predisposing VTE risk factors, using analytical methods that account for multiple risk factors in a single patient and their non-independence. For exposing risk factors, a single score was assigned to each set of factors, both medical (25 items) and surgical conditions (144 items). A CART regression model was used to integrate the risk scales into a 4-level measure of overall risk. The validity of the level of risk and the appropriateness of 11 different prophylactic approaches was assessed using the RAND/UCLA appropriateness method and validated by expert opinion ratings (n=1998) on sample case scenarios (n=108). Correlation between the level of risk calculated by the risk matrix and that offered by expert opinion for individual surgical and medical clinical cases was high (65% and 70%, respectively). The matrix over-estimated the level of risk, compared with that offered by expert opinion, in 28% and 20% of surgical and medical cases, respectively, but the appropriate prophylaxis suggested was no different. Between-expert agreement on the appropriateness of the prophylaxis recommendations was high (90-94% of indications). This computer-based electronic tool for individualized assessment of venous thromboembolic risk successfully identified both the perceived risk of thrombosis and the appropriate prophylactic approach for medical and surgical patients.
    Haematologica 02/2006; 91(1):64-70. · 6.42 Impact Factor
  • Article: OncoSurge: a strategy for improving resectability with curative intent in metastatic colorectal cancer.
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    ABSTRACT: Most patients with colorectal liver metastases present to general surgeons and oncologists without a specialist interest in their management. Since treatment strategy is frequently dependent on the response to earlier treatments, our aim was to create a therapeutic decision model identifying appropriate procedure sequences. We used the RAND Corporation/University of California, Los Angeles Appropriateness Method (RAM) assessing strategies of resection, local ablation and chemotherapy. After a comprehensive literature review, an expert panel rated appropriateness of each treatment option for a total of 1,872 ratings decisions in 252 cases. A decision model was constructed, consensus measured and results validated using 48 virtual cases, and 34 real cases with known outcomes. Consensus was achieved with overall agreement rates of 93.4 to 99.1%. Absolute resection contraindications included unresectable extrahepatic disease, more than 70% liver involvement, liver failure, and being surgically unfit. Factors not influencing treatment strategy were age, primary tumor stage, timing of metastases detection, past blood transfusion, liver resection type, pre-resection carcinoembryonic antigen (CEA), and previous hepatectomy. Immediate resection was appropriate with adequate radiologically-defined resection margins and no portal adenopathy; other factors included presence of < or = 4 or > 4 metastases and unilobar or bilobar involvement. Resection was appropriate postchemotherapy, independent of tumor response in the case of < or = 4 metastases and unilobar liver involvement. Resection was appropriate only for > 4 metastases or bilobar liver involvement, after tumor shrinkage with chemotherapy. When possible, resection was preferred to local ablation. The results were incorporated into a decision matrix, creating a computer program (OncoSurge). This model identifies individual patient resectability, recommending optimal treatment strategies. It may also be used for medical education.
    Journal of Clinical Oncology 10/2005; 23(28):7125-34. · 18.37 Impact Factor