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      An electronic application for rapidly calculating Charlson comorbidity score

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          Abstract

          Background

          Uncertainty regarding comorbid illness, and ability to tolerate aggressive therapy has led to minimal enrollment of elderly cancer patients into clinical trials and often substandard treatment. Increasingly, comorbid illness scales have proven useful in identifying subgroups of elderly patients who are more likely to tolerate and benefit from aggressive therapy. Unfortunately, the use of such scales has yet to be widely integrated into either clinical practice or clinical trials research.

          Methods

          This article reviews evidence for the validity of the Charlson Comorbidity Index (CCI) in oncology and provides a Microsoft Excel (MS Excel) Macro for the rapid and accurate calculation of CCI score. The interaction of comorbidity and malignant disease and the validation of the Charlson Index in oncology are discussed.

          Results

          The CCI score is based on one year mortality data from internal medicine patients admitted to an inpatient setting and is the most widely used comorbidity index in oncology. An MS Excel Macro file was constructed for calculating the CCI score using Microsoft Visual Basic. The Macro is provided for download and dissemination.

          The CCI has been widely used and validated throughout the oncology literature and has demonstrated utility for most major cancers. The MS Excel CCI Macro provides a rapid method for calculating CCI score with or without age adjustments. The calculator removes difficulty in score calculation as a limitation for integration of the CCI into clinical research. The simple nature of the MS Excel CCI Macro and the CCI itself makes it ideal for integration into emerging electronic medical records systems.

          Conclusions

          The increasing elderly population and concurrent increase in oncologic disease has made understanding the interaction between age and comorbid illness on life expectancy increasingly important. The MS Excel CCI Macro provides a means of increasing the use of the CCI scale in clinical research with the ultimate goal of improving determination of optimal treatments for elderly cancer patients.

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          Most cited references23

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          Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review.

          Many computer software developers and vendors claim that their systems can directly improve clinical decisions. As for other health care interventions, such claims should be based on careful trials that assess their effects on clinical performance and, preferably, patient outcomes. To systematically review controlled clinical trials assessing the effects of computer-based clinical decision support systems (CDSSs) on physician performance and patient outcomes. We updated earlier reviews covering 1974 to 1992 by searching the MEDLINE, EMBASE, INSPEC, SCISEARCH, and the Cochrane Library bibliographic databases from 1992 to March 1998. Reference lists and conference proceedings were reviewed and evaluators of CDSSs were contacted. Studies were included if they involved the use of a CDSS in a clinical setting by a health care practitioner and assessed the effects of the system prospectively with a concurrent control. The validity of each relevant study (scored from 0-10) was evaluated in duplicate. Data on setting, subjects, computer systems, and outcomes were abstracted and a power analysis was done on studies with negative findings. A total of 68 controlled trials met our criteria, 40 of which were published since 1992. Quality scores ranged from 2 to 10, with more recent trials rating higher (mean, 7.7) than earlier studies (mean, 6.4) (P<.001). Effects on physician performance were assessed in 65 studies and 43 found a benefit (66%). These included 9 of 15 studies on drug dosing systems, 1 of 5 studies on diagnostic aids, 14 of 19 preventive care systems, and 19 of 26 studies evaluating CDSSs for other medical care. Six of 14 studies assessing patient outcomes found a benefit. Of the remaining 8 studies, only 3 had a power of greater than 80% to detect a clinically important effect. Published studies of CDSSs are increasing rapidly, and their quality is improving. The CDSSs can enhance clinical performance for drug dosing, preventive care, and other aspects of medical care, but not convincingly for diagnosis. The effects of CDSSs on patient outcomes have been insufficiently studied.
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            Participation of patients 65 years of age or older in cancer clinical trials.

            Although 61% of new cases of cancer occur among the elderly, recent studies indicate that the elderly comprise only 25% of participants in cancer clinical trials. Further investigation into the reasons for low elderly participation is warranted. Our objective was to evaluate the participation of the elderly in clinical trials sponsored by the National Cancer Institute (NCI) and assess the impact of protocol exclusion criteria on elderly participation. We conducted a retrospective analysis using NCI data, analyzing patient and trial characteristics for 59,300 patients enrolled onto 495 NCI-sponsored, cooperative group trials, active from 1997 through 2000. Our main outcome measure was the proportion of elderly patients enrolled onto cancer clinical trials compared with the proportion of incident cancer patients who are elderly. Overall, 32% of participants in phase II and III clinical trials were elderly, compared with 61% of patients with incident cancers in the United States who are elderly. The degree of underrepresentation was more pronounced in trials for early-stage cancers than in trials for late-stage cancers (P <.001). Furthermore, protocol exclusion criteria on the basis of organ-system abnormalities and functional status limitations were associated with lower elderly participation. We estimate that if protocol exclusions were relaxed, elderly participation in cancer trials would be 60%. The elderly are underrepresented in cancer clinical trials relative to their disease burden. Older patients are more likely to have medical histories that make them ineligible for clinical trials because of protocol exclusions. Insurance coverage for clinical trials is one step toward improvement of elderly access to clinical trials. Without a change in study design or requirements, this step may not be sufficient.
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              The effect of comorbidity on 3-year survival of women with primary breast cancer.

              To determine the effect of comorbidity and stage of disease on 3-year survival in women with primary breast cancer. Longitudinal, observational study. Metropolitan Detroit. 936 women ages 40 to 84 years. Data on stage of breast cancer, treatment type, and comorbidity were obtained from Metropolitan Detroit Cancer Surveillance System (MDCSS) files and medical records. Personal interviews were the source of information on social and behavioral factors. Vital status and cause of death were obtained from MDCSS files. Patients who had 3 or more of 7 selected comorbid conditions had a 20-fold higher rate of mortality from causes other than breast cancer and a 4-fold higher rate of all-cause mortality when compared with patients who had no comorbid conditions. The effects of comorbidity were independent of age, disease stage, tumor size, histologic type, type of treatment, race, and social and behavioral factors. Moreover, women with severe comorbid conditions had uniformly higher mortality rates, and early diagnosis in these women conferred no survival advantage. Comorbidity in patients with breast cancer appears to be a strong predictor of 3-year survival, independent of the effects of breast cancer stage. This finding suggest that trials assessing the efficacy of screening should routinely include measures of comorbidity.
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                Author and article information

                Journal
                BMC Cancer
                BMC Cancer
                BioMed Central (London )
                1471-2407
                2004
                20 December 2004
                : 4
                : 94
                Affiliations
                [1 ]University of California, Davis, Department of Radiation Oncology, UC Davis Cancer Center, 4501 X Street, G126, Sacramento, CA 95817 USA
                [2 ]University of Chicago, Department of Radiation and Cellular Oncology, 5758 S Maryland Ave, MC 9006, Chicago, IL 60637, USA
                Article
                1471-2407-4-94
                10.1186/1471-2407-4-94
                545968
                15610554
                c3161bec-0fa6-403b-b383-68599f576a4b
                Copyright © 2004 Hall et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 March 2004
                : 20 December 2004
                Categories
                Software

                Oncology & Radiotherapy
                Oncology & Radiotherapy

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