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      Association between cancer‐specific adverse event triggers and mortality: A validation study

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          Abstract

          Background

          As there are few validated measures of patient safety in clinical oncology, creating an efficient measurement instrument would create significant value. Accordingly, we sought to assess the validity of a novel patient safety measure by examining the association of oncology‐specific triggers and mortality using administrative claims data.

          Methods

          We examined a retrospective cohort of 322 887 adult cancer patients enrolled in commercial or Medicare Advantage products for one year after an initial diagnosis of breast, colorectal, lung, or prostate cancer in 2008‐2014. We used diagnosis and procedure codes to calculate the prevalence of 16 cancer‐specific "triggers"–events that signify a potential adverse event. We compared one‐year mortality rates among patients with and without triggers by cancer type and metastatic status using logistic regression models.

          Results

          Trigger events affected 19% of patients and were most common among patients with metastatic colorectal (41%) and lung (50%) cancers. There was increased one‐year mortality among patients with triggers compared to patients without triggers across all cancer types in unadjusted and multivariate analyses. The increased mortality rate among patients with trigger events was particularly striking for nonmetastatic prostate cancer (1.3% vs 7.5%, adjusted odds ratio 1.96 [95% CI 1.49‐2.57]) and nonmetastatic colorectal cancer (4.1% vs 11.7%, 1.44 [1.19‐1.75]).

          Conclusions

          The association between adverse event triggers and poor survival among a cohort of cancer patients supports the validity of a cancer‐specific, administrative claims‐based trigger tool.

          Abstract

          In a retrospective longitudinal cohort of 322 887 newly diagnosed patients with breast, colorectal, lung, or prostate cancer, there was increased one‐year mortality among patients who experienced oncology‐specific claims‐based triggers compared to patients without triggers. Increased mortality was observed across all cancer types, stratified by metastatic status, in unadjusted and multivariate analyses.

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          Most cited references33

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          Fully conditional specification in multivariate imputation

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            Identifying complications of care using administrative data.

            The Complications Screening Program (CSP) is a method using standard hospital discharge abstract data to identify 27 potentially preventable in-hospital complications, such as post-operative pneumonia, hemorrhage, medication incidents, and wound infection. The CSP was applied to over 1.9 million adult medical/surgical cases using 1988 California discharge abstract data. Cases with complications were significantly older and more likely to die, and they had much higher average total charges and lengths of stay than other cases (P < 0.0001). For most case types, 13 chronic conditions, defined using diagnosis codes, increased the relative risks of having a complication after adjusting for patient age. Cases at larger hospitals and teaching facilities generally had higher complication rates. Logistic regression models to predict complications using demographic, administrative, clinical, and hospital characteristics variables, had modest power (C statistics = 0.64 to 0.70). The CSP requires further evaluation before using it for purposes other than research.
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              Computerized surveillance of adverse drug events in hospital patients.

              To develop a new method to improve the detection and characterization of adverse drug events (ADEs) in hospital patients. Prospective study of all patients admitted to our hospital over an 18-month period. LDS Hospital, Salt Lake City, Utah, a 520-bed tertiary care center affiliated with the University of Utah School of Medicine, Salt Lake City. We developed a computerized ADE monitor, and computer programs were written using an integrated hospital information system to allow for multiple source detection of potential ADEs occurring in hospital patients. Signals of potential ADEs, both voluntary and automated, included sudden medication stop orders, antidote ordering, and certain abnormal laboratory values. Each day, a list of all potential ADEs from these sources was generated, and a pharmacist reviewed the medical records of all patients with possible ADEs for accuracy and causality. Verified ADEs were characterized as mild, moderate, or severe and as type A (dose-dependent or predictable) or type B (idiosyncratic or allergic) reactions, and causality was further measured using a standardized scoring method. The number and characterization of ADEs detected. Over 18 months, we monitored 36,653 hospitalized patients. There were 731 verified ADEs identified in 648 patients, 701 ADEs were characterized as moderate or severe, and 664 were classified as type A reactions. During this same period, only nine ADEs were identified using traditional detection methods. Physicians, pharmacists, and nurses voluntarily reported 92 of the 731 ADEs detected using this automated system. The other 631 ADEs were detected from automated signals, the most common of which were diphenhydramine hydrochloride and naloxone hydrochloride use, high serum drug levels, leukopenia, and the use of phytonadione and antidiarrheals. The most common symptoms and signs were pruritus, nausea and/or vomiting, rash, and confusion-lethargy. The most common drug classes involved were analgesics, anti-infectives, and cardiovascular agents. We believe that screening for ADEs with a computerized hospital information system offers a potential method for improving the detection and characterization of these events in hospital patients.
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                Author and article information

                Contributors
                sweingart@tuftsmedicalcenter.org
                Journal
                Cancer Med
                Cancer Med
                10.1002/(ISSN)2045-7634
                CAM4
                Cancer Medicine
                John Wiley and Sons Inc. (Hoboken )
                2045-7634
                13 April 2020
                June 2020
                : 9
                : 12 ( doiID: 10.1002/cam4.v9.12 )
                : 4447-4459
                Affiliations
                [ 1 ] Tufts Medical Center Boston MA USA
                [ 2 ] Department of Medicine Tufts University School of Medicine Boston MA USA
                [ 3 ] OptumLabs Cambridge MA USA
                [ 4 ] Predictive Analytics and Comparative Effectiveness Center Tufts University School of Medicine Boston MA USA
                [ 5 ] Memorial Sloan Kettering Cancer Center New York NY USA
                Author notes
                [*] [* ] Correspondence

                Saul N. Weingart, Tufts Medical Center, 800 Washington St., Boston, MA 02111, USA.

                Email: sweingart@ 123456tuftsmedicalcenter.org

                Author information
                https://orcid.org/0000-0003-3262-9957
                https://orcid.org/0000-0003-2423-2220
                Article
                CAM43033
                10.1002/cam4.3033
                7300390
                32285614
                c32abd54-9eb6-4743-9c66-e2745b9b8d18
                © 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 December 2019
                : 07 March 2020
                : 09 March 2020
                Page count
                Figures: 0, Tables: 5, Pages: 13, Words: 9313
                Funding
                Funded by: AARP Foundation , open-funder-registry 10.13039/100009444;
                Award ID: n/a
                Categories
                Original Research
                Cancer Prevention
                Original Research
                Custom metadata
                2.0
                June 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.4 mode:remove_FC converted:18.06.2020

                Oncology & Radiotherapy
                adverse events,epidemiology,oncology,quality of care,trigger tool
                Oncology & Radiotherapy
                adverse events, epidemiology, oncology, quality of care, trigger tool

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