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      Incidence, complications, and costs of peripheral venous catheter-related bacteraemia: a retrospective, single-centre study

      , , , , , , , ,
      Journal of Hospital Infection
      Elsevier BV

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          Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

          Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
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            The Best Use of the Charlson Comorbidity Index With Electronic Health Care Database to Predict Mortality.

            The most used score to measure comorbidity is the Charlson index. Its application to a health care administrative database including International Classification of Diseases, 10th edition (ICD-10) codes, medical procedures, and medication required studying its properties on survival. Our objectives were to adapt the Charlson comorbidity index to the French National Health Insurance database to predict 1-year mortality of discharged patients and to compare discrimination and calibration of different versions of the Charlson index.
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              The risk of bloodstream infection in adults with different intravascular devices: a systematic review of 200 published prospective studies.

              To better understand the absolute and relative risks of bloodstream Infection (BSI) associated with the various types of intravascular devices (IVDs), we analyzed 200 published studies of adults In which every device in the study population was prospectively evaluated for evidence of associated infection and microbiologically based criteria were used to define IVD-related BSI. English-language reports of prospective studies of adults published between January 1, 1966, and July 1, 2005, were identified by MEDLINE search using the following general search strategy: bacteremla [Medical Subject Heading, MeSH] OR septicemia [MeSH] OR bloodstream Infection AND the specific type of intravascular device (e.g., central venous port). Mean rates of IVD-related BSI were calculated from pooled data for each type of device and expressed as BSIs per 100 IVDs (%) and per 1000 IVD days. Point incidence rates of IVD-related BSI were lowest with peripheral Intravenous catheters (0.1%, 0.5 per 1000 IVD-days) and midline catheters (0.4%, 0.2 per 1000 catheter-days). Far higher rates were seen with short-term noncuffed and nonmedicated central venous catheters (CVCs) (4.4%, 2.7 per 1000 catheter-days). Arterial catheters used for hemodynamic monitoring (0.8%, 1.7 per 1000 catheter-days) and peripherally inserted central catheters used in hospitalized patients (2.4%, 2.1 per 1000 catheter-days) posed risks approaching those seen with short-term conventional CVCs used in the Intensive care unit. Surgically implanted long-term central venous devices--cuffed and tunneled catheters (22.5%, 1.6 per 1000 IVD-days) and central venous ports (3.6%, 0.1 per 1000 IVD-days)--appear to have high rates of Infection when risk Is expressed as BSIs per 100 IVDs but actually pose much lower risk when rates are expressed per 1000 IVD-days. The use of cuffed and tunneled dual lumen CVCs rather than noncuffed, nontunneled catheters for temporary hemodlalysis and novel preventive technologies, such as CVCs with anti-infective surfaces, was associated with considerably lower rates of catheter-related BSI. Expressing risk of IVD-related BSI per 1000 IVD-days rather than BSIs per 100 IVDs allows for more meaningful estimates of risk. These data, based on prospective studies In which every IVD in the study cohort was analyzed for evidence of infection by microbiologically based criteria, show that all types of IVDs pose a risk of IVD-related BSI and can be used for benchmarking rates of infection caused by the various types of IVDs In use at the present time. Since almost all the national effort and progress to date to reduce the risk of IVD-related Infection have focused on short-term noncuffed CVCs used in Intensive care units, Infection control programs must now strive to consistently apply essential control measures and preventive technologies with all types of IVDs.
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                Author and article information

                Journal
                Journal of Hospital Infection
                Journal of Hospital Infection
                Elsevier BV
                01956701
                May 2023
                May 2023
                : 135
                : 67-73
                Article
                10.1016/j.jhin.2023.02.012
                36918069
                b923cbec-6987-407e-b6c4-a1dce6338138
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by/4.0/

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