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  1. AU="Pistoia, Matteo"
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  1. Article ; Online: No contraindication to internal jugular central venous catheter insertion in patients at increased risk of bleeding: Results from a prospective observational study in a internal medicine department.

    Mumoli, Nicola / Mereghetti, Marco / Capra, Riccardo / Pistoia, Matteo / Dalla Torre, Laura / Dentali, Francesco / Giarretta, Igor

    The journal of vascular access

    2024  , Page(s) 11297298241227248

    Abstract: Implantation of centrally inserted central venous catheter (CICC) may be complicated by bleedings particularly in patients with severe coagulopathy or taking antithrombotic drugs. It has been shown that the application of the Italian Group for Venous ... ...

    Abstract Implantation of centrally inserted central venous catheter (CICC) may be complicated by bleedings particularly in patients with severe coagulopathy or taking antithrombotic drugs. It has been shown that the application of the Italian Group for Venous Access Devices (GAVeCeLT) bundle reduces the incidence of bleeding in patients admitted to intensive care units (ICU), but its effectiveness has never been demonstrated in different contexts. In this study we evaluated the incidence of bleeding after urgent internal jugular CICC (J-CICC) implantation in patients with increased or no risk of bleeding complications when recommended preventive strategies are applied systematically. We included 185 patients admitted to Internal Medicine Units who underwent urgent J-CICC implantation from April 2016 to December 2018. The incidence of major and minor bleeding immediately after the procedure and in the following 30 days was recorded. None of the enrolled patients showed major bleeding. The incidence of minor bleedings was 2.1% (95% IC: 0.03-4.2) with two patients requiring line removal and repositioning (1.1%; 95% IC: -0.45 to 2.6). Bleeds were not correlated with age or sex, although they all occurred in female subjects. The incidence of bleeds was not increased in patients with increased risk of bleeding compared with those without (5.0% vs 1.3%;
    Language English
    Publishing date 2024-02-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2252820-9
    ISSN 1724-6032 ; 1129-7298
    ISSN (online) 1724-6032
    ISSN 1129-7298
    DOI 10.1177/11297298241227248
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Utility of intestinal ultrasound in the diagnosis and short-term follow-up of non-steroidal anti-inflammatory drug-induced enteropathy.

    Pistoia, Matteo / Perrone, Tiziano / Fiengo, Anna / Lenti, Marco Vincenzo / Di Sabatino, Antonio

    Internal and emergency medicine

    2019  Volume 15, Issue 4, Page(s) 729–731

    MeSH term(s) Aged ; Anti-Inflammatory Agents, Non-Steroidal/adverse effects ; Female ; Humans ; Intestinal Diseases/chemically induced ; Intestinal Diseases/diagnostic imaging ; Ultrasonography/methods
    Chemical Substances Anti-Inflammatory Agents, Non-Steroidal
    Language English
    Publishing date 2019-08-01
    Publishing country Italy
    Document type Case Reports ; Letter
    ZDB-ID 2454173-4
    ISSN 1970-9366 ; 1828-0447
    ISSN (online) 1970-9366
    ISSN 1828-0447
    DOI 10.1007/s11739-019-02161-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Derivation and validation of the clinical prediction model for COVID-19.

    Foieni, Fabrizio / Sala, Girolamo / Mognarelli, Jason Giuseppe / Suigo, Giulia / Zampini, Davide / Pistoia, Matteo / Ciola, Mariella / Ciampani, Tommaso / Ultori, Carolina / Ghiringhelli, Paolo

    Internal and emergency medicine

    2020  Volume 15, Issue 8, Page(s) 1409–1414

    Abstract: The epidemic phase of Coronavirus disease 2019 (COVID-19) made the Worldwide health system struggle against a severe interstitial pneumonia requiring high-intensity care settings for respiratory failure. A rationalisation of resources and a specific ... ...

    Abstract The epidemic phase of Coronavirus disease 2019 (COVID-19) made the Worldwide health system struggle against a severe interstitial pneumonia requiring high-intensity care settings for respiratory failure. A rationalisation of resources and a specific treatment path were necessary. The study suggests a predictive model drawing on clinical data gathered by 119 consecutive patients with laboratory-confirmed COVID-19 admitted in Busto Arsizio hospital. We derived a score that identifies the risk of clinical evolution and in-hospital mortality clustering patients into four groups. The study outcomes have been compared across the derivation and validation samples. The prediction rule is based on eight simple patient characteristics that were independently associated with study outcomes. It is able to stratify COVID-19 patients into four severity classes, with in-hospital mortality rates of 0% in group 1, 6-12.5% in group 2, 7-20% in group 3 and 60-86% in group 4 across the derivation and validation sample. The prediction model derived in this study identifies COVID-19 patients with low risk of in-hospital mortality and ICU admission. The prediction model that the study presents identifies COVID-19 patients with low risk of in-hospital mortality and admission to ICU. Moreover, it establishes an intermediate portion of patients that should be treated accurately in order to avoid an unfavourable clinical evolution. A further validation of the model is important before its implementation as a decision-making tool to guide the initial management of patients.
    MeSH term(s) Adult ; Aged ; Aged, 80 and over ; COVID-19 ; Clinical Decision Rules ; Coronavirus Infections/diagnosis ; Coronavirus Infections/physiopathology ; Female ; Humans ; Male ; Middle Aged ; Pandemics ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/physiopathology ; Reproducibility of Results ; Risk Assessment/methods ; Risk Assessment/standards ; Risk Assessment/statistics & numerical data ; Severity of Illness Index
    Keywords covid19
    Language English
    Publishing date 2020-09-15
    Publishing country Italy
    Document type Journal Article
    ZDB-ID 2454173-4
    ISSN 1970-9366 ; 1828-0447
    ISSN (online) 1970-9366
    ISSN 1828-0447
    DOI 10.1007/s11739-020-02480-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Derivation and validation of the clinical prediction model for COVID-19

    Foieni, Fabrizio / Sala, Girolamo / Mognarelli, Jason Giuseppe / Suigo, Giulia / Zampini, Davide / Pistoia, Matteo / Ciola, Mariella / Ciampani, Tommaso / Ultori, Carolina / Ghiringhelli, Paolo

    Intern Emerg Med

    Abstract: The epidemic phase of Coronavirus disease 2019 (COVID-19) made the Worldwide health system struggle against a severe interstitial pneumonia requiring high-intensity care settings for respiratory failure. A rationalisation of resources and a specific ... ...

    Abstract The epidemic phase of Coronavirus disease 2019 (COVID-19) made the Worldwide health system struggle against a severe interstitial pneumonia requiring high-intensity care settings for respiratory failure. A rationalisation of resources and a specific treatment path were necessary. The study suggests a predictive model drawing on clinical data gathered by 119 consecutive patients with laboratory-confirmed COVID-19 admitted in Busto Arsizio hospital. We derived a score that identifies the risk of clinical evolution and in-hospital mortality clustering patients into four groups. The study outcomes have been compared across the derivation and validation samples. The prediction rule is based on eight simple patient characteristics that were independently associated with study outcomes. It is able to stratify COVID-19 patients into four severity classes, with in-hospital mortality rates of 0% in group 1, 6-12.5% in group 2, 7-20% in group 3 and 60-86% in group 4 across the derivation and validation sample. The prediction model derived in this study identifies COVID-19 patients with low risk of in-hospital mortality and ICU admission. The prediction model that the study presents identifies COVID-19 patients with low risk of in-hospital mortality and admission to ICU. Moreover, it establishes an intermediate portion of patients that should be treated accurately in order to avoid an unfavourable clinical evolution. A further validation of the model is important before its implementation as a decision-making tool to guide the initial management of patients.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #758227
    Database COVID19

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  5. Article ; Online: Derivation and validation of the clinical prediction model for COVID-19

    Foieni, Fabrizio / Sala, Girolamo / Mognarelli, Jason Giuseppe / Suigo, Giulia / Zampini, Davide / Pistoia, Matteo / Ciola, Mariella / Ciampani, Tommaso / Ultori, Carolina / Ghiringhelli, Paolo

    Internal and Emergency Medicine ; ISSN 1828-0447 1970-9366

    2020  

    Abstract: Abstract The epidemic phase of Coronavirus disease 2019 (COVID-19) made the Worldwide health system struggle against a severe interstitial pneumonia requiring high-intensity care settings for respiratory failure. A rationalisation of resources and a ... ...

    Abstract Abstract The epidemic phase of Coronavirus disease 2019 (COVID-19) made the Worldwide health system struggle against a severe interstitial pneumonia requiring high-intensity care settings for respiratory failure. A rationalisation of resources and a specific treatment path were necessary. The study suggests a predictive model drawing on clinical data gathered by 119 consecutive patients with laboratory-confirmed COVID-19 admitted in Busto Arsizio hospital. We derived a score that identifies the risk of clinical evolution and in-hospital mortality clustering patients into four groups. The study outcomes have been compared across the derivation and validation samples. The prediction rule is based on eight simple patient characteristics that were independently associated with study outcomes. It is able to stratify COVID-19 patients into four severity classes, with in-hospital mortality rates of 0% in group 1, 6–12.5% in group 2, 7–20% in group 3 and 60–86% in group 4 across the derivation and validation sample. The prediction model derived in this study identifies COVID-19 patients with low risk of in-hospital mortality and ICU admission. The prediction model that the study presents identifies COVID-19 patients with low risk of in-hospital mortality and admission to ICU. Moreover, it establishes an intermediate portion of patients that should be treated accurately in order to avoid an unfavourable clinical evolution. A further validation of the model is important before its implementation as a decision-making tool to guide the initial management of patients.
    Keywords Internal Medicine ; Emergency Medicine ; covid19
    Language English
    Publisher Springer Science and Business Media LLC
    Publishing country us
    Document type Article ; Online
    DOI 10.1007/s11739-020-02480-3
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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