Browsing by Author "Alshahrani, Mohammed"

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    Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU
    (Elsevier, 2022) Elhazmi, Alyaa; Al-Omari, Awad; Sallam, Hend; Mufti, Hani N.; Rabie, Ahmed A.; Alshahrani, Mohammed; Mady, Ahmed; Alghamdi, Adnan; Altalaq, Ali; Azzam, Mohamed H.; Sindi, Anees; Kharaba, Ayman; Al-Aseri, Zohair A.; Almekhlafi, Ghaleb A.; Tashkandi, Wail; Alajmi, Saud A.; Faqihi, Fahad; Alharthy, Abdulrahman; Al-Tawfiq, Jaffar A.; Melibari, Rami Ghazi; Al-Hazzani, Waleed; Arabi, Yaseen M.; Medicine, School of Medicine
    Background: Coronavirus disease-19 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is currently a major cause of intensive care unit (ICU) admissions globally. The role of machine learning in the ICU is evolving but currently limited to diagnostic and prognostic values. A decision tree (DT) algorithm is a simple and intuitive machine learning method that provides sequential nonlinear analysis of variables. It is simple and might be a valuable tool for bedside physicians during COVID-19 to predict ICU outcomes and help in critical decision-making like end-of-life decisions and bed allocation in the event of limited ICU bed capacities. Herein, we utilized a machine learning DT algorithm to describe the association of a predefined set of variables and 28-day ICU outcome in adult COVID-19 patients admitted to the ICU. We highlight the value of utilizing a machine learning DT algorithm in the ICU at the time of a COVID-19 pandemic. Methods: This was a prospective and multicenter cohort study involving 14 hospitals in Saudi Arabia. We included critically ill COVID-19 patients admitted to the ICU between March 1, 2020, and October 31, 2020. The predictors of 28-day ICU mortality were identified using two predictive models: conventional logistic regression and DT analyses. Results: There were 1468 critically ill COVID-19 patients included in the study. The 28-day ICU mortality was 540 (36.8 %), and the 90-day mortality was 600 (40.9 %). The DT algorithm identified five variables that were integrated into the algorithm to predict 28-day ICU outcomes: need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio. Conclusion: DT is a simple tool that might be utilized in the ICU to identify critically ill COVID-19 patients who are at high risk of 28-day ICU mortality. However, further studies and external validation are still required.
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    The Saudi Critical Care Society practice guidelines on the management of COVID-19 in the ICU: Therapy section
    (Elsevier, 2021-10) Alhazzani, Waleed; Alshahrani, Mohammed; Alshamsi, Fayez; Aljuhani, Ohoud; Eljaaly, Khalid; Hashim, Samaher; Alqahtani, Rakan; Alsaleh, Doaa; Al Duhailib, Zainab; Algethamy, Haifa; Al-Musawi, Tariq; Alshammari, Thamir; Alqarni, Abdullah; Khoujah, Danya; Tashkandi, Wail; Dahhan, Talal; Almutairi, Najla; Alserehi, Haleema A.; Al-Yahya, Maytha; Al-Judaibi, Bandar; Arabi, Yaseen M.; Abualenain, Jameel; Alotaibi, Jawaher M.; Al Bshabshe, Ali; Alharbi, Reham; Al-Hameed, Fahad; Elhazmi, Alyaa; Almaghrabi, Reem S.; Almaghlouth, Fatma; Abedalthagafi, Malak; Al Khathlan, Noor; Al-Suwaidan, Faisal A.; Bunyan, Reem F.; Baw, Bandar; Alghamdi, Ghassan; Al Hazmi, Manal; Mandourah, Yasser; Assiri, Abdullah; Enani, Mushira; Alawi, Maha; Aljindan, Reem; Aljabbary, Ahmed; Alrbiaan, Abdullah; Algurashi, Fahd; Alsaawi, Abdulmohsen; Alenazi, Thamer H.; Alsultan, Mohammed A.; Alqahtani, Saleh A.; Memish, Ziad; Al-Tawfiq, Jaffar A.; Al-Jedai, Ahmed; Medicine, School of Medicine
    BACKGROUND: The rapid increase in coronavirus disease 2019 (COVID-19) cases during the subsequent waves in Saudi Arabia and other countries prompted the Saudi Critical Care Society (SCCS) to put together a panel of experts to issue evidence-based recommendations for the management of COVID-19 in the intensive care unit (ICU). METHODS: The SCCS COVID-19 panel included 51 experts with expertise in critical care, respirology, infectious disease, epidemiology, emergency medicine, clinical pharmacy, nursing, respiratory therapy, methodology, and health policy. All members completed an electronic conflict of interest disclosure form. The panel addressed 9 questions that are related to the therapy of COVID-19 in the ICU. We identified relevant systematic reviews and clinical trials, then used the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach as well as the evidence-to-decision framework (EtD) to assess the quality of evidence and generate recommendations. RESULTS: The SCCS COVID-19 panel issued 12 recommendations on pharmacotherapeutic interventions (immunomodulators, antiviral agents, and anticoagulants) for severe and critical COVID-19, of which 3 were strong recommendations and 9 were weak recommendations. CONCLUSION: The SCCS COVID-19 panel used the GRADE approach to formulate recommendations on therapy for COVID-19 in the ICU. The EtD framework allows adaptation of these recommendations in different contexts. The SCCS guideline committee will update recommendations as new evidence becomes available.