Development and internal validation of simplified predictive scoring (ICU-SEPSA score) for mortality in patients with multidrug resistant infection
Background: Mortality from multidrug-resistant (MDR) pathogens is an urgent healthcare crisis worldwide. At present we do not have any simplified screening tools to predict the risk of mortality associated with MDR infections. The aim of this study was to develop a screening tool to predict mortalit...
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| Format: | Article |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2022.938028/full |
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| author | Taranee Sirichayanugul Chansinee Srisawat Chawin Thummakomut Abhisit Prawang Nina S Huynh Surasak Saokaew Surasak Saokaew Surasak Saokaew Surasak Saokaew Surasak Saokaew Pochamana Phisalprapa Sukrit Kanchanasurakit Sukrit Kanchanasurakit Sukrit Kanchanasurakit Sukrit Kanchanasurakit |
| author_facet | Taranee Sirichayanugul Chansinee Srisawat Chawin Thummakomut Abhisit Prawang Nina S Huynh Surasak Saokaew Surasak Saokaew Surasak Saokaew Surasak Saokaew Surasak Saokaew Pochamana Phisalprapa Sukrit Kanchanasurakit Sukrit Kanchanasurakit Sukrit Kanchanasurakit Sukrit Kanchanasurakit |
| author_sort | Taranee Sirichayanugul |
| collection | DOAJ |
| description | Background: Mortality from multidrug-resistant (MDR) pathogens is an urgent healthcare crisis worldwide. At present we do not have any simplified screening tools to predict the risk of mortality associated with MDR infections. The aim of this study was to develop a screening tool to predict mortality in patients with multidrug-resistant organisms.Methods: A retrospective cohort study to evaluate mortality risks in patients with MDR infections was conducted at Phrae Hospital. Univariable and multivariable analyses were used to classify possible risk factors. The model performance was internally validated utilizing the mean of three measures of discrimination corrected by the optimism using a 1000-bootstrap procedure. The coefficients were transformed into item scores by dividing each coefficient with the lowest coefficient and then rounding to the most adjacent number. The area under the receiver operating characteristic curve (AuROC) was used to determine the performance of the model.Results: Between 1 October 2018 and 30 September 2020, a total of 504 patients with MDR infections were enrolled. The ICU-SEPSA score composed of eight clinical risk factors: 1) immunocompromised host, 2) chronic obstructive pulmonary disease, 3) urinary tract infection, 4) sepsis, 5) placement of endotracheal tube, 6) pneumonia, 7) septic shock, and 8) use of antibiotics within the past 3 months. The model showed good calibration (Hosmer-Lemeshow χ2 = 19.27; p-value = 0.50) and good discrimination after optimism correction (AuROC 84.6%, 95% confidence interval [Cl]: 81.0%–88.0%). The positive likelihood ratio of low risk (score ≤ 5) and high risk (score ≥ 8) were 2.07 (95% CI: 1.74–2.46) and 12.35 (95% CI: 4.90–31.13), respectively.Conclusion: A simplified predictive scoring tool wad developed to predict mortality in patients with MDR infections. Due to a single-study design of this study, external validation of the results before applying in other clinical practice settings is warranted. |
| first_indexed | 2024-04-13T18:04:06Z |
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| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-13T18:04:06Z |
| publishDate | 2022-08-01 |
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| series | Frontiers in Pharmacology |
| spelling | doaj.art-f78defbae457407e9713bed1b23400572022-12-22T02:36:08ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122022-08-011310.3389/fphar.2022.938028938028Development and internal validation of simplified predictive scoring (ICU-SEPSA score) for mortality in patients with multidrug resistant infectionTaranee Sirichayanugul0Chansinee Srisawat1Chawin Thummakomut2Abhisit Prawang3Nina S Huynh4Surasak Saokaew5Surasak Saokaew6Surasak Saokaew7Surasak Saokaew8Surasak Saokaew9Pochamana Phisalprapa10Sukrit Kanchanasurakit11Sukrit Kanchanasurakit12Sukrit Kanchanasurakit13Sukrit Kanchanasurakit14Division of Drug Information, Department of Pharmacy, Phrae Hospital, Phrae, ThailandDivision of Clinical Pharmacy, Department of Pharmaceutical Care, School of Pharmaceutical Sciences, University of Phayao, Phayao, ThailandDepartment of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, ThailandCollege of Pharmacy, Rangsit University, Phathum Thani, ThailandDepartment of Pharmacy Practice, University of Illinois at Chicago, Chicago, IL, United StatesDivision of Social and Administration Pharmacy, Department of Pharmaceutical Care, School of Pharmaceutical Sciences, University of Phayao, Phayao, ThailandCenter of Health Outcomes Research and Therapeutic Safety (Cohorts), School of Pharmaceutical Sciences, University of Phayao, Phayao, ThailandUnit of Excellence on Clinical Outcomes Research and IntegratioN (UNICORN), School of Pharmaceutical Sciences, University of Phayao, Phayao, ThailandNovel Bacteria and Drug Discovery Research Group, Microbiome and Bioresource Research Strength, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bander Sunway, Malaysia0Biofunctional Molecule Exploratory Research Group, Biomedicine Research Advancement Centre, School of Pharmacy, Monash University Malaysia, Bander Sunway, Malaysia1Division of Ambulatory Medicine, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, ThailandDivision of Clinical Pharmacy, Department of Pharmaceutical Care, School of Pharmaceutical Sciences, University of Phayao, Phayao, ThailandCenter of Health Outcomes Research and Therapeutic Safety (Cohorts), School of Pharmaceutical Sciences, University of Phayao, Phayao, ThailandUnit of Excellence on Clinical Outcomes Research and IntegratioN (UNICORN), School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand2Division of Pharmaceutical Care, Department of Pharmacy, Phrae Hospital, Phrae, ThailandBackground: Mortality from multidrug-resistant (MDR) pathogens is an urgent healthcare crisis worldwide. At present we do not have any simplified screening tools to predict the risk of mortality associated with MDR infections. The aim of this study was to develop a screening tool to predict mortality in patients with multidrug-resistant organisms.Methods: A retrospective cohort study to evaluate mortality risks in patients with MDR infections was conducted at Phrae Hospital. Univariable and multivariable analyses were used to classify possible risk factors. The model performance was internally validated utilizing the mean of three measures of discrimination corrected by the optimism using a 1000-bootstrap procedure. The coefficients were transformed into item scores by dividing each coefficient with the lowest coefficient and then rounding to the most adjacent number. The area under the receiver operating characteristic curve (AuROC) was used to determine the performance of the model.Results: Between 1 October 2018 and 30 September 2020, a total of 504 patients with MDR infections were enrolled. The ICU-SEPSA score composed of eight clinical risk factors: 1) immunocompromised host, 2) chronic obstructive pulmonary disease, 3) urinary tract infection, 4) sepsis, 5) placement of endotracheal tube, 6) pneumonia, 7) septic shock, and 8) use of antibiotics within the past 3 months. The model showed good calibration (Hosmer-Lemeshow χ2 = 19.27; p-value = 0.50) and good discrimination after optimism correction (AuROC 84.6%, 95% confidence interval [Cl]: 81.0%–88.0%). The positive likelihood ratio of low risk (score ≤ 5) and high risk (score ≥ 8) were 2.07 (95% CI: 1.74–2.46) and 12.35 (95% CI: 4.90–31.13), respectively.Conclusion: A simplified predictive scoring tool wad developed to predict mortality in patients with MDR infections. Due to a single-study design of this study, external validation of the results before applying in other clinical practice settings is warranted.https://www.frontiersin.org/articles/10.3389/fphar.2022.938028/fullmortalitymultidrug-resistant infectionscreening toolantibiotic resistantpredictive scoring |
| spellingShingle | Taranee Sirichayanugul Chansinee Srisawat Chawin Thummakomut Abhisit Prawang Nina S Huynh Surasak Saokaew Surasak Saokaew Surasak Saokaew Surasak Saokaew Surasak Saokaew Pochamana Phisalprapa Sukrit Kanchanasurakit Sukrit Kanchanasurakit Sukrit Kanchanasurakit Sukrit Kanchanasurakit Development and internal validation of simplified predictive scoring (ICU-SEPSA score) for mortality in patients with multidrug resistant infection Frontiers in Pharmacology mortality multidrug-resistant infection screening tool antibiotic resistant predictive scoring |
| title | Development and internal validation of simplified predictive scoring (ICU-SEPSA score) for mortality in patients with multidrug resistant infection |
| title_full | Development and internal validation of simplified predictive scoring (ICU-SEPSA score) for mortality in patients with multidrug resistant infection |
| title_fullStr | Development and internal validation of simplified predictive scoring (ICU-SEPSA score) for mortality in patients with multidrug resistant infection |
| title_full_unstemmed | Development and internal validation of simplified predictive scoring (ICU-SEPSA score) for mortality in patients with multidrug resistant infection |
| title_short | Development and internal validation of simplified predictive scoring (ICU-SEPSA score) for mortality in patients with multidrug resistant infection |
| title_sort | development and internal validation of simplified predictive scoring icu sepsa score for mortality in patients with multidrug resistant infection |
| topic | mortality multidrug-resistant infection screening tool antibiotic resistant predictive scoring |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2022.938028/full |
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