Enhancing early diagnosis of septic shock and Sepsis-induced Acute Kidney Injury (S-AKI) in patients with sepsis

Background: Sepsis and its most severe form, septic shock, are major public health crises causing substantial morbidity, mortality, and financial burdens in hospitals, particularly as leading causes of acute kidney injury (AKI) in ICUs. Sepsis-induced AKI carries a higher mortality rate and economic burden compared to other causes of AKI or sepsis alone. Although septic shock and sepsis-induced AKI can be reversed with timely intervention, the narrow window for effective treatment underscores the importance of early diagnosis. The study aims to develop a predictive model for the early diagnosis of septic shock and sepsis-induced AKI within the crucial 48 hours of hospital admission. The model utilized the innovative Maximum Partial SOFA (Max Pt-SOFA) score, vital signs, and laboratory biomarkers within the first six hours of hospital admission, which was in line with Sepsis-3 guidelines. The Max Pt-SOFA score addresses the limitations of the traditional SOFA score, such as its restriction to ICU settings, failure to reflect the patient's condition accurately, and its constraints on early assessments.

Method: In this retrospective cohort study, we analyzed electronic health records from Geisinger Health System, spanning from January 1, 2007, to June 3, 2016, identifying a total of 3,038 and 893 patients met the criteria for sepsis and septic shock within 48 hours of admission, respectively. Excluding 145 patients diagnosed with AKI at admission, 2,043 were diagnosed with Sepsis-induced AKI within 48 hours. Among the S-AKI cohort, 1384 cases originated from sepsis (n=2913), and 659 cases originated from septic shock (n=873). the Max Pt-SOFA score is re-calculated with every change in the parameters of the six organ systems within 48 hours of admission. The time to organ dysfunction was captured when the Max Pt-SOFA reached two or more, therefore meeting the sepsis-3. Additionally, The Max Pt-SOFA score was calculated within six hours of admission to predict septic shock and sepsis-induced AKI development. We incorporated forty-four variables, including comorbidities, the Max Pt-SOFA score, laboratory biomarkers, and vital signs within six hours of admission, into a stepwise logistic regression model to predict septic shock and sepsis-induced AKI within 48 hours of admission. The model successfully identified several key predictors, which were utilized to construct our comprehensive model. We conducted multiple imputations, Wilcoxon rank sum test, Chi-Square tests, sensitivity analysis, and 10-fold cross-validation, with odds ratios and 95% CIs reported to validate the model.

Results: Our predictive model demonstrated excellent performance with an AUC of 0.8998 in identifying patients at risk of septic shock within the first 48 hours of admission. The novel Max Pt-SOFA score, calculated within the first six hours, emerged as a significant predictor (value: 1.461, CI: 1.404-1.521), underscoring its significance in early diagnosis. Additionally, the Max Pt-SOFA score (1.048, CI: 1.021-1.076), along with other early predictors such as creatinine (2.712, CI: 2.403-3.061), proved significant in predicting sepsis-induced AKI. The model also showed strong predictive capability for sepsis-induced AKI with an AUC of 0.7914. For both predictive models, The sensitivity analyses indicated that the final logistic regression models are relatively robust to the issue of data missingness. However, the Hosmer and Lemeshow tests, used to assess the models' fit, suggested room for further refinement (Pr>ChiSq <0.05)

Conclusion: The Max Pt-SOFA score calculated within the first six hours of admission stands out for its predictive value in early septic shock and sepsis-induced AKI detection. This facilitates timely identification and intervention, thereby improving patient outcomes.

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Work Title Enhancing early diagnosis of septic shock and Sepsis-induced Acute Kidney Injury (S-AKI) in patients with sepsis
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Penn State
Creators
  1. Hosam Abdelhameed Farag
License In Copyright (Rights Reserved)
Work Type Professional Doctoral Culminating Experience
Sub Work Type Culminating Research Project
Program Public Health Sciences
Degree Doctor of Public Health
Publication Date 2024
Deposited July 02, 2024

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Version 1
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  • Created

    July 02, 2024 00:42 by haf5068

  • Updated

    July 02, 2024 00:42 by [unknown user]

  • Updated Description, Publication Date Show Changes

    July 02, 2024 00:46 by haf5068

    Description
    • Background
    • Sepsis and its most severe form, septic shock, are major public health crises causing substantial morbidity, mortality, and financial burdens in hospitals, particularly as leading causes of acute kidney injury (AKI) in ICUs. Sepsis-induced AKI carries a higher mortality rate and economic burden compared to other causes of AKI or sepsis alone. Although septic shock and sepsis-induced AKI can be reversed with timely intervention, the narrow window for effective treatment underscores the importance of early diagnosis. The study aims to develop a predictive model for the early diagnosis of septic shock and sepsis-induced AKI within the crucial 48 hours of hospital admission. The model utilized the innovative Maximum Partial SOFA (Max Pt-SOFA) score, vital signs, and laboratory biomarkers within the first six hours of hospital admission, which was in line with Sepsis-3 guidelines. The Max Pt-SOFA score addresses the limitations of the traditional SOFA score, such as its restriction to ICU settings, failure to accurately reflect the patient's condition, and its constraints on early assessments.
    • Methods
    • In this retrospective cohort study, we analyzed electronic health records from Geisinger Health System, spanning from January 1, 2007, to June 3, 2016, identifying a total of 3,038 and 893 patients met the criteria for sepsis and septic shock within 48 hours of admission, respectively. Excluding 145 patients diagnosed with AKI at admission, 2,043 were diagnosed with Sepsis-induced AKI within 48 hours. Among the S-AKI cohort, 1384 cases originated from sepsis (n=2913), and 659 cases originated from septic shock (n=873). the Max Pt-SOFA score is re-calculated with every change in the parameters of the six organ systems within 48 hours of admission. The time to organ dysfunction was captured when the Max Pt-SOFA reached two or more, therefore meeting the sepsis-3. Additionally, The Max Pt-SOFA score was calculated within six hours of admission to predict septic shock and sepsis-induced AKI development. We incorporated forty-four variables, including comorbidities, the Max Pt-SOFA score, laboratory biomarkers, and vital signs within six hours of admission, into a stepwise logistic regression model to predict septic shock and sepsis-induced AKI within 48 hours of admission. The model successfully identified several key predictors, which were utilized to construct our comprehensive model. We conducted multiple imputations, Wilcoxon rank sum test, Chi-Square tests, sensitivity analysis, and 10-fold cross-validation, with odds ratios and 95% CIs reported to validate the model.
    • Results
    • Our predictive model demonstrated excellent performance with an AUC of 0.8998 in identifying patients at risk of septic shock within the first 48 hours of admission. The novel Max Pt-SOFA score, calculated within the first six hours, emerged as a significant predictor (value: 1.461, CI: 1.404-1.521), underscoring its significance in early diagnosis. Additionally, the Max Pt-SOFA score (1.048, CI: 1.021-1.076), along with other early predictors such as creatinine (2.712, CI: 2.403-3.061), proved significant in predicting sepsis-induced AKI. The model also showed strong predictive capability for sepsis-induced AKI with an AUC of 0.7914. For both predictive models, The sensitivity analyses indicated that the final logistic regression models are relatively robust to the issue of data missingness. However, the Hosmer and Lemeshow tests, used to assess the models' fit, suggested room for further refinement (Pr>ChiSq <0.05)
    • Conclusion
    • The Max Pt-SOFA score calculated within the first six hours of admission stands out for its predictive value in early septic shock and sepsis-induced AKI detection. This facilitates timely identification and intervention, thereby improving patient outcomes.
    Publication Date
    • 2024
  • Added Creator Hosam Farag

    July 02, 2024 00:46 by haf5068

  • Renamed Creator Hosam A Farag Show Changes

    July 02, 2024 00:52 by haf5068

    • Hosam Farag
    • Hosam A Farag
  • Added Creator Nasrollah Ghahramani

    July 02, 2024 00:52 by haf5068

  • Added Creator Vernon Chinchilli

    July 02, 2024 00:52 by haf5068

  • Added Creator Lauren J Van Scoy

    July 02, 2024 00:52 by haf5068

  • Added Creator Casey Pinto

    July 02, 2024 00:52 by haf5068

  • Deleted Creator Nasrollah Ghahramani

    July 02, 2024 00:58 by haf5068

  • Deleted Creator Vernon Chinchilli

    July 02, 2024 00:58 by haf5068

  • Deleted Creator Lauren J Van Scoy

    July 02, 2024 00:58 by haf5068

  • Deleted Creator Casey Pinto

    July 02, 2024 00:58 by haf5068

  • Added Creator Casey Pinto

    July 02, 2024 00:58 by haf5068

  • Added Creator Nasrollah Ghahramani

    July 02, 2024 00:58 by haf5068

  • Added Creator Vernon Chinchilli

    July 02, 2024 00:58 by haf5068

  • Added Creator Lauren J Van Scoy

    July 02, 2024 00:58 by haf5068

  • Added Creator Jessica Yingst

    July 02, 2024 00:58 by haf5068

  • Added Hosam Farag_IDR_Summer_2024.pdf

    July 02, 2024 00:59 by haf5068

  • Deleted Hosam Farag_IDR_Summer_2024.pdf

    July 02, 2024 01:03 by haf5068

  • Added ENHANCING EARLY DIAGNOSIS OF SEPTIC SHOCK AND SEPSIS-INDUCED ACUTE KIDNEY INJURY (S-AKI) IN PATIENTS WITH SEPSIS_HAF_2024.pdf

    July 02, 2024 01:04 by haf5068

  • Updated License Show Changes

    July 02, 2024 01:06 by haf5068

    License
    • https://rightsstatements.org/page/InC/1.0/
  • Updated Description Show Changes

    July 02, 2024 01:21 by haf5068

    Description
    • Background
    • Sepsis and its most severe form, septic shock, are major public health crises causing substantial morbidity, mortality, and financial burdens in hospitals, particularly as leading causes of acute kidney injury (AKI) in ICUs. Sepsis-induced AKI carries a higher mortality rate and economic burden compared to other causes of AKI or sepsis alone. Although septic shock and sepsis-induced AKI can be reversed with timely intervention, the narrow window for effective treatment underscores the importance of early diagnosis. The study aims to develop a predictive model for the early diagnosis of septic shock and sepsis-induced AKI within the crucial 48 hours of hospital admission. The model utilized the innovative Maximum Partial SOFA (Max Pt-SOFA) score, vital signs, and laboratory biomarkers within the first six hours of hospital admission, which was in line with Sepsis-3 guidelines. The Max Pt-SOFA score addresses the limitations of the traditional SOFA score, such as its restriction to ICU settings, failure to accurately reflect the patient's condition, and its constraints on early assessments.
    • Methods
    • Background:
    • Sepsis and its most severe form, septic shock, are major public health crises causing substantial morbidity, mortality, and financial burdens in hospitals, particularly as leading causes of acute kidney injury (AKI) in ICUs. Sepsis-induced AKI carries a higher mortality rate and economic burden compared to other causes of AKI or sepsis alone. Although septic shock and sepsis-induced AKI can be reversed with timely intervention, the narrow window for effective treatment underscores the importance of early diagnosis. The study aims to develop a predictive model for the early diagnosis of septic shock and sepsis-induced AKI within the crucial 48 hours of hospital admission. The model utilized the innovative Maximum Partial SOFA (Max Pt-SOFA) score, vital signs, and laboratory biomarkers within the first six hours of hospital admission, which was in line with Sepsis-3 guidelines. The Max Pt-SOFA score addresses the limitations of the traditional SOFA score, such as its restriction to ICU settings, failure to reflect the patient's condition accurately, and its constraints on early assessments.
    • Method:
    • In this retrospective cohort study, we analyzed electronic health records from Geisinger Health System, spanning from January 1, 2007, to June 3, 2016, identifying a total of 3,038 and 893 patients met the criteria for sepsis and septic shock within 48 hours of admission, respectively. Excluding 145 patients diagnosed with AKI at admission, 2,043 were diagnosed with Sepsis-induced AKI within 48 hours. Among the S-AKI cohort, 1384 cases originated from sepsis (n=2913), and 659 cases originated from septic shock (n=873). the Max Pt-SOFA score is re-calculated with every change in the parameters of the six organ systems within 48 hours of admission. The time to organ dysfunction was captured when the Max Pt-SOFA reached two or more, therefore meeting the sepsis-3. Additionally, The Max Pt-SOFA score was calculated within six hours of admission to predict septic shock and sepsis-induced AKI development. We incorporated forty-four variables, including comorbidities, the Max Pt-SOFA score, laboratory biomarkers, and vital signs within six hours of admission, into a stepwise logistic regression model to predict septic shock and sepsis-induced AKI within 48 hours of admission. The model successfully identified several key predictors, which were utilized to construct our comprehensive model. We conducted multiple imputations, Wilcoxon rank sum test, Chi-Square tests, sensitivity analysis, and 10-fold cross-validation, with odds ratios and 95% CIs reported to validate the model.
    • Results
    • Results:
    • Our predictive model demonstrated excellent performance with an AUC of 0.8998 in identifying patients at risk of septic shock within the first 48 hours of admission. The novel Max Pt-SOFA score, calculated within the first six hours, emerged as a significant predictor (value: 1.461, CI: 1.404-1.521), underscoring its significance in early diagnosis. Additionally, the Max Pt-SOFA score (1.048, CI: 1.021-1.076), along with other early predictors such as creatinine (2.712, CI: 2.403-3.061), proved significant in predicting sepsis-induced AKI. The model also showed strong predictive capability for sepsis-induced AKI with an AUC of 0.7914. For both predictive models, The sensitivity analyses indicated that the final logistic regression models are relatively robust to the issue of data missingness. However, the Hosmer and Lemeshow tests, used to assess the models' fit, suggested room for further refinement (Pr>ChiSq <0.05)
    • Conclusion
    • Conclusion:
    • The Max Pt-SOFA score calculated within the first six hours of admission stands out for its predictive value in early septic shock and sepsis-induced AKI detection. This facilitates timely identification and intervention, thereby improving patient outcomes.
  • Deleted ENHANCING EARLY DIAGNOSIS OF SEPTIC SHOCK AND SEPSIS-INDUCED ACUTE KIDNEY INJURY (S-AKI) IN PATIENTS WITH SEPSIS_HAF_2024.pdf

    July 02, 2024 01:23 by haf5068

  • Added ENHANCING EARLY DIAGNOSIS OF SEPTIC SHOCK AND SEPSIS-INDUCED ACUTE KIDNEY INJURY (S-AKI) IN PATIENTS WITH SEPSIS_HAF_2024.pdf

    July 02, 2024 01:24 by haf5068

  • Deleted ENHANCING EARLY DIAGNOSIS OF SEPTIC SHOCK AND SEPSIS-INDUCED ACUTE KIDNEY INJURY (S-AKI) IN PATIENTS WITH SEPSIS_HAF_2024.pdf

    July 04, 2024 06:52 by haf5068

  • Added ENHANCING EARLY DIAGNOSIS OF SEPTIC SHOCK AND SEPSIS-INDUCED ACUTE KIDNEY INJURY (S-AKI) IN PATIENTS WITH SEPSIS_HAF_2024.pdf

    July 04, 2024 06:52 by haf5068

  • Published

    July 04, 2024 06:54 by haf5068

  • Updated

    July 04, 2024 22:04 by [unknown user]

  • Updated Work Title Show Changes

    November 21, 2024 13:38 by avs5190

    Work Title
    • ENHANCING EARLY DIAGNOSIS OF SEPTIC SHOCK AND SEPSIS-INDUCED ACUTE KIDNEY INJURY (S-AKI) IN PATIENTS WITH SEPSIS
    • Enhancing early diagnosis of septic shock and Sepsis-induced Acute Kidney Injury (S-AKI) in patients with sepsis
  • Updated Description, Degree, Program, and 1 more Show Changes

    November 21, 2024 13:39 by avs5190

    Description
    • Background:
    • Sepsis and its most severe form, septic shock, are major public health crises causing substantial morbidity, mortality, and financial burdens in hospitals, particularly as leading causes of acute kidney injury (AKI) in ICUs. Sepsis-induced AKI carries a higher mortality rate and economic burden compared to other causes of AKI or sepsis alone. Although septic shock and sepsis-induced AKI can be reversed with timely intervention, the narrow window for effective treatment underscores the importance of early diagnosis. The study aims to develop a predictive model for the early diagnosis of septic shock and sepsis-induced AKI within the crucial 48 hours of hospital admission. The model utilized the innovative Maximum Partial SOFA (Max Pt-SOFA) score, vital signs, and laboratory biomarkers within the first six hours of hospital admission, which was in line with Sepsis-3 guidelines. The Max Pt-SOFA score addresses the limitations of the traditional SOFA score, such as its restriction to ICU settings, failure to reflect the patient's condition accurately, and its constraints on early assessments.
    • Method:
    • In this retrospective cohort study, we analyzed electronic health records from Geisinger Health System, spanning from January 1, 2007, to June 3, 2016, identifying a total of 3,038 and 893 patients met the criteria for sepsis and septic shock within 48 hours of admission, respectively. Excluding 145 patients diagnosed with AKI at admission, 2,043 were diagnosed with Sepsis-induced AKI within 48 hours. Among the S-AKI cohort, 1384 cases originated from sepsis (n=2913), and 659 cases originated from septic shock (n=873). the Max Pt-SOFA score is re-calculated with every change in the parameters of the six organ systems within 48 hours of admission. The time to organ dysfunction was captured when the Max Pt-SOFA reached two or more, therefore meeting the sepsis-3. Additionally, The Max Pt-SOFA score was calculated within six hours of admission to predict septic shock and sepsis-induced AKI development. We incorporated forty-four variables, including comorbidities, the Max Pt-SOFA score, laboratory biomarkers, and vital signs within six hours of admission, into a stepwise logistic regression model to predict septic shock and sepsis-induced AKI within 48 hours of admission. The model successfully identified several key predictors, which were utilized to construct our comprehensive model. We conducted multiple imputations, Wilcoxon rank sum test, Chi-Square tests, sensitivity analysis, and 10-fold cross-validation, with odds ratios and 95% CIs reported to validate the model.
    • Results:
    • Our predictive model demonstrated excellent performance with an AUC of 0.8998 in identifying patients at risk of septic shock within the first 48 hours of admission. The novel Max Pt-SOFA score, calculated within the first six hours, emerged as a significant predictor (value: 1.461, CI: 1.404-1.521), underscoring its significance in early diagnosis. Additionally, the Max Pt-SOFA score (1.048, CI: 1.021-1.076), along with other early predictors such as creatinine (2.712, CI: 2.403-3.061), proved significant in predicting sepsis-induced AKI. The model also showed strong predictive capability for sepsis-induced AKI with an AUC of 0.7914. For both predictive models, The sensitivity analyses indicated that the final logistic regression models are relatively robust to the issue of data missingness. However, the Hosmer and Lemeshow tests, used to assess the models' fit, suggested room for further refinement (Pr>ChiSq <0.05)
    • Conclusion:
    • The Max Pt-SOFA score calculated within the first six hours of admission stands out for its predictive value in early septic shock and sepsis-induced AKI detection. This facilitates timely identification and intervention, thereby improving patient outcomes.
    • Background: Sepsis and its most severe form, septic shock, are major public health crises causing substantial morbidity, mortality, and financial burdens in hospitals, particularly as leading causes of acute kidney injury (AKI) in ICUs. Sepsis-induced AKI carries a higher mortality rate and economic burden compared to other causes of AKI or sepsis alone. Although septic shock and sepsis-induced AKI can be reversed with timely intervention, the narrow window for effective treatment underscores the importance of early diagnosis. The study aims to develop a predictive model for the early diagnosis of septic shock and sepsis-induced AKI within the crucial 48 hours of hospital admission. The model utilized the innovative Maximum Partial SOFA (Max Pt-SOFA) score, vital signs, and laboratory biomarkers within the first six hours of hospital admission, which was in line with Sepsis-3 guidelines. The Max Pt-SOFA score addresses the limitations of the traditional SOFA score, such as its restriction to ICU settings, failure to reflect the patient's condition accurately, and its constraints on early assessments.
    • Method: In this retrospective cohort study, we analyzed electronic health records from Geisinger Health System, spanning from January 1, 2007, to June 3, 2016, identifying a total of 3,038 and 893 patients met the criteria for sepsis and septic shock within 48 hours of admission, respectively. Excluding 145 patients diagnosed with AKI at admission, 2,043 were diagnosed with Sepsis-induced AKI within 48 hours. Among the S-AKI cohort, 1384 cases originated from sepsis (n=2913), and 659 cases originated from septic shock (n=873). the Max Pt-SOFA score is re-calculated with every change in the parameters of the six organ systems within 48 hours of admission. The time to organ dysfunction was captured when the Max Pt-SOFA reached two or more, therefore meeting the sepsis-3. Additionally, The Max Pt-SOFA score was calculated within six hours of admission to predict septic shock and sepsis-induced AKI development. We incorporated forty-four variables, including comorbidities, the Max Pt-SOFA score, laboratory biomarkers, and vital signs within six hours of admission, into a stepwise logistic regression model to predict septic shock and sepsis-induced AKI within 48 hours of admission. The model successfully identified several key predictors, which were utilized to construct our comprehensive model. We conducted multiple imputations, Wilcoxon rank sum test, Chi-Square tests, sensitivity analysis, and 10-fold cross-validation, with odds ratios and 95% CIs reported to validate the model.
    • Results: Our predictive model demonstrated excellent performance with an AUC of 0.8998 in identifying patients at risk of septic shock within the first 48 hours of admission. The novel Max Pt-SOFA score, calculated within the first six hours, emerged as a significant predictor (value: 1.461, CI: 1.404-1.521), underscoring its significance in early diagnosis. Additionally, the Max Pt-SOFA score (1.048, CI: 1.021-1.076), along with other early predictors such as creatinine (2.712, CI: 2.403-3.061), proved significant in predicting sepsis-induced AKI. The model also showed strong predictive capability for sepsis-induced AKI with an AUC of 0.7914. For both predictive models, The sensitivity analyses indicated that the final logistic regression models are relatively robust to the issue of data missingness. However, the Hosmer and Lemeshow tests, used to assess the models' fit, suggested room for further refinement (Pr>ChiSq <0.05)
    • Conclusion: The Max Pt-SOFA score calculated within the first six hours of admission stands out for its predictive value in early septic shock and sepsis-induced AKI detection. This facilitates timely identification and intervention, thereby improving patient outcomes.
    Degree
    • Doctor of Public Health
    Program
    • Public Health Sciences
    Sub Work Type
    • Culminating Research Project
  • Deleted Creator Casey Pinto

    November 21, 2024 13:40 by avs5190

  • Deleted Creator Nasrollah Ghahramani

    November 21, 2024 13:40 by avs5190

  • Deleted Creator Vernon Chinchilli

    November 21, 2024 13:40 by avs5190

  • Deleted Creator Lauren J Van Scoy

    November 21, 2024 13:40 by avs5190

  • Deleted Creator Jessica Yingst

    November 21, 2024 13:40 by avs5190

  • Renamed Creator Hosam Abdelhameed Farag Show Changes

    November 21, 2024 13:40 by avs5190

    • Hosam A Farag
    • Hosam Abdelhameed Farag