Potential Value of TNF-α (–376 G/A) Polymorphism and Cystatin C (CysC) in the Diagnosis of Sepsis Associated Acute Kidney Injury (S-AK I) and Prediction of Mortality in Critically Ill patients

Al-Amodi, Hiba S.; Abdelsattar, Shimaa; Kasemy, Zeinab A.; Bedair, Hanan M.; Elbarbary, Hany S.; Kamel, Hala F. M.

doi:10.3389/fmolb.2021.751299

Cited by 6 publications

(10 citation statements)

References 70 publications

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“…Nevertheless, serum cystatin C was able to predict the occurrence of AKI, one to two days earlier than serum Cr (18,25). Cystatin C has been suggested as a potential predictor for the early diagnosis of S-AKI or was an alternative to the gold standard "creatinine" in growing number of studies in recent years (18,26). Regardless of the underlying precipitating factors, previous studies have demonstrated that cystatin C played a significant role in the diagnosis and prediction of kidney injury in sepsis or other critical clinical conditions (25, 27).…”

Section: Discussionmentioning

confidence: 93%

“…It is not significantly influenced by biological factors such as age, gender, muscle mass, diet, infection, and tumors, in contrast to Creatinine (Cr). Serum Cr may not be able to detect very early changes in renal function, particularly in sepsis patients with early tubular necrosis (18). Nevertheless, serum cystatin C was able to predict the occurrence of AKI, one to two days earlier than serum Cr (18,25).…”

Section: Discussionmentioning

confidence: 95%

“…Serum Cr may not be able to detect very early changes in renal function, particularly in sepsis patients with early tubular necrosis (18). Nevertheless, serum cystatin C was able to predict the occurrence of AKI, one to two days earlier than serum Cr (18,25). Cystatin C has been suggested as a potential predictor for the early diagnosis of S-AKI or was an alternative to the gold standard "creatinine" in growing number of studies in recent years (18,26).…”

Section: Discussionmentioning

confidence: 95%

“…In addition, doctors must use a quick and precise nomogram model to anticipate MAKE30 to make tailored management decisions, which will improve the outcome and reduce mortality in sepsis patients with T2DM. Since sepsis is one of the most common causes of kidney injury, it is a prominent clinical problem in critically ill patients (18). Furthermore, more sepsis patients with T2DM 145 (35.7%) reached a MAKE30 composite outcome, than patients with acute pancreatitis (16%) and sepsis patients (28.3%) (16,17).…”

Section: Discussionmentioning

confidence: 99%

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Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus

et al. 2022

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BackgroundIn sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study.MethodsA total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed.ResultsIn sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801).ConclusionThe nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus

et al. 2022

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“… 40 Moreover, increasing studies have indicated that cystatin C was a potential predictor for the early diagnosis of S-AKI or was an alternate to the gold standard “creatinine” due to its considerable stability. 41 , 42 However, no research has been conducted to determine whether the combination of inflammation and coagulation indicators can be used to predict patients with S-AKI. In our present study, we discovered that model 3 (constructed with PCT, PLT, and PTA) (AUC = 0.855) exhibited more predictive value than either Cr (AUC = 0.807) or cystatin C (AUC = 0.765) in sepsis patients of the training cohort.…”

Section: Discussionmentioning

confidence: 99%

A Predictive Model Based on Inflammatory and Coagulation Indicators for Sepsis-Induced Acute Kidney Injury

Xin¹,

Xie²,

Chen³

et al. 2022

JIR

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Background Sepsis-induced acute kidney injury (S-AKI) is associated with systemic inflammatory responses and coagulation system dysfunction, and it is associated with an increased risk of mortality. However, there was no study to explore the predictive value of inflammatory and coagulation indicators for S-AKI. Methods In this retrospective study, 1051 sepsis patients were identified and divided into a training cohort (75%, n = 787) and a validation cohort (25%, n = 264) in chronological order according to the date they were admitted. Univariate analyses and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The logistic regression analyses (enter methods) were used to conducted the prediction models. The ROC curves were used to determine the predictive value of the constructed models on S-AKI. To test whether the increase in the AUC is significant, we used a two-sided test for ROC curves available online ( http://vassarstats.net/roc_comp.html ). The secondary outcome was different AKI stages and major adverse kidney events within 30 days (MAKE30). Stage 3B of S-AKI was defined as both meeting the stage 3 criteria [increase of Cr level by > 300% (≥ 4.0 mg/dL with an acute increase of ≥ 0.5 mg/dL) and/or UO < 0.3 mL/kg/h for > 24 h or anuria for > 12 h and/or acute kidney replacement therapy] and having cystatin C positive. MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD). Results We discovered that cardiovascular disease, white blood cell (WBC), mean arterial pressure (MAP), platelet (PLT), serum procalcitonin (PCT), prothrombin time activity (PTA), and thrombin time (TT) were independent predictors for S-AKI. The predictive value (AUC = 0.855) of the simplest model 3 (constructed with PLT, PCT, and PTA), with a sensitivity of 77.6% and a specificity of 82.4%, had a similar predictive value comparing with the model 1 (AUC = 0.872) and the model 2 (AUC = 0.864) in the training cohort (P > 0.05). Compared with the model 1 (AUC = 0.888) and the model 2 (AUC = 0.887), the model 3 (AUC = 0.887) had a similar predictive value in the validation cohort. Moreover, model 3 had the best predictive power for predicting S-AKI in the stage 3 (AUC = 0.777), especially in stage 3B (AUC = 0.771). Finally, the model 3 (AUC = 0.843) had perfect predictive power for predicting MAKE30 in sepsis patients. Conclusion Within 24 hours after admission, the simplest model 3 (constructed with PLT, PCT, and PTA) might be a robust predictor of the S-AKI in sepsis patients, providing information for timely and efficient intervention.

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An early warning model to predict acute kidney injury in sepsis patients with prior hypertension

Ma,

Liu,

Deng

et al. 2024

Heliyon

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Potential Value of TNF-α (–376 G/A) Polymorphism and Cystatin C (CysC) in the Diagnosis of Sepsis Associated Acute Kidney Injury (S-AK I) and Prediction of Mortality in Critically Ill patients

Cited by 6 publications

References 70 publications

Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus

Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus

A Predictive Model Based on Inflammatory and Coagulation Indicators for Sepsis-Induced Acute Kidney Injury

An early warning model to predict acute kidney injury in sepsis patients with prior hypertension

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