Association between Procalcitonin and Acute Kidney Injury in Patients with Bacterial Septic Shock

Fu, Guang; Zhan, Haichao; Li, Hao-li; Lu, Jun-fu; Chen, Yanhong; Wu, Le-Feng; Yang, Zi-Long; Huang, Jia-jia; Feng, Yang; Luan, Ying-yi; Wu, Ming

doi:10.1159/000512351

Cited by 11 publications

(14 citation statements)

References 41 publications

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“…It has been widely used as a biomarker for diagnosing bacterial infection and as a promising guideline for antibiotics therapy in sepsis patients. 22 , 23 Although PCT has been demonstrated to be an excellent predictor of AKI development and recovery in a variety of clinical settings, its validity as a predictor of developed S-AKI is still debated. 23 , 24 In this study, we observed that PCT was identified as an independent predictive factor for S-AKI.…”

Section: Discussionmentioning

confidence: 99%

“… 22 , 23 Although PCT has been demonstrated to be an excellent predictor of AKI development and recovery in a variety of clinical settings, its validity as a predictor of developed S-AKI is still debated. 23 , 24 In this study, we observed that PCT was identified as an independent predictive factor for S-AKI. More potential explanations of elevated PCT levels in S-AKI have been revealed: (1) S-AKI was caused by elevated blood levels of proinflammatory cytokines and chemokines during the infection and inflammation states; 5 (2) PCT can mediate direct cytotoxicity on mesangial cells by increasing the synthesis of proinflammatory cytokines and causes mesangial cell apoptosis.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…According to classical theories, renal hypotension and associated ischemia, as well as a lack of oxygen, are the main pathological mechanisms underlying AKI [ 6 ]. Additionally, PCT has been widely used to diagnose bacterial infections and guide antibiotic therapy in sepsis patients [ 26 , 27 ]. This study had similar findings to previous studies that PCT was more accurate at predicting S-AKI than other inflammatory biomarkers, such as WBC, lymphocytes, monocytes, and NEUT [ 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of an early warning model for predicting the acute kidney injury in elderly patients with sepsis

et al. 2022

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Background Sepsis-induced acute kidney injury (S-AKI) is a significant complication and is associated with an increased risk of mortality, especially in elderly patients with sepsis. However, there are no reliable and robust predictive models to identify high-risk patients likely to develop S-AKI. We aimed to develop a nomogram to predict S-AKI in elderly sepsis patients and help physicians make personalized management within 24 h of admission. Methods A total of 849 elderly sepsis patients from the First Affiliated Hospital of Xi’an Jiaotong University were identified and randomly divided into a training set (75%, n = 637) and a validation set (25%, n = 212). Univariate and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The corresponding nomogram was constructed based on those predictors. The calibration curve, receiver operating characteristics (ROC)curve, and decision curve analysis were performed to evaluate the nomogram. The secondary outcome was 30-day mortality and major adverse kidney events within 30 days (MAKE30). MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD). Results The independent predictors for nomogram construction were mean arterial pressure (MAP), serum procalcitonin (PCT), and platelet (PLT), prothrombin time activity (PTA), albumin globulin ratio (AGR), and creatinine (Cr). The predictive model had satisfactory discrimination with an area under the curve (AUC) of 0.852–0.858 in the training and validation cohorts, respectively. The nomogram showed good calibration and clinical application according to the calibration curve and decision curve analysis. Furthermore, the prediction model had perfect predictive power for predicting 30-day mortality (AUC = 0.813) and MAKE30 (AUC = 0.823) in elderly sepsis patients. Conclusion The proposed nomogram can quickly and effectively predict S-AKI risk in elderly sepsis patients within 24 h after admission, providing information for clinicians to make personalized interventions.

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“…The most common source of patient data was Medical Information Mart for Intensive Care (MIMIC) III, which was used in 15 studies [5 ▪▪ ,7 ▪▪ ,10 ▪▪ ,13–15,16 ▪▪ ,17 ▪▪ ,18,19 ▪▪ –22 ▪▪ ,23 ▪ ,24]. Although most studies ( n = 16) included mixed ICU populations [5 ▪▪ ,7 ▪▪ ,8,10 ▪▪ ,13,16 ▪▪ ,17 ▪▪ ,18,19 ▪▪ –22 ▪▪ ,23 ▪ ,25 ▪▪ ,26 ▪ ,27], there were seven studies focused on sepsis patients [11,12,14,24,28–30], four focused on surgical patients [6,9,31,32 ▪▪ ], one on diabetic ketoacidosis [15], one on haemorrhagic shock [33] and one on acute brain injury [34].…”

Section: Acute Kidney Injury Algorithmsmentioning

confidence: 99%

“…Studies also established baseline sCr from the lowest sCr prior to admission (ranging from 365 days to 1 week prior [25 ▪▪ ,29,30,32 ▪▪ ] or within 7 days prior [24]), mean sCr from 180 to 7 days prior to admission [21 ▪▪ ,26 ▪ ], preoperative sCr [6,31] or the Modified Diet and Renal Disease (MDRD) equation if a measured sCr was not available [9,12,26 ▪ ,30]. Eleven studies did not define baseline sCr [7 ▪▪ ,10 ▪▪ ,11–13,15,20 ▪▪ ,22 ▪▪ ,23 ▪ ,28,33].…”

Section: Acute Kidney Injury Algorithmsmentioning

confidence: 99%

Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury

Ozrazgat‐Baslanti

Loftus

Ren

et al. 2021

Current Opinion in Critical Care

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Purpose of reviewAcute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of this article is to assimilate and critically evaluate recently published literature regarding artificial intelligence applications for predicting, diagnosing and subphenotyping AKI among critically ill patients. Recent findingsAmong recent studies regarding artificial intelligence implementations for predicting, diagnosing and subphenotyping AKI among critically ill patients, there are many promising models, but few had external validation, clinical interpretability and high predictive performance. Deep learning techniques leveraging multimodal clinical data show great potential to provide continuous, accurate, early predictions of AKI risk, which could be implemented clinically to optimize preventive and early therapeutic management strategies.

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Association between Procalcitonin and Acute Kidney Injury in Patients with Bacterial Septic Shock

Cited by 11 publications

References 41 publications

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

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

Construction and validation of an early warning model for predicting the acute kidney injury in elderly patients with sepsis

Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury

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