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

Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study

Background and purpose: Acute kidney injury (AKI) is a common serious complication in sepsis patients with a high mortality rate. This study aimed to develop and validate a predictive model for sepsis associated acute kidney injury (SA-AKI). Methods: In our study, we retrospectively constructed a development cohort comprising 733 septic patients admitted to eight Grade-A tertiary hospitals in Shanghai from January 2021 to October 2022. Additionally, we established an external validation cohort consisting of 336 septic patients admitted to our hospital from January 2017 to December 2019. Risk predictors were selected by LASSO regression, and a corresponding nomogram was constructed. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curves (CIC) in both internal and external validation. Results: AKI incidence was 53.2% in the development cohort and 48.2% in the external validation cohort. The model included five independent indicators: chronic kidney disease stages 1 to 3, blood urea nitrogen, procalcitonin, D-dimer and creatine kinase isoenzyme. The AUC of the model in the development and validation cohorts was 0.914 (95% CI, 0.894-0.934) and 0.923 (95% CI, 0.895-0.952), respectively. The calibration plot, DCA, and CIC demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.

Establishment and Diagnostic Value of an Early Prediction Model for Acute Pancreatitis Complicated With Acute Kidney Injury

OBJECTIVES

To establish an early prediction model for acute pancreatitis (AP) complicated with acute kidney injury (AKI) and evaluate its diagnostic value.

METHOD

AP patients were recruited from the Emergency Department at Peking University People's Hospital in 2021 and stratified into AKI and control (no AKI) groups. Their clinical data were analyzed. The risk for AKI development was determined using logistic analyses to establish a risk prediction model, whose diagnostic value was analyzed using a receiver operating characteristic curve.

RESULTS

There was no significant difference in the basic renal function between the AKI (n = 79) and control (n = 179) groups. The increased triglyceride glucose index (odds ratio [OR], 2.613; 95% confidence interval [CI], 1.324-5.158; P = 0.006), age (OR, 1.076; 95% CI, 1.016-1.140; P = 0.013), and procalcitonin (OR, 1.377; 95% CI, 1.096-1.730, P = 0.006) were associated with AKI development. A model was established for prediction of AKI (sensitivity 79.75%, specificity 96.65%). The area under the receiver operating characteristic curve was 0.856 which was superior to the Ranson, Bedside Index for Severity in AP, and Acute Physiology and Chronic Health Evaluation II scores (0.856 vs 0.691 vs 0.745 vs 0.705).

CONCLUSIONS

The prediction model based on age, triglyceride glucose, and procalcitonin is valuable for the prediction of AP-related AKI.

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

Background

In the context of sepsis patients, hypertension has a significant impact on the likelihood of developing sepsis-associated acute kidney injury (S-AKI), leading to a considerable burden. Moreover, sepsis is responsible for over 50 % of cases of acute kidney injuries (AKI) and is linked to an increased likelihood of death during hospitalization. The objective of this research is to develop a dependable and strong nomogram framework, utilizing the variables accessible within the first 24 h of admission, for the anticipation of S-AKI in sepsis patients who have hypertension.

Methods

In this study that looked back, a total of 462 patients with sepsis and high blood pressure were identified from Nanfang Hospital. These patients were then split into a training set (consisting of 347 patients) and a validation set (consisting of 115 patients). A multivariate logistic regression analysis and a univariate logistic regression analysis were performed to identify the factors that independently predict S-AKI. Based on these independent predictors, the model was constructed. To evaluate the efficacy of the designed nomogram, several analyses were conducted, including calibration curves, receiver operating characteristics curves, and decision curve analysis.

Results

The findings of this research indicated that diabetes, prothrombin time activity (PTA), thrombin time (TT), cystatin C, creatinine (Cr), and procalcitonin (PCT) were autonomous prognosticators for S-AKI in sepsis individuals with hypertension. The nomogram model, built using these predictors, demonstrated satisfactory discrimination in both the training (AUC = 0.823) and validation (AUC = 0.929) groups. The S-AKI nomogram demonstrated superior predictive ability in assessing S-AKI within the hypertension grade I (AUC = 0.901) set, surpassing the hypertension grade II (AUC = 0.816) and III (AUC = 0.810) sets. The nomogram exhibited satisfactory calibration and clinical utility based on the calibration curve and decision curve analysis.

Conclusion

In patients with sepsis and high blood pressure, the nomogram that was created offers a dependable and strong evaluation for predicting S-AKI. This evaluation provides valuable insights to enhance individualized treatment, ultimately resulting in improved clinical outcomes.

Diagnostic and Prognostic Value of C1q in Sepsis-Induced Coagulopathy

Early identification of biomarkers that can predict the onset of sepsis-induced coagulopathy (SIC) in septic patients is clinically important. This study endeavors to examine the diagnostic and prognostic utility of serum C1q in the context of SIC. Clinical data from 279 patients diagnosed with sepsis at the Departments of Intensive Care, Respiratory Intensive Care, and Infectious Diseases at the Renmin Hospital of Wuhan University were gathered spanning from January 2022 to January 2024. These patients were categorized into two groups: the SIC group comprising 108 cases and the non-SIC group consisting of 171 cases, based on the presence of SIC. Within the SIC group, patients were further subdivided into a survival group (43 cases) and non-survival group (65 cases). The concentration of serum C1q in the SIC group was significantly lower than that in the non-SIC group. Furthermore, A significant correlation was observed between serum C1q levels and both SIC score and coagulation indices. C1q demonstrated superior diagnostic and prognostic performance for SIC patients, as indicated by a higher area under the curve (AUC). Notably, when combined with CRP, PCT, and SOFA score, C1q displayed the most robust diagnostic efficacy for SIC. Moreover, the combination of C1q with the SOFA score heightened predictive value concerning the 28-day mortality of SIC patients.

Serum procalcitonin levels are associated with rhabdomyolysis following exertional heatstroke: an over 10-year intensive care survey

BACKGROUND

Heatstroke has become a common emergency event in hospitals. Procalcitonin (PCT) is used as a biomarker of infection in the emergency department (ED), but its role in rhabdomyolysis (RM) following exertional heatstroke (EHS) remains unclear.

METHODS

A retrospective cohort study enrolled patients with EHS from the intensive care unit (ICU). We collected RM biomarkers, inflammation markers, critical disease scores at admission, 24 h, 48 h, and discharge, and 90-day mortality. Correlation analysis, linear regression and curve fitting were used to identify the relationship between PCT and RM.

RESULTS

A total of 162 patients were recruited and divided into RM (n=56) and non-RM (n=106) groups. PCT was positively correlated with myoglobin (Mb), acute hepatic injury, disseminated intravascular coagulation (DIC), Sequential Organ Failure Assessment (SOFA) score, and Acute Physiology and Chronic Health Evaluation II (APACHE II) score, with correlation coefficients of 0.214, 0.237, 0.285, 0.454, and 0.368, respectively (all P<0.05). Interestingly, the results of curve fitting revealed a nonlinear relationship between PCT and RM, and a two-piecewise linear regression model showed that PCT was related to RM with an odds ratio of 1.3 and a cut-off of <4.6 ng/mL. Survival analysis revealed that RM was associated with higher mortality compared to non-RM cases (P=0.0093).

CONCLUSION

High serum PCT concentrations are associated with RM after EHS in critically ill patients. Elevated PCT concentrations should be interpreted cautiously in patients with EHS in the ED.

Risk factors for BK virus infection in DCD donor kidney transplant recipients

Background

BK virus infection after kidney transplantation can negatively impact the prognosis of patients. However, current risk factor analyses primarily focus on BK virus nephropathy, while BK viruria and BK viruria progressing to BK viremia receive less attention. This study aims to analyze the risk factors associated with BK viruria and BK viruria progressing to BK viremia in recipients of donation after cardiac death (DCD), with the goal of facilitating early intervention.

Methods

Donor characteristics and clinical data of recipients before and after transplantation were evaluated, and logistic univariate and multivariate analyses were performed to determine the risk factors associated with BK viruria and the progression of BK viruria to BK viremia. Additionally, machine learning techniques were employed to identify the top five features associated with BK viruria evolving into BK viremia.

Results

During a median follow-up time of 1,072 days (range 739-1,418), 69 transplant recipients (15.6% incidence rate) developed BK viruria after transplantation, with 49.3% of cases occurring within 6 months post-transplantation. Moreover, 19 patients progressed to BK viremia. Donor age [OR: 1.022 (1.000, 1.045), p = 0.047] and donor procalcitonin (PCT) levels [0.5-10 ng/ml; OR: 0.482 (0.280, 0.828), p = 0.008] were identified as independent risk factors for BK viruria. High BK viruria [OR: 11.641 (1.745, 77.678), p = 0.011], recipient age [OR: 1.106 (1.017, 1.202), p = 0.018], and immunoinduction regimen [ATG; OR: 0.063 (0.006, 0.683), p = 0.023] were independent risk factors for BK viruria progressing to BK viremia. Machine learning analysis confirmed the importance of high BK viruria, recipient age, and immunoinduction regimen (ATG) in predicting the progression of BK viruria to BK viremia.

Conclusion

The development and progression of BK virus in DCD kidney transplant recipients is influenced by multiple factors. Early intervention and treatment could potentially extend the lifespan of the transplanted organ.

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

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.

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

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.

Predictive Ability of Procalcitonin for Acute Kidney Injury: A Narrative Review Focusing on the Interference of Infection

Acute kidney injury (AKI) is a common yet complicated clinical entity with high morbidity and mortality. An essential strategy to improve AKI patients’ prognoses is finding optimal biomarkers to identify AKI in a timely manner. Procalcitonin (PCT), a well-recognized biomarker for diagnosing infection and guiding antibiotics therapy, has been proposed to predict AKI development and recovery in many clinical settings. The current review provides comprehensive and updated information from relevant studies to evaluate PCT’s AKI-predictive ability and the influence of infection on this predictive ability. PCT has demonstrated optimal predictive ability for AKI in various populations irrespective of infection. However, the predictive ability seems to be blunted by infection since infection and inflammation have a more potent influence than AKI on PCT elevation. We furthermore explain the complicated association between elevated PCT levels and AKI in infection and inflammation situations and recommend directions for further investigations to clarify the essential issue. In conclusion, although conflicting data exist, serum PCT level is a potential biomarker for predicting AKI in many clinical settings regardless of infection. Nevertheless, further studies are warranted to clarify the association between PCT, infection, and AKI and to confirm the utilization of PCT for AKI prediction.