Incidence, risk factors and clinical outcomes of septic acute renal injury in cancer patients with sepsis admitted to the ICU: A retrospective study

Predictive value of peripheral blood indicators plus procalcitonin clearance rate for mortality in cancer patients with sepsis

This study investigated the predictive value of combining peripheral blood indicators with procalcitonin clearance rate (PCTc) to assess mortality risk in cancer patients with sepsis, aiming to develop a more sensitive and specific clinical tool. A retrospective analysis was conducted on 393 cancer patients with sepsis admitted to South China Hospital of Shenzhen University from January 2019 to January 2024. Collected data included clinical demographics, laboratory indicators such as white blood cell count, neutrophil count (NEUT), platelet count (PLT), lymphocyte count (LYC), C-reactive protein, procalcitonin (PCT), alanine aminotransferase, and the ratio of arterial oxygen partial pressure to inspired oxygen fraction, as well as functional scores like Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment. Multivariate logistic regression and receiver operating characteristic curves assessed the predictive ability of these factors for 28-day survival. Results showed significantly higher NEUT (P<0.001) and lower PLT and LYC (P<0.001) in the death group, while APACHE II score (area under the curve (AUC) = 0.776) and PCT 24h (AUC = 0.723) demonstrated strong predictive value for mortality risk. The joint projection model's AUC reached 0.966, significantly outperforming individual indicators, indicating that combining multiple indicators offers a more accurate prediction of survival versus mortality risk. Additionally, 24h LCR and 24h PCTc were notably lower in the death group compared to the survival group, reinforcing the advantage of combined indicators for prognosis. Overall, using both peripheral blood indicators and PCTc significantly improves the accuracy of mortality risk assessment in cancer patients with sepsis, enhancing prognostic evaluation and supporting optimized clinical decision-making.

A clinical risk score for predicting acute kidney injury in sepsis patients receiving normal saline in Northern Thailand: a retrospective cohort study

BACKGROUND

Normal saline is commonly used for resuscitation in sepsis patients but has a high chloride content, potentially increasing the risk of acute kidney injury (AKI). This study evaluated risk factors and developed a predictive risk score for AKI in sepsis patients treated with normal saline.

METHODS

This retrospective cohort study utilized the medical and electronic health records of sepsis patients who received normal saline between January 2018 and May 2020. Predictors of AKI used to construct the predictive risk score were identified through multivariate logistic regression models, with discrimination and calibration assessed using the area under the receiver operating characteristic curve (AUROC) and the expected-to-observed (E/O) ratio. Internal validation was conducted using bootstrapping techniques.

RESULTS

AKI was reported in 211 of 735 patients (28.7%). Eight potential risk factors, including norepinephrine, the Acute Physiology and Chronic Health Evaluation II score, serum chloride, respiratory failure with invasive mechanical ventilation, nephrotoxic antimicrobial drug use, history of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers use, history of liver disease, and serum creatinine were used to create the NACl RENAL-Cr score. The model demonstrated good discrimination and calibration (AUROC, 0.79; E/O, 1). The optimal cutoff was 2.5 points, with corresponding sensitivity, specificity, positive predictive value, and negative predictive value scores of 71.6%, 72.5%, 51.2%, and 86.4%, respectively.

CONCLUSIONS

The NACl RENAL-Cr score, consisting of eight critical variables, was used to predict AKI in sepsis patients who received normal saline. This tool can assist healthcare professionals when deciding on sepsis treatment and AKI monitoring.

Severe Acute Kidney Injury in Hospitalized Cancer Patients: Epidemiology and Predictive Model of Renal Replacement Therapy and In-Hospital Mortality

BACKGROUND

Acute kidney injury (AKI) is a common complication among cancer patients, often leading to longer hospital stays, discontinuation of cancer treatment, and a poor prognosis. This study aims to provide insight into the incidence of severe AKI in this population and identify the risk factors associated with renal replacement therapy (RRT) and in-hospital mortality.

METHODS

This retrospective cohort study included 3201 patients with cancer and severe AKI admitted to a Comprehensive Cancer Center between January 1995 and July 2023. Severe AKI was defined according to the KDIGO guidelines as grade ≥ 2 AKI with nephrological in-hospital follow-up. Data were analyzed in two timelines: Period A (1995-2010) and Period B (2011-2023).

RESULTS

A total of 3201 patients (1% of all hospitalized cases) were included, with a mean age of 62.5 ± 17.2 years. Solid tumors represented 75% of all neoplasms, showing an increasing tendency, while hematological cancer decreased. Obstructive AKI declined, whereas the incidence of sepsis-associated, prerenal, and drug-induced AKI increased. Overall, 20% of patients required RRT, and 26.4% died during hospitalization. A predictive model for RRT (AUC 0.833 [95% CI 0.817-0.848]) identified sepsis and hematological cancer as risk factors and prerenal and obstructive AKI as protective factors. A similar model for overall in-hospital mortality (AUC 0.731 [95% CI 0.71-0.752]) revealed invasive mechanical ventilation (IMV), sepsis, and RRT as risk factors and obstructive AKI as a protective factor. The model for hemato-oncological patients' mortality (AUC 0.832 [95% CI 0.803-0.861]) included IMV, sepsis, hematopoietic stem cell transplantation, and drug-induced AKI. Mortality risk point score models were derived from these analyses.

CONCLUSIONS

This study addresses the demographic and clinical features of cancer patients with severe AKI. The development of predictive models for RRT and in-hospital mortality, along with risk point scores, may play a role in the management of this population.