Incidence and risk factors for acute kidney injury following autologous stem cell transplantation for multiple myeloma

Andronesi, Andreea; Tănase, Alina; Sorohan, Bogdan; Crăciun, O; Stefan, Laura; Várady, Zsofia; Lipan, Lavinia; Obrișcă, Bogdan; Truica, Alexandra; Ismail, Gener

doi:10.1002/cam4.2187

Cited by 22 publications

(25 citation statements)

References 38 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Complete kidney recovery has been defined as a return of serum creatinine to baseline by all studies except Parikh et al [ 14 ], who defined complete recovery as the return of serum creatinine to less than 1.2 times baseline. In accord with the notion that allogeneic HSCT is associated with an increased incidence of AKI as compared to autologous, there was a signal from two studies [ 16 , 19 ] that autologous HSCT could have better recovery rates as compared to allogeneic HSCT, but more data are required to confirm these findings. Unfortunately, kidney recovery in those cases needing RRT was dismal except for the study by Irazabal et al [ 16 ] in which half the patients recovered their kidney function after RRT.…”

Section: Discussionmentioning

confidence: 98%

Kidney Recovery From Acute Kidney Injury After Hematopoietic Stem Cell Transplant: A Systematic Review and Meta-Analysis

Kanduri

Kovvuru

Cheungpasitporn

et al. 2021

Cureus

View full text Add to dashboard Cite

Patients with the recovery of kidney function after an episode of acute kidney injury (AKI) have better outcomes compared to those without recovery. The current systematic review is conducted to assess the rates of kidney function recovery among patients with AKI or severe AKI requiring kidney replacement therapy (KRT) within 100 days after hematopoietic stem cell transplant (HSCT). Methods The Ovid MEDLINE, EMBASE, and Cochrane databases were systemically searched from database inceptions through August 2019 to identify studies reporting the rates of recovery from AKI after HSCT. The random-effects and generic inverse variance methods of DerSimonian-Laird were used to combine the effect estimates obtained from individual studies. Results A total of 458 patients from eight cohort studies with AKI after HSCT were identified. Overall, the pooled estimated rates of AKI recovery among patients with AKI and severe AKI requiring KRT within 100 days were 58% (95%CI: 37%-78%) and 10% (95%CI: 2%-4%), respectively. Among patients with AKI recovery, the pooled estimated rates of complete and partial AKI recovery were 60% (95%CI: 39%-78%) and 29% (95%CI: 10%-61%), respectively. There was no clear correlation between study year and the rate of AKI recovery (p=0.26). Conclusion The rate of recovery from AKI after HSCT depends on the severity of AKI. While recovery is common, complete recovery is reported in about two-thirds of all AKI patients. The rate of recovery among those with AKI requiring renal replacement therapy (RRT) is substantially lower.

show abstract

Section: Discussionmentioning

confidence: 98%

Kidney Recovery From Acute Kidney Injury After Hematopoietic Stem Cell Transplant: A Systematic Review and Meta-Analysis

Kanduri

Kovvuru

Cheungpasitporn

et al. 2021

Cureus

View full text Add to dashboard Cite

show abstract

“…Moreover, we found that patients receiving ASCT had a higher risk of AKI. This may be related to the adverse effect of calcineurin inhibitors, graft versus host disease and hepatic sinusoidal obstruction syndrome [33].…”

Section: Discussionmentioning

confidence: 99%

Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies

Chen

Wang

et al. 2020

BMC Nephrol

View full text Add to dashboard Cite

Background: Patients who were diagnosed with hematologic malignancies (HM) had a higher risk of acute kidney injury (AKI). This study applies the Bayesian networks (BNs) to investigate the interrelationships between AKI and its risk factors among HM patients, and to evaluate the predictive and inferential ability of BNs model in different clinical settings. Methods: During 2014 and 2015, a total of 2501 inpatients with HM were recruited in this retrospective study conducted in a tertiary hospital, Shanghai of China. Patients' demographics, medical history, clinical and laboratory records on admission were extracted from the electronic medical records. Candidate predictors of AKI were screened in the group-LASSO (gLASSO) regression, and then they were incorporated into BNs analysis for further interrelationship modeling and disease prediction.

show abstract

“…Lymphadenopathy as well as drug-induced crystalluria (acyclovir, cotrimoxazole) can result in obstructive nephropathy. Moreover, ASCT was observed with a higher risk of AKI, involving potential mechanisms including the adverse effect of calcineurin inhibitors, graft versus host disease and hepatic sinusoidal obstruction syndrome, which can induce anemia and renal dysfunction [32].…”

Section: Discussionmentioning

confidence: 99%

Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies

Chen

Wang

et al. 2019

Preprint

View full text Add to dashboard Cite

Background: This study aims to explore a novel machine-learning algorithm, Bayesian networks (BNs), to delineate the interrelationships between acute kidney injury (AKI) and its associated risk factors among patients with hematologic malignancies (HM), to assess the prediction ability of BNs model, and to infer the probability of AKI under different clinical settings. Methods: From 1 October 2014 to 30 September 2015, 2501 hospitalized patients diagnosed with HM in Zhongshan Hospital, Fudan University, Shanghai of China, were recruited in this retrospective study. Data on demographics, comorbidities, and baseline clinical lab records were exported from the electronic medical records. The group-LASSO (gLASSO) regression was performed to select the candidate predictors of AKI, which were further presented in BNs analysis for interrelationship exploration and disease prediction. Results: Among 2395 eligible patients, 370 episodes were diagnosed with AKI (15.4%). Patients with multiple myeloma (24.1%) and leukemia (23.9%) shared a higher AKI incidence than lymphoma (13.4%). Screened by the gLASSO regression, variables as age, gender, diabetes, hemopathy category, anti-tumor treatment, hemoglobin, serum creatinine (SCr), estimated glomerular filtration rate (eGFR), serum uric acid, serum sodium and potassium level were found with significant associations with AKI occurrence. BNs model revealed a complex interrelationship between these factors, among which there were direct connections between AKI and age, hemoglobin, eGFR, serum sodium and potassium. Moreover, HM category and anti-tumor treatment were indirectly linked to AKI through hemoglobin and eGFR; diabetes was connected with AKI through serum sodium level. BNs inferences indicated that when poor GFR, anemia and hyponatremia occurred simultaneously, the probability of AKI might reach 78.5%. The area under the receiver operating characteristic curve (AUC) of BNs model was 0.835, higher than that in logistic score model (0.763) and showed a robust performance in 10-fold cross-validation. Conclusion: AKI is common in HM hospitalized patients and is affected by multiple risk factors. The application of Bayesian networks and gLASSO regression can provide a novel strategy to explore the potential risk factors integrally and deep into their interrelationships, in accordance with the tide of e-alert and big-data for AKI early detection.

show abstract

Incidence and risk factors for acute kidney injury following autologous stem cell transplantation for multiple myeloma

Cited by 22 publications

References 38 publications

Kidney Recovery From Acute Kidney Injury After Hematopoietic Stem Cell Transplant: A Systematic Review and Meta-Analysis

Kidney Recovery From Acute Kidney Injury After Hematopoietic Stem Cell Transplant: A Systematic Review and Meta-Analysis

Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies

Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies

Contact Info

Product

Resources

About