Prospective model for predicting renal recovery in cardiac surgery patients with acute kidney injury requiring renal replacement therapy.
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ABSTRACT: Aim
To develop a model for predicting renal recovery in cardiac surgery patients with acute kidney injury (AKI) requiring renal replacement therapy (RRT).Methods
Data from a prospective randomized controlled trial, conducted in a tertiary hospital to compare the survival effect of two dosages of hemofiltration for continuous RRT in cardiac surgery patients between 20 March 2012 and 9 August 2015, were used to develop the model. The outcome was renal recovery defined as alive and dialysis-free 90 days after RRT initiation. Multivariate logistic regression with a stepwise backward selection of variables based on Akaike Information Criterion was applied to develop the model, which was internally validated using bootstrapping. Model discrimination, calibration and clinical value were assessed using the concordance index (C-Index), calibration plots and decision curve analysis, respectively.Results
Totally, 211 patients with AKI requiring RRT (66.8% male) with median age of 57 years were included. The incidence of renal recovery was 33.2% (n = 70). The model included six variables: body mass index stratification, baseline estimated glomerular filtration rate, hypertension, sepsis, mean arterial pressure and mechanical ventilation. The C-Index for this model was 0.807 (95% CI, 0.744-0.870). After correction by the bootstrap, the C-Index was 0.780 (95% CI, 0.720-0.845). The calibration plots indicated good consistency between actual observations and model prediction of renal recovery. Decision curve analysis demonstrated the model was clinical usefulness.Conclusion
We developed and validated a model to predict the chance of renal recovery in cardiac surgery patients with AKI requiring RRT.
SUBMITTER: Hu P
PROVIDER: S-EPMC9292395 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionAfter dialysis-requiring acute kidney injury (AKI-D), recovery of sufficient kidney function to discontinue dialysis is an important clinical and patient-oriented outcome. Predicting the probability of recovery in individual patients is a common dilemma.MethodsThis cohort study examined all adult members of Kaiser Permanente Northern California who experienced AKI-D between January 2009 and September 2015 and had predicted inpatient mortality of <20%. Candidate predictors included demographic characteristics, comorbidities, laboratory values, and medication use. We used logistic regression and classification and regression tree (CART) approaches to develop and cross-validate prediction models for recovery.ResultsAmong 2214 patients with AKI-D, mean age was 67.1 years, 40.8% were women, and 54.0% were white; 40.9% of patients recovered. Patients who recovered were younger, had higher baseline estimated glomerular filtration rates (eGFR) and preadmission hemoglobin levels, and were less likely to have prior heart failure or chronic liver disease. Stepwise logistic regression applied to bootstrapped samples identified baseline eGFR, preadmission hemoglobin level, chronic liver disease, and age as the predictors most commonly associated with coming off dialysis within 90 days. Our final logistic regression model including these predictors had a correlation coefficient between observed and predicted probabilities of 0.97, with a c-index of 0.64. An alternate CART approach did not outperform the logistic regression model (c-index 0.61).ConclusionWe developed and cross-validated a parsimonious prediction model for recovery after AKI-D with excellent calibration using routinely available clinical data. However, the model's modest discrimination limits its clinical utility. Further research is needed to develop better prediction tools.
| S-EPMC6451155 | biostudies-literature
Project description:ImportanceSevere acute kidney injury (AKI) is a serious postoperative complication. A tool for predicting the risk of AKI requiring kidney replacement therapy (KRT) after major noncardiac surgery might assist with patient counseling and targeted use of measures to reduce this risk.ObjectiveTo derive and validate a predictive model for AKI requiring KRT after major noncardiac surgery.Design, setting, and participantsIn this prognostic study, 5 risk prediction models were derived and internally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Alberta, Canada, between January 1, 2004, and December 31, 2013. The best performing model and corresponding risk index were externally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Ontario, Canada, between January 1, 2007, and December 31, 2017. Data analysis was conducted from September 1, 2019, to May 31, 2021.ExposuresDemographic characteristics, surgery type, laboratory measures, and comorbidities before surgery.Main outcomes and measuresAcute kidney injury requiring KRT within 14 days after surgery. Discrimination was assessed using the C statistic; calibration was assessed using calibration intercept and slope. Logistic recalibration was used to optimize model calibration in the external validation cohort.ResultsThe derivation cohort included 92 114 patients (52.2% female; mean [SD] age, 62.3 [18.0] years), and the external validation cohort included 709 086 patients (50.8% female; mean [SD] age, 61.0 [16.0] years). A total of 529 patients (0.6%) developed postoperative AKI requiring KRT in the derivation cohort, and 2956 (0.4%) developed postoperative AKI requiring KRT in the external validation cohort. The following factors were consistently associated with the risk of AKI requiring KRT: younger age (40-69 years: odds ratio [OR], 2.07 [95% CI, 1.69-2.53]; <40 years: OR, 3.73 [95% CI, 2.61-5.33]), male sex (OR, 1.55; 95% CI, 1.28-1.87), surgery type (colorectal: OR, 4.86 [95% CI, 3.28-7.18]; liver or pancreatic: OR, 6.46 [95% CI, 3.85-10.83]; other abdominal: OR, 2.19 [95% CI, 1.66-2.89]; abdominal aortic aneurysm repair: OR, 19.34 [95% CI, 14.31-26.14]; other vascular: OR, 7.30 [95% CI, 5.48-9.73]; thoracic: OR, 3.41 [95% CI, 2.07-5.59]), lower estimated glomerular filtration rate (OR, 0.97; 95% CI, 0.97-0.97 per 1 mL/min/1.73 m2 increase), lower hemoglobin concentration (OR, 0.99; 95% CI, 0.98-0.99 per 0.1 g/dL increase), albuminuria (mild: OR, 1.88 [95% CI, 1.52-2.33]; heavy: OR, 3.74 [95% CI, 2.98-4.69]), history of myocardial infarction (OR, 1.63; 95% CI, 1.32-2.03), and liver disease (mild: OR, 2.32 [95% CI, 1.66-3.24]; moderate or severe: OR, 4.96 [95% CI, 3.58-6.85]). In external validation, a final model including these variables showed excellent discrimination (C statistic, 0.95; 95% CI, 0.95-0.96), with sensitivity of 21.2%, specificity of 99.9%, positive predictive value of 38.1%, and negative predictive value of 99.7% at a predicted risk threshold of 10% or greater.Conclusions and relevanceThe findings suggest that this risk model can predict AKI requiring KRT after noncardiac surgery using routine preoperative data. The model may be feasible for implementation in clinical perioperative risk stratification for severe AKI.
| S-EPMC8383136 | biostudies-literature
Project description: Not available
| S-EPMC7839389 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) is a common disease in the intensive care unit (ICU). AKI patients with nonrecovery of renal function have a markedly increased risk of death compared with patients with recovery. The current study aimed to explore and validate the utility of urinary cell cycle arrest biomarkers for predicting nonrecovery in patients who developed AKI after ICU admission.MethodsWe prospectively and consecutively enrolled 379 critically ill patients who developed AKI after admission to the ICU, which were divided into a derivation cohort (194 AKI patients) and a validation cohort (185 AKI patients). The biomarkers of urinary tissue inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) were detected at inclusion immediately after AKI diagnosis (day 0) and 24 h later (day 1). The optimal cut-off values of these biomarkers for predicting nonrecovery were estimated in the derivation cohort, and their predictive accuracy was assessed in the validation cohort. The primary endpoint was nonrecovery from AKI (within 7 days).ResultsOf 379 patients, 159 (41.9%) patients failed to recover from AKI onset, with 79 in the derivation cohort and 80 in the validation cohort. Urinary [TIMP-2]*[IGFBP7] on day 0 showed a better prediction ability for nonrecovery than TIMP-2 and IGFBP7 alone, with an area under the reciever operating characteristic curve (AUC) of 0.751 [95% confidence interval (CI) 0.701-0.852, p < 0.001] and an optimal cut-off value of 1.05 ((ng/mL)2/1000). When [TIMP-2]*[IGFBP7] was combined with the clinical factors of AKI diagnosed by the urine output (UO) criteria, AKI stage 2-3 and nonrenal SOFA score for predicting nonrecovery, the AUC was significantly improved to 0.852 (95% CI 0.750-0.891, p < 0.001), which achieved a sensitivity and specificity of 88.8% (72.9, 98.7) and 92.6% (80.8, 100.0), respectively. However, urine [TIMP-2]*[IGFBP7], TIMP-2 alone, and IGFBP7 alone on day 1 performed poorly for predicting AKI recovery.ConclusionUrinary [TIMP-2]*[IGFBP7] on day 0 showed a fair performance for predicting nonrecovery from AKI. The predictive accuracy can be improved when urinary [TIMP-2]*[IGFBP7] is combined with the clinical factors of AKI diagnosed by the UO criteria, AKI stage 2-3 and nonrenal SOFA score.
| S-EPMC8840946 | biostudies-literature
Project description:BackgroundAcidosis frequently occurs in severe acute kidney injury (AKI), and continuous renal replacement therapy (CRRT) can control this pathologic condition. Nevertheless, acidosis may be aggravated; thus, monitoring is essential after starting CRRT. Herein, we addressed the longitudinal trajectory of acidosis on CRRT and its relationship with worse outcomes.MethodsThe latent growth mixture model was applied to classify the trajectories of pH during the first 24 hours and those of C-reactive protein (CRP) after 24 hours on CRRT due to AKI (n = 1815). Cox proportional hazard models were used to calculate hazard ratios of all-cause mortality after adjusting multiple variables or matching their propensity scores.ResultsThe patients could be classified into 5 clusters, including the normally maintained groups (1st cluster, pH = 7.4; and 2nd cluster, pH = 7.3), recovering group (3rd cluster with pH values from 7.2 to 7.3), aggravating group (4th cluster with pH values from 7.3 to 7.2), and ill-being group (5th cluster, pH < 7.2). The pH clusters had different trends of C-reactive protein (CRP) after 24 hours; the 1st and 2nd pH clusters had lower levels, but the 3rd to 5th pH clusters had an increasing trend of CRP. The 1st pH cluster had the best survival rates, and the 3rd to 5th pH clusters had the worst survival rates. This survival difference was significant despite adjusting for other variables or matching propensity scores.ConclusionsInitial trajectories of acidosis determine subsequent worse outcomes, such as mortality and inflammation, in patients undergoing CRRT due to AKI.
| S-EPMC9792158 | biostudies-literature
Project description:Objective: We aimed to: (1) explore the risk factors that affect the prognosis of cardiac surgery-associated acute kidney injury (CS-AKI) in patients undergoing renal replacement therapy (RRT) and (2) investigate the predictive value of the Acute Physiology and Chronic Health Evaluation (APACHE) III score, Sequential Organ Failure Assessment (SOFA) score, and Vasoactive-Inotropic Score (VIS) for mortality risk in patients undergoing RRT. Methods: Data from patients who underwent cardiac surgery from January 2015 through February 2021 were retrospectively reviewed to calculate the APACHE III score, SOFA score, and VIS on the first postoperative day and at the start of RRT. Various risk factors influencing the prognosis of the patients during treatment were evaluated; the area under the receiver operating characteristics curve (AUCROC) was used to measure the predictive ability of the three scores. Independent risk factors influencing mortality were analyzed using multivariable binary logistic regression. Results: A total of 90 patients were included in the study, using 90-day survival as the end point. Of those patients, 36 patients survived, and 54 patients died; the mortality rate reached 60%. At the start of RRT, the AUCROC of the APACHE III score was 0.866 (95% CI: 0.795-0.937), the VIS was 0.796 (95% CI: 0.700-0.892), and the SOFA score was 0.732 (95% CI: 0.623-0.842). The AUCROC-value of the APACHE III score on the first postoperative day was 0.790 (95% CI: 0.694-0.885). After analyzing multiple factors, we obtained the final logistic regression model with five independent risk factors at the start of RRT: a high APACHE III score (OR: 1.228, 95% CI: 1.079-1.397), high VIS (OR: 1.147, 95% CI: 1.021-1.290), low mean arterial pressure (MAP) (OR: 1.170, 95% CI: 1.050-1.303), high lactate value (OR: 1.552, 95% CI: 1.032-2.333), and long time from AKI to initiation of RRT (OR: 1.014, 95% CI: 1.002-1.027). Conclusion: In this study, we showed that at the start of RRT, the APACHE III score and the VIS can accurately predict the risk of death in patients undergoing continuous RRT for CS-AKI. The APACHE III score on the first postoperative day allows early prediction of patient mortality risk. Predictors influencing patient mortality at the initiation of RRT were high APACHE III score, high VIS, low MAP, high lactate value, and long time from AKI to the start of RRT.
| S-EPMC8566707 | biostudies-literature
Project description:ObjectivesAcute kidney injury (AKI) is common after cardiac arrest (CA). Few data exist on survival and neurological outcomes measured at hospital discharge of patients with severe AKI requiring renal replacement therapy (RRT) within the first 72 hours (i.e., duration of post-CA syndrome).MethodsSingle-center, prospective, observation cohort of patients with in- or out-of-hospital CA who survived to intensive care unit admission and were considered for targeted temperature management between 2010 and 2016 were reviewed. After excluding preexisting RRT history, patients with new RRT requirements within the first 72 hours after CA were included. Primary outcome of survival and secondary outcome of good neurological recovery defined as cerebral performance category score of 1 to 2, were compared between patients with and without RRT. Within 24 hours of initiating RRT, illness severity, as measured by Sequential Organ Failure Assessment score, Acute Physiology and Chronic Health Evaluation-II, and Charlson Comorbidity Index, was compared between survivors and nonsurvivors.ResultsOf 524 patients, 65 (12.4%) had new RRT requirements within 72 hours. Survival rates and good neurological recovery at discharge were comparable between RRT and non-RRT groups (19 of 65 [29%] vs. 162 of 459 [35%], P = 0.3, and 8 of 19 [42%] vs. 73 of 162 [45%], respectively). Sixty-three percent (12 of 19) of survivors requiring RRT did not need dialysis on discharge. Among patients requiring RRT, prognostic factors, including illness severity scores and indications for RRT, did not differ between survivors and nonsurvivors.ConclusionsPatients with severe AKI requiring RRT during the post-CA syndrome period were not associated with any significant reduction in survival or poor neurological recovery, compared with those without RRT. Among those requiring RRT, none of the known prognostic factors predicted survival.
| S-EPMC6506695 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) is a key risk factor for chronic kidney disease in the general population, but has not been investigated in detail among renal transplant recipients (RTRs). We investigated the incidence, severity and risk factors for AKI following cardiac surgery among RTRs compared with non-RTRs with otherwise similar clinical characteristics.MethodsWe conducted a retrospective cohort study of RTRs (n = 83) and non-RTRs (n = 83) who underwent cardiac surgery at two major academic medical centers. Non-RTRs were matched 1:1 to RTRs by age, preoperative (preop) estimated glomerular filtration rate and type of cardiac surgery. We defined AKI according to Kidney Disease: Improving Global Outcomes criteria.ResultsRTRs had a higher rate of AKI following cardiac surgery compared with non-RTRs [46% versus 28%; adjusted odds ratio 2.77 (95% confidence interval 1.36-5.64)]. Among RTRs, deceased donor (DD) versus living donor (LD) status, as well as higher versus lower preop calcineurin inhibitor (CNI) trough levels, were associated with higher rates of AKI (57% versus 33% among DD-RTRs versus LD-RTRs; P = 0.047; 73% versus 36% among RTRs with higher versus lower CNI trough levels, P = 0.02). The combination of both risk factors (DD status and higher CNI trough level) had an additive effect (88% AKI incidence among patients with both risk factors versus 25% incidence among RTRs with neither risk factor, P = 0.004).ConclusionsRTRs have a higher risk of AKI following cardiac surgery compared with non-RTRs with otherwise similar characteristics. Among RTRs, DD-RTRs and those with higher preop CNI trough levels are at the highest risk.
| S-EPMC7771983 | biostudies-literature
Project description:Half of the critically ill patients with renal replacement therapy (RRT) dependent acute kidney injury (AKI) die within one year despite RRT. General intensive care prediction models perform inadequately in AKI. Predictive models for mortality would be an invaluable complementary tool to aid clinical decision making. We aimed to develop and validate new prediction models for intensive care unit (ICU) and hospital mortality customized for patients with RRT dependent AKI in a retrospective single-center study. The models were first developed in a cohort of 471 critically ill patients with continuous RRT (CRRT) and then validated in a cohort of 193 critically ill patients with intermittent hemodialysis (IHD) as the primary modality for RRT. Forty-two risk factors for mortality were examined at ICU admission and CRRT initiation, respectively, in the first univariate models followed by multivariable model development. Receiver operating characteristics curve analyses were conducted to estimate the area under the curve (AUC), to measure discriminative capacity of the models for mortality. AUCs of the respective models ranged between 0.76 and 0.83 in the CRRT model development cohort, thereby showing acceptable to excellent predictive power for the mortality events (ICU mortality and hospital mortality). The models showed acceptable external validity in a validation cohort of IHD patients. In the IHD validation cohort the AUCs of the MALEDICT RRT initiation model were 0.74 and 0.77 for ICU and hospital mortality, respectively. The MALEDICT model shows promise for mortality prediction in critically ill patients with RRT dependent AKI. After further validation, the model might serve as an additional clinical tool for estimating individual mortality risk at the time of RRT initiation.
| S-EPMC9205979 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) is associated with high morbidity and mortality in surgical patients. Nonrecovery from AKI may increase mortality and early risk stratification seems key to improving clinical outcomes. The aim of the current study was to explore and validate the value of endostatin for predicting failure to recover from AKI.MethodsWe conducted a prospective cohort study of 198 patients without known chronic kidney disease who underwent noncardiac major surgery and developed new-onset AKI in the first 48 h after admission to the ICU. The biomarkers of plasma endostatin, neutrophil gelatinase-associated lipocalin (NGAL) and cystatin C were detected immediately after AKI diagnosis. The primary endpoint was nonrecovery from AKI (within 7 days). Cutoff values of the biomarkers for predicting nonrecovery were determined in a derivation cohort (105 AKI patients). Predictive accuracy was then analyzed in a validation cohort (93 AKI patients).ResultsSeventy-six of 198 (38.4%) patients failed to recover from AKI onset, with 41 in the derivation cohort and 35 in the validation cohort. Compared with NGAL and cystatin C, endostatin showed a better prediction for nonrecovery, with an area under the receiver operating characteristic curve (AUC) of 0.776 (95% confidence interval (CI) 0.654-0.892, p < 0.001) and an optimal cutoff value of 63.7 ng/ml. The predictive ability for nonrecovery was greatly improved by the prediction model combining endostatin with clinical risk factors of Sequential Organ Failure Assessment (SOFA) score and AKI classification, with an AUC of 0.887 (95% CI 0.766-0.958, p < 0.001). The value of the endostatin-clinical risk prediction model was superior to the NGAL-clinical risk and cystatin C-clinical risk prediction models in predicting failure to recover from AKI, which was supported by net reclassification improvement and integrated discrimination improvement. Further, the endostatin-clinical risk prediction model achieved sensitivity and specificity of 94.6% (76.8-99.1) and 72.7% (57.2-85.0), respectively, when validated in the validation cohort.ConclusionPlasma endostatin shows a useful value for predicting failure to recover from AKI. The predictive ability can be greatly improved when endostatin is combined with the SOFA score and AKI classification.
| S-EPMC6240328 | biostudies-literature