&lt;p&gt;Development and Validation of a Nomogram Model to Predict Acute Kidney Disease After Nephrectomy in Patients with Renal Cell Carcinoma&lt;/p&gt;

Hu, Xiaoying; Li, Dongwei; Qiao, Ye; Zheng, Xuan; Duan, Jiayu; Pan, Shaokang; Liu, Zhang-Sou

doi:10.2147/cmar.s273244

Cited by 7 publications

(14 citation statements)

References 32 publications

(45 reference statements)

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“…Recent studies haves shown that both are mutual risk factors and risk factors for cardiovascular disease (Awdishu and Mehta, 2017). Similarly, eGFR, the main diagnostic indicator of CKD, has been used as an independent predictor by multiple AKI prediction models (Bell et al, 2020;Hu et al, 2020). We also found a significant correlation between alcohol abuse and D-AKI.…”

Section: Discussionsupporting

confidence: 61%

Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching

Guo

Yan

et al. 2021

Front. Pharmacol.

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Background: Drug-induced acute kidney injury (D-AKI) is associated with increased mortality and longer hospital stays. This study aims to establish a nomogram to predict the occurrence of D-AKI in hospitalized patients in a multi-drug environment.Methods: A single center retrospective study among adult hospitalized patients was conducted from July 2019 to September 2019 based on the Adverse Drug Events Active Surveillance and Assessment System-2 developed by our hospital. According to the propensity score matching algorithm, four controls per case were matched to eliminate the confounding bias caused by individual baseline variables. The predictors for D-AKI were obtained by logistic regression equation and used to establish the nomogram.Results: Among 51,772 hospitalized patients, 332 were diagnosed with D-AKI. After matching, 288 pairs and 1,440 patients were included in the study, including 1,005 cases in the development group and 435 cases in the validation group. Six variables were independent predictors for D-AKI: alcohol abuse, the concurrent use of nonsteroidal anti-inflammatory drugs or diuretics, chronic kidney disease, lower baseline red blood cell count and neutrophil count ≥7 × 109/L. The area under the curve (AUC) of the prediction model in the development group and validation group were 0.787 (95%CI, 0.752–0.823) and 0.788 (95%CI, 0.736–0.840), respectively. The GiViTI calibration belts showed that the model had a good prediction accuracy for the occurrence of D-AKI (p > 0.05).Conclusion: This nomogram can help identify patients at high risk of D-AKI, which was useful in preventing the progression of D-AKI and treating it in the early stages.

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Section: Discussionsupporting

confidence: 61%

Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching

Guo

Yan

et al. 2021

Front. Pharmacol.

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“…The study flowchart according to the PRISMA statement is shown in Figure 1. The review included 150 studies (encompassing 14.4 million patients) with requisite data to enable sROC curves in 103 studies. The median duration of study data collection was 4 years (range, 3 months to 17 years).…”

Section: Resultsmentioning

confidence: 99%

“…Most studies reported prospective data collection (124 [82.7%]) . Based on prespecified clinical scenario subgroups, 29 studies assessed AKI associated with nephrotoxic agents, including 26 studies examining radio-contrast medium associated AKI and 3 studies that reported AKI associated with other nephrotoxins; 64 studies assessed AKI prediction in the postoperative setting; 21 studies assessed AKI prediction in intensive care units (ICU); 35 studies assessed AKI in general hospitalizations…”

Section: Resultsmentioning

confidence: 99%

“…Based on prespecified clinical scenario subgroups, 29 studies assessed AKI associated with nephrotoxic agents, including 26 studies examining radio-contrast medium associated AKI and 3 studies that reported AKI associated with other nephrotoxins; 64 studies assessed AKI prediction in the postoperative setting; 21 studies assessed AKI prediction in intensive care units (ICU); 35 studies assessed AKI in general hospitalizations; and 1 assessed AKI in the outpatient setting . The most common region where the studies had been conducted was the Asia-Pacific region, accounting for 40.0% of the included studies (60 studies), followed by North America (37.3% [56 studies])…”

Section: Resultsmentioning

confidence: 99%

“…The AKI definitions varied across the studies, with most being the KDIGO criteria (76 [50.7%]), followed by the AKIN criteria (17 [11.3%]) and the RIFLE criteria (9 [6.0%]) . Nearly a quarter of the studies (35 [23.3%]) defined AKI using criteria specific to their particular studies .…”

Section: Resultsmentioning

confidence: 99%

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Characterization of Risk Prediction Models for Acute Kidney Injury

et al. 2023

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ImportanceDespite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.ObjectiveTo systematically review published AKI prediction models across all clinical subsettings.Data SourcesMEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.Study SelectionAll studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.Data Extraction and SynthesisTwo authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.Main Outcomes and MeasuresC statistic was used to measure the discrimination of prediction models.ResultsOf the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium–associated AKI: I2 = 99.9%; P &lt; .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.Conclusions and RelevanceIn this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

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Development and deployment of interpretable machine-learning model for predicting in-hospital mortality in elderly patients with acute kidney disease

Zhuang

Zhao

et al. 2022

Renal Failure

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Background Acute kidney injury (AKI) is more likely to develop in the elderly admitted to the intensive care unit (ICU). Acute kidney disease (AKD) affects ∼45% of patients with AKI and increases short-term mortality. However, there are no studies on the prognosis of AKD in the elderly. Methods Data from 2666 elderly patients with AKD in the Medical Information Mart for Intensive Care IV were used for model development and 535 in the eICU Collaborative Research Database for external validation. Based on 5 machine learning algorithms, 33 noninvasive parameters were extracted as features for modeling. Results In-hospital mortality of AKD in the elderly was 29.6% and 31.8% in development and validation cohorts, respectively. The comprehensive best-performing algorithm was the support vector machine (SVM), and a simplified online application included only 10 features employing SVM (AUC: 0.810 and 0.776 in the training and external validation cohorts, respectively) was deployed. Model interpretation by SHapley Additive exPlanation (SHAP) values revealed that the difference (AKD day – ICU day) in sequential organ failure assessment (delta SOFA), Glasgow coma scale (GCS), delta GCS, delta peripheral oxygen saturation (SpO2), and SOFA were the top five features associated with prognosis. The optimal target was determined by SHAP values from partial dependence plots. Conclusions A web-based tool was externally validated and deployed to predict the early prognosis of AKD in the elderly based on readily available noninvasive parameters, assisting clinicians in intervening with precision and purpose to save lives to the greatest extent.

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<p>Development and Validation of a Nomogram Model to Predict Acute Kidney Disease After Nephrectomy in Patients with Renal Cell Carcinoma</p>

Cited by 7 publications

References 32 publications

Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching

Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching

Characterization of Risk Prediction Models for Acute Kidney Injury

Development and deployment of interpretable machine-learning model for predicting in-hospital mortality in elderly patients with acute kidney disease

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