Dataset Information

Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.

ABSTRACT:

Background

One of the most prevalent complications of Partial Nephrectomy (PN) is Acute Kidney Injury (AKI), which could have a negative impact on subsequent renal function and occurs in up to 24.3% of patients undergoing PN. The aim of this study was to predict the occurrence of AKI following PN using preoperative parameters by applying machine learning algorithms.

Methods

We included all adult patients (n = 723) who underwent open PN in our department since 1995 and on whom we have data on the pre-operative renal function. We developed a random forest (RF) model with Boolean satisfaction-based pruned decision trees for binary classification (AKI or non-AKI). Hyper-parameter grid search was performed to optimize the model's performance. Fivefold cross-validation was applied to evaluate the model. We implemented a RF model with greedy feature selection to binary classify AKI and non-AKI cases based on pre-operative data.

Results

The best model obtained a 0.69 precision and 0.69 recall in classifying the AKI and non-AKI groups on average (k = 5). In addition, the model's probability to correctly classify a new prediction is 0.75. The proposed model is available as an online calculator.

Conclusions

Our model predicts the occurrence of AKI following open PN with (75%) accuracy. We plan to externally validate this model and modify it to minimally-invasive PN.

SUBMITTER: Lazebnik T

PROVIDER: S-EPMC9112450 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.

Lazebnik Teddy T Bahouth Zaher Z Bunimovich-Mendrazitsky Svetlana S Halachmi Sarel S

BMC medical informatics and decision making 20220516 1

<h4>Background</h4>One of the most prevalent complications of Partial Nephrectomy (PN) is Acute Kidney Injury (AKI), which could have a negative impact on subsequent renal function and occurs in up to 24.3% of patients undergoing PN. The aim of this study was to predict the occurrence of AKI following PN using preoperative parameters by applying machine learning algorithms.<h4>Methods</h4>We included all adult patients (n = 723) who underwent open PN in our department since 1995 and on whom we h ...[more]

PMID: 35578278

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Project description:Although partial nephrectomy is the preferred treatment for many patients with early-stage kidney cancer, recent clinical trial data, which demonstrate better survival for patients treated with radical nephrectomy, have generated new uncertainty regarding the comparative effectiveness of these treatment options.To compare long-term survival after partial vs radical nephrectomy among a population-based patient cohort whose treatment reflects contemporary surgical practice.We performed a retrospective cohort study of Medicare beneficiaries with clinical stage T1a kidney cancer treated with partial or radical nephrectomy from 1992 through 2007. Using an instrumental variable approach to account for measured and unmeasured differences between treatment groups, we fit a 2-stage residual inclusion model to estimate the treatment effect of partial nephrectomy on long-term survival.Overall and kidney cancer-specific survival.Among 7138 Medicare beneficiaries with early-stage kidney cancer, we identified 1925 patients (27.0%) treated with partial nephrectomy and 5213 patients (73.0%) treated with radical nephrectomy. During a median follow-up of 62 months, 487 (25.3%) and 2164 (41.5%) patients died following partial or radical nephrectomy, respectively. Kidney cancer was the cause of death for 37 patients (1.9%) treated with partial nephrectomy, and 222 patients (4.3%) treated with radical nephrectomy. Patients treated with partial nephrectomy had a significantly lower risk of death (hazard ratio [HR], 0.54; 95% CI, 0.34-0.85). This corresponded with a predicted survival increase with partial nephrectomy of 5.6 (95% CI, 1.9-9.3), 11.8 (95% CI, 3.9-19.7), and 15.5 (95% CI, 5.0-26.0) percentage points at 2, 5, and 8 years posttreatment (P < .001). No difference was noted in kidney cancer-specific survival (HR, 0.82; 95% CI, 0.19-3.49).Among Medicare beneficiaries with early-stage kidney cancer who were candidates for either surgery, treatment with partial rather than radical nephrectomy was associated with improved survival.

Dataset Information

Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.

Background

Methods

Results

Conclusions

Publications

Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.

Similar Datasets

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