Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Kang, Min Woo; Kim, Jayoun; Oh, Kook Hwan; Joo, Kwon Wook; Kim, Yon Su

doi:10.1186/s13054-020-2752-7

Cited by 72 publications

(79 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have applied machine learning algorithms to critically ill patients and have shown superior performance compared to existing models or scoring systems in predicting outcomes [18]. Our previous study also demonstrated that machine learning had better performance than conventional scoring systems, such as SOFA and APACHE II, in predicting mortality of CRRT patients [10]. The present study expands the utility of machine learning in predicting hypotension as other outcomes of CRRT and provides a clue on advanced management before the occurrence of hypotension.…”

Section: Discussionsupporting

confidence: 55%

“…Arti cial intelligence may have a role in this di cult assignment, particularly when the numbers of clinical features and their potential interactions rise [9]. Regarding this issue, we previously used machine learning models to predict the mortality risk in patients starting CRRT and found that the model performance was better than conventional disease-severity scores such as the Sequential Organ Failure Assessment (SOFA), the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) II, and the abbreviated mortality scoring system for acute kidney injury with CRRT (MOSAIC) [10]. The study results may widen the area of machine learning applicability, particularly in the eld of critical care using CRRT.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Model to Predict Hypotension After Starting Continuous Renal Replacement Therapy

Kang

Kim

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcome, but it is difficult to predict because several factors have interactive and complex effects on the risk. The present study applied machine learning algorithms to develop models to predict hypotension after initiating CRRT.Methods: Among 2,349 adult patients who started CRRT due to acute kidney injury, 70% and 30% were randomly assigned into the training and testing sets, respectively. Hypotension was defined as a reduction in mean arterial pressure (MAP) ≥20 mmHg from the initial value within 6 hours. The area under the receiver operating characteristic curves (AUROCs) in machine learning models, such as support vector machine (SVM), deep neural network (DNN), and light gradient boosting machine (LGBM), were compared with those in disease-severity scores such as the Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II.Results: The DNN model showed the highest AUROC (0.822 [0.789–0.856]), and the LGBM and SVM models followed with AUROCs of 0.810 (0.776–0.845) and 0.807 (0.772–0.842), respectively; all machine learning AUROC values were higher than those obtained from disease-severity scores (AUROCs <0.6). Although different definitions of hypotension were used such as a reduction of MAP ≥30 mmHg or a reduction occurring within 1 hour, the AUROCs of machine learning models were higher than those of disease-severity scores. These machine learning models were well calibrated.Conclusion: Machine learning models successfully predict hypotension after starting CRRT and can serve as the basis of systems to predict hypotension before starting CRRT.

show abstract

Section: Discussionsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Machine Learning Model to Predict Hypotension After Starting Continuous Renal Replacement Therapy

Kang

Kim

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, AKI patients with low predialysis creatinine but who require dialysis therapy implicitly have more organ dysfunctions. Because it does not consider the effect of creatinine, the SOFA score does not perform well for mortality prediction in AKI-D patients 5,9 . The nonrenal SOFA score is slightly better at mortality prediction than the SOFA score, but it still does not take into account the effect of a low creatinine level.…”

Section: Discussionmentioning

confidence: 99%

“…Although many severity of illness scoring systems have been developed for mortality prediction, they either performed well in a single-center study without external validation or had limited results during external validation [5][6][7][8] . In terms of the Sepsis-related Organ Failure Assessment (SOFA) score, it doesn't have good discrimination in this patient group 5,9 .…”

Section: Introductionmentioning

confidence: 88%

“…Creatinine and SOFA score Figure 4 shows that the SOFA and nonrenal SOFA scores were higher in the low-creatinine group. The median (range) values of the SOFA and nonrenal SOFA scores were 12 (3-21) and 10 (3-18), respectively, in the low-creatinine group and 11 (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22) and 7 (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18), respectively, in the high-creatinine group. Table S4 reveals the distribution between the eICU and MIMIC datasets.…”

Section: Creatinine and All-cause Mortalitymentioning

confidence: 94%

See 1 more Smart Citation

Associations Between Predialysis Creatinine, SOFA Score, and Mortality in Acute Kidney Injury Patients Requiring Dialysis

Chang

Chiu

2020

Preprint

View full text Add to dashboard Cite

Background: Creatinine is widely used to estimate renal function, but this is not practical in critical illness. Low creatinine has been associated with mortality in many clinical settings. However, the associations between predialysis creatinine level, Sepsis-related Organ Failure Assessment (SOFA) score, and mortality in acute kidney injury patients receiving dialysis therapy (AKI-D) has not been fully addressed. Methods: We extracted data for AKI-D patients in the eICU (n = 1,992) and MIMIC (n = 1,001) databases. We conducted a retrospective observational cohort study using the eICU dataset. The study cohort was divided into the high-creatine group and the low-creatinine group by the median value (4 mg/dL). The baseline patient information included demographic data, laboratory parameters, medications, and comorbid conditions. The independent association of creatinine level with mortality was examined using multivariate logistic regression analysis. We also carried out a sensitivity analysis using the MIMIC dataset.Results: In all 1,992 eICU participants, the 30-day intensive care unit mortality rate was 32.2%. The crude overall mortality rate in the low-creatinine group (43.7%) was significantly higher than that in the high-creatinine group (20.6%; P < 0.001). In the fully adjusted models, the high-creatinine group was associated with a lower risk of all-cause mortality (odds ratio, 0.56; 95% confidence interval, 0.42–0.75; P < 0.001) compared with the low-creatinine group. The nonrenal SOFA score was higher in the low-creatinine group. The results were consistent when the MIMIC dataset was used as an external validation dataset.Conclusions: AKI-D patients with a low predialysis creatinine value had a significantly higher risk of mortality that might be associated with more organ dysfunctions. Moreover, SOFA and nonrenal SOFA scores did not sufficiently reflect the severity of illness without considering the impact of the creatinine value in AKI-D patients.

show abstract

Unveiling Anoikis‐related genes: A breakthrough in the prognosis of bladder cancer

Jiang,

Yang,

Lin

et al. 2024

The Journal of Gene Medicine

View full text Add to dashboard Cite

BackgroundBladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis‐related genes in the prognosis of BLCA.MethodsWe obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine‐learning algorithms to develop the artificial intelligence‐derived prognostic signature (AIDPS).ResultsThe performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA‐BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high‐ and low‐risk groups.ConclusionsIn conclusion, these findings indicated Anoikis‐related prognostic genes in BLCA and constructed an innovative machine‐learning model of AIDPS with high prognostic value for BLCA.

show abstract

Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Cited by 72 publications

References 34 publications

Machine Learning Model to Predict Hypotension After Starting Continuous Renal Replacement Therapy

Machine Learning Model to Predict Hypotension After Starting Continuous Renal Replacement Therapy

Associations Between Predialysis Creatinine, SOFA Score, and Mortality in Acute Kidney Injury Patients Requiring Dialysis

Unveiling Anoikis‐related genes: A breakthrough in the prognosis of bladder cancer

Contact Info

Product

Resources

About