Artificial Intelligence for Risk Prediction of End-Stage Renal Disease in Sepsis Survivors with Chronic Kidney Disease

Lee, Kuo‐Hua; Lee, Fa-Yauh; Tsai, Ming-Tsung; Tseng, Wei Cheng; Lin, Yao‐Ping; Ou, Shuo‐Ming; Tarng, Der Cherng

doi:10.3390/biomedicines10030546

Cited by 9 publications

(9 citation statements)

References 38 publications

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“…This retrospective cohort study was conducted with medical records data from the Big Data Center of Taipei Veterans General Hospital, which contains comprehensive medical records, pharmacy orders, laboratory results, and examination reports for all inpatients, outpatients, and emergency patients [ 25 ]. All patients were aged ≥ 20 years and had had their peripheral venous TCO 2 levels measured within 1 week of discharge from hospitalization for sepsis (international classification of diseases [ICD] code 038, 995.91, A40, and A41), severe sepsis (ICD code 995.92 and R65.20), or septic shock (ICD code 785.52 and R65.21) between 1 January 2008 and 31 December 2018 [ 26 , 27 ]. The study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments.…”

Section: Methodsmentioning

confidence: 99%

Associations of the Serum Total Carbon Dioxide Level with Long-Term Clinical Outcomes in Sepsis Survivors

et al. 2023

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Introduction Sepsis is characterized by a dysregulated host response to infection that leads to multiple organ dysfunction and often complicated with metabolic acidosis. However, the associations between serum total carbon dioxide level (TCO 2 ) and long-term clinical outcomes in sepsis survivors remains unknown. Methods A total of 7212 sepsis survivors aged ≥ 20 years who were discharged from January 1, 2008 to December 31, 2018 were included in our analyses. The sepsis survivors were further divided into high TCO 2 (≥ 18 mmol/L) and low TCO 2 (< 18 mmol/L) groups, comprising 5023 and 2189 patients, respectively. The following outcomes of interest were included: all-cause mortality, myocardial infarction, ischemic stroke, hospitalization for heart failure, ventricular arrhythmia, and end-stage renal disease (ESRD). Results After propensity score matching, the low TCO 2 group was at higher risks of all-cause mortality (hazard ratio [HR] 1.28, 95% confidence interval [95% CI] 1.18–1.39), myocardial infarction (HR 1.83, 95% CI 1.39–2.43), and ESRD (HR 1.38, 95% CI 1.16–1.64) than the high TCO 2 group. The results remained similar after considering death as a competing risk. Conclusion Patients discharged from hospitalization for sepsis have higher risks of worse long-term clinical outcomes. Physicians may need to pay more attention to sepsis survivors whose TCO 2 was low. Supplementary Information The online version contains supplementary material available at 10.1007/s40121-023-00765-6.

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Section: Methodsmentioning

confidence: 99%

Associations of the Serum Total Carbon Dioxide Level with Long-Term Clinical Outcomes in Sepsis Survivors

et al. 2023

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“…Several studies have been conducted on the use of artificial intelligence (AI) in ESRD and CKD. [20,[25][26][27][28][29] However, these studies mostly focused on CKD prevention or identification. [21,[30][31][32][33] For mortality prediction in ESRD patients, most studies have focused on populations with only kidney transplant therapy or hemodialysis.…”

Section: Introductionmentioning

confidence: 99%

Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning

Chang,

Chen,

et al. 2024

Medicine

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Chronic kidney disease (CKD) is a major public health concern. But there are limited machine learning studies on non-cancer patients with advanced CKD, and the results of machine learning studies on cancer patients with CKD may not apply directly on non-cancer patients. We aimed to conduct a comprehensive investigation of risk factors for a 3-year risk of death among non-cancer advanced CKD patients with an estimated glomerular filtration rate < 60.0 mL/min/1.73m2 by several machine learning algorithms. In this retrospective cohort study, we collected data from in-hospital and emergency care patients from 2 hospitals in Taiwan from 2009 to 2019, including their international classification of disease at admission and laboratory data from the hospital’s electronic medical records (EMRs). Several machine learning algorithms were used to analyze the potential impact and degree of influence of each factor on mortality and survival. Data from 2 hospitals in northern Taiwan were collected with 6565 enrolled patients. After data cleaning, 26 risk factors and approximately 3887 advanced CKD patients from Shuang Ho Hospital were used as the training set. The validation set contained 2299 patients from Taipei Medical University Hospital. Predictive variables, such as albumin, PT-INR, and age, were the top 3 significant risk factors with paramount influence on mortality prediction. In the receiver operating characteristic curve, the random forest had the highest values for accuracy above 0.80. MLP, and Adaboost had better performance on sensitivity and F1-score compared to other methods. Additionally, SVM with linear kernel function had the highest specificity of 0.9983, while its sensitivity and F1-score were poor. Logistic regression had the best performance, with an area under the curve of 0.8527. Evaluating Taiwanese advanced CKD patients’ EMRs could provide physicians with a good approximation of the patients’ 3-year risk of death by machine learning algorithms.

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“…ML-based decision support tools have been developed across many aspects of CKD care across disease prevention, diagnosis, and treatment [7], fuelled by the growth in volume and variety of big data in nephrology and healthcare in general [9,10]. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression to ESKD may help to facilitate early disease prevention, assist with early care planning, and allocate resources for the most significant clinical benefit [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance

et al. 2023

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Chronic Kidney disease (CKD) is an important yet under-recognized contributor to morbidity and mortality globally. Machine-learning (ML) based decision support tools have been developed across many aspects of CKD care. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression may help to facilitate early disease prevention, assist with early planning of renal replacement therapy, and offer potential clinical and economic benefits to patients and health systems. Clinical implementation can be affected by the uncertainty surrounding the methodological rigor and performance of ML-based models. This systematic review aims to evaluate the application of prognostic and diagnostic ML tools in CKD development and progression. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for CKD prediction and diagnosis have been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022356704, CRD42022372378). A systematic search will be undertaken of PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the Web of Science, and the IEEE Xplore digital library. Studies in which ML has been applied to predict and diagnose CKD development and progression will be included. The primary outcome will be the comparison of the performance of ML-based models with non-ML-based models. Secondary analysis will consist of model use cases, model construct, and model reporting quality. This systematic review will offer valuable insight into the performance and reporting quality of ML-based models in CKD diagnosis and prediction. This will inform clinicians and technical specialists of the current development of ML in CKD care, as well as direct future model development and standardization.

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Artificial Intelligence for Risk Prediction of End-Stage Renal Disease in Sepsis Survivors with Chronic Kidney Disease

Cited by 9 publications

References 38 publications

Associations of the Serum Total Carbon Dioxide Level with Long-Term Clinical Outcomes in Sepsis Survivors

Associations of the Serum Total Carbon Dioxide Level with Long-Term Clinical Outcomes in Sepsis Survivors

Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning

Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance

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