Introduction: Mortality is a major primary endpoint for long-term hemodialysis (HD) patients. The clinical status of HD patients generally relies on longitudinal clinical observations such as monthly laboratory examinations and physical examinations. Methods: A total of 829 HD patients who met the inclusion criteria were analyzed. All patients were tracked from January 2009 to December 2013. Taken together, this study performed full-adjusted-Cox proportional hazards (CoxPH), stepwise-CoxPH, random survival forest (RSF)-CoxPH, and whale optimization algorithm (WOA)-CoxPH model for the all-cause mortality risk assessment in HD patients. The model performance between proposed selections of CoxPH models were evaluated using concordance index. Results: The WOA-CoxPH model obtained the highest concordance index compared with RSF-CoxPH and typical selection CoxPH model. The eight significant parameters obtained from the WOA-CoxPH model, including age, diabetes mellitus (DM), hemoglobin (Hb), albumin, creatinine (Cr), potassium (K), Kt/V, and cardiothoracic ratio, have also showed significant survival difference between low- and high-risk characteristics in single-factor analysis. By integrating the risk characteristics of each single factor, patients who obtained seven or more risk characteristics of eight selected parameters were dichotomized as high-risk subgroup, and remaining is considered as low-risk subgroup. The integrated low- and high-risk subgroup showed greater discrepancy compared with each single risk factor selected by WOA-CoxPH model. Conclusion: The study findings revealed WOA-CoxPH model could provide better risk assessment performance compared with RSF-CoxPH and typical selection CoxPH model in the HD patients. In summary, patients who had seven or more risk characteristics of eight selected parameters were at potentially increased risk of all-cause mortality in HD population.
Time-averaged serum albumin (TSA) is commonly associated with clinical outcomes in hemodialysis (HD) patients and considered as a surrogate indicator of nutritional status. The whale optimization algorithm-based feature selection (WOFS) model could address the complex association between the clinical factors, and could further combine with regression models for application. The present study aimed to demonstrate an optimal multifactor TSA-associated model, in order to interpret the complex association between TSA and clinical factors among HD patients. A total of 829 HD patients who met the inclusion criteria were selected for analysis. Monthly serum albumin data tracked from January 2009 to December 2013 were converted into TSA categories based on a critical value of 3.5 g/dL. Multivariate logistic regression was used to analyze the association between TSA categories and multiple clinical factors using three types of feature selection models, namely the fully adjusted, stepwise, and WOFS models. Five features, albumin, age, creatinine, potassium, and HD adequacy index (Kt/V level), were selected from fifteen clinical factors by the WOFS model, which is the minimum number of selected features required in multivariate regression models for optimal multifactor model construction. The WOFS model yielded the lowest Akaike information criterion (AIC) value, which indicated that the WOFS model could achieve superior performance in the multifactor analysis of TSA for HD patients. In conclusion, the application of the optimal multifactor TSA-associated model could facilitate nutritional status monitoring in HD patients.
Background Serum albumin level is a crucial nutritional indicator for patients on dialysis. Approximately one-third of patients on hemodialysis (HD) have protein malnutrition. Therefore, the serum albumin level of patients on HD is strongly correlated with mortality. Methods In study, the data sets were obtained from the longitudinal electronic health records of the largest HD center in Taiwan from July 2011 to December 2015, included 1,567 new patients on HD who met the inclusion criteria. Multivariate logistic regression was performed to evaluate the association of clinical factors with low serum albumin, and the grasshopper optimization algorithm (GOA) was used for feature selection. The quantile g-computation method was used to calculate the weight ratio of each factor. Machine learning and deep learning (DL) methods were used to predict the low serum albumin. The area under the curve (AUC) and accuracy were calculated to determine the model performance. Results Age, gender, hypertension, hemoglobin, iron, ferritin, sodium, potassium, calcium, creatinine, alkaline phosphatase, and triglyceride levels were significantly associated with low serum albumin. The AUC and accuracy of the GOA quantile g-computation weight model combined with the Bi-LSTM method were 98% and 95%, respectively. Conclusion The GOA method was able to rapidly identify the optimal combination of factors associated with serum albumin in patients on HD, and the quantile g-computation with DL methods could determine the most effective GOA quantile g-computation weight prediction model. The serum albumin status of patients on HD can be predicted by the proposed model and accordingly provide patients with better a prognostic care and treatment.
BACKGROUND Time-averaged serum albumin (TSA) is commonly associated with clinical outcomes in hemodialysis (HD) patients and considered a surrogate indicator of nutritional status. Whale optimization (WO)-based feature selection algorithm could address the challenges associated with the complex characteristics of multifactor interactions and could be combined with regression models. OBJECTIVE The present study aimed to demonstrate an optimal multifactor TSA-associated model, which could be applied in the interpretation of the association between TSA and clinical factors in HD patients. METHODS A total of 829 HD patients who met the inclusion criteria were analyzed. Monthly serum albumin data tracked from January 2009 to December 2013 were converted into TSA categories based on a critical value of 3.5 g/dL. Multivariate logistic regression was used to analyze the association between TSA categories and multiple clinical factors using three types of feature selection models, namely the fully adjusted model, stepwise model, and whale optimization algorithm (WOA) model. RESULTS The WOA yielded the lowest Akaike Information Criterion (AIC) value, which indicated that the WOA could achieve superior performance in multifactor analysis when compared to the fully adjusted and stepwise models. The significant features in the optimal multifactor TSA-associated model included age, creatinine, potassium, and HD adequacy index (Kt/V level). CONCLUSIONS The WOA algorithm could select five features from 15 clinical factors, which is the minimum number of selected features required in multivariate regression models for optimal multifactor model construction to achieve high model performance. Therefore, the application of the optimal multifactor TSA-associated model could facilitate nutritional status monitoring in HD patients. CLINICALTRIAL All data were retrospectively collected using an approved data protocol (201800595B0) with a waiver of informed consent from patients.
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