Archivos espanoles de urologia · 2023
Influencing Factors of Quality of Life and Prediction Model Building in Patients with Primary Nephrotic Syndrome: A Single-Centre Retrospective Study.
СтатьяНазвание и аннотация: Перевод в очереди
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Ji Xiaohong, Zhang Jianfang, Li Yuanyuan, Hu Lin
Аннотация
OBJECTIVE: This study aimed to explore the influencing factors of quality of life (QOL) and establish a prediction model in patients with primary nephrotic syndrome (PNS).
METHODS: A single-centre retrospective study was conducted on 245 patients with PNS admitted to Zibo Central Hospital from August 2020 to August 2022. According to the 36-Item Short-Form Health Survey (SF-36) for QOL evaluation, the patients were divided into the good QOL group (the total score ≥50 points) and poor QOL group (the total score <50 points). Univariate analysis was conducted by collecting clinical data from patients, and multiple logistic regression analysis was carried out on single factors with statistically significant differences to construct a clinical prediction model. The diagnostic efficacy of the prediction model was evaluated using the receiver operating characteristic (ROC) curve.
RESULTS: A total of 245 questionnaires were distributed, and 243 valid questionnaires were recovered, in which 143 cases had good QOL, with an average score of (71.86 ± 10.83) points, and 100 cases had poor QOL, with an average score of (40.03 ± 5.95) points. Statistical differences were observed in age, education level, monthly family average income, payment methods of medical expenses, albumin, 24-hour urinary protein quantification (24 h UPro) and serum uric acid (SUA) in both groups (p < 0.05), whereas no statistical difference was found in gender, body mass index (BMI) and marital status (p > 0.05). The multiple logistic regression analysis showed that age (X1), monthly family average income (X2), payment methods of medical expenses (X3), albumin (X4), 24 h UPro (X5) and SUA (X6) were risk factors for the QOL of patients with PNS, with Y = -12.105 + 0.130X1 + 0.457X2 + 0.448X3 + -0.161X4 + 0.823X5 + 0.025X6 as the regression prediction model. The results of ROC curve showed that the area under the curve (AUC) was 0.987 with standard error of 0.005 (p < 0.001), and 95% CI was 0.976-0.998.
CONCLUSIONS: Age, monthly family average income, payment methods of medical expenses, albumin, 24 h UPro and SUA are risk factors that affect the QOL of patients with PNS, and the construction of prediction model has good evaluation value and can provide a reference for clinical practice.
METHODS: A single-centre retrospective study was conducted on 245 patients with PNS admitted to Zibo Central Hospital from August 2020 to August 2022. According to the 36-Item Short-Form Health Survey (SF-36) for QOL evaluation, the patients were divided into the good QOL group (the total score ≥50 points) and poor QOL group (the total score <50 points). Univariate analysis was conducted by collecting clinical data from patients, and multiple logistic regression analysis was carried out on single factors with statistically significant differences to construct a clinical prediction model. The diagnostic efficacy of the prediction model was evaluated using the receiver operating characteristic (ROC) curve.
RESULTS: A total of 245 questionnaires were distributed, and 243 valid questionnaires were recovered, in which 143 cases had good QOL, with an average score of (71.86 ± 10.83) points, and 100 cases had poor QOL, with an average score of (40.03 ± 5.95) points. Statistical differences were observed in age, education level, monthly family average income, payment methods of medical expenses, albumin, 24-hour urinary protein quantification (24 h UPro) and serum uric acid (SUA) in both groups (p < 0.05), whereas no statistical difference was found in gender, body mass index (BMI) and marital status (p > 0.05). The multiple logistic regression analysis showed that age (X1), monthly family average income (X2), payment methods of medical expenses (X3), albumin (X4), 24 h UPro (X5) and SUA (X6) were risk factors for the QOL of patients with PNS, with Y = -12.105 + 0.130X1 + 0.457X2 + 0.448X3 + -0.161X4 + 0.823X5 + 0.025X6 as the regression prediction model. The results of ROC curve showed that the area under the curve (AUC) was 0.987 with standard error of 0.005 (p < 0.001), and 95% CI was 0.976-0.998.
CONCLUSIONS: Age, monthly family average income, payment methods of medical expenses, albumin, 24 h UPro and SUA are risk factors that affect the QOL of patients with PNS, and the construction of prediction model has good evaluation value and can provide a reference for clinical practice.