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Longitudinal Analysis and Predictive Modeling of Nursing-Sensitive Quality Indicators in Hemodialysis
Sikai Tang1,2, Qiao Li1,2, Li Liu1,2
1Hemodialysis Room, Department of Nephrology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China, scu.edu.cn.
Journal of Nursing Management
|July 29, 2026
Summary
Longitudinal monitoring of nursing-sensitive quality indicators (NSQIs) in hemodialysis (HD) care reveals distinct trends and interrelationships. Predictive modeling using time-series analysis aids proactive nursing quality management and improved patient outcomes.
Area of Science:
- Nursing-sensitive quality indicators (NSQIs)
- Hemodialysis (HD) care
- Quality management
Background:
- Limited long-term monitoring of NSQIs in HD care hinders quality improvement.
- Understanding trends and interrelationships among NSQIs is crucial for effective nursing management.
Purpose of the Study:
- To examine longitudinal trends and interrelationships among NSQIs in an HD unit.
- To apply analytical methods for supporting nursing quality management in HD care.
Main Methods:
- A 4-year longitudinal retrospective study in a tertiary hospital HD unit.
- Monthly monitoring of ten NSQIs (structural, process, outcome indicators).
- Analysis using descriptive statistics, correlation, principal component analysis (PCA), and autoregressive integrated moving average (ARIMA) modeling.
Main Results:
- Structural and process indicators showed stability or improvement; outcome indicators varied.
- Specific trends observed in hypotension, coagulation, weight control, and catheter infection rates.
- Correlation and PCA identified relationships between indicators and annual quality structure evolution.
- ARIMA models provided 6-month forecasts for key outcome indicators.
Conclusions:
- Continuous NSQI monitoring identifies actionable areas for HD quality improvement.
- The structure-process-outcome framework effectively captures dynamic nursing quality trends.
- Predictive modeling supports proactive management and targeted interventions for better patient outcomes.
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