Longitudinal Analysis and Predictive Modeling of Nursing-Sensitive Quality Indicators in Hemodialysis

Nursing-sensitive quality indicators (NSQIs) are essential for evaluating and improving the quality of hemodialysis (HD) care, yet long-term monitoring of their trends and interrelationships remains limited. This study aimed to examine longitudinal trends and interrelationships among NSQIs in an HD unit and to apply analytical methods to support nursing quality management.

A 4-year longitudinal retrospective study was conducted in a tertiary hospital HD unit. Ten NSQIs (two structural indicators, three process indicators, and five outcome indicators) were monitored monthly from January 2022 to December 2025. Data were analyzed using descriptive statistics, correlation analysis, principal component analysis (PCA), and autoregressive integrated moving average (ARIMA) modeling.

Structural and process indicators remained generally stable or improved over the 4 years. Among outcome indicators, incidence of hypotension in HD (O1) and patient satisfaction (O5) were stable; incidence of coagulation during extracorporeal circulation (O2) increased initially and then declined; dialysis period weight control success rate (O3) decreased and then partially recovered; incidence of central venous catheter infection (O4) decreased markedly. Spearman correlation showed a positive association between the ratio of blood purification specialist nurses to general nurses (S2) and O3, and an inverse relationship between process indicators and O4. PCA explained 49.5% of the total variance and revealed an annual evolution in the structure of nursing quality indicators. The final ARIMA models for O1-O4 all passed the Ljung-Box test (p > 0.05). Six-month forecasts indicated that O1 and O3 would remain nearly constant, O2 would increase initially and then stabilize, and O4 would rise slowly.

Continuous monitoring of NSQIs helps identify actionable areas for quality improvement in HD care. The structure-process-outcome framework effectively captured dynamic trends in nursing quality, revealing distinct patterns across indicator types and highlighting areas requiring targeted intervention. Predictive modeling supports proactive management and adjustable interventions.

Nurse leaders can use longitudinal NSQI data to guide staffing decisions, prioritize process-centered quality improvement, and design targeted interventions based on outcome trends. Time-series models help move from reactive to predictive management, making it easier to anticipate trend shifts, ultimately improving patient outcomes in HD care.