Clinicosocial determinants of hospital stay following cervical decompression: A public healthcare perspective and machine learning model

Objective: Post-operative length of hospital stay (LOS) is a valuable measure for monitoring quality of care provision, patient recovery, and guiding hospital resource management. But the impact of patient ethnicity, socio-economic deprivation as measured by the indices of multiple deprivation (IMD), and pre-existing health conditions on LOS post-anterior cervical decompression and fusion (ACDF) is under-researched in public healthcare settings.

Methods: From 2013 to 2023, a retrospective study at a single center reviewed all ACDF procedures. We analyzed 14 non-clinical predictors-including demographics, comorbidities, and socio-economic status-to forecast a categorized LOS: short (≤2 days), medium (2-3 days), or long (>3 days). Three machine learning (ML) models were developed and assessed for their prediction reliability.

Results: 2033 ACDF patients were analyzed; 79.44 % had a LOS ≤ 2 days. Significant predictors of LOS included patient sex (HR:0.81[0.74-0.88], p < 0.005), IMD decile (HR:1.38[1.24-1.53], p < 0.005), smoking (HR:1.24[1.12-1.38], p < 0.005), DM (HR:0.70[0.59-0.84], p < 0.005), and COPD (HR:0.66, p = 0.01). Asian patients had the highest mean LOS (p = 0.003). Testing on 407 patients, the XGBoost model achieved 80.95 % accuracy, 71.52 % sensitivity, 85.76 % specificity, 71.52 % positive predictive value, and a micro F1 score of 0.715. This model is available at: https://acdflos.streamlit.app.

Conclusions: Utilizing non-clinical pre-operative parameters such as patient ethnicity, socio-economic deprivation index, and baseline comorbidities, our ML model effectively predicts postoperative LOS for patient undergoing ACDF surgeries. Yet, as the healthcare landscape evolves, such tools will require further refinement to integrate peri and post-operative variables, ensuring a holistic decision support tool.