Clinical, laboratory, and medication-related factors associated with in-hospital mortality among oncology patients at a tertiary hospital in Saudi Arabia - Ahmed Alafnan; Meshal M. Alorainan; Sanad M. Alanazi; Salman F. Algharbi; Faisal R. Alshammari; Ammar Alghaslan; Hammad Saleem; Saleh Alghamdi; Mohd Farooq Shaikh; Sirajudheen Anwar (Frontiers in Pharmacology, 2026)
Background Oncology inpatients face physiological decline alongside complex pharmacology, yet clinical mortality predictors and drug-related risks remain understudied in Middle Eastern populations. This study identified independent predictors of in-hospital mortality and characterized potential drug–drug interactions (pDDIs) among hospitalized cancer patients in Saudi Arabia. Methods This retrospective cohort study evaluated 401 adult oncology inpatients admitted to a tertiary care center in Hail, Saudi Arabia (2020–2022). Demographics, clinical parameters, and medication profiles were extracted from medical records. Bivariate correlations were assessed using point-biserial ( r p b ) and phi ( ϕ ) coefficients. Independent mortality predictors were identified via multivariable binary logistic regression, and biomarker discrimination was evaluated using receiver operating characteristic (ROC) curves. Potential DDIs were screened using Drugs.com . Results The cohort was predominantly female (67.6%) and Saudi (85.0%), with solid malignancies accounting for 94.3% of cases. In-hospital mortality was 25.2% ( n = 101 / 401 ). Bivariate correlations demonstrated significant associations with mortality across clinical parameters, including mechanical ventilation requirements ( ϕ = 0.118 , p = 0.018 ). Multivariable regression identified four independent physiological predictors of mortality: renal failure ( aOR = 44.29 , 95 % CI : 1.07 − 1836.31 ), baseline albumin aOR = 0.71 per g / L , 95 % CI : 0.55 − 0.91 , serum creatinine ( aOR = 1.02 per μ mol / L , 95 % CI : 1.001 − 1.04 ), and oxygen saturation ( aOR = 0.68 per % , 95 % CI : 0.50 − 0.93 ). Serum albumin showed high individual discrimination ( AUC = 0.920 ), while the combined multivariable model achieved an AUC of 0.953. Pharmacological screening revealed near-universal interaction prevalence: 98.6% of 2,148 prescriptions contained a pDDI flag (54.9% severe), and 96.5% of patients had ≥ 1 severe pDDI flag. Conclusion In-hospital mortality is strongly driven by acute organ dysfunction and physiological decline rather than database-flagged interactions alone. However, the high prevalence of severe pDDI flags highlights a critical need to integrate structured clinical pharmacy reviews into oncology admission protocols. Targeted medication safety interventions should focus on avoiding nephrotoxic combinations and establishing pre-therapeutic pharmacogenetic screening to mitigate potential iatrogenic risks.