A total of 576 eligible patients were included in the study. Multivariate logistic regression analysis revealed that D-dimer levels (odds ratio [OR]: 4.06, 95% confidence interval [CI]: 2.52–6.55; p < 0.001), the systemic immune-inflammation index (SII) (OR: 6.61, 95% CI: 2.08–21.00; p = 0.001), and the consolidation pattern (OR: 2.82, 95% CI = 1.52–5.24; p = 0.001) were significantly associated with delayed recovery. With the training set, the integrated model outperformed the radiomics model in terms of predictive efficacy (AUC: 0.982 vs. 0.872, p = 0.01). However, there was no statistically significant difference between the integrated and clinical-imaging models (AUC: 0.982 vs. 0.894, p = 0.07). There was no significant difference in performance between the radiomics and clinical-imaging models with both the training and external validation sets (AUC: 0.894 vs. 0.872, p = 0.75; AUC: 0.807 vs. 0.837, p = 0.638). With the external validation set, the integrated model achieved an AUC of 0.865 (95% CI: 0.770–0.960), sensitivity of 0.933, and specificity of 0.720.