Status of coexisting chronic obstructive pulmonary disease and its clinicopathological features in patients undergoing lung cancer surgery: a cross-sectional study of 3,006 cases

Systemic immune-inflammation index acts as a novel diagnostic biomarker for postmenopausal osteoporosis and could predict the risk of osteoporotic fracture

Background

Postmenopausal osteoporosis (PMOP) is a bone metabolism disorder involving systematic inflammation activation. Blood routine examination is easily available in clinical practice and contains abundant information reflecting the systematic inflammation level. Thus, it is attractive to achieve early diagnosis of PMOP and predict osteoporotic fracture risk just based on the biomarkers in blood routine examination.

Methods

A multi‐centric prospective cohort study was designed and enrolled postmenopausal women from two independent institutions. All participants underwent the dual‐energy X‐ray absorptiometry (DEXA) scanning for diagnosing PMOP. Blood routine examination was conducted, and the key inflammatory biomarkers such as neutrophil‐to‐lymphocyte ratio (NLR) and systemic immune‐inflammation index (SII) were calculated. PMOP patients were followed up to observe osteoporotic fracture and identify the related risk predictors.

Results

A total of 92 participants out of 238 enrolled postmenopausal women were diagnosed with PMOP, with a prevalence of 38.66%. The main risk factors identified for PMOP included older age (OR = 2.06, 95% CI = 1.14‐3.72), longer menopause duration (OR = 3.14, 95% CI = 2.06‐4.79), higher NLR (OR = 2.11, 95% CI = 1.37‐3.25), and higher SII (OR = 3.02, 95% CI = 1.98‐4.61). Besides age and menopause duration, SII ≥834.89 was newly identified as a prominent risk factor for discriminating osteoporotic fracture risk in PMOP patients (HR = 3.66, 95% CI = 1.249‐10.71).

Conclusion

As an easy and economical biomarker calculated from blood routine examination, SII not only acts as a good risk predictor for PMOP diagnosis but also well discriminates the osteoporotic fracture risk, which deserves further investigation and application in clinical practice.

Development and validation of nomogram estimating post-surgery hospital stay of lung cancer patients: relevance for predictive, preventive, and personalized healthcare strategies

OBJECTIVE

In the era of fast track surgery, early and accurately estimating whether postoperative length of stay (p-LOS) will be prolonged after lung cancer surgery is very important, both for patient's discharge planning and hospital bed management. Pulmonary function tests (PFTs) are very valuable routine examinations which should not be underutilized before lung cancer surgery. Thus, this study aimed to establish an accurate but simple prediction tool, based on PFTs, for achieving a personalized prediction of prolonged p-LOS in patients following lung resection.

METHODS

The medical information of 1257 patients undergoing lung cancer surgery were retrospectively reviewed and served as the training set. p-LOS exceeding the third quartile value was considered prolonged. Using logistic regression analyses, potential predictors of prolonged p-LOS were identified among various preoperative factors containing PFTs and intraoperative factors. A nomogram was constructed and subjected to internal and external validation.

RESULTS

Five independent risk factors for prolonged p-LOS were identified, including older age, being male, and ratio of residual volume to total lung capacity (RV/TLC) ≥ 45.0% which is the only modifiable risk factor, more invasive surgical approach, and surgical type. The nomogram comprised of these five predictors exhibited sufficient predictive accuracy, with the area under the receiver operating characteristic curve (AUC) of 0.76 [95% confidence interval (CI) 0.73-0.79] in the internal validation. Also its predictive performance remained fine in the external validation, with the AUC of 0.70 (95% CI 0.60-0.79). The calibration curves showed satisfactory agreements between the model predicted probability and the actually observed probability.

CONCLUSIONS

Preoperative amelioration of RV/TLC may prevent lung cancer patients from unnecessary prolonged p-LOS. The integrated nomogram we developed could provide personalized risk prediction of prolonged p-LOS. This prediction tool may help patients perceive expected hospital stays and enable clinicians to achieve better bed management after lung cancer surgery.