Improving the performance of risk-adjusted mortality modeling for colorectal cancer surgery by combining claims data and clinical data

Modified Tumor Budding as a Better Predictor of Lymph Node Metastasis in Early Gastric Cancer: Possible Real-World Applications

Simple Summary

To obtain the optimal treatment effect of endoscopic resection (ER) in early gastric carcinoma (EGC), a well-established indication for post-ER surgery is needed. In addition, accurate prediction of lymph node metastasis (LNM) is necessary to achieve this goal. Here, we present modified tumor budding (mTB), which excludes signet ring cells from conventional tumor budding (TB) as a novel predictor for LNM. Conventional TB and mTB were the most predictive independent factors for LNM. Furthermore, mTB was superior to TB in predicting LNM (p = 0.0004–0.0008). In conclusion, mTB significantly enhanced the predictive power of LNM, which could be a novel indicator for determining post-ER surgery.

Abstract

Endoscopic resection (ER) is a minimally invasive treatment for early gastric cancer (EGC) with a low risk of lymph node metastasis (LNM). Recently, tumor budding (TB) has emerged as a potential predictor of LNM in EGC. We assessed the clinical significance of modified TB (mTB) that excludes the signet ring cell component and compared several TB assessment methods. Two hundred and eighty-nine patients with EGC at Uijeongbu St. Mary’s Hospital from 2010 to 2021 were enrolled. In univariate analysis, age, size, depth of invasion, tumor type, histologic type, Lauren classification, lymphatic invasion, venous invasion, poorly differentiated carcinoma (“not otherwise specified” predominant), and TB were significantly associated with LNM. Multivariate regression analysis showed that mTB (difference area under the curve [dAUC] = 0.085 and 0.087) was superior to TB (dAUC = 0.054 and 0.057) in predicting LNM. In addition, total TB counts on representative slide sections (dAUC = 0.087 and 0.057) in assessing TB and mTB and the ITBCC method (dAUC = 0.085) in mTB were superior to the presence or absence method (dAUC = 0.042 and 0.029). The mTB significantly increases LNM prediction ability, which can provide important information for patients with EGC.

Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture

BACKGROUND

The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records.

METHODS

We used PAS data from all Norwegian hospitals (2005-2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed.

RESULTS

The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes.

CONCLUSIONS

This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway.

Model for risk adjustment of postoperative mortality in patients with colorectal cancer

Background

A model was developed for risk adjustment of postoperative mortality in patients with colorectal cancer in order to make fair comparisons between healthcare providers. Previous models were derived in relatively small studies with the use of suboptimal modelling techniques.

Methods

Data from adults included in a national study of major surgery for colorectal cancer were used to develop and validate a logistic regression model for 90-day mortality. The main risk factors were identified from a review of the literature. The association with age was modelled as a curved continuous relationship. Bootstrap resampling was used to select interactions between risk factors.

Results

A model based on data from 62 314 adults was developed that was well calibrated (absolute differences between observed and predicted mortality always smaller than 0·75 per cent in deciles of predicted risk). It discriminated well between low- and high-risk patients (C-index 0·800, 95 per cent c.i. 0·793 to 0·807). An interaction between age and metastatic disease was included as metastatic disease was found to increase postoperative risk in young patients aged 50 years (odds ratio 3·53, 95 per cent c.i. 2·66 to 4·67) far more than in elderly patients aged 80 years (odds ratio 1·48, 1·32 to 1·66).

Conclusion

Use of this model, estimated in the largest number of patients with colorectal cancer to date, is recommended when comparing postoperative mortality of major colorectal cancer surgery between hospitals, clinical teams or individual surgeons.

Problems with public reporting of cancer quality outcomes data

The authors say public reporting of even the most well-defined end points—survival after treatment—can be misleading when used to compare outcomes across providers, and these data can suffer from patient-selection and treatment-intensity biases.