Performance of the Matsumiya scoring system in cervical cancer patients with bone metastasis: an external validation study.

BACKGROUND: Accurate prognostic prediction of survival in cervical cancer patients with bone metastasis is important for treatment planning. We aimed to externally validate the Matsumiya scoring system using external patient data. METHODS: We collected a retrospective cohort of patients with cervical cancer diagnosed with bone metastasis at Chiang Mai University Hospital from 1st January 2007 to 31st December 2016. The Matsumiya score was composed of 5 predictors, including the presence of extraskeletal metastasis, ECOG performance status, history of previous chemo- or radiotherapy, the presence of multiple bone metastasis, and bone metastasis-free interval < 12 months. Harrell's C-statistics and score calibration plots were used to evaluate the score performance. We also reconstructed the development study to estimate apparent performance values for comparison during external validation. RESULTS: A total of 124 cervical cancer patients with bone metastasis were included in this study. The 13-, 26-, and 52-week survival probabilities in the validation study were 70.1%, 50.5%, and 25.7%, respectively. Several differences were identified between development and validation studies regarding clinical characteristics, case-mix, and predictor-outcome associations. Harrell's C-statistics in the development and validation study were 0.714 and 0.567. The score showed poor agreement between the observed and the predicted survival probabilities in the validation study. Score reweighting and refitting showed only modest improvement in performance. CONCLUSION: A prognostic scoring system by Matsumiya et al. performed poorly in our cohort of Thai cervical cancer patients with bone metastasis. We suggested that the score should be sufficiently updated before being used.

Preoperative Correction of Low Hemoglobin Levels Can Reduce 1-Year All-Cause Mortality in Osteoporotic Hip Fracture Patients: A Retrospective Observational Study.

PURPOSE: Osteoporotic hip fracture surgery is associated with a risk of morbidity and mortality. Admission hemoglobin levels <10 g/dL have been documented as a strong predictor of mortality risk. This study aimed to investigate the mortality outcome between osteoporotic hip fracture patients who had preoperative hemoglobin levels raised to ≥10 g/dL and those with hemoglobin levels were <10 g/dL. PATIENTS AND METHODS: This 5-year retrospective observational study included 226 participants with osteoporotic hip fractures that required surgery and who had admission hemoglobin levels <10 g/dL. Patients were categorized into two groups: those with corrected preoperative hemoglobin ≥10 g/dL and those with either corrected or uncorrected preoperative hemoglobin <10 g/dL. Outcomes were analyzed using Cox proportional hazard regression adjusted for confounders. Results are presented as hazard ratio (HR) and 95% confidence interval (95% CI). RESULTS: Among 226 the patients, the overall mortality rate was 17.25% (n=39/226) of the 226 patients, 93 (41.15%) had their hemoglobin levels raised to ≥10 g/dL by red blood cell transfusion. Multivariable analysis after adjustment for confounders showed a 50% lower incidence of mortality among patients with preoperative hemoglobin levels ≥10 g/dL than among those with hemoglobin levels <10 g/dL (HR 0.50, 95% CI (0.25-0.99), p=0.048). CONCLUSION: In osteoporotic hip fracture patients with admission hemoglobin <10g/dL, raising preoperative hemoglobin levels to ≥10 g/dL can significantly reduce the risk of mortality. Testing for and correction of low preoperative hemoglobin levels is of value in hip surgery patients.

Auto-mapping Clinical Documents to ICD-10 using SNOMED-CT.

Excessive paperwork is a considerable issue that leads to additional burdens for health-care professionals. In Thai health-care systems, physicians manually review medical records to select an appropriate principle diagnosis and other co-morbidities and convert them into ICD-10s to claim financial support from the government. Accordingly, 160,000 ICD-10 codes and 46,000 in-patient discharge summaries are documented by physicians at Maharaj Nakorn Chiang Mai hospital each year. As a result, to decrease physicians' burden of manual paper-work, we created a new approach to automatically analyse discharge summary notes and map the diagnoses to ICD-10s. We combined SNOMED-CT and natural language processing techniques within the approach through 3 steps: cleaning data; extracting keywords from discharge summary notes; and matching keywords to ICD-10. In this paper, we present that mapping clinical documents by using approximate matching and SNOMED-CT shows potential to be used for automating the ICD-10 mapping process.