ABSTRACT
OBJECTIVES: The European Alliance of Associations for Rheumatology (EULAR) supports the use of nailfold videocapillaroscopy (NVC) to identify disease patterns (DPs) associated with systemic sclerosis (SSc) and Raynaud's phenomenon (RP). Recently, EULAR proposed an easy-to-manage procedure, a so-called Fast Track algorithm, to differentiate SSc from non-SSc patterns in NVC specimens. However, subjectivity among capillaroscopists remains a limitation. Our aim was to perform a software-based analysis of NVC peculiarities in a cohort of samples from SSc and RP patients and, subsequently, build a Fast Track-inspired algorithm to identify DPs without the constraint of interobserver variability. METHODS: NVCs were examined by 9 capillaroscopists. Those NVCs whose DPs were consensually agreed (≥2 out of 3 interobservers) were subsequently analysed with an in-house developed software. Each variable's results were grouped according to the consensually agreed DPs in order to identify useful hallmarks to categorise them. RESULTS: Eight-hundred and fifty-one NVCs (21 957 images) whose DPs had been consensually agreed were software-analysed. Appropriate cut-offs set in capillary density and percentage of abnormal and giant capillaries, tortuosities and hemorrhages allowed DP categorization and the development of the CAPI-Score algorithm. This consisted of 4 rules: Rule 1, SSc vs non-SSc, accuracy 0.88; Rules 2 and 3, SSc-early vs SSc-active vs SSc-late, accuracy 0.82; Rule 4, non-SSc normal vs non-SSc non-specific, accuracy 0.73. Accuracy improved when the analysis was limited to NVCs whose DPs had achieved full consensus among interobservers. CONCLUSIONS: The CAPI-Score algorithm may become a useful tool to assign DPs by overcoming the limitations of subjectivity.
ABSTRACT
OBJECTIVES: We developed a predictive model for the hospital readmission of patients with diabetes. The objective was to identify the frail population that requires additional strategies to prevent readmissions at 90 days. METHODS: Using data collected from 1977 patients in 3 studies on the national prevalence of diabetes (2015-2017), we developed and validated a predictive model of readmission at 90 days for patients with diabetes. RESULTS: A total of 704 (36%) readmissions were recorded. There were no differences in the readmission rates over the course of the 3 studies. The hospitals with more than 500 beds showed significantly (p=.02) higher readmission rates than those with fewer beds. The main reasons for readmission were infectious diseases (29%), cardiovascular diseases (24) and respiratory diseases (14%). Readmissions directly related to diabetic decompensations accounted for only 2% of all readmissions. The independent variables associated with hospital readmission were patient's age, degree of comorbidity, estimated glomerular filtration rate, degree of disability, presence of previous episodes of hypoglycaemia, use of insulin in treating diabetes and the use of systemic glucocorticoids. The predictive model showed an area under the ROC curve (AUC) of 0.676 (95% confidence interval [95% CI] 0.642-0.709; p=.001) in the referral cohort. In the validation cohort, the model showed an AUC of 0.661 (95% CI 0.612-0.710; p=.001). CONCLUSION: The model we developed for predicting readmissions for hospitalised patients with type 2 diabetes helps identify a subgroup of frail patients with a high risk of readmission.
