Incidence and clinical predictors of 30-day emergency readmission after colorectal cancer surgery - A nationwide cohort study.

AIM: The aim of this study was to investigate the risk and predictors of 30-day emergency readmission and surgical reintervention after discharge from colorectal cancer surgery with curative intent in Denmark. METHOD: This is a retrospective cohort study using Danish nationwide registry data. We included all patients who underwent colorectal tumour resection with curative intent between 1 January 2005 and 1 December 2018. The primary outcome was 30-day emergency readmission, defined as any emergency hospital visit within 30 days of discharge. Secondary outcomes were 30-day emergency readmission with a minimum duration of 2 days and 30-day emergency readmission including any abdominal procedure. Twenty-three candidate predictors including patient comorbidities, tumour characteristics, surgical treatment and length of stay were evaluated using multivariate logistic regression models. Length of stay was categorized into percentiles and standardized according to year of surgery. RESULTS: Of the 40 782 patients included in the study, 8360 (20.5%) were readmitted within 30 days of discharge. Median time to readmission was 6 days (interquartile range 2-15 days). A total of 4968 patients (12.2%) were readmitted for at least 2 days, and 793 patients (1.9%) underwent an abdominal procedure during their readmission. The strongest predictors of 30-day readmission were length of stay below the fifth percentile (OR 2.36; P < 0.001) and American Society of Anesthesiologists score IV (OR 2.21; P < 0.001). CONCLUSION: Emergency readmission is frequent after colorectal cancer surgery with curative intent, and almost 10% of readmitted patients require surgical reintervention. An increased focus on predicting preventable readmissions might facilitate interventions to reduce morbidity and hospital expenses.

Development of a method for digital assessment of tumor regression grade in patients with rectal cancer following neoadjuvant therapy.

Neoadjuvant chemo-radiotherapy (nCRT) followed by surgical resection is the standard treatment strategy in patients with locally advanced rectal cancer (RC). The pathological effect of nCRT is assessed by determining the tumor regression grade (TRG) of the resected tumor. Various methods exist for assessing TRG and all are performed manually by the pathologist with an accompanying risk of interobserver variation. Automated digital image analysis could be a more objective and reproducible approach to evaluate TRG. This study aimed at developing a digital method to assess TRG in RC following nCRT, and correlate the results to the currently used Mandard method. A deep learning-based semi-automatic Epithelium-Tumor area Percentage (ETP) algorithm enabling quantification of tumor regression by determining the percentage of residual tumor epithelium out of the total tumor area was developed. The ETP was quantified in 50 cases treated with nCRT and 25 cases with no prior nCRT served as controls. Median ETP was 39.25% in untreated compared with 6.64% in patients who received nCRT (P < .001). The ETP of the resected tumors treated with nCRT increased along with increasing Mandard grade (P < .001). As new treatment strategies in RC are emerging, performing an accurate and reproducible evaluation of TRG is important in the assessment of treatment response and prognosis. TRG is often used as an outcome point in clinical trials. The ETP algorithm is capable of performing a precise and objective value of tumor regression.

Interpretable Autoencoders Trained on Single Cell Sequencing Data Can Transfer Directly to Data from Unseen Tissues.

Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in terms of biology. Here, we show that such autoencoders can generalize to the extent that they can transfer directly without additional training. In practice, we can extract biological modules, denoise, and classify data correctly from an autoencoder that was trained on a different dataset and with different cells (a foreign model). We deconvoluted the biological signal encoded in the bottleneck layer of scRNA-models using saliency maps and mapped salient features to biological pathways. Biological concepts could be associated with specific nodes and interpreted in relation to biological pathways. Even in this unsupervised framework, with no prior information about cell types or labels, the specific biological pathways deduced from the model were in line with findings in previous research. It was hypothesized that autoencoders could learn and represent meaningful biology; here, we show with a systematic experiment that this is true and even transcends the training data. This means that carefully trained autoencoders can be used to assist the interpretation of new unseen data.

Histological disease activity in patients with microscopic colitis is not related to clinical disease activity or long-term prognosis.

BACKGROUND: Microscopic colitis (MC) is a common cause of chronic watery diarrhea. Biopsies with characteristic histological features are crucial for establishing the diagnosis. The two main subtypes are collagenous colitis (CC) and lymphocytic colitis (LC) but incomplete forms exist. The disease course remains unpredictable varying from spontaneous remission to a relapsing course. AIM: To identify possible histological predictors of course of disease. METHODS: Sixty patients from the European prospective MC registry (PRO-MC Collaboration) were included. Digitised histological slides stained with CD3 and Van Gieson were available for all patients. Total cell density and proportion of CD3 positive lymphocytes in lamina propria and surface epithelium were estimated by automated image analysis, and measurement of the subepithelial collagenous band was performed. Histopathological features were correlated to the number of daily stools and daily watery stools at time of endoscopy and at baseline as well as the clinical disease course (quiescent, achieved remission after treatment, relapsing or chronic active) at 1-year follow-up. RESULTS: Neither total cell density in lamina propria, proportion of CD3 positive lymphocytes in lamina propria or surface epithelium, or thickness of collagenous band showed significant correlation to the number of daily stools or daily watery stools at any point of time. None of the assessed histological parameters at initial diagnosis were able to predict clinical disease course at 1-year follow-up. CONCLUSIONS: Our data indicate that the evaluated histological parameters were neither markers of disease activity at the time of diagnosis nor predictors of disease course.