ObjectiveBased on the current version of Database from Colorectal Cancer (DACCA), we aimed to analyze the comorbidities and preoperative physical status of colorectal cancer patients.MethodsThe DACCA version selected for this data analysis was updated on May 9, 2019. The data items included: surgical comorbidities and classified by systems, surgical history, pelvic disease history, medical comorbidities, and some important subdivision types, infectious disease status, allergic history, nutrition risk screening 2002 (NRS2002) score, amount of weight loss after illness, anemia, low protein status, preoperative ascites status, preoperative pleural effusion status, immune system disease and immunocompromised status, and preoperative nutritional support. Characteristic analysis was performed on each selected data item.ResultsA total of 6 166 admitted data were filtered from the DACCA database. Among them, surgical comorbidities, surgical history, medical comorbidities, and allergy history had 6 166 admitted data, and weight loss had admitted 4 703. There were 2 923 (47.4%) with surgical comorbidities. According to the system, the most common one was digestive system (2 005, 68.6%), and the least one was skin tissue system (24, 0.8%). There were 4 361 (70.7%) patients without surgical history and 1 805 (29.3%) patients had surgical history. There were 2 397 (38.9%) patients without medical comorbidities and 3 769 (61.1%) had medical comorbidities, of which pneumonia/pulmonary infection/chronic bronchopneumonia/lung indeterminate nodules were the most common(2 330, 37.8%), the least was cerebral infarction (unspecified type, 63, 1.0%). There were 5 813 (94.3%) without allergy history and 353 (5.7%) had allergy history. According to the NRS2002 nutrition screening criteria, the scores ranged from 1 to 7 points, with an average of 1.22 points, which could be classified as non-nutrition risk (5 279, 85.6%, included 1 point of 4 310, 2 points of 969), nutritional risk (887, 14.4%, included 3 points of 415, 4 points of 358, 5 points of 100, 6 points of 12, and 7 points of 2), the result of linear regression analysis of NRS2002 scores with the trend of the year showed that: ŷ=0.000 2x–6.275 8, R2=0.716 2, P<0.001. A total of 2 840 (60.4%) had no weight loss while 1 863 (39.6%) had, and weight loss with the trend of year were analyzed by linear regression analysis: ŷ=0.000 2x–3.956, R2=0.685 7, P<0.001. The number of cases of other physical status and the proportion of valid data were anemia (1 194, 33.1%), preoperative ascites (1829, 51.7%), preoperative pleural effusion (171, 5.7%), hypoproteinemia (1 206, 33.6%), immune system disease and immunocompromised status (495, 56.6%), and nutritional support (824, 25.0%).ConclusionsThrough the analysis of the DACCA database, nearly 1/2 of colorectal cancer surgery patients have surgical comorbidities before surgery, more than 1/2 of the patients have medical comorbidities, and the types of diseases are various. Preoperative nutritional status in patients with colorectal cancer also shows certain characteristics, suggesting the state of preoperative risk. These data will provide a detailed big data basis for future preoperative risk assessment of colorectal cancer.
The design of wall filter in ultrasonic microvascular imaging directly affects the resolution of blood flow imaging. We compared the traditional polynomial regression wall filter algorithm and two algorithms based on singular value decomposition (SVD), Full-SVD algorithm and RS-RSVD algorithm (random sampling based on random singular value decomposition) through experiments with simulated data and human renal entity data imaging experiments. The experimental results showed that the filtering effect of the traditional polynomial regression wall filter algorithm was limited, however, Full-SVD algorithm and RS-RSVD algorithm could better extract the micro blood flow signal from the tissue or noise signal. When RS-RSVD algorithm was randomly divided into 16 blocks, the signal-to-noise ratio was the same as that of Full-SVD algorithm, reduces the contrast-to-noise ratio by 2.05 dB, and reduces the execution time by 90.41%. RS-RSVD algorithm can improve the operation efficiency and is more conducive to the real-time imaging of high frame rate ultrasound microvessels.