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BACKGROUND@#A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients.@*METHODS@#We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1-6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period.@*RESULTS@#We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1-6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77-0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75-0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%.@*CONCLUSIONS@#In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.
Subject(s)
Humans , Artificial Intelligence , Deep Learning , Electrocardiography , Hypokalemia/diagnosis , Retrospective StudiesABSTRACT
Objective To study the expression of RASSF1A gene in gastric cancer tissues and cell lines and to investigate its clinical roles. Methods Immunohistochemistry was employed to detect the expression of RASSF1A gene in 39 cases of human gastric cancer tissues and 18 cases of normal human gastric tissues, and RT-PCR was used to examine the expression of RASSF1A mRNA in 4 gastric cancer cell lines, normal gastric cell lines GES-1 and positive control Hela cells. Correlations between expression of RASSF1A gene and clinicopathological characteristics of gastric cancer were also analyzed. Results All the normal gastric tissues (18 cases) were stained with anti-RASSF1A antibody, and significantly lower expression of RASSF1A was found in the 39 cases of gastric cancer tissues (P
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<p><b>OBJECTIVE</b>To detect breast cancer specific gene 1 (BCSG1) expression in different breast tissue, analysis its correlation with clinical parameters and evaluate the prognosis of breast cancer.</p><p><b>METHODS</b>The expression of BCSG1 was detected by reverse transcription-polymerase chain reaction (RT-PCR) in surgical specimens from 84 cases of breast disease patients selected randomly at XinHua Hospital affiliated with Shanghai Second Medical University from September 1999 to December 2002. Of 84 cases, 72 case were breast cancer. Statistic analysis BCSG1 gene expression correlation with clinical parameters of breast cancer. 72 breast cancers were followed up (4 - 43 months) to set up independent prognosis factor by survival analysis.</p><p><b>RESULTS</b>BCSG1 was undetectable in all benign breast lesions, while was detectable in 36.1% of all breast cancer samples (26/72), in which 79.2% of stage III/IV cases were positive (19/24). The expression of BCSG1 was tightly correlated with the stage (P = 0.000) and the size of tumor (P = 0.007). Both ER (P = 0.027) and BCSG1 (P = 0.001) were the independent prognosis factor of breast cancer.</p><p><b>CONCLUSION</b>BCSG1 is one of independent tumor marker of breast cancer, the expression of BCSG1 is closely correlated to the stage of breast cancer and the tumor size. Maybe, BCSG1 is a new prognosis factor of breast cancer.</p>