Editor’s Perspective: What We Already Know about This Topic: What This Article Tells Us That Is New: Background: Previous research has shown different effects of anesthetics on cancer cell growth. Here, the authors investigated the association between type of anesthetic and patient survival after elective colon cancer surgery. Methods: A retrospective cohort study included patients who received elective colon cancer surgery between January 2005 and December 2014. Patients were grouped according to anesthesia received: propofol or desflurane. After exclusion of those who received combined propofol anesthesia with inhalation anesthesia or epidural anesthesia, survival curves were constructed from the date of surgery to death. After propensity matching, univariable and multivariable Cox regression models were used to compare hazard ratios for death. Subgroup analyses were performed for tumor–node–metastasis staging and postoperative metastasis. Results: A total of 706 patients (307 deaths, 43.5%) with desflurane anesthesia and 657 (88 deaths, 13.4%) with propofol anesthesia were eligible for analysis. After propensity matching, 579 patients remained in each group (189 deaths, 32.6%, in the desflurane group vs. 87, 15.0%, in the propofol group). In the matched analyses, the propofol-treated group had a better survival, irrespective of lower tumor–node–metastasis stage (hazard ratio, 0.22; 95% CI, 0.11 to 0.42; P < 0.001) or higher tumor–node–metastasis stage (hazard ratio, 0.42; 95% CI, 0.32 to 0.55; P < 0.001) and presence of metastases (hazard ratio, 0.67; 95% CI, 0.51 to 0.86; P = 0.002) or absence of metastases (hazard ratio, 0.08; 95% CI, 0.01 to 0.62; P = 0.016). Simple propensity score adjustment produced similar findings. Conclusions: Propofol anesthesia for colon cancer surgery is associated with better survival irrespective of tumor–node–metastasis stage.

Object: This study investigated the specific preoperative MRI features of patients with intracranial meningiomas that correlate with pathological grade and provide appropriate preoperative planning. Methods: From 2006 to 2012, 120 patients (36 men and 84 women, age range 20–89 years) with newly diagnosed symptomatic intracranial meningiomas undergoing resection were retrospectively analyzed in terms of radiological features of preoperative MRI. There were 90 WHO Grade I and 30 WHO Grade II or III meningiomas. The relationships between MRI features and WHO histopathological grade were analyzed and scored quantitatively. Results: According to the results of multivariate logistic regression analysis, age ≥ 75 years, indistinct tumorbrain interface, positive capsular enhancement, and heterogeneous tumor enhancement were identified factors in the prediction of advanced histopathological grade. The prediction model was quantified as a scoring scale: 2 × (age) + 5 × (tumor-brain interface) + 3 × (capsular enhancement) + 2 × (tumor enhancement). The calculated score correlated positively with the probability of high-grade meningioma. Conclusions: This scoring approach may be useful for clinicians in determining therapeutic strategy and in surgical planning for patients with intracranial meningiomas.

Background: The detection of dyskalemias—hypokalemia and hyperkalemia—currently depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia, electrocardiography (ECG) may be able to uncover clinically important dyskalemias before laboratory results. Objective: Our study aimed to develop a deep-learning model, ECG12Net, to detect dyskalemias based on ECG presentations and to evaluate the logic and performance of this model. Methods: Spanning from May 2011 to December 2016, 66,321 ECG records with corresponding serum potassium (K+) concentrations were obtained from 40,180 patients admitted to the emergency department. ECG12Net is an 82-layer convolutional neural network that estimates serum K+ concentration. Six clinicians—three emergency physicians and three cardiologists—participated in human-machine competition. Sensitivity, specificity, and balance accuracy were used to evaluate the performance of ECG12Net with that of these physicians. Results: In a human-machine competition including 300 ECGs of different serum K+ concentrations, the area under the curve for detecting hypokalemia and hyperkalemia with ECG12Net was 0.926 and 0.958, respectively, which was significantly better than that of our best clinicians. Moreover, in detecting hypokalemia and hyperkalemia, the sensitivities were 96.7% and 83.3%, respectively, and the specificities were 93.3% and 97.8%, respectively. In a test set including 13,222 ECGs, ECG12Net had a similar performance in terms of sensitivity for severe hypokalemia (95.6%) and severe hyperkalemia (84.5%), with a mean absolute error of 0.531. The specificities for detecting hypokalemia and hyperkalemia were 81.6% and 96.0%, respectively. Conclusions: A deep-learning model based on a 12-lead ECG may help physicians promptly recognize severe dyskalemias and thereby potentially reduce cardiac events.

Objective: The biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict the biological age via ECG to explore its contribution to future cardiovascular diseases (CVDs).Methods: There were 71,741 cases ranging from 20 to 80 years old recruited from the health examination center. The development set used 32,707 cases to train the DLM for estimating the ECG-age, and 8,295 cases were used as the tuning set. The validation set included 30,469 ECGs to follow the outcomes, including all-cause mortality, cardiovascular-cause mortality, heart failure (HF), diabetes mellitus (DM), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), atrial fibrillation (AF), and hypertension (HTN). Two independent external validation sets (SaMi-Trop and CODE15) were also used to validate our DLM.Results: The mean absolute errors of chronologic age and ECG-age was 6.899 years (r = 0.822). The higher difference between ECG-age and chronological age was related to more comorbidities and abnormal ECG rhythm. The cases with the difference of more than 7 years had higher risk on the all-cause mortality [hazard ratio (HR): 1.61, 95% CI: 1.23–2.12], CV-cause mortality (HR: 3.49, 95% CI: 1.74–7.01), HF (HR: 2.79, 95% CI: 2.25–3.45), DM (HR: 1.70, 95% CI: 1.53–1.89), CKD (HR: 1.67, 95% CI: 1.41–1.97), AMI (HR: 1.76, 95% CI: 1.20–2.57), STK (HR: 1.65, 95% CI: 1.42–1.92), CAD (HR: 1.24, 95% CI: 1.12–1.37), AF (HR: 2.38, 95% CI: 1.86–3.04), and HTN (HR: 1.67, 95% CI: 1.51–1.85). The external validation sets also validated that an ECG-age >7 years compare to chronologic age had 3.16-fold risk (95% CI: 1.72–5.78) and 1.59-fold risk (95% CI: 1.45–1.74) on all-cause mortality in SaMi-Trop and CODE15 cohorts. The ECG-age significantly contributed additional information on heart failure, stroke, coronary artery disease, and atrial fibrillation predictions after considering all the known risk factors.Conclusions: The ECG-age estimated via DLM provides additional information for CVD incidence. Older ECG-age is correlated with not only on mortality but also on other CVDs compared with chronological age.