The current methods available for determining insulin resistance are complicated; hence, they are only applicable to small-scale studies. Therefore, this study aimed to classify the characteristics of surrogate measures for insulin resistance and establish valid cut-off values for predicting the development of type 2 diabetes mellitus (DM) in Korean populations. This prospective study included 7,643 participants aged 40–69 years from the Ansung-Ansan cohort database (2001–2012). Four surrogate measures, namely homeostasis model assessment-insulin resistance (HOMA-IR), visceral adiposity index (VAI), lipid accumulation product (LAP), and triglycerides and glucose (TyG) index, were analyzed. We analyzed each measure using receiver operating characteristic (ROC) curve for the development of type 2 DM. The cut-off value was determined as the value with the highest Youden index score in the specificity dominant area. The area under the curve (AUC) was 0.566 (95% confidence interval [CI], 0.548–0.583) for HOMA-IR, 0.622 (95% CI, 0.605–0.639) for VAI, 0.642 (95% CI, 0.625–0.658) for LAP, and 0.672 (95% CI, 0.656–0.687) for TyG index. The AUC of TyG index was significantly higher than that of HOMA-IR, VAI, and LAP (p < 0.001). The cut-off value was 2.54 (sensitivity 36.8%; specificity 73.1%; hazard ratio [HR], 1.41, 95% CI, 1.25–1.59) for HOMA-IR, 2.54 (sensitivity 50.4%; specificity 68.8%; HR, 1.75, 95% CI, 1.55–1.96) for VAI, 36.6 (sensitivity 59.2%; specificity 63.9%; HR, 1.87, 95% CI, 1.64–2.14) for LAP, and 4.69 (sensitivity 62.1%; specificity 63.1%; HR, 2.17, 95% CI, 1.92–2.45) for TyG index. The TyG index was a better predictor for DM than HOMA-IR. VAI and LAP showed the modest predictability for DM. The TyG index could be a useful supplementary method for identifying individuals at risk for insulin resistance and DM development.

Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There is currently no alternative method to real-time reverse transcriptase polymerase chain reaction (RT-PCR), which requires 40 cycles to diagnose COVID-19. We propose a deep learning (DL) model to improve the speed of COVID-19 RT-PCR diagnosis. We developed and tested a DL model using the long short-term memory method with a dataset of fluorescence values measured in each cycle of 5810 RT-PCR tests. Among the DL models developed here, the diagnostic performance of the 21st model showed an area under the receiver operating characteristic (AUROC), sensitivity, and specificity of 84.55%, 93.33%, and 75.72%, respectively. The diagnostic performance of the 24th model showed an AUROC, sensitivity, and specificity of 91.27%, 90.00%, and 92.54%, respectively.

Decreased thyroid hormone (TH) has been considered as one of the potential predictors of mortality in sepsis. This study aimed to evaluate the prognostic impact of decreased TH on mortality in septic patients during intensive care unit (ICU) admission. We included studies that assessed thyroid function by measuring the serum thyroid hormone level and in-hospital mortality in adult septic patients. Reviews, case reports, editorials, letters, commentaries, animal studies, duplicate studies, and studies with irrelevant populations and inappropriate controls were excluded. A total of 1,578 patients from eight studies were included. Triiodothyronine levels in non-survivors were relatively lower than that of survivors (6 studies; standardized mean difference [SMD] 2.31; 95% confidence interval (CI), 0.52–4.10; I² = 97%; P = 0.01). Thyroxine levels in non-survivors were also lower than that of survivors (5 studies; SMD 2.40; 95% CI, 0.91–3.89). There were no statistically significant differences in thyroid-stimulating hormone levels between non-survivors and survivors. The present meta-analysis suggested that the decreased TH during ICU admission might be associated with the increase in mortality in adult septic patients. Hence, the measurement of TH could provide prognostic information on mortality in adult septic patients.

Background: The efficacy and safety of videolaryngoscopes (VLs) for tracheal intubation is still conflicting and changeable according to airway circumstances. This study aimed to compare the efficacy and safety of several VLs in patients undergoing general anesthesia. Methods: Medline, EMBASE, and the Cochrane Library were searched until 13 January 2020. The following VLs were evaluated compared to the Macintosh laryngoscope (MCL) by network meta-analysis for randomized controlled trials (RCTs): Airtraq, Airwayscope, C-MAC, C-MAC D-blade (CMD), GlideScope, King Vision, and McGrath. Outcome measures were the success and time (speed) of intubation, glottic view, and sore throat (safety). Results: A total of 9315 patients in 96 RCTs were included. The highest-ranked VLs for first-pass intubation success were CMD (90.6 % in all airway; 92.7% in difficult airway) and King Vision (92% in normal airway). In the rank analysis for secondary outcomes, the following VLs showed the highest efficacy or safety: Airtraq (safety), Airwayscope (speed and view), C-MAC (speed), CMD (safety), and McGrath (view). These VLs, except McGrath, were more effective or safer than MCL in moderate evidence level, whereas there was low certainty of evidence in the intercomparisons of VLs. Conclusions: CMD and King Vision could be relatively successful than MCL and other VLs for tracheal intubation under general anesthesia. The comparisons of intubation success between VLs and MCL showed moderate certainty of evidence level, whereas the intercomparisons of VLs showed low certainty evidence.