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.

Previous studies have demonstrated the potential for using smartwatches with a built-in accelerometer as feedback devices for high-quality chest compression during cardiopulmonary resuscitation. However, to the best of our knowledge, no previous study has reported the effects of this feedback on chest compressions in action. A randomized, parallel controlled study of 40 senior medical students was conducted to examine the effect of chest compression feedback via a smartwatch during cardiopulmonary resuscitation of manikins. A feedback application was developed for the smartwatch, in which visual feedback was provided for chest compression depth and rate. Vibrations from smartwatch were used to indicate the chest compression rate. The participants were randomly allocated to the intervention and control groups, and they performed chest compressions on manikins for 2 min continuously with or without feedback, respectively. The proportion of accurate chest compression depth (≥5 cm and ≤6 cm) was assessed as the primary outcome, and the chest compression depth, chest compression rate, and the proportion of complete chest decompression (≤1 cm of residual leaning) were recorded as secondary outcomes. The proportion of accurate chest compression depth in the intervention group was significantly higher than that in the control group (64.6±7.8% versus 43.1±28.3%; p = 0.02). The mean compression depth and rate and the proportion of complete chest decompressions did not differ significantly between the two groups (all p>0.05). Cardiopulmonary resuscitation-related feedback via a smartwatch could provide assistance with respect to the ideal range of chest compression depth, and this can easily be applied to patients with out-of-hospital arrest by rescuers who wear smartwatches.

Healthcare providers in emergency departments should wear respirators for infection protection. However, the wearer's vigorous movements during cardiopulmonary resuscitation may affect the protective performance of the respirator. Herein, we aimed to assess the effects of chest compressions (CCs) on the protective performance of respirators. This crossover study evaluated 30 healthcare providers from 1 emergency department who performed CC with real-time feedback. The first, second, and third groups started with a cup-type, fold-type, and valve-type respirator, respectively, after which the respirators were randomized for each group. The fit factors were measured using a quantitative fit testing device before and during the CC in each experiment. The protection rate was defined as the proportion of respirators achieving a fit factor ≥100. The fold-type respirator had a significantly greater protection rate at baseline (100.0% ± 0.0%) compared to the cup-type (73.6% ± 39.6%, P = .003) and valve-type respirators (87.5% ± 30.3%, P = .012). During the CC, the fit factor values significantly decreased for the cup-type (44.9% ± 42.8%, P < .001) and valve-type respirators (59.5% ± 41.7%, P = .002), but not for the fold-type respirator (93.2% ± 21.7%, P = .095). The protective performances of respirators may be influenced by CC. Healthcare providers should identify the respirator that provides the best fit for their intended tasks.

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.

Objective: According to the guidelines, rescuers should provide chest compressions (CC) ~1.5 inches (40 mm) for infants. Feedback devices could help rescuers perform CC with adequate rates (CCR) and depths (CCD). However, there is no CC feedback device for infant cardiopulmonary resuscitation (CPR). We suggest a smartwatch-based CC feedback application for infant CPR. Participants and methods: We created a smartwatch-based CC feedback application. This application provides feedback on CCD and CCR by colour and text for infant CPR. To evaluate the application, 30 participants were divided randomly into two groups on the basis of whether CC was performed with or without the assistance of the smartwatch application. Both groups performed continuous CC-only CPR for 2 min on an infant mannequin placed on a firm table. We collected CC parameters from the mannequin, including the proportion of correct depth, CCR, CCD and the proportion of correct decompression depth. Results: Demographics between the two groups were not significantly different. The median (interquartile range) proportion of correct depth was 99 (97–100) with feedback compared with 83 (58–97) without feedback (P = 0.002). The CCR and proportion of correct decompression depth were not significantly different between the two groups (P = 0.482 and 0.089). The CCD of the feedback group was significantly deeper than that of the control group [feedback vs. control: 41.2 (39.8–41.7) mm vs. 38.6 (36.1–39.6) mm; P=0.004]. Conclusion: Rescuers who receive feedback of CC parameters from a smartwatch could perform adequate CC during infant CPR.