Effect of preoperative warming on intraoperative hypothermia: a randomized-controlled trial

Purpose: The purpose of this study was to evaluate the effects of preoperative forced-air warming on intraoperative hypothermia. Methods: In this randomized-controlled trial, adult patients scheduled for elective, non-cardiac surgery under general anesthesia were stratified by scheduled surgical duration ( \u3c 2.5 hr or ≥ 2.5 hr) and then randomized to a pre-warming group using a BairPaws™ forced-air warming system for at least 30 min preoperatively or to a control group with warmed blankets on request. All patients were warmed intraoperatively via convective forced-air warming blankets. Perioperative temperature was measured using the SpotOn™ temperature system consisting of a single-use disposable sensor applied to the participant\u27s forehead. The primary outcome was the magnitude of intraoperative hypothermia calculated as the area under the time-temperature curve for core temperatures \u3c 36°C between induction of general anesthesia and leaving the operating room. Secondary outcomes included surgical site infections, packed red blood cell requirements, and 24 hr postoperative opioid consumption. Results: Two hundred participants were analyzed (101 control; 99 pre-warmed). Pre-warmed participants had a lower median [interquartile range] magnitude of hypothermia than controls (0.00 [0.00-0.12] °C·hr −1 vs 0.05 [0.00-0.36] °C·hr −1 , respectively; median difference, −0.01°C·hr −1 ; 95% confidence interval, −0.04 to 0.00°C·hr −1 ; P = 0.005). There were no between-group differences in the secondary outcomes. Conclusion: A minimum of 30 min of preoperative forced-air convective warming decreased the overall intraoperative hypothermic exposure. While redistribution hypothermia still occurs despite pre- and intraoperative forced-air warming, their combined application results in greater preservation of intraoperative normothermia compared with intraoperative forced-air warming alone. Trial registration: www.clinicaltrials.gov(NCT02177903). Registered 25 June 2014

Pediatric in-Hospital Death from Infectious Disease in Uganda: Derivation of Clinical Prediction Models.

BACKGROUND:Pediatric hospital mortality from infectious diseases in resource constrained countries remains unacceptably high. Improved methods of risk-stratification can assist in referral decision making and resource allocation. The purpose of this study was to create prediction models for in-hospital mortality among children admitted with suspected infectious diseases. METHODS:This two-site prospective observational study enrolled children between 6 months and 5 years admitted with a proven or suspected infection. Baseline clinical and laboratory variables were collected on enrolled children. The primary outcome was death during admission. Stepwise logistic regression minimizing Akaike's information criterion was used to identify the most promising multivariate models. The final model was chosen based on parsimony. RESULTS:1307 children were enrolled consecutively, and 65 (5%) of whom died during their admission. Malaria, pneumonia and gastroenteritis were diagnosed in 50%, 31% and 8% of children, respectively. The primary model included an abnormal Blantyre coma scale, HIV and weight-for-age z-score. This model had an area under the curve (AUC) of 0.85 (95% CI, 0.80-0.89) with a sensitivity and specificity of 83% and 76%, respectively. The positive and negative predictive values were 15% and 99%, respectively. Two alternate models with similar performance characteristics were developed withholding HIV and weight-for-age z-score, for use when these variables are not available. CONCLUSIONS:Risk stratification of children admitted with infectious diseases can be calculated based on several easily measured variables. Risk stratification at admission can be used for allocation of scarce human and physical resources and to guide referral among children admitted to lower level health facilities

Primary model receiver operating curve characteristics.

<p>Primary model receiver operating curve characteristics.</p

Consort diagram of study flow.

<p>Consort diagram of study flow.</p

Characteristics of Subjects (n = 1307).

<p>Characteristics of Subjects (n = 1307).</p

Model Characteristics.

<p>Model Characteristics.</p

Univariate analyses of candidate predictor variables for inpatient-mortality.

<p>Univariate analyses of candidate predictor variables for inpatient-mortality.</p