Computer-assisted glucose control in critically ill patients

Objective: Intensive insulin therapy is associated with the risk of hypoglycemia and increased costs of material and personnel. We therefore evaluated the safety and efficiency of a computer-assisted glucose control protocol in a large population of critically ill patients. Design and setting: Observational cohort study in three intensive care units (32 beds) in a 1,300-bed university teaching hospital. Patients: All 2,800 patients admitted to the surgical, neurosurgical, and cardiothoracic units; the study period started at each ICU after implementation of Glucose Regulation for Intensive Care Patients (GRIP), a freely available computer-assisted glucose control protocol. Measurements and results: We analysed compliance in relation to recommended insulin pump rates and glucose measurement frequency. Patients were on GRIP-ordered pump rates 97% of time. Median measurement time was 5 min late (IQR 20 min early to 34 min late). Hypoglycemia was uncommon (7% of patients for mild hypoglycemia, <3.5 mmol/l; 0.86% for severe hypoglycemia, <2.2 mmol/l). Our predefined target range (4.0 - 7.5 mmol/l) was reached after a median of 5.6h (IQR 0.2 - 11.8) and maintained for 89% (70 - 100%) of the remaining stay at the ICU. The number of measurements needed was 5.9 (4.8 - 7.3) per patient per day. In-hospital mortality was 10.1%. Conclusions: Our computer-assisted glucose control protocol provides safe and efficient glucose regulation in routine intensive care practice. A low rate of hypoglycemic episodes was achieved with a considerably lower number of glucose measurements than used in most other schemes

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase