Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: development and validation of a multivariable model.

AIM: The National Early Warning System (NEWS) is based on vital signs; the Laboratory Decision Tree Early Warning Score (LDT-EWS) on laboratory test results. We aimed to develop and validate a new EWS (the LDTEWS:NEWS risk index) by combining the two and evaluating the discrimination of the primary outcome of unanticipated intensive care unit (ICU) admission or in-hospital mortality, within 24 hours. METHODS: We studied emergency medical admissions, aged 16 years or over, admitted to Oxford University Hospitals (OUH) and Portsmouth Hospitals (PH). Each admission had vital signs and laboratory tests measured within their hospital stay. We combined LDT-EWS and NEWS values using a linear time-decay weighting function imposed on the most recent blood tests. The LDTEWS:NEWS risk index was developed using data from 5 years of admissions to PH, and validated on a year of data from both PH and OUH. We tested the risk index's ability to discriminate the primary outcome using the c-statistic. RESULTS: The development cohort contained 97,933 admissions (median age = 73 years) of which 4,723 (4.8%) resulted in in-hospital death and 1,078 (1.1%) in unanticipated ICU admission. We validated the risk index using data from PH (n = 21,028) and OUH (n = 16,383). The risk index showed a higher discrimination in the validation sets (c-statistic value (95% CI)) (PH, 0.901 (0.898-0.905); OUH, 0.916 (0.911-0.921)), than NEWS alone (PH, 0.877 (0.873-0.882); OUH, 0.898 (0.893-0.904)). CONCLUSIONS: The LDTEWS:NEWS risk index increases the ability to identify patients at risk of deterioration, compared to NEWS alone

A comparison of the ability of the National Early Warning Score and the National Early Warning Score 2 to identify patients at risk of in-hospital mortality: A multi-centre database study.

AIMS: To compare the ability of the National Early Warning Score (NEWS) and the National Early Warning Score 2 (NEWS2) to identify patients at risk of in-hospital mortality and other adverse outcomes. METHODS: We undertook a multi-centre retrospective observational study at five acute hospitals from two UK NHS Trusts. Data were obtained from completed adult admissions who were not fit enough to be discharged alive on the day of admission. Diagnostic coding and oxygen prescriptions were used to identify patients with type II respiratory failure (T2RF). The primary outcome was in-hospital mortality within 24 h of a vital signs observation. Secondary outcomes included unanticipated intensive care unit admission or cardiac arrest within 24 h of a vital signs observation. Discrimination was assessed using the c-statistic. RESULTS: Among 251,266 adult admissions, 48,898 were identified to be at risk of T2RF by diagnostic coding. In this group, NEWS2 showed statistically significant lower discrimination (c-statistic, 95% CI) for identifying in-hospital mortality within 24 h (0.860, 0.857-0.864) than NEWS (0.881, 0.878-0.884). For 1394 admissions with documented T2RF, discrimination was similar for both systems: NEWS2 (0.841, 0.827-0.855), NEWS (0.862, 0.848-0.875). For all secondary endpoints, NEWS2 showed no improvements in discrimination. CONCLUSIONS: NEWS2 modifications to NEWS do not improve discrimination of adverse outcomes in patients with documented T2RF and decrease discrimination in patients at risk of T2RF. Further evaluation of the relationship between SpO2 values, oxygen therapy and risk should be investigated further before wide-scale adoption of NEWS2

Construct an Intelligent Yield Alert and Diagnostic Analysis System via Data Analysis: Empirical Study of a Semiconductor Foundry

Part 6: Industry 4.0 - Smart FactoryInternational audienceAs semiconductor manufacturing technology advances, the process becomes longer and more complex. A critical issue is to determine how to avoid yield loss at an early stage or to diagnose the cause of yield loss soon, in order to save more money. Traditional statistical regression analysis and correlation analysis are unable to quickly and easily figure out the causes of process anomalies and potential problems. This study aims to construct an intelligent yield alert and diagnostic analysis framework combined within a big data analysis architecture. Through an intelligent detection and early warning mechanism, instant detection of yield anomalies and automatic diagnostic analysis based on good/bad wafer classification, we can effectively and rapidly find out the factors that may cause process variation to help quickly clarify the causes of abnormal product yield. The case study in this paper uses real-world data from a foundry in Taiwan. We hope to provide engineers and domain experts with a reference framework for building a yield analysis system to help improve the yield of semiconductor manufacturing and enhance the competitiveness of high-tech industries