WHAT'S KNOWN ON THIS SUBJECT:The management of term and near-term newborns suspected of early-onset sepsis, particularly when they are not clearly symptomatic, remains controversial. Methods for quantifying risk that combine maternal factors with a newborn' s evolving clinical examination have been lacking. WHAT THIS STUDY ADDS:This study provides a method for predicting risk of early-onset sepsis. It combines maternal risk factors with objective measures of a newborn' s clinical examination and places newborns into 3 risk groups (treat empirically, observe and evaluate, and continued observation). abstract OBJECTIVE: To define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns $34 weeks' gestation. METHODS:We conducted a retrospective nested case-control study that used split validation. Data collected on each infant included sepsis risk at birth based on objective maternal factors, demographics, specific clinical milestones, and vital signs during the first 24 hours after birth. Using a combination of recursive partitioning and logistic regression, we developed a risk classification scheme for EOS on the derivation dataset. This scheme was then applied to the validation dataset.

We describe a most straightforward synthetic method for preparing neurokinin‐1 (NK1) receptor antagonist derivatives by asymmetric hydrogenation of 3‐amido‐2‐arylpyridinium salts using dinuclear iridium complexes with enantiopure diphosphine ligands, affording the corresponding chiral piperidines in high cis‐diastereoselectivity (>95:5) and moderately high enantioselectivity (up to 86%). Deprotection treatments afforded the NK‐1 receptor antagonist (+)‐CP‐99,994 (83% ee). In addition, we observed unique additive effects of 10‐camphorsulfonic acid in the asymmetric hydrogenation of 3‐amido‐2‐arylpyridinium salts.

Background Patients in general medical-surgical wards who experience unplanned transfer to the intensive care unit (ICU) show evidence of physiologic derangement 6–24 h prior to their deterioration. With increasing availability of electronic medical records (EMRs), automated early warning scores (EWSs) are becoming feasible. Objective To describe the development and performance of an automated EWS based on EMR data. Materials and methods We used a discrete-time logistic regression model to obtain an hourly risk score to predict unplanned transfer to the ICU within the next 12 h. The model was based on hospitalization episodes from all adult patients (18 years) admitted to 21 Kaiser Permanente Northern California (KPNC) hospitals from 1/1/2010 to 12/31/2013. Eligible patients met these entry criteria: initial hospitalization occurred at a KPNC hospital; the hospitalization was not for childbirth; and the EMR had been operational at the hospital for at least 3 months. We evaluated the performance of this risk score, called Advanced Alert Monitor (AAM) and compared it against two other EWSs (eCART and NEWS) in terms of their sensitivity, specificity, negative predictive value, positive predictive value, and area under the receiver operator characteristic curve (c statistic). Results A total of 649,418 hospitalization episodes involving 374,838 patients met inclusion criteria, with 19,153 of the episodes experiencing at least one outcome. The analysis data set had 48,723,248 hourly observations. Predictors included physiologic data (laboratory tests and vital signs); neurological status; severity of illness and longitudinal comorbidity indices; care directives; and health services indicators (e.g. elapsed length of stay). AAM showed better performance compared to NEWS and eCART in all the metrics and prediction intervals. The AAM AUC was 0.82 compared to 0.79 and 0.76 for eCART and NEWS, respectively. Using a threshold that generated 1 alert per day in a unit with a patient census of 35, the sensitivity of AAM was 49% (95% CI: 47.6–50.3%) compared to the sensitivities of eCART and NEWS scores of 44% (42.3–45.1) and 40% (38.2–40.9), respectively. For all three scores, about half of alerts occurred within 12 h of the event, and almost two thirds within 24 h of the event. Conclusion The AAM score is an example of a score that takes advantage of multiple data streams now available in modern EMRs. It highlights the ability to harness complex algorithms to maximize signal extraction. The main challenge in the future is to develop detection approaches for patients in whom data are sparser because their baseline risk is lower.

Patients who deteriorate in the hospital outside the intensive care unit (ICU) have higher mortality and morbidity than those admitted directly to the ICU. As more hospitals deploy comprehensive inpatient electronic medical records (EMRs), attempts to support rapid response teams with automated early detection systems are becoming more frequent. We aimed to describe some of the technical and operational challenges involved in the deployment of an early detection system. This 2-hospital pilot, set within an integrated healthcare delivery system with 21 hospitals, had 2 objectives. First, it aimed to demonstrate that severity scores and probability estimates could be provided to hospitalists in real time. Second, it aimed to surface issues that would need to be addressed so that deployment of the early warning system could occur in all remaining hospitals. To achieve these objectives, we first established a rationale for the development of an early detection system through the analysis of risk-adjusted outcomes. We then demonstrated that EMR data could be employed to predict deteriorations. After addressing specific organizational mandates (eg, defining the clinical response to a probability estimate), we instantiated a set of equations into a Java application that transmits scores and probability estimates so that they are visible in a commercially available EMR every 6 hours. The pilot has been successful and deployment to the remaining hospitals has begun.

The customized eSimplified Acute Physiology Score 3 shows good potential for providing automated risk adjustment in the intensive care unit.