No abstract

Healthcare use is markedly elevated after severe sepsis, and post-discharge management may be an opportunity to reduce resource use.

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.

Purpose: Cutaneous T-cell lymphomas (CTCL), encompassing a spectrum of T-cell lymphoproliferative disorders involving the skin, have collectively increased in incidence over the last 40 years. S ezary syndrome is an aggressive form of CTCL characterized by significant presence of malignant cells in both the blood and skin. The guarded prognosis for S ezary syndrome reflects a lack of reliably effective therapy, due, in part, to an incomplete understanding of disease pathogenesis.Experimental Design: Using single-cell sequencing of RNA and the machine-learning reverse graph embedding approach in the Monocle package, we defined a model featuring distinct transcriptomic states within S ezary syndrome. Gene expression used to differentiate the unique transcriptional states were further used to develop a boosted tree classification for early versus late CTCL disease.Results: Our analysis showed the involvement of FOXP3 þ malignant T cells during clonal evolution, transitioning from FOXP3 þ T cells to GATA3 þ or IKZF2 þ (HELIOS) tumor cells. Transcriptomic diversities in a clonal tumor can be used to predict disease stage, and we were able to characterize a gene signature that predicts disease stage with close to 80% accuracy. FOXP3 was found to be the most important factor to predict early disease in CTCL, along with another 19 genes used to predict CTCL stage.Conclusions: This work offers insight into the heterogeneity of S ezary syndrome, providing better understanding of the transcriptomic diversities within a clonal tumor. This transcriptional heterogeneity can predict tumor stage and thereby offer guidance for therapy.

Claims that growth hormone enhances physical performance are not supported by the scientific literature. Although the limited available evidence suggests that growth hormone increases lean body mass, it may not improve strength; in addition, it may worsen exercise capacity and increase adverse events. More research is needed to conclusively determine the effects of growth hormone on athletic performance.