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EDITOR’S PERSPECTIVE What We Already Know about This Topic Dantrolene effectively treats malignant hyperthermia, but there are discrepant recommendations for dantrolene availability in facilities that stock succinylcholine for airway rescue but do not use volatile anesthetics. What This Article Tells Us That Is New The authors performed an analysis of data from three databases and a systematic literature review. Providers frequently use succinylcholine, including during difficult mask ventilation. Succinylcholine given without volatile anesthetics triggered 24 malignant hyperthermia events, 13 of which were treated with dantrolene. Fourteen patients experienced substantial complications, and one died. Delayed dantrolene treatment worsened patient outcomes. Background Although dantrolene effectively treats malignant hyperthermia (MH), discrepant recommendations exist concerning dantrolene availability. Whereas Malignant Hyperthermia Association of the United States guidelines state dantrolene must be available within 10 min of the decision to treat MH wherever volatile anesthetics or succinylcholine are administered, a Society for Ambulatory Anesthesia protocol permits Class B ambulatory facilities to stock succinylcholine for airway rescue without dantrolene. The authors investigated (1) succinylcholine use rates, including for airway rescue, in anesthetizing/sedating locations; (2) whether succinylcholine without volatile anesthetics triggers MH warranting dantrolene; and (3) the relationship between dantrolene administration and MH morbidity/mortality. Methods The authors performed focused analyses of the Multicenter Perioperative Outcomes Group (2005 through 2016), North American MH Registry (2013 through 2016), and Anesthesia Closed Claims Project (1970 through 2014) databases, as well as a systematic literature review (1987 through 2017). The authors used difficult mask ventilation (grades III and IV) as a surrogate for airway rescue. MH experts judged dantrolene treatment. For MH morbidity/mortality analyses, the authors included U.S. and Canadian cases that were fulminant or scored 20 or higher on the clinical grading scale and in which volatile anesthetics or succinylcholine were given. Results Among 6,368,356 queried outcomes cases, 246,904 (3.9%) received succinylcholine without volatile agents. Succinylcholine was used in 46% (n = 710) of grade IV mask ventilation cases (median dose, 100 mg, 1.2 mg/kg). Succinylcholine without volatile anesthetics triggered 24 MH cases, 13 requiring dantrolene. Among 310 anesthetic-triggered MH cases, morbidity was 20 to 37%. Treatment delay increased complications every 10 min, reaching 100% with a 50-min delay. Overall mortality was 1 to 10%; 15 U.S. patients died, including 4 after anesthetics in freestanding facilities. Conclusions Providers use succinylcholine commonly, including during difficult mask ventilation. Succinylcholine administered without volatile anesthetics may trigger MH events requiring dantrolene. Delayed dantrolene treatment increases the likelihood of MH complications. The data reported herein support stocking dantrolene wherever succinylcholine or volatile anesthetics may be used.

Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.

Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.

Sepsis, an adverse auto-immune response to an infection often causing life-threatening complications, results in the highest mortality and treatment cost of any illness in US hospitals. Several immune biomarker levels, including Interleukin 6 (IL-6), have shown a high correlation to the onset and progression of sepsis. Currently, no technology diagnoses and stratifies sepsis progression using biomarker levels. This paper reports a microfluidic biochip platform to detect proteins in undiluted human plasma samples. The device uses a differential enumeration platform that integrates Coulter counting principles, antigen specific capture chambers, and micro size bead based immunodetection to quantify cytokines. This microfluidic biochip was validated as a potential point of care technology by quantifying IL-6 from plasma samples (n = 29) with good correlation (R2 = 0.81) and agreement (Bland–Altman) compared to controls. In combination with previous applications, this point of care platform can potentially detect cell and protein biomarkers simultaneously for sepsis stratification.