Evaluation of the catheter positioning for neurally adjusted ventilatory assist

During neurally adjusted ventilatory assist (NAVA) the ventilator is driven by the patients electrical activation of the diaphragm (EAdi), detected by a special esophageal catheter. A reliable positioning of the EAdi-catheter is mandatory to trace a representative EAdi signal. We aimed to determine whether a formula that is based on the measurement from nose to ear lobe to xiphoid process of the sternum (NEX distance) modified for EAdi-catheter placement (NEX(mod)) is sufficient for predicting the accurate catheter position. Twenty-six patients were enrolled in this study. The optimal EAdi-catheter position (OPT) was defined by: (1) stable EAdi signal, (2) electrical activity highlighted in central leads of the catheter positioning tool, and (3) absence of p-wave in distal lead. Afterwards NEX(mod) was calculated and compared to the OPT finding. At NEX(mod) the EAdi signal was suitable for running NAVA in 18 out of 25 patients (72%). NEX(mod) was identical with OPT in four patients (16%). NAVA was possible in all patients at OPT. Median OPT position was 2 cm caudal of the NEX(mod) ranging from 3 cm too cranial to a position 12 cm too caudal (P < 0.01). In one patient excluded from further analysis EAdi-catheter placement led to the diagnosis of bilateral injury of the phrenic nerves. EAdi-catheter placement based on the NEX(mod) formula allows running NAVA in about two-thirds of all patients. The additional tools provided are efficient and facilitate the correct positioning of the EAdi-catheter for neurally adjusted ventilatory assist

Predicting restoration of kidney function during CRRT-free intervals

<p>Abstract</p> <p>Background</p> <p>Renal failure is common in critically ill patients and frequently requires continuous renal replacement therapy (CRRT). CRRT is discontinued at regular intervals for routine changes of the disposable equipment or for replacing clogged filter membrane assemblies. The present study was conducted to determine if the necessity to continue CRRT could be predicted during the CRRT-free period.</p> <p>Materials and methods</p> <p>In the period from 2003 to 2006, 605 patients were treated with CRRT in our ICU. A total of 222 patients with 448 CRRT-free intervals had complete data sets and were used for analysis. Of the total CRRT-free periods, 225 served as an evaluation group. Twenty-nine parameters with an assumed influence on kidney function were analyzed with regard to their potential to predict the restoration of kidney function during the CRRT-free interval. Using univariate analysis and logistic regression, a prospective index was developed and validated in the remaining 223 CRRT-free periods to establish its prognostic strength.</p> <p>Results</p> <p>Only three parameters showed an independent influence on the restoration of kidney function during CRRT-free intervals: the number of previous CRRT cycles (medians in the two outcome groups: 1 vs. 2), the "Sequential Organ Failure Assessment"-score (means in the two outcome groups: 8.3 vs. 9.2) and urinary output after the cessation of CRRT (medians in two outcome groups: 66 ml/h vs. 10 ml/h). The prognostic index, which was calculated from these three variables, showed a satisfactory potential to predict the kidney function during the CRRT-free intervals; Receiver operating characteristic (ROC) analysis revealed an area under the curve of 0.798.</p> <p>Conclusion</p> <p>Restoration of kidney function during CRRT-free periods can be predicted with an index calculated from three variables. Prospective trials in other hospitals must clarify whether our results are generally transferable to other patient populations.</p

Childlessness in France

Even though the average age at first childbirth has been increasing and education and employment options for women have improved immensely in recent decades, in France, unlike in other European countries, these developments have not led to a major increase in childlessness. Birth rates remain high and the share of the population who are childless is among the lowest in western Europe. This article discusses the historical roots as well as the societal conditions, institutional regulations, and political decisions that may explain the low levels of childlessness in France. We also discuss differences in rates of childlessness by education and occupation. Using a large representative survey on family life that was conducted parallel to the French census in 2011, we study the fertility histories of men and women born between the 1920s and late 1970s. We find that while the differences in fertility by level of education seem to have declined, having a higher education is still an obstacle to parenthood for women. For men, having a low educational and occupational status is associated with a greater likelihood of being childless. A large part of the differences in rates of childlessness between social groups can be traced back to the men and women who have never lived in a couple relationship; thus, partnership status can be regarded as a decisive parameter of the extent of childlessness

Key characteristics impacting survival of COVID-19 extracorporeal membrane oxygenation

Background Severe COVID-19 induced acute respiratory distress syndrome (ARDS) often requires extracorporeal membrane oxygenation (ECMO). Recent German health insurance data revealed low ICU survival rates. Patient characteristics and experience of the ECMO center may determine intensive care unit (ICU) survival. The current study aimed to identify factors affecting ICU survival of COVID-19 ECMO patients. Methods 673 COVID-19 ARDS ECMO patients treated in 26 centers between January 1st 2020 and March 22nd 2021 were included. Data on clinical characteristics, adjunct therapies, complications, and outcome were documented. Block wise logistic regression analysis was applied to identify variables associated with ICU-survival. Results Most patients were between 50 and 70 years of age. PaO2/FiO2 ratio prior to ECMO was 72 mmHg (IQR: 58–99). ICU survival was 31.4%. Survival was significantly lower during the 2nd wave of the COVID-19 pandemic. A subgroup of 284 (42%) patients fulfilling modified EOLIA criteria had a higher survival (38%) (p = 0.0014, OR 0.64 (CI 0.41–0.99)). Survival differed between low, intermediate, and high-volume centers with 20%, 30%, and 38%, respectively (p = 0.0024). Treatment in high volume centers resulted in an odds ratio of 0.55 (CI 0.28–1.02) compared to low volume centers. Additional factors associated with survival were younger age, shorter time between intubation and ECMO initiation, BMI > 35 (compared to < 25), absence of renal replacement therapy or major bleeding/thromboembolic events. Conclusions Structural and patient-related factors, including age, comorbidities and ECMO case volume, determined the survival of COVID-19 ECMO. These factors combined with a more liberal ECMO indication during the 2nd wave may explain the reasonably overall low survival rate. Careful selection of patients and treatment in high volume ECMO centers was associated with higher odds of ICU survival

A Implementation Plan for the Rainwater Basin Joint Venture

Nebraska\u27s Rainwater Basin (RWB) wetland area is identified by the North American Waterfowl Management Plan (NAWMP) as a waterfowl habitat area of major concern in North America. The Rainwater Basin area is recognized as the focal point of a spring migration corridor used by millions of ducks and geese annually (Figure 1). This migration corridor is shaped like an hourglass, with the Rainwater Basin and Central Platte River located at the constriction

Řízení tepelného systému s použitím neuronové sítě a genetického algoritmu

Predictive Controller of a laboratory thermal process is presented in the paper. Process model is approximated by a neural network. On-line optimization is done by a genetic algorithm. Control algorithm is tested on the laboratory thermal process and compared to the standard control methods like predictive controller with the transfer and state-space linear model and the quadratic programming optimization method or a PI controller.V článku je prezentováno řízení tepelného systému. Model je vytvořen pomocí neuronové sítě. Online optimalizace je prováděna genetických algoritmem. Výsledky jsou porovnány se standardními metodami, kterými jsou prediktivní regulátor s přenosem a stavovým popisem a kvadratickým programováním a PI regulátor