The Problem of Sequential Entry of Nurses as a Disincentive to Medical Safety and a Factor Increasing Overtime.

The purpose of this study was to visualize the nurses' approach to recording and to identify the causes that lead to overtime. The data used were those related to the performance and entry of nursing tasks (observation and care acts). Data from Hospital A, a provider of acute care with approximately 400 beds, was used. One month of work data were obtained from 12 nurses who were judged by the chief nurse to be excellent and efficient in their work. 12 nurses were divided into three groups: a high-level sequential entry rate group (>0.6), a medium-level sequential entry rate group (0.4 to <0.6), and a low-level sequential entry rate group (<0.4). The high-level sequential input rate group had data input every time they performed their work, so that the work implementation and input implementation were completed at 5:00 p.m. The medium-level sequential entry rate group tended to enter data about an hour late when they started work, which tended to result in overtime. The low-level sequential input rate group tended to input work in the afternoon, resulting in significant overtime. The data of approximately 400 nurses in a 900-bed University Hospital B, where Team Compass is implemented, were used to analyze the actual sequential input rate. 884 nurses in University Hospital B, of whom 397 had input data for 19 consecutive months. The sequential input rate in November 2020, one and a half years after the start of operation, was divided into five groups by 20%: 0-20%: 27 (7%), 20-40%: 30 (7%), 40-60%: 37 (9%), 60-80%: 69 (17%), and over 80%: 234 (59%). The number of employees who have been working overtime hours is also high. Although the importance of sequential entry to achieve results in reducing overtime hours continued to be strongly presented, there were 23% of nurses who indicated a sequential entry rate of less than 60%.

CUSUMIN: A cumulative sum interval design for cancer phase I dose finding studies.

Recently, model-assisted designs, including the Bayesian optimal interval (BOIN) design with optimal thresholds to determine the dose for the next cohort, have been proposed for cancer phase I studies. Model-assisted designs are useful because of their good performance as model-based designs in addition to their algorithm-based simplicity. In BOIN, escalation and de-escalation based on boundaries can be understood as a type of change point detection based on a sequential test procedure. Notably, the sequential test procedure is used in a wide range of fields and is known for its application to control charts, statistical monitoring methods used for detecting abnormalities in manufacturing processes. In control charts, abnormalities are detected if the control chart statistics are observed to be outside of the optimal boundaries. The cumulative sum (CUSUM) statistic, which is developed for control chart applications, derives higher power under the same erroneous judgment rate. Hence, it is expected that a more efficient model-assisted design can be achieved by the application of CUSUM statistics. In this study, a model-assisted design based on the CUSUM statistic is proposed. In the proposed design, the dose for the next cohort is decided by CUSUM statistics calculated from the counts of the dose-limiting toxicity and pre-defined boundaries, based on the CUSUM control chart scheme. Intensive simulation shows that our proposed method performs better than BOIN, and other representative model-assisted designs, including modified toxicity probability interval (mTPI) and Keyboard, in terms of controlling over-dosing rates while maintaining similar performance in the determination of maximum tolerated dose.

MN-166 (ibudilast) in amyotrophic lateral sclerosis in a Phase IIb/III study: COMBAT-ALS study design.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with motor neuron loss as a defining feature. Despite significant effort, therapeutic breakthroughs have been modest. MN-166 (ibudilast) has demonstrated neuroprotective action by various mechanisms: inhibition of proinflammatory cytokines and macrophage migration inhibitory factor, phosphodiesterase inhibition, and attenuation of glial cell activation in models of ALS. Early-phase studies suggest that MN-166 may improve survival outcomes and slow disease progression in patients with ALS. This article describes the rationale and design of COMBAT-ALS, an ongoing randomized, double-blind, placebo-controlled, multicenter Phase IIb/III study in ALS. This study is designed to evaluate the pharmacokinetics, safety and tolerability and assess the efficacy of MN-166 on function, muscle strength, quality of life and survival in ALS.

Lay abstract Amyotrophic lateral sclerosis (ALS) is a neurological disease defined by the loss of the nerve cells going to the muscles. Despite significant effort, we still do not have good treatments for ALS. MN-166 (ibudilast) can protect nerve cells by calming inflammation in several ways in models of ALS. Early human studies suggest that MN-166 may extend life and slow disease progression in ALS patients. This article describes the rationale and design of COMBAT-ALS, an ongoing randomized, double-blind, placebo-controlled, multicenter Phase IIb/III study. This study will show the drug's safety and tolerability and its effects on physical function, muscle strength, quality of life and survival in people living with ALS. Trial registration number: NCT04057898 (ClinicalTrial.gov).

Bayesian central statistical monitoring using finite mixture models in multicenter clinical trials.

BACKGROUND: Central monitoring (CM), in which data across all clinical sites are monitored, has an important role in risk-based monitoring. Several statistical methods have been proposed to compare patient outcomes among the sites for detecting atypical sites that have different trends in observed data. These methods assume that the number of clinical sites is not small, e.g., 100 or more. In addition, the proportion of atypical sites is assumed to be relatively small. However, in actuality, the central statistical monitoring (CSM) has to be implemented in small or moderate sized clinical trials such as small phase II clinical trials. The number of sites is no longer large in such situations. Therefore, it is of concern that existing methods may not work efficiently in CM of small or moderate sized clinical trials. In the light of this problem, we propose a Bayesian CSM method to detect atypical sites as the robust method against the existence of atypical sites. METHODS: We use Bayesian finite mixture models (FMM) to model patient outcome values of both atypical and typical sites. In the method, the distributions of outcome values in normal sites are determined by choosing the body distribution, which has the largest mixture parameter value of finite mixture models based on the assumption that normal sites are in the majority. Atypical sites are detected by the criterion based on the posterior predictive distribution of normal site's outcome values derived from only the chosen body distribution. RESULTS: Proposed method is evaluated by cumulative detection probability and type I error averaged over sites every round of CSM under the various scenarios, being compared with the conventional type analysis. If the total number of patients enrolled is 48, the proposed method is superior at least 10% for any shift sizes at the 2nd and the 3rd rounds. If the total number of patients is 96, both methods show similar detection probability for only one atypical site and large shift size. However, the proposed method is superior for the other scenarios. It is observed that all the type I errors averaged over sites are little difference between the methods at all the scenarios. CONCLUSION: We propose a Bayesian CSM method which works efficiently in a practical use of CM. It is shown that our method detects atypical sites with high probability regardless of the proportion of the atypical sites under the small clinical trial settings which is the target of our proposed method.

Valorizing waste iron powder in biogas production: Hydrogen sulfide control and process performances.

Biogas is composed of different gases including hydrogen sulfide (H2S), which is a hazardous gas that damages pipes and generators in anaerobic digestion system. The objective of this study was to control H2S by waste iron powder produced by laser cutting machine in a steel and iron industry. Waste iron powder was mixed with dairy manure at a concentration between 2.0 and 20.0 g/L in batch experiments, while the concentration was varied between 1.0 and 4.0 g/L in bench experiment. In batch experiment, a reduction of up to 93% of H2S was observed at waste iron powder of 2.0 g/L (T1), while the reduction was of more than 99% at waste iron powder beyond 8.0 g/L (T4 âˆ¼ T6). The total sulfide concentration (ST) increased together with waste iron powder concentration and was fitted with a quadratic equation with a maximum ST of 208.0 mg/L at waste iron powder of 20.2 g/L. Waste iron powder did not have significant effect on methane yield in batch and bench experiments. However, hydrolysis rate constant was increased by almost 100%, while the lag-phase period was reduced to half in test digesters compared to that in control digester. In bench experiment, H2S concentration was reduced by 89% at 2.0 g/L, while 50% at 1.0 g/L. Therefore, waste iron powder was effectively removed H2S and did not affect negatively anaerobic digestion process.

Effects of handling parameters on hydrogen sulfide emission from stored dairy manure.

Hydrogen sulfide (H2S) emission from liquid manure in the process preceding field application is an important issue in fertigation systems. Given that H2S poses a significant health risk, it is important to determine the effects of different handling parameters on H2S emissions to prevent health risks to farmers. In this study, the effects of total solids (TS; 3, 5, 7, 9, and 11%) and mixing speed (100, 200, 300, and 400 rpm), duration (5, 15, 30, and 60 min), and frequency (one, two, three, and four times a day) on H2S emissions from two different dairy manures were investigated. The results indicate that the quantity of sulfur-containing substrate intake determines the potential of dairy manure to emit H2S because manure from cows fed with concentrate-based feed generates higher amounts of H2S than manure from cows fed with forage-based feed. The H2S concentration increased with TS concentration and reached a maximum of 1133 ppm at a TS of 9%; thereafter, it decreased with further increases in TS concentration. H2S emission increased with mixing speed with a peak concentration of 3996 ppm at 400 rpm. A similar trend was observed for mixing duration. However, there were no significant differences between the amounts H2S emitted at different frequencies of mixing (P > 0.05). The results indicate that mixing speed, duration, and TS are the major determinants of the quantity of H2S emitted from dairy manure. Therefore, to prevent health risks associated with H2S emission from dairy manure, it is recommended that the mixing speed and duration should be kept as low as possible, while a TS concentration of above 9% should be applied during the fertigation of dairy manure.