Cvcu Case Study

While there was a policy in place for conscious sedation, even good policies rely on the vigilance of staff to adhere to them. Often times, working conditions allow for distractions, and even the best of practitioners, with the best of intentions, make errors. There were several areas presented in this scenario that require examination and improvement.

Adequate qualified medical staff must be present in all critical care areas caring for mechanically ventilated

An alpha level of 0.05 is arbitrary and was set as a standard by scientists. One of the key concepts in hypothesis testing is that of significance level or, the alpha level, which specifies the probability level for the evidence to be an unreasonable estimate. Unreasonable means that the estimate should not have taken its particular value unless some non-chance factor(s) had operated to alter the nature of the sample such that it was no longer representative of the population of interest. (Price, 2000)

Topics Distribution of the sample mean. Central Limit Theorem. Confidence intervals for a population mean. Confidence intervals for a population proportion. Sample size for a given confidence level and margin of error (proportions). Poll articles. Hypotheses tests for a mean, and differences in means (independent and paired samples). Sample size and power of a test. Type I and Type II errors. You will be given a table of normal probabilities. You may wish to be familiar with the follow formulae and their application.

5. CVS’ additional competitor Walgreens is behind them by a market cap of roughly $10

Over one-third of the surgeries in the United States are performed on patients aged 65 and older, and delirium is one of the most common postoperative complications in this population. Despite the high incidence of delirium, the syndrome often goes undiagnosed. Postoperative delirium is associated with adverse functional and cognitive outcomes, increased hospital length of stay, cost of care, and mortality rate. Knowledge of the risk factors that predict postoperative delirium will aid early identification of the patients at highest risk in order to facilitate preoperative optimization by managing comorbidities or employing targeted prevention strategies.

Original research related to sedation management occurred in the year 2000 by Kress, Pohlman, O ' Connor, and Hall. Their findings served as a landmark study and initiated the impetus related to improving our sedation practices. According to Kress et al. (2000), daily interruption of sedation led to a decrease in the number of days on the ventilator in the intensive care unit. Several studies since this time have focused on the influence of sedation protocols, and outcomes. This paper will review the synthesis of the discovered studies and highlight the noted contraindications and inconsistencies. Also, explanations including a preliminary conclusion will be discussed.

Enhanced assessment and nursing implementations to better prevent and detect ICU delirium will bring improved outcomes for this particular patient population. There are many ways to assess for ICU delirium. Two of the most reliable and easiest methods are basic observations from the bedside nurse and The Confusion Assessment Method (CAM). The CAM includes nine different criteria for delirium (1) acute onset and fluctuation, (2) inattention, (3) disorganized thinking, (4) altered level of consciousness, (5) disorientation, (6) memory impairment, (7) perceptual disturbances, (8) psychomotor agitation or retardation, and (9) altered sleep-wake cycle. A delirium diagnosis is given when criteria one and two and either three or four are present. The second assessment tool for delirium detection is made from nursing observations. The nurse observes the patient throughout their

ICU patients suffer from a broad range of pathologies, requiring MV, sedation and use of multiples devices, which do not allow patients to protect their airway (Augustyn. 2007; Kollef. 2004).

Cohen’s paper The Earth is Round (p>0.05) is a critique of null-hypothesis significance testing (NHST). In his article, Cohen presents his arguments about what is wrong with NHST and suggests ways in which researchers can improve their research, as well as the way they report their research. Cohen’s main point is that researchers who use NHST often misinterpret the meaning of p-values and what can be concluded from them (Cohen, 1994). Cohen also shows that the NHST is close to worthless. NHST is a way to show how unlikely a result would be if the null hypothesis were true. A Type I error is where the researcher incorrectly rejects a true null hypothesis and a Type II error is where the researcher incorrectly accepts the false null

The inability to sleep is a source of anxiety and stress for patients in the intensive care unit (ICU); this inability can lead to cardiorespiratory disturbances, immune system dysfunction, impaired wound healing, hormonal and metabolic imbalances, cognitive changes, and delirium. Through the use of quiet times and nursing actions that encourage sleep, or, sleep hygiene bundles, nurses can assist in improving sleep quality in the ICU. These bundles include noise and light reduction, scheduling routine procedures outside of reserved sleep times, the use of earplugs and eye masks, and the grouping of nursing care activities to minimize sleep interruptions. The aim of this proposal, therefore, will be to implement a nurse-driven, sleep hygiene protocol, in order to improve patient care by promoting quality sleep in ICU patients. Barriers to implementation include staff resistance to the concept, concerns regarding patient care during sleep times and difficulties in coordinating care with other units. Studies used to gather information for this proposal were published between the years of 2011 and 2016 and were found in several databases: Academic Search Complete, Medline, and OVID Journals.

Management of the acutely ill adult is a complex and perplexed procedure. It requires underpinning knowledge of the pathophysiology of the disease currently affecting the patient, as well as ensuring that professionals are equipped to deal with the development of a rapid deterioration. The National Institute for Clinical Excellence (2007) explain that patients are sometimes inadequately treated due to staff not acting in a sufficient time manner, and so a systematic assessment of the patient recommended by the Resuscitation Council (2006) should initially be followed (Jevon, 2009).

The investigation of how anesthesia effects cognitive functioning has had a long history. Overtime, it has been suggested that there is an association between anesthesia, surgery, delirium, dementia and postoperative cognitive dysfunction (Inan & Ozkose Satirlar, 2015). The theory of anesthesia’s impact on cognitive functioning was derived in 1887, by Savage, who began to observe the “insanity” that follows the use of anesthesia. He suggested that “Any cause which will give rise to delirium may set up a more chronic form of mental disorder quite apart from any febrile disturbance” (Savage, 1887, p. 1199). Delirium can be defined as an altered level of consciousness that may cause a sudden decline in attention and focus perception (Isik, 2015). Postoperative delirium was reevaluated in 1955 when Bedford used a series of case studies collected over a 50 year span to describe a connection between anesthesia and dementia. The results suggest that 10% of the patients had postoperative cognitive dysfunction (Bedford, 1955). Since these initial studies, research has persisted using a variety of methods, in an attempt to determine: both long- and short-term effects of anesthesia on cognitive functioning and memory; whether the anesthesia administration technique will change the outcome of postoperative cognitive dysfunction; and other risk factors that may be associated to AD.

Kirk (1996) had major criticisms of NHST. According to Kirk, the procedure does not tell researchers what they want to know: In scientific inference, what we want to know is the probability that the null hypothesis (H0) is true given that we have obtained a set of data (D); that is, p(H0|D). What null hypothesis significance testing tells us is the probability of obtaining these data or more extreme data if the null hypothesis is true, p(D|H0). (p. 747) Kirk (1996) went on to explain that NHST was a trivial exercise because the null hypothesis is always false, and rejecting it is merely a matter of having enough power. In this study, we investigated how textbooks treated this major problem of NHST. Current best practice in this area is open to debate (e.g., see Harlow, Mulaik, & Steiger, 1997). A number of prominent researchers advocate the use of confidence intervals in place of NHST on grounds that, for the most part, confidence intervals provide more information than a significance test and still include information necessary to determine statistical significance (Cohen, Gliner, Leech, & Morgan 85 1994; Kirk, 1996). For those who advocate the use of NHST, the null hypothesis of no difference (nil hypothesis) should be replaced by a null hypothesis specifying some nonzero value based on previous research (Cohen, 1994; Mulaik, Raju, & Harshman, 1997). Thus, there would be less chance that a trivial difference between intervention and control

The Theory of Learned Helplessness can explain the onset of delirium when the patient eventually feels that there is nothing that they can do to overcome their symptoms during the progression of mechanical ventilation