on 17-Jul-2017 (Mon)

In patients with AKI and oliguria, the fractional excretion of sodium (FENa) may be helpful in differentiating between prerenal AKI and AKI from renal tubular cell damage or acute tubular necrosis (ATN).

FENa measures the percent of filtered sodium excreted in the urine and is calculated as (USodium × PCr)/(UCr× PSodium) × 100. It is considered a more accurate measurement of kidney sodium avidity in prerenal states than the urine sodium concentration

FENa is less reliable when diuretics are being used because the urine sodium may not accurately reflect attempts by the kidney to retain sodium

FENa is less reliable to differentiate between prerenal and ATN if the patient use diuretics , so better to use FEUrea which is not affected by diuretics

Most patients with class III lupus nephritis and all patients with class IV lupus nephritis benefit from aggressive combination immunosuppressive therap

Patients with class I or II LN may have minimal or no kidney findings, and those with classes III and IV present with varying degrees of the nephritic syndrome. Patients with class V LN present predominantly with proteinuria. Class VI is the end stage of long-standing LN.

Class IV LN represents diffuse glomerular involvement and is the most common and severe form of nephritis associated with lupus. It is also associated with elevated anti–double stranded DNA antibody levels and hypocomplementemia, particularly during periods of active disease.

The optimal initial therapy is to increase glucocorticoid doses (typically an intravenous pulse followed by a tapering oral dose), which is accompanied by either intravenous cyclophosphamide or mycophenolate mofetil

The addition of plasmapheresis to immunosuppressive therapy has not been shown to improve outcomes in patients with LN.

treatment goal of <150/90 mm Hg for patients with hypertension who are ≥60 years

infection-related glomerulonephritis (IRGN) following a streptococcal infection. Supportive evidence includes preceding symptoms of an upper respiratory tract infection suggestive of streptococci (rapid streptococcal antigen test is positive and antistreptolysin O antibodies are elevated), followed by the nephritic syndrome in 1 week, and low C3 levels with normal C4 levels (suggesting an alternative pathway of complement activation, which is typical of IRGN).

IgA nephropathy (IgAN) is the most common form of glomerulonephritis. Asymptomatic microscopic hematuria with or without proteinuria is the most common presentation of IgAN, and episodic gross hematuria following an upper respiratory tract infection is a classic presentation. Kidney manifestations usually occur concomitantly with the respiratory infection in IgAN (“synpharyngitic” nephritis), as opposed to the typical 7- to 10-day latent period with IRGN. Moreover, complement levels are typically normal in IgAN, whereas C3 is typically low and C4 is normal in IRGN

Lupus nephritis may occasionally be precipitated by infections. Patients with lupus typically experience systemic manifestations such as rash and arthritis, although kidney-limited disease is sometimes seen. Both C3 and C4 complement levels are depressed in this condition due to the classical pathway of complement being activated.

Small-vessel vasculitis is also associated with glomerulonephritis. However, there are frequently other clinical findings of vasculitis present, and complement levels are typically normal.

Orlistat may be a cause of acute kidney injury by triggering acute oxalate nephropathy, particularly in patients with volume depletion or chronic kidney disease

The clinical presentation of MG is indistinguishable from other causes of the nephrotic syndrome, although the propensity for venous thromboembolism, and particularly renal vein thrombosis, is much higher in MG than other disorders associated with the nephrotic syndrome, such as focal segmental glomerulosclerosis or minimal change glomerulopathy

prophylactic anticoagulation is frequently given to patients with MG with a serum albumin level of ≤2.8 g/dL (28 g/L)

MG is the most common cause of idiopathic nephrotic syndrome in adult white persons but may also be associated with infections, systemic lupus erythematosus, medications, and certain malignancies.

The diagnosis of white coat hypertension is applied to patients with average blood pressure readings ≥140/90 mm Hg in the office and average readings <135/85 mm Hg as determined by ABPM

Two anti-vascular endothelial growth factor inhibitors, bevacizumab and sunitinib, have been linked to thrombotic microangiopathy, which typically subsides after stopping the drug.

All patients with stage G4 or G5 chronic kidney disease should be referred to a nephrologist for management, and referral for transplant evaluation is indicated once the estimated glomerular filtration rate is below 20 mL/min/1.73 m 2 .

Abdominal compartment syndrome should be suspected in patients with oliguria or increasing serum creatinine levels who have had abdominal surgery, who have received massive fluid resuscitation, who have a tense abdomen, or who have liver or pancreatic disease with ascites.

ACS is typically defined as new organ dysfunction with an intra-abdominal pressure >20 mm Hg

Diagnosis of intra-abdominal hypertension and ACS is accomplished by transduction of bladder pressure. Although medical treatment (diuresis, dialysis, management of ascites) can be tried, surgical decompression of the abdomen is often necessary to definitively treat ACS

Risk factors for the development of statin-induced rhabdomyolysis include advanced age, female gender, preexisting chronic kidney disease, diabetes mellitus, hypothyroidism, high-dose statin therapy, and use of medications metabolized through cytochrome P450 3A4.

Acquired cystic kidney disease is associated with a large number of small bilateral kidney cysts, reduced kidney size, and a markedly increased risk for developing renal cell carcinoma.

The hallmark of autosomal dominant polycystic kidney disease (ADPKD) is large kidneys with multiple kidney cysts, usually originating in the renal collecting duct.

Renal angiomyolipomas and bilateral renal cysts are associated with tuberous sclerosis complex (TSC). However, because this is an autosomal dominant disease, there is almost always a positive family history

• where the returns are centered (central tendency);

• how far returns are dispersed from their center (dispersion);

• whether the distribution of returns is symmetrically shaped or lopsided (skewness); and

• whether extreme outcomes are likely (kurtosis).

Just as a parameter is a descriptive measure of a population characteristic, a sample statistic (statistic, for short) is a descriptive measure of a sample characteristic.

Nominal scales represent the weakest level of measurement: They categorize data but do not rank them. If we assigned integers to mutual funds that follow different investment strategies, the number 1 might refer to a small-cap value fund, the number 2 to a large-cap value fund, and so on for each possible style. This nominal scale categorizes the funds according to their style but does not rank them.

Ordinal scales reflect a stronger level of measurement. Ordinal scales sort data into categories that are ordered with respect to some characteristic. For example, the Morningstar and Standard & Poor’s star ratings for mutual funds represent an ordinal scale in which one star represents a group of funds judged to have had relatively the worst performance, with two, three, four, and five stars representing groups with increasingly better performance, as evaluated by those services.

An ordinal scale may also involve numbers to identify categories. For example, in ranking balanced mutual funds based on their five-year cumulative return, we might assign the number 1 to the top 10 percent of funds, and so on, so that the number 10 represents the bottom 10 percent of funds. The ordinal scale is stronger than the nominal scale because it reveals that a fund ranked 1 performed better than a fund ranked 2. The scale tells us nothing, however, about the difference in performance between funds ranked 1 and 2 compared with the difference in performance between funds ranked 3 and 4, or 9 and 10.

Interval scales provide not only ranking but also assurance that the differences between scale values are equal. As a result, scale values can be added and subtracted meaningfully. The Celsius and Fahrenheit scales are interval measurement scales. The difference in temperature between 10°C and 11°C is the same amount as the difference between 40°C and 41°C. We can state accurately that 12°C = 9°C + 3°C, for example. Nevertheless, the zero point of an interval scale does not reflect complete absence of what is being measured; it is not a true zero point or natural zero. Zero degrees Celsius corresponds to the freezing point of water, not the absence of temperature. As a consequence of the absence of a true zero point, we cannot meaningfully form ratios on interval scales.

As an example, 50°C, although five times as large a number as 10°C, does not represent five times as much temperature. Also, questionnaire scales are often treated as interval scales. If an investor is asked to rank his risk aversion on a scale from 1 (extremely risk-averse) to 7 (extremely risk-loving), the difference between a response of 1 and a response of 2 is sometimes assumed to represent the same difference in risk aversion as the difference between a response of 6 and a response of 7. When that assumption can be justified, the data are measured on an interval scale.

Ratio scales represent the strongest level of measurement. They have all the characteristics of interval measurement scales as well as a true zero point as the origin. With ratio scales, we can meaningfully compute ratios as well as meaningfully add and subtract amounts within the scale. As a result, we can apply the widest range of statistical tools to data measured on a ratio scale. Rates of return are measured on a ratio scale, as is money. If we have twice as much money, then we have twice the purchasing power. Note th

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Statistics can refer to numerical data (e.g., a company's average revenue for the past 20 years). It can also refer to methods of collecting, classifying, analyzing, and interpreting numerical data. Statistical methods provide a powerful set of tools for making decisions in business and other fields.

Statistics involves two different processes:

• Describing sets of data. Descriptive statistical methods can be used to describe the important aspects of data sets that have been collected. This reading will focus on the use of descriptive statistics to consolidate a mass of numerical data into useful information.
• Drawing conclusions (making estimates, judgments, predictions, etc.).Inferential statistical methods can be used to draw conclusions about a large group from a smaller group actually observed.

We use statistical methods to analyze the results of data. Since the amount of information available may be vast, it may be extremely time-consuming and expensive to collect all the necessary data. For instance, suppose we are interested in the durability of tennis balls. Theoretically, in order to carry out an accurate assessment, we would need to collect large quantities of all different makes of tennis balls from all over the world. Clearly, this is not practical; aside from taking up lots of time, it would be cost-prohibitive to purchase all the balls we would need for our study. A more practical solution would be to use a sample.

A population consists of an entire set of objects, observations, or scores that have something in common. It comprises every possible member of the specified group. In our example above, the population of tennis balls consists of every tennis ball that has ever been manufactured anywhere in the world. This is a huge number of tennis balls. Another example of a population would be all males between the ages of 15 and 18.

A sample is a subset of a population. The sample is comprised of some of the members of the population. Since it is usually impractical (or too expensive or time-consuming) to test every member of a population, using data gathered from a sample of the population is typically the best approach available for describing that population.

In our example above, a sample might be a selection of 1,000 tennis balls of various makes collected from different sources. It would be a virtually impossible task to collect every possible tennis ball in the world; this same size provides a manageable number to work with as well as a substantial amount of possible data.

Before we move on, there are several points worth noting:

• Don't be fooled by the word "population." This does not necessarily refer to people. As with the example above, we can have a population of tennis balls. A population can consist of anything, living or not.
• Although populations are often vast, they can also be of manageable size. For example, the population of even numbers between 1 and 9 would comprise the numbers 2, 4, 6 and 8. In this case, it is possible to sample the entire population and get accurate results. This is rare, however, and for your purposes, populations can generally be considered to be vast.
• In general, the bigger the sample, the better your results will be (because you are using data from more of the population for analysis). However, this point can present difficulties, as you will see when we study variance and standard deviation later.
• The ideal process would be to select a sample that is "representative" of the population (a sample that takes into account extreme values on both sides but contains many "average" values). In this way, the results that we get will be more meaningful. Because we frequently don't know about the exact values of a population (which is why we sample in the first place), we will never really know if our sample is truly representative or not. It's all we have to work with, however, so it's all we can

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Hint: One way to easily remember these terms is to recall that "population" and "parameter" both start with a "p," and "sample" and "statistic" both start with a "s."

Inferential statistics generally require that sampling be random although some types of sampling (such as those used in voter polling) seek to make the sample as representative of the population as possible by choosing a sample that resembles the population on most important characteristics.

A typical statistical procedure:

• Define the population and identify the parameter(s) of interest.
• Draw a sample from the population.
• Determine the corresponding statistic(s) of the sample and use it (or them) to estimate the parameter(s) of the population.