Insulin Dosing Rules

Starting dose

0.5 to 1 unit / kg / day for Type 2 Diabetes
If they are already on Insulin Start with their current dose
Give 50% long acting and 50% short acting. Divide the short acting evenly for each meal.

Corrective Insulin (in addtion to meal insulin)

Estimated Blood sugar Decrease for Each Unit of Insulin

1700/ total daily insulin for Humalog and Novolog
1500/ total daily insulin for regular insulin

Insulin for Carb Counters

Number of grams of carbohydrate covered by each unit of insulin

450/ total daily insulin for Humalog and Novolog
500/ total daily insulin for regular insulin

Statistical Power

OK, so John Lennon didn’t really write this , but statistical power is a very abstract concept and the ability to "imagine" really helps.

Overview

Power is the probability to detect a difference if it is there.

Of course, your next question should be " How big a difference can the study detect?" More on that later.

The main factors that affect power are:

• The number of subjects that were studied
• the aforementioned treatment effect size (how big is the difference we expect or hope for)
• the variabliity of the data (standard deviation).

That last one is not a factor if you are studying non-parametric data (such as the percent of people who grew a second nose). For nominal and ordinal data there is are complicated equations and the calculations become just a black box for non-math-heads such as myself.

Before the Study Power Calculations

1. You need to decide what the smallest effect size you consider to be important and also what effect size you hope to find.
2. You need to decide what level of confidence you want to have that you will be able to detect that small of a difference.(this is power or 1-beta)
3. You need to estimate the variablility of the outcome, preferably from previous studies if available.

Here is a conceptual equation (don’t use this at home)

     (Variability of the Outcome ) (Power)
    ————————————————– = Number Needed (n)
      Expected effect size

As you can see:

• Increase in variability or power will increase the number needed in the study.
• Increase in expected effect size will decrease the number needed in the study.
• And Vice Versa

   

  After the Study Power Calculations

  If you found a difference, You had enough power! and you don’t need to calculate power.
  If you found "no difference":
  

  1. You need to decide what the smallest effect size you consider to be important (this will be smaller than you actually found in the study)
  2. You can now calculate the variablility of the outcome (rather than estimate)
  3. You have the number that you studied (unless you forgot to count

      (Expected effect size ) (N)
      ————————————————– = Power

        Variability of the Outcome

  • Increase in variability of the date will decrease the power.
  • Increase in expected effect size will increase the power.
  • Increase in the number studied will increase the power.
  • And Vice Versa

  

The Headline

Spironolactone saves lives in heart failure patients

The Facts

The Big Idea:
Spironolactone reduces mobidity and mortality among heart failure patients

Study Design:
Multicenter, double-blind, randomized, placebo-controlled trial (intention to treat)

Outcomes
Death from any cause, hospitalization for cardiac problems, and a change in NYHA heart failure class

Number of Patients
1663 randomized patients, 822 patients received sprionolactone 25 mg QD, 841 patients in placebo arm

Type of Patients
Patients who have severe heart failure (NYHA III-IV class) with a left ejection fraction less than 35 percent who were being treated with an angiotensin-converting–enzyme inhibitor (ACEI), a loop diuretic, and in most cases digoxin.

Important ExclusionsPatients were excluded if they had primary operable valvular heart disease (other than mitral or tricuspid regurgitation with clinical symptoms due to left ventricular systolic heart failure), congenital heart disease, unstable angina, primary hepatic failure, active cancer, or any life-threatening disease (other than heart failure). Patients who had undergone heart transplantation or were awaiting the procedure were also ineligible. Other criteria for exclusion were a serum creatinine concentration of more than 2.5 mg per deciliter (221 µmol per liter) and a serum potassium concentration of more than 5.0 mmol per liter

Interventions
Spironolactone 25 mg daily and my be increased to 50 mg, or placebo once daily

Duration
24 months, the trial was terminated early because an interim analysis determined that spironolactone was efficacious.

Statistics
Kaplan–Meier, log-rank test, Cox proportional-hazards regression models,

Funding

Supported by a grant from Searle, Skokie, Ill

Results

There were 386 deaths in the placebo group (46%) compared to 284 (35%) in the spironolactone group (CI 0.60-0.82, P < 0.001). The frequency of hospitalization for worsening heart failure (NYHA heart failure class) was 35% lower in the spironolactone group than in the placebo grou. Gynecomastia or breast pain was reported in 10% of men who were treated with spironolactone, as compared with 1% of men in the placebo group (P<0.001).

Spironolacotone 25-50 mg a day with an ACEI, and loop diuretic reduces mobidity and mortality among heart failure (NYHA III-IV class) patients

Reference

Pitt, B, Zannad, F et al. The Effect of Spironolactone on Morbidity and Mortality in Patients with Severe Heart Failure. N Eng J Med1999;341:709

The Headline

Patients with CHF taking carvedilol have significantly less mortatility than patients taking metoprolol

The Facts

The Big Idea:
This study looked to show which beta-blocker was superior, carvedilol or metoprolol.

Study Design:
Multicenter (341 centers in 15 European countries), randomized, double blind, parallel-group

Outcomes
All cause mortatility and all cause mortatility and all cause admission

Number of Patients
3029 randomized: 1511 in carvedilol arm and 1518 in metoprolol arm

Type of Patients
Symptomatic heart failure (NYHA Class II-IV), one cardio admission in past 2 years, being treated with ACEI’s and diuretics unless contraindications, LVEF of < or = 0.35 withink previous 3 months.

Important ExclusionsRecent change in therapy or recent use of beta or alpha blockers in past 2 weeks; being treated with CCBs, amiodarone, class-I antiarrhythmics or investigational drugs in past 30 days; patients with MI, unstable angina or stroke in last 2 months; uncontrolled HTN (>170/105); pregnancy; autmatic defibrillator, known drug or alcohol issues

Interventions
3.125 mg carvedilol BID or 5 mg metoprolol tartrate BID (both titrated up to goals of 25 mg carvedilol and 50 mg BID metoprolol tartrate)

Duration
58 Months

Statistics
Cox’s proportional hazard, log rank test

Funding

Roche and GlaxoSmithKline

Results

All cause mortality: Carvedilol – 34%; Metoprolol – 40% (HR 0.83 [0.74 - 0.93] p = 0.002) All cause mortality and all cause admission: Carvedilol – 74%; Metoprolol – 76% (HR 0.94 [0.86 - 1.02] p = 0.122) For the Cynic
Once major concern with this study was the use of metoprolol tartrate when compared to carvedilol. The extended release succincate salt is most often used and studied in CHF. It is possible the results may have changed someone based on the different drug formulations. Also some of the funding comes from Roche – who makes carvedilol (Coreg). Whether the formulation of metoprolol succinate would fare better, is an interesting question. Another interesting question is whethere the alpha action from the carvedilol contributes to its increased efficacy.

Carvedilol shows increased benefit in CHF patients compared to metoprolol tartrate.

Reference

Poole-Wilson PA, Swedberg K, Cleland JGF, et al. Comparison of carvedilol and metoprolol on clinical outcomes in patients with chronic heart failure in the Carvedilol Or Metoprolol European Trial (COMET): randomised controlled trial. Lancet 2003 Jul 5; 362: 7-13

Note: these are for maintenance doses only at steady state. Do NOT use these guidelines for starting someone on warfarin.

Vitamin K

Often it is NOT necessary to give vitamin K. If you want to keep the patient anticoagulated, and you cannot stop yourself from giving vitamin K … then give only 2.5 mg of phytonadione orally.

Do not give 10mg of vitamin K unless it is OK for the patient to NOT be anticoagulated for one or two weeks.

Obviously, you will usually give Vitamin K if the patient is bleeding.

• This applies to patients: with the same problem studied
• treated for the same duration as the study

Calculation:

• First calculate the Absolute Risk Reduction (ARR)
• Then take the ARR in decimal form (e.g. .05 for 5%) and divide it INTO 1. (1/ ARR = NNT)

Example:
- 8% stroke rate with A. Fib decreased to 3% with Coumadin
– Absolute risk reduction of 5%
– NNT = 1 / ARR or 1/.05 = 20
Therefore you need to treat 20 A. Fib patients for one year with warfarin to prevent one stroke.

Number Needed to Harm (NNH): this is the same concept as the Number Needed to Treat except that you use:
      Incidence of Adverse Effect MINUS Incidence in the Placebo Group = Absolute Risk Increase

The calculation is then the same using Absolute Risk Increase instead of ARR.

Example:
– Incidence of gynecomastia is almost zero with placebo
– Incidence of gynecomastia is 10% with spironolactone
– Therefore: Absolute increase in risk is 10% – 0% = 10%
– 1 / 0.10 = 10 = NNH You need to treat 10 patients with spironolactone to cause one case of gynecomastia.

]]> http://www.toomanymeds.com/pro/one-minute-genius/statistics/number-needed-to-treat/feed/ 0 http://www.toomanymeds.com/pro/one-minute-genius/statistics/warfarin-dosing-adjustment/ http://www.toomanymeds.com/pro/one-minute-genius/statistics/warfarin-dosing-adjustment/#comments Wed, 19 Aug 2009 20:06:05 +0000 admin http://www.toomanymeds.com/pro/one-minute-genius/pharmacology/warfarin-dosing-adjustment/

Albert and I developed an acute interest in risk reduction at about 3500 feet.


 

Examples:
Example 1A:

• Consider the benefit of using Coumadin for Stroke prevention in Atrial Fibrillation. Moderate risk patients on
…
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Albert and I developed an acute interest in risk reduction at about 3500 feet.


 

Examples:
Example 1A:

• Consider the benefit of using Coumadin for Stroke prevention in Atrial Fibrillation. Moderate risk patients on placebo have 8% risk of stroke in ONE year
• Coumadin decreases that to 3% risk of stroke in ONE year
• Quick !! Instinctively, what is the risk reduction? ….. 5% , right? That’s absolute risk reduction, NOT relative to anything else.

 

Relative Risk Reduction is RELATIVE to the baseline 8% so… 0.05/0.08 or 5% reduction /8% baseline = .62 or 62% relative risk reduction

Example 1B:
OK, now consider if there was a very high baseline risk of 93%

• Suppose Coumadin decreased the risk to 88%
• Quick !! The absolute reduction is? …. You’re right! 5% (the same as the first example)
The relative risk though is different. 5 / 93 = 5.3% relative risk reduction

So which is the most important? Absolute reduction or Relative reduction. Well, they each give you different kinds of information. I prefer the absolute risk reduction, but both are important. See also the Number Needed To Treat

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