Posts tagged with "liver"

Expert Offers 10 Tips for Surviving Bottomless Brunch Without Hurting Your Liver

Now that brunch season is upon us, the last thing anyone wants to think about at their weekend gathering is how the fluffy French toast and fruity sangria they’re enjoying is affecting their health and how best to avoid hurting their liver. Lucky for them, that is where Dr. Tarek Hassanein of Southern California Liver Centers comes in.

Dr. Tarek Hassanein is a liver specialist who doesn’t believe in completely eliminating the fun from your life in the name of health. Instead, he has bcreated 10 tips to navigating your bottomless brunch. By offering your audience this advice, you are helping them navigate their upcoming boozy gathering with friends and family without negatively affecting your health…or your fun.


10 Tips for Navigating Your Next Boozy Brunch

1.       Savor your meal before drinking. Don’t drink on an empty stomach.

2.       Enjoy your beverage. Do not overeat and Sip your drink.

3.       Avoid binging. Binge drinking is 5 drinks or more in less than 4-5 hours.

4.       Keep your number of drinks as low as possible. Don’t consume more than 3 drinks for a man and 2 for a woman.

5.       Know your alcohol. One beer is equivalent to a glass of wine or a shot of liquor.

6.       Find a driver. Don’t drive after drinking. It is hard to judge your blood alcohol level and its effects on your cognitive ability and reflexes.

7.       Take your meds. If you are a diabetic or hypertensive, suffering from a heart or liver condition, take your daily medications, and check with your doctor to avoid alcohol interactions with your medications.

8.       Avoid overdoing pain meds. If you are going to use Tylenol, don’t exceed more than 3 grams in one day. Be aware that a lot of headache medicines or pain killers contain acetaminophen (Tylenol), so avoid accidental overdosing.

9.       Don’t mix. Avoid mixing alcohol with other recreational drugs.

10.     Space your beverages out. Allow your body the ability to metabolize what you ingested and avoid intoxication.

10 Ways to Monitor Your Drinking this Cinco de Mayo

1. Don’t drink on an empty stomach. Savor your meal before you start drinking an alcoholic beverage.

2. Do not overeat and Sip your drink. Enjoy your beverage.

3. Avoid binging. The definition of binging is 5 drinks or more in less than 4-5 hours.

4. Keep your consumption of drinks as low as possible – not more than 3 drinks for a man and 2 for a woman.

5. Alcoholic beverages are similar in alcohol content. One beer is equivalent to a glass of wine or a shot of liquor.

6. Find a driver. Don’t drive after drinking. It is hard to judge your blood alcohol level and its effects on your cognitive ability and reflexes.

7. If you are a diabetic or hypertensive, suffering from a heart or liver condition, take your daily medications, and check with your doctor to avoid alcohol interactions with your medications.

8. If you are going to use Tylenol, don’t exceed more than 3 grams in one day. Be aware that a lot of headache medicines or pain killers contain acetaminophen (Tylenol), so avoid accidental overdosing.

9. Don’t mix alcohol with other recreational drugs.

10. Space your beverages to allow your body the ability to metabolize what you ingested and avoid intoxication.

Savor, Sip and Space

Curated by Dr. Tarek Hassanein of Southern California Liver Centers

MIT: Liver transplant deaths reduced by 20%

Demand for liver transplants is much higher than organ supply, resulting in approximately 2,400 deaths every year. Also problematic is the current model used to identify and prioritize the “sickest” patients, which does not allow for equitable access to all waitlisted candidates, with a particular disadvantage to women. To address these issues, MIT Sloan School of Management Prof. Dimitris Bertsimas and Prof. Nikos Trichakis utilized machine learning to create a model that reduces mortality by 20%, averting nearly 400 deaths each year. Their model, Optimized Prediction of Mortality (OPOM), also provides a fairer and more equitable allocation to candidate groups, including women.

“There are many significant benefits to using this new model over the current system. Unlike the current system, which makes some arbitrary choices and results in bias against certain populations, OPOM’s methodology for prioritization is clear and understandable to surgeons — and it can save hundreds of additional lives every year,” says Bertsimas.

Trichakis noted, “OPOM fixes many of the current system’s problems because it was designed specifically for liver patients using real data. As a result, it can accurately prioritize patients across all populations without bias. This shows the potential of machine learning technology to help guide clinical practice and national policy on transplants.”

The researchers explain that the current model created in 2002 depends on the Model for End-Stage Liver Disease (MELD) score to rank disease severity and priority for receiving a liver transplant. As certain patient populations are at risk of death or of becoming too sick or unsuitable for transplantation based upon disease progressions that are not captured in their MELD score, the system arbitrarily grants them “exception” points. While the overall MELD score has led to a more objective ranking of candidates awaiting liver transplantation, the process of MELD exception point granting has resulted in inequitable and undesirable outcomes.

More specifically, the MELD exception points policy has disadvantaged women. “Data shows that women have historically had less access to liver transplantation and have had higher death rates on the wait list,” notes Trichakis. “This is due to the awarding of exception points to cancer patients, as more than 75% of those patients are men. Women also tend to have lower muscle mass and higher sodium levels, which lowers their MELD scores.”

Using a state-of-the-art machine learning method developed at the MIT Operations Research Center and real historical data from liver patients, the researchers sought a better way to prioritize the allocation of organs. With OPOM, they asked the question: What is the probability that a patient will either die or become unsuitable for liver transplantation within three months, given his or her individual characteristics?

They found that the OPOM allocation outperformed the MELD-based prediction method in terms of accuracy and fairness. In simulations, OPOM averted significantly more waitlist deaths and removed the bias against women. As a result, it allowed for more equitable and efficient allocation of liver transplants.

“Unlike MELD, which relies on an inexact approach of exception point assignment, OPOM allows for accurate prioritization of all candidates and removes bias for or against particular groups,” says Trichakis.

Bertsimas adds, “If we use this model to change how we measure mortality and allocate livers, the death rate will decrease by 20%, which is very significant. We’re hopeful that our findings will affect the national policy.”

Bertsimas and Trichakis are coauthors of “Development and validation of an Optimized Prediction of Mortality (OPOM) for candidates awaiting liver transplantation” with transplant surgeons Dr. Ryutaro Hirose of the University of California and Dr. Parsia A. Vagefi of the University of Southwestern Medical Center. Additional coauthors include MIT Sloan students Yuchen Wang and Jerry Kung. Their paper has appeared online in the American Journal for Transplantation.