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The Etiquette of Help

The Etiquette of Help

“Any surgeon to OR 6 STAT. Any surgeon to OR 6 STAT.” No surgeon wants to hear or respond to a call like that. It means someone is in deep kimchee and needs help right away. I was in the locker room, just about to strip off my scrubs and dress to go out with my wife for the evening. We had finished a full day of routine surgery—two gallbladders and a colon resection—and had plans for dinner. Our older son was home from college and had offered to watch his younger brother for us. I closed my locker and walked back out to the OR control desk. Michele, my wife and first assistant, was already there. A glance at the control board showed me that Dr. S was in room 6. She was a gynecologist, and according to the board, was doing a routine diagnostic laparoscopy. The bustle of technicians and nurses running in and out of the room indicated that it was anything but routine. We made our way to the room, and I stuck my head in. My friend Jon was the anesthesiologist. He was squeezing a bag of packed red blood cells to make them run into the IV faster. “We could use some help,” he said, calmly as ever. But he rolled his eyes toward the table. Dr S. stood there, blood coating her arms and chest, her eyes looking at me but somehow also looking far away, the thousand yard stare of someone out of their depth and very afraid. “Hey, Lou,” I said, using her first name as I stepped into the...
Predicting Stroke Risk in Patients With ACS

Predicting Stroke Risk in Patients With ACS

Clinical outcomes after stroke can be devastating and include high risks of mortality and severe debilitation. In a study published in the American Journal of Cardiology, my colleagues and I sought to develop a clinical risk prediction model that would allow physicians to identify patients with acute coronary syndrome (ACS) who are at a higher risk of developing in-hospital stroke. We wanted to identify independent risk factors for developing a stroke in the acute time period of an ACS. The final model was created based on data from 63,118 patients with a total of 217 ischemic strokes. Testing a New Model Using a multivariable analysis, eight baseline and presenting clinical characteristics were independently associated with the occurrence of ischemic stroke: 1. Older age. 2. Atrial fibrillation or flutter on index electrocardiogram. 3. Systolic blood pressure ≥160 mm Hg. 4. Positive initial cardiac biomarkers. 5. No previous or current smoking. 6. ST segment change. 7. Killip class II to IV. 8. Lower body weight. The risk prediction model for the primary endpoint of ischemic stroke within 14 days of hospitalization was developed by converting final model estimates to points. For example, each 10-kg decrease in weight, starting with 210 kg to 219 kg, was assigned 1 point. Atrial fibrillation or flutter was assigned 4 points. Points for each of any of the eight patient factors were summed to obtain a total patient risk score. Patients with risk scores of 21 to 27 had a 14-day stroke risk of about 0.4%. This risk increased to more than 1.0% when scores rose above 30 points and to 1.0% or 2.0% in those...
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