Hello, I’m Dr Jeff Kingsley and welcome to another edition of Riding in Cars With Researchers. Today we’re going to talk to physicians and research coordinators about your research data and how to evaluate that data. For some reason, unbeknownst to me, we seem to treat research study data differently than we do normal healthcare data and I don’t know why.
Treat the Patient Not the Data
In normal health care, I used to train medical residents and I would talk to them about treating the patient, don’t treat the data points. If you have a patient in front of you and you look at that patient’s monitor at a hospital bed and it says that the patient is in asystole and the patient is awake, smiling and talking to you, don’t start CPR. The monitor is wrong and the patient’s not in asystole. Perhaps the leads fell off, but you don’t start CPR. If you have a patient in front of you and you have a lab saying that the person is wildly hyperkalemic and the person is looking beautiful and they’ve got an EKG that’s stone cold normal, it’s likely a lab error. Perhaps there was hemolysis. Repeat the lab and don’t panic.
Lab Tests Aren’t Always Right
It’s the constellation of things that help point us in the right direction. A single data point is not as telling as a constellation of data points. You have a patient who has ST segment elevation in one area of a 12 lead EKG. What does that mean? You have a patient with ST-segment elevation in 3 contiguous leads of an EKG – that’s got my attention. You’ve got a patient with ST-segment elevation in three contiguous leads on an EKG who has substernal crushing chest pain, is diaphoretic, nauseated, short of breath, and has radiation to their jaw and left arm. The constellation of symptoms means so much more. I have a patient in a NASH trial (non-alcoholic steatohepatitis) and we got labs back last week with panic results on liver enzymes. Everyone was immediately upset, immediately discontinued study medicine, immediately filed the SAEs, etc. I said, wait, wait, hold on…this doesn’t make sense. Nothing has changed and we are getting routine labs. This is a big move in a very short period of time. Before doing an early termination on this patient, we immediately repeat the labs, and the labs were completely and utterly normal. Catastrophe averted and the patient gets to continue in the trial. Everyone is happy.
I encourage you in your research trials, think the same way you do in your medical practice. Individual data points are far less significant than constellations of data points. Doing so will refine your quality. Doing so will ensure that your adverse event list is higher quality than if you don’t. The patients you put in research trials will be higher quality patients. The patients that you screen fail or early terminate will have screen failed or early terminated for the right reasons, not the wrong reasons.
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