Welcome to Lesson 2 of this learning activity titled Metric 1, PCI in Hospital Risk-Adjusted Mortality All Patients. The content in this lesson was developed by myself, Kate Malish. I will also be narrating this lesson. Let's try a practice scenario and question to understand risk-adjusted mortality for Metric 1. Our patient is Mark. He underwent shoulder surgery at your facility, which was successful and unremarkable. However, the next day he reported acute onset chest pain and per nurse documentation became diaphoretic and hypertensive. A STAT ECG revealed ST elevation and the cardiologist on duty was consulted. The patient was immediately taken to the cath lab and found to have a 90% thrombotic lesion in the proximal LAD. The physician performed thrombectomy and attempted to place a stent, however, Mark's condition began to worsen. He developed cardiogenic shock and ongoing ST elevations. The physician placed an impella device and arranged for transfer to a tertiary care facility for high-risk PCI or CABG. Our question is, will Mark be included in this facility's risk-adjusted mortality? Here is your review slide so that you may formulate a response. The answer is no. Looking back at the model specifications for Metric 1, patients transferred to another acute care facility upon discharge are excluded from the risk-adjusted model. This means that Mark will not be factored into your facility's overall risk of mortality. Our next patient scenario is Jane. She presented to your facility for diagnostic coronary angiography. Her symptoms were those of typical angina and this was the first time she experienced these symptoms. Her diagnostic coronary angiography revealed complex multivessel disease, which prompted a surgical consult. The physicians decided on a hybrid procedure, starting with PCI on the proximal circumflex. The PCI was successful and the patient was scheduled for CABG the following day. Unfortunately, the CABG procedure had several complications and the patient was sent for an emergent high-risk PCI due to a failed lemagraft. The patient developed cardiogenic shock followed by two episodes of cardiac arrest. An impella device was emergently placed and the patient survived the PCI procedure, however, the following day she passed away. Our question is, will Jane be an observed mortality? Here is your review slide so that you may formulate a response. The answer is yes, Jane will be an observed mortality for your facility since her discharge status in sequence 10105 will be coded deceased. If you answered no, Jane will not be an observed mortality since she passed away after the second PCI procedure and the model only includes index PCI procedures. Please allow me to clarify. At any time a patient is discharged as deceased, he or she will be an observed mortality. In this scenario, Jane underwent two PCI procedures in her episode of care. Looking at the model eligibility at the patient level for metric 1, we see only the index PCI procedure is included when patients have multiple PCI procedures. This does not mean Jane's mortality will be excluded from the metric. It simply means the procedure variables for the subsequent PCI procedure will not be used to determine her risk of mortality. Only those variables coded for the index PCI are used to determine Jane's mortality risk. What are those variables used in determining risk of mortality? We will review these in the next few slides. This chart lists the variable, the variable type, which is either a yes or no response or a number that is coded by the abstractor. Then the data elements which capture the variables are listed in the right-hand column. Looking at this list of variables, you can see it contains history and risk factors such as age and renal function, along with comorbidities such as diabetes, cerebrovascular and peripheral arterial disease, and chronic lung disease. A variable which weighs heavily on risk adjustment is whether or not the patient presented to the cath lab visit in cardiogenic shock. Finally, details about the cath lab visit are included in the list of variables such as having multivessal disease, instant thrombosis, or a lesion in the left main coronary artery or proximal LAD. A feature to note on the data collection form is the key at the top of the form, which tells you what data elements are factored into certain metrics and or AUC. Whenever there is an M next to a data element, this indicates that data element is used in metric one in hospital risk-adjusted mortality. This next scenario will illustrate how coding of these data elements will form the facility's data for metric one. Henry is a 55-year-old African-American male. He is 5'9 and weighs 160 pounds. He has type 1 diabetes but has no cardiac history. He presents to the ED with chest pain. An ECG shows inferior STEMI, which moves him to the cath lab for an immediate PCI for acute STEMI involving an occluded RCA. There is non-obstructive CAD in the other coronary vessels. Henry's pre-procedure serum creatinine is 1.0 mg per deciliter. Based on the information provided for this scenario so far, we can begin to fill out the data collection form based on metric variables for metric one. Henry's date of birth is 5-17-1965, which makes him less than or equal to 70 years old. His race is Black African-American, which plays a part in calculating GFR or glomerular filtration rate. He is 5'9, which equals 175 centimeters and weighs 72 kilograms. To calculate BMI, you take the weight in kilograms divided by height in meters squared. Henry's BMI equals 23.5, which puts him in the less than or equal to 30 category. We can also fill out the rest of this portion of the form with information we know. He has no prior PCI, no cerebrovascular or peripheral arterial disease, no chronic lung disease. He does have diabetes mellitus, but is not currently on dialysis. Henry's PCI indication is STEMI, immediate PCI for acute STEMI. He does present to the cath lab with cardiovascular instability in that he has persistent ischemic symptoms of chest pain and ST elevations. His pre-procedure creatinine is 1.0, which is used to calculate GFR. Henry's GFR is 98 milliliters per minute. There is no history of heart failure, and finally, he is not having PCI in the presence of multivessel disease. By filling out the data collection form with pertinent information, we can see what variables are used to calculate risk of mortality for Henry. In the next lesson, you will see how patients such as Henry can be viewed by using the patient drill-down functionality in the dashboard. This concludes Lesson 2 of Metric 1, PCI in Hospital Risk-Adjusted Mortality, All Patients. Thank you for your participation.

Metric 1

PCI

Hospital Risk-Adjusted Mortality

patient scenarios

risk-adjusted model