Good afternoon, everyone. Thank you for this opportunity. I'll be presenting on the association between nurse co-patient illness severity and mortality in ICU. I first want to begin by acknowledging my funding. I'm funded generously by a T32, and my mentor is Dr. Jeremy Kahn. And I've got to acknowledge also my co-authors, because it takes a village to write a good research paper, and so my co-authors. So to start out, as I'm sure you're all aware, nursing workload and outcomes are related. But what we really don't understand is what are these levers for managing nurse workload? Sure, one to two nurse to patient ratios are ideal. And we've had legislation that has, in some states, mandated ratios. But these ratios are not always feasible. And in the context of a one to two nurse to patient ratio, workload can vary. One to two nurse to patient ratios don't always look the same. You can have two patients that look very differently from each other, from assignment to assignment. So I venture you to think about some more creative ways that we can manage workload. And one of those ways is considering the co-patient. So in a one to two nurse to patient ratio, each patient has a co-patient. So if two people are assigned to a nurse, one patient's illness severity may potentially affect the other one's outcome. So in this study, my goal was to ask, does the condition of one patient affect the nurse's capacity to care for the other? So as I'm sure you're aware, and any nurses in the audience, when you're caring for two patients, there are times when you are in one patient's room most of the shift, and the other patient cannot necessarily get the same care. So I wanted to study this and understand this a little bit more, because plenty of times when I've been working, I've been in one patient's room most of the time. So the objective of the study, as I said, to examine the effect of co-patient illness severity on ICU mortality. So this was a retrospective cohort study using electronic health records from 24 ICUs in eight hospitals from 2018 to 2020. So the way that I linked these nurses and these patients. So one big limitation of a lot of existing nurse workforce research is that it's based on aggregate data on the unit level or the hospital level to outcomes. But using EHR data and metadata, so nurse digital footprint, their breadcrumbs they leave in the EHR, documentation of their assessment data, and medications, I linked nurses to individual patients. And I got those nurse-to-patient assignments. So I could link the individual nurse to the patient that they were caring for. So using this validated algorithm and the metadata, I was able to find these assignments to do this study. And you can look at further methods in the paper in JMR Medical Informatics. So the exposure. So I operationalized the exposure co-patient illness severity as a categorical variable, with the reference category being neither mechanical ventilation nor vasoactive support. And then there's also mechanical ventilation only, vasoactive support only, and then both mechanical ventilation or vasoactive support. And outcome was 28-day ICU mortality. So I wanted to break down statistical analysis here, because we have multiple levels of data in this study. So I did a proportional hazards model with time-varying covariates, and this was at the level of the 12-hour nurse shift. So each patient, because it's 28-day ICU mortality, they could have 56 shifts. Not all patients had 56 shifts. So at each shift, the end can vary, because a patient can be discharged or die prior to that 56th shift. So then you can kind of see from, there were time-invariant covariates, which included age, gender, admission source, and comorbidities, as operationalized by Elixhauser. And then covariates that varied by shift, including the patient SOFA score, there are a number of co-patients. So in this sample, I did a sensitivity analysis in samples that had different nurse-to-patient ratios. But in this primary sample, it was either 1-to-2 ratio or 1-to-3 ratio, specifically. But I also did a sensitivity analysis in a cohort that was only 1-to-2 the entire time, and also 1-to-1, 1-to-2, and 1-to-3, to make sure that these findings continued through the other cohorts. And so, in addition, co-patient illness severity is covariate. And so by saying that these vary by shift, meaning from shift to shift, they are different values. So let's look at the patient cohort. So it's 20,650 patients, with most being white, and with four or more comorbidities, and most admitted via the emergency department. You can see the average SOFA score on admission, and keep in mind that this is at admission because it varies by shift, is about 2.8, with about 8% within ICU mortality. And then looking at the shift, the average SOFA score at shift was 3.1, and most co-patients were neither mechanically ventilated nor had vasoactive support. And you know, I think this is probably largely attributed to surgical ICUs in the sample, you know, the patient that get extubated pretty quickly and is transferred to the floor. So the findings of the study generally were that when your co-patient is sicker, your risk of death is higher. And keep in mind, this is a retrospective cohort study, so we have limitations on being able to see what the mechanisms are and causality, but you can see that when a patient's on both mechanical ventilation or vasoactive support compared to the reference group, their risk of death is higher. And this is more pronounced in the vasoactive support group only, which I have some hypotheses on why this occurs, and I believe mechanical ventilation only was not observed because, you know, we are largely supported by our respiratory therapists, and they decrease our workload tremendously. And so I think vasoactive support only had the largest effect because the nurse is the effector limb in there, they're delivering the care, and they don't have the support of their respiratory therapist colleagues who are so often helping us in the ICU. So what are some of my conclusions? Well, one to two staffing creates interdependencies that affect patient outcomes. I think that we might be able to intelligently pair patients within assignments to improve outcomes and I, you know, shameless self-promotion, but I would like to tell you to come to my research snapshot theater because I did a qualitative study looking at what nurses think about the nurse-to-patient assignment process because I think the next step is looking more about how these assignments are made and how is this affecting the nurse workload. And I want to better understand the assignment process from the perspective of nurses because before we implement any intervention that is going to change the way the nurse assignment process is done, you have to hear from the stakeholders themselves because they're the ones who are in the trenches. And so beyond that, I have future plans to look at, I'm curious if, you know, co-patient illness severity affects the completion of SATs and SBTs, requisite care that we need to deliver that maybe the workload of a co-patient might be preventing you from giving to the index patient. Thank you so much for this opportunity and, well, I will hand it off to the next speaker.

ICU mortality

nurse workloads

co-patient severity

electronic health records

patient assignment