Good afternoon, everybody. Thank you, Dr. Buckman, for that introduction, and it is a pleasure to present our work in front of this audience, and thank you to the Congress Programming Committee for giving us this opportunity. I'm going to be presenting our work on behalf of the APICS I study team. So our work was funded by the U.S. Department of Defense and by means of disclosure. This is opinions that we're going to be expressing are solely those of ours and not necessarily endorsed by the U.S. Department of Defense. I do not have any relevant disclosures. So jumping on to the topic of survivorship, we are all aware that as care has improved, respiratory failure has increased. What this means is that we have larger amounts of survivors of critical care who experience new or worsened health states across multiple domains, and this has been increasingly recognized as APICS. And post-intensive care syndrome is an urgent research priority, and there are a lot of efforts being done to mitigate the burden of post-intensive care syndrome. Now we all know that as patients move from one phase of recovery to another, their needs evolve. And for the purposes of this presentation, we are going to be focusing on this early post-discharge recovery period, which is somewhat understudied as of now. So if we look into the post-discharge needs, these can be broadly categorized into two. We could classify them as medication needs and non-medication needs. Now if you think about the needs, non-medication needs could be used as an umbrella term for durable medical equipment, home health services, and follow-up appointments. So we know from our prior literature that there are major gaps in care as following recovery from critical illness. But what we do not know is whether there is an association between early non-medication needs and subsequent outcomes. Now we have in the past presented the complex relationship that exists between unmet medication needs and outcomes. So for the purposes of this study, we are going to be focusing on the unmet non-medication needs. So we had three broad aims for this study. One was to characterize the proportion of non-medication needs that go unmet, analyze the association between such unmet non-medication needs and a composite outcome of readmission or death within three months of discharge, and as an exploratory analysis, try to identify predictors of unmet discharge needs. So ours was a prospective multi-center cohort study, which was conducted across six academic medical centers across the breadth of the United States. Here is the inclusion criteria. For patients to be included in this cohort, they had to meet these two inclusion criteria. They had to be adults with acute respiratory failure. And for the purposes of the study, acute respiratory failure was defined as the need for more than 24 consecutive hours of mechanical ventilation via an endotracheal tube or non-invasive ventilation or high-flow nasal cannula. And they had to be, after their ICU stay, be discharged home. Looking into the major exclusion criteria, respiratory support solely for area protection was an exclusion criteria. As dementia, according to IQ code screening, was an exclusion criteria, and inability to complete telephone-based follow-up excluded patients as well. Here is a schematic of our study. So the exposure for the study was a proportion of unmet non-medication needs, which could either be durable medical equipment, home health services, and follow-up appointments. The exposure was ascertained within 7 to 28 days of discharge home. And this was done through a centralized data coordination center, which was housed at Johns Hopkins Medical Center. And this was done with the help of a questionnaire, which was designed solely for this purpose with the help of multidisciplinary input. The outcome of interest was a composite of free admission or death within 90 days of hospital discharge. And we also looked at the components of the primary outcome. Here's a brief overview of the statistical analysis. The exposure unmet non-medication discharge needs were dichotomized as above or below median. And we expected, in our cohort, to have reverse causation and confounding by indication. So propensity score adjustment was used to estimate the average effect. And for the primary analysis, a Poisson regression was used with the propensity score as a covariate. Exploratory analysis was conducted using a random forest model. And for all analysis, a two-tailed significance threshold of 0.05 was considered significant. So coming to the sample size, we did some simulation before embarking on this trial. And we came to the conclusion that we would need about 152 patients to detect a difference of about 23% difference between patients who had unmet needs or higher unmet needs categories versus those in the lower category. And we built in an attrition rate of around 24% and enrolled 200 patients. So we've gone through some of these before. But the study was overseen by the central IRB at Vanderbilt. And we obtained informed consent prior to enrollment. So here is the CONSAR diagram. So over a period of about 20 months, we screened about 5,700 patients. Only 200 patients were enrolled, and 195 patients were included in the analytic cohort. To give you an idea of what our cohort looked like, the median age was around 55 years with about 53% females. The population was mostly insured. So this is something to bear in mind with only about 5.6% patients who are uninsured. And we had about 16.9% patients with COVID in this cohort, because this literally finished as COVID was ramping up. Aftercare, and only about a sixth of patients were enrolled in any form of ICU recovery program. So this is a snapshot of how the non-medication discharge needs that were identified. So almost everybody, about 98% patients, had about at least one identified non-medication needs. Of these, follow-up appointments were the commonest, with about 97% patients having an identified follow-up appointment, and about 70% needing home health services. And if you looked at the intersection of all these, about 48% patients had a need identified in at least each of the three categories, and about 31.3% had needs in two categories. This schematic shows the needs and the proportion that were unfulfilled. So if you look at absolute numbers, follow-up appointments were the highest at about 600, and about 37% of home health services remained unfulfilled. So this is giving a snapshot of the proportion of needs which went unmet. So looking at all comers, patients had a median of five needs, with an interquartile range of four to eight. But what I would like you to focus is the number on the extreme right, which shows that about two-fifths, or 44%, of patients had at least one or more unmet needs at the time the exposure was ascertained. And the proportion of unmet needs varied depending on which category you looked at, anywhere from 0% to 15% for durable medical equipment, to about 0% to 50% amongst home health services. Now another important information that can be gleaned from this table is that the median number of unmet needs was zero, with an interquartile range of zero to one. So any comparisons that we are going to be talking about are essentially between a cohort, between comparisons between patients who had no unmet needs and those who had unmet needs. So coming to our 90-day outcomes, among the cohort of 195 patients, about 29% of patients met, were either dead or readmitted within three months. And numbers were predominantly driven by readmission rather than death. And another key part of this key observation, I would say, is the time to readmission. And if you look at that, it's about 12 days for patients who were readmitted, which is extremely, extremely early. Coming to the primary outcome. So looking at the primary outcome, we did not find a statistically significant difference between patients who had higher unmet needs, in this case, any unmet needs versus those who did not. And looking into the constituents of the primary outcome, we did not find any statistically significant relationship. Then we looked to explore what are the drivers of these unmet discharge needs. And we did a random forest model. And the best-performing random forest model had an out-of-bag error rate of around 33%. And the four top predictors were hospital length of stay, APACHE-2 scores, multidimensional perceived social support, and age. And of these two only, actually, like none of these seem to be quite modifiable. Now once these were placed into PASAR regression, only two variables came out to be significant, namely the hospital length of stay and age. So putting all of these into perspective, what we found was that most respiratory failure survivors had non-medication needs identified at discharge. And the proportion of needs that went unmet varied from 0 to 15 to 0 to 50%. And greater than about two-fifths of acute respiratory failure survivors had one or more needs unmet. However, we were unable to find any statistically significant relationship between unmet needs and the composite outcome. So why are our results important? First, they show the magnitude of mismatch that exists and specifically identifies areas that are vulnerable. Now to put a few things into perspective, about 31.3% of patients prescribed non-invasive ventilation did not receive them at the time that the exposure was ascertained. And about 50% of physical, occupational, and speech-language pathology appointments remain unfulfilled as well. And readmissions occurred early. Why is this important? Because our traditional follow-up systems, even where we have robust ICU recovery clinics, do not start until four weeks to three months. However, the readmissions, as we see here, happened at around a median of 12 days. We were, however, unable to ascertain if there was a clear impact of post-recovery ICU clinics in our cohort because only about 16% were enrolled in such programs. And it seems that older people, older patients with long hospital length of stay appear to be the most vulnerable. So what are our strengths? So ours was a multicenter study conducted across the breadth of the United States. We had an exceptionally high cohort retention rate of about 95% to 96%. And we had developed this discharge needs tool with multidisciplinary input. What are the limitations? The biggest limitation of our study is the early readmission rate. The median time to readmission was about 12 days, which may have led to inaccurate or misclassification of exposure in a certain subset of patients. And although we quantified the discharge needs as either met or unmet, we were not designed to assess the quality of services that were provided. Generalizability of our study can also be called into question because this was conducted at tertiary level academic medical centers. And these might not apply to smaller centers with less robust follow-up infrastructure. And although we were able to rule out a large effect due to our small sample size, we can certainly not rule out a more modest effect that may exist. And as with any observation studies, we cannot rule out residual confounding. So in conclusion, our study demonstrated that unmet non-medication needs remain extremely common among the survivors of acute respiratory failure. And these point out to areas which might need more careful introspection as we plan and optimize recovery for our patients. So if there was one take-home message, that is the stark mismatch that exists even to this date even at tertiary level medical centers. With that, I conclude. Thank you very much.

acute respiratory failure survivors

unmet non-medication needs

follow-up appointments

home health services

readmission or death within three months