Expert Conversations: Management of Severe Pneumonia
In this podcast, George Tetz, MD, PhD; Danai Khemasuwan, MD, FCCP; Timothy Schwarz, DO; and Asim Kichloo, MD, discuss their respective studies recently presented at CHEST 2021 under the topic "New Insights and Approaches to Management of Severe Pneumonia and Its Complications."
- File TM, Khemasuwan D, Tetz G, Schwarz TJ. New insights and approaches to management of severe pneumonia and its complications. Talk presented at: CHEST 2021 Annual Meeting. October 17-20, 2021; Virtual. https://chestmeeting.chestnet.org/session/new-insights-and-approaches-to-management-of-severe-pneumonia-and-its-complications/
- Khemasuwan D, Sorensen JS, Colt HG. Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. Eur Respir Rev. 2020;29(157):200181. doi: 10.1183/16000617.0181-2020
- Kichloo A, Schwarz T, El-amir Z, Wani F, Shaka H. Predictors for inpatient mortality among hospitalizations with ventilator-associated pneumonia: national inpatient database 2016-2017. Chest. 2021;160(4):A562. doi: 10.1016/j.chest.2021.07.543
- Tetz G, Kardava K, Gembitskaya T, Vecherkovskaya M, Tetz V. Clinical efficacy data for new diagnostic tool for the selection of the most effective antibiotics for patients with cystic fibrosis. Eur Respir Rev. 2020;56(64):699. doi: 10.1183/13993003.congress-2020.699
George Tetz, MD, PhD, is affiliated with the Human Microbiology Institute and is the CEO of CLS Therapeutics in New York City.
Danai Khemasuwan, MD, FCCP, is an assistant professor in the Department of Internal Medicine in the Division of Pulmonary Disease and Critical Care Medicine at Virginia Commonwealth University in Richmond, VA.
Asim Kichloo, MD, is the program director of the Internal Medicine Residency program and the Apogee Hospitalist program at Samaritan Medical Center in Watertown, NY.
Timothy Schwarz, DO, is an internal medicine resident in the Samaritan Medical Academic Residency Training (SMART) Clinic at Samaritan Medical Center in Watertown, NY.
Leigh Precopio: Hello everyone, and welcome to another installment of Podcasts360, your go‑to resource for medical news and clinical updates. I'm your moderator, Leigh Precopio, with Consultant360.
Today we get the opportunity to speak with several researchers who recently presented at CHEST 2021, under the topic “New Insights and Approaches to Management of Severe Pneumonia and Its Complications.” Each of these presenters contributed their research on a different aspect in this area of medicine and is here with us today to further discuss their respective studies. Thank you all for taking the time to speak with me today. Let’s start with introductions.
George Tetz: I’m George Tetz, MD, PhD, affiliated with the Human Microbiology Institute and the company named TGV‑DX. Thank you.
Danai Khemasuwan: My name is Danai Khemasuwan, MD. I'm a faculty at Virginia Commonwealth University in Richmond.
Asim Kichloo: Hi, I’m Dr Asim Kichloo, MD. I'm the program director of the Internal Medicine Residency Program at Samaritan Medical Center and also the associate professor of medicine at Central Michigan University.
Timothy Schwarz: Good morning. I'm Dr Tim Schwarz, DO. I'm a resident physician in internal medicine at Samaritan Medical Center in Watertown, New York. Which is northern New York. I'm in the program, Dr Kichloo is our program director, and he was also the primary investigator on our paper that was accepted.
Leigh Precopio: Can you give us an overview of your sessions?
George Tetz: Sure, absolutely. We have developed a novel culture‑based diagnostic method name AtbFinder that helps the medical doctors to select the most effective antibiotics just within hours.
The study that we presented at CHEST is the outline of our first in-human clinical trial when we used the AtbFinder to select the antibiotics for patients with cystic fibrosis. We confirmed the clinical response to antibiotics selected with AtbFinder, chosen with AtbFinder, that it is significantly higher in that selected with conventional methods.
Today, the main flaw of literally all antimicrobial suitability tests is that they rely on evaluating the inhibition of growth to offer lead bacterial pathogen with their various concentrations of antibiotics. But that happens in a very constrained and very artificial setting. I will tell you what I mean. Whereas in these tests the lead pathogen is grown as a pure mono pathogen culture. Therefore, as these tests, they simply ignore critical characteristics of real-life infections when at the site of infection, for example in the lungs, the lead pathogen is surrounded by dozens of other bacteria that interact with them.
In other words, the intrabacterial interactions such as biofilm formation, or collective antibiotic resistance, or the modulation of resistance, phenotype, lead pathogen by surrounding bacteria are simply not reproduced by conventional antimicrobial stability tests, so in not accounting for this test contributes to the failure to select effective antibiotics.
We develop the AtbFinder that is culture‑based microbiological tests, but it's based on a novel paradigm selecting antibiotics effective against not only its elite pathogen, but also are supporting bacteria that help them withstand the antibiotics. In other words, we select antibiotics that treat the bacterial community of the site of infection as the whole. We conduct this testing in conditions that mimic real life infection. With AtbFinder, we're able to simultaneously identify the efficacy of up to 130 antibiotics for each patient and we can bring the results for the physician just within 4 hours.
The clinical trial that was conducted, it was 3‑year perspective and versus retrospective data analysis. We compared the clinical performance of patients with cystic fibrosis during the time when they were treated with antibiotics selected with AtbFinder, versus the time when they were treated with antibiotics selected with a routine and conventional antimicrobial stability test.
We have received an absolute unbelievable result. Because first of all we found that there was a significant reduction in the number of hospitalizations due to pulmonary exacerbations. And actually, antibiotics selected within AtbFinder, were able to completely arrest the development of hospitalizations due to pulmonary exacerbations in these patients. There were 35 total patients with cystic fibrosis.
We then evaluated the effect of these antibiotics on burden of Pseudomonas aeruginosa. That is a critical life-threatening pathogen causing the most significant lung function decline in patients with cystic fibrosis. Once antibiotics started to be selected to the AtbFinder, we were able to eradicate Pseudomonas aeruginosa in 81% of patients. It's absolutely unbelievable result because once established, Pseudomonas aeruginosa was known to be impossible to be eradicated.
We analyzed specifically the difference in antibiotics suggested as effective through AtbFinder versus those that are selectively their conventional methods, and found that there was a significant difference in the type of antibiotics selected with AtbFinder. However, while the standard methods suggested to use over 70 systemic antibiotic courses during the same time period, with AtbFinder we're able to prescribe only 45 systemic antibiotic courses. Meaning that we could dramatically reduce the antibiotic exposure specifically for these patients.
Finally, the cream of the crop was the dynamic of lung function. The pulmonary function is critical in subjects with cystic fibrosis since it's decline is the reduction of life expectancy in these subjects. All patients responded beautifully. In all patients before the antibiotics were selected with AtbFinder the mean FEV1 was 44%, and after the use of antibiotics selected with AtbFinder, there was a significant increase of up to 65%.
We have received these amazing results, now we're getting ready for the second tier of our next step of clinical trials, and that was the most important outcome of our study.
Danai Khemasuwan: Basically – this is a general statement – in the patient with pneumonia they usually develop parapneumonic effusion or empyema, which is a pus in the pleural space between the lungs.
The standard treatment for that in the past is just antibiotics and chest tube drainage. The study that we referred to back in 2011 is the MIST (Multicenter Intrapleural Sepsis Trial) trial published in The New England Journal of Medicine that installation of alteplase and dornase, which is a fibrinolytic medication, that will help to liquefy the pus and drainage through the chest tube.
Usually at the center of the treatment is about 3 days, that's twice a day dose of 6 doses total. About 10‑15% of cases under the study, the patients still need surgery anyway because they didn't get improvement.
The principal of my research is to try to find a predictive model of which patient characteristics will fail the treatment. If you know that, we can expedite the intrapleural fibrinolytic treatment and get the patient to surgery. We save about 3 days of time or more, and the patient can get through the surgery faster.
What my study did was we compiled the data from 5 different centers in the US. We came up with about 430 something patients, and we used a novel machine‑learning model. It's a little different than the regression model that usually use in these predictive models, to build a predictive model. We used a machine‑learning model with 4 different classifiers. The classifier, meaning that they allocate the variables differently. We took different samples, and then we run that test. And what we've seen was we randomly selected 80% of the data to train the models. Once the model has a good predictive model relative accuracy, then we test in the 20% which the data that model has seen before, and we see a correlation.
We use 17 clinical variables including patient characteristics, demographics, radiographic findings for the initial CT scans. And then flow fluid analysis, which usually helps with diagnosis. We drain the fluid, we send it for chemistry testing, and we run some numbers.
So, with all those 17 variables, we rank which 1 is most important. All these 4classifiers came out that persons of necrotizing pneumonia or abscess and poor thickening, all leads to very highly predictive output failure, and the patient would need surgery. That's the overview of my study.
Asim Kichloo: The basis of our study is looking at the readmissions for ventilator‑associated pneumonia. We know that the cost of care in the health system in the United States is very, very dependent upon readmissions, which we have for multiple comorbidities and diseases here.
The aim of our study was to quantify these readmissions. We have found that there are gaps in the literature stating and quantifying the causes and the predictors of these readmissions in ventilator‑associated pneumonia.
Our study is a retrospective study in which we looked at the index admissions for ventilator‑associated pneumonias and 30‑day readmissions for those admissions again, so index admissions along with readmissions for 30 days. We looked at not only what were the top 10 most important causes of which were leading to these readmissions, but at the same time, what were the actual predictors in terms of which were leading to the readmissions for these patients.
I will let Dr Schwarz to dig in into the result section part of it, and then I will expand a little bit more.
Timothy Schwarz: Thank you, Dr Kichloo. Yes, effectively, we wanted to look at both the rate and the top 10 reasons for re‑admission, as well as a comparison between index and readmission statistics, looking at things like in‑hospital mortality, total hospital cost, length of stay.
Then, try to see if we could find statistically significant data that would lead us to, as Dr Kichloo said, the goal of our study. Which is seeing if we can quantify a predictor of re‑admission within 30 days of an initial index admission for ventilator‑associated pneumonia. Effectively we found overall all‑cause readmission was 20%. Essentially, where we got our data from was the National Readmission Database, over the year of 2018, looking at some‑odd 40,000 ventilator‑associated cases of which identified 14,000‑plus that met our criteria. Then approximately a little bit over 2,300 who had a 30‑day readmission.
Statistically significant data we found comparing index and readmission was, generally, older age, those that had a higher Charlson Comorbidity Index (CCI) – that is a marker that is used to tabulate 10‑year survival – so we found that a moderately‑elevated CCI, or Charlson Comorbidity Index, had a statistical significance for readmission. The moderate elevated scores of 1 or 2 – it goes from 0 to 3, 3 being the highest burden of comorbidity – scores of 1 or 2 had a higher rate of readmission.
Then, moving forward in addition to that, we found that total length of stay, hospital cost, in‑hospital mortality, was much higher for the initial, the index admission, at which point the ventilator‑associated pneumonia was diagnosed compared to the 30‑day readmission.
In terms of going back to the top 10 reasons, the most common reason was sepsis of unspecified cause. That was approximately, almost 28%, 27.5%. Then a bit of a gap but the next highest number 2 was ventilator‑associated pneumonia, again, on readmission. That was just south of 5%.
Other things that showed statistical significance in terms of if patients are diagnosed with, or their readmission reason for the 30‑day readmission, things like COPD, stroke, dyslipidemia, all of those. If patients had those comorbidities they had a much higher chance of being readmitted at 30 days. We also found, interestingly, in terms of hospital size. So in terms of total number of beds of a hospital, that those - smaller hospitals in terms of bed size - had those patients at a higher risk of readmission at 30 days. That's the data we compared ‑‑ index and 30‑day readmission.
Essentially, in terms of predictors, we're able to quantify 2 specific ones. One, the first of which was a moderate elevated Charlson Comorbidity Index, or CCI, score. Those patients, that is a predictor. Should they have a score of 1 or 2, it predicted that they would a higher chance to be readmitted.
Then, the other is hospital size. Again, if they're diagnosed with ventilator‑associated pneumonia, the index admission at a smaller size hospital in terms of the number of beds, that's a predictor that they have an increased risk for a 30‑day all‑cause readmission.
Statistically significant comparison data, as far as our measured outcomes, and then from that, we had 2 statistically significant predictors ‑‑ the comorbidity index and the size of the hospital.
Leigh Precopio: How can the take‑home messages from your studies be implemented into clinical practice?
George Tetz: The most important take‑home message is that taking into consideration the complex interbacterial interactions at the site of infection. These are critical items in order to select effective antibiotics and to decrease the number of antibiotic failures.
Danai Khemasuwan: The finding is in a patient with pneumonia and parapneumonic effusion or empyema, if initial CT scan shows that the patient has necrotizing pneumonia and abscess, and pleural thickening on the CT scan, defined as two-millimeter thickness on the initial CT, those 2 variables are predictors of the failure for the intrapleural fibrinolytic treatment with alteplase and dornase so the patient should go for the surgery without trying the treatment.
Timothy Schwarz: For us, 2 main things I'm going to take away is, the optimization of outpatient management, just common things you see in primary care, comorbidities such as diabetes, dyslipidemia, COPD, very chronic, very common disease entities in our population. Optimizing them may benefit patients in terms of decreasing the risk for readmission. Because again going back to 1 of our 2 predictors statistically significant data points being that comorbidity index. Those patients with higher scores, they're at a higher risk of readmission. That directly leads to the importance of outpatient management of said conditions. That's the first takeaway.
The second is, it's something that generally has been worked on, but the treatment and care plans for ventilator‑associated pneumonia. Going back to our second data point on the predictors that was statistically‑significant which was the size of the hospital. Generally, bigger hospitals usually have greater infrastructure, in terms of staffing, education, and staff power, to be able to recognize the factors that put patients at greater risk for ventilator‑associated pneumonia and preventing that. Then, as well as optimizing care, once if there's the diagnosis made of ventilator‑associated pneumonia. I think that, generally, our take‑home is that smaller facilities may lack the staffing power, and/or the access to resources, and/or the common repetition of exposure to ventilator‑associated pneumonia to have that mental sequence of how to prevent, and then once it's diagnosed, address ventilator‑associated pneumonia.
Leigh Precopio: What is the next step for your research in this area?
George Tetz: Currently, we're launching additional clinical trials in the Children's National Hospital at the University of Buffalo, in order to utilize AtbFinder in patients with cystic fibrosis including those who are impacted with Burkholderia cepacia infection as well.
Danai Khemasuwan: Mine is, this is a retrospective multi‑center study, even though we have a large amount of patients and relatively patients. But prospective validation of the model is necessary. Basically, we just have to prospectively collect the data and validate these models.
Asim Kichloo: For us, the next step is going to be two-fold. One is to develop a score, which we have the predictors now, and now develop a score which can directly lead to identification of the patients who are at higher risk, number 1.
Number 2, to develop quality improvement projects at this point of time, which will lead to certain endpoints with regards to a setup of certain home health setups, especially with these patients with ventilator‑associated pneumonia, so that we can see whether we can intervene for these comorbid conditions in certain ways to see whether we will be able to reduce this readmission.
Leigh Precopio: What is your biggest challenge when managing patients with severe pneumonia?
Danai Khemasuwan: For me, it's a two-fold. One is, managing a patient with a pneumonia when they get into the separate shock, sometimes it's irreversible to get them back. It's harder to figure it out, the infection. From there, through my research, from an efficient standpoint, to find the optimal timing to refer the patient for surgical intervention is difficult. That's how I come up with the research.
Asim Kichloo: For us, related to our study, in these patients with a very high index, Charlson Comorbidity Index, to find the interventions we can make to decrease the mortality, to decrease the length of stay, and at the same time decrease the readmissions for these patients.
Timothy Schwarz: Just to add, patients that get ventilator‑associated pneumonia usually are going to be sicker in general for their baseline. It's a very complex approach to them. Obviously you have this significant airway infection, but what was it that led to them needing to be intubated and then how do you try and, as Dr. Kichloo said, if we can implement some type of a way of scoring to get ahead of things and maybe prevent the decline that leads to an intubation and understanding the full holistic management of multiple comorbidities in order to prevent the cascade that would lead to an index ventilator‑associated pneumonia admission. Just very complex, these patients usually have a lot of things going on, so it kind of a loaded medical question and approach.
Leigh Precopio: Great. I appreciate you all taking the time to speak with me today.