142 Ctsi Translational Research Trainee Symposium Talk
142.1 Summary
- Improved Recognition of Inpatient Hypercapnic Respiratory Failure
- Hypercapnic Respiratory Failure
- E.g.: Home PAP improves mortality in ↑CO2-COPD
- By the time patients are caught: ↑morbidity
- OVERALL: There is an important burden of unrecognized hypercapnia among hospitalized patients.
- Aim 1: To determine the local accuracy of non-invasive assessments of blood PaCO2
- Aim 2: To estimate the prevalence of unrecognized hypercapnia in inpatients
- Aim 3: To estimate the potential impact of early & reliable identification
- Additional Design Thoughts:
142.2 Slide outline
142.2.1 Slide 1
- Improved Recognition of Inpatient Hypercapnic Respiratory Failure
- Brian Locke, MD
- T32 Fellow
- Division of Pulmonary, Critical Care, and Occupational Pulmonary Medicine
- Department of Internal Medicine
- University of Utah School of Medicine
- This research was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award 5T32HL105321 from the NIH
- and The American Thoracic Society through the Academic Sleep and Pulmonary Integrated Research/Clinical Fellowship (ASPIRE) Program ### Slide 2
- Hypercapnic Respiratory Failure
- Hypoxemic (Low O2) Respiratory Failure
- Hypercapnic (High CO2) Respiratory Failure
- Low arterial blood oxygen tension
- High arterial blood carbon dioxide tension
- (Relatively) Reliably Identified by Pulse Oximetry
- No routine non-invasive testing
- Build up here CO2 in blood rises
- “What gets measured gets managed”
- Patients with hypercapnia are identified unreliably and late in their illness ### Slide 3
- E.g.: Home PAP improves mortality in ↑CO2-COPD
- Weight loss
- Medication Adjustment
- Pulmonary Rehab
- Hypercapnia is common, is probably becoming more common, and is treatable.
- Common contributors to Hypercapnic Respiratory Failure:
- Advanced Age
- Opiate Use
- Obesity
- Advanced Lung Dz
- Multimorbidity
- Population-standardized prevalence PaCO2 > 45 mmHg after excluding iatrogenic causes:
- 150 per 100,000 person/year
- Only inpatients, only those with blood gasses checked
- Comparison: Decompensated Cirrhosis 94.9 (US, 2017) per 100,000 person year ### Slide 4
- By the time patients are caught: ↑morbidity
- Inpatient ICD code for Hypercapnic Respiratory Failure
- 23% 30d readmission rate
- Same as CHF, more than MI
- 66% with recurrence
- Could we catch them earlier? Would it help?
- Lung Adenocarcinoma, for reference ### Slide 5
- OVERALL: There is an important burden of unrecognized hypercapnia among hospitalized patients.
- OVERALL: There is a significant burden of unrecognized hypercapnia among hospitalized patients.
- 3 aims
- Aim 1: Non-invasive Validation
- Aim 2: Prevalence Estimate
- Aim 3: Potential Impact
- Hypothesis
- Less invasive methods of blood CO2 assessment (TcCO2) Risk Stratification) can identify hypercapnia with sufficient accuracy to estimate prevalence.
- There is a sizable prevalence of unrecognized hypercapnia among hospitalized patients.
- Inpatients who were predicted, but not confirmed, to have hypercapnic respiratory failure are at comparable risk of readmission to those identified.
- Barrier
- TcCO2 and bicarbonate+ are perceived as problematic for individual patient use.
- ABGs a selectively and unreliably obtained in clinical practice, and cannot be indiscriminately obtained for research
- Prior work showing high readmission rates only assess patients who have been identified as hypercapnic.
- Insight
- Scatter, without bias, can allow accurate estimation of prevalence
- Risk modeling allows for stratified sampling better efficiency.
- ABG PaCO2 is an imperfect reference standard
- Approach
- Obtain paired samples with ordered ABGs (inpatient).
- Non-invasively sample (with TcCO2) patients by strata of predicted hypercapnia risk.
- Among patients with an admission for hypercapnia that is identified, how many had preceding admissions likely to have hypercapnia? ### Slide 6
- Aim 1: To determine the local accuracy of non-invasive assessments of blood PaCO2
- Tool
- Health Record-Based Modeling
- Transcutaneous CO2
- Purpose
- Refine Estimation of Pre-test Odds of Hypercapnia
- Allow non-invasive CO2 assessment (patients unlikely to consent for research ABGs)
- Limitation
- Approach only externally validated in outpatient OHS assessment
- Prior 95% agreement +/- 6 mmHg insufficient for unstratified clinical use
- Total
- No ABG Obtained
- ABG Obtained
- N879,019
- N675,620
- N203,399
- Ambulatory
- 39% (343,699)
- 50% (334,457)
- 5% (9,242)
- Emergency
- 20% (175,792)
- 22% (146,066)
- 15% (29,726)
- Inpatient
- 41% (359,528)
- 29% (195,097)
- 81% (164,431) ### Slide 7
- Aim 1: To determine the local accuracy of non-invasive assessments of blood PaCO2
- N~65 power of 0.8 for TcCO2-PaCO2 LOA ~8mmHg
- Expected Outcomes:
- TcCO2 feasibility assessment
- TcCO2 local limits of agreement
- Predictive modeling external validation
- Career development aim: prospective study design and execution ### Slide 8
- Aim 2: To estimate the prevalence of unrecognized hypercapnia in inpatients
- Statistical Insights:
- Accuracy needed for clinical care ≠ Required accuracy for prevalence estimate
- Pre-test probability estimate allows stratified sampling → efficiency
- …
- 49: Correct 93%, Wrong 7%: weight: 1.98%
- 48: Correct 88%, Wrong 12%: weight: 2.39%
- 47: Correct 80%, Wrong 20%: weight: 2.47%
- 46: Correct 69%, Wrong 31%: weight: 2.92%
- 45: Correct 57%, Wrong 43%: weight: 3.11%
- SENSITIVITY
- SPECIFICITY
- 44: Correct 57%, Wrong 43% 3.56%
- 43: Correct 69%, Wrong 31% weight 3.73
- 42: Correct 80%, Wrong 20% weight 4.16%
- 41: Correct 88%, Wrong 12% weight 4.25%
- 40: Correct 93%, Wrong 7% weight 4.58%
- ….
- Expected test characteristics:
- sensitivity 89%
- specificity 92%
- if
- agreement ~6 mmHg
- same distribution of CO2 ### Slide 9
- Aim 2: To estimate the prevalence of unrecognized hypercapnia in inpatients
- Design:
- Within 24 hours of admit
- Stratified sampling by decile of the predicted likelihood of hypercapnia
- Overall prevalence estimate weighted [by overall hospital] average of decile prevalences.
- Apply TcCO2
- Sample Size Calculation:
- Complex: Upper bound unstratified: n200 would give 5% 95CI width at expected prevalence of 2.5%
- Outcomes:
- Estimated in-hospital prevalence
- % recognized(?) – should information be shared with teams? (or, vs historical control)
- How will this fail?
- Several pieces of data will update power calc
- (distribution of predicted hypercapnia deciles, bias/agreement of TcCO2)
- Can I access EHR data fast enough?
- Hawthorne effect?
- What if TcCO2 isn’t good enough? (VBG?)
- Elevation adjustment? Due to hypoxemia, do they get identified earlier?
- Nowbar et al. 2004: All w/ BMI ≥35 kg/m2 admitted to inpatient medicine service at the Denver VA. Everyone (n277) gets an ABG. 31% had hypercapnia. Only 1/3 of those had therapy started (teams were told) ### Slide 10
- Aim 3: To estimate the potential impact of early & reliable identification
- More aggressive case identification could lead to underwhelming improvements in important outcomes if:
- Clinicians already identify the important cases
- Or, if earlier identification doesn’t actually facilitate helpful treatment
- Perhaps, nothing bad would have happened in the period between identifications
- Or, nothing could be done to change that trajectory
- Rigor of the supporting research:
- Marik et al. 2013: patient diagnosed with OHS are repeatedly admitted and missed diagnosed prior to their ultimate recognition
- Single center with non-representative enrollment (only obese patients)
- Meservey et al 2020. and Vonderbank et al 2021: readmission rates are high after diagnosis (~1/3 of patients) - likely because the pipeline from identification to treatment is very unreliable.
- This only studies patients who are identified as hypercapnic
- Cavalot et al 2022: across etiologies (enrolled in an emergency department), patients with hypercapnia (by ABG) have worse outcomes than patients without
- Not matched for other prognostic indicators ### Slide 11
- Aim 3: To estimate the potential impact of early & reliable identification
- Hypothesis: Patients predicted to have had hypercapnia that was not recognized have a similar or excess risk of subsequent readmission as those identified (by blood gas and/or ICD code)
- Study Design:
- Retrospective EHR-based review
- Dataset: Longitudinal, ideal self-contained system
- Exposures of interest:
- Hypercapnic Respiratory Failure by ICD code
- Hypercapnic Respiratory Failure by ABG (or by VBG) & not ICD code
- Predicted Risk of Hypercapnic Respiratory Failure by commonly obtained data-elements
- Effect modifier: PAP/NIV therapy
- Outcome: Hazard of death or rehospitalization with hypercapnia
- Those who likely had hypercapnia, but were not sampled
- Model predicted to have hypercapnia
- Comparison in consequences ### Slide 12
- Additional Design Thoughts:
- Data requirements: need to be able to accurately obtain relevant data elements for prediction model (if they were obtained by providers)
- Musts: Demographics, labs (BMP), ABG & VBG data, Diagnostic Codes, Admissions, Deaths
- Ideal? PAP and oxygen prescriptions
- Possible Designs
- Single arm
- Case-Control
- Matched Cohort
- Inclusion: 1st identified hypercapnic respiratory failure admission
- Case: 1st identified Hypercap-RF
- Control: matched hypox RF?
- Exposure: %Predicted likelihood of hypercapnia [including 100% when confirmed]
143 and rate of preceding likely-hypercap admissions
- OR of a prior “likely to have hypercapnia” encounter
- HR of readmission or death, modeled with restricted cubic spline [or categories] ### Slide 13
- Additional Design Thoughts:
- Threats to validity:
- Would be difficult to show it’s the predicted hypercapnia risk that raises risk, per-se (rather than things associated with it… negative control outcomes?)
- Risk of readmission may be lower when hypercapnia is identified and proper treatment is instituted (which will be hard to determine from either ICD codes or procedure codes, I think)
- Risk among patients with hypercapnia is not binary [ie risk PaCO290 >>> 50] ### Slide 14
- What is expected to come from this research?
- Validation of a method of stratification that can be used to:
- Facilitate the identification of patients with high CO2 → improved patient care
- Identify patients with hypercapnia (± a verification method) for study inclusion → more representative cohorts of patients.
- Enable future studies assessing:
- Overall burden of disease from hypercapnic respiratory failure
- Effect of exposures (medications, environmental, policy) on risk of hypercapnia
- Test the effect of treatment interventions on broader types of high-risk patients.
- Career-development:
- Development of models (statistical, ML, etc.) exploding in healthcare
- a dearth of validated uses → investigators with experience in prospective model application and validation needed
143.1 Learning objectives
- Improved Recognition of Inpatient Hypercapnic Respiratory Failure
- Hypercapnic Respiratory Failure
- E.g.: Home PAP improves mortality in ↑CO2-COPD
- By the time patients are caught: ↑morbidity
- OVERALL: There is an important burden of unrecognized hypercapnia among hospitalized patients.
143.2 Bottom line / summary
- Improved Recognition of Inpatient Hypercapnic Respiratory Failure
- Hypercapnic Respiratory Failure
- E.g.: Home PAP improves mortality in ↑CO2-COPD
- By the time patients are caught: ↑morbidity
- OVERALL: There is an important burden of unrecognized hypercapnia among hospitalized patients.
143.3 Approach
- TODO: Outline the initial assessment or decision point.
- TODO: Outline the next diagnostic or management step.
- TODO: Outline follow-up or escalation criteria.
143.4 Red flags / when to escalate
- TODO: List red flags that require urgent escalation.
143.5 Common pitfalls
- TODO: Capture common errors or missed steps.
143.6 References
TODO: Add landmark references or guideline citations.