Draft

199  Locke PGR

199.1 Summary

  • Who has hypercapnia and how much should we care?
  • What makes a problem worth researching?
  • How should we study these patients?
  • Shared Management
  • Would GLP-1RA work in patients with obesity and hypercapnia regardless of cause?
  • What are the most important (population) causes of Hypercapnia?
  • What do we know about sleep?
  • Analogy to decompensated cirrhosis: is hypercapnia decompensated ventilation?
  • Beware of extrapolation: who counts is tricky

199.2 Slide outline

199.2.1 Slide 1

  • Who has hypercapnia and how much should we care?
  • BRIAN LOCKE PGR
  • (Title Redacted)
  • This work is supported by the American Thoracic Society ASPIRE Fellowship and the National Institutes of Health under the Ruth L. Kirschstein National Research Service Award 5T32HL105321
  • Disclosure: I hold a financial stake and advise a local startup called Mountain Biometrics (focused on data processing of wearable sensor data) ### Slide 2
  • What makes a problem worth researching?
  • INT Framework (World Health Organization and other grant-makers)
  • Importance of the problem:
  • How many people (will be) affected by the problem, and how intensely?
  • Neglected-ness
  • How much effort is already going toward solving the problem? (is it going to be solved anyway? Will a bit more effort help?)
  • Tractability
  • Is the problem solvable? (What has prevented others from solving it?)
  • Hypercapnic Respiratory Failure scores high in all 3 ### Slide 3
  • How should we study these patients?
  • Paradigm
  • Disease-Based
  • Syndrome-Based
  • Pros
  • The causative disease is usually an effect modifier for treatments → homogenous response better for efficacy trials.
  • Funders often interested in specific diseases
  • Hypercapnia is often recognized before the cause; outpatient workup, so initial management is empiric.
  • Efficacious management shares features.
  • Enables assessment and improvement of processes of care.
  • Cons
  • Difficult to extrapolate results to patients with multiple contributing conditions.
  • Evidence only applies to definitely-evaluated patients
  • May distract from better understanding the way different cases differ
  • Lumping vs Splitting (e.g. Sepsis, ARDS) ### Slide 4
  • Shared Management
  • Annals ATS 2024 ### Slide 5
  • Would GLP-1RA work in patients with obesity and hypercapnia regardless of cause?
  • 10 RCTs (4 surgical; 6 med. n854)
  • Study-level meta-regression
  • 1% weight loss → 0.45 less AHI event/hr
  • How often does obesity or OSA contribute to hypercapnic respiratory failure? ### Slide 6
  • What are the most important (population) causes of Hypercapnia?
  • 1 hospital serves population of Liverpool, AUS.
  • Random sample of ED cases (PaCO2 ≥ 45 mmHg) and stratified sample of community non-cases.
  • Assess: Med Use, BNP, Spiro, HSAT, SNIP
  • Estimate Population Attributable Fraction: cases that would be prevented if factor eliminated.
  • Fatal problems: 10% got HSAT; causal pathways
  • (Spiro)
  • (BNP)
  • (HSAT)
  • (SNIP) ### Slide 7
  • What do we know about sleep? ### Slide 8
  • Analogy to decompensated cirrhosis: is hypercapnia decompensated ventilation?
  • (more properly, acute/chronic liver failure- as cirrhosis is the pathology, not the syndrome)
  • Consider: Liver failure requires shared management regardless of disease cause.
  • 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 to decompensated cirrhosis: 94.9 per 100,000 person-year (2017)
  • Inpatient ICD code for Hypercapnic Respiratory Failure
  • 23% 30-day readmission rate (2 in 3 with recurrence of hypercapnia)
  • Comparison to decompensated cirrhosis 12.9% in 30d, 21.2% in 90d in 2012 ### Slide 9
  • Beware of extrapolation: who counts is tricky
  • Data Source for several studies:
  • Encounters: Jan 1 to Dec 31, 2022
  • TriNetX research network: EHR data from 76 medical centers, roughly 115 million patients
  • Enriched data set mirroring the spectrum of patients a good provider should consider that hypercapnia might be present
  • One of: Predisposing condition (incl. BMI > 40), any resp failure, received ventilatory support, received ABG or VBG (regardless of result)
  • Data quality check: each encounter must have 1 of each data type ### Slide 10
  • Beware of extrapolation: who counts is tricky ### Slide 11
  • Beware of extrapolation: who counts is tricky
  • Structured, AI-assisted review identified 14 studies pertaining to the prognosis of any-cause hypercapnic respiratory failure.
  • Inclusion crit: Variety of ABG, VBG, ICD, & procedure codes used
  • TriNetX Emulation: Limited overlap, ICD codes very insensitive. ### Slide 12
  • Beware of extrapolation: who counts is tricky
  • If you have hypercapnia, context influences the likelihood of Hypercap RF Label
  • Cohorts identified by the various case definitions varied widely. E.g:
  • 16% Black using Wilson to 26% using Calvo
  • 43% receiving a COPD diagnosis code in Meservey to 23% using Adler, Thille, and Cavalot
  • Case definitions requiring a procedure code for ventilatory assistance → more patients who received critical care services, and died
  • Crux of the problem: Many patients who have hypercapnia don’t get an ABG. P(ABG) varies. ### Slide 13
  • Epidemiologic Theory
  • What is the effect of an exposures (E) on an outcomes (O)
  • Often, a confounder (influences both E and O) distorts the relationship.
  • If there is an association between E and O, there are 4 explanations:
  • Chance
  • Confounding
  • Bias
  • Selection
  • Measurement
  • A real effect
  • Argument for cause is the disjunctive syllogism:
  • “When you have eliminated the impossible, whatever remains, however improbable, must be the truth.”
  • If you can’t reliably identify who has hypercapnia, you can’t study its impact on outcomes, control for it in studies of other things, or use it as an outcome. ### Slide 14
  • How often is respiratory depression reported in clinical trials?
  • Severe adverse drug events (SAE): death, hospitalization, prolongation of hospitalization, disability, incapacity, or situations where action is required to prevent one of the preceding events. [no attribution to intervention]
  • ADEs must be submitted to clinicaltrials.gov.
  • Shi & Du used BioBERT to extract arm-level data, including SAEs
  • 6,429 trials (summarizing data from 4,863,170 participants) reported outcome & adverse even
  • Additional 5,300 trials (4,097,827 participants) uploaded at least one adverse event.
  • Categorized: plausible (e.g. ‘respiratory failure’) or definitive (e.g. ‘hypoventilation’)
  • 3.8% of completed trials, and 2.6% of partially reported trials reported 1+ definitive event.
  • Mostly NOT medications known to suppress resp. drive: opioids, benzos, gabapentinoids
  • Dustin Anderson-Bell & Brian Locke
  • CHEST 2024 abstract ### Slide 15
  • How can we improve identification? Make better use of the data elements we have
  • Venous Blood Gas
  • ABG: PaCO2 ≥ 45 mmHg calendar day of admit
  • VBG: VBG PCO2 ≥ 50 mmHg calendar day of admit
  • VBGcorr (Farkas): If VBG SO2 < 90%, then PCO2 - 0.2 (diff. in A-V O2 sats)
  • How much are VBGs currently used?
  • Patients with an ICD for Hypercap. RF
  • Admit Day:
  • No VBG
  • Had VBG
  • No ABG
  • 16.6%
  • 13.2%
  • Had ABG
  • 39.9%
  • 30.3%
  • Association of evidence of hypercapnia with clinical course
  • Test showing ↑ CO2
  • ABG
  • VBG
  • VBG Corr
  • OR of Hypercapnic RF ICD
  • 6.5
  • 5.6
  • 6.1
  • Absolute Increase (%)
  • 10.3%
  • 9.8%
  • 10.2%
  • OR of subsequent events during hospitalization
  • Steroid
  • 1.51
  • 1.13
  • 1.2
  • Crit Care
  • 1.83
  • 1.74
  • 1.86
  • NIV or IMV
  • 2.6
  • 2.02
  • 2.29 ### Slide 16
  • How can we improve identification? Make better use of the data elements we have
  • Paired Serum HCO3- (BMP) and PaCO2 (ABG) measurements
  • Optimal HCO3- Threshold (Youden Index)
  • Ambulatory
  • ≥ 27 mEq/L
  • Se: 60.06%; Sp: 83.55%
  • Emergency
  • ≥ 26 mEq/L
  • Se: 59.19%; Sp: 75.37%
  • Inpatient
  • Se: 54.44% Sp: 80.97%
  • Critical Care
  • ≥ 25 mEq/L
  • Se: 57.07% Sp: 76.30% ### Slide 17
  • TODO: No text extracted from this slide. ### Slide 18
  • TODO: No text extracted from this slide. ### Slide 19
  • TODO: No text extracted from this slide. ### Slide 20
  • A list of important answered questions in Hypercapnic Respiratory Failure
  • What are the reasons providers do or don’t order blood gasses?
  • What are the most common causes of hypercapnia?
  • What proportion of patients identified as inpatients are referred for and receive definitive evaluation (e.g. PFTs, Sleep Study, Echo)?
  • What proportion of inpatients with hypercapnia are recognized?
  • What happens to the patients who are not recognized?
  • Is hypercapnia a predictive or causal agent of harm?
  • Are VBGs a sufficient surrogate for ABGs?
  • Should we be aggressively identifying hypercapnia (or will this lead to overdiagnosis)?
  • What are the burdens of disease associated with any-cause hypercapnic respiratory failure?
  • How should we manage multifactorial hypercapnic respiratory failure?
  • … etc … etc … etc ..
  • Scoping the research is challenging. Where to start? ### Slide 21
  • K23: Bioinformatics Tools to Quantify the Disease Burden Associated with Hypercapnia
  • Gaps:
  • Identification of hypercapnia appears to be idiosyncratic, which is a problem for patient care and science.
  • Admissions with hypercapnia are associated with substantial health burdens, but estimates are from single-centers, rely on local identification approaches, and may not extrapolate.
  • It is unclear if the occurrence of hypercapnic respiratory failure is a general marker of illness, a specific marker of respiratory frailty, or a causal agent of harm. ### Slide 22
  • K23: Bioinformatics Tools to Quantify the Disease Burden Associated with Hypercapnia
  • My goal is to be an expert in chronic respiratory failure that uses clinical research informatics to enable embedded clinical studies of this population
  • Career Development Objectives:
  • Build experience characterizing care processes and variation
  • Informatics deployment: Gain expertise and experience deploying informatics tools (LLMs and Risk Models) for use in clinical research
  • Learn Clinical investigation: Learn to lead prospective studies ### Slide 23
  • Aim 1: Characterize Current Diagnosis and Workup of Hypercapnic Respiratory Failure
  • Problem: Many patients are identified as inpatients at decompensation but need outpatient workup and management. There is ~no guidance.
  • Gap: There is no modern estimates of how well we diagnose or evaluate patients presenting with hypercapnic respiratory failure
  • Hypotheses:
  • There is substantial variation by institution, provider (training), and patient factor in who receives ABG, VBG, diagnosis, and workup
  • Most patients are identified as having hypercapnia do not receive definitive workup (PFTs, PSG, pulmonary referral) ### Slide 24
  • Aim 1: Characterize Current Diagnosis and Workup of Hypercapnic Respiratory Failure
  • Solution:
  • Linkage of Intermountain Health medical records and UT All Payor Claims Database, which contains information on follow-up
  • Includes (outpatient) testing, referrals, readmissions, deaths
  • All patients with hypercapnia identified by VBG, ABG, ICD code, and risk modeling (experimental aim: augmented with NLP)
  • Multi-level logistic regression modeling to characterize factors influencing test ordering
  • Summary of referral for testing, receipt of testing, readmission, death. ### Slide 25
  • Aim 2: Augment and Locally Validate Hypercapnia Diagnostic Modeling
  • Problem: Methods of identifying hypercapnia are haphazardly applied, inconsistent, and not patient-centered.
  • Gap: Reliable, routine methods for identification of hypercapnia are needed.
  • Hypothesis: The likelihood a patient has hypercapnia can be estimated from routinely obtained data elements, like labs and notes. ### Slide 26
  • Diagnostic Risk Modeling
  • Solution: Diagnostic Modeling (Diagnostic Risk Modeling ~ Clinical Decision Rule ~ Prediction Modeling, Diagnostic nomogram)
  • Wells score is a logistic regression ### Slide 27
  • Diagnostic Risk Modeling
  • Estimate the likelihood of hypercapnia at admission in inpatients.
  • Predictors: must be routinely available, obtained before/independently of blood gas
  • 2 approaches: LASSO Logistic Regression; Random Forest
  • Assess distribution of predictions, discrimination, calibration
  • Partial verification: vs any blood gas (ABG > 45, VBG > 50); inverse propensity weighted ABG
  • Predictors: age, sex, body mass index [BMI], components of basic blood chemistry testing [sodium, potassium, chloride, bicarbonate, urea, and creatinine], and hemoglobin. Selected a priori on clinical relevance and availability. ### Slide 28
  • Partial Verification: you can only train on data with outcomes available. ### Slide 29
  • Aim 2: Augment
  • Problem: model accuracy is imperfect
  • Gap: Symptoms, which greatly improve prediction accuracy, are in notes.
  • Hypothesis: extracting this data using locally-deployed LLMs will improve the accuracy of predictions about the risk of hypercapnia ### Slide 30
  • Aim 2: Augment Model
  • Solution: Large language models enable transfer learning (more on this next week).
    1. Extract: Chief concern, confusion, dyspnea, etc. from nursing triage note.
    1. Use as predictor in existing model
    1. Assess vs limited annotated corpus, but mainly for impact on model. ### Slide 31
  • Aim 2: Validate Model
  • Problem: We’re interested in people who haven’t received blood gas verification. How well will it work in practice?
  • Gap: Research consent for ABGs will be biased (and patients with hypercapnia are confused)
  • Hypothesis: Risk prediction modeling can identify a group of patients highly likely to have hypercapnia. ### Slide 32
  • Is TcCO2 usable for hypercapnia ascertainment?
  • Conway et al.: 7021 paired measurements taken from 2817 participants in 73 studies
  • mean bias: 0.085 mmHg
  • within study SD(agreement): 3.51 mmHg
  • In-silico simulation of paired TcCO2 measurement for each PaCO2 obtained in 2022 via TriNetX.
  • Sensitivity
  • Specificity
  • Neg. Pred. Val.
  • Pos. Pred. Val
  • 86.8%
  • 93.7%
  • 85.7%
  • 94.2% ### Slide 33
  • Aim 2: Validate Model
  • Solution: Validate model predictions using TcCO2 on newly admitted adults to IMED medical floors.
  • Protocol:
  • Admit to medicine – apply TcCO2 within 24 hours to patients with predicted high likelihood of hypercapnia
  • N63 patients for power to approximate PaCO2 +/- 12.5 mmHg in patients predicted 50+% likely.
  • Deferred (vs waived) consent ### Slide 34
  • Outcomes of this work:
  • Three novel contributions:
  • Estimate current use (and variation) of diagnostic surrogates like venous blood gas in a large integrated health system.
  • Improve estimates of the prognostic significance of any-cause hypercapnia
  • Establish ABG & VBG independent methods of enrollment
  • Potential weaknesses: reliance on diagnostic modeling and transcutaneous CO2 sensors. Broad Scope
  • Strengths: extensive preliminary modeling, contingency plans,
  • Development of an informaticist-investigator with technical expertise in rapidly progressing subfield of data-science tools, such as LLMs. ### Slide 35
  • Acknowledgements:
  • UU PCCM Collaborators: Krishna Sundar, Jeanette Brown
  • UU PCCM Advisors: Rob Paine, Barb Jones
  • UU DBMI Collaborators: Ram Gouripeddi, Wayne Richards
  • IMED PCCM Advisors/Collaborators: Ithan Peltan, Sam Brown, ACR Research Group

199.3 Learning objectives

  • Who has hypercapnia and how much should we care?
  • What makes a problem worth researching?
  • How should we study these patients?
  • Shared Management
  • Would GLP-1RA work in patients with obesity and hypercapnia regardless of cause?

199.4 Bottom line / summary

  • Who has hypercapnia and how much should we care?
  • What makes a problem worth researching?
  • How should we study these patients?
  • Shared Management
  • Would GLP-1RA work in patients with obesity and hypercapnia regardless of cause?

199.5 Approach

  1. TODO: Outline the initial assessment or decision point.
  2. TODO: Outline the next diagnostic or management step.
  3. TODO: Outline follow-up or escalation criteria.

199.6 Red flags / when to escalate

  • TODO: List red flags that require urgent escalation.

199.7 Common pitfalls

  • TODO: Capture common errors or missed steps.

199.8 References

TODO: Add landmark references or guideline citations.

199.9 Slides and assets

199.10 Source materials