Draft

164  Hypercapnia Update Jan 2023

164.1 Summary

  • Hypercapnia Research GroupJan 6 Meeting
  • Goal of project
  • Hypothesis 2: We can construct methods that select a more representative sample of patients (would require expanded data)
  • Hypothesis 3: validate how well hypercapnia definitions that can be based on features that can be created from other datasets (e.g. NIS) would perform and whether they are usable.
  • Data pre-processing
  • Data sanity checking
  • Admission VS (skewed center) e.g. for BMI – version w missing data?
  • Inpatient medication categories
  • Procedures. No TTE, EMG/NCS, PSG. No crit care time?
  • Chronic conditions: Heart failure high – rest not?
  • for labs e.g. ( value27441 (ABG pH) )- missing values are currently 0. They should be 7.4 (should we mean center?). Also exclude <6.5 and above 7.8
  • TODO: still need to sanity check acute diagnoses.

164.2 Slide outline

164.2.1 Slide 1

  • Hypercapnia Research GroupJan 6 Meeting ### Slide 2
  • Goal of project
  • Hypothesis 1: Commonly used methods of identifying hypercapnia identify different patients, and those patients differ in ways expected to influence prognosis and distribution of effect modifiers to treatments.
  • Question: how best to demonstrate this difference?
  • Principal Components are Hard to Understand
  • Stratification by a few key covarites (e.g. age, gender) by frequency of causative diagnoses. (or other features too?)
  •  idea, this improved stratification might allow better ‘conditional’ estimates knowing a patients comorbidities/clinical circumstance (as compared to more ‘marginal’ estimates that apply to the entire population. ### Slide 3
  • Hypothesis 2: We can construct methods that select a more representative sample of patients (would require expanded data)
  • In some sense, the fundamental problem of the research endeavor is that the reference standard diagnostic test (ABG) is frequently missing
  • Modeling P(obtaining ABG) and P(Hypercapnia | ABG obtained) may be one avenue.
  • Partial verification formulation: clinicians only seek a reference standard test in cases where some other features suggest hypercapnia is likely
  • If you make certain assumptions… can you do better? IPW can extend things to work if data is assumed to be missing at random. To what extent is it?
  • in order to know if that assumption is valid (enough), you’d need a gold standard reference… which would require prospective study on the population of interest (enriched population) ### Slide 4
  • Hypothesis 3: validate how well hypercapnia definitions that can be based on features that can be created from other datasets (e.g. NIS) would perform and whether they are usable. ### Slide 5
  • Data pre-processing
  • Missing data: For hypercapnia icd codes, treatment modalities, and ABG results -> the missingness of the data is a feature of interest
  • For other features: should we impute? ### Slide 6
  • Data sanity checking
  • Total
  • ABG Group
  • ICD Group
  • Procedure Group
  • N785,667
  • N678,424
  • N105,126
  • N2,117
  • Age
  • 60 (16)
  • 60 (17)
  • 62 (16)
  • 52 (19)
  • Gender
  • 46% (360,445)
  • 45% (304,832)
  • 52% (54,726)
  • 42% (887)
  • Asian
  • 1% (9,673)
  • 1% (8,420)
  • 1% (1,245)
  • 0% (8)
  • Black
  • 18% (138,026)
  • 18% (119,911)
  • 17% (18,026)
  • 4% (89)
  • Native American / Alaska Native
  • 0% (3,545)
  • 0% (3,041)
  • 0% (504)
  • 0% (0)
  • Native Hawaiian / Pacific Islander
  • 0% (800)
  • 0% (644)
  • 0% (156)
  • White
  • 62% (489,151)
  • 62% (418,632)
  • 66% (68,948)
  • 74% (1,571)
  • Latino
  • 6% (31,744)
  • 6% (27,896)
  • 7% (3,818)
  • 2% (30)
  • Location
  • ex
  • 10% (73,473)
  • 11% (72,632)
  • 1% (841)
  • midwest
  • 16% (123,411)
  • 17% (114,279)
  • 10% (9,132)
  • northeast
  • 23% (177,929)
  • 22% (148,086)
  • 34% (29,843)
  • south
  • 45% (348,616)
  • 47% (316,478)
  • 34% (30,021)
  • 100% (2,117)
  • west
  • 6% (43,175)
  • 4% (23,934)
  • 22% (19,241)
  • split out group membership into 3 binaries (yes/no ABG, yes/no ICD, yes/no Procedure)
    • currently too few procedure. But, that may be a function of sequential evaluation of criteria? ### Slide 7
  • Admission VS (skewed center) e.g. for BMI – version w missing data?
  • Total
  • ABG Group
  • ICD Group
  • Procedure Group
  • N785,667
  • N678,424
  • N105,126
  • N2,117
  • Age
  • 60 (16)
  • 60 (17)
  • 62 (16)
  • 52 (19)
  • Gender
  • 46% (360,445)
  • 45% (304,832)
  • 52% (54,726)
  • 42% (887)
  • BMI
  • 24 (5)
  • 24 (6)
  • 24 (4)
  • Respiratory Rate
  • 15 (3)
  • 14 (0)
  • Temperature (C)
  • 37 (1)
  • 37 (0)
  • Systolic BP
  • 123 (19)
  • 123 (17)
  • 122 (12)
  • Diastolic BP
  • 73 (12)
  • 72 (12)
  • 74 (11)
  • 75 (7)
  • Other features: [ ] spo2? o2? ### Slide 8
  • Inpatient medication categories
  • Total
  • ABG Group
  • ICD Group
  • Procedure Group
  • N785,667
  • N678,424
  • N105,126
  • N2,117
  • Age
  • 60 (16)
  • 60 (17)
  • 62 (16)
  • 52 (19)
  • Gender
  • 46% (360,445)
  • 45% (304,832)
  • 52% (54,726)
  • 42% (887)
  • PO Corticosteroid
  • 41% (320,131)
  • 41% (281,124)
  • 37% (38,901)
  • 5% (106)
  • Narcan or other antidote
  • 23% (183,224)
  • 25% (167,285)
  • 15% (15,896)
  • 2% (43)
  • Inhaled Treatments Rx’d
  • 37% (293,266)
  • 37% (252,748)
  • 38% (40,403)
  • 5% (115)
  • Vasodilators
  • 14% (107,701)
  • 14% (97,077)
  • 10% (10,554)
  • 3% (70)
  • Procedures. No TTE, EMG/NCS, PSG. No crit care time?
  • Total
  • ABG Group
  • ICD Group
  • Procedure Group
  • N785,667
  • N678,424
  • N105,126
  • N2,117
  • Age
  • 60 (16)
  • 60 (17)
  • 62 (16)
  • 52 (19)
  • Gender
  • 46% (360,445)
  • 45% (304,832)
  • 52% (54,726)
  • 42% (887)
  • CPAP proc. code
  • 7% (58,615)
  • 6% (43,070)
  • 15% (15,524)
  • 1% (21)
  • Aerosolized Treatment
  • 15% (119,865)
  • 14% (97,697)
  • 21% (22,093)
  • 4% (75)
  • Inhaler Teaching
  • 3% (22,402)
  • 3% (19,943)
  • 2% (2,401)
  • 3% (58)
  • 1 View CXR
  • 23% (177,806)
  • 22% (147,529)
  • 29% (30,156)
  • 6% (121)
  • 2 View CXR
  • 6% (46,579)
  • 6% (39,661)
  • 7% (6,903)
  • 1% (15)
  • CT Chest Non-Contrast
  • 4% (32,650)
  • 4% (27,317)
  • 5% (5,325)
  • 0% (8)
  • CT Chest w Contrast
  • 4% (30,349)
  • 4% (26,256)
  • 4% (4,072) ### Slide 10
  • Chronic conditions: Heart failure high – rest not?
  • Total
  • ABG Group
  • ICD Group
  • Procedure Group
  • N785,667
  • N678,424
  • N105,126
  • N2,117
  • Age
  • 60 (16)
  • 60 (17)
  • 62 (16)
  • 52 (19)
  • Gender
  • 46% (360,445)
  • 45% (304,832)
  • 52% (54,726)
  • 42% (887)
  • Asthma
  • 0% (2,934)
  • 0% (1,828)
  • 1% (1,106)
  • 0% (0)
  • Cystic Fibrosis
  • 0% (1,226)
  • 0% (1,041)
  • 0% (185)
  • CHF
  • 21% (166,238)
  • 19% (130,533)
  • 34% (35,641)
  • 3% (64)
  • CKD
  • 1% (5,485)
  • 1% (3,698)
  • 2% (1,787)
  • COPD
  • 0% (1,680)
  • 0% (1,232)
  • 0% (448)
  • Connective Tissue Disease
  • 0% (1,112)
  • 0% (813)
  • 0% (299)
  • Dementia
  • 0% (2,076)
  • 0% (1,394)
  • 1% (682)
  • Diabetes
  • 1% (7,894)
  • 1% (5,274)
  • 2% (2,620)
  • Nicotine dependence
  • 1% (4,727)
  • 0% (3,250)
  • 1% (1,477)
  • Neuromuscular Disease
  • 0% (924)
  • 0% (673)
  • 0% (251)
  • OSA
  • 1% (5,675)
  • 1% (3,523)
  • 2% (2,152)
  • Opiate Use Disorder
  • 0% (936)
  • 0% (680)
  • 0% (256)
  • Pulmonary Hypertension
  • 0% (2,703)
  • 0% (1,675)
  • 1% (1,028)
  • Peripheral Vascular Disease
  • 0% (2,095)
  • 0% (1,666)
  • 0% (429)
  • Stroke
  • 0% (2,124)
  • 0% (1,734)
  • 0% (390) ### Slide 11
  • for labs e.g. ( value27441 (ABG pH) )- missing values are currently 0. They should be 7.4 (should we mean center?). Also exclude <6.5 and above 7.8
  • Total
  • ABG Group
  • ICD Group
  • Procedure Group
  • p-value
  • N785,667
  • N678,424
  • N105,126
  • N2,117
  • Age
  • 60 (16)
  • 60 (17)
  • 62 (16)
  • 52 (19)
  • <0.001
  • Gender
  • 46% (360,445)
  • 45% (304,832)
  • 52% (54,726)
  • 42% (887)
  • pH (ABG)
  • 7.32 (0.11)
  • 7.37 (0.12)
  • HCO3 (ABG)
  • 24.8 (6.3)
  • 21.6 (5.8)
  • pH (VBG)
  • 7.35 (0.12)
  • 7.33 (0.12)
  • HCO3 (VBG)
  • 25.5 (6.6)
  • 25.4 (6.5)
  • 26.6 (7.4)
  • Serum Sodium
  • 138 (5)
  • 137 (5)
  • Serum Hemoglobin
  • 12.1 (2.5)
  • 12.2 (2.6)
  • 13.0 (2.5)
  • // what is value 21600? ### Slide 12
  • TODO: still need to sanity check acute diagnoses. ### Slide 13
  • PCA output
  • Knowing which data features are the major contributors to the principal components might guide which data elements should be included in future models?
  • Is the PCA useful on its own?
  • PCA: feature generation for other algorithms
  • Which algorithms and for what purpose?
  • K-means clustering -> what would this get us?
  • Regression? ### Slide 14
  • Partial verification formulation:
  • ABG is reference standard, but how well can things like VBG work as imperfect standards?
  • Latent class approach? ### Slide 15
  • Expanded Data Set
  • Target population: all patients in whom a clinician would consider that the patient has acute hypercapnic respiratory failure
  • Analytic sample: widened criteria that includes both patients who had hypercapnic failure, and patients who might reasonably have been expected to. (at admission) ### Slide 16
  • need robust truthfulness of data… integrity checks
  • Aim: only patient admissions where all the types of data needed WOULD have been reported if they were obtained in clinical care.
  • For diagnoses, only diagnosis that were known/made prior to that time. ### Slide 17
  • Next steps:
    • currently too few procedure. But, that may be a function of sequential evaluation of criteria?
  • Problematic features: why no PSG EMG and NCS billed in the entire dataset? - restrictions on location of care?. Yet, TTE also isn’t but clearly that is done inpatient frequently.
  • What pre-screening can we do to improve the completeness of the sample?
    • require at least 1 result from all categories to be included?
    • eliminate iatrogenic cases… somehow? ### Slide 18
  • Structure to final paper:
  • Currently used methods A, B, C, and D over-selected type and underselected type compared to
  • Possible features from the data that could be used to identify, or phenotype, patients include
  • For research that aims to enroll a representative sample of patients to estimates the prognosis, burden to the healthcare system, or improve processes of care should consider to identify these patients.
  • A future prospective study evaluating the performance of these methods is warranted. ### Slide 19
  • Stratifying causes
  • Also from that - could I create —> key figure of comorbidities by age in hypercapnia.
  • By age? by BMI ; stratified by gender ### Slide 20
  • U of U Data

164.3 Learning objectives

  • Hypercapnia Research GroupJan 6 Meeting
  • Goal of project
  • Hypothesis 2: We can construct methods that select a more representative sample of patients (would require expanded data)
  • Hypothesis 3: validate how well hypercapnia definitions that can be based on features that can be created from other datasets (e.g. NIS) would perform and whether they are usable.
  • Data pre-processing

164.4 Bottom line / summary

  • Hypercapnia Research GroupJan 6 Meeting
  • Goal of project
  • Hypothesis 2: We can construct methods that select a more representative sample of patients (would require expanded data)
  • Hypothesis 3: validate how well hypercapnia definitions that can be based on features that can be created from other datasets (e.g. NIS) would perform and whether they are usable.
  • Data pre-processing

164.5 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.

164.6 Red flags / when to escalate

  • TODO: List red flags that require urgent escalation.

164.7 Common pitfalls

  • TODO: Capture common errors or missed steps.

164.8 References

TODO: Add landmark references or guideline citations.

164.9 Slides and assets

164.10 Source materials