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https://github.com/zimolzak/covid-datathon

Analysis of COVID outcome predictors, and of datathon survey responses.
https://github.com/zimolzak/covid-datathon

covid-19 datathon hackathon

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Analysis of COVID outcome predictors, and of datathon survey responses.

Awesome Lists containing this project

README 

          
Lessons learned from an enterprise-wide clinical datathon

========



[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13261085.svg)](https://doi.org/10.5281/zenodo.13261085)



This repository contains code that was used in the research reported

in the following journal article:



Zimolzak AJ, Davila JA, Punugoti V, et al. Lessons learned from an

enterprise-wide clinical datathon. J Clin Transl Sci. 2022;6(1):e125.

Published 2022 Aug 24.

[doi:10.1017/cts.2022.450](https://doi.org/10.1017/cts.2022.450)



- [PMID 36590351](https://pubmed.ncbi.nlm.nih.gov/36590351/)



- Free full text at [PMCID PMC9794964](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794964/)



- Direct [link to

  paper](https://www.cambridge.org/core/journals/journal-of-clinical-and-translational-science/article/lessons-learned-from-an-enterprisewide-clinical-datathon/A399543C00670D6A43F1911D28A111ED)

  at *Journal of Clinical and Translational Science* and

  [PDF](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/A399543C00670D6A43F1911D28A111ED/S2059866122004502a.pdf/lessons-learned-from-an-enterprise-wide-clinical-datathon.pdf)

  from the journal. (Paper is licensed CC BY-NC-SA 4.0.)



* * *



Items from my own datathon project below this line.



Datathon: Predictors of severe COVID-19 outcomes

========



1. Characterize the BCM experience with COVID, including

hospitalization rate by comorbidity, and ICU utilization.



2. Train a multivariable predictive model for severity of COVID (ICU

admission, incidence of ARDS criteria, length of stay, and in-hospital

mortality) as a function of known and novel factors such as

comorbidity.



3. Study the covariation in severe outcomes with treatment modalities

to evaluate population-level changes.



The file you probably want

--------



`bslmc_v6.R` (click on it up above).



`Routputs_v6.txt` for results (although you should treat them as proof

of concept only and not believe them. Not even hypothesis-generating.)



Also I feel the `outputs/makefile` file is useful and nifty.



Analytic data set sketch

--------



* one row per ED encounter

    * patient ID of course

    * ER visit diagnoses

    * admission diagnoses, if admitted

    * covid test date(s) & results

    * ER visit index date

* outcomes as follows:

    * admitted yes/no

    * ER directly to ICU yes/no

    * length of stay, if admitted (continuous)

    * pao2:fio2 ratio (future summary measure, of oxygenation)

    * mortality yes/no (and date)

    * intubated yes/no (and date)

    * maybe future ICU admit & date if I can manage it

* predictors as follows

    * demographics

        * age

        * sex

        * race

        * ethnicity

        * ZIP

    * comorbidities (two columns for each: N prior visits or rate, and prob list yes/no)

        * diabetes

        * copd

        * asthma

        * hypertension

        * coronary disease

        * cancer

    * number of prior hospital admissions (or rate)

    * number of prior ER visits (or rate)

    * vitals (summary meas if needed)

        * temp

        * pulse

        * respirations

        * BP

        * SpO2

        * height

        * weight

    * labs (summary measure of labs just before/on index date)

        * wbc

        * hgb

        * plt

        * sodium

        * K

        * bicarb

        * creatinine

        * d-dimer

        * CRP

        * LDH

        * BUN

        * HDL

        * direct bilirubin

        * RDW

        * albumin

        * neutrophils

        * lymphocytes

        * ALT

        * P:F ratio can be predictor for more "hard" downstream events



Data pull spec

--------



Include: anyone with *positive* covid test. BSLMC and BCM outpatient.

If more than 10,000 patients, OK to randomly sample. (Seems to be 1100

to 1800). 700 ish at office visit in person.



Tables (inpatient/BSLMC):



- PAT_ID



- ENC_DX including distant past



- PROBLEM including distant past



- ORDER_RESULTS only need recent dates like 2020



- HSP, including all types of visits (ER, inpatient, etc.). OK to

  limit to 2020 only, but not necessary.



- possibly flowsheet: height weight systolic diastolic pulse

  respirations spo2. limit to intubated or other o2 params, and spo2

  po2.



- discharge disposition (looking for discharge disposition)



Tables (outpatient):



- TBD



Where to look in this code

--------



High value areas would be `bslmc_v4_DataSets_pipe.R` and `bslmc_v6.R` for inpatient

data and `analysis_outpat.R` for outpatient. Pay the most attention to

variables & values mentioned within `select()` and `filter()`

statements to get a sense of how the data is structured.



Requirements for current repo

--------



- R, Rscript

- R packages: dplyr, ggplot2, tidyr, lubridate, here, earth, ROCR

- make and usual UNIX-like toolchain (mv, rm, cp)

- pandoc (only for documentation)



Datathon "alpha phase" use case examples

========



|ID| Hard? | Waiting on:   | Description                                                 |

|--|-------|---------------|-------------------------------------------------------------|

|1 | Easy  | **Done**      | Test volume, positive tests, by date. **Foundational.**     |

|2 | Easy  | **Done**      | Count tests, positive tests, by comorbidity (see below).    |

|3 | Easy  | Andy          | Retest volume, likelihood of positivity. By clinic.         |

|4o| Int.  | **Done**      | Pulse oximetry (SpO2) by positive/negative.                 |

|4i| Int.  | **Done**/Rory | " " "                                                       |

|5 | Int.  | Andy          | Basic labs (see below) by positive/negative.                |

|6 | Adva. | Rory          | *I:* People who "touch" the chart (PPE estimate).           |

|7 | Adva. | Andy          | *I:* Rate of testing late in an admission, rate of positive.|

|8 | Adva. | Rory          | *I:* Basic descriptives (LOS, floor/hosp census by date).   |

|9 | Adva. | Andy          | *O:* Rt. of admission/ER (manual rev.?); risk factors.      |

|10| Adva. | Andy          | Anything to do with mortality.                              |



Capture what Baylor Medicine clinic it was sent from. Capture what lab

(vendor) it went to (LabCorp, or whoever), called "test perfomed by."

Most common "lab facility" are CPL and LabCorp. Slicer has the

*confirmed* and the *suspected* registries for COVID.



Define "comorbidity"

--------



1. The "big four": Asthma, COPD, DM, HTN.[^ehrn]



    a. Define first using just the problem list. This is "middle

    school level."



    b. Then define using encounters, "high school:" anyone with 2 or more

    encounters, from the beginning of time, is defined has having the

    diabetes (copd, htn, etc.) phenotype. Also note that we want to

    know the *value* of the counts: really the *distribution* of the

    count over patients. (100 patients have 2 COPD encounters, 10 have

    3 encounters, and 1 patient had 10 encounters, since beginning of

    the database, which means 5 years.) Final note: for big four, this

    depends only on the encounters, not on problem list. (A woman with 3

    COPD office visits in past year should be considered as having

    COPD according to this rule, even if she does not have it in the

    problem list.)



    c. Then meds, "college level." Requires increasingly more work.



    d. Then more fancy, such as moving beyond rules based phenotype

    definitions. Probably don't do this for purposes of this stage in

    the datathon.



2. Also want a broad view of all "medical history" items in problem

list (not just big four). *This is shown on one of Gloria's SlicerDicer

slides as a bar graph.*



Epic has notion of encounter for office, procedure, and rx. Gloria

knows someone for data quality of tobacco use.



Define "labs"

--------



Labs are called "procedure." Labs are of definite interest. Big ones

(values are interesting): cmp, cbc, flu, bmp, crp, d-dimer, ferritin. Within

those lab panels, the values I care most about are: sodium,

creatinine, and white blood cell count, detailed white cell

differential (neutrophils, lymphocytes, monocytes, eosinophils,

basophils).



In broader "non big-one" sense, I *do* want to know *whether* they

have the lab ordered (e.g. A1c, lipid panel: I don't care so much

about the exact value, but I want to know whether or not the patient

had the lab ordered/done). *This is shown on one of Gloria's

SlicerDicer slides.*



[^ehrn]: Epic Health Research Network, https://ehrn.org/