Surgery, anesthesiology, and emergency and critical care medicine

The Biostatistics Clinic on Wednesdays is dedicated to biostatistics applications in surgery, anesthesiology, and emergency and critical care medicine.

Click here for 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, and before.

Current Notes (2022)

2022 July 20

Isaura Diaz, Pediatric Critical Care

We devised a national survey to evaluate the number of PICU programs in our Nation have a cardiac simulation curriculum in place. Out of 72 programs we had 32 programs were represented and 16 of these programs reported a cardiac simulation curriculum that was in place. We are now writing up a manuscipt regarding our findings and would like to confirm the statistical portion of our findings.

2022 July 13

Shiayin Yang, Otolaryngology

We are investigating the effect that reconstruction on the nose after cancer removal has on breathing. We would like to determine if certain nasal areas, depth of defect, and type of reconstruction have increased likelihood of nasal obstruction after Mohs surgery.

2022 June 29

Ankush Ratwani (Fabien Maldonado), Pulmonary and Critical Care

Question for Chris Lindsell, already aware of the project. Question relating to exclusion after randomization and design for our study. Mentor confirmed.

2022 June 22

Raymond Zhou (Louise Mawn), Ophthalmology and Visual Sciences

IIH is a growing health concern affecting obese young women, with risk factors and treatments that are under continual study. We want to identify what risk factors and treatments make severe vision loss, measured by visual field mean defect (VFMD) and visual acuity. in patients with IIH more or less likely. We intend to do so by studying all patients with IIH that were previously treated at VUMC. We intend to collect data on the presence or absence of various risk factors, including obesity, race, and gender in these patients. We also intend to collect data on the provision of various invasive treatments that release pressure and divert fluid pushing on the optic nerve. Our primary statistical analysis will utilize a multivariable linear regression model with the outcomes being the continuous variables VFMD and Visual Acuity. Exactly as suggested by VICTR, this will increase the power of our study. Secondary analyses will be done using the dichotomous variable +/-decrease in visual acuity, and +/- decrease in VFMD. While previous studies have studied these dichotomous variables, this has several drawbacks including loss of efficiency and potential for ceiling/floor effects. We appreciate the question and recommendation that was provided by VICTR. We would like to further obtain feedback regarding our proposed statistical analyses. Additionally, we would like to potentially explore the potential need to obtain additional biostatistical support, as our co-collaborators will be beginning residency soon. The Department of Ophthalmology has a designated institutional partnership with Dr. Chen, who we would love to work with. It is my understanding that, to collaborate with Dr. Chen, projects must be discussed at a Biostats clinic. Mentor confirmed.

Shaan Setia (Ryan Hsi), Urology

ITS analysis. Mentor confirmed.

2022 April 20

Kathy Mendieta (Katie Boyle), Pediatric Critical Care

Our QI project entails the use of a CPR Coach during resuscitation events in both the medical and cardiac ICU. Since implementing this new role (April 2021) we have collected data via survey following a resuscitation event. We are in the process of reviewing objective data (blood pressure, end-tidal; if compressors switched every 2 minutes, and presence of equipment required for high-quality CPR) and comparing codes with a CPR coach versus without. Mentor confirmed.
  • Meeting Notes:
    • Main goal: after implementation of the new role, want to compare codes with and without a CPR coach (compare vital signs and use of new tools)
    • N = 46 codes collected in the survey currently (around 60% with a CPR coach)
      • All after the implementation of the CPR coach
  • Recommendations:
    • Recognizing this is an observational study, can go ahead with the current number of cases.
    • Can estimate the magnitude of the difference between the two groups
    • Need to recognize that it is unknown who got a coach and why
    • Recommended to apply for a VICTR voucher?

Allison Weatherly (Kristina Betters), Pediatric Critical Care

We want to look at the relationship between hyperoxia on bypass and delirium outcomes in patients in the pediatric cardiac ICU via a retrospective chart review. Delirium is prevalent in this population (seen in up 60%+ of patients) and so we're wondering how many patients we would need to power this study. Additionally, we have been discussing how to measure/analyze hyperoxia. We are thinking about looking at time spent at a given paO2 vs change in NIRS while on bypass (we have minute to minute data on NIRS) and so discussions about what data to pull from the bypass records and how we would potentially analyze this. Other hyperoxia studies that we have looked at, look at AUC vs separate paO2 into quartiles. I think that this particularly needs to be flushed out as it will affect data collection significantly. Mentor confirmed.
  • Meeting Notes:
    • Multiple ways to define delirium
    • Is a slightly hyperoxia over a long time and large hyperoxia in a short time the same thing?
    • Around 40-60% in this group will have delirium
  • Recommendations:
    • Change the research question to: Do different measures of hyperoxia have different associations with delirium?
    • Write down the different ways to measure hyperoxia
    • Will be an exploratory study with multiple exposures
    • If about 50% of the group has delirium, this will maximize the power
    • Recommended to apply for a VICTR voucher

2022 March 2

Chiara Foster (Kristina Betters), Pediatric Critical Care

Cost analysis of a sedation weaning protocol. Assessing how different the two groups are and comparing costs with median and interquartile ranges as well as discussing if secondary analyses are indicated. Mentor confirmed.
  • Meeting Notes:
    • Costs are usually not distributing normally
    • Costs in this study are purely pharmaceutical (no hospital costs)
    • Three different drugs: some patients are on all 3, but some are only on 1 or 2
      • Have a risk score for each drug, but not an overall risk score
  • Recommendations:
    • To statistically compare the 2 groups, could compare the medians
      • Mann-Whitely U test (does not adjust for risk)
      • To adjust for previous exposure, could use a proportional odds model or a Cox model, adjusting for risk
    • Can use a chi-square test to compare the risk between the two groups
    • For the VICTR voucher, Chris will help with the appeal process

Christina Boncyk, Anesthesiology

The primary aim of this observational study is to utilize the electronic health record to identify and validate environmental and structural SDOH and to determine the association between these SDOH factors and ICU-free days over the first 28 days following admission in critically ill patients with COVID-19. We believe that there will be a correlation between the identified markers of SDOH and ICU free days at 28-days following admission.

We plan to use logistic regression adjusting for the Charlson comorbidity index (CCI), the Elixhauser-based comorbidity summary, and the van Walraven summary score. Secondary outcomes will include ventilator-free days over 28-days, 28-day in-hospital mortality, duration of hospitalization, and discharge location. We will perform linear regression modelling adjusting for the same confounders of CCI, Elixhauser comorbidities, and van Walraven summary score.
  • Meeting Notes:
    • Hypothesis: SDOH is associated with in-hospital outcomes among critically ill adults diagnosed with COVID-19
    • Primary outcome: ICU-free days over first 28 days
    • Secondary outcomes: ventilator-free days, 28-day in-hospital mortality, hospital length of stay, discharge location
    • Overall goal: look at different SDOH associations with the outcomes
  • Recommendations:
    • Can test the SDOH one by one
    • A VICTR voucher may be appropriate
      • First need to identify variables to include
      • Chris can help with the SAP
    • Generating and validating a predictive model is likely too big for a VICTR voucher

Current planning and questions we are hoping to answer would be discussion on how study plan could benefit from a biostatistician with an expertise in geospatial/geographic data, what we need to include while constructing our data output, and other experience within this type of analysis.

2022 February 16

Bo Stubblefield, Emergency Medicine

Analysis of bleeding outcomes in a subset of patient with acute pulmonary embolism and high risk features.
  • Meeting Notes:
    • Main question: Do higher risk patients have worse bleeding outcomes?
    • Primary outcome: could make a composite outcome (unscheduled visit for bleeding, rehospitalization for bleeding, transfusion) – all within 30 days
    • Potential secondary outcomes: complications (renal failure, liver failure, stroke, MI); rehospitalization for PE of DVT
  • Recommendations:
    • Any conclusions can only be applied to those who are discharged home
    • Seems more like an exploratory risk factor analysis - explore the prevalence of each exposure
    • Look into the derivation of the PESI score and find all their candidate risk factors
      • Can look at ones that were considered and ones that were not, but it will be important to note

2022 January 19

Montana (Lori) Fleenor (Joanna Stollings), Pharmacy

This study is a retrospective cohort study looking at the effects of tocilizumab on prevalence of delirium and coma in COVID-19 patients. Sample size will consist of a control vs. tocilizumab group (anticipate ~120-150 pts total). We aim to characterize the incidence and duration of delirium/coma in these patients. Independent variable: control vs. tocilizumab group. Dependent variable: Days alive without delirium/coma (denominator of 21 days). At least one instance of delirium by a positive CAM-ICU will be considered delirious for that day. At least one instance of coma by a RASS -4/-5 will be considered comatose for that specific day. Also need to incorporate death into the equation because this is looking at days alive without delirium or coma. Covariates: Age, sex, CRP, Charlson Comorbidity Index, SOFA, and vent status. Proposed statistical model: Proportional odds logistic regression. Questions: 1)What is your opinion on adding these other covariates? -sepsis, steroids use, delirium, total daily dose of analgesics and sedatives (benzodiazepine, propofol, opioids, dexmedetomidine) 2)Will it be beneficial to also consider precision variables, that only affect dependent variables? 3)Do we need additional statistical models? 4)Should we look at ICU, hospital length of stay, and 90 day mortality? VICTR Biostatistics voucher. Mentor confirmed.
  • Meeting Notes:
    • Looking at delirium outcomes before and after TOCI was used as the standard practice for COVID patients
    • Proposed model is a proportional odds logistic regression
    • Primary outcome: days alive in the ICU without delirium/coma within 21 days of receiving TOCI (believe TOCI effects will be gone by 21 days)
  • Recommendations:
    • Might consider an interrupted time series model
    • Clarify how to count days alive for subjects who die in less than 21 days
    • Include any covariates that would be clinically would be a confounder of delirium
    • Include analyses for secondary outcomes of interest
    • Recommended to apply for a VICTR voucher

2022 January 12

Margaret Barton (Donald Arnold), Pediatric Emergency Medicine

Children discharged from emergency departments (EDs) are at risk to return to the ED. Those who return within 48-72 hours of their visit may represent gaps in patient care including proper assessment, treatment, or follow-up instructions. Additionally, there are likely demographic factors that influence rate of ED return visits. Identifying areas of possible intervention to reduce ED return visits may help hospitals take care of patients better, reduce ED crowding, and reduce costs. The aim of this study is to describe the incidence of and identify risk factors for 48 and 72-hour ED return visits among this patient population. We will obtain demographic (age, language, zip-code, PCP status, insurance) and visit characteristic data (chief complaint, triage acuity, diagnosis, reason for return, medication/imaging received during visit, medication prescribed after visit, follow-up plan in place). We will conduct a single-center retrospective study at our children’s hospital of all 48 and 72-hour return visits during a 6-year period (January 2017 to December 2022). Questions: How to organize demographic data of both 48h and 72h return visits (some patients will overlap)? We would like to see if certain zip codes correspond to different rates of return. In particular we are interested to see whether zip-codes with significantly different median incomes vary in their rate of return. How would you recommend comparing this data? Should we use excel or redcap? Overall, we would like help in organizing our data collection so it is easy to analyze. VICTR Biostatistics voucher. Mentor confirmed.
  • Meeting Notes:
    • Overall objective is to describe the incidence of 48 and 72-hour return ED visits and to identify risk factors (demographic + clinical data from first visit)
    • Most patients that come to Vanderbilt Children’s ED would return here for a return visit
  • Recommendations:
    • Need to work on focusing the research question
    • One issue to consider: patients come back for different reasons, and this may be hard to tease out, but could look at comparing the reason for why they left the ER in the first place
    • Consider today’s discussion and then come back to another Wednesday clinic with Chris
Topic attachments
I Attachment Action Size Date Who Comment
BradBeaudoin.docdoc BradBeaudoin.doc manage 31.5 K 10 Oct 2012 - 11:36 JoAnnAlvarez  
For_Pediatric_Pulmonary_Physicians.docxdocx For_Pediatric_Pulmonary_Physicians.docx manage 15.3 K 23 Oct 2012 - 17:08 JoAnnAlvarez Frank Virgin
Jackson-Stripling.docdoc Jackson-Stripling.doc manage 48.0 K 16 Sep 2013 - 10:40 JoAnnAlvarez Protocol for Heather Jackson
MVD.Tables.3-24-13docx.docxdocx MVD.Tables.3-24-13docx.docx manage 12.8 K 22 May 2013 - 11:23 JoAnnAlvarez  
Niesner.Surgical.Biostat.Clinic.5-22-13.xlsxls Niesner.Surgical.Biostat.Clinic.5-22-13.xls manage 46.0 K 21 May 2013 - 14:26 JoAnnAlvarez  
PORC_Dataset_16Jul2013.xlsxxlsx PORC_Dataset_16Jul2013.xlsx manage 159.3 K 17 Jul 2013 - 11:18 JoAnnAlvarez from Jennifer Morse and Breanna Michaels
Questions_for_Pediatric_Otolaryngologists.docxdocx Questions_for_Pediatric_Otolaryngologists.docx manage 15.7 K 23 Oct 2012 - 17:08 JoAnnAlvarez Frank Virgin
Readmission_Study_APM_2-26.docxdocx Readmission_Study_APM_2-26.docx manage 17.3 K 29 Feb 2012 - 11:23 JoAnnAlvarez doc from Andre Marshall
Residency_Project_Proposal-_Juliana_Kyle_2012.docxdocx Residency_Project_Proposal-_Juliana_Kyle_2012.docx manage 44.8 K 13 Sep 2012 - 16:47 JoAnnAlvarez  
SRNACRNASurvey.xlsxxlsx SRNACRNASurvey.xlsx manage 42.7 K 06 Mar 2013 - 09:52 JoAnnAlvarez survey for Jennifer Morse
gestational.docdoc gestational.doc manage 23.4 K 15 Feb 2012 - 11:27 JoAnnAlvarez Document from Sapna Sanjay Shah
propeff.pngpng propeff.png manage 54.4 K 12 Jan 2011 - 11:45 FrankHarrell Simulation study showing efficiency of simple proportions with cutoffs
sarahHill.xlsxxlsx sarahHill.xlsx manage 64.9 K 08 Oct 2012 - 14:52 JoAnnAlvarez  
tmp.txttxt tmp.txt manage 1.0 K 01 Mar 2006 - 13:03 ChuanZhou For Muyibat
Topic revision: r829 - 28 Jun 2022, YueGao

This site is powered by FoswikiCopyright © 2013-2022 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding Vanderbilt Biostatistics Wiki? Send feedback