Angela Presson, PhD, MS
- Departments: Family & Preventive Medicine - Adjunct Associate Professor, Internal Medicine - Research Professor, Orthopaedics - Adjunct Associate Professor, Pediatrics - Adjunct Assistant Professor, Population Health Sciences - Adjunct Associate Professor
- Divisions: Biostatistics, Epidemiology, Pediatric Critical Care, Public Health
Academic Office Information
295 Chipeta Way
Salt Lake City, UT 84108
Dr. Angela Presson is a Research Associate Professor in the Division of Epidemiology in the Department of Internal Medicine at the University of Utah. She also holds adjunct appointments in the Department of Pediatrics (Pediatric Critical Care), Department of Orthopaedics and Department of Population Health Sciences (Biostatistics); and an adjunct appointment in the University of California, Los Angeles (UCLA)’s Department of Biostatistics. Prior to starting at the University of Utah, she was a full-time Adjunct Assistant Professor in Biostatistics and Pediatrics at UCLA (2007-2010). She has a PhD from the UCLA Department of Statistics and a BS in Biological Sciences from Cornell University.
My research career began at UCLA in the area of statistical genetics, where both my dissertation research and early career research focused on this topic. More recently, I have collaborated with faculty in Orthopaedics and Surgery, where there is interest in studying patient outcomes both in terms of comparative effectiveness of treatments and patient satisfaction with care. The following research topics are summarized below with selected publications.
Statistical Genetics – Early in my research career I developed MicroMerge software (Presson et al. 2006, 2008) and identified genetic predictors of chronic fatigue syndrome using weighted network analysis WNA (Presson et al. 2008). MicroMerge is freely available software that implements a Bayesian Markov Chain Monte Carlo method to merge microsatellite marker data sets generated on different genotyping platforms (http://www.genetics.ucla.edu/software/micromerge). I have also developed methodology for identifying integration site patterns of gene therapy vectors to evaluate their safety (Presson et al. 2011). At the University of Utah, I have collaborated with clinical scientists to apply WNA methods to the discovery of phenotypes in Acute Respiratory Distress Syndrome (Brown et al. 2017).
- Presson AP, Sobel E, Lange K, Papp JC (2006). Merging microsatellite data. J Comput Biol, 13(6), 1131-47.
- Presson AP, Sobel EM, Pajukanta P, Plaisier C, Weeks DE, Aberg K, Papp JC (2008). Merging microsatellite data: enhanced methodology and software to combine genotype data for linkage and association analysis. BMC Bioinformatics, 9, 317.
- Presson AP, Sobel EM, Papp JC, Suarez CJ, Whistler T, Rajeevan MS, Vernon SD, Horvath S. Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome. BMC Syst Biol. 2008 Nov 6;2:95. PMCID: PMC2625353
- Presson AP, Kim N, Xiaofei Y, Chen IS, Kim S. Methodology and software to detect viral integration site hot-spots. BMC Bioinformatics. 2011 Sep 14;12:367. doi: 10.1186/1471-2105-12-367. PMCID: PMC3203353
- Brown SM, Wilson EL, Presson AP, Dinglas VD, Greene T, Hopkins RO, Needham DM; with the National Institutes of Health NHLBI ARDS Network. Understanding patient outcomes after acute respiratory distress syndrome: identifying subtypes of physical, cognitive and mental health outcomes. Thorax. 2017 Dec; 72(12):1094-1103. PMCID: PMC5690818
- Brown SM, Wilson E, Presson AP, Zhang C, Dinglas VD, Greene T, Hopkins RO, Needham DM (2017). Predictors of 6-month health utility outcomes in survivors of acute respiratory distress syndrome. Thorax, 72(4), 311-317.
Comparative Effectiveness & Patient Outcomes Research – To evaluate patient outcomes following surgical techniques in observational research, it is becoming increasingly popular to apply causal inference methods such as propensity scores (Brooke et al. 2017, Scaife et al. 2018, Dy et al. 2018) and marginal structural models (Patel et al. 2017). The gold standard approach for evaluating pre/post study designs is to use segmented regression models when data is available at the patient level (Tonna et al. 2018) or interrupted time-series analysis to aggregated data. For example, see the pre/post study quality checklist from the NIH https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools.
- Patel DP, Lenherr SM, Stoffel JT, Elliott SP, Welk B, Presson AP, Jha A, Rosenbluth J, Myers JB (2017). Study protocol: patient reported outcomes for bladder management strategies in spinal cord injury. BMC Urol, 17(1), 95.
- Myers JB, Lenherr SM, Stoffel JT, Elliott SP, Presson AP, Zhang C, Rosenbluth J, Jha A, Patel DP, Welk B; Neurogenic Bladder Research Group NBRG.org. Patient reported bladder-related symptoms and quality of life after spinal cord injury with different bladder management strategies. J Urol. 2019 Apr 8. PubMed PMID: 30958741.
- Brooke BS, Sarfati MR, Zhang Y, Zhang Y, Presson AP, Greene TH, Kraiss LW (2017). Cardiac Stress Testing during Workup for Abdominal Aortic Aneurysm Repair Is Not Associated with Improved Patient Outcomes. Ann Vasc Surg, 42, 222-230.
- Scaife CL, Mone MC, Bowen ME, Swords DS, Zhang C, Presson AP, Nelson EW (2018). Perioperative antibiotics should be used for placement of implanted central venous ports: A propensity analysis evaluating risk. Am J Surg, 216(6), 1135-1143.
- Dy CJ, Osei DA, Maak TG, Gottschalk MB, Zhang AL, Maloney MD, Presson AP, O'Keefe RJ (2018). Safety of Overlapping Inpatient Orthopaedic Surgery: A Multicenter Study. J Bone Joint Surg Am, 100(22), 1902-1911.
- Tonna JE, Kawamoto K, Presson AP, Zhang C, Mone MC, Glasgow RE, Barton RG, Hoidal JR, Anzai Y (2018). Single intervention for a reduction in portable chest radiography (pCXR) in cardiovascular and surgical/trauma ICUs and associated outcomes. J Crit Care, 44, 18-23.
Assessment of Patient Satisfaction –Much of my recent research has been in collaboration with faculty in the Departments of Surgery and Orthopaedics on studies that evaluate patient satisfaction outcomes. Specifically, we have studied the psychometric properties of the Press Ganey® satisfaction survey, which is used to evaluate patient satisfaction at the University of Utah (Presson et al. 2017). We have also published on patient characteristics associated with satisfaction (Abtahi et al. 2015, Martin et al. 2017, Tyser et al. 2018), and evidence of non-response bias on the the Press Ganey® satisfaction survey (Tyser et al. 2016).
- Abtahi AM, Presson AP, Zhang C, Saltzman CL, Tyser AR. Association Between Orthopaedic Outpatient Satisfaction and Non-Modifiable Patient Factors. J Bone Joint Surg Am. 2015 Jul 01;97(13):1041-1048. Epub 2015/07/03. PMCID: PMC4574907
- Tyser AR, Abtahi AM, McFadden M, Presson AP. Evidence of non-response bias in the Press-Ganey patient satisfaction survey. BMC Health Serv Res. 2016 Aug 04;16(a):350. Epub 2016/08/05. PMCID: PMC4972948
- Presson AP, Zhang C, Abtahi AM, Kean J, Hung M, Tyser AR. Psychometric properties of the Press Ganey(R) Outpatient Medical Practice Survey. Health Qual Life Outcomes. 2017 Feb 10;15(1):32. Epub 2017/02/12. PMCID: PMC5301343
- Robins RJ, Anderson MB, Zhang Y, Presson AP, Burks RT, Greis PE. Convergent Validity of the Patient-Reported Outcomes Measurement Information System's Physical Function Computerized Adaptive Test for the Knee and Shoulder Injury Sports Medicine Patient Population. Arthroscopy. 2017 Mar;33(3):608-616. Epub 2016/12/15.
- Martin L, Presson AP, Zhang C, Ray D, Finlayson S, Glasgow R (2017). Association between surgical patient satisfaction and nonmodifiable factors. J Surg Res, 214, 247-253.
- Tyser AR, Gaffney CJ, Zhang C, Presson AP (2018). The Association of Patient Satisfaction with Pain, Anxiety, and Self-Reported Physical Function. J Bone Joint Surg Am, 100(21), 1811-1818.
- Chen J, Presson A, Zhang C, Ray D, Finlayson S, Glasgow R (2018). Online physician review websites poorly correlate to a validated metric of patient satisfaction. J Surg Res, 227, 1-6.
|Doctoral Training||University of California, Los Angeles
|Graduate Training||University of California, Los Angeles
Biological Sciences - Genetics
- Fuller T, Langfelder P, Presson A, Horvath H (2011). Review of Weighted Gene Coexpression Network Analysis. In Lu HHS, Scholkopf B, Zhao H (Eds.), Handbook of Statistical Bioinformatics (1st, pp. 369-379). Springer.
- Presson A, Sobel E, Papp J, Horvath S (2009). Co-expression Modules and Genes Related to Chronic Fatigue Syndrome Severity. In McConnell P, Lim S, Cuticchia AJ, (Eds.), Methods of Microarray Data Analysis (pp. 141-159). Scotts Valley: CreateSpace Publishing.