Douglas D Gunzler1, Nathan Morris2, Adam Perzynski3, Daniel Ontaneda4, Farren Briggs5, Deborah Miller6, Robert A Bermel7. 1. Case Western Reserve University, Center for Health Care Research & Policy, MetroHealth Medical Center, Cleveland, OH, USA. Electronic address: dgunzler@metrohealth.org. 2. Case Western Reserve University, Department of Epidemiology and Biostatistics, Cleveland, OH, USA. Electronic address: njm18@case.edu. 3. Case Western Reserve University, Center for Health Care Research & Policy, MetroHealth Medical Center, Cleveland, OH, USA. Electronic address: Adam.Perzynski@case.edu. 4. Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH, USA. Electronic address: ontanedad@ccf.org. 5. Case Western Reserve University, Department of Epidemiology and Biostatistics, Cleveland, OH, USA. Electronic address: farren.briggs@case.edu. 6. Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH, USA. Electronic address: millerd@ccf.org. 7. Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH, USA. Electronic address: bermelr@ccf.org.
Abstract
BACKGROUND: Trajectories of depression over time may be heterogeneous in Multiple Sclerosis (MS) patients. Describing these trajectories will help clinicians understand better the progression of depression in MS patients to aid in patient care decisions. METHODS: Latent class growth analysis (LCGA) was applied to 3507 MS patients using an electronic health records (EHR) data base to identify subgroups of MS patients based on self-reported depression screening (PHQ-9). Latent trajectory classes were used for group comparisons based on baseline clinical characteristics. RESULTS: Three subgroups were found characterized by high (10.0% [of participants]), wavering above and below moderate (26.2%) and low and variable (63.8%) depression level trajectories. The subpopulation trajectories, respectively, were also characterized by high, moderate and low MS disability at baseline. In contrast, the overall average trajectory was slightly declining and below the moderate depression threshold. CONCLUSION: The LCGA approach described in this paper and applied to MS patients provides a template for improved use of an EHR data base for understanding heterogeneous depression screening trajectories. Clinicians may use such information to more closely monitor patients that are expected to maintain high or unstable depression levels.
BACKGROUND: Trajectories of depression over time may be heterogeneous in Multiple Sclerosis (MS) patients. Describing these trajectories will help clinicians understand better the progression of depression in MSpatients to aid in patient care decisions. METHODS: Latent class growth analysis (LCGA) was applied to 3507 MSpatients using an electronic health records (EHR) data base to identify subgroups of MSpatients based on self-reported depression screening (PHQ-9). Latent trajectory classes were used for group comparisons based on baseline clinical characteristics. RESULTS: Three subgroups were found characterized by high (10.0% [of participants]), wavering above and below moderate (26.2%) and low and variable (63.8%) depression level trajectories. The subpopulation trajectories, respectively, were also characterized by high, moderate and low MS disability at baseline. In contrast, the overall average trajectory was slightly declining and below the moderate depression threshold. CONCLUSION: The LCGA approach described in this paper and applied to MSpatients provides a template for improved use of an EHR data base for understanding heterogeneous depression screening trajectories. Clinicians may use such information to more closely monitor patients that are expected to maintain high or unstable depression levels.
Authors: R Rudick; J Antel; C Confavreux; G Cutter; G Ellison; J Fischer; F Lublin; A Miller; J Petkau; S Rao; S Reingold; K Syndulko; A Thompson; J Wallenberg; B Weinshenker; E Willoughby Journal: Ann Neurol Date: 1996-09 Impact factor: 10.422
Authors: Douglas Gunzler; Steven Lewis; Allison Webel; Mallika Lavakumar; Diana Gurley; Katherine Kulp; McKenzie Pile; Victoria El-Hayek; Ann Avery Journal: AIDS Behav Date: 2020-06