Prediction of Readmission Following Sepsis Using Social Determinants of Health

被引:2
|
作者
Amrollahi, Fatemeh [1 ]
Kennis, Brent D. [2 ]
Shashikumar, Supreeth Prajwal [1 ]
Malhotra, Atul [3 ]
Taylor, Stephanie Parks [4 ]
Ford, James [5 ]
Rodriguez, Arianna [6 ]
Weston, Julia [6 ]
Maheshwary, Romir [6 ]
Nemati, Shamim [1 ]
Wardi, Gabriel [3 ,7 ]
Meier, Angela [8 ]
机构
[1] Univ Calif San Diego, Dept Biomed Informat, La Jolla, CA USA
[2] Univ Calif San Diego, Sch Med, La Jolla, CA USA
[3] Univ Calif San Diego, Div Pulm Crit Care & Sleep Med, La Jolla, CA 92093 USA
[4] Univ Michigan, Div Hosp Med, Ann Arbor, MI USA
[5] Univ Calif San Francisco, Dept Emergency Med, San Francisco, CA USA
[6] Univ Calif San Diego, Dept Med, La Jolla, CA USA
[7] Univ Calif San Diego, Dept Emergency Med, San Diego, CA 92093 USA
[8] Univ Calif San Diego, Dept Anesthesiol, Div Crit Care, La Jolla, CA USA
基金
美国国家卫生研究院;
关键词
readmission; sepsis; social determinants of health; HOSPITAL READMISSION; EPIDEMIOLOGY; LIMITATIONS; VALIDATION; COST;
D O I
10.1097/CCE.0000000000001099
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
OBJECTIVES:To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables.DESIGN:Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data.SETTINGS:Thirty-five hospitals across the United States from 2017 to 2021.PATIENTS:Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization.INTERVENTIONS:None.MEASUREMENTS AND MAIN RESULTS:Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission.CONCLUSIONS:In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Characteristics and Readmission Risks Following Sepsis Discharges to Home
    You, Sang Bin
    Song, Jiyoun
    Hsu, Jesse Y.
    Bowles, Kathryn H.
    MEDICAL CARE, 2025, 63 (02) : 89 - 97
  • [22] The Impact of Social Determinants of Health on 30-Day Readmission After Ischemic Stroke
    Kovi, Shivakrishna
    Man, Shumei
    Uchino, Ken
    Tang, Anne S.
    Lopez, Rocio
    STROKE, 2020, 51
  • [23] Health Care Disparity in Asthma-COPD Overlap Syndrome: Social Determinants for Readmission
    Day, G. L.
    Jackson, N. J.
    Buhr, R. G.
    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2020, 201
  • [24] Factors influencing readmission patterns following radical cystectomy: An analysis of social determinants and discharge outcomes
    Grajales, Valentina
    Lin, Jonathan Y.
    Sharbaugh, Danielle
    Pere, Maria
    Sharbaugh, Adam
    Miller, David T.
    Pelzman, Dan
    Sun, Zhaojun
    Eom, Kirsten Y.
    Davies, Benjamin J.
    Yabes, Jonathan G.
    Sabik, Lindsay M.
    Jacobs, Bruce L.
    UROLOGIC ONCOLOGY-SEMINARS AND ORIGINAL INVESTIGATIONS, 2024, 42 (12) : 449e13 - 449e19
  • [25] Target-based fusion using social determinants of health to enhance suicide prediction with electronic health records
    Sacco, Shane J.
    Chen, Kun
    Wang, Fei
    Aseltine, Robert
    PLOS ONE, 2023, 18 (04):
  • [26] Social determinants of health and the prediction of missed breast imaging appointments
    Shahabeddin Sotudian
    Aaron Afran
    Christina A. LeBedis
    Anna F. Rives
    Ioannis Ch. Paschalidis
    Michael D. C. Fishman
    BMC Health Services Research, 22
  • [27] Social determinants of health and the prediction of missed breast imaging appointments
    Sotudian, Shahabeddin
    Afran, Aaron
    LeBedis, Christina A.
    Rives, Anna F.
    Paschalidis, Ioannis Ch.
    Fishman, Michael D. C.
    BMC HEALTH SERVICES RESEARCH, 2022, 22 (01)
  • [28] Social determinants of health associated with the development of sepsis in adults: a scoping review protocol
    Machon, Christina
    Sheikh, Fatima
    Fox-Robichaud, Alison
    BMJ OPEN, 2020, 10 (10):
  • [29] SOCIAL DETERMINANTS OF HEALTH IMPACT HOSPITAL LENGTH OF STAY FOR CHILDREN WITH SEVERE SEPSIS
    West, Alina
    Hamilton, Hunter
    Ammar, Nariman
    Gunturkun, Fatma
    Jones, Tamekia
    Chinthala, Lokesh
    Burroughs, Anna
    Shaban-Nejad, Arash
    Shah, Samir
    CRITICAL CARE MEDICINE, 2022, 50 (01) : 249 - 249
  • [30] READMISSION PREVENTION IN SEPSIS: DEVELOPMENT AND VALIDATION OF A PREDICTION MODEL: REPRESS
    Grek, Ami
    Rogers, Emily
    Peacock, Sarah
    Hartjes, Tonja
    White, Launia
    Li, Zhuo
    Naessens, James
    Franco, Pablo Moreno
    CRITICAL CARE MEDICINE, 2020, 48