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Understanding the association between admission source and in-hospital delirium: a cross-sectional study

Meghan K Thomas, Marc E Heincelman, Jingwen Zhang, Justin Marsden, Jennifer Dulin, Patrick Robbins, Kelly Hunt, Patrick Mauldin, William P Moran, Benjamin Kalivas
DOI: 10.1136/jim-2022-002342 Published 6 September 2022
Meghan K Thomas
1Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
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Marc E Heincelman
1Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
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Jingwen Zhang
1Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
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Justin Marsden
1Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
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Jennifer Dulin
1Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
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Patrick Robbins
2Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
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Kelly Hunt
3Public Health, Medical University of South Carolina, Charleston, South Carolina, USA
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Patrick Mauldin
1Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
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William P Moran
1Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
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Benjamin Kalivas
1Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
4Psychiatry, Medical University of South Carolina, Charleston, South Carolina, USA
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  • Figure 1
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    Figure 1

    Cohort selection. bCAM, brief confusion assessment method; ED, emergency department; IHT, interhospital transfer; OBGYN, obstetrics and gynecology.

  • Figure 2
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    Figure 2

    Sequential model receiver operating characteristic (ROC) curves. AUC, area under the curve.

Tables

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  • Table 1

    Demographics

    ED
    n=19,915
    Clinic
    n=4214
    IHT
    n=5971
    P value
    Age (mean±SD)58.1±17.457.5±16.158.6±17.20.0008
    Age group (%)<0.0001
     <5029.328.328.7
     50 to <6529.833.429.2
     65 to <8031.932.532.6
     80+9.05.89.4
    Gender (%)<0.0001
     Male48.049.254.0
     Female52.050.946.0
    Race (%)<0.0001
     Black37.029.733.6
     Other3.23.24.7
     White59.967.161.7
    Marital status (%)<0.0001
     Married46.655.147.9
     Single19.717.020.6
     Other33.727.931.5
    Distance to MUSC (>50 miles) (%)34.658.276.0<0.0001
    Poverty (%)30.327.130.20.0002
    CCI score (mean±SD)4.1±3.24.1±3.14.3±3.1<0.0001
    CCI score (%)<0.0001
     012.810.110.0
     1–225.025.622.4
     3–424.427.726.0
     5+37.836.641.7
    Medication exposure (%)
     Antipsychotic30.433.629.1<0.0001
     Anticholinergic63.368.959.6<0.0001
     Opioid81.986.182.3<0.0001
     Benzodiazepine (anytime)38.343.249.5<0.0001
    ICU during hospitalization (%)18.111.940.2<0.0001
    Group of specialty (%)<0.0001
     ICU0.30.40.5
     General Internal Medicine50.540.445.1
     Neurology13.811.524.6
     OBGYN1.93.32.1
     Surgery33.544.427.7
    Alcohol abuse7.83.07.9<0.0001
    Dementia4.92.45.0<0.0001
    Myocardial infarction12.48.616.6<0.0001
    Congestive heart failure19.416.223.1<0.0001
    Cerebrovascular disease9.07.719.9<0.0001
    Chronic pulmonary disease21.119.420.00.0185
    Peptic ulcer disease1.71.02.5<0.0001
    Uncomplicated diabetes10.99.711.20.0347
    Complicated diabetes19.117.619.60.0341
    Hemiplegia3.31.59.0<0.0001
    Renal disease20.320.219.80.6385
    Cancer19.529.319.1<0.0001
    AIDS/HIV1.41.41.10.2560
    Liver disease7.97.19.9<0.0001
    • CCI, Charlson Comorbidity Index; ED, emergency department; ICU, intensive care unit; IHT, interhospital transfer; MUSC, Medical University of South Carolina; OBGYN, obstetrics and gynecology.

  • Table 2

    Outcomes

    ED
    n=19,915
    Clinic
    n=4214
    IHT
    n=5971
    P value
    Primary outcome
     bCAM positive (%)11.85.822.3<0.0001
    Secondary outcome
     Discharge to a facility (%)13.05.822.8<0.0001
     LOS (mean±SD)5.7±6.35.6±7.28.9±9.3<0.0001
     Mortality (%)0.80.61.9<0.0001
    • bCAM, brief confusion assessment method; ED, emergency department; IHT, interhospital transfer; LOS, length of stay.

  • Table 3

    Clinical variables associated with delirium used in the multivariable logistic regression model

    OR95% CIP value
    Admission source (ref: ED)
     Clinic0.5570.481 to 0.644<0.0001
     IHT1.9121.741 to 2.099<0.0001
    Group of specialty (ref: General Internal Medicine)
     ICU3.1641.900 to 5.267<0.0001
     Neurology0.9920.881 to 1.1160.8903
     OBGYN0.4220.286 to 0.624<0.0001
     Surgery0.7540.682 to 0.834<0.0001
    Age group (ref: 18–49)
     50–641.4381.287 to 1.607<0.0001
     65–792.0181.799 to 2.264<0.0001
     80+2.82.405 to 3.259<0.0001
    Female0.9960.921 to 1.0770.9153
    Race (ref: White)
     Black1.1821.083 to 1.2910.0002
     Other1.1550.939 to 1.4230.1733
    Marital status (ref: Married)
     Other1.1511.042 to 1.2720.0056
     Single1.2361.122 to 1.362<0.0001
    Distance_MUSC≥500.8820.809 to 0.9620.0045
    Poverty1.0090.928 to 1.0960.8362
    Antipsychotic3.2202.977 to 3.483<0.0001
    Anticholinergics0.8260.761 to 0.897<0.0001
    Opioid1.0320.931 to 1.1450.5481
    Benzodiazepine2.4222.235 to 2.625<0.0001
    Falls2.7732.107 to 3.648<0.0001
    Alcohol abuse1.7281.516 to 1.971<0.0001
    Dementia3.9223.431 to 4.484<0.0001
    Myocardial infarction1.2841.157 to 1.425<0.0001
    Congestive heart failure1.1461.039 to 1.2650.0067
    Cerebrovascular disease2.3772.132 to 2.650<0.0001
    Chronic pulmonary disease1.0200.930 to 1.1190.6776
    Peptic ulcer disease1.3921.084 to 1.7890.0096
    Uncomplicated diabetes0.9360.826 to 1.0610.2998
    Complicated diabetes1.2061.091 to 1.3330.0002
    Hemiplegia2.4942.144 to 2.901<0.0001
    Renal disease1.0420.939 to 1.1550.4409
    Cancer0.8960.811 to 0.9890.0302
    AIDS/HIV1.3080.960 to 1.7830.089
    Liver disease1.5891.398 to 1.807<0.0001
    • ED, emergency department; ICU, intensive care unit; IHT, interhospital transfer; MUSC, Medical University of South Carolina; OBGYN, obstetrics and gynecology.

Supplementary Materials

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  • Supplementary data

    [jim-2022-002342supp002.pdf]

  • Supplementary data

    [jim-2022-002342supp001.xlsx]

Additional Files

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  • Supplementary Materials
  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    • Data supplement 1
    • Data supplement 2
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Understanding the association between admission source and in-hospital delirium: a cross-sectional study
Meghan K Thomas, Marc E Heincelman, Jingwen Zhang, Justin Marsden, Jennifer Dulin, Patrick Robbins, Kelly Hunt, Patrick Mauldin, William P Moran, Benjamin Kalivas
Journal of Investigative Medicine Sep 2022, jim-2022-002342; DOI: 10.1136/jim-2022-002342

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Understanding the association between admission source and in-hospital delirium: a cross-sectional study
Meghan K Thomas, Marc E Heincelman, Jingwen Zhang, Justin Marsden, Jennifer Dulin, Patrick Robbins, Kelly Hunt, Patrick Mauldin, William P Moran, Benjamin Kalivas
Journal of Investigative Medicine Sep 2022, jim-2022-002342; DOI: 10.1136/jim-2022-002342
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Understanding the association between admission source and in-hospital delirium: a cross-sectional study
Meghan K Thomas, Marc E Heincelman, Jingwen Zhang, Justin Marsden, Jennifer Dulin, Patrick Robbins, Kelly Hunt, Patrick Mauldin, William P Moran, Benjamin Kalivas
Journal of Investigative Medicine Sep 2022, jim-2022-002342; DOI: 10.1136/jim-2022-002342
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