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Research ArticlePatient SafetyF
Open Access

A 5-Item Prediction Rule to Identify Severe Renal Dysfunction in Patients with Acute Stroke

M.D.I. Vergouwen, J. Fang, L.K. Casaubon, M.K. Kapral, M. Stamplecoski, A. Robertson and F.L. Silver on behalf of the Investigators of the Registry of the Canadian Stroke Network
American Journal of Neuroradiology September 2012, 33 (8) 1449-1454; DOI: https://doi.org/10.3174/ajnr.A3013
M.D.I. Vergouwen
aFrom the Division of Neurology (M.D.I.V., L.K.C., F.L.S.), Department of Medicine
cUMC Utrecht Stroke Center (M.D.I.V.), Department of Neurology and Neurosurgery, Rudolf Magnus Institute of Neuroscience, UMC Utrecht, Utrecht, the Netherlands
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J. Fang
eInstitute for Clinical Evaluative Sciences (J.F., M.S., A.R., M.K.K., F.L.S.), Toronto, Ontario, Canada
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L.K. Casaubon
aFrom the Division of Neurology (M.D.I.V., L.K.C., F.L.S.), Department of Medicine
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M.K. Kapral
bDivision of General Internal Medicine and Clinical Epidemiology and Women's Health Program (M.K.K.), University Health Network, University of Toronto, Toronto, Canada
dDepartment of Health Policy, Management and Evaluation (M.K.K.), University of Toronto, Toronto, Ontario, Canada
eInstitute for Clinical Evaluative Sciences (J.F., M.S., A.R., M.K.K., F.L.S.), Toronto, Ontario, Canada
fthe Canadian Stroke Network (M.K.K, F.L.S.).
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M. Stamplecoski
eInstitute for Clinical Evaluative Sciences (J.F., M.S., A.R., M.K.K., F.L.S.), Toronto, Ontario, Canada
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A. Robertson
eInstitute for Clinical Evaluative Sciences (J.F., M.S., A.R., M.K.K., F.L.S.), Toronto, Ontario, Canada
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F.L. Silver
aFrom the Division of Neurology (M.D.I.V., L.K.C., F.L.S.), Department of Medicine
eInstitute for Clinical Evaluative Sciences (J.F., M.S., A.R., M.K.K., F.L.S.), Toronto, Ontario, Canada
fthe Canadian Stroke Network (M.K.K, F.L.S.).
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    Fig 1.

    Cohort populations selection process.

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    Fig 2.

    Comparison of severe renal dysfunction in the derivation and validation cohorts according to Renal Risk Score categories.

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    Fig 3.

    A, Receiver operating characteristic curve for predicting severe renal dysfunction in the RCSN cohort (model 1). B, Receiver operating characteristic curve for predicting severe renal dysfunction in the OSA cohort.

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    Table 1:

    Values for the following equation: GFR = a × (serum creatinine/b)c × (0.993)age

    Values
    The variable a takes on the following values on the basis of race:
        Black
            Women = 166
            Men = 163
        White/other
            Women = 144
            Men = 141
    The variable b takes on the following values on the basis of sex:
        Women = 0.7
        Men = 0.9
    The variable c takes on the following values on the basis of sex and creatinine measurement:
        Women
            Serum creatinine, ≤0.7 mg/dL = −0.329
            Serum creatinine, >0.7 mg/dL = −1.209
        Men
            Serum creatinine, ≤0.9 mg/dL = −0.411
            Serum creatinine, >0.9 mg/dL = −1.209
    • View popup
    Table 2:

    Baseline characteristics

    VariableRCSN3 2003–2008OSA 2002/03 and 2004/05Comparison of all Patients from RCSN versus OSA (P value)
    All (n = 9872)GFR ≥30 (n = 9393)GFR <30 (n = 479)P ValueAll (n = 2544)GFR ≥30 (n = 2411)GFR <30 (n = 133 )P Value
    Age
        Median (IQR) (yr)75 (65–83)74 (64–82)82 (77–88)<.000177 (68–83)76 (67–83)82 (77–87)<.0001<.0001
        ≥65 yr (No.) (%)7389 (74.8)6936 (73.8)453 (94.6)<.00012066 (81.2)1936 (80.3)130 (97.7)<.0001<.0001
    No. of women (No.) (%)4827 (48.9)4827 (48.9)294 (61.4)<.00011258 (49.4)1177 (48.8)81 (60.9).007.62
    Stroke type (No.) (%)<.0001.005<.0001
        Ischemic stroke5496 (55.7)5175 (55.1)321 (67.0)1101 (43.3)1026 (42.6)75 (56.4)
        TIA3307 (33.5)3176 (33.8)131 (27.3)1251 (49.2)1198 (49.7)53 (39.8)
        ICH1069 (10.8)1042 (11.1)27 (5.6)192 (7.5)187 (7.8)≤5 (≤3.8)
    Diabetes mellitus (No.) (%)2178 (22.1)2033 (21.6)145 (30.3)<.0001549 (21.6)497 (20.6)52 (39.1)<.0001.60
    Hypertension (No.) (%)6552 (66.4)6159 (65.6)393 (82.0)<.00011512 (59.4)1417 (58.8)95 (71.4).004<.0001
    Hyperlipidemia (No.) (%)3478 (35.2)3298 (35.1)180 (37.6).27718 (28.2)684 (28.4)34 (25.6).48<.0001
    Peripheral vascular disease (No.) (%)529 (5.4)483 (5.1)46 (9.6)<.0001105 (4.1)92 (3.8)13 (9.8).0008.01
    Current smoking (No.) (%)1481 (15.0)1450 (15.4)31 (6.5)<.0001313 (12.3)304 (12.6)9 (6.8).046.0006
    Previous myocardial infarction (No.) (%)1492 (15.1)1382 (14.7)110 (23.0)<.0001381 (15.0)343 (14.2)38 (28.6)<.0001.86
    Previous stroke or TIA (No.) (%)3357 (34.0)3149 (33.5)208 (43.4)<.0001979 (38.5)919 (38.1)60 (45.1)011<.0001
    Preadmission medications (No.) (%):
        Metformin1038 (10.5)1006 (10.7)32 (6.7).005253 (9.9)237 (9.8)16 (12.0).41.40
        Insulin414 (4.2)368 (3.9)46 (9.6)<.000199 (3.9)84 (3.5)15 (11.3)<.0001.49
    • Note:—ICH indicates intracerebral hemorrhage.

    • View popup
    Table 3:

    Results of multivariable logistic regression analyses using data from the RCSN (model 1)

    VariableUnitOR (95% CI)Estimateβ AgeEstimate β AgePoints Assigned
    Age (yr)11.07 (1.06–1.08)0.06880.068811 (per yr)
    Female sex11.27 (1.04–1.54)0.23620.06883.445
    History of diabetes mellitus11.34 (1.06–1.69)0.2920.06884.255
    Preadmission insulin use12.52 (1.74–3.67)0.92550.068813.4615
    History of hypertension11.86 (1.46–2.37)0.62190.06889.0510
    • View popup
    Table 4:

    Results of multivariable logistic regression analyses using data from the RCSN (model 2)

    VariableUnitOR (95% CI)Estimateβ AgeEstimate/β AgePoints Assigned
    Age (yr)11.07 (1.06–1.08)0.06850.068511 (per yr)
    Female sex11.32 (1.09–1.61)0.28010.06854.095
    History of diabetes mellitus11.31 (1.03–1.66)0.26820.06853.925
    Preadmission insulin use12.35 (1.61–3.42)0.85450.068512.4715
    History of hypertension11.80 (1.41–2.29)0.58660.06858.5610
    Previous myocardial infarction11.41 (1.12–1.77)0.34250.06855.005
    Peripheral vascular disease11.70 (1.23–2.36)0.53130.06857.7510
    • View popup
    Table 5:

    Prevalence of renal insufficiency according to Renal Risk Score categories

    Renal Risk Score CutoffSevere Renal Dysfunction (GFR < 30)
    RCSN3 (n/N) (%)OSA (n/N) (%)P Value
    10–60≤5/981 (≤0.005)≤5/196 (≤0.03).84
    61–707/1011 (0.7)≤5/210 (≤0.02).72
    71–8034/1622 (2.1)10/457 (2.2).90
    81–9091/2488 (3.7)33/731 (4.5).29
    91–100180/2519 (7.1)44/665 (6.6).64
    101–110132/1055 (12.5)33/249 (13.3).75
    111–12029/182 (15.9)9/32 (28.1).10
    121–130––.13
    • Note:— – indicates small cell number (≤5).

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American Journal of Neuroradiology: 33 (8)
American Journal of Neuroradiology
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M.D.I. Vergouwen, J. Fang, L.K. Casaubon, M.K. Kapral, M. Stamplecoski, A. Robertson, F.L. Silver
A 5-Item Prediction Rule to Identify Severe Renal Dysfunction in Patients with Acute Stroke
American Journal of Neuroradiology Sep 2012, 33 (8) 1449-1454; DOI: 10.3174/ajnr.A3013

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A 5-Item Prediction Rule to Identify Severe Renal Dysfunction in Patients with Acute Stroke
M.D.I. Vergouwen, J. Fang, L.K. Casaubon, M.K. Kapral, M. Stamplecoski, A. Robertson, F.L. Silver
American Journal of Neuroradiology Sep 2012, 33 (8) 1449-1454; DOI: 10.3174/ajnr.A3013
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