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Figure 1. Scatterplot and regression line for Nt-proBNP vs eGFR
0 30 60 90 120 150 180eGFR (ml/min/1.73 m-sq)
10
100
1000
10000
NT-
proB
NP
Figure 2. Boxplot of Nt-proBNP by WHO criteria for anemia
0 35 70 105 140 175e G F R [M D R D ] m l/m in /1 .7 3 m -s q
0500
100015002000250030003500400045005000550060006500700075008000850090009500
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Nt-p
roB
NP
0.0 0.5 1.0W H O c r ite r ia
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100015002000250030003500400045005000550060006500700075008000850090009500
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Nt-p
roB
NP
Figure 3. Scatterplot of Nt-proBNP by hemoglobin
8 10 12 14 16H e m o g lo b in (g )
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100015002000250030003500400045005000550060006500700075008000850090009500
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Nt-p
roB
NP
Figure 4. 3D fitting plot of egFR and hemoglobin for separating by Nt-proBNP
Table I. Regression of eGFR and hemoglobin to predict Nt-proBNP
Step number : 0R : 0.376R-square : 0.141
In Effect Coefficient Standard Error
Std.Coefficient Tolerance df F-ratio p-value
1 Constant 2 eGFR -83.499 14.063 -0.297 0.951 1 35.256 0.0003 Hgb -910.224 260.436 -0.175 0.951 1 12.215 0.001
Information CriteriaAIC 7785.028AIC (Corrected) 7785.139Schwarz's BIC 7800.628
Dependent Variable NTproBNP(pg/ml)
N 365Multiple R 0.376Squared Multiple R 0.141Adjusted Squared Multiple R 0.137
Standard Error of Estimate 10287.156
Analysis of Variance
Source SS df Mean Squares F-ratio p-value
Regression 6.309E+009 2 3.155E+009 29.809 0.000Residual 3.831E+010 362 1.058E+008
Figure 5. Age normalized 1000*Log(Nt-proBNP)/eGFR by eGFR and hemoglobin
Table II. Linear regression of NKLog(Nt-proBNP0/eGFR by eGFR and hemoglobinLog transform flattens the high Nt-proBNP scale and eGFR and age are normalized
R : 0.597R-square : 0.357
In Effect Coefficient Standard Error
Std.Coefficient Tolerance df F-ratio p-value
1 Constant 2 eGFR -1.873 0.144 -0.573 0.933 1 170.011 0.0003 Hgb -4.259 2.436 -0.077 0.933 1 3.056 0.081
Information CriteriaAIC 4299.786AIC (Corrected) 4299.899Schwarz's BIC 4315.331
Dependent Variable NKLogNTGFRN 360Multiple R 0.597Squared Multiple R 0.357Adjusted Squared Multiple R 0.353
Standard Error of Estimate 94.260
Regression Coefficients B = (X'X)-1X'Y
Effect Coefficient
Standard Error
Std.Coefficient
Tolerance t p-value
CONSTANT 256.151 27.745 0.000 . 9.232 0.000MDRD_GFR -1.873 0.144 -0.573 0.933 -13.039 0.000Hgb -4.259 2.436 -0.077 0.933 -1.748 0.081
Figure 7. Means and 95% confidence intervals for D-dimer for positive and negative duple scans. Many false positive D-dimers not delineated without use of Receiver Operator Characteristic curve.
Least Squares Means
0 1VENDUP
4684.0
8636.6
12589.2
16541.8
20494.4
24447.0
D_D
IME
R
Table III. One-way ANOVA of D-dimer for positive and negative scans
Dependent Variable D_DIMER
N 817
Analysis of VarianceSource Type III SS df Mean Squares F-ratio p-valueVENDUP 43456570.851 1 43456570.851 68.278 0.000Error 5.187E+008 815 636461.763
Figure 8. Discriminant Function multivariable graph for anemia-CHF-renal insufficiency
Table 4. Discriminant function for CHF, renal insufficiency and anemia by age, NT-proBNP, creatinine and hemoglobin
Group Frequencies0 1 2135 335 235
Group Means 0 1 2NTproBNP (pg/ml) 1516.369 5964.054 12902.662Creatinine 0.716 1.654 2.103Hgb 11.972 11.533 11.305Age 60.570 71.373 74.966
Between Groups F-matrixdf : 4 699 0 1 20 0.000 1 23.445 0.000 2 45.108 11.788 0.000
Wilks's Lambda
Lambda : 0.778df : (4,2,702)Approx. F-ratio : 23.337df : (8,1398)p-value : 0.000
Classification Functions 0 1 2CONSTANT -32.018 -35.196 -37.394
Variable F-to-remove Tolerance5 NTproBNP
(pg/ml)13.489 0.801
6 Creatinine 21.368 0.7997 Hgb 0.190 0.9283 Age 38.632 0.948
Test StatisticStatistic Value Approx. F-ratio df p-valueWilks's Lambda 0.778 23.337 8 1398 0.000Pillai's Trace 0.226 22.295 8 1400 0.000Lawley-Hotelling Trace 0.279 24.382 8 1396 0.000
Figure 9. Same data using quadratic DSA with age-normalized 1000*Log(NT-proBNP)/eGFR and
GFRe using the MDRD calculation.
Table V. The DFA calculations for Figure 9.
Group Frequencies 0 1 2221.000 631.000 571.000
MeansNKLgNTproGFRe 15.589 55.971 81.159MDRD 123.130 61.940 48.748
Group 0 Discriminant Function Coefficients NormKLgNTproGFR-
eMDRDest Constant
NKLgNTproGFRe -0.015 MDRD -0.001 0.000 Constant 0.588 0.052 -15.590
Group 1 Discriminant Function Coefficients NormKLgNTproGFR-
eMDRDest Constant
NKLgNTproGFRe 0.000 MDRD 0.000 -0.001 Constant 0.024 0.089 -12.106
Group 2 Discriminant Function Coefficients NormKLgNTproGFR-
eMDRDest Constant
NKLgNTproGFRe 0.000 MDRD 0.000 -0.001 Constant 0.015 0.147 -13.077
Between Groups F-matrixdf : 2 1419 0 1 20 0.000 1 236.650 0.000 2 335.228 21.342 0.000
Wilks's Lambda for the Hypothesis
Lambda : 0.671df : (2,2,1420)Approx. F-ratio : 156.542df : (4,2838)p-value : 0.000
Classification Matrix (Cases in row categories classified into columns) 0 1 2 %correct0 206 15 0 931 237 363 31 582 69 459 43 8
Classification Matrix (Cases in row categories classified into columns) 0 1 2 %correctTotal 512 837 74 43
Jackknifed Classification Matrix 0 1 2 %correct0 205 16 0 931 237 363 31 582 69 462 40 7Total 511 841 71 43
Test StatisticStatistic Value Approx. F-ratio df p-valueWilks's Lambda 0.671 156.542 4 2838 0.000Pillai's Trace 0.330 140.347 4 2840 0.000Lawley-Hotelling Trace 0.488 173.026 4 2836 0.000
Canonical Discriminant Functions 1 2Constant -1.912 -1.075
NKLgNTproGFRe 0.001 0.009MDRD 0.028 0.008
Canonical Discriminant Functions : Standardized by Within Variances 1 2NKLgNTproGFRe 0.085 1.061MDRD 1.026 0.284
Canonical Scores of Group Means 1 20 1.576 0.0341 -0.122 -0.0692 -0.476 0.063
Table VI Ordinal regression model for combined 3 predictors of malnutrition risk.
Predictor L 2 p exp(beta)
Poor oral intake 60.29 8.2e-15 5.3
Malnutrition related condition 46.29 1.0e-11 3.06
Albumin 152.01 6.3e-35 3.16
Figure 10. Association Model of Malnutrition Risk
1 2 3 4RISKLEV
0,0,1
0,1,1
1,0,1
0,1,3
1,1,4
X-profile
0
5
10
15
20
log(Odds-Ratios) Explained L²=267.68 df=3 p=9.7e-58Residual L²=19.88 df=42 p=1.00
Joint Y
Table VII. Expected Odds Ratios – Diagnosis Thalassemia
Odds-RatiosMe,M,A2(e) Thalassemia1,1,1 97131,1,0 16961,0,1 2630,1,1 212 1,0,0 460,1,0 370,0,1 60,0,0 1
Table VIII. Probabilities of RDS given by gestational age and S/A ratio.
Dependent variable: Respiratory outcome (Resp_Sca)Predictors: Surfactant to albumin (S/A) Ratio_45: 0, > 45; 1, 21-44; 2, < 21; Gestational age at delivery: 0, > 36; 1, 34-36; 2, < 34.S/A Ratio_45 p = 8.7*10-22
Gestational Age at Delivery Scaled p = 4.2*10-9
Combined variables: ChiSq = 130.14, p = 5.1*10-28, R2 = 0.433, phi = 0.8231, exp(beta) = 2.16 (S/A), 1.88 (GA)
Definition (S/A, GA)
Exp. Probabilities Exp. Odds-Ratios
0-20, < 34 0.84 44270-20, 34-36 0.64 66821-44, < 34 0.57 4410-20, > 36 0.31 10121-44, 34-36 0.25 67> 45, < 34 0.19 4421-44, > 36 0.06 10> 45, 34-36 0.04 7> 45, > 36 0.01 1
Table IX. Ordinal regression of EKG and troponin T on diagnoses
Association Summary L² df p-value R² phiExplained by Model 206.52 2 1.4e-45 0.686 1.3856Residual 48.64 14 1.0e-5Total 255.16 16 4.5e-45
Odds Ratios and probabilities for diagnosesaverage 1 2 0 1 2score 0.00 0.00
2,3 2.87 466.82 10086.03 0.01 0.11 0.882,2 2.67 105.78 1087.95 0.04 0.20 0.751,3 2.64 95.35 931.05 0.05 0.21 0.742,1 1.95 23.97 117.35 0.26 0.27 0.471,2 1.87 21.61 100.43 0.29 0.26 0.450,3 1.79 19.48 85.95 0.32 0.26 0.421,1 0.67 4.90 10.83 0.73 0.15 0.120,2 0.61 4.41 9.27 0.75 0.14 0.110,1 0.12 1.00 1.00 0.95 0.04 0.01
Figure 11. Plot of Effective Information (Y-axis, OVINFO) by CA125 t1/2 (X-axis, OVHALFLIFE)
10 30 50 70OVHALFLIFE
1.35
1.40
1.45
1.50
1.55
1.60
OVI
NFO
Figure 12. Kaplan Meier plot of survival with t1/2 of 10 days
Survival Time
Cumulative Proportion of Survival
0 7 14 21 28 35 42 49 56 630.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
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0.9
1.0
HL > 10HL < 10
Kaplan-Meier Cumulative Survival Plot
Table X. Observed and expected odds and odds-ratios of remission, relapse and no response by half-
life
Half-life exp. odds exp. odds-ratios(range, days) Rem short none Rem short none
> 20 1 4.16 17.11 1 12.49 56.07
16-20 1 2.21 4.84 1 6.64 44.16
11-15 1 1.18 1.37 1 3.53 12.49
6-10 1 0.63 0.39 1 1.88 3.53
< 6 1 0.33 0.11 1 1 1
HL-ref 1 0.33 0.11 1 1 1
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