Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patient Population
2.2. Data Collection
2.3. Clustering Analysis
2.4. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Patient Characteristics | Overall | Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 | p-Value |
---|---|---|---|---|---|---|
(n = 4289) | (n = 1201) | (n = 1396) | (n = 1191) | (n = 501) | ||
Age (years) | 67.3 ± 16.2 | 53.0 ± 14.3 | 73.8 ± 12.0 | 76.2 ± 10.9 | 62.4 ± 15.5 | <0.001 |
Male sex | 2566 (60) | 754 (63) | 869 (62) | 637 (53) | 306 (61) | <0.001 |
Race | <0.001 | |||||
-White | 4042 (94) | 1096 (91) | 1336 (96) | 1143 (96) | 467 (93) | |
-Black | 65 (2) | 32 (3) | 13 (1) | 5 (0.4) | 15 (3) | |
-Others | 182 (4) | 73 (6) | 47 (3) | 43 (4) | 19 (4) | |
BMI (kg/m2) | 30.4 ± 8.3 | 32.1 ± 9.8 | 29.5 ± 7.1 | 29.3 ± 7.2 | 31.2 ± 9.5 | <0.001 |
Principal diagnosis | <0.001 | |||||
-Cardiovascular | 820 (19) | 141 (12) | 287 (21) | 352 (30) | 40 (8) | |
-Endocrine/metabolic | 190 (4) | 38 (3) | 54 (4) | 55 (5) | 43 (9) | |
-Gastrointestinal | 437 (10) | 103 (9) | 149 (11) | 120 (10) | 65 (13) | |
-Genitourinary | 499 (12) | 98 (8) | 145 (10) | 106 (9) | 150 (30) | |
-Hematology/oncology | 741 (17) | 296 (25) | 274 (20) | 126 (11) | 45 (9) | |
-Infectious disease | 381 (9) | 74 (6) | 135 (10) | 83 (7) | 89 (18) | |
-Respiratory | 216 (5) | 40 (3) | 70 (5) | 94 (8) | 12 (2) | |
-Injury/poisoning | 488 (11) | 198 (16) | 152 (11) | 105 (9) | 33 (7) | |
-Other | 517 (12) | 213 (18) | 130 (9) | 150 (13) | 24 (5) | |
Charlson Comorbidity Score | 3.0 ± 2.7 | 1.4 ± 1.7 | 3.6 ± 2.8 | 3.7 ± 2.8 | 3.2 ± 2.9 | <0.001 |
Comorbidities | ||||||
-Coronary artery disease | 530 (12) | 43 (4) | 210 (15) | 223 (19) | 54 (11) | <0.001 |
-Congestive heart failure | 632 (15) | 33 (3) | 199 (14) | 334 (28) | 66 (13) | <0.001 |
-Peripheral vascular disease | 272 (6) | 12 (1) | 121 (9) | 117 (10) | 22 (4) | <0.001 |
-Dementia | 119 (3) | 5 (0.4) | 63 (63) | 46 (4) | 5 (1) | <0.001 |
-Stroke | 518 (12) | 34 (3) | 218 (16) | 209 (18) | 57 (11) | <0.001 |
-COPD | 629 (15) | 56 (5) | 222 (16) | 293 (25) | 58(12) | <0.001 |
-Diabetes mellitus | 1390 (32) | 198 (16) | 516 (37) | 459 (39) | 217 (43) | <0.001 |
-Cirrhosis | 236 (6) | 40 (3) | 90 (6) | 47 (4) | 59 (12) | <0.001 |
Laboratory test | ||||||
-eGFR (mL/min/1.73 m2) | 68 ± 27 | 92 ± 23 | 55 ± 20 | 59 ± 21 | 71 ± 29 | <0.001 |
-Sodium (mEq/L) | 137 ± 5 | 138 ± 4 | 138 ± 4 | 136 ± 5 | 133 ± 6 | <0.001 |
-Potassium (mEq/L) | 4.5 ± 0.8 | 4.3 ± 0.6 | 4.7 ± 0.8 | 4.4 ± 0.7 | 5.0 ± 1.0 | <0.001 |
-Chloride (mEq/L) | 102 ± 6 | 103 ± 4 | 106 ± 4 | 98 ± 5 | 99 ± 7 | <0.001 |
-Bicarbonate (mEq/L) | 24 ± 5 | 25 ± 3 | 22 ± 4 | 27 ± 4 | 19 ± 5 | <0.001 |
-Anion gap | 11 ± 4 | 10 ± 3 | 9 ± 3 | 11 ± 4 | 15 ± 6 | <0.001 |
-Strong ion difference | 39.2 ± 4.3 | 39.4 ± 3.2 | 36.3 ± 3.4 | 42.4 ± 3.5 | 38.9 ± 5.2 | <0.001 |
-Hemoglobin (g/dL) | 11.6 ± 2.3 | 12.5 ± 2.2 | 10.7 ± 2.1 | 11.9 ± 2.0 | 11.4 ± 2.6 | <0.001 |
Acute kidney injury stage | <0.001 | |||||
-Stage 1 | 3517 (82) | 1092 (91) | 1289 (92) | 1092 (92) | 44 (9) | |
-Stage 2 | 408 (10) | 102 (8) | 93 (7) | 86 (7) | 127 (25) | |
-Stage 3 | 364 (8) | 7 (1) | 14 (1) | 13 (1) | 330 (66) |
Hospital Mortality | OR (95% CI) | 1-Year Mortality | HR (95% CI) | |
---|---|---|---|---|
Cluster 1 | 1.7% | 1 (ref) | 8.4% | 1 (ref) |
Cluster 2 | 4.4% | 2.74 (1.65–4.57) | 29.7% | 3.97 (3.14–5.03) |
Cluster 3 | 3.9% | 2.37 (1.39–4.04) | 31.2% | 4.22 (3.33–5.35) |
Cluster 4 | 11.2% | 7.43 (4.41–12.53) | 33.7% | 4.98 (3.82–6.48) |
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Thongprayoon, C.; Vaitla, P.; Nissaisorakarn, V.; Mao, M.A.; Genovez, J.L.Z.; Kattah, A.G.; Pattharanitima, P.; Vallabhajosyula, S.; Keddis, M.T.; Qureshi, F.; et al. Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering. Med. Sci. 2021, 9, 60. https://doi.org/10.3390/medsci9040060
Thongprayoon C, Vaitla P, Nissaisorakarn V, Mao MA, Genovez JLZ, Kattah AG, Pattharanitima P, Vallabhajosyula S, Keddis MT, Qureshi F, et al. Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering. Medical Sciences. 2021; 9(4):60. https://doi.org/10.3390/medsci9040060
Chicago/Turabian StyleThongprayoon, Charat, Pradeep Vaitla, Voravech Nissaisorakarn, Michael A. Mao, Jose L. Zabala Genovez, Andrea G. Kattah, Pattharawin Pattharanitima, Saraschandra Vallabhajosyula, Mira T. Keddis, Fawad Qureshi, and et al. 2021. "Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering" Medical Sciences 9, no. 4: 60. https://doi.org/10.3390/medsci9040060