Subtyping Hyperchloremia among Hospitalized Patients 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 (n = 11,394) | Cluster 1 (n = 3237) | Cluster 2 (n = 4059) | Cluster 3 (n = 4098) | p-Value |
---|---|---|---|---|---|
Age (years) | 60.8 (17.9) | 61.3 (15.9) | 48.9 (16.5) | 72.3 (12.3) | <0.001 |
Male sex | 5706 (50) | 1446 (45) | 2047 (50) | 2213 (54) | <0.001 |
Race | <0.001 | ||||
- White | 10,528 (92) | 2944 (91) | 3668 (90) | 3916 (96) | |
- Black | 191 (2) | 56 (2) | 94 (2) | 41 (1) | |
- Others | 675 (6) | 237 (7) | 297 (7) | 141 (3) | |
BMI (kg/m2) | 29.0 (6.8) | 28.4 (6.6) | 28.7 (6.7) | 29.7 (6.9) | <0.001 |
Principal diagnosis | <0.001 | ||||
- Cardiovascular | 3750 (33) | 1192 (37) | 1094 (27) | 1464 (36) | |
- Endocrine/metabolic | 195 (2) | 61 (2) | 40 (1) | 94 (2) | |
- Gastrointestinal | 974 (9) | 367 (11) | 253 (6) | 354 (9) | |
- Genitourinary | 403 (4) | 93 (3) | 84 (2) | 226 (6) | |
- Hematology/oncology | 1233 (11) | 348 (11) | 234 (6) | 651 (16) | |
- Infectious disease | 379 (3) | 198 (6) | 54 (1) | 127 (3) | |
- Respiratory | 243 (2) | 92 (2) | 72 (2) | 109 (3) | |
- Injury/poisoning | 1933 (17) | 467 (14) | 970 (24) | 496 (12) | |
- Other | 2284 (20) | 449 (14) | 1258 (31) | 577 (14) | |
Charlson Comorbidity Score | 1.6 (2.2) | 1.6 (2.0) | 0.5 (1.0) | 2.7 (2.6) | <0.001 |
Comorbidities | |||||
- Coronary artery disease | 859 (8) | 193 (6) | 117 (3) | 549 (13) | <0.001 |
- Congestive heart failure | 785 (7) | 178 (6) | 122 (3) | 485 (12) | <0.001 |
- Peripheral vascular disease | 374 (3) | 92 (3) | 25 (1) | 257 (6) | <0.001 |
- Dementia | 169 (1) | 29 (1) | 13 (0) | 127 (3) | <0.001 |
- Stroke | 858 (8) | 203 (6) | 125 (3) | 530 (13) | <0.001 |
- COPD | 807 (7) | 244 (8) | 85 (2) | 478 (12) | <0.001 |
- Diabetes mellitus | 2040 (18) | 586 (18) | 220 (5) | 1234 (30) | <0.001 |
- Cirrhosis | 347 (3) | 164 (5) | 47 (1) | 136 (3) | <0.001 |
- End-stage kidney disease | 378 (3) | 77 (2) | 11 (0) | 290 (7) | <0.001 |
Laboratory test | |||||
- eGFR (mL/min/1.73 m2) | 74 (24) | 75 (21) | 91 (18) | 56 (20) | <0.001 |
- Sodium (mEq/L) | 141 (3) | 139 (3) | 141 (3) | 142 (3) | <0.001 |
- Potassium (mEq/L) | 4.2 (0.6) | 4.0 (0.6) | 4.1 (0.4) | 4.4 (0.7) | <0.001 |
- Chloride (mEq/L) | 110 (3) | 112 (4) | 109 (2) | 110 (2) | <0.001 |
- Bicarbonate (mEq/L) | 23 (3) | 22 (4) | 24 (3) | 23 (4) | <0.001 |
- Anion gap | 8 (4) | 6 (4) | 8 (3) | 10 (3) | <0.001 |
- Strong ion difference | 34.8 (3.4) | 31.5 (3.0) | 35.6 (2.5) | 36.7 (2.6) | <0.001 |
- Hemoglobin (g/dL) | 11.5 (2.2) | 10.2 (1.9) | 13.2 (1.6) | 10.9 (1.9) | <0.001 |
- Albumin (g/dL) | 3.5 (0.4) | 3.1 (0.3) | 3.8 (0.3) | 3.5 (0.3) | <0.001 |
Acute kidney injury | 1542 (14) | 434 (13) | 170 (4) | 938 (23) | <0.001 |
Hospital Mortality | OR (95% CI) | 1-Year Mortality | HR (95% CI) | |
---|---|---|---|---|
Cluster 1 | 2.4% | 3.60 (2.33–5.56) | 12.5% | 4.49 (3.53–5.70) |
Cluster 2 | 0.7% | 1 (ref) | 2.8% | 1 (ref) |
Cluster 3 | 3.3% | 4.83 (3.21–7.28) | 18.8% | 6.96 (5.56–8.72) |
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Thongprayoon, C.; Nissaisorakarn, V.; Pattharanitima, P.; Mao, M.A.; Kattah, A.G.; Keddis, M.T.; Dumancas, C.Y.; Vallabhajosyula, S.; Petnak, T.; Erickson, S.B.; et al. Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering. Medicina 2021, 57, 903. https://doi.org/10.3390/medicina57090903
Thongprayoon C, Nissaisorakarn V, Pattharanitima P, Mao MA, Kattah AG, Keddis MT, Dumancas CY, Vallabhajosyula S, Petnak T, Erickson SB, et al. Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering. Medicina. 2021; 57(9):903. https://doi.org/10.3390/medicina57090903
Chicago/Turabian StyleThongprayoon, Charat, Voravech Nissaisorakarn, Pattharawin Pattharanitima, Michael A. Mao, Andrea G. Kattah, Mira T. Keddis, Carissa Y. Dumancas, Saraschandra Vallabhajosyula, Tananchai Petnak, Stephen B. Erickson, and et al. 2021. "Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering" Medicina 57, no. 9: 903. https://doi.org/10.3390/medicina57090903