| Literature DB >> 35747431 |
Jun Su1,2, Hao Huang1,3, Yuan-Jun Xin1, Yi-Dan Liang1,4, Xin-Tong Wu1, Xiao-Lin Yang1, Xiao-Zhu Liu5, Zhaohui He1.
Abstract
Objective: A nomogram was developed in this work to predict the probability of delayed cerebral infarction (DCI) after ruptured intracranial aneurysms (RIA) clipping.Entities:
Keywords: clipping; delayed cerebral infarction; nomogram; risk factors; ruptured Intracranial aneurysm
Year: 2022 PMID: 35747431 PMCID: PMC9209644 DOI: 10.3389/fsurg.2022.886237
Source DB: PubMed Journal: Front Surg ISSN: 2296-875X
Patient information and univariate analysis of DCI for intracranial aneurysm clipping.
| Index | Observation group ( | Control group ( |
| |
|---|---|---|---|---|
| Age / years old | 53.25 ± 9.61 | 52.58 ± 9.50 | 0.347 | 0.728 |
| Women | 32(86.5) | 250 (65.4) | 6.787 | 0.009 |
| Hypertension | 14 (37.8) | 159 (41.6) | 0.199 | 0.655 |
| Diabetes | 0 (0) | 12 (3.1) | 0.334 | 0.563 |
| Smoking history | 5 (13.5) | 111 (29.1) | 4.071 | 0.044 |
| Dringking history | 5 (13.5) | 70 (18.3) | 0.531 | 0.466 |
| Small aneurysms | 25 (67.6) | 187 (49.0) | 4.676 | 0.031 |
| Uniform shape | 34 (91.9) | 320 (83.8) | 1.698 | 0.193 |
| Location of aneurysm | 2.674 | 0.6142 | ||
| ACoA | 11 (29.7) | 164 (41.1) | ||
| MCA | 9 (24.3) | 64 (16.8) | ||
| Supraclinoid | 13 (35.1) | 123 (32.2) | ||
| Paraclinoid | 2 (5.4) | 13 (3.4) | ||
| Multiple | 2 (5.4) | 18 (4.7) | ||
| WFNS Classification – Advanced | 12 (32.4) | 81 (21.2) | 2.463 | 0.117 |
| Hunt and Hess grade | - | 0.428 | ||
| I–III level | 36 (97.3) | 377 (98.7) | ||
| IV–V level | 1 (2.7) | 5 (1.3) | ||
| Improved Fisher score | 0.480 | 0.488 | ||
| I–II level | 5 (13.5) | 69 (18.1) | ||
| III–IV level | 32 (86.5) | 313 (81.9) | ||
| Temporary blocking | 5 (13.5) | 38 (10.0) | 0.159 | 0.690 |
| Operation time /min | 213.76 ± 64.30 | 215.57 ± 75.08 | 0.142 | 0.887 |
| Intraoperative aneurysm rupture | 4 (10.8) | 6 (1.6) | – | 0.007 |
| Temporary blocking time /min | 3.80 ± 0.837 | 3.95 ± 1.207 | −0.561 | 0.575 |
| Vasospasm | 26 (70.3) | 105 (27.5) | 28.732 | <0.001 |
Multivariate Logistic regression analysis of DCI in intracranial aneurysm clipping.
| Factors | OR | 95% CI |
|
|---|---|---|---|
| Gender | 0.386 | 0.102–1.465 | 0.162 |
| Small aneurysms | 3.332 | 1.563–7.104 | 0.002 |
| smoking history | 1.117 | 0.102–1.465 | 0.871 |
| Intraoperative aneurysm rupture | 0.122 | 0.029–0.504 | 0.004 |
| Cerebral vasospasm | 0.153 | 0.070–0.333 | <0.001 |
Figure 1Nomogram for predicting 7-,14-,and 21-day probability of DCI for individual patients.
Figure 27-, 14-and 21-day calibration curve of nomogram. X-axis refers to probability of DCI and the y-axis means actual probability. The lines represent the perfect calibration models in which the predicted probabilities are identical to the actual probabilities.
Figure 3The clinical decision curve for predicting the occurrence of DCI. The x-axis represents the threshold probabilities, and the y-axis measures the net benefit calculated by adding the true positives and subtracting the false positives.