Influencing factors and prediction model construction of re-tearing risk after arthroscopic rotator cuff repair

doi:10.11958/20252352

Abstract

Abstract:

Objective To explore the factors affecting the risk of postoperative re-tearing in patients undergoing arthroscopic rotator cuff repair, and to construct a nomogram prediction model. Methods A total of 587 patients who underwent arthroscopic rotator cuff repair were divided into the training set (470 cases) and the validation set (117 cases). The training set was sub-divided into the re-tearing group and the healing group based on whether re-tearing occurred within 12 months after surgery. Clinical data were compared between the two groups. Multivariate Logistic regression analysis was used to identify the influencing factors of re-tearing, and a nomogram prediction model for re-tearing was constructed. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the model. The clinical decision curve was used to evaluate its clinical applicability. Results In the training set, re-tearing occurred in 97 cases (the re-tearing group) and healing was observed in 373 cases (the healing group). There were 23 cases of re-tearing in the validation set. Multivariate Logistic regression analysis results showed that age ≥ 60 years, osteoporosis, length of tearing >3 cm, tendon retraction, fat infiltration ≥ grade 2 and use of corticosteroids were independent risk factors for re-tearing (P<0.05). The C-index of the constructed nomogram model was 0.814 (95%CI: 0.810-0.819). The calibration curve and the ideal curve were close in both the training and validation sets. Internally validation using Bootstrap method found that the mean absolute errors were 0.011 and 0.027, respectively. Hosmer-Lemeshow test showed good goodness of fit (χ²=4.531, P=0.806). The clinical decision curve showed that the model had a significant positive net benefit. The area under the curve (AUC), sensitivity and specificity of the nomogram prediction model for predicting postoperative re-tearing in the training set were 0.818 (95%CI: 0.771-0.865), 78.69% and 79.60%, which in the validation set were 0.807 (95%CI: 0.730-0.884), 77.94% and 76.82%, respectively. Conclusion The nomogram model constructed based on risk factors can better predict the risk of re-tearing after arthroscopic repair.

Key words: rotator cuff injury, arthroscopy, laceration, nomogram, arthroscopic rotator cuff repair, postoperative re-tearing

CLC Number:

R687.4

Figures/Tables 6

References 20

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组别	n	女性	年龄≥60岁	BMI/（kg/m²）	吸烟	饮酒	高血压	糖尿病	高脂血症	伴骨质疏松	病程/月
愈合组	373	250（67.02）	153（41.02）	22.62±1.33	35（9.38）	16（4.29）	138（37.00）	95（25.47）	91（24.40）	104（27.88）	5.23±1.32
再撕裂组	97	71（73.20）	62（63.92）	22.85±1.34	12（12.37）	7（7.22）	40（41.24）	40（41.24）	30（30.93）	50（51.55）	5.37±1.32
χ²或t		1.354	16.264^＊＊	1.567	0.764	1.417	0.588	9.349^＊＊	1.718	19.569^＊＊	0.936
组别	撕裂长度		疼痛VAS评分/分	肌腱回缩	脂肪浸润分级		手术方式		应用皮质类固醇
组别	>3 cm	≤3 cm	疼痛VAS评分/分	肌腱回缩	≥2级	<2级	单排	双排	应用皮质类固醇
愈合组	150（40.21）	223（59.79）	6.48±1.35	112（30.03）	49（13.14）	324（86.86）	142（38.07）	231（61.93）	86（23.06）
再撕裂组	70（72.16）	27（27.84）	6.30±1.28	49（50.52）	35（36.08）	62（63.92）	47（48.45）	50（51.55）	33（34.02）
χ²	31.562^＊＊		1.252	14.349^＊＊	27.613^＊＊		3.452		4.894^＊

组别	n	女性	年龄≥60岁	BMI/（kg/m²）	吸烟	饮酒	高血压	糖尿病	高脂血症	伴骨质疏松	病程/月
愈合组	373	250（67.02）	153（41.02）	22.62±1.33	35（9.38）	16（4.29）	138（37.00）	95（25.47）	91（24.40）	104（27.88）	5.23±1.32
再撕裂组	97	71（73.20）	62（63.92）	22.85±1.34	12（12.37）	7（7.22）	40（41.24）	40（41.24）	30（30.93）	50（51.55）	5.37±1.32
χ²或t		1.354	16.264^＊＊	1.567	0.764	1.417	0.588	9.349^＊＊	1.718	19.569^＊＊	0.936
组别	撕裂长度		疼痛VAS评分/分	肌腱回缩	脂肪浸润分级		手术方式		应用皮质类固醇
组别	>3 cm	≤3 cm	疼痛VAS评分/分	肌腱回缩	≥2级	<2级	单排	双排	应用皮质类固醇
愈合组	150（40.21）	223（59.79）	6.48±1.35	112（30.03）	49（13.14）	324（86.86）	142（38.07）	231（61.93）	86（23.06）
再撕裂组	70（72.16）	27（27.84）	6.30±1.28	49（50.52）	35（36.08）	62（63.92）	47（48.45）	50（51.55）	33（34.02）
χ²	31.562^＊＊		1.252	14.349^＊＊	27.613^＊＊		3.452		4.894^＊

变量	β	SE	Wald χ²	P	OR	95% CI
年龄	0.980	0.268	13.406	<0.001	2.664	1.577~4.502
合并糖尿病	0.519	0.272	3.647	0.056	1.680	0.986~2.862
伴骨质疏松	0.905	0.265	11.641	<0.001	2.473	1.470~4.159
撕裂长度	1.230	0.274	20.171	<0.001	3.422	2.001~5.854
肌腱回缩	0.835	0.265	9.912	0.002	2.304	1.370~3.874
脂肪浸润分级	1.461	0.299	23.864	<0.001	4.311	2.399~7.748
应用皮质类固醇	0.606	0.283	4.592	0.032	1.832	1.053~3.188
常数项	-3.943	0.371	112.746	<0.001	0.019	-

变量	β	SE	Wald χ²	P	OR	95% CI
年龄	0.980	0.268	13.406	<0.001	2.664	1.577~4.502
合并糖尿病	0.519	0.272	3.647	0.056	1.680	0.986~2.862
伴骨质疏松	0.905	0.265	11.641	<0.001	2.473	1.470~4.159
撕裂长度	1.230	0.274	20.171	<0.001	3.422	2.001~5.854
肌腱回缩	0.835	0.265	9.912	0.002	2.304	1.370~3.874
脂肪浸润分级	1.461	0.299	23.864	<0.001	4.311	2.399~7.748
应用皮质类固醇	0.606	0.283	4.592	0.032	1.832	1.053~3.188
常数项	-3.943	0.371	112.746	<0.001	0.019	-