Risk factors associated with early implant failure: A 5-year retrospective clinical study

2016 ◽  
Vol 115 (2) ◽  
pp. 150-155 ◽  
Author(s):  
Maris Victoria Olmedo-Gaya ◽  
Francisco J. Manzano-Moreno ◽  
Esther Cañaveral-Cavero ◽  
Juan de Dios Luna-del Castillo ◽  
Manuel Vallecillo-Capilla
2017 ◽  
Vol 46 (2) ◽  
pp. 267-273 ◽  
Author(s):  
T. Hasegawa ◽  
S. Kawabata ◽  
D. Takeda ◽  
E. Iwata ◽  
I. Saito ◽  
...  

2021 ◽  
Vol 20 (3) ◽  
pp. 212-216
Author(s):  
IVAN ANDREEVICH STEPANOV ◽  
VLADIMIR ANATOL’EVICH BELOBORODOV ◽  
MARIYA ANATOL’EVNA SHAMEEVA ◽  
EDUARD BORISOVICH BORISOV

ABSTRACT Objective This retrospective clinical study was carried out to generate and cross-validate a scoring system for the identification of patients at risk of SSIs after spinal surgery. Methods A retrospective study was conducted, which included patients who underwent spinal surgery. The potential variables for SSIs were extracted from the database, including preoperative, intraoperative and postoperative risk factors for univariate and multivariate regression analyses. Results A total of 2347 patients were included in this retrospective clinical study. Postoperative SSIs were observed in 53 patients (2.2%). The multivariate logistic regression analysis revealed the following risk factors for SSIs after spinal surgery: diabetes mellitus ( P =0.029), body mass index ( P =0.008), low serum calcium concentration ( P =0.012), low pre- and postoperative albumin ( P =0.023, P =0.037), more than three operated segments ( P =0.008), operation time of more than 180 minutes ( P =0.019), estimated blood loss ( P =0.011), low postoperative hemoglobin ( P =0.017) and prolonged drainage time ( P =0.025). Each of these factors contributed 1 point to the risk score. The predicted rates of incidence for the low-, intermediate-, high-, and extremely high-risk categories in the validation set were 1.4%, 12%, 41.6%, and 66.6%, respectively. Conclusions Our scoring system allows for easy and validated risk stratification of SSIs after spinal surgery. Level of evidence III; Cross-sectional Observational Study.


Author(s):  
Abeer Essam Hakam ◽  
Gabriela Vila ◽  
Poliana Mendes Duarte ◽  
Marcia Phemba Mbadu ◽  
Dannia Sulaiman AI Angary ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Qimin Chen ◽  
Ming Liu ◽  
Bo Liu ◽  
Wei Li ◽  
Daixiu Gao ◽  
...  

Background. Noninvasive ventilation (NIV) has been reported to be beneficial for patients with acute respiratory failure in intensive care unit (ICU); however, factors that influence the clinical outcome of NIV were unclarified. We aim to determine the factors that predict the failure of NIV in critically ill patients with acute respiratory failure (ARF). Setting. Adult mixed ICU in a medical university affiliated hospital. Patients and Methods. A retrospective clinical study using data from critical adult patients with initial NIV admitted to ICU in the period August 2016 to November 2017. Failure of NIV was regarded as patients needing invasive ventilation. Logistic regression was employed to determine the risk factor(s) for NIV, and a predictive model for NIV outcome was set up using risk factors. Results. Of 101 included patients, 50 were unsuccessful. Although more than 20 variables were associated with NIV failure, multivariate logistic regression demonstrated that only ideal body weight (IBW) (OR 1.110 (95%1.027–1.201), P=0.009), the maximal heart rate during NIV period (HR-MAX) (OR 1.024 (1.004–1.046), P=0.021), the minimal respiratory rate during NIV period (RR-MIN) (OR 1.198(1.051–1.365), P=0.007), and the highest body temperature during NIV period (T-MAX) (OR 1.838(1.038–3.252), P=0.037) were independent risk factors for NIV failure. We set up a predictive model based on these independent risk factors, whose area under the receiver operating characteristic curve (AUROC) was 0.783 (95% CI: 0.676–0.899, P<0.001), and the sensitivity and specificity of model were 68.75% and 71.43%, respectively, with the optimal cut-off value of 0.4863. Conclusion. IBW, HR-MAX, RR-MIN, and T-MAX were associated with NIV failure in patients with ARF. A predictive model based on the risk factors could help to discriminate patients who are vulnerable to NIV failure.


2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Henning Staedt ◽  
Martin Rossa ◽  
Karl Martin Lehmann ◽  
Bilal Al-Nawas ◽  
Peer W. Kämmerer ◽  
...  

Abstract Background The aim of this study was to analyze potential risk factors for early and late dental implant failure (DIF) in a clinical cohort trial. In a private practice, 9080 implants were inserted during a period of 10 years. In case of DIF, data were classified into early and late DIF and compared to each other in regard of gender, age, site of implantation, implant geometry, and patients’ systemic diseases. Results Three hundred fifty-one implants failed within the observation period (survival rate: 96.13%). Early DIF occurred in 293 implants (83.48%) compared to late DIF in 58 implants (16.52%). Significant earlier DIF was seen in the mandible (OR = 3.729, p < 0.001)—especially in the posterior area—and in younger patients (p = 0.017), whereas an increased likelihood of late DIF was associated with maxillary implants (OR = 3.729, p < 0.001) and older patients. Conclusions Early DIF is about twice as common as late DIF. Main risk factors for early DIF are implant location in the (posterior) mandible as well as younger age. On contrary, late DIF is rather associated with older patients, cancellous bone quality, and longer implants.


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