Evaluation of bedside tests and proposal of a model for predicting difficult laryngoscopy: an observational prospective study
Avaliação de testes à beira leito e proposta de modelo para prever laringoscopia difícil: estudo prospectivo observacional
Chara Liaskou, Eleftherios Vouzounerakis, Anastasia Trikoupi, Chryssoula Staikou
Abstract
Background and objectives
The prediction of difficult laryngoscopy is based on tests that assess anatomic characteristics of face and neck. We aimed to identify the most accurate tests and propose a multivariate predictive model.
Methods
This prospective observational study included 1134 patients. Thyromental Distance (TMD), Sternomental Distance (STMD), Ratio of Height-to-Thyromental Distance (R-H/TMD), Neck Circumference (NC), Ratio of Neck Circumference-to-Thyromental Distance (R-NC/TMD), Hyomental Distance with head in Neutral Position (HMD-NP) and at Maximal Extension (HMD-HE), Ratio of Hyomental Distance at Maximal head extension-to-hyomental distance in neutral position (R-HMD), Mallampati Class (MLC), Upper Lip Bite Test (ULBT), Mouth Opening (MO) and Head Extension (HE) were assessed preoperatively. A Cormack-Lehane Grade ≥ 3 was defined as Difficult Laryngoscopy. Sensitivity, specificity, positive and negative predictive values were assessed for all tests. Multivariate analysis with logistic regression was used to create the predictive models.
Results
A model incorporating MLC, ULBT, HE, HMD-HE and R-NC/TMD showed high prognostic accuracy; x2(5) = 109.12, p < 0.001, AUC = 0.86, p < 0.001). Its sensitivity, specificity and negative predictive value were 82.3%, 74.8% and 97.4%, respectively. A second model including two measurements not requiring patient’s cooperation (R-NC/TMD and HMD-HE) exhibited good prognostic performance; x2(2) = 63.5, p < 0.001, AUC = 0.77, p < 0.001. Among single tests, HE had the highest sensitivity (78.5%) and negative predictive value (96%).
Conclusions
A five-variable model incorporating MLC, ULBT, HE, HMD-HE and R-NC/TMD showed satisfyingly high predictive value for difficult laryngoscopy. A model including R-NC/TMD and HMD-HE could be useful in incapable patients. The most accurate single predictor was HE.
Keywords
Resumo
Justificativa e objetivos
A previsão de laringoscopia difícil se baseia em testes que avaliam as características anatômicas da face e pescoço. Nosso objetivo foi identificar os testes mais precisos e propor modelo preditivo multivariado.
Método
Estudo observacional prospectivo incluiu 1134 pacientes e avaliou no pré-operatório: Distância Tireomentoniana (DTM), Distância Esternomentoniana (DEM), razão Altura-Distância Tireomentoniana (A/DTM), Circunferência Cervical (CC), razão Circunferência Cervical-Distância Tireomentoniana (CC/DTM), Distância Hiomentoniana com a cabeça na Posição Neutra (DHM-PN) e em Extensão Máxima (DHM-EM), razão Distância Hiomentoniana com Cabeça em Extensão Máxima/Distância Hiomentoniana na posição Neutra (DHME/DHMN), Classe Mallampati (CML), Teste da Mordida do Lábio Superior (TMLS), Abertura da Boca (AB) e Extensão da Cabeça (EC). Grau Cormack-Lehane ≥ 3 foi definido como Laringoscopia Difícil. A sensibilidade, especificidade, valores preditivos positivo e negativo foram avaliados para todos os testes. A análise multivariada com regressão logística foi usada para criar modelos preditivos.
Resultados
Um modelo incorporando CML, TMLS, EC, DHM-EM e CC/DTM demonstrou alta precisão prognóstica (x2(5) = 109,12, p < 0,001, AUC = 0,86, p < 0,001). A sensibilidade, especificidade e valor preditivo negativo foram 82,3%, 74,8% e 97,4%, respectivamente. Um segundo modelo incluindo duas medidas que não necessitavam da cooperação do paciente (CC/DTM e DHM-EM) demonstrou bom desempenho prognóstico (x2 (2) = 63,5; p < 0,001; AUC = 0,77, p < 0,001). Entre os testes individuais, EC teve a maior sensibilidade (78,5%) e valor preditivo negativo (96%).
Conclusões
O modelo de cinco variáveis incorporando CML, TMLS, EC, DHM-EM e CC/DTM mostrou valor preditivo satisfatoriamente alto para laringoscopia difícil. Um modelo que incluísse CC/DTM e DHM-EM poderia ser útil em pacientes com incapacidade. O preditor individual mais preciso foi EC.
References
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