CLAIR Score: A Risk-Screening Tool for Unanticipated Difficult Airways — A Retrospective Case– Control Study

doi:10.21203/rs.3.rs-8331091/v1

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Research Article

CLAIR Score: A Risk-Screening Tool for Unanticipated Difficult Airways — A Retrospective Case– Control Study

https://doi.org/10.21203/rs.3.rs-8331091/v1

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Background

Unanticipated difficult airways are a major cause of perioperative morbidity. Existing bedside airway assessments often fail to predict difficulty when anatomical predictors are absent, highlighting the need for a pragmatic tool that integrates physiological, procedural, and demographic factors to flag patients at risk for unanticipated difficult intubation.

Results

In this retrospective case–control study of 62,111 anesthetized patients between 2015 and 2020, we identified 98 unanticipated difficult airway events and matched them 1:3 to 294 controls. The incidence of unanticipated difficult airways was 0.16%. Multivariate logistic regression determined four independent predictors: coagulopathy or hypocalcaemia, female sex, potentially difficult airway on physical exam, and intubation by a less-experienced resident. These variables were combined into the CLAIR score which demonstrated an area under the receiver operating characteristic curve of 0.633, with high sensitivity (98%) despite low specificity (16%). A cutoff of 0 effectively stratified patients into high (≥ 1), and low (≤-1) risk groups to airway management strategies while a web-based risk calculator and QR code enhanced accessibility for real-time clinical applications.

Conclusion

The CLAIR score, based on routinely available preoperative and procedural data, provides a high-sensitivity screening tool to identify patients at risk of unanticipated difficult airway. Although external validation is required, its simplicity and reliance on routine information make it a valuable tool in routine anesthetic practice to prompt preparedness and risk mitigation.

patient safety

preoperative assessment

predictors

unanticipated difficult airway

Unanticipated difficult airway continues to be a major challenge in anesthesia practice. The incidence of difficult intubation has been reported between 1.5–8.5% of cases, pose significant challenges and can result in life-threatening complications [1–6]. The 2015 Perioperative and Anesthetic Adverse Events in Thailand (PAAd Thai) study demonstrated a 2.3% incidence of unanticipated difficult intubation [7]. Ineffective management of such cases can lead to severe complications, including emergency surgery, airway trauma, brain hypoxia, and death due to oxygen deprivation [8, 9]. Traditional airway assessments -such as the Mallampati classification, upper lip bite test (ULBT), interincisor distance (IID), and thyromental distance (TMD) assess anatomical predictors but often fail when airway anatomy appears normal [10]. While comprehensive assessment models integrating multiple factors improve accuracy, they are complex and time-consuming, limiting their suitability for daily practice [11–14]. Advanced airway ultrasound techniques offer promising sensitivity and specificity [15–17] but remain inaccessible in resource-limited settings. A more practical, efficient, and accurate risk stratification tool remains a critical need. This study aims to identify independent predictors of unanticipated difficult airway to develop a practical screening score for day-to-day anesthetic practice, especially in high-throughput settings.

This retrospective observational case-control study was conducted at an 858-bed tertiary care teaching hospital in southern Thailand from January 2015 to December 2020. All procedures adhered to relevant laws and institutional guidelines. The study protocol was approved by the Human Research Ethics Unit, Faculty of Medicine, Prince of Songkla University (approval number 63-560-8-1), which granted a waiver for informed consent due to its retrospective design. The privacy rights of all participants were rigorously observed. As this study was retrospective and observational in nature and did not involve any prospective intervention, clinical trial registration was not required.

Patient selection

The inclusion comprised patients undergoing general anaesthesia with endotracheal intubation for elective or emergency surgery who encountered difficult airway management, defined as requiring three or more intubation attempts. Patients who were already on preoperative mechanical ventilation or had anticipated difficult airways were excluded.

Matching procedure

A rigorous matching algorithm was implemented to minimize selection bias and control for potential confounding variables. Cases of difficult airway management were matched with controls who underwent routine airway management based on age (± 5 years), surgery type, and year, in a 1:3 ratio. For each difficult airway case, three controls were selected: one with a preoperatively anticipated difficult airway and two with normal airway assessment.

Definition of unanticipated difficult intubation

Unanticipated difficult intubation was defined as three or more attempts at tracheal tube intubation using direct or video laryngoscopy in a patient not previously identified as having a difficult airway. Intubations were performed by anaesthesia staff with at least one year of experience, including anaesthesiology residents, nurse anaesthetists, and attending anaesthesiologists.

Potential risk factors and confounding variables

Potential predictors included patient-, surgery-, and anaesthesia-related factors such as sex, age, weight, height, body mass index (BMI), American Society of Anesthesiologists (ASA) physical status, preoperative airway assessment, history of difficult intubation and ventilation, comorbidities (e.g., medical conditions, congenital heart disease, obstructive sleep apnoea, head and neck radiation, anatomical abnormalities due to infection, trauma, tumours, or burns, and abnormal laboratory values), type of surgery, laryngoscopic view grades, intubation attempts, intubation devices, incidence of emergency tracheostomy, first intubation attempt by attending staff, and intubation experience.

Sample size determination

A sample size calculation estimated that 70 unanticipated difficult airway cases and 280 controls were required to detect an odds ratio of 2.5, assuming a 15% prevalence of exposure among controls, with 80% power and a 0.05 significance level. Based on the institutional annual incidence of 10–15 cases, a 6-year study period was required.

Statistical analysis

Statistical analyses were performed using R version 4.3.1 (R Foundation, Vienna, Austria). Descriptive statistics are reported as medians with interquartile ranges for continuous variables and as frequencies with percentages for categorical variables. Associations between categorical variables and difficult intubation were assessed using the chi-squared or Fisher’s exact test, while continuous variables were evaluated with Student’s t-tests or Mann–Whitney U tests, depending on data distribution. Collinearity diagnostics and bivariate correlation matrices were evaluated for all variables. In cases of multicollinearity, only one variable was retained for multivariate analysis. Variables with a p-value < 0.25 in the univariate analysis were considered for inclusion in the initial multivariate logistic regression model. The final regression model was derived using backward selection, retaining all significant variables. The optimal cut off point was identified using Youden’s index. Statistical significance was defined as a p-value < 0.05.

Score derivation and validation

The risk prediction score system was developed using predictors derived from the multivariate logistic regression model. Risk scores were calculated by assigning weights to regression coefficients and scaling the total to 5.

In the final model, the total predictor score was used to estimate the likelihood of unanticipated difficult airways. Youden’s index determined the cutoff value that maximized specificity and sensitivity. The performance of the final model was reported as the area under the receiver operating characteristic curve (AUC), with an AUC greater than 80 indicating excellent predictive accuracy.

Among 62,111 patients undergoing general anaesthesia over 6 years, 168 cases of difficult airways were identified. After excluding 70 anticipated cases, 98 unanticipated difficult airway cases and 294 matched controls were analysed (Figure 1), yielding an incidence of 0.16%. Patient demographics and perioperative characteristics are presented in Table 1. Notably, only 3.1% of patients in the unanticipated difficult airway group were preoperatively assessed as having a probable difficult airway, compared with 16% in the control group (p=0.002). Most first-attempt intubations (66.1%) were performed by anaesthesiology residents. The unanticipated difficult airway group had significantly more intubation attempts (median 4 (3,5) vs. 1 (1,1), p<0.001) and worse laryngoscopic view grades (p<0.001). They were also more likely to require video laryngoscope (78.6 % vs. 13.7%, p<0.001).

Table 1. Patient demographics and perioperative characteristics

Characteristics	Unanticipated difficult airway (n=98)	Non-difficult airway (n=294)	p-value
Sex			0.243
Male	56 (57.1)	146 (49.7)
Female	42 (42.9)	148 (50.3)
Age (years), median (IQR)	55 (38.0, 65.8)	56 (36.2, 64.0)	0.790
Age (years)			match
≤ 7	12 (12.2)	34 (11.6)
8–20	3 (3.1)	14 (4.8)
21–64	56 (57.1)	180 (61.2)
≥ 65	27 (27.6)	66 (22.4)
Weight (kg), median (IQR)	58.8 (49.1, 70.0)	59.5 (48.4, 69.0)	0.980
Height (cm), median (IQR)	160 (152.2,165.9)	158.5 (150,165)	0.346
BMI (kg/m²) median (IQR)	23.3 (19,25.9)	22.7 (19.2,26)	0.494
BMI (kg/m²)			0.286
< 15	8 (8.2)	21 (7.1)
15–29	82 (83.7)	231 (78.6)
≥ 30	8 (8.2)	42 (14.3)
ASA classification			0.798
I	2 (2)	13 (4.4)
II	52 (53.1)	155 (52.7)
III	43 (43.9)	123 (41.8)
IV	1 (1)	3 (1)
Preoperative probable difficult airway			0.002
No	95 (96.9)	247 (84)
Yes	3 (3.1)	47 (16)
Modified Mallampati classification			0.911
1–2	79 (80.6)	239 (81.3)
3–4	10 (10.2)	26 (8.8)
unknown	9 (9.2)	29 (9.9)
Thyromental distance			0.832
< 3 finger breadths	3 (3.1)	8 (2.7)
3 finger breadths	64 (65.3)	181 (61.6)
> 3 finger breadths	22 (22.4)	80 (27.2)
unknown	9 (9.2)	25 (8.5)
Inter-incisor gap			0.824
1–2 cm	7 (7.1)	16 (5.4)
3–4 cm	82 (83.7)	251 (85.4)
unknown	9 (9.2)	27 (9.2)
Limited neck flexion and extension			1
No	94 (95.9)	284 (96.6)
Yes	3 (3.1)	8 (2.7)
Cannot evaluate	1 (1)	2 (0.7)
Upper lip bite test classification			0.681
1	53 (54.1)	175 (59.5)
2	26 (26.5)	75 (25.5)
3	1 (1)	3 (1)
unknown	18 (18.4)	41 (13.9)
Facial appearance or syndrome			0.418
Normal	95 (96.9)	289 (98.3)
Abnormal	3 (3.1)	5 (1.7)
Edentulous			0.840
No	88 (89.8)	268 (91.2)
Yes	10 (10.2)	26 (8.8)
Overbite			0.250
No	97 (99)	294 (100)
Yes	1 (1)	0 (0)
Previous history of difficult intubation and ventilation			1
No	98 (100)	293 (99.7)
Yes	0 (0)	1 (0.3)
Medical conditions			1
No	93 (94.9)	278 (94.9)
Yes	5 (5.1)	16 (5.4)
Congenital heart disease			0.770
No	95 (96.9)	281 (95.6)
Yes	3 (3.1)	13 (4.4)
Airway/neck/oral deformity			0.639
No	90 (91.8)	276 (93.9)
Yes	8 (8.2)	18 (6.1)
Foreign body aspiration			0.438
No	97 (99)	293 (99.7)
Yes	1 (1)	1 (0.3)
Infection: retropharyngeal abscess, epiglottitis, supraglottitis			1
No	97 (99)	292 (99.3)
Yes	1 (1)	2 (0.7)
Post-surgical procedure: thyroid, cervical vertebrae			0.504
No	94 (95.9)	286 (97.3)
Yes	4 (4.1)	8 (2.7)
OSA/Snoring			0.875
No	83 (84.7)	245 (83.3)
Yes	15 (15.3)	49 (16.7)
Tumours: thyroid, pharynx, larynx and tracheobronchus, oesophagus			0.634
No	86 (87.8)	265 (90.1)
Yes	12 (12.2)	29 (9.9)
Trauma: face, neck			0.643
No	96 (98)	290 (98.6)
Yes	2 (2)	4 (1.4)
Burns (head, neck, face), smoke inhalation, massive burn			0.261
No	96 (98)	292 (99.3)
Yes	2 (2)	2 (0.7)
History radiation of head, neck			0.697
No	95 (96.9)	288 (98)
Yes	3 (3.1)	6 (2)
Laryngeal oedema: angioedema, allergic, post rigid bronchoscopy			1
No	97 (99)	292 (99.3)
Yes	1 (1)	2 (0.7)
Coagulopathy and hypocalcaemia			0.102
No	95 (96.9)	292 (99.3)
Yes	3 (3.1)	2 (0.7)
Type of surgery			match
Remote	14 (14.3)	41 (13.9)
Neuro/Orthopaedic	6 (6.1)	18 (6.1)
Eye/superficial	12 (12.2)	40 (13.6)
ENT	31 (31.6)	90 (30.6)
Thoracic/Vascular	12 (12.2)	34 (11.6)
Abdomen	23 (23.5)	71 (24.1)
Intubation attempts, median (IQR)	4 (3,5)	1 (1,1)	< 0.001
Intubation device Direct laryngoscope Video laryngoscope Supraglottic airway device Other Fiberoptic intubation	19 (19.4) 77 (78.6) 6 (6.1) 2 (2) 2 (2)	252 (85.7) 40 (13.7) 4 (1.4) 2 (0.7) 5 (1.7)	< 0.001
Emergency tracheostomy No	97 (99)	293 (99.7)	0.439
Yes	1 (1)	1 (0.3)
First-attempt intubation personnel			0.152
Anaesthesia instructors	9 (9.2)	31 (10.5)
Anaesthesiology residents	72 (73.5)	187 (63.6)	0.096
Certified registered nurse anaesthetists	3 (3.1)	28 (9.5)
Nurse anaesthetist students	14 (14.3)	48 (16.3)
Intubation experience (years)			0.837
< 5	93 (94.9)	275 (93.5)
5–10	3 (3.1)	13 (4.4)
11–20	2 (2)	4 (1.4)
> 20	0 (0)	2 (0.7)
Laryngoscopic view			< 0.001
Grade 1	10 (10.2)	227 (77.2)
Grade 2	14 (14.3)	49 (16.7)
Grade 3	43 (43.9)	14 (4.8)
Grade 4	30 (30.6)	0 (0)
Unknown	1 (1)	4 (1.4)

Data are presented as numbers (%) unless otherwise indicated.

IQR, interquartile range; BMI, body mass index; ASA, American Society of Anesthesiologists; OSA, obstructive sleep apnea; ENT, ear, nose, and throat.

Development of the CLAIR risk prediction tool

The initial multivariate model included seven variables with p-values <0.25: Sex, preoperative assessment of probable difficult airway, overbite, coagulopathy, hypocalcaemia, first-attempt intubation, and laryngoscopic view. Since laryngoscopic view represents an outcome rather than a predictor, it was excluded from the final model. Table 2 presents the final multivariate analysis leading to the development of the CLAIR score, integrating four key predictors: Coagulopathy and hypocalcaemia (C), Lady (L), potential airway difficulty (A), and first-attempt intubation by residents (In-experienced Residents; IR). The score ranged from −4 to 6 (IQR -1 to 1), with an AUC of 0.633 (Figure 2). A cut off score of 0 provided 98% sensitivity and 16% specificity. The CLAIR scores were classified into high (≥ 1), and low (≤ -1) risk groups. The diagnostic performance of the score is detailed in Table 3.

Table 2. Multivariate logistic regression of predictive factors of unanticipated difficult airway (CLAIR score)

Predictive factors	Coefficient	Adjusted OR (95% CI)	p-value	Risk score
C: Coagulopathy and hypocalcaemia	2.36	10.62 (1.27, 88.81)	0.029	5
L: Lady	-0.34	0.71 (0.44, 1.14)	0.157	-1
A: Potential Airway difficulty	-2.04	0.13 (0.04, 0.47)	< 0.001	-4
IR: Inexperienced Residents	0.48	1.62 (0.97, 2.72)	0.063	1

OR, odds ratio; CI, confidence interval.

Table 3. Diagnostic performance of the CLAIR score for predicting unanticipated difficult airway

Score	Sensitivity	Specificity	PPV	NPV	Accuracy	+LR	-LR
-4	100.00	0	25.00	NA	50.00	1.00	NA
-3	100.00	5.10	25.99	100.00	52.55	1.05	0
-2	97.96	15.65	27.91	95.83	56.80	1.16	0.13
-1	97.96	15.65	27.91	95.83	56.80	1.16	0.13
0	97.96	15.65	27.91	95.83	56.80	1.16	0.13
1	73.47	46.94	31.58	84.15	60.20	1.39	0.57
2	2.04	99.32	50.00	75.26	50.68	2.99	0.99
3	2.04	99.66	66.67	75.32	50.85	6.00	0.98
4	2.04	99.66	66.67	75.32	50.85	6.00	0.98
5	2.04	99.66	66.67	75.32	50.85	6.00	0.98
6	1.02	99.66	50.00	75.13	50.34	3.00	0.99

PPV, positive predictive value; NPV, negative predictive value; +LR, positive likelihood ratio; -LR, negative likelihood ratio.

Web-based risk calculator and accessibility

The CLAIR score calculator, now a web-based tool, enables clinicians to quickly categorize patients into risk groups for tailored airway management. Accessible via a QR code (Figure 3), the tool supports real-time use during preoperative assessments or emergency scenarios, helping to readily identify at-risk patients, optimize resource allocation, and enhance airway management strategies, ultimately improving patient safety and outcomes.

Key findings

The CLAIR risk prediction tool represents a significant advancement in the identification of unanticipated difficult airways, addressing a critical gap in anaesthesiology practice. Our study revealed an incidence of unanticipated difficult airways of 0.16%, significantly lower than the 2.3% reported in the 2015 PAAd Thai study [7]. This may be due to the development of preoperative assessment techniques, increased awareness of airway difficulties, or differences in patient populations and clinical settings. However, it also underscores the persistent challenges in airway assessment and highlights the potential value of our novel approach.

Interpretation and implications

CLAIR score: A paradigm shift in airway risk prediction

The CLAIR score demonstrated a moderate predictive performance for unanticipated difficult airways, with an AUC of 0.633. Notably, the sensitivity was high (98%), enabling early identification of at-risk patients. Although its specificity is lower (16%), the model remains a valuable screening tool due to its simplicity, practicality, and ease of use in clinical settings. Unlike traditional models that emphasize anatomical factors, CLAIR incorporates patient, clinical, and procedural elements, aligning with evolving airway management guidelines [18]. By enhancing routine airway assessment and preparedness, the CLAIR score provides a user-friendly approach to improve patient safety and first-attempt success in airway management. The score incorporates four key factors:

Coagulopathy and hypocalcaemia

Coagulopathy (abnormal rotational thromboelastometry, international normalized ratio > 1.3, prothrombin time > control by 3 s, or partial thromboplastin time > control by 10 s) and hypocalcaemia (serum calcium < 8.2 mg/dL or ionized calcium < 4.4 mg/dL) pose significant challenges in airway management. Coagulopathy increases bleeding risk during airway manipulation, whereas hypocalcaemia increases neuromuscular excitability, potentially causing masseter spasms despite neuromuscular blocking drugs use [19]. These conditions complicate airway management and increase intubation difficulties. Including this factor in the CLAIR score emphasizes the importance of a comprehensive preoperative evaluation that extends beyond traditional airway assessment.

Lady (female sex)

Several studies have identified male sex as a risk factor for both difficult mask ventilation and intubation [20–22]. Moreover, Hindman et al. reported a significantly higher intubation force required for male patients compared to females [23]. Similarly, our findings suggest that female sex may serve as a protective factor against unanticipated difficult airways.

Potential airway difficulty

The limited predictive value of conventional preoperative airway assessments, with an observed accuracy of only 3.1% in this study, aligns with previous findings [24]. This underscores the need for a more comprehensive, multimodal approach to identifying risk factors for unanticipated difficult airways. The CLAIR score addresses this limitation by integrating multiple criteria, thereby enhancing prediction accuracy, as evidenced by previous studies [25–27]. Despite routine screening, difficult airway scenarios may still arise unexpectedly, highlighting the importance of heightened vigilance and preparedness.

Inexperienced residents

Our findings regarding the increased likelihood of unanticipated difficult airways among less experienced anesthesiology residents highlight the importance of ongoing training and supervision. In this study, inexperienced individuals were defined as having less than one year of intubation experience, including first-year residents, nursing students, and medical students. These findings align with previous studies examining learning curves in airway management skills. Integrating the CLAIR score into resident training programs, alongside simulation-based education and regular assessments, could enhance skill development, improve decision-making, and strengthen preparedness for managing challenging airway situations [28, 29].

Implications for anaesthesiology practice

The CLAIR risk prediction tool offers a practical approach for enhancing preoperative risk stratification and airway management. Its implementation could optimize resource allocation, ensure the availability of advanced airway tools, and guide the use of specialized techniques such as video laryngoscopy. The stratification of patients into high, intermediate, and low-risk categories enables tailored airway management strategies, potentially reducing adverse events and improving safety, especially in resource-limited settings or emergency scenarios where rapid, accurate risk assessment is essential.

Strengths and limitations

Despite the large sample size and rigorous matching, the retrospective design and reliance on existing medical records introduced potential data variability. The single-centre setting may limit generalizability to other healthcare systems or patient populations. Additionally, this case-control study limited the estimation of absolute risk, as the artificially set prevalence may bias predicted probabilities. While ORs remain valid, the risk calculator might require prevalence adjustment.

Future research directions

Future studies should validate the CLAIR score in a prospective cohort and refine its predictive accuracy through prevalence-adjusted recalibration. Beyond validation, integrating the CLAIR score into existing airway management protocols and developing targeted strategies for high-risk patients are essential next steps. Additionally, assessing its economic impact will provide insights into its feasibility for widespread adoption. These efforts will not only enhance its accuracy, reliability, and clinical utility but also contribute to a more standardized and effective approach to difficult airway management. Ultimately, this could transform anaesthesia practice, improving patient safety across diverse clinical settings.

The CLAIR score is a simple, pragmatic, high-sensitivity screening tool for unanticipated difficult airways that integrates physiologic, demographic, anatomical/clinical, and procedural risk factors. While external validation is required, it shows promise as an adjunct to standard airway assessment to enhance perioperative safety and preparedness.

PAAd Thai Perioperative and Anesthetic Adverse Events in Thailand

BMI body mass index

ASA American Society of Anesthesiologists

AUC area under the receiver operating characteristic curve

Acknowledgements

Ethics approval and consent to participate

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand (Approval Reference: REC.63-560-8-1) on January 15, 2021. Patient consent was waived due to the retrospective nature of the study.

Consent for publication:

Not applicable

Competing interests:

The authors declare no conflict of interest.

Funding:

This research received no external funding.

Author Contribution

MO and PB conceived of the study. MO developed the methodology. CK, WJ, ND, WJ, and PP collected the data. MO and PV conducted the formal analysis. CK and QY were responsible for validation. CK and WJ wrote the original draft. CK, MO, ND, WJ, and PP reviewed and edited the manuscript. PB supervised the study. QY was responsible for project administration. PV gathered the resources for the study. All authors have read and agreed to the published version of the manuscript.

Data Availability

The datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author upon reasonable request and with permission of the Human Research Ethics Unit (HREU), Faculty of Medicine, Prince of Songkla University.

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No competing interests reported.

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CLAIR Score: A Risk-Screening Tool for Unanticipated Difficult Airways — A Retrospective Case– Control Study

Status:

Version 1

Abstract

Figures

Background

Methods

Patient selection

Matching procedure

Definition of unanticipated difficult intubation

Potential risk factors and confounding variables

Sample size determination

Statistical analysis

Score derivation and validation

Results

Discussion

Key findings

Interpretation and implications

CLAIR score: A paradigm shift in airway risk prediction

Implications for anaesthesiology practice

Strengths and limitations

Future research directions

Conclusions

Abbreviations

Declarations

Acknowledgements

Competing interests:

Funding:

Author Contribution

Data Availability

References

Additional Declarations

Status:

Version 1