We constructed a statistical model for predicting severe OSAHS based on 1920 hospitalized Chinese patients extracted from the hospital electronic medical record system with guaranteed data quality. Further we provided a visual nomogram as an easy-to-use clinical screening tool accordingly. The nomogram requires only routinely collected information on admission (including gender, BMI, blood pressure, choke, sleepiness, apnea, WBC, hemoglobin, and triglyceride), but it still has a satisfactory performance in terms of discrimination and calibration. The sensitivity and specificity of our model were 71% and 68% respectively, and the model outperformed SBQ and ESS.
Although many screening tools and predictive models had been developed to identify high-risk patients with severe OSAHS, most of them had low performance. A meta-analysis mentioned that the pooled specificity levels of Berlin questionnaire (BQ), SBQ, and STOP ranged 28%~38%, which were lower than our model, although the pooled sensitivity levels of these tools were higher (84%~93%). According to our result, the specificity and accuracy of SBQ were relatively lower than our model, but with higher sensitivity. The reason might be that Far-East Asian men were nonobese, despite the presence of severe OSAHS, which mean that Asian were more sensitive to BMI. Nevertheless, the measurement of neck circumference in SBQ also required certain skill. In agreement with our findings, ESS seemed to be inferior to our model. A relatively low specificity (36.2%) and high sensitivity (87.2%) were also reported for the American Society of Anesthesiologists checklist when applied in surgical patients in Canada. Questionnaires, scale and checklist mentioned above tend to have a low specificity and more non severe OSAHS patients will be misdiagnosed, which means that the time-consuming and expensive PSG would be applied to a certain number of healthy patients when these tools are used. At the same time, it would further lead to a waste of medical resources and patients' unwarranted panic and anxiety. Compared with these tools, our model has a relatively balanced sensitivity and specificity. Similarly, DES-OAS performed satisfactorily in differentiating between severe OSAHS and others, with a sensitivity and specificity of 89% and 65%, respectively among susceptible surgical patients in Belgium. However, most of the items in aforementioned questionnaires, scale, and checklist are not routinely obtained partially due to that it is difficult to collect the information during a hospital visit. (e.g. thyromental distance, thyroid-chin distance) and some are subjective (e.g. degree of sleepiness, degree of snoring), resulting in patients-report bias of the clinic sample. Besides, it takes time to complete such a file and therefore it might be inefficient to screen severe OSAHS using these tools, especially for urgent cases.
A limited number of studies used statistical methods to predict severe OSAHS with less information (and most of which are professional) as compared with the questionnaires, scale and checklist mentioned above. Huang et al. applied a support vector machine (sensitivity: 70%, specificity: 70%) and a logistic regression model (sensitivity: 65%, specificity: 79%), while Amra et al. used the decision tree algorithm to predict the severe OSAHS (sensitivity: 57%, specificity: 90%). The performance of these models is comparable to our nomogram. Nevertheless, these models require data which are not commonly collected during a consultation in most non-otolaryngology departments (such as neck circumference and waist circumference for the former and Mallampati indices for the later). Our model would be of more clinical significance in the screening of severe OSAHS, since all of the data needed are often obtained on admission.
To our knowledge, this is the first study using indices from routine blood tests for the screening of severe OSAHS. We found that patients with ≥ 9.5 ×109/L WBC counts were more likely to be severe OSAHS compared with those with WBC counts ranging from 3.5 to 9.5 ×109/L. Similarly, a study reported that the severity of OSA might be positively associated with WBC counts. In addition, our findings suggested that patients with a high level of hemoglobin (≥ 175 g/L) were at an elevated risk of severe OSAHS. Patients with severe OSAHS are chronically hypoxic. Hypoxia can induce inflammation and cause a compensatory increase in hemoglobin, therefore high levels of WBC and hemoglobin would be predictors of severe OSAHS.
Our results show that rising triglyceride to 1.7 mmol/L increases the risk of OSAHS. Previous studies have shown that OSAHS is independently associated with cardiovascular risk factors, such as hypertension and dyslipidemia.Consistent with previous studies, we observed that males, high BMI, high blood pressure, choke, sleepiness, and apnea were predictors of severe OSAHS.
Our study had some limitations. First, although we collected data from 1920 patients, only 167 patients had information of ESS and 100 patients had the information of Stop-Bang. The relatively small sample size may explain the statistically non-significant difference between Stop-Bang and our model with the optimal thresholds. Nevertheless, in terms of point estimation of sensitivity, specificity and accuracy, our model is not inferior to SBQ, and has better clinical maneuverability. Besides, when the specificity of our model is equal to the specificity of 79.55% for SBQ, the sensitivity obtained is significantly higher than SBQ. Second, we did not perform external validation for the model since no data were available for such an assessment. However, we did attempt internal validation and temporal validation in an effort to prevent data overinterpretation, and the result suggested satisfactory validation of the model. Third, some factors that may be related to OSAHS have not been considered, such as cardiovascular disease and ethnic differences. In view of these limitations, further efforts in forward-looking and multicenter data collection are encouraged to demonstrate the robustness of nomogram.
To sum up, demographical characteristics and indices obtained from routine blood tests can be used for screening the severe OSAHS. Our findings have important implications for identifying the severe OSAHS and improving the prognosis of surgical patients. Further studies are warranted to detect more predictors of severe OSAHS and thereafter to improve the predictive power of the nomogram.