INTRODUCTION

According to the 2017 World Health Organization report^1-5, tobacco smoking remains a major worldwide public health threat, with >7 million deaths directly related to tobacco. Many studies have recognized smoking as a risk factor for chronic diseases, such as chronic respiratory diseases (asthma, chronic obstructive pulmonary disease), hypertension, cardiovascular disease, atherosclerosis, diabetes, cancer, and microbial infections (respiratory infections, bacterial meningitis)^6,7. Smoking also burdens healthcare systems and increases social costs. Given the impact of cigarette smoking, the development of effective interventions to address tobacco addiction is a major public health need. There are 350 million smokers in China, which accounts for one-third of the world’s smokers⁸. Unfortunately, smoking cessation services and counseling are at an early stage of development in China. Moreover, healthcare workers in China do not exert much effort in helping smokers to quit tobacco use⁹. Therefore, the effectiveness of existing smoking cessation interventions and services are largely unknown in China.

Numerous prior studies have examined individuallevel predictors of successful and unsuccessful cessation attempts^2,10,11, including socio-economic status groups, increasing tobacco prices, both peer and family smoking groups, and use of smoking cessation medications, but researchers have come to differing conclusions. In addition, another study shows that it is important to understand the differential roles that pre-quitting and post-quitting experiences play in smoking cessation and to provide help to smokers for not resuming cigarette smoking¹². It is important to understand the characteristics of smokers and identify factors predicting successful smoking cessation to improve the efficacy of interventions. Some smokers are unwilling (i.e. not ready, not motivated, or not able) to attempt quitting in the near future, so identification of predictors and determinants of success in smoking cessation is a key component in smoking cessation programs. Therefore, we assessed the outcomes of smokers in our smoking cessation clinic and investigated factors predictive of successful smoking cessation treatment. The primary objective of this study was to develop a valid but simple prediction tool by using only characteristics easily determined at the beginning of treatment to assess the factors associated with successful smoking cessation, with the goal of enabling physicians in smoking cessation clinics to provide individualized treatment strategies.

METHODS

RESULTS

Feature selection

Based on 278 patients in the cohort, 25 features were reduced to four potential predictors (Figures 1A and B), and the coefficients were non-zero in the LASSO regression model: reason for quitting smoking, number of other smokers in the household, number of visits to the outpatient department, and varenicline use (Table 2).

Table 2

Factors predictive of successful smoking cessation

	β	OR (95% CI)	p
Intercept	1.2693	3.558 (1.164–11.132)	0.027
Reason for quitting smoking
Mobilization of others vs prevention and treatment of own diseases	−2.1006	0.122 (0.056–0.255)	<0.001
Others vs prevention and treatment of own diseases	−2.2011	0.111 (0.049–0.239)	<0.001
Living with a smoker or being exposed to workplace smoking yes vs no	−0.8220	0.439 (0.219–0.878)	0.020
Number of outpatient department visits ≥2 vs 1	1.0526	2.865 (1.439–5.844)	0.003
Varenicline use yes vs no	0.6943	2.002 (0.825–4.999)	0.128

[i] OR: odds ratio. β: regression coefficient.

Figure 1

Demographic and clinical feature selection using the LASSO binary logistic regression model

Generation of an individualized prediction model

Based on the multivariate analysis results, predictive factors, including the reason for quitting smoking, living with a smoker or being exposed to workplace smoking, number of visits to the outpatient department, and varenicline use, were incorporated into the nomogram; these characteristics are shown in Table 2. A model containing the above independent predictors was established and is represented as a nomogram in Figure 2.

Figure 2

Nomogram to predict the probability of quitting smoking

Apparent performance of the nomogram for prediction of successful smoking cessation

The calibration curve of the nomogram for prediction of successful smoking cessation showed good consistency (Figure 3). The C-index of the predictive nomogram was 0.816 (95% CI: 0.761– 0.871) for this cohort and was confirmed by internal validation as 0.804, indicating that this model had good discriminatory ability. The nomogram had good power for predicting success in quitting smoking.

Figure 3

Calibration curves of nomogram prediction of successful smoking cessation in the cohort

Clinical value of the model

The DCA for predicting the success of quitting smoking is shown in Figure 4. The decision curve shows that the use of this nomogram increased the ability to predict successful smoking cessation when the patient and physician threshold probabilities were 19% and 92%, respectively. In this range, according to the successful smoking cessation nomogram, the net benefit was comparable to several overlaps.

Figure 4

Decision curve analysis for the nomogram predicting successful smoking cessation

DISCUSSION

In the analysis of predictors of quitting smoking and reasons for quitting, living with a smoker or being exposed to workplace smoking, number of outpatient department visits, and varenicline use were associated with successful cessation rate. This nomogram suggested that treatment with varenicline, quitting for health-related reasons, more visits, and not coexisting with other smokers may be key factors that determine the success of smoking cessation.

The efficacy data in our study showed that varenicline was more effective than bupropion for smoking cessation. However, there is strong evidence from multiple randomized clinical trials that both bupropion and varenicline increased smoking cessation rates when used in a quitting attempt^17,18. One potential explanation of this discrepancy between our results and those of the trials is that the low-usage rate of bupropion may have resulted in underestimation of its potential efficacy.

To our surprise, the number of outpatient department visits was the most influential factor affecting smoking cessation. In addition, we found that the reason for quitting smoking could predict the success of the attempt. These results indicate that individual motivation, especially intrinsic motivation, was predictive of the smoking cessation result. These results are in accordance with those of many reports from the medical literature^12,19-21, which suggest that smokers subjectively recognize the harm of smoking and the benefits of quitting smoking and that taking the initiative to quit smoking is very important for success. From a pooled estimate of 65 trials, Hartmann–Boyce et al.²² concluded that increasing the amount of behavioral support is likely to increase the chance of success by approximately 10% to 20%. Smokers with more outpatient visits may be able to obtain more behavioral and psychosocial support, thus achieving better smoking cessation results.

As the results show, not co-existing with other smokers also was a predictor of smoking cessation success. This finding is consistent with the results of previous reports that living with a smoker or being exposed to workplace smoking made individuals less likely to quit^23-26. It is possible that exposure to other people smoking decreases quitting rates and increases the risk of starting to smoke²³. Smoking is not only a personal behavior in China, which has a high smoking rate, but also deeply influenced by social factors. Smokers who are often surrounded by other smokers perceive higher approval and acceptance of smoking behavior, thus further strengthening smoking behavior. From this point of view, we should pay more attention to decreasing passive smoking. It should be noted that age, sex, education, occupation, and health status were not predictive factors for the success of smoking cessation in our study. Although the investigators observed that some of these factors were independent predictors^21,27,28, the findings of our study appeared to be inconsistent with some published evidence and could not confirm all the previous findings. This inconsistency may be because of differences between countries and regions. Some studies have found that measuring exhaled CO levels were a useful biomarker for predicting successful smoking cessation^3-5. However, in our study, exhaled CO levels at the first visit were not associated with success in quitting. In addition, neither the average number of cigarettes smoked daily nor the FTCD scores related to nicotine dependence were associated with the success of smoking cessation. This finding differs from the results in previous studies^{19, 29,30}. In their study, Huang et al.⁵ found that smokers with lower FTCD scores, those with lower exhaled CO concentrations, and those who smoked <20 cigarettes per day on average, had higher success rates. These differences in conclusions may be because our follow-up time was limited, the self-reports may have underestimated cigarette consumption, and only the first measurements of CO levels were compared.

In our study, the 3-month continuous abstinence rate of 59.4% (165/278) was indeed a relatively high level, compared with other studies that generally report proportions of quitting lower than 50%. We think that it may be related to the following reasons. First, because most Chinese smokers think drug therapy is the most important type of treatment, psychological therapy and behavioral therapy could only play a supplementary role. Drug therapy is the beginning of medical behavior for many patients. We have tried to correct smokers’ perceptions many times, but the effect of our previous efforts was not obvious. In our study, 94.6% (263/278) of the enrolled smokers used adjuvant treatment with smoking cessation drugs, especially varenicline, and received a standard course of treatment. Studies have proved that smoking cessation drugs, especially varenicline, obviously improved the success rate of smoking cessation. Second, the choice of medications and timing of giving smoking cessation drugs were also important factors. Varenicline has an acceptable safety/tolerability profile and has been proven to be more effective than placebo, bupropion, and NRT, in the general population^31-33. In a flexible quit date study that evaluated this paradigm (n=659), continuous abstinence rates of weeks 9–12 (CAR9– 12) were significantly higher with varenicline than with placebo (53.1% vs 19.3%, respectively), as were CAR9–24³⁴. Hajek et al.³⁵ hypothesized that if varenicline reduced the rewarding properties associated with cigarette smoking, pre-loading the drug prior to TQD could help weaken this association and enhance efficacy. In the study, participants (n=101) received varenicline for 4 weeks prior to the TQD and received varenicline for 12 weeks. The study found that the effect on early quitting rates (CARs at week 4) did not reach statistical significance (varenicline 49.1% vs placebo 33.3%), but there was a statistically significant improvement in self-reported abstinence at 12 weeks (varenicline 47.2% vs placebo 20.8%). In our study, pre-loading of the drug prior to TQD also gave similar results. As discussed earlier, studies that further investigate varenicline pre-loading could confirm the potential benefits of this approach. While currently not included in the prescribing information, varenicline pre-loading^35-37 has shown improved efficacy, particularly in highly dependent smokers. Third, the popularity of smart phones eliminates the limitation of time and space. Popular mobile social software, such as WeChat, shows great potential in promoting healthy behavioral changes in China. We have developed systematic follow-up mechanisms, including sending smokers vivid and easily understood smoking cessation information regularly and extensively. These measures ensure continuous psychological and behavioral support for smokers. In addition, one of the reasons for the high cessation rate is that our doctors patiently provide timely answers and help answer smokers’ questions or clarify points of confusion. Unfortunately, in China, smoking cessation drugs are not included in the scope of medical insurance reimbursement. It is hoped that more work will be done in this area.

CONCLUSIONS

We developed a novel nomogram with relatively good accuracy to help clinicians assess the probability of a smoker successfully quitting after initiation of smoking cessation treatment, which is also the question smokers most want to know when they are ready to quit. By evaluating the predictors of success, clinicians can target more important smoking cessation interventions, such as more individualized behavioral therapy, psychotherapy, or medication. This nomogram requires external validation, and further studies are needed to determine if individual interventions based on this nomogram can improve the success rates and effectiveness of smoking cessation therapies.