Heliyon (Jan 2024)
Risk assessment of severe adult tetanus using the NLR and AST level and construction of a nomogram prediction model
Abstract
Objective: We sought to examine high-risk factors for severe tetanus, construct a nomogram model, and predict the risk probability of severe tetanus in adult patients to provide a theoretical basis for clinical intervention. Methods: A retrospective analysis was employed in this study, which enrolled 65 adult patients with tetanus diagnosed at the Second Affiliated Hospital of Hainan Medical University from January 2017 to September 2022. Study participants were divided into severe and mild groups based on the Ablett classification. The general data and laboratory markers of both groups were compared, and logistic regression analysis was used to screen for independent risk factors for severe tetanus. A nomogram prediction model was constructed, and receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were constructed and used to assess discrimination, calibration, and net benefit. Results: Of the 65 adults patients with tetanus, 28 were placed in the severe group and 37 were placed in the mild group. Univariate logistic regression analysis showed that there were statistically significant differences in the incubation period, time from disease onset to treatment, white blood cell count (WBC), neutrophil count (NEU), lymphocyte count (LYM), platelet count (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lactate dehydrogenase level (LDH), myoglobin level (Mb), and aspartate aminotransferase (AST) level between the two groups (P 0.05). Multivariate analysis showed that NLR (odds ratio [OR] = 4.998, 95 % confidence interval [CI] = 1.154–21.649, P = 0.031), AST (OR = 1.074, 95 % CI = 1.007–1.146, P = 0.031), PLT (OR = 1.055, 95 % CI = 1.006–1.106, P = 0.027), and incubation period (OR = 0.597, 95 % CI = 0.423–0.843, P = 0.003) are independent risk factor for severe tetanus. A Nomogram for predicting Severe Tetanus (N-ST) prediction model was constructed based on variables in the multivariate analysis with P < 0.05. The ROC curve showed that the optimal cutoff point was 108.044 points. At this point, the sensitivity was 86.5 %, the specificity was 89.3 %, the area under the ROC curve was 0.936, and model discrimination was good. The calibration curve overlapped with the ideal curve, and the DCA curve showed that the model can provide clinical benefits. Conclusion: NLR, AST, PLT, and incubation period are predictors of severe tetanus. The constructed N-ST model can provide a new, convenient, and rapid method to predict the risk probability of severe tetanus in adults and guide early clinical intervention.
