Development and validation of a novel predictive model and web calculator for evaluating transfusion risk after spinal fusion for spinal tuberculosis: a retrospective cohort study

The incidence with regard to tuberculosis was approximately a quarter of global population and the Mycobacterium tuberculosis causes 1.5 million mortalities annually [1]. Although developing countries, particularly southern Africa, China and India, account for more than half of all cases, unfortunately, emerging immunodeficiency and anti-tuberculosis drugs resistance place a burden on TB treatment in western countries [2]. In TB patients, anemia is a frequent comorbidity with estimated prevalence more than one third [3]. The causes of TB patients with anemia are variable, which mainly include iron-deficiency anemia and anemia of inflammation or both. The conclusion of Minchella et al. [3] and Gil-santana et al. [4] identified that inflammation is a more plausible explanation for TB-associated anemia.

Skeletal TB is diagnosed in nearly 10% of all TB cases, and spine is the most commonly involved site representing 50% of these cases [5]. With its insidious onset and chronic progression, patients initially present with back pain and limited vertebral motion in addition to symptoms of TB systemic toxicity. Complications such as clod abscess, kyphosis and neurological deficits may arise if diagnosis and treatment are delayed. Late-onset paraplegia is the most devastating clinical presentation, occurring in 10 to 30% of patients with spinal TB [2, 6].

With the advancement of anti-tuberculosis drugs, modern surgical purpose is primarily to debride lesion, furthermore to decompress the spinal cord and nerves and reestablish spinal stability. Because of the superiority of spinal fusion (SF) in stabilizing the vertebra, this approach is widely used in many spinal diseases [7]. However, it is necessary to be aware the significant blood loss in the perioperative period of SF including estimated blood loss (EBL) and hidden blood loss (HBL) [8, 9]. In particular, according to the high incidence of anemia in TB patients, blood transfusion seems to be an inescapable problem for these patients.

Artificial intelligence (AI) is leading the medical revolution, and the performance of machine learning (ML) in deep mining of data is satisfactory. These algorithms learn autonomously and can train to recognize patterns faster and more accurately than humans when researchers provide them with high-latitude data [10]. As previously reported, machine learning has been widely incorporated into tuberculosis research [11]. Chen and his colleagues [12] have also used machine learning algorithms such as multilayer perceptrons to perform detailed analysis of the genotypes of multidrug resistance Mycobacterium tuberculosis, which has greatly facilitated the clinical management of tuberculosis.

In the present study, we developed a traditional statistical algorithm and machine learning algorithms to assess transfusion risk factors in patients with spinal tuberculosis who underwent spinal fusion. This human-computer interactive predictive model provides a great predictive accuracy. The optimal prediction model obtained from the validation is used to build an online calculator.

The gold standard for the diagnosis of spinal TB has reached a consensus that culture confirms the presence of M.tuberculosis [6]. However, limited by the low detection rate of mycobacteria in skeleton, diagnosis commonly consisted of clinical symptoms, physical examinations, radiographic manifestations, anti-tuberculosis drugs response, molecular methods, tissue and microbiological assessment, polymerase chain reaction (PCR) and gene detection [5‐7]. Due to its disguised clinical presentation and complicated diagnostic targets, vertebral body (VB) destruction was discovered at the patient’s first visit. Previous report demonstrated VB involvement in 98% patients [6]. Antitubercular drugs, the primary approach of managing TB, significantly improves adverse events in these cases, but the surgical intervention of patients with spinal TB cannot be ignored. Notably, patients with spinal TB have lower preoperative hemoglobin [19] and preoperative albumin [20], higher transfusion rates [19‐22], longer operative time [21‐23], more intraoperative blood loss [23], and more fused vertebrae [23] than patients undergoing SF for other etiologies, suggesting that allogeneic transfusion as a management measure is more frequently used in spinal TB. And the length of hospital stay is thus prolonged [23, 24]. Whereas, the complex intra-human environment in TB patients place them at higher risk for serious transfusion complication [4]. Thus, it is urgent to investigate the risk factors of blood transfusion after SF, develop a convenient and sensitive prediction model, tailor the surgical strategy to the patient’s characteristics, and ensure the optimal functional outcome.

In the current article, there were seven parameters significantly associated with transfusion supported by strong evidences, including preoperative hemoglobin, preoperative albumin, preoperative MCHC, surgical duration, number of fused vertebra and anticoagulation history. As the most readily available test result, we chose the first blood test result after the patient arrived at our department. We reckon that this strategy can minimize heterogeneity and facilitate the model predict the patients’ blood transfusion risk at an early stage.

Preoperative hemoglobin has been verified as a significant predictor of transfusion among published clinical trials conducted in patients undergoing spinal surgeries [21, 25]. In view of the persistent inflammation in TB patients, the balance of iron in the body is disrupted and its absorption is reduced and consumption increased [3]. Therefore, anemia in TB patients is not infrequent and has a complex pathology. In our study, preoperative Hb was 106.80 ± 9.37 in the transfusion set versus 111.01 ± 11.06 in the non-transfusion group. Soliman et al. [19] report that a 49.6-fold increase in the probability of transfusion in patients with preoperative Hb < 11 g/dL compared to those with preoperative Hb > 14 g/dL. We detected that lower preoperative albumin result in higher transfusion requirements (37.02 ± 4.63 vs. 38.59 ± 4.04; p = 0.030), which might be related to the poor general condition of TB patients. For patients undergoing surgery for spinal TB, lower preoperative albumin maps out to worsening health status, more complications, longer hospital stays, and possibly increased mortality [26, 27]. Furthermore, low protein-related malnutrition is a risk factor for TB infection and both might be mutually reinforcing [28]. Accordingly, preoperative serum albumin was a potent predictor in evaluating the both of transfusion risk and long-term survival, and initiative preoperative management should be adopted to correct perioperative status and reduce transfusion risk [29]. The potential association between MCHC and blood transfusions appears to be related to the correction of anemia. Some studies have shown that drugs to improve anemia also increase MCHC in patients [30, 31].

As for intraoperative factors, we found that patients that undergone longer surgical duration, more fused vertebrae, and more intraoperative blood loss (IBL) tended to request blood transfusions. The number of fused vertebrae was deemed as the strongest predictor and our speculation is supported by a report from China that it may be the dependent variable affecting the length of surgery and IBL [32]. Multilevel vertebral fusion requires a matching extensive incision and sustained retraction forces to expose the surgical field, which means that a large amount of muscle behind the posterior spine and the posterior complex will hardly be preserved. Medically induced muscle and soft tissue injuries and disruptions in spinal integrity and stability, as well as prolonged surgery times, can increase blood loss during surgery [33]. In the systematic review by Elgafy et al. [34], the threshold for intraoperative blood loss in patients requiring blood transfusion is 650 ml, which is comparable to our report. Morcos et al. [24] found that each additional 60 min of surgery time increases the chance of blood transfusion by 4.2% after a retrospective cohort study of postoperative transfusion risk in posterior fusion.

We found evidence that anticoagulant history be associated with blood transfusion, which was comparable to part of the previous reports. In the literature by Fiasconaro et al. [35], the investigators confirmed the increased odds of cases using aspirin and regular heparin by exploring the potential risk of heparin, low-molecular heparin, and aspirin on blood transfusions. In addition, due to the favorable safety demonstrated by low molecular weight heparin with negligible risk of bleeding and blood transfusion, it is recommended for patients in need [36]. Interestingly, there were contradictions in the patient’s anticoagulant history and routine coagulation test results (PT, APTT and platelet count). The underlying mechanism might be that these measurement methods are not sensitive. Such as PT, this indicator generally serves in evaluating common pathways of procoagulant factors (factors II, V, and X) and significantly reduced tissue factor pathways (factor VII) [37].

Based on the results of logistic regression analysis, we constructed the first predictive model that correlates the risk of blood transfusion with both SF surgery and spinal TB. We acknowledge that nomogram has made a great contribution to clinical decision-making via intuitive visualization and low cost. Both the decision curve analysis and the clinical impact curve indicate the great clinical value of this established nomogram in risk stratification. Furthermore, we found that nomogram is the best prediction model compared to ML with an average AUC of 0.75 and a maximum AUC of 0.93. However, in the digital era, artificial intelligence has penetrated several fields of medicine such as genetics and radiographic and we should not be stagnant [38, 39]. Machine learning techniques has the potential to deliver early and accurate diagnosis, and the ability to evaluate prognosis and develop management among complex clinical datasets. Han et al. [40] and Goyal et al. [41] reported the machine learning algorithms for adverse events and unplanned readmissions associated with SF, respectively. In our modeling, the machine learning algorithms we aggressively selected were support vector machines, decision trees, multilayer perceptron, naive Bayesian, k-nearest neighbors and random forest. The results of five algorithms and nomogram reveal gratifying predictive performance with an average AUC greater than 0.5 for all 10-fold cross-validation.

With the interactive shiny application [42], we created a web calculator that make it easy to compare the effects of different parameters individually and in combination, in order to supply a viable tool for clinical screening of high-risk transfusion patients. (https://​drwenleli.​shinyapps.​io/​STTapp/​) Based on this platform, we provided users with eight modifiable parameters: surgical duration, IBL, number of fused vertebrae, anticoagulant history, preoperative Hb, preoperative Albumin, preoperative MCHC.

Although we have highlighted the prominent advantages of this constructed model and algorithms, several limitations of present literature are still worthy of attention. First, the small sample size of the collected patients and the fact that they were all from one central institution led to some possible overfitting during the data processing phase. Secondly, there may be bias in our retrospective study. Third, although there are a number of clinical parameters that we use to predict transfusion risk, the interaction between M. tuberculosis and the host remains unclear [43]. Further studies should focus on a broader range of assay metrics and surgical parameters.

In conclusion, our study achieved the objective of identifying predictors of transfusion in patients with spinal TB undergoing SF and demonstrated multiple predictive models including machine learning algorithms. Although AI is still under development, the broad application prospect of machine learning in clinical big data can be foreseen, and it facilitates the evolution towards precision medicine.