| Background:With the deepening of China’s aging,the number of elderly surgical patients continues to rise,and their surgical safety has also received widespread attention.Surgical complications are an important aspect that affects the safety of surgery.However,the effect of blood transfusion remains controversial among all possible factors contributing to postoperative complications.Specifically,in the non-elderly population,the "gold standard" randomized controlled trial of epidemiology did not find whether blood transfusion within a specific hemoglobin threshold range(such as 8～10g/dL,that is,the gray interval of whether blood should be transfused)affected the mortality and complication rate of patients,that is,to support the restrictive transfusion strategy from the perspective of blood conservation.Most of this evidence focuses on orthopedic and cardiac surgery patients.However,many observational studies conducted in patients undergoing more extensive surgery have found that blood transfusions increase the risk of adverse perioperative outcomes in patients.In the face of this paradox of blood transfusion,there is an urgent need to identify the reasons for the divergence between the results of the two types of study designs,develop methods to bridge the gap,and promote the generation of relevant evidence in the elderly population to guide the practice of blood transfusion.In addition,accurately predicting elderly patients at increased risk of surgical complications is of great significance to guide clinicians to prevent and reduce adverse morbidity and mortality in high-risk elderly patients.At present,there are surgical risk prediction models based on demographic characteristics and some preoperative factors,but the factors and outcome definitions of these models are inconsistent,and most of them only include preoperative risk factors,but the risk of postoperative complications is affected by the three aspects of preoperative,intraoperative and postoperative,involving the whole body,and the traditional model fails to consider the complex interaction between predictors,and machine learning methods that are not affected by the variable distribution pattern and the relationship between variables can overcome this shortcoming.Previous studies have widely shown that machine learning has high prediction accuracy,and interpreting machine learning combined with SHAP value will give full play to the advantages and potential of machine learning methods in clinical prediction.Methods:This study is first based on a new observational study design to resolve the above differences,named Hemoglobin-based Transfusion Study Design.We limited the study population to elderly(≥60 years old)patients with stable hemoglobin in the range of 7.5g/dL～9.5g/dL,so as to exclude unreasonable blood transfusion,major bleeding(bleeding volume≥500 mL)and severe anemia(≤7.5g/dL)and other factors that are prone to serious confounding,and then the propensity score was used to match the American Society of Anesthesiologists(ASA)score(comprehensively reflecting the patient’s preoperative state),surgery duration and other key covariates,and then logistic regression was used to control the remaining variables that could not be controlled by the tendency score,so as to explore the impact of blood transfusion on the adverse postoperative outcomes of patients.Adverse outcomes were defined as death(within 30 days of hospitalisation or discharge)and possible complications during hospitalisation,including ischaemic events(myocardial infarction,stroke,and acute renal failure);infections(surgical site infections,pneumonia,sepsis,septic shock and urinary tract infections);and others(cardiac arrest requiring cardiopulmonary resuscitation,heart failure,re-intubation,mechanical ventilation≥48 hours postoperatively,atelectasis,respiratory failure,wound dehiscence,delayed incision healing,pulmonary embolism,venous thrombosis,and multiple organ dysfunction syndrome).Further use the random forest and XGBoost models recommended by "Guidelines for the Development and Reporting of Machine Learning Predictive Models in Biomedical Research:A Multidisciplinary Perspective",and supplement the traditional Logistic model as a comparison,compare AUC values between models and use SHAP values to explain machine learning models.Results:A total of 6141 patients undergoing general surgery were included in this study,of which 662(10.78%)received red blood cell transfusion.Heterogeneity was large in the transfusion and non-transfusion groups,particularly in the measures of intraoperative haemorrhage(transfusion vs.no transfusion:37.9%vs.2.1%)and hypohaemoglobin(transfusion vs.non-transfusion:29.7%vs.22.6%).By excluding patients with major bleeding in the basal population,limiting the hemoglobin level to 7.5～9.5g/dL(i.e.,the study population,n=715),and using the method of propensity score matching,the heterogeneity of the transfusion group and the non-transfused group was greatly reduced(the standardized mean difference of the key variables was less than 10%).At the same time,the association between blood transfusion and adverse outcomes also changed qualitatively in the process of reducing patient heterogeneity,with blood transfusion associated with adverse postoperative outcomes in the basal population(OR:2.68,95%CI:[1.86,3.88]);In the study population,blood transfusion was not associated with adverse outcomes(0.77,[0.32,1.86]),and there was no association between blood transfusion and adverse outcomes after further matching with propensity scores(0.66,[0.23,1.89]).This study found that blood transfusion may have a protective effect on adverse postoperative outcomes in elderly patients within the study range of 7.5-9.5g/dL,but the results are not statistically significant.In addition,the results showed that the random forest model(AUC:0.756,95%CI:[0.701,0.810])had the best predictive performance,although there was no statistically significant difference compared with the logistic regression model(0.750,[0.697,0.803])(Delong test P=0.466).The model calibration curve also shows that the model is well calibrated.ASA≥Ⅲ.,ICU admission,low serum albumin,transfusion volume of more than 4 units within 72 hours,and long operative time were the top five risk factors with the greatest impact on adverse postoperative outcomes.Conclusion:The results of this study are consistent with the results of the few randomized controlled trials involving elderly surgical patients,and provide a new reference for the safety of blood transfusion in the elderly in general surgery.At the same time,the results suggest that when conducting correlation studies based on complex observational clinical data,full attention should be paid to study design rather than purely statistical correction methods.Finally,this study establishes a prediction model for postoperative adverse outcomes based on perioperative risk factors,which provides a new explainable prediction method for clinical studies under real-world conditions. |
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