Development and Validation of a Nomograph Model for Post-Operative Central Nervous System Infection after Craniocerebral Surgery

Round 1

Reviewer 1 Report

The authors present a paper describing the use of a predictive model for post-operative infection after surgery in the central nervous system. There is no significant novelty in what they state and most of the factors indicated as good predictors for the infection are well known toplay a role in infections. However, when reading the text, it gives a thorough and well conducted analysis and the results are consequential and well evidenced. In my opinion the paper deserves publication 

Author Response

The authors present a paper describing the use of a predictive model for post-operative infection after surgery in the central nervous system. There is no significant novelty in what they state and most of the factors indicated as good predictors for the infection are well known toplay a role in infections. However, when reading the text, it gives a thorough and well conducted analysis and the results are consequential and well evidenced. In my opinion the paper deserves publication.

Response : Thank you very much for your professional review. The present study does have similarities with previous published literature in terms of predictor screening [1-4], however, most of them were Logistic regression analysis or univariate risk factor screening, and there are few clinical prediction model studies. In this study, we screened predictive variables based on the clinical characteristics of neurosurgical patients, which comprehensively considered the basic status of patients, surgical conditions and postoperative complications, and developed and validated a nomograph model to predict the risk of PCNSI, which can be used to guide the treatment decision of perioperative prophylactic use of anti-infection and provide beneficial clinical reference for the early prevention and control of PCNSI.

References：

[1] Zhan R, Zhu Y, Shen Y, et al. Post-operative central nervous system infections after cranial surgery in China: incidence, causative agents, and risk factors in 1,470 patients[J].Eur J Clin Microbiol Infect Dis. 2014,33(5):861-6. doi: 10.1007/s10096-013-2026-2.

[2] Sun C, Du H, Yin L, et al. Choice for the removal of bloody cerebrospinal fluid in postcoiling aneurysmal subarachnoid hemorrhage: external ventricular drainage or lumbar drainage?[J]. Turk Neurosurg. 2014,24(5):737-44. doi: 10.5137/1019-5149.JTN.9837-13.2.

[3] Fu P, Zhang Y, Zhang J, et al. Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study[J].J Clin Med. 2022,11(14):3973. doi: 10.3390/jcm11143973.

[4] Lu G, Liu Y, Huang Y, et al. Prediction model of central nervous system infections in patients with severe traumatic brain injury after craniotomy [J]. J Hosp Infect. 2023,136:90-99. doi: 10.1016/j.jhin.2023.04.004.

Reviewer 2 Report

I would like to congratulate the authors for their work. The aim of their study was to identify the risk factors for post operative CNS infections and construct a predicting model to guide clinical decision. The study included 1864 patients. Taking into account that CNS infection is a major complication of cranial surgery, predicting tools may be proved useful into preventing post op infections. The authors focused mostly into describing their statistical methodology and validation of their model. However, the discussion section is lacking of how to utilize the model and their clinical recommendations based on the scoring system of the model.

Recommendations:

1.       Grammatical and syntactic corrections

2.       Description in the discussion section of the application and interpretation of the model on clinical settings

Corrections of grammatical and syntactic errors

Author Response

I would like to congratulate the authors for their work. The aim of their study was to identify the risk factors for post operative CNS infections and construct a predicting model to guide clinical decision. The study included 1864 patients. Taking into account that CNS infection is a major complication of cranial surgery, predicting tools may be proved useful into preventing post op infections. The authors focused mostly into describing their statistical methodology and validation of their model. However, the discussion section is lacking of how to utilize the model and their clinical recommendations based on the scoring system of the model.

Point 1: Grammatical and syntactic corrections.

Response 1：Thank you very much for your critical review. In view of my unprofessional grammar and syntactic expressions, I have transferred this manuscript to other authors for revision, and the revised content has been marked red in the manuscript.

Point 2: Description in the discussion section of the application and interpretation of the model on clinical settings

Response 2：Thank you very much for your professional review. According to your suggestion, relevant paragraphs have been added to the discussion section of the manuscript and marked in red. The revised content is as follows: The nomograph model constructed in this study can directly predict the magnitude of the risk probability of PCNSI. For example, a patient aged 75 years with a previous history of diabetes mellitus was admitted to the hospital for emergency surgery, with operation time duration of 5 h, intraoperative blood lost amounting to 500 ml, intracranial drainage tube placed for more than 5 d, no lumbar cistern drainage tube placed, no preoperative, intraoperative, or postoperative shock combined, a postoperative ALB of 28.8 g/L, and a 5-d ICU stay. The scores of each predictor variable were calculated according to the nomograph model as 68, 68, 58, 22, 44, 0, 50, 0, 42, and 48, respectively, with a total score of 400, then the probability of PCNSI was more than 80%, which should be paid enough attention by clinicians. After evaluation of the prediction model, the following diagnostic and treatment decisions were made for this patient: 1. Strengthen the management of hand hygiene in health care; 2. Improve blood culture, cerebrospinal fluid culture or NGS of cerebrospinal fluid as soon as possible to clarify the pathogenic bacteria; 3. Remove the intracranial drainage tube as soon as possible according to the condition; 4. Timely supplement human serum albumin to ALB≥30g/L, monitor and control blood glucose; 5. Transfer the patient out of ICU as soon as possible when the patient's condition was stable; 6, According to the epidemiological characteristics of the pathogenic bacteria in the region and unit, vancomycin combined with meropenem, ceftriaxone, cefepime and other bacterial meningitis systemic intravenous dosing regimens were empirically selected [1], and adjust the target therapy in time after pathogenic return, and if the treatment effect was poor, it could be combined with ventricular or intrathecal injection [2].

References：

[1] van de Beek D, Cabellos C, Dzupova O, et al. ESCMID Study Group for Infections of the Brain (ESGIB). ESCMID guideline: diagnosis and treatment of acute bacterial meningitis. Clin Microbiol Infect. 2016,22 Suppl 3:S37-62. doi: 10.1016/j.cmi.2016.01.007.

[2] The neurocritical care expert committee of the neurosurgeon branch of the chinese medical association, the neurosurgery critical care group of the neurosurgery branch of the beijing medical association. Chinese expert consensus on the diagnosis and treatment of central nervous system infection in neurosurgery (2021 edition). Chin J Neurosurg,2021,37(01):2-15. doi:10.3760/cma.j.cn112050-20200831-00480.

Round 2

Reviewer 2 Report

I would like to congratulate the authors. I believe that their text editing and their additions to the discussion section are adequate and strengthened the manuscript.