Paramasivam Palaniyandi, Jaganathasamy Nagaraj, Ramalingam Srinivasan, Mahalingam Vasantha, Nagarajan Selvam, Shaik Fayaz Ahamed, Karuppasamy Sundarakumar, Bhaskar Adhin, Srinivasan Padmanaban, Manoharan Tamizhselvan, Natesan Adalarasan, Chinnaiyan Ponnuraja
Department of Statistics, ICMR-National Institute for Research in Tuberculosis, Chennai 600 031, Tamil Nadu, India.
University of Madras, Chennai 600 005, Tamil Nadu, India.
Diseases. 2025 Mar 14;13(3):83. doi: 10.3390/diseases13030083.
Globally, neonatal fungal sepsis (NFS) is a leading cause of neonatal mortality, particularly among vulnerable populations in neonatal intensive care units (NICU). The use of spatial frailty models with a Bayesian approach to identify hotspots and risk factors for neonatal deaths due to fungal sepsis has not been explored before.
A cohort of 80 neonates admitted to the NICU at a Government Hospital in Tamil Nadu, India and diagnosed with fungal sepsis through blood cultures between 2018-2020 was considered for this study. Bayesian spatial frailty models using parametric distributions, such as Log-logistic, Log-normal, and Weibull proportional hazard (PH) models, were employed to identify associated risk factors for NFS deaths and hotspot areas using the R version 4.1.3 software and QGIS version 3.26 (Quantum Geographic Information System).
The spatial parametric frailty models were found to be good models for analyzing NFS data. Abnormal levels of activated thromboplastin carried a significantly higher risk of death in neonates across all PH models (Log-logistic, Hazard Ratio (HR), 95% Credible Interval (CI): 22.12, (5.40, 208.08); Log-normal: 20.87, (5.29, 123.23); Weibull: 18.49, (5.60, 93.41). The presence of hemorrhage also carried a risk of death for the Log-normal (1.65, (1.05, 2.75)) and Weibull models (1.75, (1.07, 3.12)). Villivakkam, Tiruvallur, and Poonamallee blocks were identified as high-risk areas.
The spatial parametric frailty models proved their effectiveness in identifying these risk factors and quantifying their association with mortality. The findings from this study underline the importance of the early detection and management of risk factors to improve survival outcomes in neonates with fungal sepsis.
在全球范围内，新生儿真菌败血症（NFS）是新生儿死亡的主要原因，尤其是在新生儿重症监护病房（NICU）的脆弱人群中。此前尚未探索使用贝叶斯方法的空间脆弱模型来识别真菌败血症导致新生儿死亡的热点地区和风险因素。
本研究纳入了印度泰米尔纳德邦一家政府医院新生儿重症监护病房收治的80例新生儿队列，这些新生儿在2018年至2020年期间通过血培养被诊断为真菌败血症。使用R版本4.1.3软件和QGIS版本3.26（量子地理信息系统），采用贝叶斯空间脆弱模型，如对数逻辑斯蒂、对数正态和威布尔比例风险（PH）模型，来识别NFS死亡的相关风险因素和热点地区。
发现空间参数脆弱模型是分析NFS数据的良好模型。在所有PH模型（对数逻辑斯蒂，风险比（HR），95%可信区间（CI）：22.12，（5.40，208.08）；对数正态：20.87，（5.29，123.23）；威布尔：18.49，（5.60，93.41））中，活化部分凝血活酶水平异常的新生儿死亡风险显著更高。出血的存在对对数正态模型（1.65，（1.05，2.75））和威布尔模型（1.75，（1.07，3.12））也有死亡风险。维利瓦卡姆、蒂鲁瓦勒尔和波纳马利街区被确定为高风险地区。
空间参数脆弱模型证明了其在识别这些风险因素以及量化它们与死亡率的关联方面的有效性。本研究结果强调了早期发现和管理风险因素对于改善真菌败血症新生儿生存结局的重要性。
