Department of Oral & Maxillofacial Plastic Surgery, Universitätsklinikum Bonn, Sigmund-Freud-Strasse 25, D- 53127, Bonn, Germany.
Institut für Medizinische Biometrie, Informatik und Epdidemiologie (IMBIE), Universitätsklinikum Bonn Sigmund-Freud-Strasse 25, D- 53127, Bonn, Germany.
J Craniomaxillofac Surg. 2019 Feb;47(2):334-340. doi: 10.1016/j.jcms.2018.12.002. Epub 2018 Dec 11.
Increasing rates of hospitalization of patients diagnosed with acute odontogenic infection have become a burden for public health care, with significant economic concerns. The aim of this study was to investigate factors that tend to prolong hospital length of stay (LOS) in the treatment of severe infections. We present a statistical model that enables the prediction of LOS by exposing the feasibility of the essential statistical determinants.
A 5-year retrospective study investigated records of 303 in-hospital patients with abscess of odontogenic origin. Time-to-event models were used to analyse data where the outcome variable is the time to the occurrence of a specific event. Here, the focus is on a statistical model for the prediction of LOS of patients.
The group of all patients (n = 303) was analysed by considering seven characteristics of the patients (age, gender, spreading of infection, localization of infection focus, type of administered antibiotics, diagnosed diabetes mellitus, and existence of a remaining infection focus). Age (p = 0.049; rc = -0.007) and spreading of infection (p < 0.001; rc = -0.965) showed a significant impact on the LOS. Subjects were divided into two groups. Group A (n = 185) consisted of patients who presented with a severe odontogenic infection and not yet removed infection focus; group B were patients having undergone outpatient operative tooth removal (n = 118). To group A patients' data, two new risk factors ("days between abscess incision and removal of infection focus" = dbir and "removal of infection focus during the same stay as abscess incision" = riss) replaced the risk factors "remaining infection focus." A significant impact on the LOS was detected for dbir (p < 0.001; rc = -0.15) and riss (p < 0.001; rc = -1.76). Our statistical model explicitly describes how the probability for discharge depends on the time and how specific characteristics affect the LOS. We observed a significantly higher LOS in older patients and subjects with infection spreading. In group A patients, dbir and riss had a highly significant impact on the LOS.
Predicting the LOS may promote transparency to costs and management of patients under inpatient treatment. Our statistical model describes the probability of a discharge at time t compared to a discharge later than t (a LOS longer than t). Furthermore, the model enables a prediction of the LOS of each patient for practitioners in an easy way.
患有急性牙源性感染的患者住院率不断上升，给公共医疗保健带来了负担，同时也存在重大的经济问题。本研究旨在探讨导致严重感染住院时间（LOS）延长的因素。我们提出了一个统计模型，可以通过揭示基本统计决定因素的可行性来预测 LOS。
回顾性研究了 303 例住院患者的脓肿来源，时间事件模型用于分析结局变量为特定事件发生时间的数据。这里，重点是针对患者 LOS 预测的统计模型。
对所有患者（n=303）组进行分析，考虑了患者的七个特征（年龄、性别、感染扩散、感染焦点定位、使用的抗生素类型、诊断为糖尿病、以及是否存在残留感染灶）。年龄（p=0.049；rc=-0.007）和感染扩散（p<0.001；rc=-0.965）对 LOS 有显著影响。将患者分为两组。A 组（n=185）为患有严重牙源性感染且尚未去除感染灶的患者；B 组为门诊手术拔牙的患者（n=118）。对 A 组患者的数据，两个新的危险因素（“脓肿切开与感染灶切除之间的天数”=dbir 和“在脓肿切开的同一住院期间切除感染灶”=riss）取代了“残留感染灶”的危险因素。dbir（p<0.001；rc=-0.15）和 riss（p<0.001；rc=-1.76）对 LOS 有显著影响。我们的统计模型明确描述了出院概率如何随时间变化，以及特定特征如何影响 LOS。我们观察到年龄较大的患者和感染扩散的患者 LOS 明显较高。在 A 组患者中，dbir 和 riss 对 LOS 有很大的影响。
预测 LOS 可以提高住院治疗患者成本和管理的透明度。我们的统计模型描述了在时间 t 时出院的概率与 t 后出院的概率（t 后 LOS 较长）。此外，该模型可以方便地预测每位患者的 LOS。
