Decision tree based diagnostic system for moderate to severe obstructive sleep apnea.

One of the major modern medical issues, obstructive sleep apnea (OSA), particularly at moderate to severe levels, may potentially cause cardiovascular morbidity and mortality. However, polysomnography (PSG), a gold standard tool in diagnosing OSA, is cumbersome, has limited availability, and is costly and time-consuming. Clinical prediction models thus are absolutely necessary in screening patients with OSA. Furthermore, the performance of the published prediction formulas is not satisfactory for Chinese populations. The aim of this study was to develop and validate a simple and accurate prediction system for the diagnosis of moderate to severe OSA by integrating an expert-based feature extraction technique with decision tree algorithms which have automatic feature selection capability in screening the moderate to severe OSA cases in Taiwan. Moreover, the backward stepwise multivariable logistic regression model and four other decision tree algorithms were also employed for comparison. The results showed that the proposed best prediction formula, with an overall accuracy reaching to 96.9 % in sensitivity = 98.2 % and specificity = 93.2 %, could present a good tool for screening moderate and severe Taiwanese OSA patients who require further PSG evaluation and medical intervention. Results also indicate that the proposed best prediction formula is simple, accurate, and reliable, and outperforms all the other prediction formulae considered in the present study. The proposed clinical prediction formula derived from three non-invasive features (Sex, Age, and AveSBP) may help prioritize patients for PSG studies as well as avoid a diagnosis of PSG in subjects who have a low probability of having the disease.

[ICU project to improve pressure ulcer healing rates].

Pressure ulcers represent a very common complication in elderly and patients receiving bedridden care. Inappropriate management of this condition can lead to delayed healing, serious infections and even mortality. The rate of healing for pressure ulcers in our department was 0% between January and June, 2003. We studied this situation and identified the following problems: (1) improper diagnoses; (2) failure to implement a pressure ulcer care protocol; (3) lack of proper instruments to reduce pressure; and (4) failure to care properly for skin following excretion. Nursing knowledge and practice were not updated with new concepts and methods related to clinical pressure ulcer care. To solve these problems and improve pressure ulcer care effectiveness, we organized a special unit in July of the same year, which proceeded to arrange lectures and promotional campaigns, published a standardized care protocol, designed water cushions, and established proper post-excretion care procedures. According to observed results, our department improved its pressure ulcer healing rate by 41.2% within 6 months. This project improved skin care quality and reduced pressure ulcer complications. We recommend that findings and measures be promoted in clinical practice.