Background Hospital and ICU structural factors are key factors affecting the quality of care as well as ICU patient outcomes. However, the data from China are scarce. This study was designed to investigate how differences in patient outcomes are associated with differences in hospital and ICU structure variables in China throughout 2019. Methods This was a multicenter observational study. Data from a total of 2820 hospitals were collected using the National Clinical Improvement System Data that reports ICU information in China. Data collection consisted of a) information on the hospital and ICU structural factors, including the hospital type, number of beds, staffing, among others, and b) ICU patient outcomes, including the mortality rate as well as the incidence of ventilator-associated pneumonia (VAP), catheter-related bloodstream infections (CRBSIs), and catheter-associated urinary tract infections (CAUTIs). Generalized linear mixed models were used to analyse the association between hospital and ICU structural factors and patient outcomes. Results The median ICU patient mortality was 8.02% (3.78%, 14.35%), and the incidences of VAP, CRBSI, and CAUTI were 5.58 (1.55, 11.67) per 1000 ventilator days, 0.63 (0, 2.01) per 1000 catheter days, and 1.42 (0.37, 3.40) per 1000 catheter days, respectively. Mortality was significantly lower in public hospitals (β = − 0.018 (− 0.031, − 0.005), p = 0.006), hospitals with an ICU-to-hospital bed percentage of more than 2% (β = − 0.027 (− 0.034, -0.019), p < 0.001) and higher in hospitals with a bed-to-nurse ratio of more than 0.5:1 (β = 0.009 (0.001, 0.017), p = 0.027). The incidence of VAP was lower in public hospitals (β = − 0.036 (− 0.054, − 0.018), p < 0.001). The incidence of CRBSIs was lower in public hospitals (β = − 0.008 (− 0.014, − 0.002), p = 0.011) and higher in secondary hospitals (β = 0.005 (0.001, 0.009), p = 0.010), while the incidence of CAUTIs was higher in secondary hospitals (β = 0.010 (0.002, 0.018), p = 0.015). Conclusion This study highlights the association between specific ICU structural factors and patient outcomes. Modifying structural factors is a potential opportunity that could improve patient outcomes in ICUs.
The Food and Drug Administration (FDA) is responsible for protecting public health by regulating medical devices, radiation-emitting electronic devices, drugs, biologics, food, tobacco, and cosmetics. After a product reaches the market, the Agency continues to evaluate a product's safety by monitoring adverse events. FDA's Center for Devices and Radiological Health facilitates reporting of medical device events and complaints by manufacturers, importers, healthcare facilities, and the general public to help FDA identify potential safety signals and/or improve medical device performance. FDA has established 2 reporting mechanisms (1) reporting through the public MedWatch system and (2) direct user facility collaboration via the MedSun program.Does this scenario sound familiar to you? You are meeting your responsibilities to keep a fleet of medical devices properly maintained and functioning well to be used on the hospital floor, but despite your best efforts, the devices seem to keep failing, all in a similar way. How do you report the malfunctions and stop this problem from continuing, or worse-from impacting delivery of safe and timely patient care?Reporting of adverse events helps to ensure the continued safety of medical devices and other products regulated by the Food and Drug Administration (FDA) once they are introduced into the US market. When problems with FDAregulated products occur, FDA wants to know about them because timely reporting by consumers, health professionals, and manufacturers allows the Agency and manufacturers to take prompt action to mitigate the problem. Because of their responsibilities with setting up, maintaining, and repairing a range of medical devices used throughout hospital departments, healthcare technology management (HTM) professionals are in a distinct position to first observe and, subsequently, report device-related adverse events and concerns, which can be critical in notifying FDA of new safety signals or an increase in known risks. 1 Only after the details surrounding the device event or concern have been identified and reported can FDA use the information to inform solutions through follow-up with the device manufacturer or identify other mechanisms (eg, safety communications or recalls) to resolve problems resulting in risk mitigation to both patients and healthcare providers. However, in order to improve patient safety through reporting medical device events and concerns to FDA, you must first understand what to report and where to report. So far, we have covered the "Why?" of reporting medical device events. This article aims to help HTM professionals get from observation to action by explaining regulatory reporting requirements at a high level, as well as the who, what, when, where, and how to report medical device events and concerns related to device safety, efficacy, or reliability, to FDA. What Events Should Be Reported?An adverse event is any undesirable experience associated with the use of a medical product in a patient. 2 FDA is interested in receiving repor...
Satellite signal distortions, or so called ''evil waveforms'' (EWFs), may cause severe distortions of the cross-correlation function in receivers. Undetected EWFs could result in large range errors and threaten the integrity of Global Navigation Satellite System (GNSS). Analog distortion and digital distortion are two classical types of signal distortions. With the advent of modernized GNSS signals, more failure types are considered to cover potential EWFs of Binary offset carrier (BOC) modulated signals, such as codeonly distortion and subcarrier-only distortion. Different failure types affect the correlation functions and the tracking precision in different ways. Therefore, it is useful to identify the failure type of a detected distortion. Conventional multi-correlator method can detect signal distortions; however, it is infeasible to identify the failure types. We developed a novel multi-correlator method based on Quadratic Discriminant Analysis (QDA). The QDA-based method also uses correlation-domain detection metrics but performs distortion type classification by supervised learning algorithm. Experimentally measured results on Beidou B1C data signals are presented, which show the effectiveness and robustness of proposed method. Compared with conventional multi-correlator method, the QDA-based signal quality monitoring (SQM) method shows better performance on detecting EWFs and provides an extra capability to identify the failure types accurately. INDEX TERMS Evil waveforms (EWFs), quadratic discriminant analysis (QDA), signal quality monitoring (SQM), multi-correlator method.
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