Fuck, we don't need predictions of pneumonia you blithering idiots, solve the problem of preventing that pneumonia in the first place. I'd have you all fired.
You've known about this problem for a long time. GET THERE!
Just maybe this vaccine!
Pneumonia Vaccine (3 posts to July 2020)
11% Stroke-associated pneumonia (2 posts to October 2020)
Predictive value of the Oxford Acute Severity of Illness Score in acute stroke patients with stroke-associated pneumonia
Ximei Wang1 
Haiyan Sun- 1Department of General Critical Care Medicine, Zhumadian Central Hospital, Zhumadian, China
- 2Department of Neurology, Zhumadian Central Hospital, Zhumadian, China
- 3Department of Scientific Research Management, Zhumadian Central Hospital, Zhumadian, China
- 4Department of Neurology, Jilin Province First Auto Work General Hospital, Jilin, China
Background: Stroke-associated pneumonia (SAP) is associated with a poor prognosis and a high mortality rate in stroke patients. However, the accuracy of early prediction of SAP is insufficient, and there is a lack of effective prognostic evaluation methods. Therefore, in this study, we investigated the predictive value of the Oxford Acute Severity of Illness Score (OASIS) in SAP to provide a potential reference index for the incidence and prognosis of SAP.
Methods: We recruited a total of 280 patients with acute ischemic stroke who had been diagnosed and treated in the Zhumadian Central Hospital between January 2021 and January 2023. These patients were divided into an SAP group (86 cases) and a non-SAP group (194 cases) according to SAP diagnostic criteria by expert consensus on the diagnosis and treatment of SAP. We collated general and clinical data from all patients, including the survival of SAP patients during the follow-up period. Multivariate logistic regression was used to analyze the risk factors for SAP. Kaplan–Meier and multivariate COX regression analyses were used to investigate the relationship between OASIS and the prognosis of SAP, and a receiver operating characteristic (ROC) curve was drawn to analyze the predictive value of OASIS for SAP.
Results: Our analyses identified body temperature, C-reactive protein, procalcitonin, OASIS, and a prolonged length of intensive care unit (ICU) stay as the main risk factors for SAP (all Ps < 0.05). Advanced age and an elevated OASIS were identified as the main risk factors for death in SAP patients (all Ps < 0.05). The risk of death in patients with OASIS of 31–42 points was significantly higher than that in patients with OASIS of 12–20 points (HR = 5.588, 95% CI = 1.531–20.401, P = 0.009). ROC curve analysis further showed that OASIS had a high predictive value for morbidity and the incidence of death in SAP patients.
Conclusion: OASIS can effectively predict the onset and death of SAP patients and provides a potential reference index for early diagnosis and the prediction of prognosis in patients with SAP. Our findings should be considered in clinical practice.
Introduction
Stroke is a common clinical disease. The results of a survey conducted in 31 provinces in China in 2020 showed the estimated prevalence (2.6%), incidence (505.2 per 100,000), and mortality (343.4 per 100,000) of stroke in people over 40 years of age in 2020, with ischemic stroke accounting for 86.8% of the total incidence (1). Stroke patients not only have to face high medical costs but also poor prognosis. According to statistics, the hospitalization cost of stroke in 2020 was as high as 58 billion yuan, of which patients paid ~19.8 billion yuan. The rate of death/discharge in hospital against medical advice was 9.2%, while ~12.5% (2.2 million people) of stroke survivors had stroke-related disability, and the disability rate at 3 and 12 months was 14.8 and 14.0%, respectively. The mortality rate of stroke at 3 and 12 months was 4.2 and 8.5%, respectively, and the recurrence rate of stroke at 3 and 12 months was 3.6 and 5.6%, respectively (2), indicating that it is urgent to improve stroke prevention and treatment strategies and strengthen assessment and early intervention in stroke prognosis in China, to improve stroke status. Stroke-associated pneumonia (SAP) is a common complication that often occurs within the 1st week after the onset of acute stroke. This condition predominantly refers to the symptoms of lung infection caused by nervous system damage and the reduced immune status that follows the onset of stroke. Previous epidemiological surveys have reported the incidence of SAP as 7–38% (3, 4). Furthermore, the risk of death within 30 days of stroke with SAP was 3-fold higher than in patients without SAP. This fact not only increases the difficulty of clinical treatment and prolongs the treatment period but also increases the incidence of severe disability (5). Over recent years, many researchers have designed SAP prediction models for different stroke patients using multivariate regression models, such as the acute ischemic stroke-associated pneumonia score (AIS-APS) and the intra cerebral hemorrhage-associated pneumonia score (ICH-APS) models, which are recommended by the Chinese Expert Consensus on the Diagnosis and Treatment of Stroke-related Pneumonia in 2019. However, the predictive accuracy of these models is not ideal, and there is a lack of predictive models to evaluate the prognosis of stroke when complicated by SAP (6, 7). Therefore, it is of great significance to continue developing early identification and accurate prognostic risk assessment methods for high-risk SAP patients to strengthen the individualized monitoring of stroke patients and implement targeted interventions to improve survival rates. Many severity scores have been used to evaluate the prognosis of critically ill patients. Of these, the acute physiology and chronic health evaluation II (APACHE II) model is the most common disease severity scoring system used in the ICU; this has had a significant effect on the prognostic evaluation of ICU patients (including those with acute stroke). However, the APACHE II system involves many parameters; most of these parameters involve laboratory tests. Furthermore, data collection can be laborious; this is not conducive to early and rapid diagnosis (8, 9). The Oxford Acute Severity of Illness Score (OASIS) is a scoring system that does not involve laboratory tests or imaging examinations. OASIS is widely used for the differential diagnosis of acute disease severity and has also been confirmed to have high identification and calibration efficiency for the prognosis of ICU patients. Consequently, OASIS can replace the more complex existing prediction systems (10, 11). However, few studies have investigated the predictive value of OASISs in the prognosis of patients with acute stroke. Moreover, nothing is known about the efficacy of OASISs when evaluating the incidence and prognosis of SAP. Therefore, in this study, we collected relevant data from patients with acute ischemic stroke and analyzed the effect of OASIS on the risk and prognosis of SAP. In addition, we investigated the predictive value of OASISs to provide reference guidelines for the prevention, treatment, and improved prognostic evaluation of patients with acute stroke complicated with SAP.
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