Wearable Device Based on IoT in the Healthcare System for Disease Detection and Symptom Recognition

Humans in good shape face many challenges in their lives, such as food habits and climate change. The result must be aware of the health situation to survive. Lack of accurate patient information, preventive errors, data risks, overdiagnosis, and delayed implementation are challenges that health support services face. Wearable sensors that connect extensive data, data mining analysis for healthcare, and the Internet of things (IoT) have been proposed to solve this problem. This research, Disease Prediction and Symptom Recognition Model using IoT (DDSR-IoT) framework, is proposed for reasoning with regression rules to gather patient information. The Boltzmann network to train Artificial Intelligence (AI) feedback is introduced in the end. As a result, the broad interaction analysis of genomes is used to predict conditions. If those infections have affected people, emails are sent to warn them and provide them with prescriptions and medical advice. In the recommended approach, the experimental study resulted in an enhanced forecast rate of 97.4 percent and a precision of 97.42 percent.

Stuck in Stage 3: The Case of an Effective Depression Intervention for African American Older Adults

Beat the Blues (BTB) is a culturally tailored depression program for older African Americans. Tested in an NIA Stage 3 efficacy trial, findings showed statistically and clinically significant benefits, including decreased depressive symptoms, improved depression knowledge and symptom recognition, and behavioral activation. The multi-component intervention was co-constructed in partnership with a large senior center. Drawn from previously tested depression programs and tailored to preferences/needs of the targeted population, its five components included care management, depression education and symptom recognition, resources/referrals, and stress reduction and behavioral activation techniques. Despite significant findings, strong effect sizes and high acceptability, moving BTB to NIA Stage 4 (effectiveness) or 5 (dissemination) has been challenging. Challenges that will be discussed include lack of senior center funding to support training and delivery and infrastructure to embed BTB in community-based programs, and reluctance of health systems to adopt BTB because of its focus on one racial group.

Estimating nocturnal stroke onset times by magnetic resonance imaging in the WAKE-UP trial

Background Fluid-attenuated inversion recovery (FLAIR) sequences have gained a role to guide treatment of patients with unknown time of stroke symptom onset. Evolution of signal intensities in FLAIR is associated with time since stroke onset with continuous linear increases. Aims Estimating symptom onset during night-sleep in patients from the WAKE-UP trial based on relative signal intensities FLAIR (FLAIR-rSI) from acute stroke lesions an independent dataset (PRE-FLAIR study). Methods FLAIR-rSI was quantified in stroke lesions in PRE-FLAIR and WAKE-UP. The PRE-FLAIR study was a multicenter observational trial establishing FLAIR as a surrogate parameter for time since stroke onset. WAKE-UP was a randomized controlled trial that revealed a benefit for alteplase in patients selected based on a DWI-FLAIR mismatch. Stroke onset times were recorded in PRE-FLAIR and used to fit a linear regression model with FLAIR-rSI, adjusted for patient age and lesion volume. The model was applied to FLAIR-rSI of stroke lesions to estimate onset times in those patients enrolled in WAKE-UP who had symptom onset during night-sleep. Results FLAIR-rSI was quantified in 399 patients from PRE-FLAIR. Linear regression indicated a significant association of age ( p = 0.001), lesion volume ( p = 0.005) and FLAIR-rSI ( p < 0.001) with time since symptom onset (adjusted R2 = 0.179). In 813 patients from WAKE-UP, distribution of times of last seen well, symptom recognition and MRI examination were recorded. Median times of last seen well were 1 h before midnight (IQR 2.4 h) and symptom recognition 7 h after midnight (IRQ 2.2 h). Based on the FLAIR-rSI profiles, we estimated median stroke onset 6.1 h after midnight (IQR 2.7 h). Conclusion Nocturnal strokes during night-sleep may predominantly occur during the early morning hours. Our results are in line with evidence of characteristic diurnal patterns of cardiovascular events.

Abstract 1122‐000022: Delays in Care for Patients with Acute Stroke During Hospitalization for Other Reasons

Introduction : In‐hospital stroke (IHS) is defined as stroke that occurs during hospitalization for non‐stroke conditions. We aimed to understand the timing of symptom recognition for patients who experienced IHS and its impact on the care they receive. Methods : A prospective, single center registry of adult patients (9/20/19‐2/28/21) was queried for acute anterior circulation IHS. Indications for hospitalization, delays from last known well (LKW) to symptom recognition, imaging, and treatment were explored. Results : Of 928 consecutively evaluated adults with acute stroke, 85 (9%) developed an anterior circulation IHS, 39 (46%) of whom were female, with a median age of 67 years (IQR 60–76) and median NIHSS of 15 (IQR 4–22). Sixty‐eight (80%) had a >1 hour delay from last known well to symptom recognition. Two patients (2%) received IV thrombolysis, although another 38 (45%) would have been eligible if not for a delay in symptom recognition. An ICA, M1, or M2 occlusion was observed in 18 patients (21%), 7 of whom were treated at a median of 174 minutes after LKW (IQR 65–219). Compared to the 11 patients who did not undergo thrombectomy with large vessel occlusion, those who underwent thrombectomy had non‐significantly shorter delays from LKW until neuroimaging (median 85 [IQR 65‐162] vs. 216 [IQR 133‐507], p = 0.12). Conclusions : While uncommon, patients with IHS experience delays in symptom recognition and treatment, which lead to exclusion from acute care treatment such as thrombolysis and thrombectomy. Earlier detection with more frequent nursing assessments or advanced neuromonitoring devices in at‐risk patients may reduce delays in care.

Acute Ischemic Stroke Evaluation and Treatment

Acute ischemic stroke is a neurologic emergency where an estimated 2 million neurons a minute are lost secondary to ischemia. Treatments of acute stroke are directed at early revascularization of the occluded vessel and to preserve neuronal death and improve collateral flow. Treatments are time sensitive, an aspect that places great importance on early symptom recognition, correct diagnosis, and clinical management. In acute ischemic stroke, in short, “time is brain.”