Pilot monitoring study in patients with diabetic kidney disease using NORA application.

INTRODUCTION: Diabetic Kidney Disease (DKD) is the most common cause of end-stage chronic kidney disease (CKD), conditioning these patients to a worse renal prognosis and higher cardiovascular mortality and/or requirement for renal replacement therapy. The use of novel information and communication technologies (ICTs) focused on the field of health, may facilitates a better quality of life and disease control in these patients. Our objective is to evaluate the effect of monitoring DKD patients using NORA-app. MATERIAL AND METHODS: Prospective feasibility/validation study of NORA-app in patients with DKD stage G3bA3 or higher, followed in outpatient clinics of a tertiary care hospital. NORA-app is an application for smartphones designed to control risk factors, share educational medical information, communicate via chat with health professionals, increase treatment compliance (Morisky-Green), and collect patient reported outcomes such as anxiety and depression using HADs scale. Clinical-laboratory variables were collected at 3 months and compared to control patients who declined using NORA-app. RESULTS: From 01/01/2021 to 03/03/2022 the use of NORA-app was offered to 118 patients, 82 accepted and 36 declined (controls). After a mean follow-up period of 6,04 months and at the time of data extraction 71 (86.6%) NORA-app patients remain active users, 2 have completed the follow-up at one year and 9 are inactive (3 due to death and 6 due to non-locatable). There were no differences in baseline characteristics including Creatinine [2.1 (1.6-2.4) vs. 1.9 (1.5-2.5)] mg/dL and alb/creat [962 (475-1784) vs. 1036 (560-2183)] mg/gr between Nora and control patients respectively. The therapeutic compliance rate in the NORA-app group was 77%, improving at 90 days to 91%. Patients in the NORA-group showed significantly lower levels of alb/creat than controls (768(411-1971) mg/g Vs 2039 (974-3214) p = 0.047) at 90-day follow-up. CONCLUSIONS: In patients with DKD the use of NORA-app was maintained in the long-term, leading to high levels of treatment compliance, and achieving a better disease control. Our study suggests that the generalized use of ICTs may help in the personalized monitoring of these patients to delay the progression of kidney disease.

PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke.

BACKGROUND: Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence. METHODS: We analyzed clinical and socioeconomic data from a prospectively collected public health care-based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014-2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and >365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models. RESULTS: Overall, 16.21% (5932 of 36 114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74-0.77), 0.60 (95% CI, 0.58-0.61), and 0.71 (95% CI, 0.69-0.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72-0.75), 0.59 (95% CI, 0.57-0.61), and 0.67 (95% CI, 0.66-0.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement (P<0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors. CONCLUSIONS: PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.

Redefining Disability: Patient-Reported Outcome Measures After Minor Stroke and Transient Ischemic Attack.

BACKGROUND: Long-term outcome assessment patients with stroke is not fully captured by usual clinical scales such as the modified Rankin Scale (mRS). Patient-reported outcome measures (PROMs) are standardized and validated assessments that consider clinical outcomes from the patient perspective. We aim to analyze the added value of PROMs in patients with transient ischemic attack and minor stroke. METHODS: We included consecutive patients with minor stroke or transient ischemic attack (National Institutes of Health Stroke Scale score 0-5) from April 2020 to October 2021 that participated in the PROMs-through-App program (NORA, NoraHealth Barcelona Spain). Clinician and self-evaluated outcomes were assessed at 90 days: clinician-evaluated mRS, self-reported mRS, the 10-item patient-reported outcome measures questionnaire global health survey (v1.2), Hospital Anxiety and Depression Scale, and the Fatigue Assessment Scale. We evaluated the acceptability (response rate), reliability (internal consistency), and construct validity (correlation with mRS and between scales) of each questionnaire. RESULTS: We included 355 patients in the analysis, response rate was patient-reported outcome measures questionnaire 71.3% (253), Hospital Anxiety and Depression Scale 70.7% (251), Fatigue Assessment Scale 71.8% (255), and self-assessed mRS 66.8% (237). PROMS internal consistency was good or excellent, while agreement between clinician and self-reported mRS was fair (k=0.34). Rate of abnormal PROMS scores were as follows (all responders versus clinician-reported mRS score 0-2): patient-reported outcome measures questionnaire mental health (43.1% versus 36.3%), physical health (48.6% versus 43.6%); Hospital Anxiety and Depression Scale-anxiety (21.9% versus 17.7%) and depression (17.1% versus 13.3%); and Fatigue Assessment Scale (40.8% versus 36.4%). PROMs scores correlated with clinician and self-reported mRS at 90 days. CONCLUSIONS: Evaluation of PROMs using a mobile-app-based communication system is a reliable and valid strategy to assess the outcome of patients from their perspective after a mild stroke or transient ischemic attack.

Patient-reported outcome measures after thrombectomy in patients with acute stroke: fine-tuning the modified Rankin Scale.

BACKGROUND: In patients with stroke undergoing endovascular treatment (EVT), long-term outcome is usually only evaluated by the modified Rankin Scale (mRS). Patient-reported outcomes (PROMs) are standardized assessments that consider clinical outcomes from the perspective of the patient. We aimed to evaluate PROMs through a smartphone-based communication platform in patients with stroke who received EVT. METHODS: Consecutive patients with stroke who underwent EVT were offered to participate in the PROMs-through-App program (NORA). A set of standardized PROMs were collected at 7, 30 and 90 days after discharge. Disability was determined by clinicians (mRS) at 90 days. To characterize the potential ceiling effect of mRS in the assessment of different domains, the rate of abnormal PROMs among patients with excellent outcome (mRS 0-1) was calculated. RESULTS: From June 2020 to October 2021, 186 patients were included. The median PROMs collection rate per patient was 80% (50-100%). A correlation was consistently seen between disability measured by mRS and the different PROMs. The rate of abnormal PROMs ranged from 20.83% (HADS at 7 days) to 59.61% (Mental PROMIS at 7 days). At 90 days, among patients with an excellent outcome, the rate of abnormal PROMs ranged from 8.7% (HADS) to 47.83% (Physical PROMIS). CONCLUSIONS: A specifically designed digital platform allows a high collection rate of PROMs among stroke patients who underwent EVT. The mRS score shows a ceiling effect and seems insufficient to fine-tune long-term clinical results. The use of PROMs may allow a better characterization of long-term outcome profiles after EVT.

Added value of patient-reported outcome measures (PROMs) after an acute stroke and early predictors of 90 days PROMs.

INTRODUCTION: Value-based health care represents a patient-centered approach by valuing Patient-Reported Outcome Measures (PROMs). Our aim was to describe the additional value of PROMs after an acute stroke over conventional outcome measures and to identify early predictors of poor PROMs. METHODS: Acute stroke patients discharged from a tertiary care hospital followed by a web/phone-based PROMs collection program in the post hospitalization phase. Main PROMs involve anxiety and depression (HADS) (each defined by HADS ≥ 10) and global physical (PHY-) and mental (M-) health (PROMIS-10). PROMIS cut-off raw values of normality were: PHY-PROMIS ≥ 13 and M-PROMIS ≥ 11. An overall health status (OHS) from 0 to 100 was also determined. PROMs related to the different modified Rankin Scale (mRS) grades were defined. Early predictors of PROMs were evaluated. RESULTS: We included 1321 stroke patients, mean age 75 (± 8.6) and 55.7% male; 77.7% returned home. Despite a favorable mRS at 3 months (< 3), a relevant rate of patients considered without symptoms or with mild disability showed unfavorable results in the measured PROMs (8% unfavorable OHS, 15% HAD-depression, 12.1% HAD-anxiety, 28.7% unfavorable M-PROMIS and 33.1% unfavorable PHY-PROMIS results). Along follow-up, only PHY-PROMIS and OHS showed significant improvement (p < 0.01 and 0.03, respectively). The multivariate analysis including discharge variables showed that female sex, higher discharge mRS and discharge to socio-rehabilitation-center (SRC) were independent predictors of unfavorable results in PROMs (p < 0.01). When adding 7 days PROMs results, they emerged as the strongest predictors of 3 months PROMs. CONCLUSIONS: A high proportion of stroke patients show unfavorable results in PROMs at 3 months, even those with favorable mRS, and most results obtained by PROMs during follow-up continued to indicate alterations. Female sex, mRS and discharge to SRC predicted unfavorable results in PROMs, but the strongest predictors of 3 months PROMs were the results of the 7 days PROMs.

Farmalarm.

Background and Purpose- Risk factor control and treatment compliance in the following months after stroke are poor. We aim to validate a digital platform for smartphones to raise awareness among patients about the need to adopt healthy lifestyle, improve communication with medical staff, and treatment compliance. Methods- Farmalarm is an application (app) for smartphones designed to increase stroke awareness by medication alerts and compliance control, chat communication with medical staff, didactic video files, exercise monitoring. Patients with stroke discharged home were screened for participation and divided into groups: to follow the FARMALARM program for 3 to 4 weeks or standard of care follow-up. We determined achievement of risk factor control goals at 90 days. Results- From August 2015 to December 2016, from the 457 patients discharged home, 159 (34.8%) were included: Farmalarm (n=107); age 57±12, Control (n=52), age 59±10; without significant differences in baseline characteristics between groups. At 90 days, knowledge of vascular risk factors was higher in FARMALARM group (86.0% versus 69.2%, P<0.01). The rate of patients with diabetes mellitus (83.2% versus 63.5%, P<0.01) and hypercholesterolemia (80.3% versus 63.5%, P=0.03) under control and the rate of patients with 4 out of 4 risk factors under control was higher in FARMALARM group (50.4% versus 30.7%, P=0.02). A regression model showed that the use of Farmalarm was independently associated with all risk factors under control at 90 days (odds ratio, 2.3; 95% CI, 1.14-4.6; P=0.02). Conclusions- In patients with stroke discharged home, the use of mobile apps to monitor medication compliance and increase stroke awareness is feasible and seems to improve the control of vascular risk factors.