Survival, but not the severity of hypoxic-ischemic encephalopathy, is associated with higher mean arterial blood pressure after cardiac arrest: a retrospective cohort study.

Background: This study investigates the association between the mean arterial blood pressure (MAP), vasopressor requirement, and severity of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA). Methods: Between 2008 and 2017, we retrospectively analyzed the MAP 200 h after CA and quantified the vasopressor requirements using the cumulative vasopressor index (CVI). Through a postmortem brain autopsy in non-survivors, the severity of the HIE was histopathologically dichotomized into no/mild and severe HIE. In survivors, we dichotomized the severity of HIE into no/mild cerebral performance category (CPC) 1 and severe HIE (CPC 4). We investigated the regain of consciousness, causes of death, and 5-day survival as hemodynamic confounders. Results: Among the 350 non-survivors, 117 had histopathologically severe HIE while 233 had no/mild HIE, without differences observed in the MAP (73.1 vs. 72.0 mmHg, pgroup = 0.639). Compared to the non-survivors, 211 patients with CPC 1 and 57 patients with CPC 4 had higher MAP values that showed significant, but clinically non-relevant, MAP differences (81.2 vs. 82.3 mmHg, pgroup < 0.001). The no/mild HIE non-survivors (n = 54), who regained consciousness before death, had higher MAP values compared to those with no/mild HIE (n = 179), who remained persistently comatose (74.7 vs. 69.3 mmHg, pgroup < 0.001). The no/mild HIE non-survivors, who regained consciousness, required fewer vasopressors (CVI 2.1 vs. 3.6, pgroup < 0.001). Independent of the severity of HIE, the survivors were weaned faster from vasopressors (CVI 1.0). Conclusions: Although a higher MAP was associated with survival in CA patients treated with a vasopressor-supported MAP target above 65 mmHg, the severity of HIE was not. Awakening from coma was associated with less vasopressor requirements. Our results provide no evidence for a MAP target above the current guideline recommendations that can decrease the severity of HIE.

Digital thErapy For Improved tiNnitus carE Study (DEFINE): Protocol for a randomised controlled trial.

Tinnitus is a common health condition, affecting approximately 15% of the UK population. The tinnitus treatment with the strongest evidence base is Cognitive Behavioural Therapy (CBT), with standard tinnitus therapy typically augmented with education, relaxation and other techniques. Availability of CBT and conventional tinnitus therapy more broadly is limited for tinnitus sufferers. The DEFINE trial aims to assess whether smartphone-delivered tinnitus therapy, the Oto app, is as effective as current standard care, one-to-one therapist-delivered tinnitus treatment for the treatment of tinnitus in adults. The trial is registered in the ISRCTN Registry: ISRCTN99577932. DEFINE is an open-label, non-inferiority, prospective, parallel design, randomised-controlled trial. Recruitment, interventions and assessments will be remote, enabling UK-wide participant involvement. 198 participants aged 18 years or more will be recruited via social media advertisement or via primary care physicians. A screening process will identify those with tinnitus that impacts health-related quality of life, and following consent smartphone-based audiometry will be performed. Randomisation 1:1 to the Oto app or one-to-one therapist-led tinnitus therapy will be performed centrally by computer, matching groups for age, sex and hearing level. Following participant allocation, the Oto app will be provided for immediate use, or a one-to-one remote therapy appointment booked to occur within approximately 1 week, with up to 6 sessions delivered. Participant outcomes will be collected at 4,12, 26 and 52 weeks via questionnaire and phone call. The primary outcome is the change in Tinnitus Functional Index (TFI) total score measured at 26 weeks following allocation. Adverse events will be recorded. A health economic evaluation in the form of a cost-utility analysis will be performed using data from participant submitted EuroQol 5D-5L and Health Utilities Index Mark 3 scores and resource use data. Trial results will be made publicly available, including a plain English summary.

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review.

BACKGROUND: Diagnostic delays in autism are common, with the time to diagnosis being up to 3 years from the onset of symptoms. Such delays have a proven detrimental effect on individuals and families going through the process. Digital health products, such as mobile apps, can help close this gap due to their scalability and ease of access. Further, mobile apps offer the opportunity to make the diagnostic process faster and more accurate by providing additional and timely information to clinicians undergoing autism assessments. OBJECTIVE: The aim of this scoping review was to synthesize the available evidence about digital biomarker tools to aid clinicians, researchers in the autism field, and end users in making decisions as to their adoption within clinical and research settings. METHODS: We conducted a structured literature search on databases and search engines to identify peer-reviewed studies and regulatory submissions that describe app characteristics, validation study details, and accuracy and validity metrics of commercial and research digital biomarker apps aimed at aiding the diagnosis of autism. RESULTS: We identified 4 studies evaluating 4 products: 1 commercial and 3 research apps. The accuracy of the identified apps varied between 28% and 80.6%. Sensitivity and specificity also varied, ranging from 51.6% to 81.6% and 18.5% to 80.5%, respectively. Positive predictive value ranged from 20.3% to 76.6%, and negative predictive value fluctuated between 48.7% and 97.4%. Further, we found a lack of details around participants' demographics and, where these were reported, important imbalances in sex and ethnicity in the studies evaluating such products. Finally, evaluation methods as well as accuracy and validity metrics of available tools were not clearly reported in some cases and varied greatly across studies. Different comparators were also used, with some studies validating their tools against the Diagnostic and Statistical Manual of Mental Disorders criteria and others through self-reported measures. Further, while in most cases, 2 classes were used for algorithm validation purposes, 1 of the studies reported a third category (indeterminate). These discrepancies substantially impact the comparability and generalizability of the results, thus highlighting the need for standardized validation processes and the reporting of findings. CONCLUSIONS: Despite their popularity, systematic evaluations and syntheses of the current state of the art of digital health products are lacking. Standardized and transparent evaluations of digital health tools in diverse populations are needed to assess their real-world usability and validity, as well as help researchers, clinicians, and end users safely adopt novel tools within clinical and research practices.

Safety of Triage Self-assessment Using a Symptom Assessment App for Walk-in Patients in the Emergency Care Setting: Observational Prospective Cross-sectional Study.

BACKGROUND: Increasing use of emergency departments (EDs) by patients with low urgency, combined with limited availability of medical staff, results in extended waiting times and delayed care. Technological approaches could possibly increase efficiency by providing urgency advice and symptom assessments. OBJECTIVE: The purpose of this study is to evaluate the safety of urgency advice provided by a symptom assessment app, Ada, in an ED. METHODS: The study was conducted at the interdisciplinary ED of Marburg University Hospital, with data collection performed between August 2019 and March 2020. This study had a single-center cross-sectional prospective observational design and included 378 patients. The app's urgency recommendation was compared with an established triage concept (Manchester Triage System [MTS]), including patients from the lower 3 MTS categories only. For all patients who were undertriaged, an expert physician panel assessed the case to detect potential avoidable hazardous situations (AHSs). RESULTS: Of 378 participants, 344 (91%) were triaged the same or more conservatively and 34 (8.9%) were undertriaged by the app. Of the 378 patients, 14 (3.7%) had received safe advice determined by the expert panel and 20 (5.3%) were considered to be potential AHS. Therefore, the assessment could be considered safe in 94.7% (358/378) of the patients when compared with the MTS assessment. From the 3 lowest MTS categories, 43.4% (164/378) of patients were not considered as emergency cases by the app, but could have been safely treated by a general practitioner or would not have required a physician consultation at all. CONCLUSIONS: The app provided urgency advice after patient self-triage that has a high rate of safety, a rate of undertriage, and a rate of triage with potential to be an AHS, equivalent to telephone triage by health care professionals while still being more conservative than direct ED triage. A large proportion of patients in the ED were not considered as emergency cases, which could possibly relieve ED burden if used at home. Further research should be conducted in the at-home setting to evaluate this hypothesis. TRIAL REGISTRATION: German Clinical Trial Registration DRKS00024909; https://www.drks.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00024909.

Optimization of Patient Flow in Urgent Care Centers Using a Digital Tool for Recording Patient Symptoms and History: Simulation Study.

BACKGROUND: Crowding can negatively affect patient and staff experience, and consequently the performance of health care facilities. Crowding can potentially be eased through streamlining and the reduction of duplication in patient history-taking through the use of a digital symptom-taking app. OBJECTIVE: We simulated the introduction of a digital symptom-taking app on patient flow. We hypothesized that waiting times and crowding in an urgent care center (UCC) could be reduced, and that this would be more efficient than simply adding more staff. METHODS: A discrete-event approach was used to simulate patient flow in a UCC during a 4-hour time frame. The baseline scenario was a small UCC with 2 triage nurses, 2 doctors, 1 treatment/examination nurse, and 1 discharge administrator in service. We simulated 33 scenarios with different staff numbers or different potential time savings through the app. We explored average queue length, waiting time, idle time, and staff utilization for each scenario. RESULTS: Discrete-event simulation showed that even a few minutes saved through patient app-based self-history recording during triage could result in significantly increased efficiency. A modest estimated time saving per patient of 2.5 minutes decreased the average patient wait time for triage by 26.17%, whereas a time saving of 5 minutes led to a 54.88% reduction in patient wait times. Alternatively, adding an additional triage nurse was less efficient, as the additional staff were only required at the busiest times. CONCLUSIONS: Small time savings in the history-taking process have potential to result in substantial reductions in total patient waiting time for triage nurses, with likely effects of reduced patient anxiety, staff anxiety, and improved patient care. Patient self-history recording could be carried out at home or in the waiting room via a check-in kiosk or a portable tablet computer. This formative simulation study has potential to impact service provision and approaches to digitalization at scale.

Predictive modeling of nontuberculous mycobacterial pulmonary disease epidemiology using German health claims data.

OBJECTIVES: Administrative claims data are prone to underestimate the burden of non-tuberculous mycobacterial pulmonary disease (NTM-PD). METHODS: We developed machine learning-based algorithms using historical claims data from cases with NTM-PD to predict patients with a high probability of having previously undiagnosed NTM-PD and to assess actual prevalence and incidence. Adults with incident NTM-PD were classified from a representative 5% sample of the German population covered by statutory health insurance during 2011-2016 by the International Classification of Diseases, 10th revision code A31.0. Pre-diagnosis characteristics (patient demographics, comorbidities, diagnostic and therapeutic procedures, and medications) were extracted and compared to that of a control group without NTM-PD to identify risk factors. RESULTS: Applying a random forest model (area under the curve 0.847; total error 19.4%) and a risk threshold of >99%, prevalence and incidence rates in 2016 increased 5-fold and 9-fold to 19 and 15 cases/100,000 population, respectively, for both coded and non-coded vs. coded cases alone. CONCLUSIONS: The use of a machine learning-based algorithm applied to German statutory health insurance claims data predicted a considerable number of previously unreported NTM-PD cases with high probabilty.

Do children have Bayesian intuitions?

Can children solve Bayesian problems, given that these pose great difficulties even for most adults? We present an ecological framework in which Bayesian intuitions emerge from a match between children's numerical competencies and external representations of numerosity. Bayesian intuition is defined here as the ability to determine the exact Bayesian posterior probability by minds untutored in probability theory or in Bayes' rule. As we show, Bayesian intuitions do not require processing of probabilities or Arabic numbers, but basically the ability to count tokens in icon arrays and to understand what to count. A series of experiments demonstrates for the first time that icon arrays elicited Bayesian intuitions in children as young as second-graders for 22% to 32% of all problems; fourth-graders achieved 50% to 60%. Most surprisingly, icon arrays elicited Bayesian intuitions in children with dyscalculia, a specific learning disorder that has been attributed to genetic causes. These children could solve an impressive 50% of Bayesian problems, a level similar to that of children without dyscalculia. By seventh grade, children solved about two thirds of Bayesian problems with natural frequencies alone, without the additional help of icon arrays. We identify four non-Bayesian rules. On the basis of these results, we propose a common solution for the phylogenetic, the ontogenetic, and the 1970s puzzles in the Bayesian literature and argue for a revision of how to teach statistical thinking. In accordance with recent work on infants' numerical abilities, these findings indicate that children have more numerical ability than previously assumed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs.

OBJECTIVES: To compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps. DESIGN: Vignettes study. SETTING: 200 primary care vignettes. INTERVENTION/COMPARATOR: For eight apps and seven general practitioners (GPs): breadth of coverage and condition-suggestion and urgency advice accuracy measured against the vignettes' gold-standard. PRIMARY OUTCOME MEASURES: (1) Proportion of conditions 'covered' by an app, that is, not excluded because the user was too young/old or pregnant, or not modelled; (2) proportion of vignettes with the correct primary diagnosis among the top 3 conditions suggested; (3) proportion of 'safe' urgency advice (ie, at gold standard level, more conservative, or no more than one level less conservative). RESULTS: Condition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%; WebMD: 93.0%. Top-3 suggestion accuracy was GPs (average): 82.1%±5.2%; Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps excluded certain user demographics or conditions and their performance was generally greater with the exclusion of corresponding vignettes. For safe urgency advice, tested GPs had an average of 97.0%±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 SD of the GPs-Ada: 97.0%; Babylon: 95.1%; Symptomate: 97.8%. One app had a safety performance within 2 SDs of GPs-Your.MD: 92.6%. Three apps had a safety performance outside 2 SDs of GPs-Buoy: 80.0% (p<0.001); K Health: 81.3% (p<0.001); Mediktor: 87.3% (p=1.3×10-3). CONCLUSIONS: The utility of digital symptom assessment apps relies on coverage, accuracy and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.

Hidradenitis Suppurativa and Concurrent Psoriasis: Comparison of Epidemiology, Comorbidity Profiles, and Risk Factors.

INTRODUCTION: Hidradenitis suppurativa (HS) is a chronic, debilitating, and inflammatory skin disease. The epidemiology of HS varies greatly, with an estimated prevalence ranging from 0.03% to 4% worldwide. Similar to psoriasis (PsO), HS also exhibits a systemic inflammatory nature with a spectrum of systemic comorbidities. A large health insurance claims (HICs) database is analyzed to determine the demography and epidemiology of HS, PsO, and HS with concurrent PsO (HS-PsO) patients. Furthermore, the comorbidity profiles, including the comorbidity risk of these patient populations, are analyzed. METHODS: This is a noninterventional retrospective analysis of anonymized HICs data using a subset of the Institute of Applied Health Research Berlin (InGef) database. The primary outcome is the prevalence and incidence of HS, PsO, and HS-PsO. Secondary outcomes include comorbidity profiles and a comorbidity risk analysis. RESULTS: The prevalence and incidence of HS were 0.0681% and 0.0101%, respectively. The prevalence of HS-PsO was 0.004% (6% of the total HS population). HS patients frequently suffered from arterial hypertension (45%), nicotine dependence (46%), obesity (41%), and depression (36%), which were more common in HS-PsO patients compared with HS alone. HS patients had an increased prevalence of metabolic, psychiatric, immune-mediated, and cardiovascular diseases, e.g., overweight/obesity [odds ratio (OR): 2.65, 95% confidence interval (CI) 2.37-2.96], depression (OR: 1.55, 95% CI 1.42-1.76), or seronegative rheumatoid arthritis (OR: 2.82, 95% CI 1.61-4.94) compared with the overall population. The increased risk of myocardial infarction in HS patients (OR: 4.1, 95% CI 3.5-4.8, adjusting for age/sex) was largely attributed to patient's current smoking status (OR: 1.1, 95% CI 0.8-1.5, adjusting for smoking/age/sex). CONCLUSIONS: HS patients show a broad spectrum of inflammatory and metabolic syndrome-related comorbidities, with an increased risk by concurrent PsO. Important for clinical practice, the elevated cardiovascular risk of HS patients can be largely attributed to smoking.

Costs of patients with chronic kidney disease in Germany.

BACKGROUND: This study aimed to determine the costs and distribution of healthcare spending of patients with chronic kidney disease (CKD) at stages 3 and 4 and on dialysis both at the individual and population level in Germany. METHODS: The study took the perspective of the German statutory health insurance (SHI) system and analyzed claims data on 3,687,015 insurees from the year 2014. To extrapolate costs to the whole SHI population, a literature search on the prevalence of CKD was conducted. RESULTS: Average costs per person per year in an age- and gender-matched control group of the normal population were €2,876 (95% confidence interval [CI], €2,798 to €2,955) and ≥2.8-fold higher in CKD patients (€8,030 [95% CI, €7,848 to €8,212] at CKD stage 3, €9,760 [95% CI, €9,266 to €10,255] at CKD stage 4, and €44,374 [95% CI, €43,608 to €45,139] on dialysis). At CKD stages 3 and 4 the major cost driver was hospitalizations, contributing to more than 50% of total expenditures. Among dialysis patients, hospitalizations and dialysis-treatment costs contributed to 23% and 53% of total healthcare spending, respectively. At CKD stages 3 and 4, patients with the highest 20% of healthcare spending showed a considerable increase in per-patient costs over the reference population, while the bottom 80% of patients generated only moderately higher per-patient costs (p < 0.001). Comparing total CKD costs to total SHI expenditures yields that 10.2% of SHI expenditures was driven by patients at CKD stages 3 and 4 and 1.6% by dialysis patients. CONCLUSIONS: Healthcare spending of patients with CKD at stages 3 and 4 and on dialysis is concentrated among a small number of high-need patients. As hospitalizations and dialysis treatment are key drivers of total expenditures, strategies that lead to a reduction in hospitalizations, delay in dialysis onset, or increase in the availability of kidney donors should become important considerations by policymakers.