Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure

Aims Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD). Methods and results In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods. Conclusion The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement

Accurate differentiation between physiological and pathological ripples recorded with scalp-EEG

OBJECTIVE: To compare scalp-EEG recorded physiological ripples co-occurring with vertex waves to pathological ripples co-occurring with interictal epileptiform discharges (IEDs). METHODS: We marked ripples in sleep EEGs of children. We compared the start of ripples to vertex wave- or IED-start, and duration, frequency, and root mean square (RMS) amplitude of physiological and pathological ripples using multilevel modeling. Ripples were classified as physiological or pathological using linear discriminant analysis. RESULTS: We included 40 children with and without epilepsy. Ripples started (χ2(1) = 38.59, p < 0.001) later if they co-occurred with vertex waves (108.2 ms after vertex wave-start) than if they co-occurred with IEDs (4.3 ms after IED-start). Physiological ripples had longer durations (75.7 ms vs 53.0 ms), lower frequencies (98.3 Hz vs 130.6 Hz), and lower RMS amplitudes (0.9 μV vs 1.8 μV, all p < 0.001) than pathological ripples. Ripples could be classified as physiological or pathological with 98 % accuracy. Ripples recorded in children with idiopathic or symptomatic epilepsy seemed to form two subgroups of pathological ripples. CONCLUSIONS: Ripples co-occurring with vertex waves or IEDs have different characteristics and can be differentiated as physiological or pathological with high accuracy. SIGNIFICANCE: This is the first study that compares physiological and pathological ripples recorded with scalp EEG

Data-driven monitoring in patients on left ventricular assist device support

Introduction: Despite an increasing population of patients supported with a left ventricular assist device (LVAD), it remains a complex therapy, and patients are frequently admitted. Therefore, a strict follow-up including frequent hospital visits, patient self-management and telemonitoring is needed. Areas covered: The current review describes the principles of LVADs, the possibilities of (tele)monitoring using noninvasive and invasive devices. Furthermore, possibilities, challenges, and future perspectives in this emerging field are discussed. Expert Opinion: Several studies described initial experiences on telemonitoring in LVAD patients, using mobile phone applications to collect clinical data and pump data. This may replace frequent hospital visits in near future. In addition, algorithms were developed aiming to early detect pump thrombosis or driveline infections. Since not all complications are reflected by pump parameters, data from different sources should be combined to detect a broader spectrum of complications in an early stage. We need to focus on the development of sophisticated but understandable algorithms and infrastructure combining different data sources, while addressing essential aspects such as data safety, privacy, and cost-effectiveness

Identifying patients at risk: multi-centre comparison of HeartMate 3 and HeartWare left ventricular assist devices

Aims: Since the withdrawal of HeartWare (HVAD) from the global market, there is an ongoing discussion if and which patients require prophylactically exchange for a HeartMate 3 (HM3). Therefore, it is important to study outcome differences between HVAD and HM3 patients. Because centres differ in patient selection and standard of care, we performed a propensity score (PS)-based study including centres that implanted both devices and aimed to identify which HVAD patients are at highest risk. Methods and results: We performed an international multi-centre study (n = 1021) including centres that implanted HVAD and HM3. PS-matching was performed using clinical variables and the implanting centre. Survival and complications were compared. As a sensitivity analysis, PS-adjusted Cox regression was performed. Landmark analysis with conditional survival >2 years was conducted to evaluate long-term survival differences. To identify which HVAD patients may benefit from a HM3 upgrade, Cox regression using pre-operative variables and their interaction with device type was performed. Survival was significantly better for HM3 patients (P 2 years after implantation (P = 0.03). None of the pre-operative variable interactions in the Cox regression were significant. Conclusions: HM3 patients have a significantly better survival and a lower incidence of ischaemic strokes and pump thrombosis than HVAD patients. This survival difference persisted after 2 years of implantation. Additional research using post-operative variables is warranted to identify which HVAD patients need an upgrade to HM3 or expedited transplantation

Hidden Markov model detection of interpersonal interaction dynamics in predicting patient depression improvement in psychotherapy: Proof-of-concept study

Background: Previous human ethology studies have demonstrated that the interpersonal interactions displayed in therapy by both patients and therapists influences a patient's depression improvement. Pairing novel statistical techniques such as the hidden Markov model (HMM), interpersonal interaction dynamics can be uncovered by partitioning time into empirically-derived nonverbal behavioral states. This approach allows for better patient-therapist behavioral dynamics distinctions in predicting depression improvement and, subsequently, for the processes behind depression improvement. Methods: For the 39 participating patients, the first 15 min of the first or second therapy session was recorded on video to examine the interpersonal interaction behaviors of patients and therapists. The video recordings were encoded for vocalization, looking and leg movement behavior events at a 1 s frequency. A Bayesian multivariate multilevel HMM was fitted on the behavioral event data. Results: It is demonstrated that patients that show improvement in the depression score are characterized by interpersonal interaction dynamics of hyperfocus when listening to their therapist in psychotherapy when compared to non-improving patients. The data supports evidence for the emergence of differences in interpersonal interaction dynamics through changed durations of the patient hyper focused listening states, but not through changed state-switching dynamics over time. Limitations: Due to our relatively small sample size we could not fit multilevel HMMs composed of more than three hidden states. Conclusions: We suggest that applying HMMs will aid human ethological behavior studies in uncovering interpersonal interaction dynamics that occur in therapy and be able to use these dynamics to predict patient depression symptom improvement

Classroom bullying norms and peer status: Effects on victim-oriented and bully-oriented defending

Defending a victimized peer is a socially risky behavior that may require high peer status and may depend on how popular or disliked bullies are in the classroom (i.e., within-classroom correlations between bullying and status). Past research has investigated defending as a unidimensional construct, though it can involve confronting the bully (bully-oriented defending) or supporting the victim (victim-oriented defending). This study used multilevel modeling to examine the effects of individual peer status, gender, and bullying as well as two indicators of classroom norms—the bullying-popularity norm and the bullying-rejection norm—on both types of defending. Our sample included 1,460 Dutch adolescents (50% girls; M age 11 years) from 59 classrooms in 50 schools. Likability and popularity were positively associated with both types of defending. Being female and lower in bullying was associated with victim-oriented defending, whereas being male and higher in bullying was associated with bully-oriented defending. In classrooms where bullies were more rejected, both types of defending were more prevalent, and the positive associations of likability and popularity with victim-oriented defending were stronger. The positive effect of the bullying-rejection norm on victim-oriented defending was stronger for girls. Moreover, the effect of popularity on bully-oriented defending was stronger in classrooms where bullies were less popular

Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives

BACKGROUND: In neuroscience, experimental designs in which multiple measurements are collected in the same research object or treatment facility are common. Such designs result in clustered or nested data. When clusters include measurements from different experimental conditions, both the mean of the dependent variable and the effect of the experimental manipulation may vary over clusters. In practice, this type of cluster-related variation is often overlooked. Not accommodating cluster-related variation can result in inferential errors concerning the overall experimental effect. RESULTS: The exact effect of ignoring the clustered nature of the data depends on the effect of clustering. Using simulation studies we show that cluster-related variation in the experimental effect, if ignored, results in a false positive rate (i.e., Type I error rate) that is appreciably higher (up to ~20-~50 %) than the chosen [Formula: see text]-level (e.g., [Formula: see text] = 0.05). If the effect of clustering is limited to the intercept, the failure to accommodate clustering can result in a loss of statistical power to detect the overall experimental effect. This effect is most pronounced when both the magnitude of the experimental effect and the sample size are small (e.g., ~25 % less power given an experimental effect with effect size d of 0.20, and a sample size of 10 clusters and 5 observations per experimental condition per cluster). CONCLUSIONS: When data is collected from a research design in which observations from the same cluster are obtained in different experimental conditions, multilevel analysis should be used to analyze the data. The use of multilevel analysis not only ensures correct statistical interpretation of the overall experimental effect, but also provides a valuable test of the generalizability of the experimental effect over (intrinsically) varying settings, and a means to reveal the cause of cluster-related variation in experimental effect

Accompanying code for "Multilevel Hidden Markov Modelling to Identify personalized crisis state dynamics based on experience sampling of cognitive, affective, and behavioral factors"

Background: Personality disorders (PDs) are characterized by regular mental health crises, which are a complex interplay of symptoms on several levels: cognitive, affective, and behavioral (CAB) symptoms. Isolating crisis states based on CAB symptoms and quantifying patient specific crisis dynamics over time may improve care and calls for fine-grained observations paired with advanced statistical models. Methods: To isolate empirically-derived crisis states, a multilevel hidden Markov model was applied to ESM of data of twenty-six patients with PD, measured three times per day with a total of 60 measurements per patient. ESM included self-report items measuring cognitive (loss of [self-]control), affective (negative mood) and behavioral (social contact avoidance and desire and suicidal ideation) factors. Results: In this proof-of-concept study, the multilevel HMM isolated four distinctive CAB-based crisis states, with crisis severity increasing over subsequent states. At the sample level patients were most likely to remain within the current CAB crisis state from one interval to the next instead of transitioning. When residing in CAB crisis state 2 or up, it was unlikely to transition directly back to CAB crisis state 1. However, large heterogeneity was observed in CAB crisis state dynamics between patients, indicated both by the patient individual state transition probabilities and the patient specific CAB crisis state trajectories over time. Conclusions: The uncovered crisis states using multilevel HMM quantify and visualize the pattern of crisis dynamics, holding the promise to quantify CAB crisis trajectories on a patient individual level. The considerable variation between patients highlights the need for a personalized method. This repository contains the accompanying code for the manuscript: "Multilevel Hidden Markov Modelling to Identify personalized crisis state dynamics based on experience sampling of cognitive, affective, and behavioral factors". It comprehends R code to: (1) run the multilevel hidden Markov model on the cognitive, affective, and behavioral factors and (2) post-process the obtained results in (1). Please note that the empirical data used in (1) is not available as part of this repository

MOESM2 of Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives

Additional file 2. Calculating the optimal allocation of sample sizes and estimating statistical power to detect the overall experimental effect. Explanation on how to calculate the optimal allocation of sample sizes over clusters and within clusters given the available resources, and explanation on how to estimate power for a balanced (i.e., the number of observations per condition are equal both between conditions and between clusters) 2-level multilevel model without covariates