Patient and Caregiver Symptom Trajectory: The Last 2 Months of Cancer Home Hospice

Patient symptom management is a fundamental goal of cancer home hospice care. However, informal family caregivers, who are primarily responsible for daily patient care, also experience negative symptoms, especially at the end of the patient’s life. While research has attended to patient symptom progression in home hospice, little research focuses on caregiver symptoms. To address this, we examined the frequency of both patient and caregiver symptoms to determine how these symptoms change in the last two months of the patient’s life. Sixty-three cancer hospice caregivers from 4 US states prospectively reported daily patient and caregiver symptoms via an Interactive Voice Response phone system. We analyzed data from up to the last 60 days of the patient’s life. Most caregivers were female (71.4%), Caucasian (88.9%), spouses of the patient (46%); average age was 59 years old (SD=13). Patients were mostly female (54%), with diverse solid tumor cancer diagnoses, and 72 years old (SD=11) on average. Most commonly reported moderate-to-severe patient symptoms were fatigue (67%), pain (47.5%), and loss in appetite (42.3%). Most common moderate-to-severe caregiver symptoms were fatigue (57.8%), trouble sleeping (45.1%), anxiety (52%), and depression (40.4%). Patient and caregiver symptoms were significantly correlated (Pearson r = .51, p<.001). Mixed-effects models found that both patient and caregiver symptoms (collapsed by week) worsened as patient death approached (ps <.01). Researchers and clinicians who are aware of the strong relationship between patient and caregiver symptoms are best able to address caregiver symptoms as part of hospice care, particularly as patient death approaches.

Causal Reasoning Model Based on Medical Knowledge Graph for Disease Diagnosis

In view of the non-interpretability of disease diagnosis models based on deep learning, a knowledge reasoning model based on medical knowledge graph for intelligent diagnosis is proposed. Given the patient symptom set, the co-occurrence of the patient and the disease is calculated, then the patient suffering from one disease is calculated. Based on the dynamic threshold value, the final disease diagnosis result of the patient is outputted. According to the symptoms of patients and the symptoms in the knowledge graph, the causal reasoning of the disease diagnosis is interpretable. Experiments on 145,712 pediatric electronic medical records in Chinese show that the proposed model can predict diseases with interpretability, and the accuracy reaches-82.12%.

Trigonal-Sparing vs. Trigonal-Involved OnabotulinumtoxinA Injection for the Treatment of Overactive Bladder: A Systematic Review and Meta-Analysis

Objective: Overactive bladder (OAB) is a disease characterized by the presence of urinary urgency. We carried out a meta-analysis to assess the effectiveness and safety of trigonal-involved injection of onabotulinumtoxinA (BoNT-A) in comparison with the trigonal-sparing technique in cases with OAB [neurogenic detrusor overactivity (NDO) and idiopathic detrusor overactivity (IDO)].Methods: Randomized controlled trials (RCTs) of BoNT-A injection for OAB were searched systematically by using EMBASE, MEDLINE, and the Cochrane Controlled Trials Register. The datum was calculated by RevMan version 5.3.0. The original references of relating articles were also reviewed.Results: In total, six RCTs involving 437 patients were included in our analysis. For OAB, the trigone-including group showed a different patient symptom score (p = 0.03), complete dryness rate (p = 0.002), frequency of incontinence episodes (p = 0.01), detrusor pressure at maximum flow rate (p = 0.01), and volume at the first desire to void (p = 0.0004) compared with the trigone-sparing group. Also, a trigone-including intradetrusor injection demonstrated a significant improvement in the patient symptom score (p = 0.0004), complete dryness rate (p = 0.0002), frequency of incontinence episodes (p = 0.0003), detrusor pressure at maximum flow rate (p = 0.01), and volume at the first desire to void (p = 0.00006) compared with the trigone-sparing group for treatment of NDO. The adverse events rates were similar in both groups.Conclusions: The meta-analysis has demonstrated that trigone-including BoNT-A injection was more effective compared with the trigone-sparing injection for the treatment of OAB, especially for NDO.

A standardized system and App for continuous patient symptom logging in gastroduodenal disorders: design, implementation, and validation

Background Functional gastroduodenal disorders include functional dyspepsia, chronic nausea and vomiting syndromes, and gastroparesis. These disorders are common, but their overlapping symptomatology poses challenges to diagnosis, research, and therapy. This study aimed to introduce and validate a standardized patient symptom-logging system and App to aid in the accurate reporting of gastroduodenal symptoms for clinical and research applications. Methods The system was implemented in an iOS App including pictographic symptom illustrations, and two validation studies were conducted. To assess convergent and concurrent validity, a diverse cohort with chronic gastroduodenal symptoms undertook App-based symptom logging for 4-hours after a test meal. Individual and total post-prandial symptom scores were averaged and correlated against two previously validated instruments: PAGI-SYM (for convergent validity) and PAGI-QOL (for concurrent validity). To assess face and content validity, semi-structured qualitative interviews were conducted with patients. Key Results App-based symptom reporting demonstrated robust convergent validity with PAGI-SYM measures of nausea (rS=0.68), early satiation (rS=0.55), bloating (rS=0.48), heartburn (rS=0.47), upper gut pain (rS=0.40) and excessive fullness (rS=0.40); all p<0.001 (n=79). The total App-reported Gastric Symptom Burden Score correlated positively with PAGI-SYM (rS=0.56; convergent validity; p<0.001), and negatively with PAGI-QOL (rS=-0.34; concurrent validity; p=0.002). Interviews demonstrated that the pictograms had adequate face and content validity. Conclusions and Inferences The continuous patient symptom-logging App demonstrated robust convergent, concurrent, face, and content validity when used within a 4-hour post-prandial test protocol. The App will enable standardized symptom reporting and is anticipated to provide utility in both research and clinical practice.

Costs and cost-effectiveness of a comprehensive tuberculosis case finding strategy in Zambia

Introduction Active-case finding (ACF) programs have an important role in addressing case detection gaps and halting tuberculosis (TB) transmission. Evidence is limited on the cost-effectiveness of ACF interventions, particularly on how their value is impacted by different operational, epidemiological and patient care-seeking patterns. Methods We evaluated the costs and cost-effectiveness of a combined facility and community-based ACF intervention in Zambia that utilized mobile chest X-ray with computer-aided reading/interpretation software and laboratory-based Xpert MTB/RIF testing. Programmatic costs (in 2018 US dollars) were assessed from the health system perspective using prospectively collected cost and operational data. Cost-effectiveness of the ACF intervention was assessed as the incremental cost per TB death averted over a five-year time horizon using a multi-stage Markov state-transition model reflecting patient symptom-associated care-seeking and TB care under ACF compared to passive care. Results Over 18 months of field operations, the ACF intervention costed $435 to diagnose and initiate treatment for one person with TB. After accounting for patient symptom-associated care-seeking patterns in Zambia, we estimate that this one-time ACF intervention would incrementally diagnose 407 (7,207 versus 6,800) TB patients and avert 502 (611 versus 1,113) TB-associated deaths compared to the status quo (passive case finding), at an incremental cost of $2,284 per death averted over the next five-year period. HIV/TB mortality rate, patient symptom-associated care-seeking probabilities in the absence of ACF, and the costs of ACF patient screening were key drivers of cost-effectiveness. Conclusions A one-time comprehensive ACF intervention simultaneously operating in public health clinics and corresponding catchment communities can have important medium-term impact on case-finding and be cost-effective in Zambia. The value of such interventions increases if targeted to populations with high HIV/TB mortality, substantial barriers (both behavioral and physical) to care-seeking exist, and when ACF interventions can optimize screening by achieving operational efficiency.

Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?

The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson’s disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.

A Study of Fuzzy Relation and Its Application in Medical Diagnosis

Objective: Medical diagnosis process extends within the degree to which they plan to affect different complicating aspects of diagnosis. In this research work, the concept of fuzzy relation with medical diagnosis is studied and the application of fuzzy relations to such problems by extending the Sanchez’s approach is introduced. Method: An application of fuzzy relation with Sanchez's approach for medical diagnosis is presented. Based on the composition of the fuzzy relations, an algorithm for medical diagnosis as follows- first input the number of objects and attributes to obtain patient symptom matrix, symptom-disease matrix and the composition of fuzzy relations to get the patient-diagnosis matrix. Then find the maximum value to evaluate which patient is suffering from what disease. Result: Using the algorithm for medical diagnosis, the disease for which the membership value is maximum gives the final decision. If almost equal values for different diagnosis in composition are obtained, the case for which non-membership is minimum and hesitation is least is considered. The output matched well with the doctor’s diagnosis. Conclusion: In the process of medical diagnosis, state of patient are given by the patient through linguistic terminology like as temperature, cough, stomach pain etc., consideration of fuzzy sets as grades for association instead of membership grades in [0,1] is more advantageous to model the state of the patient. Similarly fuzzy relation has been introduced representing the association between symptoms and diseases. Sanchez’s approach has been extended for medical diagnosis in this reference. The approach used to form fuzzy matrix showing the association of symptoms and diseases is based on the sanchez’s approach.