Reducing diagnostic delays in Acute Hepatic Porphyria using electronic health records data and machine learning: a multicenter development and validation study.

Importance: Acute Hepatic Porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of fifteen years on average. The advent of electronic health records (EHR) data and machine learning (ML) may improve the timely recognition of rare diseases like AHP. However, prediction models can be difficult to train given the limited case numbers, unstructured EHR data, and selection biases intrinsic to healthcare delivery. Objective: To train and characterize models for identifying patients with AHP. Design Setting and Participants: This diagnostic study used structured and notes-based EHR data from two centers at the University of California, UCSF (2012-2022) and UCLA (2019-2022). The data were split into two cohorts (referral, diagnosis) and used to develop models that predict: 1) who will be referred for testing of acute porphyria, amongst those who presented with abdominal pain (a cardinal symptom of AHP), and 2) who will test positive, amongst those referred. The referral cohort consisted of 747 patients referred for testing and 99,849 contemporaneous patients who were not. The diagnosis cohort consisted of 72 confirmed AHP cases and 347 patients who tested negative. Cases were female predominant and 6-75 years old at the time of diagnosis. Candidate models used a range of architectures. Feature selection was semi-automated and incorporated publicly available data from knowledge graphs. Main Outcomes and Measures: F-score on an outcome-stratified test set. Results: The best center-specific referral models achieved an F-score of 86-91%. The best diagnosis model achieved an F-score of 92%. To further test our model, we contacted 372 current patients who lack an AHP diagnosis but were predicted by our models as potentially having it (≥ 10% probability of referral, ≥ 50% of testing positive). However, we were only able to recruit 10 of these patients for biochemical testing, all of whom were negative. Nonetheless, post hoc evaluations suggested that these models could identify 71% of cases earlier than their diagnosis date, saving 1.2 years. Conclusions and Relevance: ML can reduce diagnostic delays in AHP and other rare diseases. Robust recruitment strategies and multicenter coordination will be needed to validate these models before they can be deployed.

Quantifying the impact of symptomatic acute hepatic porphyria on well-being via patient-reported outcomes: Results from the Porphyria Worldwide Patient Experience Research (POWER) study.

Acute hepatic porphyria (AHP) is a group of rare genetic diseases of heme biosynthesis resulting in severe neurovisceral attacks and chronic complications that negatively impact patients' well-being. This study evaluated the impacts of AHP on patients' physical and emotional health from a global perspective. Adult patients from the United States, Italy, Spain, Australia, Mexico, and Brazil with AHP with >1 porphyria attack within the past 2 years or receiving intravenous hemin and/or glucose for attack prevention completed an online survey assessing demographics, health characteristics, and patient-reported outcomes. Results were analyzed collectively and by patient subgroups. Ninety-two patients with AHP across the six countries completed the survey. More than 70% of patients reported that their physical, emotional, and financial health was fair or poor. Among patients who reported pain, fatigue, and muscle weakness, 94.3%, 95.6%, and 91.4%, respectively, reported that these symptoms limited daily activities. Moderate to severe depression was present in 58.7% of patients, and moderate to severe anxiety in 48.9% of patients. Of the 47% of patients who were employed, 36.8% reported loss in productivity while at work. Among patients, 85.9% reported that they had to change or modify goals that were important to them because of AHP. Aside from differences in healthcare utilization and pain severity, scores did not significantly vary with attack rate or use of hemin or glucose prophylactic treatments. AHP substantially impacts patients' physical and emotional well-being, regardless of hemin or glucose prophylactic treatment or frequency of attacks. This multinational study demonstrates that there is substantial disease burden for patients with AHP, even among those experiencing sporadic attacks or using prophylactic treatment.

Patient Perspective on Acute Hepatic Porphyria with Sporadic Attacks: A Chronic Disease with Substantial Health-Related Quality of Life Impacts.

INTRODUCTION: Acute hepatic porphyria (AHP) is a family of rare metabolic diseases characterized by potentially life-threatening acute attacks and, in some patients, chronic debilitating symptoms. While patients with frequent or recurrent attacks (three or more attacks annually) are known to have reduced health-related quality of life (HRQoL) as most aspects of daily living are impacted, limited data exist in patients with sporadic attacks. This research aims to identify porphyria-related symptoms between attacks, characterize the frequency, severity, and bothersomeness of these symptoms, and more generally understand the burden of this disease in patients who experience attacks sporadically. METHODS: Patients with AHP with sporadic attacks (AHP-SA) (at least one porphyria attack in the past 2 years, but no more than two attacks per year in the previous 2 years) were recruited, via outreach performed by patient advocacy groups, for participation in qualitative telephone interviews. Interviews were conducted using a semi-structured guide and were audio-recorded, transcribed, anonymized, coded, and analyzed to determine if saturation was reached. RESULTS: A total of 14 participants with AHP-SA were interviewed (mean age 45 years, 100% female). The most frequently reported chronic symptoms were fatigue, pain, heartburn, and constipation. The most frequently experienced chronic impacts were difficulty performing daily activities, difficulty exercising, negative impact on work, need for a special diet, anxiety, and depression. Beyond these chronic symptoms and impacts, participants also frequently described flares in their porphyria that were severe, did not qualify in their minds as an acute attack, but were nonetheless more severe than their typical chronic experience. CONCLUSION: Patients with acute hepatic porphyria who experience sporadic attacks face significant chronic symptoms and impacts that frequently require significant pharmacological and clinical treatment. The reported severity of these symptoms and impacts suggests that the humanistic burden of AHP-SA is substantial and may lead to a significant decrease in health-related quality of life in these patients between acute attacks. The presence of flares that do not reach the level of what is considered an acute attack by patients is a unique finding of this study not reported elsewhere and requires additional investigation.

Patient and caregiver experiences of living with acute hepatic porphyria in the UK: a mixed-methods study.

BACKGROUND: This study used quantitative and qualitative research methods to analyze how acute hepatic porphyria (AHP) affects patients with varying annualized porphyria attack rates. The overall impact of AHP on patients and caregivers, including their quality of life, was explored. The nature and treatment of acute attacks, experiences of long-term heme arginate treatment and access to other appropriate treatment, and the extent of and treatment for chronic symptoms were also investigated within this study. METHODS: Patient and caregiver data were collected via an online survey of members of the British Porphyria Association, followed by an optional 1-h telephone interview. RESULTS: Thirty-eight patients and 10 caregivers responded to the survey. Of those, 10 patients and three caregivers completed follow-up interviews. Overall, 19 patients (50%) had experienced an acute attack within the previous 2 years, and the severity and types of symptoms experienced during or between acute attacks varied considerably. There were no clear definitions among patients for 'mild' or 'severe' attacks. Treatments and treatment settings used to manage attacks also varied. Following unsatisfactory care experiences at hospitals, some patients reported avoiding further hospital services for later attacks. Therefore, using settings of care as a measure of attack severity should be avoided. Ninety-four percent of patients also experienced chronic symptoms, which were as varied as acute attacks. Pain was the predominant chronic symptom and was managed with opioids in severe cases. Regardless of AAR, porphyria heavily impacted the daily lives of patients and caregivers. Although patients experiencing frequent attacks generally endured a greater impact on their daily life, patients with less frequent attacks also experienced impacts on all domains (social, leisure activities, relationship with family, relationships, psychological wellbeing, finances, employment, and study). Caregivers were most affected in the finance, relationships with family, and employment domains, and just over half of the caregivers reported a moderate impact on their psychological wellbeing. CONCLUSIONS/IMPLICATIONS: The burden of illness with AHP is high across all patients, regardless of frequency of attacks, and AHP negatively affects patients and caregivers alike.

Correction: Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

[This corrects the article DOI: 10.1371/journal.pone.0235574.].

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

BACKGROUND: With the growing adoption of the electronic health record (EHR) worldwide over the last decade, new opportunities exist for leveraging EHR data for detection of rare diseases. Rare diseases are often not diagnosed or delayed in diagnosis by clinicians who encounter them infrequently. One such rare disease that may be amenable to EHR-based detection is acute hepatic porphyria (AHP). AHP consists of a family of rare, metabolic diseases characterized by potentially life-threatening acute attacks and chronic debilitating symptoms. The goal of this study was to apply machine learning and knowledge engineering to a large extract of EHR data to determine whether they could be effective in identifying patients not previously tested for AHP who should receive a proper diagnostic workup for AHP. METHODS AND FINDINGS: We used an extract of the complete EHR data of 200,000 patients from an academic medical center and enriched it with records from an additional 5,571 patients containing any mention of porphyria in the record. After manually reviewing the records of all 47 unique patients with the ICD-10-CM code E80.21 (Acute intermittent [hepatic] porphyria), we identified 30 patients who were positive cases for our machine learning models, with the rest of the patients used as negative cases. We parsed the record into features, which were scored by frequency of appearance and filtered using univariate feature analysis. We manually choose features not directly tied to provider attributes or suspicion of the patient having AHP. We trained on the full dataset, with the best cross-validation performance coming from support vector machine (SVM) algorithm using a radial basis function (RBF) kernel. The trained model was applied back to the full data set and patients were ranked by margin distance. The top 100 ranked negative cases were manually reviewed for symptom complexes similar to AHP, finding four patients where AHP diagnostic testing was likely indicated and 18 patients where AHP diagnostic testing was possibly indicated. From the top 100 ranked cases of patients with mention of porphyria in their record, we identified four patients for whom AHP diagnostic testing was possibly indicated and had not been previously performed. Based solely on the reported prevalence of AHP, we would have expected only 0.002 cases out of the 200 patients manually reviewed. CONCLUSIONS: The application of machine learning and knowledge engineering to EHR data may facilitate the diagnosis of rare diseases such as AHP. Further work will recommend clinical investigation to identified patients' clinicians, evaluate more patients, assess additional feature selection and machine learning algorithms, and apply this methodology to other rare diseases. This work provides strong evidence that population-level informatics can be applied to rare diseases, greatly improving our ability to identify undiagnosed patients, and in the future improve the care of these patients and our ability study these diseases. The next step is to learn how best to apply these EHR-based machine learning approaches to benefit individual patients with a clinical study that provides diagnostic testing and clinical follow up for those identified as possibly having undiagnosed AHP.

Penicillin skin testing in methicillin-sensitive staphylococcus aureus bacteremia: A cost-effectiveness analysis.

BACKGROUND: Beta-lactams are the mainstay for treating methicillin-susceptible Staphylococcus aureus (MSSA) infections complicated by bacteremia due to superior outcomes compared with vancomycin. With approximately 11% of inpatients reporting a penicillin (PCN) allergy, many patients receive suboptimal treatment for MSSA bacteremia. OBJECTIVE: Evaluate the cost-effectiveness of penicillin skin testing (PST) in adult patients with self-reported PCN allergy in an inpatient setting undergoing treatment for MSSA bacteremia. METHODS: A decision analytic model was developed comparing an acute care PST intervention to a scenario with no confirmatory allergy testing. The primary outcome was the incremental cost-effectiveness ratio (ICER) from the health-sector perspective over a 1-year time horizon using quality-adjusted life years (QALYs) as the measure for effectiveness. One-way and probabilistic sensitivity analyses were conducted to assess the uncertainty of the ICER estimation. RESULTS: Over a 1-year time horizon, PST services applied to all MSSA bacteremia patients reporting a PCN-allergy would result in a cost per patient of $12,559 and 0.73 QALYs while no PST services would have a higher cost per patient of $13,219 and 0.66 QALYs per patient. This resulted in a cost-effectiveness estimate of -$9,429 per QALY gained. Varying the cost of implementing PST services determined a break-even point of $959.98 where any PST cost less than this amount would actually be cost saving. CONCLUSIONS: Patients reporting a PCN allergy on admission may receive sub-optimal alternative therapies to beta-lactams, such as vancomycin, for MSSA bacteremia. This economic analysis demonstrates that inpatient PST services confirming PCN allergy are cost-effective for patients with MSSA bacteremia.