Electronic health record signatures identify undiagnosed patients with common variable immunodeficiency disease.

Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases and uses this knowledge to rank patients by likelihood of having CVID. PheNet could have diagnosed more than half of our patients with CVID 1 or more years earlier than they had been diagnosed. When applied to a large EHR dataset, followed by blinded chart review of the top 100 patients ranked by PheNet, we found that 74% were highly probable to have CVID. We externally validated PheNet using >6 million records from disparate medical systems in California and Tennessee. As artificial intelligence and machine learning make their way into health care, we show that algorithms such as PheNet can offer clinical benefits by expediting the diagnosis of rare diseases.

Prevalence of Diagnosed and Undiagnosed Depression Among US Adults with Human Immunodeficiency Virus: Data from the Medical Monitoring Project.

People with human immunodeficiency virus (PWH) are disproportionately affected by depression, but the recent national estimates for US PWH encompassing both current symptoms and clinical diagnoses to assess missed diagnoses and lack of symptom remission are lacking. We used data from CDC's Medical Monitoring Project (MMP) to report nationally representative estimates of diagnosed and undiagnosed depression among US adult PWH. During June 2021 to May 2022, MMP collected interview data on symptoms consistent with major or other depression and depression diagnoses from medical records of 3928 PWH. We report weighted percentages and prevalence ratios (PRs) to quantify differences between groups on key social and health factors. Overall, 34% of PWH experienced any depression (diagnosis or Patient Health Questionnaire-8); of these, 26% had symptoms but no diagnosis (undiagnosed depression), 19% had both diagnosis and symptoms, and 55% had a diagnosis without symptoms. Among those with depression, persons with a disability (PR: 1.52) and food insecurity (PR: 1.67) were more likely to be undiagnosed. Unemployed persons (PR: 1.62), those experiencing a disability (PR: 2.78), food insecurity (PR: 1.46), or discrimination in human immunodeficiency virus (HIV) care (PR: 1.71) were more likely to have diagnosed depression with symptoms. Those with symptoms (undiagnosed or diagnosed) were less likely to be antiretroviral therapy (ART) dose adherent (PR: 0.88; PR: 0.73) or have sustained viral suppression (PR: 0.62; PR: 0.91) and were more likely to have unmet needs for mental health services (PR: 2.38, PR: 2.03). One-third of PWH experienced depression, of whom nearly half were undiagnosed or still experiencing clinically relevant symptoms. Expanding screening and effective treatment for depression could improve quality of life and HIV outcomes.

Suspected Undiagnosed ADRD Among Middle Eastern and North African Americans.

ADRD underdiagnosis among minority populations is well-established and known to be more prevalent among women. Yet, it remains unclear if these patterns exist among adults of Middle Eastern and North African (MENA) descent. We estimated ADRD underdiagnosis among adults of MENA descent and other US- and foreign-born non-Hispanic Whites and compared sex-stratified results. We linked 2000-2017 National Health Interview Survey and 2001-2018 Medical Expenditure Panel Survey data (ages > = 65 years, n = 23,981). Undiagnosed ADRD was suspected if participants reported cognitive limitations without corresponding ADRD diagnosis. Undiagnosed ADRD was highest among adults of MENA descent (15.8%) compared to non-Hispanic Whites (US-born = 8.1%; foreign-born = 11.8%). Women of MENA descent had 2.52 times greater odds (95% CI = 1.31-4.84) of undiagnosed ADRD compared to US-born White women after adjusting for risk factors. This study contributes the first national estimates of undiagnosed ADRD among adults of MENA descent. Continued research is needed to facilitate policy changes that more comprehensively address health disparities and related resource allocation.

Tertiary survey by trauma nurse specialist at a paediatric trauma centre.

INTRODUCTION: Tertiary surveys aim to detect injuries missed in the initial assessment of trauma. We introduced a process by which the trauma nurse specialist performed a number of the tertiary surveys (NTSs) at our paediatric trauma centre. METHODS: Data from the first six months following introduction of the NTS were compared to retrospective data from the six months prior to NTS implementation (pre-NTS), when trauma surveys were completed by medical staff. RESULTS: Over the 12-month period, 130 children met the criteria for a tertiary survey. Pre-NTS, 57/62 eligible patients received a tertiary survey, compared to 61/68 during NTS (p=0.77). There were significantly more road traffic crash patients in the NTS group (p=0.008) but no significant differences by demographics, injury pattern, injury severity score or outcomes. New injuries were found in three patients pre-NTS compared to five patients during NTS (odds ratio 1.3 (95%CI 1.3-2.0, p=0.73)). CONCLUSION: This study conservatively supports the hypothesis that, with training and support, a trauma nurse specialist can perform tertiary surveys as effectively as doctors. A larger study is required to confirm these findings.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

Online questionnaire on genetic testing for intractable diseases in Japan: response to and issues associated with the revised medical care act.

In Japan, most genetic testing for intractable diseases has been conducted in research laboratories in the past. However, since the Revised Medical Care Act came into effect on December 1, 2018, genetic testing in compliance with this act has become a major issue. To collect information on this topic, we conducted an online survey of members of the research groups for intractable diseases, which play a central role in medical care and research on intractable diseases with the support of the Ministry of Health, Labor and Welfare, five months after the enactment of the act. We separated the surveyed facilities into those that conducted genetic testing in their own laboratories ("testing facilities") and those that outsourced genetic testing ("outsourcing facilities"). Ninety-five and 66 responses regarding genetic testing were obtained from the testing and outsourcing facilities, respectively. Genetic analysis was the most commonly conducted genetic testing method, accounting for 60% or more of the tests. At the testing facilities that conducted comprehensive analysis with a next-generation sequencer, the number of target diseases for genetic testing was observed to be higher. In these testing facilities, more than 70% were research laboratories. In contrast, at the outsourcing facilities, testing was outsourced to registered clinical laboratories in many cases or to research laboratories. The proportion of genetic testing covered by public medical insurance at the outsourcing facilities was two times higher than that at the testing facilities. The importance of quality control for genetic testing was generally well acknowledged, but there was apprehension regarding the increased cost and burden on staff of quality control assurance, and many testing facilities viewed genetic testing as difficult. The research groups could handle the examination and interpretation of the genetic testing results, and many groups gathered and registered patient information. Within the intractable disease medical support network, there was a relatively large number of collaborations, with studies supported by the Japan Agency for Medical Research and Development (AMED) and Initiative on Rare and Undiagnosed Diseases (IRUD) projects. There were many requests for genetic testing to be covered by public medical insurance. In the future, the implementation of genetic testing using a next-generation sequencer at clinical laboratories with guaranteed quality control and the development of a system for collaboration with research groups will be necessary.

A resource of lipidomics and metabolomics data from individuals with undiagnosed diseases.

Every year individuals experience symptoms that remain undiagnosed by healthcare providers. In the United States, these rare diseases are defined as a condition that affects fewer than 200,000 individuals. However, there are an estimated 7000 rare diseases, and there are an estimated 25-30 million Americans in total (7.6-9.2% of the population as of 2018) affected by such disorders. The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Mass spectrometry-based metabolomics and lipidomics analyses could advance the collective understanding of individual symptoms and advance diagnoses for individuals with heretofore undiagnosed disease. Here, we report the mass spectrometry-based metabolomics and lipidomics analyses of blood plasma, urine, and cerebrospinal fluid from 148 patients within the UDN and their families, as well as from a reference population of over 100 individuals with no known metabolic diseases. The raw and processed data are available to the research community so that they might be useful in the diagnoses of current or future patients suffering from undiagnosed disorders.

Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network.

Importance: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Objective: To develop computational models that effectively predict admission outcomes for applicants seeking UDN evaluation and to rank the applications based on the likelihood of patient admission to the UDN. Design, Setting, and Participants: This prognostic study included all applications submitted to the UDN from July 2014 to June 2019, with 1209 applications accepted and 1212 applications not accepted. The main inclusion criterion was an undiagnosed condition despite thorough evaluation by a health care professional; the main exclusion criteria were a diagnosis that explained the objective findings or a review of the records that suggested a diagnosis. A classifier was trained using information extracted from application forms, referral letters from health care professionals, and semantic similarity between referral letters and textual description of known mendelian disorders. The admission labels were provided by the case review committee of the UDN. In addition to retrospective analysis, the classifier was prospectively tested on another 288 applications that were not evaluated at the time of classifier development. Main Outcomes and Measures: The primary outcomes were whether a patient was accepted or not accepted to the UDN and application order ranked based on likelihood of admission. The performance of the classifier was assessed by comparing its predictions against the UDN admission outcomes and by measuring improvement in the mean processing time for accepted applications. Results: The best classifier obtained sensitivity of 0.843, specificity of 0.738, and area under the receiver operating characteristic curve of 0.844 for predicting admission outcomes among 1212 accepted and 1210 not accepted applications. In addition, the classifier can decrease the current mean (SD) UDN processing time for accepted applications from 3.29 (3.17) months to 1.05 (3.82) months (68% improvement) by ordering applications based on their likelihood of acceptance. Conclusions and Relevance: A classification system was developed that may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Accelerating the admission process may improve the diagnostic journeys for these patients and serve as a model for partial automation of triaging or referral for other resource-constrained applications. Such classification models make explicit some of the considerations that currently inform the use of whole-genome sequencing for undiagnosed disease and thereby invite a broader discussion in the clinical genetics community.

Relation of Antecedent Symptoms to the Likelihood of Detecting Subclinical Atrial Fibrillation With Inserted Cardiac Monitors.

Atrial fibrillation (AF) comes to attention clinically during an evaluation of symptoms, an evaluation of its adverse outcomes, or because of incidental detection during a routine examination or electrocardiogram. However, a notable number of additional individuals have AF that has not yet been clinically apparent or suspect-subclinical AF (SCAF). SCAF has been recognized during interrogation of pacemakers and defibrillators. More recently, SCAF has been demonstrated in prospective studies with long-term monitors-both external and implanted. The REVEAL AF trial enrolled a demographically "enriched" population that underwent monitoring for up to 3 years with an insertable cardiac monitor. SCAF was noted in 40% by 30 months. None of these patients had AF known before the study; however, some had nonspecific symptoms common to patients with known AF. The current study assessed whether patients with versus without such symptoms were more likely to have SCAF detected. We found that only palpitations had an association with AF detection when controlling for other baseline symptoms (hazard ratio 1.61 (95% confidence interval 1.12 to 2.32; p = 0.011). No other prescreening symptoms evaluated were associated with an increased likelihood of SCAF detection although patients without detected SCAF had an even higher frequency of symptoms than those with detected SCAF. Thus, REVEAL AF demonstrated that the presence of palpitations is associated with an increased likelihood of SCAF whereas other common symptoms are not; and, symptoms, per se, may more likely be consequent to associated disorders than they are a direct consequence of SCAF.

Acute HIV at the Time of Initiation of Pre-exposure or Post-exposure Prophylaxis: Impact on Drug Resistance and Clinical Outcomes.

BACKGROUND: Initiating pre-exposure or post-exposure prophylaxis (PrEP/PEP) in the setting of undiagnosed acute HIV (AHI) could cause antiretroviral resistance. We sought to characterize clinical outcomes and drug resistance mutations among individuals prescribed PrEP/PEP with undiagnosed AHI at a San Francisco sexually transmitted disease clinic. SETTING: In our PrEP/PEP program, patients are tested for HIV using a point-of-care antibody test. If negative, patients are started on prophylaxis and screened for AHI using pooled HIV RNA (5-10 days turn-around). We used 2-drug PEP until 05/2016. METHODS: We identified patients who had as-yet-undiagnosed AHI on the day of PrEP/PEP start between 2011 and 2018, then used our clinical record and surveillance data to describe HIV resistance and clinical outcomes. RESULTS: Of 1758 PrEP and 2242 PEP starts, there were 7 AHI cases among PrEP users (0.40%) and 6 among PEP users (0.30%). Median times for linkage to HIV care, initiation of HIV treatment, and viral suppression were 7, 12, and 43 days. On initiation of HIV care, 3 patients (23%) were found to have an M184 mutation 7-12 days after starting PrEP/PEP. All 3 had genotyping performed on stored serum available from the date of PrEP/PEP start, each of which demonstrated wild-type virus. All 3 patients achieved durable viral suppression. CONCLUSIONS: Although rare (occurring <0.5% of the time), AHI in the setting of PrEP/2-drug PEP can result in an M184 within days. Even with M184, persons with AHI achieve viral suppression when rapidly linked to care and initiated on antiretroviral therapy. Providers should consider AHI screening when starting PrEP/PEP.

Previously Undetected Obstructive Sleep Apnea in Patients With New-Onset Atrial Fibrillation.

Obstructive sleep apnea-hypopnea syndrome (OSA) compromises the efficacy of atrial fibrillation (AF) control strategies. Continuous positive airway pressure (CPAP) may ameliorate arrhythmia control especially in early AF stages (new-onset AF). We investigated a practical screening strategy to determine the likelihood of CPAP indication in new-onset AF patients. Seventy-seven consecutive patients with new-onset (<1 month) AF were prospectively evaluated. Of them, 4 were excluded due to previously diagnosed OSA. The remaining 73 (68% persistent AF) fulfilled the Epworth, Berlin and STOP-BANG questionnaires, an ambulatory polysomnography being performed thereafter in all them in order to determine the apnea-hipopnea index (AHI). CPAP was indicated following conventional criteria. The variables associated with the diagnosis of OSA, with the AHI value and with CPAP indication were investigated by means of descriptive, univariate and multivariate analysis. The prevalence of OSA of any degree and CPAP indication was 82% and 37%, respectively. The variables associated (p < 0.05) with a higher AHI were male gender, body mass index, obesity, hypertension, and high-risk scoring at the Berlin and STOP-BANG questionnaires. In the multivariate analysis, the STOP-BANG scoring proved superior to conventional risk factors and became the only variable predicting CPAP indication (odds ratio 4.5 [1.9 to 10.6]; p = 0.01), an optimized cutoff value of ≥4 being newly established (sensitivity/specificity 76/65%). In conclusion, in patients referred with new-onset AF we documented a high risk of OSA and of need for CPAP. A STOP-BANG scoring of ≥4 in our population was a practical screening alternative to direct polysomnography in this setting.

Exome and genome sequencing in adults with undiagnosed disease: a prospective cohort study.

BACKGROUND: Exome and genome sequencing have been demonstrated to increase diagnostic yield in paediatric populations, improving treatment options and providing risk information for relatives. There are limited studies examining the clinical utility of these tests in adults, who currently have limited access to this technology. METHODS: Patients from adult and cancer genetics clinics across Toronto, Ontario, Canada were recruited into a prospective cohort study evaluating the diagnostic utility of exome and genome sequencing in adults. Eligible patients were ≥18 years of age and suspected of having a hereditary disorder but had received previous uninformative genetic test results. In total, we examined the diagnostic utility of exome and genome sequencing in 47 probands and 34 of their relatives who consented to participate and underwent exome or genome sequencing. RESULTS: Overall, 17% (8/47) of probands had a pathogenic or likely pathogenic variant identified in a gene associated with their primary indication for testing. The diagnostic yield for patients with a cancer history was similar to the yield for patients with a non-cancer history (4/18 (22%) vs 4/29 (14%)). An additional 24 probands (51%) had an inconclusive result. Secondary findings were identified in 10 patients (21%); three had medically actionable results. CONCLUSIONS: This study lends evidence to the diagnostic utility of exome or genome sequencing in an undiagnosed adult population. The significant increase in diagnostic yield warrants the use of this technology. The identification and communication of secondary findings may provide added value when using this testing modality as a first-line test.

Singapore Undiagnosed Disease Program: Genomic Analysis aids Diagnosis and Clinical Management.

OBJECTIVE: Use next-generation sequencing (NGS) technology to improve our diagnostic yield in patients with suspected genetic disorders in the Asian setting. DESIGN: A diagnostic study conducted between 2014 and 2019 (and ongoing) under the Singapore Undiagnosed Disease Program. Date of last analysis was 1 July 2019. SETTING: Inpatient and outpatient genetics service at two large academic centres in Singapore. PATIENTS: Inclusion criteria: patients suspected of genetic disorders, based on abnormal antenatal ultrasound, multiple congenital anomalies and developmental delay. EXCLUSION CRITERIA: patients with known genetic disorders, either after clinical assessment or investigations (such as karyotype or chromosomal microarray). INTERVENTIONS: Use of NGS technology-whole exome sequencing (WES) or whole genome sequencing (WGS). MAIN OUTCOME MEASURES: (1) Diagnostic yield by sequencing type, (2) diagnostic yield by phenotypical categories, (3) reduction in time to diagnosis and (4) change in clinical outcomes and management. RESULTS: We demonstrate a 37.8% diagnostic yield for WES (n=172) and a 33.3% yield for WGS (n=24). The yield was higher when sequencing was conducted on trios (40.2%), as well as for certain phenotypes (neuromuscular, 54%, and skeletal dysplasia, 50%). In addition to aiding genetic counselling in 100% of the families, a positive result led to a change in treatment in 27% of patients. CONCLUSION: Genomic sequencing is an effective method for diagnosing rare disease or previous 'undiagnosed' disease. The clinical utility of WES/WGS is seen in the shortened time to diagnosis and the discovery of novel variants. Additionally, reaching a diagnosis significantly impacts families and leads to alteration in management of these patients.

Prevalence and correlates of diagnosed and undiagnosed epilepsy and migraine headache among people with severe psychiatric disorders in Ethiopia.

BACKGROUND: There is a paucity of research on the prevalence of diagnosed as well as undiagnosed neurological disorders with episodic manifestations such as epilepsy and migraine headaches in people with severe psychiatric disorders (SPD). To the best of our knowledge, this is the first study analyzing and comparing the prevalence of diagnosed and undiagnosed chronic neurological disorders with episodic manifestations including epilepsy and migraine headache in people with SPD. METHOD: This quantitative cross-sectional survey was undertaken among 309 patients with SPD selected by a systematic random sampling technique. The Structured Clinical Interview for DSM-IV Axis I Disorders (SCID) was used to confirm SPD among the participants. The International Classification of Headache Disorders (ICHD-3) and International League Against Epilepsy (ILAE) were used to define migraine headache and epilepsy, respectively]. Risk factors for chronic neurologic disorders were explored by using logistic regression models. RESULT: In this study, the prevalence of overall neurological disorders, epilepsy, and migraine headache among people with SPD were found to be 5.2% (95%CI 3.2-8.3), 1.6% (95%CI 0.7-3.9), and 3.9% (95%CI 2.2-6.7), respectively. We found that a considerably higher proportion of people with SPD had undiagnosed overall neurological disorder (87.5%; 14/16), epilepsy (60%; 3/5), as well as migraine headaches (100%; 12/12). On the other hand, in this study, 12.5%, 40%, and 0% of patients with overall neurologic disorder, epilepsy, and migraine headaches respectively were diagnosed by the professionals. Higher disability score (WHODAS score) was associated with increased odds of having neurological disorders compared with the lower WHODAS score [OR = 1.30 (95% CI 1.02-1.66)]. CONCLUSION: Whilst the prevalence estimates of neurological disorders with episodic manifestations including epilepsy and migraine headache was high among people with SPD, the vast majority of them remained undiagnosed. The diagnosis rates of those disorders were significantly low, perhaps surprisingly zero for migraine headache. High WHODAS score was associated with increased odds of having neurological disorders. Routine screening and management of epilepsy and migraine headache are imperative among people with SPD.

Prevalence and Characteristics of Undiagnosed COPD in Adults 40 Years and Older - Reports from the Tunisian Population-Based Burden of Obstructive Lung Disease Study.

This study aimed to investigate the underdiagnosis of COPD and its determinants based on the Tunisian Burden of Obstructive Lung Disease study. We collected information on respiratory history symptoms and risk factors for COPD. Post-bronchodilator (Post-BD) FEV1/FVC < the lower limit of normal (LLN) was used to define COPD. Undiagnosed COPD was considered when participants had post-BD FEV1/FVC < LLN but were not given a diagnosis of emphysema, chronic bronchitis or COPD. 730 adults aged ⩾40 years selected from the general population were interviewed, 661 completed spirometry, 35 (5.3%) had COPD and 28 (80%) were undiagnosed with the highest prevalence in women (100%). When compared with patients with an established COPD diagnosis, undiagnosed subjects had a lower education level, milder airway obstruction (Post-BD FEV1 z-score -2.2 vs. -3.7, p < 0.001), fewer occurrence of wheezing (42.9% vs. 100%, p = 0.009), less previous lung function test (3.6% vs. 42.8%, p = 0.019) and less visits to the physician (32.1% vs. 85.7%, p = 0.020) in the past year. Multivaried analysis showed that the probability of COPD underdiagnosis was higher in subjects who had mild to moderate COPD and in those who did not visit a clinician and did not perform a spirometry in the last year. Collectively, our results highlight the need to improve the diagnosis of COPD in Tunisia. Wider use of spirometry should reduce the incidence of undiagnosed COPD. Spirometry should also predominately be performed not only in elderly male smokers but also in younger women in whom the prevalence of underdiagnosis is the highest.

Missed diagnoses: Clinically relevant lessons learned through medical mysteries solved by the Undiagnosed Diseases Network.

BACKGROUND: Resources within the Undiagnosed Diseases Network (UDN), such as genome sequencing (GS) and model organisms aid in diagnosis and identification of new disease genes, but are currently difficult to access by clinical providers. While these resources do contribute to diagnoses in many cases, they are not always necessary to reach diagnostic resolution. The UDN experience has been that participants can also receive diagnoses through the thoughtful and customized application of approaches and resources that are readily available in clinical settings. METHODS: The UDN Genetic Counseling and Testing Working Group collected case vignettes that illustrated how clinically available methods resulted in diagnoses. The case vignettes were classified into three themes; phenotypic considerations, selection of genetic testing, and evaluating exome/GS variants and data. RESULTS: We present 12 participants that illustrate how clinical practices such as phenotype-driven genomic investigations, consideration of variable expressivity, selecting the relevant tissue of interest for testing, utilizing updated testing platforms, and recognition of alternate transcript nomenclature resulted in diagnoses. CONCLUSION: These examples demonstrate that when a diagnosis is elusive, an iterative patient-specific approach utilizing assessment options available to clinical providers may solve a portion of cases. However, this does require increased provider time commitment, a particular challenge in the current practice of genomics.

Cochlear implantation in children under 12 months: Prevalence and implications of 'hidden' disabilities.

Introduction: While cochlear implants (CI) prior to 12 months of age have become common, the prevalence and impact of issues that either arise or were not evident prior to implantation is unknown. Methods: Retrospective chart review of children implanted under 12 months of age with minimum 3 years follow up. The children were divided into three groups: those with no identified additional disabilities, those with no known disabilities at time of implantation but diagnosed with additional disabilities following implantation, and those that had known anticipated additional disabilities at time of implantation. Results: 108 children under the age of 12 months were implanted at our Center between 2000 and 2013 with an average age of 9 months at time of implantation and n = 93 met inclusion criteria. In 79.6% (74/93) of children, there were no additional issues detected. In 11.8% (11/93), additional issues were known at the time of implantation while in 8.6% (8/93) of the children were diagnosed with additional issues that were not evident prior to implantation. The auditory and linguistic benefits vary commensurate with the severity of their disabilities. Those with anticipated issues preoperatively did not perform as well. Conclusions: Children implanted below one year of age but diagnosed with additional disabilities following implantation obtained substantial though varying degrees of benefit. In none of these cases would knowledge of the disability have altered the decision to offer early CI. It is important to address these potential issues when counseling families about outcomes.

Use of dental practices for the identification of adults with undiagnosed type 2 diabetes mellitus or non-diabetic hyperglycaemia: a systematic review.

AIM: Type 2 diabetes is a growing global challenge. Evidence exists demonstrating the use of primary care (non-hospital based) dental practices to identify, through risk assessments, those who may be at increased risk of type 2 diabetes or who may already unknowingly have the condition. This review aimed to synthesize evidence associated with the use of primary care dental services for the identification of undiagnosed non-diabetic hyperglycaemia or type 2 diabetes in adults, with particular focus on the pick-up rate of new cases. METHOD: Electronic databases were searched for studies reporting the identification of non-diabetic hyperglycaemia/type 2 diabetes in primary care dental settings. Returned articles were screened and two independent reviewers completed the data-extraction process. A descriptive synthesis of the included articles was undertaken due to the heterogeneity of the literature returned. RESULTS: Nine studies were identified, the majority of which utilized a two-stage risk-assessment process with risk score followed by a point-of-care capillary blood test. The main barriers cited were cost, lack of adequate insurance cover and people having previously been tested elsewhere. The pick-up rate of new cases of type 2 diabetes and non-diabetic hyperglycaemia varied greatly between studies, ranging from 1.7% to 24% for type 2 diabetes and from 23% to 45% for non-diabetic hyperglycaemia, where reported. CONCLUSION: This review demonstrates that although it appears there may be benefit in using the dental workforce to identify undiagnosed cases of non-diabetic hyperglycaemia and type 2 diabetes, further high-quality research in the field is required assessing both the clinical and cost effectiveness of such practice. (Prospero Registration ID: PROSPERO 2018 CRD42018098750).

Engaging Community Pharmacies in Early Detection of Missing Tuberculosis Patients through Public-Private Mix Intervention in Pakistan.

Globally, Pakistan ranks fifth in terms of missing tuberculosis (TB) patients' burden. Missed TB cases are either undiagnosed or diagnosed but not notified to the national TB database. Public-private mix interventions are contributing significantly to the case detection, diagnosis, and treatment of TB in Pakistan. However, it is estimated that many cases of infected TB patients go undetected. It is likely that these "undiagnosed" active TB cases seek treatment from community pharmacies, among other venues. This study aimed at assessing the feasibility of community pharmacy-based TB case detection. Case detection protocol implementation in three Pakistani districts in a nonrandom selection of pharmacies was followed by a review of routinely maintained prospective records of patients referred from these private community pharmacies to general practitioner (GP) clinics. The study engaged 500 community pharmacies for referring presumptive TB patients to GP clinics. In total, 85% of the engaged pharmacies remained active in providing referrals during the study period. The community pharmacy-referral network achieved an annual referral rate of 3,025 presumptive TB patients and identified 547 active TB cases for the period January-December 2017. Every fifth referral among presumptives presenting and counseled at pharmacies was diagnosed with TB at GP clinics. This contribution was 9% of all new TB cases identified in these districts through all other private venues linked with the Greenstar Social Marketing setup. Identified barriers and facilitators to implementation and cost effectiveness of pharmacy models for TB case detection should be considered if the model were to be scaled up.