Risperidone-Induced Neutropenia in a Schizophrenic Patient: A Case Report and Literature Review

Neutropenia is an adverse effect of various pharmacological therapies, including antipsychotics. Among the second-generation antipsychotic (SGA) medications, clozapine is most notable for neutropenic adverse effect. Risperidone, another SGA drug, is linked mainly with metabolic adverse effects, but rarely, blood dyscrasia adverse reactions have been reported. Hence, we report the case of a 56-year-old African American woman who developed severe neutropenia following two weeks of oral risperidone treatment. Her neutrophil levels returned to normal limits following discontinuation of risperidone and switching to haloperidol.

Hypocupraemia-induced anaemia, sensory ataxia and cognitive impairment secondary to zinc-containing dental adhesive

A 67-year-old man presented with 5 months of worsening memory impairment and sensory gait ataxia on the background of symptomatic anaemia. He experienced falls, agitation and became socially withdrawn over 3 weeks, resulting in hospital admission. On examination, he had sensory gait ataxia consistent with a dorsal column syndrome. He scored 13/30 on the Montreal Cognitive Assessment. Serum analysis showed normocytic anaemia and leucopenia, severe hypocupraemia, reduced caeruloplasmin and normal zinc levels. Overuse of zinc-containing denture cream was the cause of excess zinc ingestion and resultant copper deficiency, leading to blood dyscrasia and myelopathy. The cream was withdrawn and intravenous and then oral copper supplementation was implemented. Direct questions with regard to excess zinc in the diet and serological testing of copper and zinc should be considered in any patient with a dorsal column syndrome, particularly with concurrent anaemia. Copper deficiency may also have a role in exacerbating pre-existing cognitive impairment.

The haematological side effects of clozapine: literature review and meta-analysis

AimsThis review and meta-analysis aim to estimate the cumulative incidence of clozapine induced agranulocytosis and leukopenia the impact of the associated factors such as dose of clozapine, duration of follow-up, gender and race on the cumulative incidence.BackgroundClozapine is the only medication licensed for treatment-resistant schizophrenia. There has been a renewed interest in the role of Clozapine in the treatment of Schizophrenia based on strong evidence that favours its efficacy and safety. Despite the evidence that Clozapine has superior efficacy and has been recommended for treatment-resistant cases by the national guidelines, the drug is underutilised.MethodWe included all studies in which clozapine was used for a psychotic illness. We included studies which provided data on two primary indices; Leucopenia or agranulocytosis and neutropenia; defined according to the cut off used by CPMS for total WBC and neutrophil count. Additionally we included studies reporting another blood dyscrasia or death due to agranulocytosis. Studies were identified by searching AMED, BIOSIS, CINAHL, EMBASE, MEDLINE, PsycINFO, PubMed, and registries of Clinical Trials and their monthly updates, hand searches, gray literature, and conference proceedings from the first available date until 2nd February, 2015. The search was updated on 15th March, 2017. The Protocol was initiated and then registered with PROSPERO International prospective register of systematic reviews University of York, Centre for Reviews and Dissemination.ResultThe cumulative incidence of the agranulocytosis in all studies was 00.32 % (CI 00.1-0.63). The cumulative incidence in all studies for different types of blood dyscrasia were following: leucopenia 00.96 % (CI 0.39-1.70), neutropenia 2.93 % (CI 1.49-4.72), other blood dyscrasias 4.64% (CI 2.34-7.52) and any blood dyscrasia was 2.23 (CI 1.46-3.12).ConclusionThe limitations of this review are mainly due to the nature of evidence from the included studies. We adopted a broad inclusion criteria to include all the available evidence. Number of patients started on Clozapine may be withdrawn from the Clozapine on the earliest signs of blood dyscrasias since the introduction of Clozapine monitoring services. This means that the true incidence of agranulocytosis and neutropenia may be higher and this may be a major bias in finding the true incidence of Clozapine induced neutropenia.

Laboratory Testing in Psychiatry

We review common laboratory testing encountered in psychiatric practice. It seems likely that in areas where the most evidence exists driving laboratory testing (e.g., metabolic monitoring for people on atypical antipsychotics), testing still is not universally done, and we often do not adequately address the results. However, in areas where there is little evidence supporting the practice (extensive laboratory testing on people being admitted to a psychiatric hospitals), we order tests extensively. We cover common tests encountered in the use of antipsychotics, antidepressants, mood stabilizers, antiepileptic drugs, and lithium. We also discuss the role of thyroid, vitamin B12, and folate testing and the special circumstance of caring for those with eating disorders. This review contains 2 highly rendered figures, 20 tables, and 44 references. Key words: agranulocytosis, antidepressant, antiepileptic drug, antipsychotic, blood dyscrasia, clozapine, eating disorders, metabolic monitoring, QT interval, urine drug screen

70 Deep learning to diagnose cardiac amyloidosis from cardiac magnetic resonance findings

Aims Cardiac magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA. Methods and results 1.5 T CMR was performed in 187 subjects with suspected CA (n = 92, 49% with unexplained left ventricular—LV—hypertrophy; n = 95, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients were randomly assigned to the training (n = 121, 65%), validation (n = 28, 15%), and testing subgroups (n = 38, 20%). Short axis (SA), 2-chamber (2 C), 4-chamber (4 C) late gadolinium enhancement (LGE) images were evaluated by 3 networks (DL algorithms). The tags “amyloidosis present” or “absent” were attributed when the average probability of CA from the 3 networks was ≥50% or < 50%, respectively. The DL strategy was compared to a machine learning (ML) algorithm considering all manually extracted features (LV volumes, mass and function, LGE pattern, early blood-pool darkening, pericardial and pleural effusion, etc.), to reproduce exam reading by an experienced operator. The DL strategy displayed good diagnostic accuracy (84%), with an area under the curve (AUC) of 0.96. The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 78%, 94%, and 86% respectively. A ML algorithm considering all CMR features had a similar diagnostic yield to DL strategy (AUC 0.93 vs. 0.96; p = 0.45). Conclusion A DL approach evaluating LGE acquisitions displayed a similar diagnostic performance for CA to a ML-based approach, which simulates CMR reading by experienced operators.

Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance

Background Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA. Methods 1.5 T CMR was performed in 206 subjects with suspected CA (n = 100, 49% with unexplained left ventricular (LV) hypertrophy; n = 106, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients were randomly assigned to the training (n = 134, 65%), validation (n = 30, 15%), and testing subgroups (n = 42, 20%). Short axis, 2-chamber, 4-chamber late gadolinium enhancement (LGE) images were evaluated by 3 networks (DL algorithms). The tags “amyloidosis present” or “absent” were attributed when the average probability of CA from the 3 networks was ≥ 50% or < 50%, respectively. The DL strategy was compared to a machine learning (ML) algorithm considering all manually extracted features (LV volumes, mass and function, LGE pattern, early blood-pool darkening, pericardial and pleural effusion, etc.), to reproduce exam reading by an experienced operator. Results The DL strategy displayed good diagnostic accuracy (88%), with an area under the curve (AUC) of 0.982. The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 83%, 95%, and 89% respectively. A ML algorithm considering all CMR features had a similar diagnostic yield to DL strategy (AUC 0.952 vs. 0.982; p = 0.39). Conclusions A DL approach evaluating LGE acquisitions displayed a similar diagnostic performance for CA to a ML-based approach, which simulates CMR reading by experienced operators.

Deep learning to diagnose cardiac amyloidosis from CMR findings

Background Cardiac magnetic resonance (CMR) is an important diagnostic technique for cardiac amyloidosis (CA). A deep learning (DL) approach to define the likelihood of CA based on automated interpretation of CMR images has never been attempted so far. Methods 187 subjects underwent standard 1.5 T CMR examination (GE-Healthcare, Milwaukee, USA) as part of a diagnostic workup for either unexplained left ventricular hypertrophy or blood dyscrasia with suspected light-chain (AL) amyloidosis. Patients were randomly assigned to 3 subgroups, which were used for training (n=121, 65%), internal validation (n=28, 15%), and model testing (n=38, 20%). LGE images in different orientations (short-axis, 2- and 4-chambers) were selected as the most informative CMR features. A deep convolutional neural network was trained to classify CMR examinations as “amyloidosis” (probability ≥50%) or “no amyloidosis” (probability &lt;50%) based on these features. Different learning strategies (data augmentation, batch normalization in convolutional layers, dropout before dense layers) were adopted to prevent model overfitting. Binary cross entropy was used as loss function. For comparison, a machine learning (ML) model based on gradient boosting trees was built for the binary classification of patients (amyloidosis vs no amyloidosis) based on clinical and imaging features extracted from the CMR exam. Results CA was diagnosed in 101 subjects (54%; 45 AL, 56 transthyretin amyloidosis). A model including 2C, 4C and SA LGE images was created. In the test cohort, it allowed to diagnose CA with good diagnostic accuracy (84.2%), and an area under the curve (AUC) of 0.96 (Figure). The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 0.78, 0.94, and 0.86, respectively. An ML algorithm considering all available parameters (LV volumes and function, LGE presence and pattern, early darkening, pericardial and pleural effusion, etc.) displayed a similar diagnostic performance than the DL method (AUC 0.93 vs. 0.96; p=0.45). Conclusions The deep learning technique allowed to create an accurate diagnostic tool for CA based on LGE patterns, which could be easily converted into an online platform for automated image analysis. Funding Acknowledgement Type of funding source: None

Two common echocardiographic variables to diagnose cardiac amyloidosis: the AMYLoidosis Index (AMYLI) score

Background Early diagnosis of cardiac amyloidosis (CA) is warranted to initiate specific treatment and improve outcome. The amyloid light chain (AL) and inferior wall thickness (IWT) scores have been proposed to assess patients referred by hematologists or with unexplained left ventricular (LV) hypertrophy, respectively. These scores are composed of 4 or 5 variables, respectively, including strain data, and no decisional cut-offs were introduced. Methods Based on 2 variables common to the AL and IWT scores, we defined a simple score named AMYLoidosis Index (AMYLI) as the product of relative wall thickness (RWT) and E/e' ratio, and assessed its diagnostic performance. Optimal rule-out cut-offs were searched as those with negative likelihood ratio (LR−) &lt;0.1. Results In the derivation cohort (n=251), CA was ultimately diagnosed in 111 patients (44%). The 2.22 score value was selected as rule-out cut-off (LR- 0.0). In the hematology subset, AL CA was finally diagnosed in 32 patients (48%), with 2.36 as rule-out cut-off (LR− 0.0). In the hypertrophy subset, ATTR CA was diagnosed in 79 patients (43%), with 2.22 as best rule-out cut-off (LR− 0.0). In the validation cohort (n=691), where more patients were diagnosed with CA (94% and 68% in the hematology and in the hypertrophy subsets, respectively), the 2.22 rule-out cut-off had a LR− = ∞ (as no patient scoring &lt;2.22 had CA). In the hematology and hypertrophy subsets, the 2.36 and 2.22 cut-offs were effective for ruling-out CA, with both LR− = ∞ (as no patient scoring &lt;2.36 or 2.22, respectively, had CA). Conclusions The AMYLI score (RWT* E/e') is simpler than those proposed and similarly accurate. A 2.22 cut-off value excludes CA diagnosis in patients undergoing a diagnostic screening for CA, while a &lt;2.36 and a &lt;2.22 value may be better considered in the subsets with either blood dyscrasia or unexplained hypertrophy, respectively. Funding Acknowledgement Type of funding source: None

Cutaneous Pedal Manifestations in Patient With Hepatitis C Virus–Associated Cryoglobulinemia: Case Report and Review of Literature

Cryoglobulinemia is an uncommon blood dyscrasia that can manifest itself in the lower extremity. Due to the insidious nature of this disease, dermatological symptoms and ulcerations can easily be mistaken for more common entities. The authors present an overview of cryoglobulinemia and a case report of a patient with lower extremity manifestations of this disorder. This can provide specific guidance on the steps necessary to accurately establish the diagnosis of cryoglobulinemia or rule it out and pursue other etiologies causing lower extremity ulceration.