Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach

Background Early accurate diagnosis and risk assessment for malaria are crucial for improving patients’ terminal prognosis and preventing them from progressing to a severe or critical stage. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteristics and the factors that affect the accuracy in the context of the agenda for a world free of malaria. Methods A retrospective study was conducted on 494 patients admitted to hospitals with a diagnosis of malaria from January 2014 through December 2016. Descriptive statistics were calculated, and decision tree analysis was performed to predict the probability of patients who may be misdiagnosed. Results Of the 494 patients included in this study, the proportions of patients seeking care in county-level, prefecture-level and provincial-level hospitals were 27.5% (n = 136), 26.3% (n = 130) and 8.3% (n = 41), respectively; the proportions of patients seeking care in clinic, township health centre and Centres for Disease Control and Prevention were 25.9% (n = 128), 4.1% (n = 20), and 7.9% (n = 39), respectively. Nearly 60% of malaria patients were misdiagnosed on their first visit, and 18.8% had complications. The median time from onset to the first visit was 2 days (IQR: 0-3 days), and the median time from the first visit to diagnosis was 3 days (IQR: 0–4 days). The decision tree classification of malaria patients being misdiagnosed consisted of six categorical variables: healthcare facilities for the initial diagnosis, time interval between onset and initial diagnosis, region, residence type, insurance status, and age. Conclusions Insufficient diagnostic capacity of healthcare facilities with lower administrative levels for the first visit was the most important risk factor in misdiagnosing patients. To reduce diagnostic errors, clinicians, government decision-makers and communities should consider strengthening the primary care facilities, the time interval between onset and initial diagnosis, residence type, and health insurance status.

Variation between Initial and Final Diagnosis as A Predictor of Adverse Obstetric Outcome among Post-Natal Women at Bungoma Hospitals, Kenya

Despite diagnosis being an important part of clinical or medical consultations, the diagnosis might fail leading to adverse effects. This is a global problem, where developed and developing countries go through. In sub-Saharan Africa, variations between initial diagnosis and final diagnosis lead to diagnostic errors with high maternal mortalities. In Kenya, a lot of measures have been put in place but still, variation in diagnosis appear to have become rampant. Bungoma county is one of the counties with a high mortality ratio, especially for pregnant women attributed to the variation between initial and final diagnosis. Therefore, it was crucial to investigate the variation between initial and final diagnosis in relation to obstetric outcomes at hospitals in Bungoma County. The cross-sectional research design was used (Bungoma and Webuye hospitals). Systematic sampling was used to obtain 384 respondents after proportionate allocation to each hospital, and purposive sampling to select 8 health care workers as key informants. Data was collected using a structured questionnaire and an interview guide. The pre-test was done with validity established through crosschecking and reliability calculated using the Cronbach method (0.89). Using a statistical package for social sciences version 25, descriptive and inferential statistics was run where chi-square and odds ratio was used to determine the influence between variables, significance and prediction. The study revealed a variation between initial diagnosis and the final diagnosis was 20.8% while diagnostic errors were significant predictors of obstetric outcomes among post-natal mothers at level five with a p-value of 0.045 at a significance of 5%(P=0.045). Demographic characteristics showed no relationship with obstetric outcomes (P=0.54>0.05). Matched diagnostic had no variations (N=327, M=1.00, SD=0.000); while unmatched diagnostic had variations (N=327, M=1.82, SD=.384). There was a relationship between diagnostic errors and obstetric outcome (ꭓ2 (1) = 251.86, p< .001). An association between diagnostic error with unsafe obstetric outcomes was significant at the odds ratio of 2.03(OR 2.03, 95% CI 1.31–2.16). The study demonstrates that a correct diagnosis is a viable strategy in preventing unsafe obstetric outcomes and by extension minimizing morbidity and mortality among pregnant women. The study concluded that there was a variation between initial diagnosis and final diagnosis which had an adverse obstetric outcome. it was recommended to build capacities for the health workers in order to address increased diagnostic errors.

Affective influences on clinical reasoning and diagnosis: insights from social psychology and new research opportunities

Psychological research consistently demonstrates that affect can play an important role in decision-making across a broad range of contexts. Despite this, the role of affect in clinical reasoning and medical decision-making has received relatively little attention. Integrating the affect, social cognition, and patient safety literatures can provide new insights that promise to advance our understanding of clinical reasoning and lay the foundation for novel interventions to reduce diagnostic errors and improve patient safety. In this paper, we briefly review the ways in which psychologists differentiate various types of affect. We then consider existing research examining the influence of both positive and negative affect on clinical reasoning and diagnosis. Finally, we introduce an empirically supported theoretical framework from social psychology that explains the cognitive processes by which these effects emerge and demonstrates that cognitive interventions can alter these processes. Such interventions, if adapted to a medical context, hold great promise for reducing errors that emerge from faulty thinking when healthcare providers experience different affective responses.

Levamisole-Induced Leukoencephalopathy in Russia: Analysis of 30 Cases

Aims: To raise medical specialists’ awareness regarding the severity of possible complications of levamisole administration and demonstrate the role of accurate medical history collection in differential diagnosis. Background: Levamisole, an anthelmintic drug with immunomodulatory effects, has long been used worldwide till early 2000s, when its association with demyelinating leukoencephalopathy was established. However, in the developing countries it is still widely used for prevention and treatment of helminthic invasion in humans. Actual prevalence of levamisole-induced multiple inflammatory leukoencephalopathy (LEV-induced MIL) in Russia remains unknown, and therefore, the study of its frequency and characteristics is indisputably important. Objectives: To determine the clinical features and MRI findings of levamisole-induced MIL in the Russian population and to analyse the frequency of diagnostic errors at the initial assessment. Methods: A single-center retrospective analysis of total 30 patients who were diagnosed with LEV-induced MIL and attended Research Center of Neurology was conducted. Inclusion criteria were 1) clinically: acute or subacute polysymptomatic onset of neurological disturbances, 2) MRI: multifocal demyelinating lesion with no evidence of dissemination in time, 3) anamnestic data: levamisole exposure from 2 to 8 weeks before symptoms onset as well as monophasic disease course (absence of relapses according to follow up assessments up to 3 years). Results: Clinically, presentation with constitutional symptoms, including headache, fever, fatigue and myalgia, focal motor disturbances and dysarthria prevailed in our cohort. On the brain MRI, multiple foci of demyelination with simultaneous gadolinium enhancement were observed. The link between neurological symptoms and levamisole intake has often been detected only during follow-up assessments. Patients were most often misdiagnosed with acute disseminated encephalomyelitis, stroke and multiple sclerosis. In most cases LEV-induced MIL was successfully treated with intravenous corticosteroids and/or plasma exchange (PLEX), however, residual neurologic symptoms preserved in some patients. Additionally, two detailed clinical cases of patients being initially misdiagnosed are presented in the article. Conclusion: The differential diagnosis remains difficult for suspected cases of LEV-induced MIL that could lead to delayed therapy initiation, and consequently incomplete recovery. Growing evidence suggests that a single administration of levamisole even in low doses might potentially lead to severe neurological deficit or death. Therefore, changes in medication management policies are required in order to prevent uncontrolled use of levamisole.

Explainable analytics: Understanding causes, correcting errors, and increasingly achieving perfect accuracy from nature of distinguishable patterns

In addition to pursue accurate analytics, it is invaluable to clarify how and why inaccuracy exists. We propose a transparent classification method (TC). In training, we discover patterns from positive and negative observations respectively; next, patterns are excluded if they appear in both types. In testing, observations are scored by the pure patterns and connected like social networks. Based on set theory, pure patterns have explanatory power for distinguishing tangled relationship between negative and positive observations. Experimental results demonstrate that TC can identify all positive (e.g., malignant) observations at low ratios of training to testing, e.g., 1:9 in Breast Cancer Wisconsin (Original) and 3:7 in Contraceptive Method Choice dataset. Without fine-tuned parameters and random selection, TC eliminates uncertainty of the methodology. TC can visualize causes, and therefore, prediction errors are traceable and can be corrected. Further, TC shows potential of identifying whether the ground truth is incorrect (e.g., diagnostic errors).

Pocus as a tool to avoid diagnostic errors in covid-19 era

X-ray flips and wrong labelling has been uncomfortably common, and often results in wrong side intervention. Wrong side surgery is indeed the most dramatic and visible form of human errors. Until the 1999 Institute of Medicine report ‘To Err is Human’, the medical fraternity was largely unaware of such preventable medical errors and near misses. We herein, describe a case where X-ray flip and mislabelling led to wrong side intervention in a COVID-19 ICU. Active errors in human performance are inevitable while practising medicine in the current COVID-19 pandemic where difficulties in performing comprehensive systemic examination with the protective gear on, long working hours, work stress, emotions, and fatigue interplay with the errors in technology and increase the chances of errors. We propose the use of point of care ultrasound (POCUS) in COVID-19 ICU’s to aid in the diagnosis and management.

Prevalence of curable STIs and bacterial vaginosis during pregnancy in sub-Saharan Africa: a systematic review and meta-analysis

ObjectiveSTIs remain a global public health problem with a high burden among pregnant women. STIs in pregnant women may lead to various adverse pregnancy outcomes. In most sub-Saharan African countries, syndromic management is used for screening and treatment of STIs. We aimed to update and summarise pooled prevalence of curable STIs and bacterial vaginosis (BV) among pregnant women in sub-Saharan Africa.MethodsElectronic databases and reference lists of relevant published and unpublished studies were searched from March 2015 to October 2020. Studies were included if they estimated prevalence of Chlamydia trachomatis (CT), Trichomonas vaginalis (TV), Neisseria gonorrhoeae (NG), Treponema pallidum (syphilis), Mycoplasma genitalium (MG) and BV among pregnant women in sub-Saharan Africa. Meta-analyses were performed with observed prevalences corrected for diagnostic errors to estimate the pooled prevalence of diagnosed infections by region.ResultsA total of 48 studies met the inclusion criteria, providing 85-point prevalence estimates for curable STIs and BV. Pooled prevalence estimates (with 95% CI and number of women tested) were as follows: MG: 13.5% (4.0–27.2, n=1076); CT: 10.8% (6.9–15.5, n=6700); TV: 13.8% (10.0–18.0, n=9264); NG: 3.3% (2.1–4.7, n=6019); syphilis: 2.9% (2.0–4.0, n=95 308) and BV: 36.6% (27.1–46.6, n=5042). By region, BV was the most prevalent and ranged from 28.5% (24.5–32.8, n=1030) in Eastern Africa to 52.4% (33.5–70.9, n=2305) in Southern Africa; NG had the lowest prevalence, ranging from 1.4% (95% CI 0.1 to 3.1, n=367) in Central Africa to 4.4% (95% CI 2.6 to 6.4, n=4042) in Southern Africa.ConclusionThe prevalence of curable STIs and BV in sub-Saharan Africa is substantial in pregnant women but most prevalent in Southern Africa where HIV prevalence is highest. It is crucial to integrate screening of curable STIs into antenatal care programmes that have previously focused on diagnosis and treatment of syphilis and HIV.

Research of diagnostic models of subsystems of power supply of radioelectronic means

The complex indicator of the reliability of electronic means - the readiness factor - significantly depends on the average recovery time. At the same time, the largest labor costs are spent by repair specialists on finding a faulty element. Diagnostic repair support depends on the models used in the development of defect detection algorithms. The most common use of diagnostic models in the form of a graph of information and energy connections, which consists of three types of structures: sequential connection of elements, converging and diverging. The latter did not receive the necessary research. In the article as a result of research of influence of forms of the graph of information and power communications on indicators of quality of diagnostic maintenance of subsystems of power supply of radio electronic means analytical dependences of quantitative estimation of the controlled variables on average recovery time are received for the first time. This allows to improve the quality of diagnostic support of existing and promising samples during their design. Minimization of diagnostic errors makes it possible to verify the feasibility of using diagnostic and metrological support during the current repair of electronic devices by the aggregate method, which reduces the recovery time, especially in the field. Depending on the volume of initial data, possible methods for quantifying the probability of the preferred choice of branches of power subsystems of radio electronic means, which also reduces the average recovery time by checking primarily the least reliable and do not require much time to perform checks and troubleshooting. The obtained results should be used in improving the diagnostic and metrological support of power supply subsystems of existing electronic devices and its development for promising samples in order to improve the quality of maintenance, regardless of the structure of choice.