Nevin Manimala

Niger Postgrad Med J. 2023 Jul-Sep;30(3):200-209. doi: 10.4103/npmj.npmj_80_23.

ABSTRACT

BACKGROUND: Kidney diseases requiring haemodialysis are becoming an emerging public health problem. This study aimed to determine the burden, socio-demographic and other risk factors associated with haemodialysis in Northwest Nigeria.

METHODOLOGY: The study was a retrospective multicentre review of secondary data from the dialysis centres of public hospitals in northwestern Nigeria. Risk factors for renal failure were examined in 1329 patients who were enrolled for haemodialysis between 2019 and 2022 in the four public dialysis centres in Kano and Jigawa state. Data were analysed using IBM SPSS Statistics for Windows, version 22.0, with statistical significance set at P ≤ 0.05.

RESULTS: The minimum age of the patients was 2 years and the maximum was 100 years with a median of 48 (interquartile range = 31, 60) years. Chronic kidney disease (CKD) was found amongst more than two-thirds 998 (75.1%) of the patients. Up to 51 (3.8%), 62 (4.7%) and 10 (0.8%) were seropositive for HIV, hepatitis B and hepatitis C, respectively. Acute kidney injury (AKI) was significantly higher (67.9%, P < 0.001) amongst patients with post-partum haemorrhage (PPH), and were 24 times more likely to develop AKI when compared with those with no PPH (adjusted odds ratio [aOR] =24, 95% confidence interval [CI] = [13.5-44.5]). CKD was significantly higher amongst patients with hypertension (HTN) (84.9%, P < 0.001), and were 3.2 times more likely to develop CKD compared with non-hypertensives (aOR = 3.2, 95% CI = [2.4-4.1]). Acute on CKD (AOCCKD) was significantly higher amongst patients with chronic glomerulonephritis (CGN) (28.1%, P < 0.001), and were three times more likely to develop AOCCKD than those without CGN (aOR = 3, 95% CI = [2.1-4.2]).

CONCLUSION: PPH was the major reason for AKI related haemodialysis while diabetes and HTN were the major causes of CKD-requiring haemodialysis. CGN was more amongst patients who had dialysis due to acute exacerbation of CKD. The government and relevant stakeholders should ensure a favourable policy for the screening and management of patients.

PMID:37675696 | DOI:10.4103/npmj.npmj_80_23

Niger Postgrad Med J. 2023 Jul-Sep;30(3):193-199. doi: 10.4103/npmj.npmj_56_23.

ABSTRACT

INTRODUCTION: Sleep disturbance is common in persons with stroke and when unrecognised and untreated may hinder rehabilitation efforts and lead to poor functional outcome. It may also result in increased risk for stroke recurrence.

AIM: We investigated the frequency and associated factors of sleep disturbances amongst stroke survivors.

METHODOLOGY: One hundred and ten stroke survivors attending the neurology outpatient clinics of two tertiary hospitals, from February 2021 to January 2022, were interviewed after obtaining ethical approval and informed consent. We used a structured questionnaire to obtain their socio-demographic, clinical characteristics and sleep disturbances. Excessive daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS). Data were analysed with statistical significance set at P < 0.05.

RESULTS: Eighty (72.7%) patients were males with a mean age of 61.4 ± 11.8, slightly older than the females (30, 27.3%) with a mean age of 60.9 ± 2.9. Their median follow-up duration was 7.5 months. Majority (84, 76.4%) had ischaemic stroke, and the frequency of sleep disturbances was 37 (33.6%) consisting of insomnia (19, 17.3%), hypersomnia (10, 9.0%), sleep-disordered breathing (5, 4.5%) and sleep-related movement disorder (3, 2.7%), respectively. Using the ESS score, 22 (20.0%) had mild, 10 (9.0%) had moderate and 7 (6.4%) had severe ESS scores, respectively. Univariate analysis showed depression to be significantly associated with ESS (P = 0.006) whereas multivariate analysis revealed age and sex as significant associated factors (P = 0.008 and P = 0.009) of ESS.

CONCLUSION: More than one-third of participants reported sleep disturbances with depression, age and gender as associated factors.

PMID:37675695 | DOI:10.4103/npmj.npmj_56_23

Indian J Med Res. 2023 Sep 6. doi: 10.4103/ijmr.ijmr_3493_21. Online ahead of print.

ABSTRACT

BACKGROUND & OBJECTIVES: Despite being a tropical country, vitamin D deficiency is highly prevalent in India with studies indicating 40-99 per cent prevalence. Apart from calcium and phosphate metabolism, vitamin D is involved in cell cycle regulation, cardiovascular, hepatoprotection. The metabolism of vitamin D is regulated by vitamin D tool genes (CYP2R1/CYP27B1/CYP24A1/VDR). The promoter regions of some of these genes have CpG islands, making them prone to methylation induced gene silencing, which may cause a reduction in circulating vitamin D levels. Epigenetic basis of vitamin D deficiency is yet to be studied in India, and hence, this pilot study was aimed to analyze whether methylation levels of CYP2R1 gene were correlated with the levels of 25(OH)D in healthy, adult individuals in Indian population.

METHODS: In this cross-sectional study, healthy adults of 18-45 yr of age with no history of malabsorption, thyroidectomy, chronic illness or therapeutic vitamin D supplementation were recruited. DNA methylation analysis was carried out by methylation specific quantitative PCR. Serum calcium, phosphate and vitamin D levels were also quantified. Statistical analysis was done by R 4.0.5 software.

RESULTS: A total of 61 apparently healthy adults were analyzed. The serum vitamin D levels did not correlate with CYP2R1 methylation levels in our study population. Significant positive correlation was observed between age and serum vitamin D levels. Significant association of gender was found with CYP2R1 methylation levels.

INTERPRETATION & CONCLUSIONS: This study found no significant correlation between levels of CYP2R1 methylation and circulating 25(OH)D deficiency. Further studies on the Indian population having a larger sample size including entire vitamin D tool genes, among different ethnic groups may be conducted to elucidate molecular etiology of circulating 25(OH)D deficiency. The high prevalence of normal serum calcium and phosphate levels among vitamin D deficient subjects in this study coupled with the strikingly high prevalence of the deficiency at the national level, may suggest the need to revise the cut-off criteria for vitamin D deficiency in the Indian population.

PMID:37675692 | DOI:10.4103/ijmr.ijmr_3493_21

Shokuhin Eiseigaku Zasshi. 2023;64(4):154-160. doi: 10.3358/shokueishi.64.154.

ABSTRACT

An inter-laboratory study involving 24 laboratories was conducted to validate the modified analytical method for the migration solution of heptane for the determination of bisphenol A migrating from polycarbonate food processing materials. In this study, two concentrations of samples were blindly coded. Each laboratory determined the analyte (bisphenol A, phenol and p-tert-butylphenol) concentration in each sample according to the established protocol. The obtained values were analyzed statistically using internationally accepted guidelines. Horwitz ratios were calculated based on the reproducibility relative standard deviation (RSD_R), which was estimated from the inter-laboratory study, and predicted RSD_R, which was calculated using the Horwitz/Thompson equation. Horwitz ratios of the two samples ranged from 0.15 to 0.37 for the three compounds, meeting the performance criteria of less than 2 set by the Codex Alimentarius for analytical method approval. These results showed that this modified analytical method shows good performance as an analytical method for the migration solution of heptane.

PMID:37673605 | DOI:10.3358/shokueishi.64.154

Arch Dis Child Fetal Neonatal Ed. 2023 Sep 6:fetalneonatal-2023-326132. doi: 10.1136/archdischild-2023-326132. Online ahead of print.

NO ABSTRACT

PMID:37673594 | DOI:10.1136/archdischild-2023-326132

Artif Intell Med. 2023 Sep;143:102605. doi: 10.1016/j.artmed.2023.102605. Epub 2023 Jun 7.

ABSTRACT

Machine learning (ML) has demonstrated its ability to exploit important relationships within data collection, which can be used in the diagnosis, treatment, and prediction of outcomes in a variety of clinical contexts. Anxiety mental disorder analysis is one of the pending difficulties that ML can help with. A thorough study is demanded to gain a better understanding of this illness. Since the anxiety data is generally multidimensional, which complicates processing and as a result of technology improvements, medical data from several perspectives, known as multiview data (MVD), is being collected. Each view has its own data type and feature values, so there is a lot of diversity. This work introduces a novel preprocessing feature selection (FS) approach, multiview harris hawk optimization (MHHO), which has the potential to reduce the dimensionality of anxiety data, hence reducing analytical effort. The uniqueness of MHHO originates from combining a multiview linking methodology with the power of the harris hawk optimization (HHO) method. The HHO is used to identify the lowest optimal MVD feature subset, while multiview linking is utilized to find a promising fitness function to direct the HHO FS while accounting for all data views’ heterogeneity. The complexity of MHHO is O(THL²), where T is the number of iterations, H is the number of involved harris hawks, and L is the number of objects. Using two publicly available anxiety MVDs, MHHO is validated against ten recent rivals in its category. The experimental findings show that MHHO has a considerable advantage in terms of convergence speed (converging in less than ten iterations), subset size (removing 75% of the views; reducing feature size by 66%), and classification accuracy (approaching 100%). Furthermore, statistical analyses reveal that MHHO is statistically different from its competitors, bolstering its applicability. Finally, feature importance is evaluated, shedding light on the most anxiety-inducing characteristics. The likelihood of developing additional disorders (such as depression or stress) is also investigated.

PMID:37673574 | DOI:10.1016/j.artmed.2023.102605

Artif Intell Med. 2023 Sep;143:102622. doi: 10.1016/j.artmed.2023.102622. Epub 2023 Jul 9.

ABSTRACT

Civil registration and vital statistics systems capture birth and death events to compile vital statistics and to provide legal rights to citizens. Vital statistics are a key factor in promoting public health policies and the health of the population. Medical certification of cause of death is the preferred source of cause of death information. However, two thirds of all deaths worldwide are not captured in routine mortality information systems and their cause of death is unknown. Verbal autopsy is an interim solution for estimating the cause of death distribution at the population level in the absence of medical certification. A Verbal Autopsy (VA) consists of an interview with the relative or the caregiver of the deceased. The VA includes both Closed Questions (CQs) with structured answer options, and an Open Response (OR) consisting of a free narrative of the events expressed in natural language and without any pre-determined structure. There are a number of automated systems to analyze the CQs to obtain cause specific mortality fractions with limited performance. We hypothesize that the incorporation of the text provided by the OR might convey relevant information to discern the CoD. The experimental layout compares existing Computer Coding Verbal Autopsy methods such as Tariff 2.0 with other approaches well suited to the processing of structured inputs as is the case of the CQs. Next, alternative approaches based on language models are employed to analyze the OR. Finally, we propose a new method with a bi-modal input that combines the CQs and the OR. Empirical results corroborated that the CoD prediction capability of the Tariff 2.0 algorithm is outperformed by our method taking into account the valuable information conveyed by the OR. As an added value, with this work we made available the software to enable the reproducibility of the results attained with a version implemented in R to make the comparison with Tariff 2.0 evident.

PMID:37673565 | DOI:10.1016/j.artmed.2023.102622

Artif Intell Med. 2023 Sep;143:102618. doi: 10.1016/j.artmed.2023.102618. Epub 2023 Jul 1.

ABSTRACT

The world has recently been exposed to a fierce attack from many viral diseases, such as Covid-19, that exhausted medical systems around the world. Such attack had a negative impact not only on the health status of people or the high death rate, but also had a bad impact on the economic situation, which affected all countries of the world especially the poor and the developing ones. Monkeypox is one of the latest viral diseases that may cause a pandemic in the near future if not dealt and diagnosed with appropriately. This paper provides a new strategy for diagnosing monkeypox, which is called; Accurate Monkeypox Diagnosing Strategy (AMDS). The proposed AMDS consists of two phases, which are; (i) pre-processing and (ii) classification. During the pre-processing phase, the most effective feature are selected using Binary Tiki-Taka Algorithm (BTTA). On the other hand, in the classification phase, ensemble classification is used for diagnosing new cases, which combines evidence from three different new classifiers, namely; (a) Layered K-Nearest Neighbors (LKNN), (b) Statistical Naïve Bayes (SNB), and (c) Deep Learning Classifier (DLC). Moreover, the decisions of the proposed classifiers are merged in a new voting scheme called Fuzzified Voting Scheme (FVS). AMDS has been compared against recent diagnostic strategies. Experimental results have proven that AMDS outperforms other monkeypox diagnostic strategies as it introduces the most accurate diagnosis according to two different datasets.

PMID:37673562 | DOI:10.1016/j.artmed.2023.102618

Clin Chem Lab Med. 2023 Sep 7. doi: 10.1515/cclm-2023-0786. Online ahead of print.

ABSTRACT

OBJECTIVES: Currently, most medical laboratories do not have a dedicated software for managing report recalls, and relying on traditional manual methods or laboratory information system (LIS) to record recall data is no longer sufficient to meet the quality management requirements in the large regional laboratory center. The purpose of this article was to describe the research process and preliminary evaluation results of integrating the Medical Laboratory Electronic Record System (electronic record system) laboratory report recall function into the iLab intelligent management system for quality indicators (iLab system), and to introduce the workflow and methods of laboratory report recall management in our laboratory.

METHODS: This study employed cluster analysis to extract commonly used recall reasons from laboratory report recall records in the electronic record system. The identified recall reasons were validated for their applicability through a survey questionnaire and then incorporated into the LIS for selecting recall reasons during report recall. The statistical functionality of the iLab system was utilized to investigate the proportion of reports using the selected recall reasons among the total number of reports, and to perform visual analysis of the recall data. Additionally, we employed P-Chart to establish quality targets and developed a “continuous improvement process” electronic flow form.

RESULTS: The reasons for the recall of laboratory reports recorded in the electronic recording system were analyzed. After considering the opinions of medical laboratory personnel, a total of 12 recall reasons were identified, covering 73.05 % (1854/2538) of the recalled laboratory reports. After removing data of mass spectra lab with significant anomalies, the coverage rate increased to 82.66 % (1849/2237). The iLab system can generate six types of statistical graphs based on user needs, including statistical time, specialty labs (or divisions), test items, reviewers, reasons for report recalls, and distribution of the recall frequency of 0-24 h reports. The control upper limit of the recall rate of P-Chart based on laboratory reports can provide quality targets suitable for each professional group at the current stage. Setting the five stages of continuous process improvement reasonably and rigorously can effectively achieve the goal of quality enhancement.

CONCLUSIONS: The enhanced iLab system enhances the intelligence and sustainable improvement capability of the recall management of laboratory reports, thus improving the efficiency of the recall management process and reducing the workload of laboratory personnel.

PMID:37673465 | DOI:10.1515/cclm-2023-0786

BMJ Open. 2023 Sep 6;13(9):e073479. doi: 10.1136/bmjopen-2023-073479.

ABSTRACT

INTRODUCTION: There is a limited understanding of the early nutrition and pregnancy determinants of short-term and long-term maternal and child health in ethnically diverse and socioeconomically vulnerable populations within low-income and middle-income countries. This investigation programme aims to: (1) describe maternal weight trajectories throughout the life course; (2) describe child weight, height and body mass index (BMI) trajectories; (3) create and validate models to predict childhood obesity at 5 years of age; (4) estimate the effects of prepregnancy BMI, gestational weight gain (GWG) and maternal weight trajectories on adverse maternal and neonatal outcomes and child growth trajectories; (5) estimate the effects of prepregnancy BMI, GWG, maternal weight and interpregnancy BMI changes on maternal and child outcomes in the subsequent pregnancy; and (6) estimate the effects of maternal food consumption and infant feeding practices on child nutritional status and growth trajectories.

METHODS AND ANALYSIS: Linked data from four different Brazilian databases will be used: the 100 Million Brazilian Cohort, the Live Births Information System, the Mortality Information System and the Food and Nutrition Surveillance System. To analyse trajectories, latent-growth, superimposition by translation and rotation and broken stick models will be used. To create prediction models for childhood obesity, machine learning techniques will be applied. For the association between the selected exposure and outcomes variables, generalised linear models will be considered. Directed acyclic graphs will be constructed to identify potential confounders for each analysis investigating potential causal relationships.

ETHICS AND DISSEMINATION: This protocol was approved by the Research Ethics Committees of the authors’ institutions. The linkage will be carried out in a secure environment. After the linkage, the data will be de-identified, and pre-authorised researchers will access the data set via a virtual private network connection. Results will be reported in open-access journals and disseminated to policymakers and the broader public.

PMID:37673446 | DOI:10.1136/bmjopen-2023-073479