Machine learning-aided algorithm design for prediction of severity from clinical, demographic, biochemical and immunological parameters: Our COVID-19 experience from the pandemic.

Background: The severity of laboratory and imaging finding was found to be inconsistent with clinical symptoms in COVID-19 patients, thereby increasing casualties. As compared to conventional biomarkers, machine learning algorithms can learn nonlinear and complex interactions and thus improve prediction accuracy. This study aimed at evaluating role of biochemical and immunological parameters-based machine learning algorithms for severity indexing in COVID-19. Methods: Laboratory biochemical results of 5715 COVID-19 patients were mined from electronic records including 509 admitted in COVID-19 ICU. Random Forest Classifier (RFC), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and K-Nearest Neighbours (KNN) classifier models were used. Lasso regression helped in identifying the most influential parameter. A decision tree was made for subdivided data set, based on randomization. Results: Accuracy of SVM was highest with 94.18% and RFC with 94.04%. SVM had highest PPV (1.00), and NBC had highest NPV (0.95). QUEST modelling ignored age, urea and total protein, and only C-reactive protein and lactate dehydrogenase were considered to be a part of decision-tree algorithm. The overall percentage of correct classification was 78.31% in the overall algorithm with a sensitivity of 87.95% and an AUC of 0.747. Conclusion: C-reactive protein and lactate dehydrogenase being routinely performed tests in clinical laboratories in peripheral setups, this algorithm could be an effective predictive tool. SVM and RFC models showed significant accuracy in predicting COVID-19 severity and could be useful for future pandemics.

Endoscopic Tympanoplasty as an Alternative to Microscopic Tympanoplasty: A Comparative Study on Surgical Outcomes and patient satisfaction.

Background: Chronic otitis media treatment has evolved, with microscopic surgeries as the gold standard and endoscopic surgeries as a newer addition. Materials and Methods: This retrospective study includes 209 patients who underwent type 1 tympanoplasty, utilizing both endoscopic and microscopic techniques, between January 2019 and December 2022 at a tertiary care institute in India. The study aims to compare hearing outcomes, graft uptake, hospital stay, postoperative pain, and cosmesis between the two groups. Results: Mean AB gap closure was 17.09 + 5.98 dB in the endoscopic group and 16.74 + 5.05 dB in the microscopic group (P = 0.687). The duration of surgery was 79.26 ± 17.37 min in the Endoscopic group and 91.92 ± 15.35 min in the Microscopic group. (P = < 0.00001). The Endoscopic group experienced less post-operative pain, shorter hospital stay (P = < 0.00001, P = 0.0008), and exhibited better cosmetic outcomes (P = 0.00001) compared to the microscopic group. Conclusion: Endoscopic tympanoplasty is a better alternative to microscopic tympanoplasty, delivering comparable hearing outcomes, shorter surgical duration, improved pain scores, and better cosmesis. Supplementary Information: The online version contains supplementary material available at 10.1007/s12070-023-04184-4.

Comparing accuracy of cochlear measurements on magnetic resonance imaging and computed tomography: A step towards radiation-free cochlear implantation.

Objective: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly employed in pre-operative evaluation for cochlear implant surgery. However, with a decrease in the age of implantation, even minor radiation exposure can cause detrimental effects on children over their lifetime. The current study compares different cochlear measurements from CT and MRI scans and evaluates the feasibility of using only an MRI scan for radiological evaluation before cochlear implantation. Methods: A longitudinal observational study was conducted on 94 ears/47 children, employing CT and MRI scans. The CT and MRI scan measurements include, A value, B value, Cochlear duct length (CDL), two-turn cochlear length, alpha and beta angles to look for cochlear orientation. Cochlear nerve diameter was measured using MRI. The values were compared. Results: The mean difference between measurements from CT and MRI scans for A value, B value, CDL, and two-turn cochlear length values was 0.567 ± 0.413 mm, 0.406 ± 0.368 mm, 2.365 ± 1.675 mm, and 2.063 ± 1.477 mm respectively without any significant difference. The alpha and beta angle measures were comparable, with no statistically significant difference. Conclusion: The study suggests that MRI scans can be the only radiological investigation needed with no radiation risk and reduces the cost of cochlear implant program in the paediatric population. There is no significant difference between the measurements obtained from CT and MRI scans. However, observed discrepancies in cochlear measurements across different populations require regionally or race-specific standardized values to ensure accurate diagnosis and precision in cochlear implant surgery. This aspect must be addressed to ensure positive outcomes for patients.

An Evaluation of T-Regulatory Cells and Inflammatory Cytokines in Preeclampsia.

Background Preeclampsia (PE) is a prenatal hypertension condition with unknown aetiology which is one of the leading causes of maternal morbidity and mortality, premature delivery, and foetal and neonatal mortality. T-regulatory cells (T-regs) are the specific subsets of T-lymphocytes that play a key role in the mechanisms of maternal-foetal tolerance, contributing to an effective immunological role in protecting the allogenic foetus during pregnancy and preventing pregnancy-related complications. This study evaluated the T-regs in PE and correlated the T-regs with inflammatory markers in the pathophysiology and for early diagnosis of PE. Methods After clearance from Institutional Ethics Committee, the participants were recruited from the Department of Obstetrics and Gynaecology. Three study groups were included a) normal reproductive age group women b) normal pregnant women c) PE pregnant women. 5 ml of venous blood was collected from each participant. Biochemical and haematological parameters estimation was done in Hospital's central laboratory. T-regs (CD4, CD25, FOXP3) were assessed using a flow-cytometer, and inflammatory markers (TGF-ß1, IL-6, hsCRP) were assessed by ELISA and Beckman Coulter autoanalyzer in the Department of Biochemistry, AIIMS, Bhubaneswar. Results We found that the levels of CD4+CD25+ T-regs were lower in PE than in normal pregnancy, but this difference was not statistically significant (p = 0.349). The levels of CD4+FOXP3+ T-regs in PE were significantly lower compared to both normal pregnant women (p = 0.001) and normal non-pregnant women (p = 0.001). In comparison to women with PE, the levels of TGF-ß1 were significantly higher in normal non-pregnant women (p = 0.020) and were higher, although not significantly so, in normal pregnant women (p = 0.994). The levels of IL-6 in women with PE were significantly higher than in normal pregnant women (p = 0.01) and normal non-pregnant women (p = 0.048). The levels of hsCRP in women with PE were significantly higher than in normal pregnant women (p = 0.045) and were higher, but not statistically significant, compared to normal non-pregnant women (p = 0.094). Conclusion The results of the study, showing a decrease in T-regs and an increase in inflammatory markers like TGF-ß1, IL-6, and hsCRP levels in PE, have potential implications for the early diagnosis and management of the condition. Incorporating assessments of CD4+FOXP3+ T-regs and inflammatory markers into screening protocols, along with regular prenatal care and monitoring, can aid in the timely detection and implementation of appropriate management strategies. By intervening early, the risks associated with PE can be reduced, optimizing both maternal and fetal health.

Altered T-cell profile in sickle cell disease.

Background: Impaired immune status due to altered T-cell response in sickle cell disease (SCD) might provide substantial insight into immune activity in SCD patients. Materials & methods: A total of 30 healthy control, 20 SCD patients in a crisis state and 38 SCD patients in a steady state were evaluated for T-cell subsets. Results: A significant decrease in CD8+ (p = 0.012) and CD8+45RA-197+ (p = 0.015) T-cells were observed among SCD patients. Naive T-cells (45RA+197+; p < 0.01) were elevated and effector (RA-197-) and central memory (RA-197+) T-cells were grossly reduced in the crisis state. Negative regression of naive T-cells with CD8+57+ affirmed immune inactivation. The predictor score reflected 100% sensitivity for predicting the crisis state (area under the curve = 0.851; p < 0.001). Conclusion: Monitoring naive T-cells with predictive scores can help assess the early shift from a steady state to a crisis state.

The sickle-shaped hemoglobin in sickle cell disease (SCD) patients are known to cause frequent episodes of blockage in small vessels. Repeated episodes of blockage result in tissue injury and create a state of chronic inflammation. In response, a series of inflammatory reactions initiate such that the immune response in these patients is quite altered. To understand these changes, this study was conducted to observe alterations in T-cell subtypes and gain substantial insight into immune activity in SCD patients. A total of 30 healthy control, 20 SCD patients in a crisis state and 38 SCD patients in a steady state were evaluated for T-cell subsets. The SCD patient experienced a gross decrease in T-cells with killing ability and memorizing ability for immune responses. The SCD patients in crisis state reported a significant increase in inactivated T-cells but the levels of activated T-cells that can defend and memorize the immune response were quite low. The finding suggested that this group of SCD patients had compromised immune activation that hindered the activation and differentiation of inactivated T-cells to their effector and memory cells. An equation was derived considering all the parameters that were significantly altered to derive a predictive score that showed 100% sensitivity for predicting a crisis state. Hence, it is proposed that monitoring the inactivated T-cell population or predictive score might help clinicians to assess clinical severity at an early stage and initiate appropriate preventive measures.

Microbiological Characterization and Clinical Facets of Elizabethkingia Bloodstream Infections in a Tertiary Care Hospital of Eastern India.

Purpose: Elizabethkingia is an emerging non-fermenting Gram-negative bacillus (NFGNB) causing bloodstream infections (BSI) associated with high mortality. It demonstrates a unique antimicrobial profile in showing susceptibility to antimicrobials effective against Gram-positive bacteria. This study was undertaken to determine the overall frequency of Elizabethkingia BSI, associated risk factors, microbiological susceptibility, and clonal relationship of Elizabethkingia isolates using Enterobacterial Repetitive Intergenic Consensus Polymerase Chain Reaction (ERIC-PCR). Patients and Methods: Elizabethkingia isolates obtained from the blood culture of admitted patients (August 2020-December 2021) were identified by the VITEK 2 system and subjected to an antimicrobial susceptibility test by standard procedures. Demographics, co-morbidities, risk factors for survival, and outcome were summarized and analyzed by Chi-square test, Kaplan-Meier curve, and Cox regression. Clonal relatedness between Elizabethkingia isolates was analyzed using ERIC­PCR fingerprinting with the "PAST: Paleontological statistics software package". Results: Of 13,747 blood samples received during the study period, 13.59% were culture positive, and 14.60% were NFGNBs. The frequency of Elizabethkingia spp. among all NFGNBs in BSI was 29.30%, and the overall prevalence in BSI was 4.21%. In patients with Elizabethkingia BSI, Foley's catheter was present in 81.25% of the cases. 100% susceptibility was observed to linezolid, followed by vancomycin (98.75%) and chloramphenicol (89.5%). The 30-day mortality rate in the patients of Elizabethkingia BSI was 26.25%. The Presence of COVID-19, pneumonia, diabetes mellitus (DM), mechanical ventilation (MV), and prior antibiotics were significantly different (p<0.05) between the survival and death groups. ERIC-PCR profile dendrogram of Elizabethkingia isolates showed ten major clusters indicating high genetic diversity. Conclusion: Elizabethkingia was responsible for one-third of NFGNB BSI in a single-center study, with approximately 26% of 30-day all-cause mortality. Most isolates were susceptible to linezolid, vancomycin, and chloramphenicol. COVID-19 was the most significant risk factor associated with mortality. ERIC-PCR of Elizabethkingia isolates exhibited high genetic diversity.

Exploring the Novelty in Lipid Profiling of Patients: A Non-fasting Approach from Eastern India.

Objective To date, no reference interval is available for lipid profile, including total cholesterol (TC), triglycerides (TGs), high-density lipoprotein (HDL), or low-density lipoprotein (LDL)-cholesterol, etc., in a non-fasting state. Hence, the study was taken up with the objective of exploring the possibility of establishing a reference interval for non-fasting lipid profile consisting of serum TC, TG, LDL, HDL, and very low-density lipoprotein (VLDL) cholesterol. Materials and Methods A total of 1,350 apparently healthy subjects, including 636 healthy men and 714 healthy women of 18 years and beyond of age, were enrolled in the study. Reference individuals were recruited using cluster sampling method from various villages and semi-urban regions irrespective of their sex, religion, socioeconomic status, or any other demographic profile, and samples were analyzed in Beckman Coulter AU480 analyzer. Results The mean age of 1,350 participants was 38.23 ± 15.94 years. We found that all the test parameters require a different reference interval than the established fasting reference range, except for HDL cholesterol in females. The data were subdivided into subjects below 40 years, between 40 and 60 years, and older than 60 years of age. All five parameters in the lipid profile were individually analyzed and were compared age group-wise and gender-wise with the total study population. Significant differences in the various dataset were found. Conclusion A shift toward non-fasting lipid interval measurement is, thus, a piece of evidence-driven mechanism. Even from a patient's perspective, it sets in ease and convenience in lipid-profile testing, subsequently leading to a more compliant cardiovascular management and monitoring.

Processing Validation Metrics of Syva Enzyme-Multiplied Immunoassay Technique (EMIT) Methotrexate Assay for Beckman Coulter System.

BACKGROUND: High-dose methotrexate (HDMTX), defined as a dose greater than 500 mg/m2, is used to treat a variety of cancers; and though safe, it can cause major toxicity. Syva enzyme-multiplied immunoassay technique (EMIT) methotrexate (MTX) assay (Gurgaon, India: Siemens Healthcare Diagnostics Ltd.) uses a homogeneous enzyme immunoassay method. Low-end precision performances are very important for laboratory methods, especially when their results have clinical significance at these levels. METHODOLOGY: A total of 25 replicates (five replicates per run, for five runs) were analyzed for profiling. Precision, accuracy, linearity, limit of blank, limit of detection, and limit of quantification were determined using existing guidelines. Imprecision profile and limit of quantitation (LoQ) at 10% were determined by fitting data with hyperbolic regression. RESULTS: The coefficient of variation percentage (CV%) for low, mid, and high-level internal quality control (IQC) was 1.25%, 3.45%, and 1.55%, respectively. Similarly, estimated bias was -4.58%, -3.54%, -7.21% for each level. The assay linearity was maintained from a range of 0.041-1.993 mmol/L with an R2 of 0.959. The limit of detection was estimated to be 0.07 mmol/L. CONCLUSION: Syva EMIT MTX assay can be precisely and accurately used to measure low levels of serum methotrexate at levels lower than claimed by the manufacturer, aiding in the monitoring of toxicity in patients.

Analysis of Biochemical and Inflammatory Markers for Predicting COVID-19 Severity: Insights From a Tertiary Healthcare Institution of Eastern India.

Background Coronavirus disease-19 (COVID-19) patients often deteriorate rapidly based on viral infection-related inflammation and the subsequent cytokine storm. The clinical symptoms were found to be inconsistent with laboratory findings. There is a need to develop biochemical severity score to closely monitor COVID-19 patients. Methods This study was conducted in the department of biochemistry at All India Institute of Medical Sciences (AIIMS) Bhubaneswar in collaboration with the intensive care unit. Laboratory data of 7,395 patients diagnosed with COVID-19 during the first three waves of the pandemic were analyzed. The serum high sensitivity high-sensitivity C-reactive protein (hs-CRP, immuno-turbidity method), lactate dehydrogenase (LDH, modified Wacker et al. method), and liver enzymes (kinetic-UV method) were estimated by fully automated chemistry analyzer. Serum ferritin and interleukin-6 (IL-6) were measured by one-step immunoassay using chemiluminescence technology. Three models were used in logistic regression to check for the predictive potential of biochemical parameters, and a COVID-19 biochemical severity score was calculated using a non-linear regression algorithm. Results The receiver operating characteristic curve found age, urea, uric acid, CRP, ferritin, IL6, and LDH with the highest odds of predicting ICU admission for COVID-19 patients. COVID-19 biochemical severity scores higher than 0.775 were highly predictive (odds ratio of 5.925) of ICU admission (AUC=0.740, p<0.001) as compared to any other individual parameter. For the validation, 30% of the total dataset was used as testing data (n=2095) with a sensitivity of 68.3%, specificity of 74.5%, and odds ratio of 6.304. Conclusion Age, urea, uric acid, ferritin, IL6, LDH, and CRP-based predictive probability algorithm calculating COVID-19 severity was found to be highly predictive of ICU admission for COVID-19 patients.

The Evaluation of the Quality Performance of Biochemical Analytes in Clinical Biochemistry Laboratory Using Six Sigma Matrices.

Introduction This study was conducted to assess the analytical performance of biochemical tests using Six Sigma methodology and to assess the underlying causes of unsatisfied performance of analytes with a sigma value of less than 4 using quality goal index (QGI) and root cause analysis (RCA). Methodology Daily data for internal quality control (IQC) for both level 1 (L1) and level 2 (L2) and monthly data for external quality assessment for a period of six months were recorded. The coefficient of variation (CV), bias, and total allowable error (TEa) were calculated to analyze the sigma (σ) values for 19 biochemical analytes. Quality goal index (QGI) analysis was done to analyze impressions and inaccuracies in analyte performance having a sigma value of less than 4. Root cause analysis (RCA) was done to evaluate the possible causes that can improve quality performance. Results Creatinine and high-density lipoprotein (HDL) had sigma metrics of ≤2.0, and chloride, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) had sigma values between 2 and 3. Glucose, total protein (TP), phosphate (Phos), and potassium had sigma values between 4 and 5 in level 1 QC. Sigma grading for level 2 quality control (QC) also gave similar results. For analytes with σ < 4, QGI analysis exposed inaccuracy or imprecision issues and identified errors such as the reconstitution of IQC, storage temperature, and air bubbles while processing the QC, being common causes of poor performance. Conclusion Six Sigma approach is helpful for quality assurance and identifying areas for improvement. Assessing Six Sigma metrics should be a routine practice to decide the frequency of QC run and to detect errors in analysis.

Impact of Time Delay in the Analysis of Serum Ionized Calcium, Sodium, and Potassium.

Introduction Delay in the analysis of serum electrolytes along with clot contact time can lead to difference in results significant enough to affect clinical decisions. This study was undertaken to evaluate the effect of time lag between centrifugation and analysis on levels of serum sodium, potassium, and ionized calcium in a tertiary level health care set up. Materials and Methods In this cross-sectional study, 70 serum samples were analyzed for ionized calcium, sodium, and potassium under different conditions with respect to time lag and clot contact time. The analysis of ionized calcium was done on Eschweiler Combiline 2, a direct ion-selective electrode (ISE) analyzer. Serum sodium and potassium were analyzed on fully automated chemistry analyzer, which is an indirect ISE analyzer. The statistical analysis was done in IBM SPSS software version 21. Results The results for intergroup comparison with different time lag and clot contact time between all the four groups for sodium, potassium, and ionized calcium were statistically significant, as obtained by application of Kruskal-Wallis test. There was consistent decrease in the concentration of sodium and ionized calcium, and an increase in serum potassium with increased delay in analysis and clot contact time. Conclusion The accurate measurement of electrolytes is of paramount importance for the treatment and better prognosis of critically ill patients. This can be accomplished by better management of the preanalytical phase of analysis by maintaining a standard protocol in the laboratory and sample transportation.

Ophthalmology as a career choice among medical students in Eastern India - A cross-sectional study.

Purpose: Several factors influence medical students to choose a specialty branch for post-graduate training, and it is important to understand them so that strategies can be made to make it more attractive and hence the shortage in the workforce can be addressed. This study aimed to identify the factors that influenced under-graduate medical students to choose ophthalmology as their specialty for post-graduate training. Methods: It was a descriptive, cross-sectional, questionnaire-based study, carried out among medical students at a tertiary academic institute in eastern India. In this survey tool, 25 questions were divided into different sections: demographic data, factors influencing the choice and perceptions about ophthalmology as a career option, and the impact of rotatory internship posting on the choice of subject. The various factors were then scored and indexed appropriately. Results: There were 515 participants. The median age was 23 years. The major influencing factor for choosing ophthalmology was "adequate time" (52.04%), and the discouraging factor was "steep learning curve" (58.64%). Age had an odds ratio of 0.781, showing that the lower the age, the higher the chances of being positively inclined toward ophthalmology as a career choice. Similarly, major deciding factors, perceptions, and experiences in ophthalmology had odds ratios of 1.841, 1.725, and 2.057, respectively, showing a positive inclination. Conclusion: The study states that a steep learning curve, personal attitudes, and misconceptions may dissuade the students from taking this subject, but adequate clinical exposure and experience, as well as role models during the internship, can create a positive impact on career choice.

Gestational Diabetes Mellitus with autoimmune subclinical hypothyroidism in pregnancy in relation to gravida.

Introduction: Gestational diabetes mellitus (GDM) and hypothyroidism are the most common endocrinological abnormalities associated with pregnancy. The association of gravida with incidence of autoimmune subclinical hypothyroidism (SCH) and GDM in pregnancy has not been studied extensively with availability of very limited data in this context. So, this study was done to find out the association between GDM and autoimmune SCH in pregnancy as per gravida status of the study population. Materials and Methods: 382 antenatal cases, both primi and multigravida, were screened for thyroid dysfunction and GDM in their first ANC coming to a tertiary level health care institution. 75 gm GCT was used for diagnosis of GDM and serum TSH, fT4, and anti-TPO antibody were measured for assessment of thyroid dysfunction. Prevalence of SCH was evaluated taking the ATA 2011 guidelines. Data obtained was also compared with ATA 2017 recommendations. Anti-TPO antibody level more than 60 U/L was considered to be raised value. Observation: The percentage of GDM was higher in autoimmune SCH participants compared to euthyroid cases with raised anti-TPO Ab Titer. GDM, SCH, and raised anti-TPO Ab titer were overall more prevalent in multigravida cases compared to primigravida participants. Conclusion: GDM and SCH with high anti-TPO Ab titer were more prevalent in multigravida participants compared to primigravida cases though not statistically significant. As occurrence of SCH varies with nutritional and geographical factors, hence internal trimester specific range should be calculated and used in practice as recommended by ATA 2017 guidelines.

Eastern India Collaboration on Multisystem Inflammatory Syndrome in Children (EICOMISC): A Multicenter Observational Study of 134 Cases.

Background: Few single center studies from resource-poor settings have reported about the epidemiology, clinical feature and outcome of multisystem inflammatory syndrome in children (MIS-C). However, larger data from multi-center studies on the same is lacking including from Indian setting. Methods: This retrospective collaborative study constituted of data collected on MIS-C from five tertiary care teaching hospitals from Eastern India. Children ≤ 15 years of age with MIS-C as per the WHO criteria were included. Primary outcome was mortality. Results: A total of 134 MIS-C cases were included (median age, 84 months; males constituted 66.7%). Fever was a universal finding. Rash was present in 40%, and conjunctivitis in 71% cases. Gastro-intestinal and respiratory symptoms were observed in 50.7% and 39.6% cases, respectively. Co-morbidity was present in 23.9% cases. Shock at admission was noted in 35%, and 27.38% required mechanical ventilation. Fifteen (11.2%) children died. The coronary abnormalities got normalized during follow-up in all except in one child. Initial choice of immunomodulation had no effect on the outcomes. Presence of underlying co-morbidity, lymphopenia, thrombocytosis, hyponatremia, increased LDH (>300 U/L), and hypoalbuminemia were the factors significantly associated an increased mortality. Conclusions: MIS-C has myriad of manifestations. Underlying co-morbidity, lymphopenia, thrombocytosis, hyponatremia, increased LDH (>300 U/L), and hypoalbuminemia were associated with an increased mortality. No difference in outcome was noted with either steroid or IVIg or both. Coronary artery abnormalities resolved in nearly all cases.

Correlation of Serum Retinol and Atherogenic Indices in Type 2 Diabetes Mellitus: A Case-Control Study.

Dyslipidemia is an important risk factor for atherosclerosis and coronary heart disease, leading to mortality and morbidity in subjects with T2DM. This risk is higher in subjects with diabetes who are on retinoid therapy for some other indication, where hypercholesterolemia, hypertriglyceridemia, and low serum high-density lipoprotein cholesterol (HDL-C), and sudden cardiovascular deaths have been reported. Our study aimed to find the correlation of serum retinol and atherogenic index (AI) in subjects with T2DM and compare them with healthy controls. We found there was a significant difference in systolic and diastolic blood pressure, body mass index, waist circumference, waist hip ratio, total cholesterol (TC), Triglycerides (TG), non-high density lipoprotein cholesterol (non-HDL-C), the atherogenic ratio of cholesterol (ARC), atherogenic index of plasma (AIP) and AI between the two groups. There was a significant positive correlation of serum retinol with TC, TG, LDL-C, Non-HDL-C, ARC, AIP, and AI. In our study we found an association of serum retinol with atherogenic index and dyslipidemia in subjects with T2DM. Serum retinol can be a novel predictor of cardiovascular risk in subjects with T2DM.

Prevalence of Subclinical Hypothyroidism in Pregnancy and Its Association With Anti-thyroperoxidase Antibody and the Occurrence of Gestational Diabetes Mellitus.

Introduction Subclinical hypothyroidism (SCH) and gestational diabetes mellitus (GDM) are common endocrinological abnormalities associated with pregnancy. The presence of a raised anti-thyroperoxidase (anti-TPO) antibody titer increases the risk of progression of subclinical hypothyroidism to overt hypothyroidism. Subclinical hypothyroidism and GDM are known to affect maternal and fetal outcomes adversely. A few studies have shown an increased risk of GDM with autoimmune hypothyroidism. However, data regarding this association between GDM, SCH, and anti-TPO Ab are scarce. This study aimed to find the prevalence of autoimmune subclinical hypothyroidism and its association with GDM in pregnancy. Materials and methods In a cross-sectional study, 382 pregnant women at their first antenatal checkup (ANC) were enrolled in the study. Serum thyroid-stimulating hormone (TSH), free T4 (FT4), anti-TPO Ab, and the 75 g oral glucose tolerance test (OGTT) were evaluated. The results obtained were analyzed in Systat Version 13.2 (SPSS Inc., Chicago, IL). Observations Results showed an SCH prevalence of 37.69% with a raised anti-TPO Ab titer in 49.31% of the diagnosed SCH cases, pointing towards an autoimmune etiology. Our study revealed a GDM prevalence of 12.04%. Out of the 46 GDM cases, 16 were found to have SCH and 3 cases had raised anti-TPO Ab titers. In our study, 27.73% of euthyroid pregnant women had a raised anti-TPO Ab titer. Our study revealed no significant association between GDM, SCH, and raised anti-TPO Ab titer. Conclusion Anti-TPO antibody subsequently leads to hypothyroxinemia, for which it is necessary that cases with high titer of anti-TPO antibody though euthyroid should be meticulously followed up and screened for to detect development of hypothyroidism or SCH, particularly in future pregnancies. However, GDM prevalence was at par with the national figure, but with no significant association of SCH and a high anti-TPO ab titer was found with GDM in our study. Further studies with a larger cohort may establish a causal association between the two most common endocrinological disorders observed in pregnancy.

Maternal and neonatal outcomes among women with and without severe acute respiratory syndrome corona virus-2 infection: A retrospective analytical study.

BACKGROUND: Corona virus disease (COVID-19) is an infectious disease caused by the novel corona virus known as severe acute respiratory syndrome corona virus 2 (SARS Cov-2). Physiological changes occurring during pregnancy can have a positive or negative effect on the disease progression. The objective of the study was to evaluate the maternal and neonatal outcomes in pregnant women with COVID-19 compared to pregnant women without COVID-19 and to determine its influence on the healthcare system. MATERIAL AND METHODS: This was a retrospective analytical study conducted at a tertiary care hospital in Odisha, India, over 3 months, from 1 September 2020 until 30 November 2020. Results were compared in both groups. RESULTS: Three hundred and three (303) women delivered, out of whom 92 were COVID-19 positive. Incidence of COVID-19 positivity was 30.3% with 93.47% asymptomatic patients. The majority of the patients were 26-35years of age. Average gestational age at delivery for both groups was 37-40 weeks. COVID-19 positivity was seen more in primigravidas than in multigravidas. Comorbidities such as GDM/type 2 DM, PIH, PROM, APH and jaundice were similar in both groups and statistically non-significant, whereas association of anaemia and hypothyroidism were statistically significant (p<0.05) in the positive group. A single maternal death was reported in the positive group. There was an increase in Caesarean section (p=0.002) with higher incidence of preterm births and lowbirth weights in the positive group. Only 3 babies tested positive for COVID-19, so vertical transmission probability was low. Overall, all babies were healthy and the majority of women were discharged without any complications. CONCLUSION: There was no significant effect of the infection on maternal and fetal outcomes, but further studies and long-term follow-up is needed to look for any delayed effects on the babies and mothers.

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C.

BACKGROUND: LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models-random forests, XGBoost, and support vector Rregression (SVR) to predict LDL-C from total cholesterol, triglyceride, and HDL-C in comparison to linear regression model and some existing formulas for LDL-C calculation, in eastern Indian population. METHODS: The lipid profiles performed in the clinical biochemistry laboratory of AIIMS Bhubaneswar during 2019-2021, a total of 13,391 samples were included in the study. Laboratory results were collected from the laboratory database. 70% of data were classified as train set and used to develop the three machine learning models and linear regression formula. These models were tested in the rest 30% of the data (test set) for validation. Performance of models was evaluated in comparison to best six existing LDL-C calculating formulas. RESULTS: LDL-C predicted by XGBoost and random forests models showed a strong correlation with directly estimated LDL-C (r = 0.98). Two machine learning models performed superior to the six existing and commonly used LDL-C calculating formulas like Friedewald in the study population. When compared in different triglycerides strata also, these two models outperformed the other methods used. CONCLUSION: Machine learning models like XGBoost and random forests can be used to predict LDL-C with more accuracy comparing to conventional linear regression LDL-C formulas.