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Nevin Manimala Statistics

Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model

PLoS One. 2021 Jul 13;16(7):e0254550. doi: 10.1371/journal.pone.0254550. eCollection 2021.

ABSTRACT

BACKGROUND: COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients.

MATERIALS AND METHODS: We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model’s discriminatory ability was assessed with Harrell’s C-statistic and the goodness-of-fit was evaluated with calibration plot.

RESULTS: 242 patients were included [median age, 64 years (56-71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6-18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82).

CONCLUSIONS: We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

PMID:34255793 | DOI:10.1371/journal.pone.0254550

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Nevin Manimala Statistics

Pulmonary rehabilitation in Lebanon “What do we have”? A national survey among chest physicians

PLoS One. 2021 Jul 13;16(7):e0254419. doi: 10.1371/journal.pone.0254419. eCollection 2021.

ABSTRACT

BACKGROUND: Pulmonary rehabilitation (PR) is not very often used by physicians in Lebanon despite evidence on its positive effects on health-related quality of life.

AIM: This study assesses the knowledge, attitudes and practices of PR among physicians in Lebanon. In addition, the study identifies the main barriers to access to PR according to chest physicians. Insight into these issues will help to increase awareness about the need for PR programs and can contribute to designing such programs in the country.

METHODS: A survey was conducted during the regional conference of the Lebanese Pulmonary Society. One week after the initial survey, the survey questionnaire was sent by email to all chest physicians who were registered with the Lebanese Pulmonary Society but did not attend the conference. A 25-item questionnaire was used to collect information on PR.

RESULTS: Responses were analyzed using descriptive statistics. The response rate was 40%. Results show that only one-third of Lebanese chest physicians have good knowledge about the nature and multidisciplinary content of PR. Physicians generally support the current “Pulmonary Rehabilitation Program” in Beirut. Key barriers found are the lack of referral, lack of motivation by patients due to their health, cost of care and lack of qualified health care specialists in Lebanon.

CONCLUSION: Absence of awareness and education about PR among healthcare providers plays an important role in increasing access to the “Pulmonary Rehabilitation Program”. Awareness campaigns and education for physicians, health care professionals and patients should be considered to increase PR in the country.

PMID:34255790 | DOI:10.1371/journal.pone.0254419

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Nevin Manimala Statistics

A comparison of liver fat fraction measurement on MRI at 3T and 1.5T

PLoS One. 2021 Jul 13;16(7):e0252928. doi: 10.1371/journal.pone.0252928. eCollection 2021.

ABSTRACT

PURPOSE: Volumetric liver fat fraction (VLFF) measurements were made using the HepaFat-Scan® technique at 1.5T and 3T to determine the agreement between the measurements obtained at the two fields.

METHODS: Sixty patients with type 2 diabetes (67% male, mean age 50.92 ± 6.56yrs) and thirty healthy volunteers (50% male, mean age 48.63 ± 6.32yrs) were scanned on 1.5T Aera and 3T Skyra (Siemens, Erlangen, Germany) MRI scanners on the same day using the HepaFat-Scan® gradient echo protocol with modification of echo times for 3T (TEs 2.38, 4.76, 7.14 ms at 1.5T and 1.2, 2.4, 3.6 ms at 3T). The 3T analyses were performed independently of the 1.5T analyses by a different analyst, blinded from the 1.5T results. Data were analysed for agreement and bias using Bland-Altman methods and intraclass correlation coefficients (ICC). A second cohort of 17 participants underwent interstudy repeatability assessment of VLFF measured by HepaFat-Scan® at 3T.

RESULTS: A small, but statistically significant mean bias of 0.48% was observed between 3T and 1.5T with 95% limits of agreement -2.2% to 3.2% VLFF. The ICC for agreement between field strengths was 0.983 (95% CI 0.972-0.989). In the repeatability cohort studied at 3T the repeatability coefficient was 4.2%. The ICC for agreement was 0.971 (95% CI 0.921-0.989).

CONCLUSION: There is minimal bias and excellent agreement between the measures of VLFF using the HepaFat-Scan® at 1.5 and 3T. The test retest repeatability coefficient at 3T is comparable to the 95% limits of agreement between 1.5T and 3T suggesting that measurements can be made interchangeably between field strengths.

PMID:34255778 | DOI:10.1371/journal.pone.0252928

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Nevin Manimala Statistics

Learning epistatic gene interactions from perturbation screens

PLoS One. 2021 Jul 13;16(7):e0254491. doi: 10.1371/journal.pone.0254491. eCollection 2021.

ABSTRACT

The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.

PMID:34255784 | DOI:10.1371/journal.pone.0254491

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Nevin Manimala Statistics

Shuanghuanglian oral preparations combined with azithromycin for treatment of Mycoplasma pneumoniae pneumonia in Asian children: A systematic review and meta-analysis of randomized controlled trials

PLoS One. 2021 Jul 13;16(7):e0254405. doi: 10.1371/journal.pone.0254405. eCollection 2021.

ABSTRACT

BACKGROUND: Mycoplasma pneumoniae is one of the main causes of community-acquired pneumonia. Due to the imperfect immune system of children, this also causes Mycoplasma pneumoniae pneumonia (MPP) to be more common in children. Globally, the incidence of MPP in children is gradually increasing. This study was the first to systematically review the clinical efficacy and safety of Shuanghuanglian (SHL) oral preparations combined with azithromycin in the treatment of MPP in children.

METHODS: This study fully retrieved 3 Chinese databases and 5 English databases to search the randomized controlled trials (RCTs) of SHL oral preparations combined with azithromycin in the treatment of children with MPP. The search time is from the inception to September 2020. Data extraction and risk bias evaluation were performed independently by two researchers. We conducted a Meta-analysis of all the outcome indicators. Besides, Meta-regression, subgroup analysis, and heterogeneity analysis were used for the primary outcomes to find the possible potential confounding factors.

RESULTS: Finally, we included 27 RCTs involving 2884 patients. SHL oral preparations combined with azithromycin were better than azithromycin alone in response rate (RR = 1.14, 95% CI[1.11, 1.18]; low certainty evidence), disappearance time of fever(MD = -1.72, 95% CI[-2.47, -0.97]; low certainty evidence), disappearance time of cough (MD = -2.95, 95% CI[-3.55, -2.34]; low certainty evidence), and disappearance time of pulmonary rales (MD = -2.13, 95% CI[-2.88, -1.38]; low certainty evidence). The Meta-regression results showed that the course of disease, age, and method of administration may be the source of heterogeneity. Subgroup analysis and sensitivity analysis have found that the results were stable. For other related clinical symptoms, T lymphocytes, and Serum inflammatory factors, SHL oral preparations combined with azithromycin was better than azithromycin alone, and the difference was statistically significant. For adverse events with low certainty evidence, safety needs further verification.

CONCLUSION: Based on the results of meta-analysis with low certainty evidence, we believed that SHL oral preparations combined with azithromycin likely be effectively improved clinical symptoms compared with azithromycin alone. Low certainty evidence showed that SHL may safety with no serious adverse events. Due to these limitations, the safety needs further verification. More high-quality, multicenter, and large-sample RCTs should be tested and verified in the future.

PMID:34255785 | DOI:10.1371/journal.pone.0254405

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Nevin Manimala Statistics

Inhibition of mTOR signaling and clinical activity of metformin in oral premalignant lesions

JCI Insight. 2021 Jul 13:147096. doi: 10.1172/jci.insight.147096. Online ahead of print.

ABSTRACT

BACKGROUND: The aberrant activation of the PI3K/mTOR signaling circuitry is one of the most frequently dysregulated signaling events in head and neck squamous cell carcinoma (HNSCC). Here, we conducted a single-arm, open label phase IIa clinical trial (NCT02581137) in individuals with oral premalignant lesions (OPL) to explore the potential of metformin to target PI3K/mTOR signaling for HNSCC prevention.

METHODS: Subjects with OPL, otherwise healthy and without diabetes, underwent pre- and post-treatment clinical exam and biopsy. Participants received metformin for 12 weeks (week 1, 500 mg; week 2, 1,000 mg; week 3-12, 2,000 mg daily). Pre- and post-treatment biopsies, saliva, and blood were obtained for biomarker analysis, including immunohistochemical (IHC) assessment of mTOR signaling and exome sequencing.

RESULTS: Twenty-three participants were evaluable for response. The clinical response rate (defined as ≥50% reduction in lesion size) was 17%. While lower than the proposed threshold for favorable clinical response, the histologic response rate (improvement in histologic grade) was 60%, including 17% complete responses and 43% partial responses. Logistic regression analysis revealed that when compared to never smokers, current and former smokers had statistically significantly increased histologic responses (p=0.016). Remarkably, a significant correlation existed between decreased mTOR activity (pS6 IHC staining) in the basal epithelial layer of OPL and the histological (p=0.04) and clinical (p=0.01) responses.

CONCLUSIONS: This is the first phase II trial of metformin in individuals with OPL, providing evidence that metformin administration results in encouraging histological responses and mTOR pathway modulation, thus supporting its further investigation as a chemopreventive agent.

PMID:34255745 | DOI:10.1172/jci.insight.147096

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Nevin Manimala Statistics

Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study

PLoS Med. 2021 Jul 13;18(7):e1003693. doi: 10.1371/journal.pmed.1003693. eCollection 2021 Jul.

ABSTRACT

BACKGROUND: With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines.

METHODS AND FINDINGS: County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R2); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%-29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R2. Mortality is estimated to increase by 43 per thousand residents (95% CI: 37-49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34-44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors.

CONCLUSIONS: Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

PMID:34255766 | DOI:10.1371/journal.pmed.1003693

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Nevin Manimala Statistics

Impact of Isoniazid Preventive Therapy on Tuberculosis incidence among people living with HIV: A secondary data analysis using Inverse Probability Weighting of individuals attending HIV care and treatment clinics in Tanzania

PLoS One. 2021 Jul 13;16(7):e0254082. doi: 10.1371/journal.pone.0254082. eCollection 2021.

ABSTRACT

BACKGROUND: Information on how well Isoniazid Preventive Therapy (IPT) works on reducing TB incidence among people living with HIV (PLHIV) in routine settings using robust statistical methods to establish causality in observational studies is scarce.

OBJECTIVES: To evaluate the effectiveness of IPT in routine clinical settings by comparing TB incidence between IPT and non-IPT groups.

METHODS: We used data from PLHIV enrolled in 315 HIV care and treatment clinic from January 2012 to December 2016. We used Inverse Probability of Treatment Weighting to adjust for the probability of receiving IPT; balancing the baseline covariates between IPT and non-IPT groups. The effectiveness of IPT on TB incidence was estimated using Cox regression using the weighted sample.

RESULTS: Of 171,743 PLHIV enrolled in the clinics over the five years, 10,326 (6.01%) were excluded leaving 161,417 available for the analysis. Of the 24,800 who received IPT, 1.00% developed TB disease whereas of the 136,617 who never received IPT 6,085 (4.98%) developed TB disease. In 278,545.90 person-years of follow up, a total 7,052 new TB cases were diagnosed. Using the weighted sample, the overall TB incidence was 11.57 (95% CI: 11.09-12.07) per 1,000 person-years. The TB incidence among PLHIV who received IPT was 10.49 (95% CI: 9.11-12.15) per 1,000 person-years and 12.00 (95% CI: 11.69-12.33) per 1,000 person-years in those who never received IPT. After adjusting for other covariates there was 52% lower risk of developing TB disease among those who received IPT compared to those who never received IPT: aHR = 0.48 (95% CI: 0.40-0.58, P<0.001).

CONCLUSION: IPT reduced TB incidence by 52% in PLHIV attending routine CTC in Tanzania. IPTW adjusted the groups for imbalances in the covariates associated with receiving IPT to achieve comparable groups of IPT and non-IPT. This study has added evidence on the effectiveness of IPT in routine clinical settings and on the use of IPTW to determine impact of interventions in observational studies.

PMID:34255776 | DOI:10.1371/journal.pone.0254082

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Nevin Manimala Statistics

Precision of Serologic Testing from Dried Blood Spots Using a Multiplex Bead Assay

Am J Trop Med Hyg. 2021 Jul 12:tpmd210140. doi: 10.4269/ajtmh.21-0140. Online ahead of print.

ABSTRACT

Multiplex bead assays (MBAs) for serologic testing have become more prevalent in public health surveys, but few studies have assessed their test performance. As part of a trachoma study conducted in a rural part of Ethiopia in 2016, dried blood spots (DBS) were collected from a random sample of 393 children aged 0 to 9 years, with at least two separate 6-mm DBS collected on a filter card. Samples eluted from DBS were processed using an MBA on the Luminex platform for antibodies against 13 antigens of nine infectious organisms: Chlamydia trachomatis, Vibrio cholera, enterotoxigenic Escherichia coli, Cryptosporidium parvum, Entamoeba histolytica, Camplyobacter jejuni, Salmonella typhimurium Group B, Salmonella enteritidis Group D, and Giardia lamblia. Two separate DBS from each child were processed. The first DBS was run a single time, with the MBA set to read 100 beads per well. The second DBS was run twice, first at 100 beads per well and then at 50 beads per well. Results were expressed as the median fluorescence intensity minus background (MFI-BG), and classified as seropositive or seronegative according to external standards. Agreement between the three runs was high, with intraclass correlation coefficients of > 0.85 for the two Salmonella antibody responses and > 0.95 for the other 11 antibody responses. Agreement was also high for the dichotomous seropositivity indicators, with Cohen’s kappa statistics exceeding 0.87 for each antibody assay. These results suggest that serologic testing on the Luminex platform had strong test performance characteristics for analyzing antibodies using DBS.

PMID:34255738 | DOI:10.4269/ajtmh.21-0140

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Nevin Manimala Statistics

Cervicovaginal microbiota and metabolome predict preterm birth risk in an ethnically diverse cohort

JCI Insight. 2021 Jul 13:149257. doi: 10.1172/jci.insight.149257. Online ahead of print.

ABSTRACT

The syndrome of spontaneous preterm birth (sPTB) presents a challenge to mechanistic understanding, effective risk stratification, and management. Individual associations between sPTB, ethnicity, vaginal microbiota, metabolome and innate immune response are known, but not fully understood and knowledge has yet to impact clinical practice. Here we use multi-data type integration and composite statistical models to gain insight into sPTB risk by exploring the cervicovaginal environment of an ethnically heterogenous pregnant population (n=346 women; n=60 sPTB <37 weeks’ gestation, including n=27 sPTB <34 weeks). Analysis of cervicovaginal samples (10-15+6 weeks) identified novel interactions between risk of sPTB and microbiota, metabolite, and maternal host defense molecules. Statistical modelling identified a composite of metabolites (leucine, tyrosine, aspartate, lactate, betaine, acetate and Ca2+) associated with risk of sPTB <37 weeks (Area Under the Curve – AUC 0.752). A combination of glucose, aspartate, Ca2+ and Lactobacillus crispatus and L. acidophilus relative abundance, identified risk of early sPTB <34 weeks, (AUC 0.758); improved by ethnicity stratification (AUC 0.835). Increased relative abundance of L. acidophilus appeared protective against sPTB <34 weeks. By using cervicovaginal fluid samples, we demonstrate the potential of multi-datatype integration for developing composite models towards understanding the contribution of the vaginal environment to risk of sPTB.

PMID:34255744 | DOI:10.1172/jci.insight.149257