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Updates in Surgery Oct 2023Laser-assisted resection (LAR) of pulmonary metastases offers several potential advantages compared to conventional surgical techniques. However, the technical details,... (Review)
Review
Laser-assisted resection (LAR) of pulmonary metastases offers several potential advantages compared to conventional surgical techniques. However, the technical details, indications and outcomes of LAR have not been extensively reviewed. We conducted a systematic literature search to identify all original articles reporting on LAR of pulmonary metastases. All relevant outcomes, including morbidity rate, R0 rate, pulmonary function tests, overall- (OS) and relapse-free survival (RFS) rates were collected. Additionally, a comparison between outcomes obtained by laser-assisted and conventional resection techniques was provided. Of 2629 articles found by the initial search, 12 were selected for the systematic review. Following LAR, the R0 rate ranged between 72 and 100% and the morbidity rate ranged from 0 to 27.5%. The postoperative decline in forced expiratory volume in 1 s varied between 3.4 and 11%. Median OS and RFS were 42-77.6 months and 9-34.1 months, respectively. Compared with patients treated by other resection techniques, patients treated by LAR frequently had a higher number of metastases and a higher rate of bilateral disease. Despite this, no significant differences were observed in R0 rate, morbidity rate, and median OS rate, while only 1 study found a lower RFS rate in the LAR cohort. Although selection bias limits the comparability of outcomes, the findings of this review suggest that LAR is a valid alternative to conventional procedures of lung metastasectomy. The main difficulties of this technique consist in the adoption of a video-assisted thoracoscopic approach, and in the pathologic assessment of resection margins.
PubMed: 37347356
DOI: 10.1007/s13304-023-01564-x -
BMC Medical Research Methodology Aug 2023When conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from... (Review)
Review
BACKGROUND
When conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from observational data is challenging because of the risk of several statistical biases. In 2016 Hernán and Robins put forward the 'target trial framework' as a guide to best design and analyse observational studies whilst preventing the most common biases. This framework consists of (1) clearly defining a causal question about an intervention, (2) specifying the protocol of the hypothetical trial, and (3) explaining how the observational data will be used to emulate it.
METHODS
The aim of this scoping review was to identify and review all explicit attempts of trial emulation studies across all medical fields. Embase, Medline and Web of Science were searched for trial emulation studies published in English from database inception to February 25, 2021. The following information was extracted from studies that were deemed eligible for review: the subject area, the type of observational data that they leveraged, and the statistical methods they used to address the following biases: (A) confounding bias, (B) immortal time bias, and (C) selection bias.
RESULTS
The search resulted in 617 studies, 38 of which we deemed eligible for review. Of those 38 studies, most focused on cardiology, infectious diseases or oncology and the majority used electronic health records/electronic medical records data and cohort studies data. Different statistical methods were used to address confounding at baseline and selection bias, predominantly conditioning on the confounders (N = 18/49, 37%) and inverse probability of censoring weighting (N = 7/20, 35%) respectively. Different approaches were used to address immortal time bias, assigning individuals to treatment strategies at start of follow-up based on their data available at that specific time (N = 21, 55%), using the sequential trial emulations approach (N = 11, 29%) or the cloning approach (N = 6, 16%).
CONCLUSION
Different methods can be leveraged to address (A) confounding bias, (B) immortal time bias, and (C) selection bias. When working with observational data, and if possible, the 'target trial' framework should be used as it provides a structured conceptual approach to observational research.
Topics: Humans; Biomedical Research; Selection Bias; Databases, Factual; MEDLINE; Medical Oncology; Observational Studies as Topic
PubMed: 37587484
DOI: 10.1186/s12874-023-02000-9 -
Environmental Health Perspectives Aug 2023Neural tube defects (NTDs) affect pregnancies worldwide annually. Few nongenetic factors, other than folate deficiency, have been identified that may provide... (Review)
Review
BACKGROUND
Neural tube defects (NTDs) affect pregnancies worldwide annually. Few nongenetic factors, other than folate deficiency, have been identified that may provide intervenable solutions to reduce the burden of NTDs. Prenatal exposure to toxic metals [arsenic (As), cadmium (Cd), mercury (Hg), manganese (Mn) and lead (Pb)] may increase the risk of NTDs. Although a growing epidemiologic literature has examined associations, to our knowledge no systematic review has been conducted to date.
OBJECTIVE
Through adaptation of the Navigation Guide systematic review methodology, we aimed to answer the question "does exposure to As, Cd, Hg, Mn, or Pb during gestation increase the risk of NTDs?" and to assess challenges to evaluating this question given the current evidence.
METHODS
We selected available evidence on prenatal As, Cd, Hg, Mn, or Pb exposure and risk of specific NTDs (e.g., spina bifida, anencephaly) or all NTDs via a comprehensive search across MEDLINE, Embase, Web of Science, and TOXLINE databases and applied inclusion/exclusion criteria. We rated the quality and strength of the evidence for each metal. We applied a customized risk of bias protocol and evaluated the sufficiency of evidence of an effect of each metal on NTDs.
RESULTS
We identified 30 studies that met our criteria. Risk of bias for confounding and selection was high in most studies, but low for missing data. We determined that, although the evidence was limited, the literature supported an association between prenatal exposure to Hg or Mn and increased risk of NTDs. For the remaining metals, the evidence was inadequate to establish or rule out an effect.
CONCLUSION
The role of gestational As, Cd, or Pb exposure in the etiology of NTDs remains unclear and warrants further investigation in high-quality studies, with a particular focus on controlling confounding, mitigating selection bias, and improving exposure assessment. https://doi.org/10.1289/EHP11872.
Topics: Female; Pregnancy; Humans; Cadmium; Lead; Prenatal Exposure Delayed Effects; Neural Tube Defects; Mercury; Manganese; Arsenic
PubMed: 37647124
DOI: 10.1289/EHP11872 -
The Annals of Applied Statistics Dec 2023Coronavirus case-count data has influenced government policies and drives most epidemiological forecasts. Limited testing is cited as the key driver behind minimal...
Coronavirus case-count data has influenced government policies and drives most epidemiological forecasts. Limited testing is cited as the key driver behind minimal information on the COVID-19 pandemic. While expanded testing is laudable, measurement error and selection bias are the two greatest problems limiting our understanding of the COVID-19 pandemic; neither can be fully addressed by increased testing capacity. In this paper, we demonstrate their impact on estimation of point prevalence and the effective reproduction number. We show that estimates based on the millions of molecular tests in the US has the same mean square error as a small simple random sample. To address this, a procedure is presented that combines case-count data and random samples over time to estimate selection propensities based on key covariate information. We then combine these selection propensities with epidemiological forecast models to construct a estimation method that accounts for both measurement-error and selection bias. This method is then applied to estimate Indiana's active infection prevalence using case-count, hospitalization, and death data with demographic information, a statewide random molecular sample collected from April 25-29th, and Delphi's COVID-19 Trends and Impact Survey. We end with a series of recommendations based on the proposed methodology.
PubMed: 38939875
DOI: 10.1214/23-aoas1744 -
International Journal of Epidemiology Aug 2023Adjusting for multiple biases usually involves adjusting for one bias at a time, with careful attention to the order in which these biases are adjusted. A novel,...
BACKGROUND
Adjusting for multiple biases usually involves adjusting for one bias at a time, with careful attention to the order in which these biases are adjusted. A novel, alternative approach to multiple-bias adjustment involves the simultaneous adjustment of all biases via imputation and/or regression weighting. The imputed value or weight corresponds to the probability of the missing data and serves to 'reconstruct' the unbiased data that would be observed based on the provided assumptions of the degree of bias.
METHODS
We motivate and describe the steps necessary to implement this method. We also demonstrate the validity of this method through a simulation study with an exposure-outcome relationship that is biased by uncontrolled confounding, exposure misclassification, and selection bias.
RESULTS
The study revealed that a non-biased effect estimate can be obtained when correct bias parameters are applied. It also found that incorrect specification of every bias parameter by +/-25% still produced an effect estimate with less bias than the observed, biased effect.
CONCLUSIONS
Simultaneous multi-bias analysis is a useful way of investigating and understanding how multiple sources of bias may affect naive effect estimates. This new method can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies.
Topics: Humans; Selection Bias; Bias; Computer Simulation; Probability; Longitudinal Studies
PubMed: 36718093
DOI: 10.1093/ije/dyad001 -
Epidemiology (Cambridge, Mass.) Nov 2023We propose a novel definition of selection bias in analytic epidemiology using potential outcomes. This definition captures selection bias under both the structural...
We propose a novel definition of selection bias in analytic epidemiology using potential outcomes. This definition captures selection bias under both the structural approach (where conditioning on selection into the study opens a noncausal path from exposure to disease in a directed acyclic graph) and the traditional definition (where a given measure of association differs between the study sample and the population eligible for inclusion). This approach is nonparametric, and selection bias under the approach can be analyzed using single-world intervention graphs both under and away from the null hypothesis. It allows the simultaneous analysis of confounding and selection bias, it explicitly links the selection of study participants to the estimation of causal effects using study data, and it can be adapted to handle selection bias in descriptive epidemiology. Through examples, we show that this approach provides a novel perspective on the variety of mechanisms that can generate selection bias and simplifies the analysis of selection bias in matched studies and case-cohort studies.
PubMed: 37708480
DOI: 10.1097/EDE.0000000000001660 -
PeerJ 2023In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to...
BACKGROUND
In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction index (BCI) approach for quantifying the influence and direction of selection bias.
METHODS
We used a BCI, a function of selection probabilities conditional on outcome and covariates, with different selection bias scenarios in a logistic regression setting. A bias-correction sensitivity plot was illustrated to analyze the associations between proctoscopy examination and sociodemographic variables obtained using the data from the Taiwan National Health Interview Survey (NHIS) and of a subset of individuals who consented to having their health insurance data further linked.
RESULTS
We included 15,247 people aged ≥20 years, and 87.74% of whom signed the informed consent. When the entire sample was considered, smokers were less likely to undergo proctoscopic examination (odds ratio (OR): 0.69, 95% CI [0.57-0.84]), than nonsmokers were. When the data of only the people who provided consent were considered, the OR was 0.76 (95% CI [0.62-0.94]). The bias-correction sensitivity plot indicated varying ORs under different degrees of selection bias.
CONCLUSIONS
When data are only available in a subsample of a population, a bias-correction sensitivity plot can be used to easily visualize varying ORs under different selection bias scenarios. The similar strategy can be applied to models other than logistic regression if an appropriate BCI is derived.
Topics: Humans; Selection Bias; Surveys and Questionnaires; Insurance, Health; Odds Ratio; Informed Consent
PubMed: 38025739
DOI: 10.7717/peerj.16411 -
World Journal of Emergency Surgery :... Dec 2023To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional... (Review)
Review
BACKGROUND
To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes.
MAIN BODY
A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics.
RESULTS
In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues.
CONCLUSION
AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.
Topics: Adult; Humans; Artificial Intelligence; Appendicitis; Prognosis; Algorithms; Machine Learning; Acute Disease
PubMed: 38114983
DOI: 10.1186/s13017-023-00527-2 -
American Journal of Epidemiology Apr 2024The World Health Organization specifies that sexual health requires the potential for pleasurable and safe sexual experiences. Yet epidemiologic research into sexual...
The World Health Organization specifies that sexual health requires the potential for pleasurable and safe sexual experiences. Yet epidemiologic research into sexual pleasure and other positive sexual outcomes has been scant. In this commentary, we aim to support the development and adoption of sex-positive epidemiology, which we define as epidemiology that incorporates the study of pleasure and other positive features alongside sexually transmitted infections and other familiar negative outcomes. We first call epidemiologists' attention to the potential role that stigma plays in the suppression of sex-positive research. We further describe existing measures of sex-positive constructs that may be useful in epidemiologic research. Finally, the study of sex-positive constructs is vulnerable to biases that are well-known to epidemiologists, especially selection bias, information bias, and confounding. We outline how these biases influence existing research and identify opportunities for future research. Epidemiologists have the potential to contribute a great deal to the study of sexuality by bringing their considerable methodological expertise to long-standing challenges in the field. We hope to encourage epidemiologists to broaden their sexual health research to encompass positive outcomes and pleasure.
PubMed: 38634632
DOI: 10.1093/aje/kwae054 -
The Journal of Mental Health Policy and... Mar 2024Consensus-guidelines for prescribing antidepressants recommend that clinicians should be vigilant to match antidepressants to patient's medical history but provide no... (Observational Study)
Observational Study
BACKGROUND
Consensus-guidelines for prescribing antidepressants recommend that clinicians should be vigilant to match antidepressants to patient's medical history but provide no specific advice on which antidepressant is best for a given medical history.
AIMS OF THE STUDY
For patients with major depression who are in psychotherapy, this study provides an empirically derived guideline for prescribing antidepressant medications that fit patients' medical history.
METHODS
This retrospective, observational, cohort study analyzed a large insurance database of 3,678,082 patients. Data was obtained from healthcare providers in the U.S. between January 1, 2001, and December 31, 2018. These patients had 10,221,145 episodes of antidepressant treatments. This study reports the remission rates for the 14 most commonly prescribed single antidepressants (amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine) and a category named "Other" (other antidepressants/combination of antidepressants). The study used robust LASSO regressions to identify factors that affected remission rate and clinicians' selection of antidepressants. The selection bias in observational data was removed through stratification. We organized the data into 16,770 subgroups, of at least 100 cases, using the combination of the largest factors that affected remission and selection bias. This paper reports on 2,467 subgroups of patients who had received psychotherapy.
RESULTS
We found large, and statistically significant, differences in remission rates within subgroups of patients. Remission rates for sertraline ranged from 4.5% to 77.86%, for fluoxetine from 2.86% to 77.78%, for venlafaxine from 5.07% to 76.44%, for bupropion from 0.5% to 64.63%, for desvenlafaxine from 1.59% to 75%, for duloxetine from 3.77% to 75%, for paroxetine from 6.48% to 68.79%, for escitalopram from 1.85% to 65%, and for citalopram from 4.67% to 76.23%. Clearly these medications are ideal for patients in some subgroups but not others. If patients are matched to the subgroups, clinicians can prescribe the medication that works best in the subgroup. Some medications (amitriptyline, doxepin, nortriptyline, and trazodone) always had remission rates below 11% and therefore were not suitable as single antidepressant therapy for any of the subgroups.
DISCUSSIONS
This study provides an opportunity for clinicians to identify an optimal antidepressant for their patients, before they engage in repeated trials of antidepressants.
IMPLICATIONS FOR HEALTH CARE PROVISION AND USE
To facilitate the matching of patients to the most effective antidepressants, this study provides access to a free, non-commercial, decision aid at http://MeAgainMeds.com.
IMPLICATIONS FOR HEALTH POLICIES
Policymakers should evaluate how study findings can be made available through fragmented electronic health records at point-of-care. Alternatively, policymakers can put in place an AI system that recommends antidepressants to patients online, at home, and encourages them to bring the recommendation to their clinicians at their next visit.
IMPLICATIONS FOR FURTHER RESEARCH
Future research could investigate (i) the effectiveness of our recommendations in changing clinical practice, (ii) increasing remission of depression symptoms, and (iii) reducing cost of care. These studies need to be prospective but pragmatic. It is unlikely random clinical trials can address the large number of factors that affect remission.
Topics: Humans; Citalopram; Fluoxetine; Paroxetine; Sertraline; Bupropion; Nortriptyline; Amitriptyline; Duloxetine Hydrochloride; Venlafaxine Hydrochloride; Desvenlafaxine Succinate; Escitalopram; Trazodone; Doxepin; Prospective Studies; Cohort Studies; Retrospective Studies; Antidepressive Agents; Psychotherapy
PubMed: 38634393
DOI: No ID Found