Tag: statistics

J Am Coll Surg. 2022 Aug 1;235(2):278-284. doi: 10.1097/XCS.0000000000000220. Epub 2022 Apr 8.

ABSTRACT

BACKGROUND: Older trauma patients present with poor preinjury functional status and more comorbidities. Advances in care have increased the chance of survival from previously fatal injuries with many left debilitated with chronic critical illness and severe disability. Palliative care (PC) is ideally suited to address the goals of care and symptom management in this critically ill population. A retrospective chart review was done to identify the impact of PC consults on hospital length of stay (LOS), ICU LOS, and surgical decisions.

STUDY DESIGN: A Level 1 Trauma Center Registry was used to identify adult patients who were provided PC consultation in a selected 3-year time period. These PC patients were matched with non-PC trauma patients on the basis of age, sex, race, Glasgow Coma Scale, and Injury Severity Score. Chi-square tests and Student’s t-tests were used to analyze categorical and continuous variables, respectively. Any p value >0.05 was considered statistically significant.

RESULTS: PC patients were less likely to receive a percutaneous endoscopic gastric tube or tracheostomy. PC patients spent less time on ventilator support, spent less time in the ICU, and had a shorter hospital stay. PC consultation was requested 16.48 days into the patient’s hospital stay. Approximately 82% of consults were to assist with goals of care.

CONCLUSION: Specialist PC team involvement in the care of the trauma ICU patients may have a beneficial impact on hospital LOS, ICU LOS, and surgical care rendered. Earlier consultation during hospitalization may lead to higher rates of goal-directed care and improved patient satisfaction.

PMID:35839403 | DOI:10.1097/XCS.0000000000000220

J Am Coll Surg. 2022 Aug 1;235(2):227-239. doi: 10.1097/XCS.0000000000000236. Epub 2022 Apr 18.

ABSTRACT

BACKGROUND: Although satisfactory volume reduction in secondary unilateral lower limb lymphedema after lymphaticovenous anastomosis (LVA) in the affected limb has been well reported, alleviation of muscle edema and the impact of LVA on the contralateral limb have not been investigated.

STUDY DESIGN: This retrospective cohort study enrolled patients who underwent supermicrosurgical LVA between November 2015 and January 2017. Pre- and post-LVA muscle edema were assessed using fractional anisotropy (FA) and apparent diffusion coefficient (ADC). The primary endpoint was changes in limb/subfascial volume assessed with magnetic resonance volumetry at least 6 months after LVA.

RESULTS: Twenty-one patients were enrolled in this study. Significant percentage reductions in post-LVA muscle edema were found in the affected thigh (83.6% [interquartile range = range of Q1 to Q3; 29.8-137.1] [FA], 53.3% [27.0-78.4] [ADC]) as well as limb (21.7% [4.4-26.5]) and subfascial (18.7% [10.7-39.1]) volumes. Similar findings were noted in the affected lower leg: 71.8% [44.0-100.1] (FA), 59.1% [45.8-91.2] (ADC), 21.2% [6.8-38.2], and 28.2% [8.5-44.8], respectively (all p < 0.001). Significant alleviation of muscle edema was also evident in the contralateral limbs (thigh: 25.1% [20.4-57.5] [FA]; 10.7% [6.6-17.7] [ADC]; lower leg: 47.1% [35.0-62.8] [FA]; 14.6% [6.5-22.1] [ADC]; both p < 0.001), despite no statistically significant difference in limb and subfascial volumes.

CONCLUSIONS: Our study found significant reductions in muscle edema and limb/subfascial volumes in the affected limb after LVA. Our findings regarding edema in the contralateral limb were consistent with possible lymphedema-associated systemic influence on the unaffected limb, which could be surgically relieved.

PMID:35839398 | DOI:10.1097/XCS.0000000000000236

Scand J Med Sci Sports. 2022 Jul 15. doi: 10.1111/sms.14212. Online ahead of print.

ABSTRACT

The aim of this systematic review and meta-analysis was to identify the genetic variants of (inter)national competing long-distance runners and road cyclists compared to controls. The Medline and Embase databases were searched until 15 November 2021. Eligible articles included genetic epidemiological studies published in English. A homogenous group of endurance athletes competing at (inter)national level and sedentary controls were included. Pooled odds ratios based on genotype frequency with corresponding 95% confidence intervals (95%CI) were calculated using random effects models. Heterogeneity was addressed by Q-statistics and I² . Sources of heterogeneity were examined by meta-regression and risk of bias was assessed with the Clark Baudouin scale. This systematic review comprised of 43 studies including a total of 3938 athletes and 10752 controls in the pooled analysis. Of the 42 identified genetic variants, 13 were investigated in independent studies. Significant associations were found for five polymorphisms. Pooled odds ratio [95%CI] favoring athletes compared to controls was 1.42 [1.12-1.81] for ACE II (I/D), 1.66 [1.26-2.19] for ACTN3 TT (rs1815739), 1.75 [1.34-2.29] for PPARGC1A GG (rs8192678), 2.23 [1.42-3.51] for AMPD1 CC (rs17602729), and 2.85 [1.27-6.39] for HFE GG+CG (rs1799945). Risk of bias was low in 25 (58%) and unclear in 18 (42%) articles. Heterogeneity of the results was low (0-20%) except for HFE (71%), GNB3 (80%), and NOS3 (76%). (Inter)national competing runners and cyclists have a higher probability to carry specific genetic variants compared to controls. This study confirms that (inter)national competing endurance athletes constitute a unique genetic make-up, which likely contributes to their performance level.

PMID:35839336 | DOI:10.1111/sms.14212

J Addict Med. 2022 Jul 16. doi: 10.1097/ADM.0000000000001034. Online ahead of print.

ABSTRACT

BACKGROUND: Los Angeles County Department of Health Services provides medical care to a diverse group of patients residing in underresourced communities. To improve patients’ access to addiction medications during the COVID-19 pandemic, Los Angeles County Department of Health Services established a low-barrier telephone service for DHS providers in March 2020, staffed by DATA-2000-waivered providers experienced with prescribing addiction medications. This study describes the patient population and medications prescribed through this service during its initial 12 months.

METHODS: We performed a retrospective evaluation of a provider-entered call registry for the telephone consult line. Information was collected between March 31, 2020, and March 30, 2021. The registry includes information related to patient demographics, the reason for visit, and which addiction medications were prescribed. We conducted descriptive statistics in each of these domains.

RESULTS: During the study period, 11 providers on the MAT telephone service logged 713 calls. These calls represented a total of 557 unique patients (mean age of 40 years, 75% male, 41% Latino, 49% experiencing homelessness). Most patients either had Medicaid insurance (77%) or were uninsured (20%). The most prescribed addiction medication was buprenorphine-naloxone (90%), followed by nicotine replacement therapy (5.3%), naltrexone (4.2%), and buprenorphine monotherapy (1.8%).

CONCLUSION: A telephone addiction medication service is feasible to deliver low-barrier medications to treat addiction in underresourced communities, especially to individuals experiencing homelessness. This can mitigate but does not eliminate disparities in access to addiction medications for communities of color.

PMID:35839323 | DOI:10.1097/ADM.0000000000001034

Colorectal Dis. 2022 Jul 15. doi: 10.1111/codi.16266. Online ahead of print.

ABSTRACT

BACKGROUND: Learning curve of total mesorectal excision (TME) by minimally invasive surgery (MIS) beyond the competency phase has not been adequately reported with large numbers or using a statistical control limit. We aimed to study the learning curves of MIS TME in the proficiency phase.

METHODS: Risk-adjusted (RA) cumulative sum (CUSUM) and RA Bernoulli CUSUM charts were plotted for sequential MIS TME performed by a surgical team over 1000 cases. Surgical failure, a composite endpoint of conversions, ≥ grade IIIA complications, R1 resections and inadequate nodal yield was used to monitor the performance.

RESULTS: The risk-adjusted CUSUM detected an inflection point around the 600^th operation. Two peaks were identified that could be traced back to probable causes for surgical failures. Similar inflection points were detected at 450^th case for laparoscopic TME and 367^th case for sphincter preservation. No single definite threshold point was noticed for robotic or abdominoperineal operations. At no point did the curves cross the safety threshold. Surgical failure probability reduced with increasing experience in the multivariate regression (OR – 0.899; p – 0.000). This association persisted irrespective of the surgical approach (laparoscopic vs robotic) or the type of operation (sphincter preservation vs abdominoperineal resection).

CONCLUSIONS: The learning curves for MIS TME did not cross the safety threshold beyond the competency phase. However, a 10% relative risk reduction in surgical failures was observed for every 100 cases operated.

PMID:35839321 | DOI:10.1111/codi.16266

Bipolar Disord. 2022 Jul 15. doi: 10.1111/bdi.13243. Online ahead of print.

ABSTRACT

BACKGROUND: The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established.

OBJECTIVES: To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators.

METHODS: We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges’ g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668).

RESULTS: The literature search identified 6,034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators.

CONCLUSION: We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.

PMID:35839276 | DOI:10.1111/bdi.13243

Biometrics. 2022 Jul 15. doi: 10.1111/biom.13719. Online ahead of print.

ABSTRACT

Inverse probability weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of causal effects. By adjusting for selection biases via a weighting mechanism, these procedures estimate an effect of interest by constructing a pseudo-population in which selection biases are eliminated. Despite their ease of use, these estimators require the correct specification of a model for the weighting mechanism, are known to be inefficient, and suffer from the curse of dimensionality. We propose a class of nonparametric inverse probability weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso, a nonparametric regression function proven to converge at nearly -rate to the true weighting mechanism. We demonstrate that our estimators are asymptotically linear with variance converging to the nonparametric efficiency bound. Unlike doubly robust estimators, our procedures require neither derivation of the efficient influence function nor specification of the conditional outcome model. Our theoretical developments have broad implications for the construction of efficient inverse probability weighted estimators in large statistical models and a variety of problem settings. We assess the practical performance of our estimators in simulation studies and demonstrate use of our proposed methodology with data from a large-scale epidemiologic study.

PMID:35839293 | DOI:10.1111/biom.13719

PLoS One. 2022 Jul 15;17(7):e0271265. doi: 10.1371/journal.pone.0271265. eCollection 2022.

ABSTRACT

AIMS: The progesterone response of the nuclear progesterone receptor plays an important role in the female reproductive system. Changes in the function of the progesterone receptor gene may increase the risk of reproductive cancer. The present study performed a meta-analysis to examine whether the progesterone receptor gene PROGINS polymorphism was a susceptibility factor for female reproductive cancer.

MATERIALS AND METHODS: We searched the PubMed, Cochrane Library, Web of Science and EMBASE databases for literature on PROGINS polymorphisms and female reproductive cancer published before September 2020. We evaluated the risk using odds ratios [ORs] and 95% confidence intervals via fixed effects models and random-effects models, which were calculated for all five genetic models. We grouped the analyses by race, cancer, and HWE.

RESULTS: Thirty studies comprised of 25405 controls and 19253 female reproductive cancer cases were included in this meta-analysis. We observed that the Alu insertion polymorphism and the V660L polymorphism were significantly associated with female reproductive cancer in the allele and dominant genetic models. The allele genetic model and (Alu-insertion polymorphism: OR = 1.22, 95% CI = 1.02-1.45; V660L polymorphism: OR = 1.02, 95% CI = 1.00-1.13) dominant genetic model (Alu-insertion polymorphism: OR = 1.27, 95% CI = 1.03-1.58; V660L polymorphism: OR = 1.10, 95% CI = 1.011.19) demonstrated a significantly increased risk of female reproductive cancer. A subgroup analysis according to ethnicity found that the Alu insertion was associated with female reproductive cancer incidence in white (Allele model: OR = 1.21, 95% CI = 1.00-1.45; Heterozygous model: OR = 3.44, 95% CI = 1.30-9.09) and Asian (Dominant model: OR = 3.12, 95% CI = 1.25-7.79) populations, but the association disappeared for African and mixed racial groups. However, the V660L polymorphism was significantly associated with female reproductive cancer in the African (Allele model: OR = 2.52, 95% CI = 1.14-5.56; Heterozygous model: OR = 2.83, 95% CI = 1.26-6.35) and mixed racial groups (Dominant model: OR = 1.28, 95% CI = 1.01-1.62). Subgroup analysis by cancer showed that the PROGINS polymorphism increased the risk of cancer in the allele model, dominant mode and heterozygous model, but the confidence interval for this result spanned 1 and was not statistically significant. This sensitivity was verified in studies with HWE greater than 0.5.

CONCLUSION: Our meta-analysis showed that the progesterone receptor gene Alu insertion and the V660L polymorphism contained in the PROGINS polymorphism were susceptibility factors for female reproductive cancer.

PMID:35839271 | DOI:10.1371/journal.pone.0271265

PLoS Comput Biol. 2022 Jul 15;18(7):e1010212. doi: 10.1371/journal.pcbi.1010212. Online ahead of print.

ABSTRACT

Longitudinal ‘omics analytical methods are extensively used in the field of evolving precision medicine, by enabling ‘big data’ recording and high-resolution interpretation of complex datasets, driven by individual variations in response to perturbations such as disease pathogenesis, medical treatment or changes in lifestyle. However, inherent technical limitations in biomedical studies often result in the generation of feature-rich and sample-limited datasets. Analyzing such data using conventional modalities often proves to be challenging since the repeated, high-dimensional measurements overload the outlook with inconsequential variations that must be filtered from the data in order to find the true, biologically relevant signal. Tensor methods for the analysis and meaningful representation of multi-way data may prove useful to the biological research community by their advertised ability to tackle this challenge. In this study, we present tcam-a new unsupervised tensor factorization method for the analysis of multi-way data. Building on top of cutting-edge developments in the field of tensor-tensor algebra, we characterize the unique mathematical properties of our method, namely, 1) preservation of geometric and statistical traits of the data, which enables uncovering information beyond the inter-individual variation that often takes-over the focus, especially in human studies. 2) Natural and straightforward out-of-sample extension, making tcam amenable for integration in machine learning workflows. A series of re-analyses of real-world, human experimental datasets showcase these theoretical properties, while providing empirical confirmation of tcam’s utility in the analysis of longitudinal ‘omics data.

PMID:35839259 | DOI:10.1371/journal.pcbi.1010212

PLoS Comput Biol. 2022 Jul 15;18(7):e1010328. doi: 10.1371/journal.pcbi.1010328. Online ahead of print.

ABSTRACT

Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods.

PMID:35839250 | DOI:10.1371/journal.pcbi.1010328