Category: Statistics

JMIR Bioinform Biotechnol. 2022 Aug 19;3(1):e38226. doi: 10.2196/38226.

ABSTRACT

BACKGROUND: Femoral neck fracture (FNF) accounts for approximately 3.58% of all fractures in the entire body, exhibiting an increasing trend each year. According to a survey, in 1990, the total number of hip fractures in men and women worldwide was approximately 338,000 and 917,000, respectively. In China, FNFs account for 48.22% of hip fractures. Currently, many studies have been conducted on postdischarge mortality and mortality risk in patients with FNF. However, there have been no definitive studies on in-hospital mortality or its influencing factors in patients with severe FNF admitted to the intensive care unit.

OBJECTIVE: In this paper, 3 machine learning methods were used to construct a nosocomial death prediction model for patients admitted to intensive care units to assist clinicians in early clinical decision-making.

METHODS: A retrospective analysis was conducted using information of a patient with FNF from the Medical Information Mart for Intensive Care III. After balancing the data set using the Synthetic Minority Oversampling Technique algorithm, patients were randomly separated into a 70% training set and a 30% testing set for the development and validation, respectively, of the prediction model. Random forest, extreme gradient boosting, and backpropagation neural network prediction models were constructed with nosocomial death as the outcome. Model performance was assessed using the area under the receiver operating characteristic curve, accuracy, precision, sensitivity, and specificity. The predictive value of the models was verified in comparison to the traditional logistic model.

RESULTS: A total of 366 patients with FNFs were selected, including 48 cases (13.1%) of in-hospital death. Data from 636 patients were obtained by balancing the data set with the in-hospital death group to survival group as 1:1. The 3 machine learning models exhibited high predictive accuracy, and the area under the receiver operating characteristic curve of the random forest, extreme gradient boosting, and backpropagation neural network were 0.98, 0.97, and 0.95, respectively, all with higher predictive performance than the traditional logistic regression model. Ranking the importance of the feature variables, the top 10 feature variables that were meaningful for predicting the risk of in-hospital death of patients were the Simplified Acute Physiology Score II, lactate, creatinine, gender, vitamin D, calcium, creatine kinase, creatine kinase isoenzyme, white blood cell, and age.

CONCLUSIONS: Death risk assessment models constructed using machine learning have positive significance for predicting the in-hospital mortality of patients with severe disease and provide a valid basis for reducing in-hospital mortality and improving patient prognosis.

PMID:38935949 | DOI:10.2196/38226

JMIR Mhealth Uhealth. 2024 Jun 27;12:e54634. doi: 10.2196/54634.

ABSTRACT

BACKGROUND: Rising rates of psychological distress (symptoms of depression, anxiety, and stress) among adults in the United States necessitate effective mental wellness interventions. Despite the prevalence of smartphone app-based programs, research on their efficacy is limited, with only 14% showing clinically validated evidence. Our study evaluates Noom Mood, a commercially available smartphone-based app that uses cognitive behavioral therapy and mindfulness-based programming. In this study, we address gaps in the existing literature by examining postintervention outcomes and the broader impact on mental wellness.

OBJECTIVE: Noom Mood is a smartphone-based mental wellness program designed to be used by the general population. This prospective study evaluates the efficacy and postintervention outcomes of Noom Mood. We aim to address the rising psychological distress among adults in the United States.

METHODS: A 1-arm study design was used, with participants having access to the Noom Mood program for 16 weeks (N=273). Surveys were conducted at baseline, week 4, week 8, week 12, week 16, and week 32 (16 weeks’ postprogram follow-up). This study assessed a range of mental health outcomes, including anxiety symptoms, depressive symptoms, perceived stress, well-being, quality of life, coping, emotion regulation, sleep, and workplace productivity (absenteeism or presenteeism).

RESULTS: The mean age of participants was 40.5 (SD 11.7) years. Statistically significant improvements in anxiety symptoms, depressive symptoms, and perceived stress were observed by week 4 and maintained through the 16-week intervention and the 32-week follow-up. The largest changes were observed in the first 4 weeks (29% lower, 25% lower, and 15% lower for anxiety symptoms, depressive symptoms, and perceived stress, respectively), and only small improvements were observed afterward. Reductions in clinically relevant anxiety (7-item generalized anxiety disorder scale) and depression (8-item Patient Health Questionnaire depression scale) criteria were also maintained from program initiation through the 16-week intervention and the 32-week follow-up. Work productivity also showed statistically significant results, with participants gaining 2.57 productive work days from baseline at 16 weeks, and remaining relatively stable (2.23 productive work days gained) at follow-up (32 weeks). Additionally, effects across all coping, sleep disturbance (23% lower at 32 weeks), and emotion dysregulation variables exhibited positive and significant trends at all time points (15% higher, 23% lower, and 25% higher respectively at 32 weeks).

CONCLUSIONS: This study contributes insights into the promising positive impact of Noom Mood on mental health and well-being outcomes, extending beyond the intervention phase. Though more rigorous studies are necessary to understand the mechanism of action at play, this exploratory study addresses critical gaps in the literature, highlighting the potential of smartphone-based mental wellness programs to lessen barriers to mental health support and improve diverse dimensions of well-being. Future research should explore the scalability, feasibility, and long-term adherence of such interventions across diverse populations.

PMID:38935946 | DOI:10.2196/54634

Aging (Albany NY). 2024 Jun 18;16. doi: 10.18632/aging.205953. Epub 2024 Jun 18.

ABSTRACT

OBJECTIVE: The primary objective of this study was to assess the diagnostic potential of galectin-3 (Gal-3), fractalkine (FKN), interleukin (IL)-6, microRNA(miR)-21, and cardiac troponin I (cTnI) in patients with ischemic cardiomyopathy (ICM).

METHOD: A total of 78 ICM patients (Case group) and 80 healthy volunteers (Control group) admitted to our hospital for treatment or physical examination from Aug. 2018 to Feb. 2020 were included in the current study. The serum concentration of Gal-3, FKN, IL-6, miR-21, and plasma expression of cTnI of both groups were determined. The severity of ICM was classified using New York Heart Association (NYHA) scale.

RESULTS: When compared with the control group, the case group had a significantly high blood concentration of Gal-3, FKN, IL-6, miR-21, and cTnI (P < 0.001). NYHA class II patients had lower blood levels of Gal-3, FKN, IL-6, miR-21, and cTnI than that in patients of NYHA class III and IV without statistical significance (P > 0.05). However, statistical significance could be achieved when comparing the above-analyzed markers in patients classified between class III and IV. Correlation analysis also revealed that serum levels of Gal-3, FKN, IL-6, miR-21, and cTnI were positively correlated with NYHA classification (R = 0.564, 0.621, 0.792, 0.981, P < 0.05).

CONCLUSION: Our study revealed that up-regulated serum Gal-3, FKN, IL-6, miR-21, and cTnI levels were closely related to the progression of ICM. This association implies that these biomarkers have diagnostic potential, offering a promising avenue for early detection and monitoring of ICM progression.

PMID:38935941 | DOI:10.18632/aging.205953

JMIR Bioinform Biotechnol. 2022 Sep 1;3(1):e38976. doi: 10.2196/38976.

ABSTRACT

BACKGROUND: Hashimoto thyroiditis (HT) is an autoimmune thyroid disease and the leading cause of hypothyroidism in areas with sufficient iodine intake. The quality-of-life impact and financial burden of hypothyroidism and HT highlight the need for additional research investigating the disease etiology with the aim of revealing potential modifiable risk factors.

OBJECTIVE: Implementation of measures against such risk factors, once identified, has the potential to lessen the financial burden while also improving the quality of life of many individuals. Therefore, we aimed to examine the potential seasonality of HT in Europe using the Google Trends data to explore whether there is a seasonal characteristic of Google searches regarding HT, examine the potential impact of the countries’ geographic location on the potential seasonality, and identify potential modifiable risk factors for HT, thereby inspiring future research on the topic.

METHODS: Monthly Google Trends data on the search topic “Hashimoto thyroiditis” were retrieved in a 17-year time frame from January 2004 to December 2020 for 36 European countries. A cosinor model analysis was conducted to evaluate potential seasonality. Simple linear regression was used to estimate the potential effect of latitude and longitude on seasonal amplitude and phase of the model outputs.

RESULTS: Of 36 included European countries, significant seasonality was observed in 30 (83%) countries. Most phase peaks occurred in spring (14/30, 46.7%) and winter (8/30, 26.7%). A statistically significant effect was observed regarding the effect of geographical latitude on cosinor model amplitude (y = -3.23 + 0.13 x; R²=0.29; P=.002). Seasonal increases in HT search volume may therefore be a consequence of an increased incidence or higher disease activity. It is particularly interesting that in most countries, a seasonal peak occurred in spring and winter months; when viewed in the context of the statistically significant impact of geographical latitude on seasonality amplitude, this may indicate the potential role of vitamin D levels in the seasonality of HT.

CONCLUSIONS: Significant seasonality of HT Google Trends search volume was observed in our study, with seasonal peaks in most countries occurring in spring and winter and with a significant impact of latitude on seasonality amplitude. Further studies on the topic of seasonality in HT and factors impacting it are required.

PMID:38935939 | DOI:10.2196/38976

Med Mycol. 2024 Jun 27;62(6):myad130. doi: 10.1093/mmy/myad130.

ABSTRACT

The World Health Organization, in response to the growing burden of fungal disease, established a process to develop a fungal priority pathogens list (FPPL). This systematic review aimed to evaluate the epidemiology and impact of invasive fungal disease due to Mucorales. PubMed and Web of Science were searched to identify studies published between January 1, 2011 and February 23, 2021. Studies reporting on mortality, inpatient care, complications and sequelae, antifungal susceptibility, risk factors, preventability, annual incidence, global distribution, and emergence during the study time frames were selected. Overall, 24 studies were included. Mortality rates of up to 80% were reported. Antifungal susceptibility varied across agents and species, with the minimum inhibitory concentrations lowest for amphotericin B and posaconazole. Diabetes mellitus was a common risk factor, detected in 65%-85% of patients with mucormycosis, particularly in those with rhino-orbital disease (86.9%). Break-through infection was detected in 13.6%-100% on azole or echinocandin antifungal prophylaxis. The reported prevalence rates were variable, with some studies reporting stable rates in the USA of 0.094-0.117/10 000 discharges between 2011 and 2014, whereas others reported an increase in Iran from 16.8% to 24% between 2011 and 2015. Carefully designed global surveillance studies, linking laboratory and clinical data, are required to develop clinical breakpoints to guide antifungal therapy and determine accurate estimates of complications and sequelae, annual incidence, trends, and global distribution. These data will provide robust estimates of disease burden to refine interventions and better inform future FPPL.

PMID:38935901 | DOI:10.1093/mmy/myad130

JCO Precis Oncol. 2024 Jun;8:e2300699. doi: 10.1200/PO.23.00699.

ABSTRACT

PURPOSE: Patients with hereditary cancer syndromes (HCS) have a high lifetime risk of developing cancer. Historically underserved populations have lower rates of genetic evaluation. We sought to characterize demographic factors that are associated with undergoing HCS evaluation in an urban safety-net patient population.

METHODS: All patients who met inclusion criteria for this study from 2016 to 2021 at an urban safety-net hospital were included in this analysis. Inclusion criteria were pathologically confirmed breast, ovarian/fallopian tube, colon, pancreatic, and prostate cancers. Patients also qualified for hereditary breast and ovarian cancers or Lynch syndrome on the basis of National Comprehensive Cancer Network guidelines. Institutional review board approval was obtained. Demographic and oncologic data were collected through retrospective chart review. Univariable and multivariable logistic regression models were constructed.

RESULTS: Of the 637 patients included, 40% underwent genetic testing. Variables associated with receiving genetic testing on univariable analysis included patients living at the time of data collection, female sex, Latinx ethnicity, Spanish language, family history of cancer, and referral for genetic testing. Patients identifying as Black, having Medicare, having pancreatic or prostate cancer, having stage IV disease, having Eastern Cooperative Oncology Group (ECOG) prognostic score ≥1, having medium or high Charlson comorbidity index, with current or previous cigarette use, and with previous alcohol use were negatively associated with testing. On multivariable modeling, family history of cancer was positively associated with testing. Patients identifying as Black, having colon or prostate cancer, and having ECOG score of 2 had significantly lower association with genetic testing.

CONCLUSION: Uptake of HCS was lower in patients identifying as Black, those with colon or prostate cancer, and those with an ECOG score of 2. Efforts to increase HCS testing in these patients will be important to advance equitable cancer care.

PMID:38935898 | DOI:10.1200/PO.23.00699

JCO Precis Oncol. 2024 Jun;8:e2300623. doi: 10.1200/PO.23.00623.

ABSTRACT

PURPOSE: Fluoropyrimidine-related toxicity and mortality risk increases significantly in patients carrying certain DPYD genetic variants with standard dosing. We implemented DPYD genotyping at a multisite cancer center and evaluated its impact on dosing, toxicity, and hospitalization.

METHODS: In this prospective observational study, patients receiving (reactive) or planning to receive (pretreatment) fluoropyrimidine-based chemotherapy were genotyped for five DPYD variants as standard practice per provider discretion. The primary end point was the proportion of variant carriers receiving fluoropyrimidine modifications. Secondary end points included mean relative dose intensity, fluoropyrimidine-related grade 3+ toxicities, and hospitalizations. Fisher’s exact test compared toxicity and hospitalization rates between pretreatment carriers, reactive carriers, and wild-type patients. Univariable and multivariable logistic regression identified factors associated with toxicity and hospitalization risk. Kaplan-Meier methods estimated time to event of first grade 3+ toxicity and hospitalization.

RESULTS: Of the 757 patients who received DPYD genotyping (median age 63, 54% male, 74% White, 19% Black, 88% GI malignancy), 45 (5.9%) were heterozygous carriers. Fluoropyrimidine was modified in 93% of carriers who started treatment. In 442 patients with 3-month follow-up, 64%, 31%, and 30% of reactive carriers, pretreatment carriers, and wild-type patients had grade 3+ toxicity, respectively (P = .085); 64%, 25%, and 13% were hospitalized (P < .001). Reactive carriers had 10-fold higher odds of hospitalization compared with wild-type patients (P = .001), whereas no significant difference was noted between pretreatment carriers and wild-type patients. Time-to-event of toxicity and hospitalization were significantly different between genotype groups (P < .001), with reactive carriers having the earliest onset and highest incidence.

CONCLUSION: DPYD genotyping prompted fluoropyrimidine modifications in most carriers. Pretreatment testing reduced toxicities and hospitalizations compared with reactive testing, thus normalizing the risk to that of wild-type patients, and should be considered standard practice.

PMID:38935897 | DOI:10.1200/PO.23.00623

JCO Precis Oncol. 2024 Jun;8:e2400103. doi: 10.1200/PO.24.00103.

ABSTRACT

PURPOSE: The National Cancer Institute-Children’s Oncology Group (NCI-COG) Pediatric MATCH trial assigns patients age 1-21 years with refractory malignancies to phase II treatment arms of molecularly targeted therapies on the basis of genetic alterations detected in their tumor. Patients with activating alterations in the mitogen-activated protein kinase pathway were treated with ulixertinib, an extracellular signal-regulated kinase (ERK)1/2 inhibitor.

METHODS: As there were no previous pediatric data, ulixertinib was initially tested in a dose escalation cohort to establish the recommended phase II dose (RP2D) before proceeding to the phase II cohort. Ulixertinib was administered at 260 mg/m²/dose orally twice a day (dose level 1 [DL1], n = 15) or 350 mg/m²/dose orally twice a day (DL2, n = 5). The primary end point was objective response rate; secondary end points included safety/tolerability and progression-free survival (PFS).

RESULTS: Twenty patients (median 12 years; range, 5-20) were treated, all evaluable for response. CNS tumors comprised 55% (11/20) of diagnoses, with high-grade glioma and low-grade glioma most common (n = 5 each). All CNS tumors except one harbored BRAF fusions or V600E mutations. Rhabdomyosarcoma (n = 5) was the most frequent non-CNS diagnosis. DL1 was declared the RP2D in the dose escalation cohort after dose-limiting toxicities in Cycle 1 occurred in 1/6 patients at DL1 and 2/5 patients at DL2, including fatigue, anorexia, rash, nausea, vomiting, diarrhea, dehydration, hypoalbuminemia, and hypernatremia. No objective responses were observed. Six-month PFS was 37% (95% CI, 17 to 58). Three patients with BRAF-altered CNS tumors achieved stable disease >6 months.

CONCLUSION: Ulixertinib, a novel targeted agent with no previous pediatric data, was successfully evaluated in a national precision medicine basket trial. The pediatric RP2D of ulixertinib is 260 mg/m²/dose orally twice a day. Limited single-agent efficacy was observed in a biomarker-selected cohort of refractory pediatric tumors.

PMID:38935895 | DOI:10.1200/PO.24.00103

JCO Clin Cancer Inform. 2024 Jun;8:e2300249. doi: 10.1200/CCI.23.00249.

ABSTRACT

PURPOSE: The expanding presence of the electronic health record (EHR) underscores the necessity for improved interoperability. To test the interoperability within the field of oncology research, our team at Vanderbilt University Medical Center (VUMC) enabled our Epic-based EHR to be compatible with the Minimal Common Oncology Data Elements (mCODE), which is a Fast Healthcare Interoperability Resources (FHIR)-based consensus data standard created to facilitate the transmission of EHRs for patients with cancer.

METHODS: Our approach used an extract, transform, load tool for converting EHR data from the VUMC Epic Clarity database into mCODE-compatible profiles. We established a sandbox environment on Microsoft Azure for data migration, deployed a FHIR server to handle application programming interface (API) requests, and mapped VUMC data to align with mCODE structures. In addition, we constructed a web application to demonstrate the practical use of mCODE profiles in health care.

RESULTS: We developed an end-to-end pipeline that converted EHR data into mCODE-compliant profiles, as well as a web application that visualizes genomic data and provides cancer risk assessments. Despite the complexities of aligning traditional EHR databases with mCODE standards and the limitations of FHIR APIs in supporting advanced statistical methodologies, this project successfully demonstrates the practical integration of mCODE standards into existing health care infrastructures.

CONCLUSION: This study provides a proof of concept for the interoperability of mCODE within a major health care institution’s EHR system, highlighting both the potential and the current limitations of FHIR APIs in supporting complex data analysis for oncology research.

PMID:38935887 | DOI:10.1200/CCI.23.00249

Ann Behav Med. 2024 Jun 27:kaae035. doi: 10.1093/abm/kaae035. Online ahead of print.

ABSTRACT

BACKGROUND: Adults with cancer have higher rates of comorbidity compared to those without cancer, with excess burden in people from lower socioeconomic status (SES). Social deprivation, based on geographic indices, broadens the focus of SES to include the importance of “place” and its association with health. Further, social support is a modifiable resource found to have direct and indirect effects on health in adults with cancer, with less known about its impact on comorbidity.

PURPOSE: We prospectively examined associations between social deprivation and comorbidity burden and the potential buffering role of social support.

METHODS: Our longitudinal sample of 420 adults (Mage = 59.6, SD = 11.6; 75% Non-Hispanic White) diagnosed with cancer completed measures at baseline (~6 months post-diagnosis) and four subsequent 3-month intervals for 1 year.

RESULTS: Adjusting for age, cancer type, and race/ethnicity, we found a statistically significant interaction between social support and the effect of social deprivation on comorbidity burden (β = -0.11, p = 0.012), such that greater social support buffered the negative effect of social deprivation on comorbidity burden.

CONCLUSION: Implementing routine screening for social deprivation in cancer care settings can help identify patients at risk of excess comorbidity burden. Clinician recognition of these findings could trigger a referral to social support resources for individuals high on social deprivation.

PMID:38935875 | DOI:10.1093/abm/kaae035