Tag: statistics

JAMA Surg. 2022 Oct 26. doi: 10.1001/jamasurg.2022.4898. Online ahead of print.

NO ABSTRACT

PMID:36287542 | DOI:10.1001/jamasurg.2022.4898

JAMA Dermatol. 2022 Oct 26. doi: 10.1001/jamadermatol.2022.3911. Online ahead of print.

ABSTRACT

IMPORTANCE: Acne and rosacea have substantial implications for quality of life, and it is therefore important to ensure the patient’s voice is being captured in pivotal randomized clinical trials (RCTs). Although patient-reported outcome measures (PROMs) are a valuable tool to capture the patient perspective, little is known about use of PROMs in RCTs on acne and rosacea.

OBJECTIVE: To characterize the use of PROMs in RCTs on acne and rosacea.

EVIDENCE REVIEW: A systematic literature search was conducted using the search terms acne vulgaris and rosacea in the following databases: MEDLINE through PubMed, Embase, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews. A modified search hedge for RCTs from the McGill Library was applied. All phase 2, 3, and 4 RCTs published between December 31, 2011, through December 31, 2021, that evaluated the efficacy and safety of therapies for acne and rosacea vs any comparator were eligible for inclusion.

FINDINGS: A total of 2461 publications describing RCTs were identified, of which 206 RCTs met the inclusion criteria (163 trials [79%] on acne and 43 [21%] on rosacea). At least 1 PROM was used in 53% of trials (110) included; PROM use was more common in rosacea RCTs (67% [n = 29]) compared with acne RCTs (50% [n = 81]). At least 1 dermatology-specific (13% [n = 27]) or disease-specific (14% [n = 28]) PROM was included in the RCTs analyzed. Only 7% of trials (14) included a PROM as a primary outcome measure. There was no statistically significant increase in PROM inclusion over the study period (11 of 21 trials in 2011 vs 5 of 12 trials in 2021).

CONCLUSIONS AND RELEVANCE: In this systematic review, PROMs were included in approximately one-half of acne and rosacea RCTs performed over the study period. In addition, PROMs were rarely used as a primary outcome measure, and inclusion of PROMs has not increased substantially over the past 10 years. Increasing use of PROMs in RCTs can ensure that the patient’s perspective is captured during the development of new treatments for acne and rosacea.

PMID:36287541 | DOI:10.1001/jamadermatol.2022.3911

JAMA Surg. 2022 Oct 26. doi: 10.1001/jamasurg.2022.4904. Online ahead of print.

NO ABSTRACT

PMID:36287539 | DOI:10.1001/jamasurg.2022.4904

JAMA Psychiatry. 2022 Oct 26. doi: 10.1001/jamapsychiatry.2022.3475. Online ahead of print.

ABSTRACT

IMPORTANCE: Psychiatric disorders are common among autistic children and adults. Little is known about sex differences in psychiatric disorders and hospitalization in early adulthood.

OBJECTIVE: To examine sex differences in psychiatric diagnoses and hospitalizations in autistic compared with nonautistic young adults.

DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study assessed all individuals born in Sweden between 1985 and 1997. A total of 1 335 753 individuals, including 20 841 autistic individuals (7129 [34.2%] female individuals), were followed up from age 16 through 24 years between 2001 and 2013. Analysis took place between June 2021 and August 2022.

EXPOSURES: Autism was defined as having received at least 1 clinical diagnosis of autism based on the International Classification of Diseases.

MAIN OUTCOMES AND MEASURES: The cumulative incidence of 11 psychiatric diagnoses up until age 25 years was estimated, and birth year-standardized risk difference was used to compare autistic female and male individuals directly. Sex-specific birth year-adjusted hazard ratios (HRs) with 95% CIs were calculated using Cox regression. Analyses were repeated for inpatient diagnoses to assess psychiatric hospitalization.

RESULTS: Of 1 335 753 individuals included in this study, 650 314 (48.7%) were assigned female at birth. Autism was clinically diagnosed in 20 841 individuals (1.6%; 7129 [34.2%] female) with a mean (SD) age of 16.1 (5.1) years (17.0 [4.8] years in female individuals and 15.7 [5.2] years in male individuals) for the first recorded autism diagnosis. For most disorders, autistic female individuals were at higher risk for psychiatric diagnoses and hospitalizations. By age 25 years, 77 of 100 autistic female individuals and 62 of 100 autistic male individuals received at least 1 psychiatric diagnosis. Statistically significant standardized risk differences were observed between autistic female and male individuals for any psychiatric disorder (-0.18; 95% CI, -0.26 to -0.10) and specifically for anxiety, depressive, and sleep disorders. Risk differences were larger among autistic than nonautistic individuals. Compared with nonautistic same-sex individuals, autistic female individuals (HR range [95% CI], 3.17 [2.50-4.04.]-20.78 [18.48-23.37]) and male individuals (HR range [95% CI], 2.98 [2.75-3.23]-18.52 [17.07-20.08]) were both at increased risk for all psychiatric diagnoses. Any psychiatric hospitalization was statistically significantly more common in autistic female individuals (32 of 100) compared with autistic male individuals (19 of 100). However, both autistic female and male individuals had a higher relative risk for psychiatric hospitalization compared with nonautistic female and male individuals for all disorders (female individuals: HR range [95% CI], 5.55 [4.63-6.66]-26.30 [21.50-32.16]; male individuals: HR range [95% CI], 3.79 [3.22-4.45]-29.36 [24.04-35.87]).

CONCLUSIONS AND RELEVANCE: These findings highlight the need for profound mental health services among autistic young adults. Autistic female individuals, who experience more psychiatric difficulties at different levels of care, require increased clinical surveillance and support.

PMID:36287538 | DOI:10.1001/jamapsychiatry.2022.3475

J Basic Microbiol. 2022 Oct 26. doi: 10.1002/jobm.202200144. Online ahead of print.

ABSTRACT

A consortium isolated from the Persian Gulf is evaluated for its ability to bioremediate petroleum-contaminated soils. The soil sample was collected from oil fields of South Western Iran. The crude oil concentrations were set to 1000-10,000 mg/kg, and the sodium chloride concentration was set to 0.5%, 1%, 1.5%, 2%, and 2.5%. Operational parameters including volume (2-20 ml) and soil moisture (25%, 50%, and 100%) were studied consecutively according to one factor at the time of experimental design. A total number of eight different isolates capable of degrading crude oil were isolated from hydrocarbon-contaminated sites (KL1-KL8). The removal efficiency of Total petroleom hydrocarbons (TPH) with an initial concentration of 1000 mg/kg for numbers of bacterial cells per gram soil of 2, 10, and 20 CFU/g was 20.9%, 45%, and 60%, respectively. The removal efficiency of TPHs (initial concentration of 1000 mg/kg) at the end of fifth week for salinity amounts of 0.5%, 1%, 1.5%, 2%, and 2.5% was 10.87%, 22.4%, 25.7%, 68.6%, and 60.5%, respectively. The TPHs biodegradation efficiencies at different soil/water ratios of 25%, 50%, and 100% (slurry) were 12%, 28.7%, and 60.8%, respectively. In sunflowers, there was no statistically significant difference in seed germination for different levels of soil pollution (p > 0.05). The results of the current work suggest that this process is a viable and efficient method for remediating contaminated sites. To enhance the removal results in real soil, a scale-up study should also be conducted.

PMID:36285670 | DOI:10.1002/jobm.202200144

Biom J. 2022 Oct 26. doi: 10.1002/bimj.202100361. Online ahead of print.

ABSTRACT

Joint analysis of recurrent and nonrecurrent terminal events has attracted substantial attention in literature. However, there lacks formal methodology for such analysis when the event time data are on discrete scales, even though some modeling and inference strategies have been developed for discrete-time survival analysis. We propose a discrete-time joint modeling approach for the analysis of recurrent and terminal events where the two types of events may be correlated with each other. The proposed joint modeling assumes a shared frailty to account for the dependence among recurrent events and between the recurrent and the terminal terminal events. Also, the joint modeling allows for time-dependent covariates and rich families of transformation models for the recurrent and terminal events. A major advantage of our approach is that it does not assume a distribution for the frailty, nor does it assume a Poisson process for the analysis of the recurrent event. The utility of the proposed analysis is illustrated by simulation studies and two real applications, where the application to the biochemists’ rank promotion data jointly analyzes the biochemists’ citation numbers and times to rank promotion, and the application to the scleroderma lung study data jointly analyzes the adverse events and off-drug time among patients with the symptomatic scleroderma-related interstitial lung disease.

PMID:36285659 | DOI:10.1002/bimj.202100361

Am J Sports Med. 2022 Oct 26:3635465221129870. doi: 10.1177/03635465221129870. Online ahead of print.

ABSTRACT

BACKGROUND: Sports levels, baseline patient-reported outcome measures (PROMs), and surgical procedures are correlated with the outcomes of anterior cruciate ligament reconstruction (ACLR). Machine learning may be superior to conventional statistical methods in making repeatable and accurate predictions.

PURPOSE: To identify the best-performing machine learning models for predicting the objective and subjective clinical outcomes of ACLR and to determine the most important predictors.

STUDY DESIGN: Case-control study; Level of evidence, 3.

METHODS: A total of 432 patients who underwent anatomic double-bundle ACLR with hamstring tendon autograft between January 2010 and February 2019 were included in the machine learning analysis. A total of 15 predictive variables and 6 outcome variables were selected to validate the logistic regression, Gaussian naïve Bayes machine, random forest, Extreme Gradient Boosting (XGBoost), isotonically calibrated XGBoost, and sigmoid calibrated XGBoost models. For each clinical outcome, the best-performing model was determined using the area under the receiver operating characteristic curve (AUC), whereas the importance and direction of each predictive variable were demonstrated in a Shapley Additive Explanations summary plot.

RESULTS: The AUC and accuracy of the best-performing model, respectively, were 0.944 (excellent) and 98.6% for graft failure; 0.920 (excellent) and 91.4% for residual laxity; 0.930 (excellent) and 91.0% for failure to achieve the minimal clinically important difference (MCID) of the Lysholm score; 0.942 (excellent) and 95.1% for failure to achieve the MCID of the International Knee Documentation Committee (IKDC) score; 0.773 (fair) and 70.5% for return to preinjury sports; and 0.777 (fair) and 69.2% for return to pivoting sports. Medial meniscal resection, participation in competitive sports, and steep posterior tibial slope were top predictors of graft failure, whereas high-grade preoperative knee laxity, long follow-up period, and participation in competitive sports were top predictors of residual laxity. High preoperative Lysholm and IKDC scores were highly predictive of not achieving the MCIDs of PROMs. Young age, male sex, high preoperative IKDC score, and large graft diameter were important predictors of return to preinjury or pivoting sports.

CONCLUSION: Machine learning analysis can provide reliable predictions for the objective and subjective clinical outcomes (graft failure, residual laxity, PROMs, and return to sports) of ACLR. Patient-specific evaluation and decision making are recommended before and after surgery.

PMID:36285651 | DOI:10.1177/03635465221129870

Clin Nurs Res. 2022 Oct 26:10547738221132096. doi: 10.1177/10547738221132096. Online ahead of print.

ABSTRACT

Smoking adversely affects tuberculosis (TB) outcomes and may be associated with depression and anxiety among people diagnosed with TB in Botswana. We conducted a cross-sectional study among patients newly diagnosed with TB in Gaborone, Botswana, evaluating factors associated with self-reported cigarette smoking. We performed Poisson regression analyses with robust variance to examine whether depressive and anxiety symptoms were associated with smoking. Among 180 participants with TB enrolled from primary health clinics, depressive symptoms were reported in 47 (26.1%) participants and anxiety symptoms were reported in 85 (47.2%) participants. Overall, 45 (25.0%) participants reported current smoking. Depressive symptoms were associated with a higher prevalence of smoking (adjusted prevalence ratio [aPR]: 2.04; 95% confidence interval [CI]: 1.29-3.25) in the adjusted analysis. The association between anxiety symptoms and smoking did not reach statistical significance (aPR: 1.26; 95% CI: 0.77-2.05). Future studies should further investigate these associations when addressing TB care.

PMID:36285635 | DOI:10.1177/10547738221132096

NMR Biomed. 2022 Oct 26:e4856. doi: 10.1002/nbm.4856. Online ahead of print.

ABSTRACT

Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean-kurtosis (MK)-Curve. A total of 10 volunteers (four women, age: 41.4±9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3T scanner and included a T1-weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK-Curve DKI fit was performed including intra-class correlation (ICC) and Bland-Altman (BA)plot statistics. A p-value <.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor- and diffusion tensor-derived MK-Curve corrected metrics (ICC values: in the range 0.77 – 0.98 and 0.87 – 0.98,resp.) with the exception of, two DKI derived metrics (Axial kurtosis in cortex: ICC=0.68, and radial kurtosis in deep grey matter: ICC =0.544). Non-MK-Curve corrected kurtosis tensor-derived metrics ranged between 0.01 – 0.52 and diffusion tensor-derived metrics between 0.06 – 0.66 indicating poor to moderate reliability. No structural bias was observed in the Bland-Altman plots for any of the diffusion metrics. In conclusion, MK-Curve corrected DKI metrics of the human brain can be reliably acquired in white and grey matter at 3T and DKI metrics have good to excellent agreement in a test-retest setting.

PMID:36285630 | DOI:10.1002/nbm.4856

J Magn Reson Imaging. 2022 Oct 26. doi: 10.1002/jmri.28487. Online ahead of print.

ABSTRACT

BACKGROUND: Cerebral microbleeds (CMBs) are microscopic brain hemorrhages with implications for various diseases. Automated detection of CMBs is a challenging task due to their wide distribution throughout the brain, small size, and visual similarity to their mimics. For this reason, most of the previously proposed methods have been accomplished through two distinct stages, which may lead to difficulties in integrating them into clinical workflows.

PURPOSE: To develop a clinically feasible end-to-end CMBs detection network with a single-stage structure utilizing 3D information. This study proposes triplanar ensemble detection network (TPE-Det), ensembling 2D convolutional neural networks (CNNs) based detection networks on axial, sagittal, and coronal planes.

STUDY TYPE: Retrospective.

SUBJECTS: Two datasets (DS1 and DS2) were used: 1) 116 patients with 367 CMBs and 12 patients without CMBs for training, validation, and testing (70.39 ± 9.30 years, 68 women, 60 men, DS1); 2) 58 subjects with 148 microbleeds and 21 subjects without CMBs only for testing (76.13 ± 7.89 years, 47 women, 32 men, DS2).

FIELD STRENGTH/SEQUENCE: A 3 T field strength and 3D GRE sequence scan for SWI reconstructions.

ASSESSMENT: The sensitivity, FP_avg (false-positive per subject), and precision measures were computed and analyzed with statistical analysis.

STATISTICAL TESTS: A paired t-test was performed to investigate the improvement of detection performance by the suggested ensembling technique in this study. A P value < 0.05 was considered significant.

RESULTS: The proposed TPE-Det detected CMBs on the DS1 testing set with a sensitivity of 96.05% and an FP_avg of 0.88, presenting statistically significant improvement. Even when the testing on DS2 was performed without retraining, the proposed model provided a sensitivity of 85.03% and an FP_avg of 0.55. The precision was significantly higher than the other models.

DATA CONCLUSION: The ensembling of multidimensional networks significantly improves precision, suggesting that this new approach could increase the benefits of detecting lesions in the clinic.

EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

PMID:36285604 | DOI:10.1002/jmri.28487