Tag: pubmed

Artif Intell Med. 2023 May;139:102536. doi: 10.1016/j.artmed.2023.102536. Epub 2023 Mar 29.

ABSTRACT

OBJECTIVE: Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs.

METHODS: Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly.

RESULTS: Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes.

CONCLUSION: There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.

PMID:37100507 | DOI:10.1016/j.artmed.2023.102536

Artif Intell Med. 2023 May;139:102534. doi: 10.1016/j.artmed.2023.102534. Epub 2023 Mar 28.

ABSTRACT

BACKGROUND: Indigenous peoples often have higher rates of morbidity and mortality associated with cardiometabolic disease (CMD) than non-Indigenous people and this may be even more so in urban areas. The use of electronic health records and expansion of computing power has led to mainstream use of artificial intelligence (AI) to predict the onset of disease in primary health care (PHC) settings. However, it is unknown if AI and in particular machine learning is used for risk prediction of CMD in Indigenous peoples.

METHODS: We searched peer-reviewed literature using terms associated with AI machine learning, PHC, CMD, and Indigenous peoples.

RESULTS: We identified 13 suitable studies for inclusion in this review. Median total number of participants was 19,270 (range 911-2,994,837). The most common algorithms used in machine learning in this setting were support vector machine, random forest, and decision tree learning. Twelve studies used the area under the receiver operating characteristic curve (AUC) to measure performance. Two studies reported an AUC of >0.9. Six studies had an AUC score between 0.9 and 0.8, 4 studies had an AUC score between 0.8 and 0.7. 1 study reported an AUC score between 0.7 and 0.6. Risk of bias was observed in 10 (77 %) studies.

CONCLUSION: AI machine learning and risk prediction models show moderate to excellent discriminatory ability over traditional statistical models in predicting CMD. This technology could help address the needs of urban Indigenous peoples by predicting CMD early and more rapidly than conventional methods.

PMID:37100506 | DOI:10.1016/j.artmed.2023.102534

Tob Control. 2023 Apr 26:tc-2023-057931. doi: 10.1136/tc-2023-057931. Online ahead of print.

ABSTRACT

BACKGROUND: In 2020, Ethiopia passed a landmark tax proclamation implementing an evidence-based mixed excise system aimed at curbing tobacco use. This study evaluates the impact of the tax increase of more than 600% on both legal and illegal cigarette prices in order to gauge the impact of the tax reform in the presence of a sizeable illicit cigarette market.

METHODS: Data on 1774 cigarette prices were obtained from retailers during Empty Cigarette Pack Surveys in the capital and major regional cities conducted in 2018 and 2022. Packs were categorised as ‘legal’ or ‘illicit’ using criteria from the tobacco control directives. Descriptive and regression analyses were used to study the cigarette price changes during the period of 2018-2022, capturing the impact of the 2020 tax increase.

RESULT: Prices of both legal and illegal cigarettes increased in response to the tax increase. In 2018, the stick prices ranged from ETB0.88 (Ethiopian birr) to ETB5.00 for legal cigarettes while they ranged from ETB0.75 to ETB3.25 for illegal ones. In 2022, a legal stick sold for ETB01.50-ETB2.73 and an illegal stick for ETB1.92-ETB8.00. The average real price of legal and illegal brands increased by 18% and 37%, respectively. The multivariate analysis confirms that prices of illicit cigarettes grew faster compared with the legal ones. By 2022, illicit brands were on average more expensive compared with their legal counterparts. This result is statistically significant at p<0.01.

CONCLUSION: The prices of both legal and illegal cigarettes increased following the 2020 tax increase, with the average real cigarette price increasing by 24%. As a result, the tax increase likely had a positive impact on public health despite a sizeable illicit cigarette market.

PMID:37100452 | DOI:10.1136/tc-2023-057931

Acta Psychiatr Scand. 2023 Apr 26. doi: 10.1111/acps.13561. Online ahead of print.

ABSTRACT

INTRODUCTION: Use of antipsychotic drugs, especially second-generation agents, has been suggested to cause acute pancreatitis in multiple case reports; however, such an association has not been corroborated by larger studies. This study examined the association of antipsychotic drugs with risk of acute pancreatitis.

METHODS: Nationwide case-control study, based on data from several Swedish registers and including all 52,006 cases of acute pancreatitis diagnosed in Sweden between 2006 and 2019 (with up to 10 controls per case; n = 518,081). Conditional logistic regression models were used to calculate odds ratios (ORs) in current and past users of first-generation and second-generation antipsychotic drugs (dispensed prescription <91 and ≥91 days of the index date, respectively) compared with never users of such drugs.

RESULTS: In the crude model, first-generation and second-generation antipsychotic drugs were associated with increased risk of acute pancreatitis, with slightly higher ORs for past use (1.58 [95% confidence interval 1.48-1.69] and 1.39 [1.29-1.49], respectively) than for current use (1.34 [1.21-1.48] and 1.24 [1.15-1.34], respectively). The ORs were largely attenuated in the multivariable model-which included, among others, alcohol abuse and the Charlson comorbidity index-up to the point where only a statistically significant association remained for past use of first-generation agents (OR 1.18 [1.10-1.26]).

CONCLUSION: There was no clear association between use of antipsychotic drugs and risk of acute pancreatitis in this very large case-control study, indicating that previous case report data are most likely explained by confounding.

PMID:37100434 | DOI:10.1111/acps.13561

Acta Psychiatr Scand. 2023 Apr 26. doi: 10.1111/acps.13556. Online ahead of print.

NO ABSTRACT

PMID:37100433 | DOI:10.1111/acps.13556

Int J Sports Physiol Perform. 2023 Apr 26:1-8. doi: 10.1123/ijspp.2022-0458. Online ahead of print.

ABSTRACT

BACKGROUND: The development and influence of physical capabilities and game action performance over the course of the season are a big challenge for coaches and players.

PURPOSE: The aims of the present study were to examine (1) the seasonal changes in the physical capabilities (mechanical and kinematic) and game-performance indicators in top-level men volleyball players and (2) the relationship between these physical capabilities and game-performance indicators in official matches.

METHODS: Eleven top-level players participated. Players were physically tested 3 times during the season. Before each test, players’ match performance (11 sets) was analyzed according to the level of opposition and match location. The percentage of change, statistical differences over the season (Friedman and Wilcoxon tests), and associations between variables (Spearman r) were calculated (P < .05) among mechanical (force-velocity profile during vertical jump and bench press), kinematic (jump height and spike ball speed), and game action performance features (coefficient, efficacy, and percentage of errors in serve, attack, and block).

RESULTS: The theoretical maximal force and velocity during vertical jump and bench press, respectively; the peak spike ball speed; and the serve efficacy significantly increased over the season. Moreover, there was a significant reduction in serve errors as the jump height increased (r = -.44; P = .026), as well as a significant increase in serve errors as the peak spike ball speed increased (r = -.62; P = .001).

CONCLUSION: These findings reveal how the physical and game action performance variables evolve and interact during the season. This may help coaches and trainers to monitor and analyze the most relevant volleyball performance factors.

PMID:37100426 | DOI:10.1123/ijspp.2022-0458

Global Spine J. 2023 Apr 26:21925682231172431. doi: 10.1177/21925682231172431. Online ahead of print.

NO ABSTRACT

PMID:37100407 | DOI:10.1177/21925682231172431

Environ Anal Health Toxicol. 2023 Mar;38(1):e2023006-0. doi: 10.5620/eaht.2023006. Epub 2023 Mar 23.

ABSTRACT

Recent studies reported bisphenol A (BPA) and propyl paraben (PrP) are found in human urine, blood, and breast milk samples as well as in food, packaging, socks, and clothes. This means that the two chemicals co-exist in consumer products, and humans are exposed simultaneously to the mixture chemicals. However, the studies on the mixture effects of the two chemicals on human health are not enough. This study was designed to elucidate the effects of orally administered PrP, BPA, and their mixture effects on the uterotrophic response using ovariectomized rats. In addition, the correlation between the uterotrophic response and tissue concentrations of the two chemicals was studied to investigate whether one chemical has any effect on the absorption, distribution, or excretion of the other chemical. Histopathology, hematology, and plasma biochemistry analysis were also performed to evaluate the chemicals’ toxicological effects in the treated rats. Although a significant increase in uterus weight (absolute and relative) was observed in the positive chemical (17β-estradiol) treated group, there were no statistical differences in the uterus weight between the vehicle control and the chemical-treated groups. However, a slight increase in the endometrial glands and a change in the cuboidal to columnar epithelium of the endometrial epithelium were observed in the mixture-treated group. There was no significant toxicity in all treated groups by the hematology and plasma biochemistry analysis results. The results of tissue distribution showed that BPA was mostly detected in the liver while PrP was not detected in most tissues, and the BPA level was higher when the rats were treated with PrP than without PrP, suggesting that PrP may increase the absorption of BPA after oral administration.

PMID:37100401 | DOI:10.5620/eaht.2023006

Curr Probl Cardiol. 2023 Apr 24:101746. doi: 10.1016/j.cpcardiol.2023.101746. Online ahead of print.

ABSTRACT

AIM: This study aimed to assess the association between aortic valve calcification and lipoprotein (a).

METHODS: We searched PUBMED, WOS, and SCOPUS databases. Inclusion criteria were any controlled clinical trials or observational studies that reported the level of Lipoprotein A in patients with aortic valve calcifications, excluding case reports, editorials and animal studies. RevMan software (5.4) was used to perform the meta-analysis.

RESULTS: After complete screening, 7 studies were included with a total number of 446179 patients included in the analysis. The pooled analysis showed a statistically significant association between the incidence of aortic valve calcium and increased levels of lipoprotein (a) compared with controls (SMD = 1.71, 95% CI = 1.04 to 2.38, p-value < 0.00001).

CONCLUSION: This meta-analysis showed a statistically significant association between the incidence of aortic valve calcium and increased levels of lipoprotein (a) compared with controls. Patients with high levels of lipoprotein (a) are at increased risk of developing aortic valve calcification. Medications targeting lipoprotein (a) in future clinical trials may be useful in primary prevention of aortic valve calcification in high risk patients.

PMID:37100357 | DOI:10.1016/j.cpcardiol.2023.101746

Am J Infect Control. 2023 Apr 24:S0196-6553(23)00335-8. doi: 10.1016/j.ajic.2023.04.165. Online ahead of print.

ABSTRACT

BACKGROUND: Surgical site infection (SSI) surveillance is a labor-intensive endeavor. We present the design and validation of an algorithm for SSI detection after hip replacement surgery, and a report of its successful implementation in four public hospitals in Madrid, Spain.

METHODS: We designed a multivariable algorithm, AI-HPRO, using natural language processing (NLP) and extreme gradient-boosting to screen for SSI in patients undergoing hip replacement surgery. The development and validation cohorts included data from 19661 healthcare episodes from four hospitals in Madrid, Spain.

RESULTS: Positive microbiological cultures, the text variable “infection”, and prescription of clindamycin were strong markers of SSI. Statistical analysis of the final model indicated high sensitivity (99.18%) and specificity (91.01%) with a F1-score of 0.32, AUC of 0.989, accuracy of 91.27% and NPV of 99.98%.

DISCUSSION: Implementation of the AI-HPRO algorithm has reduced surveillance time from 975 person/hours to 63.5 person/hours and has permitted an 88.95% reduction in total volume of clinical records to be reviewed manually. The model presents a higher NPV (99.98%) than algorithms relying on NLP alone (94%) or NLP and logistic regression (97%).

CONCLUSIONS: This is the first report of an algorithm combining NLP and extreme gradient-boosting to permit accurate, real-time orthopedic SSI surveillance.

PMID:37100291 | DOI:10.1016/j.ajic.2023.04.165