Classification And Regression Tree Analysis Research Articles

Characterisation of the ringed seal (Pusa hispida) acoustic repertoire during spring in the Western Canadian Arctic

ABSTRACT Understanding the acoustic repertoire of a species is crucial for comprehending its ecology and can also provide a valuable tool in the analysis of passive acoustic monitoring data. Despite being the most abundant seal species in the Arctic, the call repertoire of ringed seals (Pusa hispida) remains poorly studied. This paper aims to provide a comprehensive and quantitative description of the calls produced by ringed seals. Data collection occurred in May 2022 near Ulukhaktok in the Western Canadian Arctic. Acoustic recorders were deployed in cracks in the sea ice that were regularly used by ringed seals as haul-out areas. All calls were counted and classified into one of three categories: yelps, barks and growls. High-quality calls were further analysed with ten acoustic parameters calculated for each signal. Cluster and classification and regression tree (CART) analyses were used to assess if the visual classification of calls was supported by the acoustic parameters calculated. The cluster and CART analyses affirmed the presence of all three distinct call types in the ringed seal acoustic repertoire. Classification of unseen calls using the CART model demonstrated an accuracy of 98%. The findings presented here serve as foundational information on the acoustic repertoire of ringed seals.

Expression of CD163 and major histocompatibility complex class I as diagnostic markers for idiopathic inflammatory myopathies

BackgroundTo develop an inflammation-related immunohistochemistry marker-based algorithm that confers higher diagnostic ability for idiopathic inflammatory myopathies (IIMs) than IIM-related histopathologic features.MethodsMuscle biopsy tissues from 129 IIM patients who met the 2017 EULAR/ACR criteria and 73 control tissues from patients with non-inflammatory myopathies or healthy muscle specimens were evaluated for histological features and immunostaining results of CD3, CD4, CD8, CD20, CD68, CD163, MX1, MHC class I, MHC class II, and HLA-DR. Diagnostic algorithms for IIM were developed based on the results of the classification and regression tree (CART) analysis, which used immunostaining results as predictor variables for classifying patients with IIMs.ResultsIn the analysis set (IIM, n = 129; control, n = 73), IIM-related histopathologic features had a diagnostic accuracy of 87.6% (sensitivity 80.6%; specificity 100.0%) for IIMs. Notably, muscular expression of CD163 (99.2% vs. 20.8%, p < 0.001) and MHC class I (87.6% vs. 23.1%, p < 0.001) was significantly higher in the IIM group than in controls. Based on the CART analysis results, we developed an algorithm combining CD163 and MHC class I expression that conferred a diagnostic accuracy of 95.5% (sensitivity 96.1%; specificity 94.5%). In addition, our algorithm was able to correctly diagnose IIM in 94.1% (16/17) of patients who did not meet the 2017 EUALR/ACR criteria but were diagnosed as having IIMs by an expert physician.ConclusionsCombination of CD163 and MHC class I muscular expression may be useful in diagnosing IIMs.

PECARN prediction rule for cervical spine imaging of children presenting to the emergency department with blunt trauma: a multicentre prospective observational study.

Cervical spine injuries in children are uncommon but potentially devastating; however, indiscriminate neck imaging after trauma unnecessarily exposes children to ionising radiation. The aim of this study was to derive and validate a paediatric clinical prediction rule that can be incorporated into an algorithm to guide radiographic screening for cervical spine injury among children in the emergency department. In this prospective observational cohort study, we screened children aged 0-17 years presenting with known or suspected blunt trauma at 18 specialised children's emergency departments in hospitals in the USA affiliated with the Pediatric Emergency Care Applied Research Network (PECARN). Injured children were eligible for enrolment into derivation or validation cohorts by fulfilling one of the following criteria: transported from the scene of injury to the emergency department by emergency medical services; evaluated by a trauma team; and undergone neck imaging for concern for cervical spine injury either at or before arriving at the PECARN-affiliated emergency department. Children presenting with solely penetrating trauma were excluded. Before viewing an enrolled child's neck imaging results, the attending emergency department clinician completed a clinical examination and prospectively documented cervical spine injury risk factors in an electronic questionnaire. Cervical spine injuries were determined by imaging reports and telephone follow-up with guardians within 21-28 days of the emergency room encounter, and cervical spine injury was confirmed by a paediatric neurosurgeon. Factors associated with a high risk of cervical spine injury (>10%) were identified by bivariable Poisson regression with robust error estimates, and factors associated with non-negligible risk were identified by classification and regression tree (CART) analysis. Variables were combined in the cervical spine injury prediction rule. The primary outcome of interest was cervical spine injury within 28 days of initial trauma warranting inpatient observation or surgical intervention. Rule performance measures were calculated for both derivation and validation cohorts. A clinical care algorithm for determining which risk factors warrant radiographic screening for cervical spine injury after blunt trauma was applied to the study population to estimate the potential effect on reducing CT and x-ray use in the paediatric emergency department. This study is registered with ClinicalTrials.gov, NCT05049330. Nine emergency departments participated in the derivation cohort, and nine participated in the validation cohort. In total, 22 430 children presenting with known or suspected blunt trauma were enrolled (11 857 children in the derivation cohort; 10 573 in the validation cohort). 433 (1·9%) of the total population had confirmed cervical spine injuries. The following factors were associated with a high risk of cervical spine injury: altered mental status (Glasgow Coma Scale [GCS] score of 3-8 or unresponsive on the Alert, Verbal, Pain, Unresponsive scale [AVPU] of consciousness); abnormal airway, breathing, or circulation findings; and focal neurological deficits including paresthesia, numbness, or weakness. Of 928 in the derivation cohort presenting with at least one of these risk factors, 118 (12·7%) had cervical spine injury (risk ratio 8·9 [95% CI 7·1-11·2]). The following factors were associated with non-negligible risk of cervical spine injury by CART analysis: neck pain; altered mental status (GCS score of 9-14; verbal or pain on the AVPU; or other signs of altered mental status); substantial head injury; substantial torso injury; and midline neck tenderness. The high-risk and CART-derived factors combined and applied to the validation cohort performed with 94·3% (95% CI 90·7-97·9) sensitivity, 60·4% (59·4-61·3) specificity, and 99·9% (99·8-100·0) negative predictive value. Had the algorithm been applied to all participants to guide the use of imaging, we estimated the number of children having CT might have decreased from 3856 (17·2%) to 1549 (6·9%) of 22 430 children without increasing the number of children getting plain x-rays. Incorporated into a clinical algorithm, the cervical spine injury prediction rule showed strong potential for aiding clinicians in determining which children arriving in the emergency department after blunt trauma should undergo radiographic neck imaging for potential cervical spine injury. Implementation of the clinical algorithm could decrease use of unnecessary radiographic testing in the emergency department and eliminate high-risk radiation exposure. Future work should validate the prediction rule and care algorithm in more general settings such as community emergency departments. The Eunice Kennedy Shriver National Institute of Child Health and Human Development and the Health Resources and Services Administration of the US Department of Health and Human Services in the Maternal and Child Health Bureau under the Emergency Medical Services for Children programme.

Predicting pre- and post-operative acute kidney injury in elderly patients with coronary artery disease

BackgroundLimited evidence exists regarding the clinical baseline characteristics at admission for acute kidney injury (AKI) before and after interventional cardiac procedures (ICP) in elderly patients with coronary artery disease (CAD). MethodsA total of 488 elderly patients were enrolled in this retrospective single-center study conducted from January 2019 to July 2022, and a classification and regression tree (CART) analysis was performed to identify the high-risk population. ResultsThe AKI incidence was 21.1 % (103/488) in this study, with 27 and 76 individuals developing AKI before and after ICP, respectively. CART analysis revealed that exposure to nephrotoxic drugs and diuretics had the strongest predictive capacities for identifying patients at risk of developing pre-ICP AKI, with the incidence among these high-risk patients ranging from 6.5 % to 13.8 %. Meanwhile, the optimum discriminators for identifying those at high risk of post-ICP AKI were the administration of diuretics, D-value ≤ −860 mL, age >73 years, and administration of nephrotoxic drugs, and the latter model predicted that the AKI incidence among high-risk patients was between 50.0 % and 60.0 %. ConclusionsElderly patients with CAD exhibited an elevated incidence of AKI. CART models suggested that exposure to nephrotoxic drugs and diuretics, D-value, and age were significantly associated with AKI in the elderly with CAD. Importantly, these baseline characteristics at admission could be utilized to identify elderly patients at high risk of pre- and post-ICP AKI.

A CHEAPLY NON-DESTRUCTIVE TECHNIQUE TO ESTIMATE HONEY QUALITY: THERMAL IMAGING AND MACHINE LEARNING

The aim of this study was to estimate honey quality based on proline and Brix content using a thermal imaging and machine learning algorithm. The proline, Brix and color properties of twenty honey samples were determined. Proline and Brix values were classified and estimated using the classification and regression tree (CART) algorithm. The mean proline and Brix content in honey samples was 678.83±192.16 mg/kg and 83.2±0.79%, respectively. CART analysis revealed that high proline honey samples had L values above 48.143 and b* values below 35.416. In contrast, honey samples with low Brix values were characterized by L and a* values below 55.860 and 53.660, respectively, and were identified as freshly harvested. The CART algorithm classified the proline and Brix values with an accuracy of 95% and 100%, respectively (p&lt; 0.001). As a result, whitish, bluish, blackish and greenish honeys are of higher quality due to high proline and low Brix content. However, to accurately assess honey quality based on its color traits, comprehensive studies with more honey samples and origin, are required.

Investigation of melt flow index and tensile properties of dual metal reinforced polymer composites for 3D printing using machine learning approach: Biomedical and engineering applications

This study investigates the enhancement of mechanical properties of metal/polymer composites produced through fused deposition modeling and the prediction of the ultimate tensile strength (UTS) by machine learning using a Classification and Regression Tree (CART). The composites, comprising 80% acrylonitrile butadiene styrene matrix and 10% each of aluminum (Al) and copper (Cu) fillers, were subjected to a comprehensive exploration of printing parameters, including printing temperature, infill pattern, and infill density using the Taguchi method. The CART unveiled a hierarchical tree structure with four terminal nodes, each representing distinct subgroups of materials characterized by similar UTS properties. The predictors’ importance was assessed, highlighting their role in determining material strength. The model exhibited a high predictive power with an R-squared value of 0.9154 on the training data and 0.8922 on the test data, demonstrating its efficacy in capturing variability. The optimal combination of parameters for maximizing UTS was a zigzag infill pattern, a printing temperature of 245 °C, and an infill density of 10%, which is associated with the highest UTS of 680 N. The model’s reliability was confirmed through a paired t-test and test and confidence interval for two variances, revealing no significant difference between the observed and predicted UTS values. This research contributes to advancing additive manufacturing processes by leveraging CART analysis to optimize printing parameters and predict material strength. The identified optimal conditions and subgroup characteristics pave the way for developing robust and predictable metal/polymer composites, offering valuable insights for material design in the era of advanced manufacturing technologies.

A Potential Screening Strategy to Identify Probable Syphilis Infections in the Urban Emergency Department Setting.

Syphilis diagnosis in the emergency department (ED) setting is often missed due to the lack of ED-specific testing strategies. We characterized ED patients with high-titer syphilis infections (HTSIs) with the goal of defining a screening strategy that most parsimoniously identifies undiagnosed, untreated syphilis infections. Unlinked, de-identified remnant serum samples from patients attending an urban ED, between 10 January and 9 February 2022, were tested using a three-tier testing algorithm, and sociodemographic variables were extracted from ED administrative database prior to testing. Patients who tested positive for treponemal antibodies in the first tier and positive at high titer (≥1:8) for nontreponemal antibodies in the second tier were classified as HTSI. Human immunodeficiency virus (HIV) status was determined with Bio-Rad enzyme-linked immunosorbent assay and confirmatory assays. Exact logistic regression and classification and regression tree (CART) analyses were performed to determine factors associated with HTSI and derive screening strategies. Among 1951 unique patients tested, 23 (1.2% [95% confidence interval, .8%-1.8%]) had HTSI. Of those, 18 (78%) lacked a primary care physician, 5 (22%) were HIV positive, and 8 (35%) were women of reproductive age (18-49 years). CART analysis (area under the curve of 0.67) showed that using a screening strategy that measured syphilis antibodies in patients with HIV, without a primary care physician, and women of reproductive age would have identified most patients with HTSI (21/23 [91%]). We show a high prevalence of HTSI in an urban ED and propose a feasible, novel screening strategy to curtail community transmission and prevent long-term complications.

Dissecting Alzheimer's Disease Risk in Asian American Elders: A Classification and Regression Tree Approach.

Alzheimer's disease (AD) poses a growing public health challenge, particularly with an aging population. While extensive research has explored the relationships between AD, socio-demographic factors, and cardiovascular risk factors, a notable gap exists in understanding these connections within the Asian American elderly population. This study aims to address this gap by employing the Classification and Regression Tree (CART) approach to investigate the intricate interplay of socio-demographic variables, cardiovascular risk factors, sleep patterns, prior antidepressant use, and AD among Asian American elders. Data from the 2017 Uniform Data Set, provided by the National Alzheimer's Coordinating Center, were analyzed, focusing on a sample of Asian American elders (n = 4,343). The analysis utilized the Classification and Regression Tree (CART) approach. CART analysis identified critical factors, including levels of independence, specific age thresholds (73.5 and 84.5 years), apnea, antidepressant use, and body mass index, as significantly associated with AD risk. These findings have far-reaching implications for future research, particularly in examining the roles of gender, cultural nuances, socio-demographic factors, and cardiovascular risk elements in AD within the Asian American elderly population. Such insights can inform tailored interventions, improved healthcare access, and culturally sensitive policies to address the complex challenges posed by AD in this community.

Classification and Regression Tree Predictive Model for Acute Kidney Injury in Traumatic Brain Injury Patients.

Acute kidney injury (AKI) is prevalent in hospitalized patients with traumatic brain injury (TBI), and increases the risk of poor outcomes. We designed this study to develop a visual and convenient decision-tree-based model for predicting AKI in TBI patients. A total of 376 patients admitted to the emergency department of the West China Hospital for TBI between January 2015 and June 2019 were included. Demographic information, vital signs on admission, laboratory test results, radiological signs, surgical options, and medications were recorded as variables. AKI was confirmed since the second day after admission, based on the Kidney Disease Improving Global Outcomes criteria. We constructed two predictive models for AKI using least absolute shrinkage and selection operator (LASSO) regression and classification and regression tree (CART), respectively. Receiver operating characteristic (ROC) curves of these two predictive models were drawn, and the area under the ROC curve (AUC) was calculated to compare their predictive accuracy. The incidence of AKI on the second day after admission was 10.4% among patients with TBI. Lasso regression identified five potent predictive factors for AKI: glucose, serum creatinine, cystatin C, serum uric acid, and fresh frozen plasma transfusions. The CART analysis showed that glucose, serum uric acid, and cystatin C ranked among the top three in terms of the feature importance of the decision tree model. The AUC value of the decision-tree predictive model was 0.892, which was higher than the 0.854 of the LASSO regression model, although the difference was not statistically significant. The decision tree model is valuable for predicting AKI among patients with TBI. This tree-based flowchart is convenient for physicians to identify patients with TBI who are at high risk of AKI and prompts them to develop suitable therapeutic strategies.

Predictors of mortality in hospitalised patients with COVID-19: a 1-year case–control study

BackgroundA paucity of predictive models assessing risk factors for COVID-19 mortality that extend beyond age and gender in Latino population is evident in the current academic literature.ObjectivesTo determine the associated...

Land-use classification of Malaysian soils by ultra-high performance liquid chromatography (UHPLC)-based untargeted data combined with chemometrics for forensic provenance

Soil has become the most precious trace evidence attributed to its transference and persistence, but little is known about the capability of non-volatile organic fractions of soil in predicting land-use class. Hence, this study devoted to discriminating soils into four land-use classes based on chromatographic data and predictive modelling. Nine sites representing four different land-use classes were selected for collecting 73 soil samples via a grid method. Then, all the soil samples were dried, homogenized, sieved and eventually extracted using acetonitrile. The extracts were further analyzed via an isocratic elution in an ultra-high performance liquid chromatography (UHPLC) system. The pixel-level chromatograms were carefully assessed via diverse data preprocessing (DP) methods including baseline correction, normalization and derivative algorithms. Furthermore, the retention time (RT) window was also segmented into several sub-windows and thoroughly evaluated. Prediction accuracy of classification and regression tree (CART) modelling as estimated using 100 subsets of training samples on the corresponding testing and blind samples were ranked using TOPSIS method for identifying the most desired sub-window and DP strategy. The sub-RT window covering 0–5 min preprocessed via modified polynomial fitting algorithm followed by vector normalization emerged to be the most desired sub-dataset. The best sub-dataset modelled using CART and partial least squares-discriminant analysis (PLS2-DA) algorithms achieved 79.5 % and 97.4 % accuracy in predicting the land-use classes of 13 mixed samples. In conclusion, UHPLC-based fingerprint technique coupled with predictive modelling shows great potential in inferring land-use class of trace amount of soil.

Interactions of genetic variations in FAS, GJB2 and PTPRN2 are associated with noise-induced hearing loss: a case-control study in China

BackgroundThis study aimed to screen and validate noise-induced hearing loss (NIHL) associated single nucleotide polymorphisms (SNPs), construct genetic risk prediction models, and evaluate higher-order gene-gene, gene-environment interactions for NIHL in Chinese population.MethodsFirst, 83 cases and 83 controls were recruited and 60 candidate SNPs were genotyped. Then SNPs with promising results were validated in another case-control study (153 cases and 252 controls). NIHL-associated SNPs were identified by logistic regression analysis, and a genetic risk model was constructed based on the genetic risk score (GRS), and classification and regression tree (CART) analysis was used to evaluate interactions among gene-gene and gene-environment.ResultsSix SNPs in five genes were significantly associated with NIHL risk (p < 0.05). A positive dose-response relationship was found between GRS values and NIHL risk. CART analysis indicated that strongest interaction was among subjects with age ≥ 45 years and cumulative noise exposure ≥ 95 [dB(A)·years], without personal protective equipment, and carried GJB2 rs3751385 (AA/AB) and FAS rs1468063 (AA/AB) (OR = 10.038, 95% CI = 2.770, 47.792), compared with the referent group. CDH23, FAS, GJB2, PTPRN2 and SIK3 may be NIHL susceptibility genes.ConclusionGRS values may be utilized in the evaluation of the cumulative effect of genetic risk for NIHL based on NIHL-associated SNPs. Gene-gene, gene-environment interaction patterns play an important role in the incidence of NIHL.

Can predictive factors determine the time to treatment initiation for oral and oropharyngeal cancer? A classification and regression tree analysis.

This ecological study aimed to identify the factors with the greatest power to discriminate the proportion of oral and oropharyngeal cancer (OOC) records with time to treatment initiation (TTI) within 30 days of diagnosis in Brazilian municipalities. A descriptive analysis was performed on the variables grouped into five dimensions related to patient characteristics, access to health services, support for cancer diagnosis, human resources, and socioeconomic characteristics of 3,218 Brazilian municipalities that registered at least one case of OOC in 2019. The Classification and Regression Trees (CART) technique was adopted to identify the explanatory variables with greater discriminatory power for the TTI response variable. There was a higher median percentage of records in the age group of 60 years or older. The median percentage of records with stage III and IV of the disease was 46.97%, and of records with chemotherapy, radiation, or both as the first treatment was 50%. The median percentage of people with private dental and health insurance was low. Up to 75% had no cancer diagnostic support services, and up to 50% of the municipalities had no specialist dentists. Most municipalities (49.4%) started treatment after more than 30 days. In the CART analysis, treatment with chemotherapy, radiotherapy, or both explained the highest TTI in all municipalities, and it was the most relevant for predicting TTI. The final model also included anatomical sites in the oral cavity and oropharynx and the number of computed tomography services per 100,000. There is a need to expand the availability of oncology services and human resources specialized in diagnosing and treating OOC in Brazilian municipalities for a timely TTI of OOC.

Alzheimer’s disease susceptibility in African American elders: a classification and regression tree (CART) analysis approach

Alzheimer's disease (AD) is increasingly prevalent, especially among African American older adults. Despite its widespread nature, accurate and timely diagnosis of AD remains challenging. Addressing the research gap in sociodemographic and cardiovascular risk factor research associated with AD in African American older adults, this study aimed to identify and analyze distinct subgroups within this population that are particularly vulnerable to AD, thereby contributing to the development of targeted interventions and healthcare strategies. This study employs a rigorous methodology utilizing classification and regression tree (CART) analysis to examine data from the 2017 Uniform Data Set (UDS). This approach enables a nuanced analysis of AD susceptibility among African American older adults. The CART analysis revealed significant associations between the studied sociodemographic and cardiovascular risk factors and AD susceptibility among African American older adults. The results indicate the presence of specific subgroups with increased vulnerability to AD, shaped by varying levels of education [relative importance (RI): 100%], antidepressant usage (RI: 83.1%), BMI (RI: 71.2%), use of antipsychotic agents (RI: 35.5%), and age of smoking cessation (RI: 21.5%). These findings underscore the importance of culturally specific research and interventions for addressing AD among African Americans. This study's findings, revealing significant associations between sociodemographic and cardiovascular risk factors and AD susceptibility among African American older adults, underscore the necessity of developing healthcare policies and interventions specifically tailored to address these risks.

Increased IL-6 Levels and the Upregulation of Iron Regulatory Biomarkers Contribute to the Progression of Japanese Encephalitis Virus Infection's Pathogenesis.

Integrated analysis of iron regulatory biomarkers and inflammatory response could be an important strategy for Japanese encephalitis viral (JEV) infection disease management. In the present study, the inflammatory response was assessed by measuring serum Interleukin-6 (IL-6) levels using ELISA, and the transcription levels of iron homeostasis regulators were analyzed via RT-PCR. Furthermore, inter-individual variation in the transferrin gene was analyzed by PCR-RFLP and their association with clinical symptoms, susceptibility, severity, and outcomes was assessed through binary logistic regression and classification and regression tree (CART) analysis. Our findings revealed elevated levels of IL-6 in serum as well as increased expression of hepcidin (HAMP), transferrin (TF), and transferrin receptor-1 (TFR1) mRNA in JEV infection cases. Moreover, we found a genetic variation in TF (rs4481157) associated with clinical symptoms of meningoencephalitis. CART analysis indicates that individuals with the wild-type TF genotype are more susceptible to moderate JEV infection, while those with the homozygous type are in the high-risk group to develop a severe JEV condition. In summary, the study highlights that JEV infection induces alteration in both IL-6 levels and iron regulatory processes, which play pivotal roles in the development of JEV disease pathologies.

Sociodemographic and Health Indicators of Diet Quality in Pre-Frail Older Adults in New Zealand.

We collected detailed dietary intake from pre-frail (FRAIL scale 1-2) older adults using NZ Intake24, an online version of 24 h multiple pass dietary recall. Diet quality was ascertained with the Diet Quality Index-International (DQI-I). We used regression generalized linear models to determine predictors of diet quality as well as classification and regression tree (CART) analysis to examine the complex relationships between predictors and identified profiles of sub-groups of older adults that predict diet quality. The median age in this sample (n = 468) was 80.0 years (77.0-84.0). Living with others, a high deprivation index and a higher BMI were independent predictors of poorer diet quality. With CART analysis, we found that those with a BMI > 29 kg/m2, living with others and younger than 80 years were likely to have a lower diet quality. We found that BMI, living arrangement and socioeconomic status were independent predictors of diet quality in pre-frail older adults, with BMI being the most important variable in this sample when the interaction of these variables was considered. Future research is needed to determine the similarities and/or differences in the profile of subgroups of older adults with poorer diet quality.

Factors That Influence the Life Satisfaction of Afghan Refugees Living in Eastern Turkey: The Role of Their Migration Causes

In this study, the impact of the reasons for leaving their country (such as social, economic, and political) of Afghan asylum seekers who illegally entered Turkey from the eastern border on their life satisfaction in the country of destination was analyzed. The sample comprised 500 individuals (54.8% women; 42.4% &lt; 30 age) who came as refugees from Turkey’s eastern border. Three-stages of analysis were carried out in the study: the Mantel–Haenszel test, ordered logit, and CART (Classification and Regression Trees) decision tree. The main findings obtained from these analyses show that individuals leaving their country for economic reasons and because of war/terrorism are happier, while those leaving their country because of religious and cultural pressures are unhappier. According to the results of the CART analysis, the most frequently repeated variables are economic and life satisfaction of individuals who are satisfied with their household income and save money is at its highest level. In the analysis it is also seen that the life satisfaction level of individuals who are not satisfied with their household income, leave their country for reasons other than economic reasons, and make a living on debt is very low. This study also focuses on the relationship between happiness and sustainable development (SD). It has associated the reasons for migrants leaving their countries with the Sustainable Development Goals (SDGs), highlighting the significance of happiness studies in achieving the SDGs.

_Combining return to sport, psychological readiness, body mass, hamstring strength symmetry, and hamstring/quadriceps ratio increases the risk of a second anterior cruciate ligament injury.

To investigate the combinations of variables that comprise the biopsychosocial model domains to identify clinical profiles of risk and protection of second anterior cruciate ligament injury. One hundred and forty-five patients for return-to-sport testing after anterior cruciate ligament (ACL) reconstruction (ACLR) were contacted, and 97 were deemed eligible. All were evaluated between 6 and 24months and followed up for 2years. Participants answered the International Knee Documentation Committee (IKDC) and Anterior Cruciate Ligament-Return to Sport after Injury Scale (ACL-RSI), performed the postural stability assessment using the Biodex Balance System, and assessed muscle strength at 60° and 300°/s on the isokinetic dynamometer. Personal factors (age, gender, body mass index), body structures (graft type and concomitant injuries), and environmental factors (time between surgery and evaluation) were also collected. The participants were asked about the occurrence of a second ACL injury and return to sport after 2years of follow-up. Classification and regression tree (CART) analysis was used to determine predictors of a second ACL injury. The receiver operating characteristic (ROC) curve was performed to verify the accuracy of the CART analysis, in addition to the sensitivity, specificity, and relative risk (RR) of the model. Of the initial 97 participants, 88 (89.8%) responded to follow-up and 14 (15.9%) had a second ACL injury (11 graft ruptures and three contralateral ACL). CART analysis identified the following variables as predictors of second ACL injury: return to sport, hamstring strength symmetry at 300°/s, ACL-RSI score, hamstrings/quadriceps ratio at 60°/s, and body mass index (BMI). CART correctly identified 9 (64.3%) of the 14 participants who were reinjured and 71 (95.9%) of the 74 participants who were not. The total correct classification was 90.9%. The area under the ROC curve was 0.88 (95% CI 0.72-0.99; p < 0.001), and the model showed a sensitivity of 75% (95% CI 42.8-94.5), specificity of 93.4% (95% CI 85.3-97.8), and RR of 15.9 (95% CI 4.9-51.4; p < 0.0001). The combination of hamstring strength symmetry, hamstring/quadriceps ratio (body functions); return to sport (activity and participation); psychological readiness; and BMI (personal factors) could identify three clinical risk profiles for a second ACL injury with good accuracy. IV.

A Predictive Rule for COVID-19 Pneumonia Among COVID-19 Patients: A Classification and Regression Tree (CART) Analysis Model.

In this study, we aimed to identify predictive factors for coronavirus disease 2019 (COVID-19) patients with complicated pneumonia and determine which COVID-19 patients should undergo computed tomography (CT) using classification and regression tree (CART) analysis. This retrospective cross-sectional survey was conducted at a university hospital. We recruited patients diagnosed with COVID-19 between January 1 and December 31, 2020. We extracted clinical information (e.g., vital signs, symptoms, laboratory results, and CT findings) from patient records. Factors potentially predicting COVID-19 pneumonia were analyzed using Student's t-test, the chi-square test, and a CART analysis model. Among 221 patients (119 men (53.8%); mean age, 54.59±18.61 years), 160 (72.4%) had pneumonia. The CART analysis revealed that patients were at high risk of pneumonia if they had C-reactive protein (CRP) levels of >1.60 mg/dL (incidence of pneumonia: 95.7%); CRP levels of ≤1.60 mg/dL + age >35.5 years + lactate dehydrogenase (LDH)>225.5 IU/L (incidence of pneumonia: 95.5%); and CRP levels of ≤1.60 mg/dL + age >35.5 years + LDH≤225.5 IU/L + hemoglobin ≤14.65 g/dL (incidence of pneumonia: 69.6%). The area of the curve of the receiver operating characteristicof the model was 0.860 (95% CI: 0.804-0.915), indicating sufficient explanatory power. The present results are useful for deciding whether to perform CTin COVID-19 patients. High-risk patients such as those mentioned above should undergo CT.

Chemotherapy-Induced Nausea and Vomiting in Patients With Breast Cancer: Risk Factor and Predictive Model Using Classification and Regression Tree (CART).

Introduction Chemotherapy-induced nausea and vomiting (CINV) is a common and debilitating adverse effect of breast cancer chemotherapy. The incidence of CINV in the first cycle of chemotherapy is essential, as it sets the tone for anticipatory CINV and the overall patients' treatment experience. We aimed to investigate the risk factors of first cycle CINV in breast cancer patients and to develop a classification and regression tree (CART) model to predict its occurrence. Methods This is a cross-sectional study that nested in a prospective cohort. One hundred and thirty-seven female breast cancer patients receiving highly emetogenic chemotherapy were included. We used the Common Toxicity Criteria for Adverse Events (CTCAE) version 4.0 to assess patient-reported nausea and vomiting in the first chemotherapy cycle. The proportional difference of CINV between sociodemographic and clinicopathologic variables was analyzed using chi-square, and the strength and direction of the relationship with CINV were analyzed using bivariable logistic regression analysis. Multivariable logistic regression and CART analysis included variables with a p-value <0.250. Results The incidence of first-cycle CINV was 43.1%. The chi-square test revealed a significant association between insurance status and CINV (p<0.001) and between the stage at diagnosis and CINV (p<0.001). Underweight to normal body mass index (BMI) patients are significantly associated with an increased risk of first-cycle CINV (OR =2.17, 95% CI 1.03-4.56, p =0.041). In hierarchical order, three variables (stage at diagnosis, BMI, and age) were included in the CART model, which significantly influenced the probability of first cycle CINV. With an accuracy of 61.3%, the CART model had a sensitivity of 28.8%, a specificity of 85.9%, a positive predictive value of 60.7%, a negative predictive value of 61.5%, and an area under curve (AUC) of 0.602. Conclusion Breast cancer patients with an underweight to normal BMI have a higher risk of developing first-cycle CINV. Our CART model was better at identifying patients who would not develop CINV than those who would. The CART model may provide a simple and effective way to individualize patient care for first-cycle CINV.