Patient Classification System Research Articles

Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm

The global impact of the COVID-19 pandemic has been profound, affecting economies and societal structures worldwide. Indonesia, with a high caseload, has encountered significant challenges across various sectors. Virus transmission primarily occurs through physical contact, and the surge in active cases has strained hospital capacities, leading to the hospitalization of only severe cases. The remaining patients receive home telecare, but some experience sudden health deterioration with fatal consequences. To address this issue, this study proposes a remote outpatient care system utilizing Internet of Things (IoT) technology and medical electronics. This integrated system aims to provide an effective response to the COVID-19 pandemic. The research includes a comparative analysis of three machine-learning algorithms: decision tree, gradient tree boosting, and random forest for the classification of COVID-19 patients. The results reveal that the random forest algorithm outperforms the others with an accuracy rate of 70%, as compared to 67% for the decision tree and 62% for the gradient tree boosting algorithm. This integrated system not only addresses immediate healthcare delivery challenges but also offers data-driven insights for patient classification, thereby enhancing the effectiveness and reach of medical interventions

Reform of hospital financing: revenue-independent flat rates for acute inpatient obstetrics facilities

The patient classification system according to diagnosis related groups (DRGs) is highly related to performance, which was the reason for its national introduction in the early 2000s. Since 2003, hospital services have therefore been remunerated via the DRG system by means of flat rates per case. With few exceptions, all hospital services were remunerated via these flat rates per case until 2019. In 2020, the personnel costs for the nursing staff working in the hospital bed were then covered by the DRG-classified remuneration in order to relieve the enormous economic cost pressure on the nursing staff employed in German hospitals. This pressure is particularly evident in hospital wards with a low proportion of elective procedures (such as obstetrics). The economic management of such hospital wards is challenging. Now an even more far-reaching health policy proposal is being made. According to this proposal, hospitals should be financed for their maintenance costs. This is to be done by means of normatively determined budgets. In this way, a financial relief of the hospitals on the one hand and the promotion of the quality of service provision on the other hand should succeed. In the following paper, the challenges of acute inpatient obstetrics due to the current and planned financing system are examined. For this purpose, six selected experts, who belong to very different levels of care in the hospital sector, give an outlook on the introduction of the reserved financing.

Prevalence and risk factors of bone metastasis and the development of bone metastatic prognostic classification system: a pan-cancer population study.

The prevalence of bone metastasis (BM) varies among primary cancer patients, and it has a significant impact on prognosis. However, there is a lack of research in this area. This study aims to explore the clinical characteristics, prevalence, and risk factors, and to establish a prognostic classification system for pan-cancer patients with BM. The data obtained from the Surveillance, Epidemiology and End Results database were investigated. The prevalence and prognosis of patients with BM were analyzed. Hierarchical clustering was used to develop a prognostic classification system. From 2010 to 2019, the prevalence of BM has increased by 41.43%. BM most commonly occurs in cancers that originate in the adrenal gland, lung and bronchus and overlapping lesion of digestive systems. Negative prognostic factors included older age, male sex, poorer grade, unmarried status, low income, non-metropolitan living, advanced tumor stages, previous chemotherapy, and synchronous liver, lung, and brain metastasis. Three categories with significantly different survival time were identified in the classification system. The clinical features, prevalence, risk factors, and prognostic factors in pan-cancer patients with BM were investigated. A prognostic classification system was developed to provide survival information and aid physicians in selecting personalized treatment plans for patients with BM.

A prognostic nomogram and risk classification system of elderly patients with extraosseous plasmacytoma: a SEER database analysis.

The survival trends and prognostic factors of patients with extraosseous plasmacytoma (EOP) or extramedullary plasmacytoma (EMP) have not been reported in recent years. The objective of this study was to develop a novel nomogram and risk stratification system for predicting the overall survival (OS) of elderly patients with EOP based on the Surveillance, Epidemiology, and End Results (SEER) database. The demographic characteristics of 900 patients aged 60years and above, diagnosed with EOP between 2000 and 2019, were extracted from the SEER database. The patient population was randomly divided into a training cohort and an internal validation cohort in a ratio of 7:3. Univariate and multivariate Cox regression analyses were conducted to identify independent predictors of prognosis in elderly EOP patients, followed by developing a nomogram for prognostic assessment. The performance of the model was evaluated through receiver-operating characteristic (ROC) curves, C-index, calibration curves for calibration accuracy assessment, and decision curve analysis (DCA) to assess its clinical utility. All elderly EOP patients were stratified into three risk subgroups by cutoff value utilizing X-tile software based on their total OS scores for comparative analysis purposes. Kaplan-Meier (K-M) survival curve analysis was employed to validate any observed differences in OS among these three risk groups. Six factors including age, year of diagnosis, marital status, primary site, surgery, and prior tumor history were identified to be independently predictive of the OS of elderly patients with EOP, and these predictors were included in the construction of the nomogram. The 1-, 3-, and 5-year area under the curves (AUCs) for OS were 0.717, 0.754, and 0.734 in the training cohort and 0.740, 0.730, and 0.765 in the validation cohort, respectively. The C-index values in the two cohorts were 0.695 and 0.690. The calibration curves and DCA exhibit commendable consistency and validity, respectively, thereby demonstrating their robust performance. The training set was stratified into low-, medium-, and high-risk subgroups based on the optimal cutoff points (167.8 and 264.8) identified. The K-M curve and cumulative risk curve exhibited statistically significant disparities in survival rates among the groups. We developed a nomogram and risk classification system, which can serve as an intuitive and effective tool for clinicians to enhance the prediction of OS in elderly EOP patients, thereby facilitating the formulation of more rational and personalized treatment strategies.

A Cost-Effective, Machine Learning-Based New Unified Risk-Classification Score (NU-CATS) for Patients with Endometrial Cancer

Treatment for endometrial cancer (EC) with radiotherapy is increasingly guided by molecular risk classifications. Derived from genomic profiling of The Cancer Genome Atlas (TCGA) project, several EC risk classification systems, including ProMisE and Leiden/TransPORTEC, have been developed. However, the current systems were developed on a relatively homogeneous population. Black or African American (BOAA) patients have consistently been demonstrated to have worse stage-adjusted prognosis than Caucasians. Given this, we intended to develop a new unified risk classification system (NU-CATS) for EC patients using machine learning (ML) utilizing datasets with demographically diverse populations. TCGA-Uterine Corpus Endometrial Carcinoma (n = 596), Memorial Sloan Kettering-Metastatic Events and Tropisms (MSK-MET, n = 1,315) and the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange (AACR-GENIE, n = 4,561) were used to identify genetic alterations and clinicopathological features, including age, race, stage, histologic grade and features, and distribution of metastatic disease. Software packages including Keras, Pytorch, and Scikit Learn were tested to build artificial neural networks (ANNs) with a binary output as either intra-abdominal metastatic lesions vs. non-metastatic. A 5-layered ANN (5-6-4-2-1) using 5 inputs ('age at surgery', 'histology', 'race', 'mismatch repair status' and 'TP53'). The optimal performing ANN was selected and cross validated. The weights and biases of the trained ANN were used to reconstruct the algorithm. BOAA patients with EC have worse prognosis than Caucasians, adjusting for TP53 or POLE mutation status. TP53 is the most common gene differentially altered by race in EC. Over 75% of BOAA patients carry TP53 mutations as compared to approximately 40% of Caucasians. Older age is associated with an increasing likelihood of TP53 mutations, high risk histology, and distant metastasis. For patients above age 70, 91% of BOAA and 60% of Caucasian EC patients carry TP53 mutations. The NU-CATS that incorporates age, race, histology, mismatch repair (MMR) status, and TP53 mutation status showed 75% accuracy in prognosticating intra-abdominal metastasis. A higher NU-CATS (>50) is associated with about 2-fold increased risk of having positive pelvic or para-aortic lymph nodes (LNs) and distant. NU-CATS was shown to outperformed TransPORTEC model for estimating risk of FIGO Stage I/II disease progression and survival in BOAA EC patients. Despite adjusting for molecular classification, race and age retain prognostic importance in EC. NU-CATS, a ML-based, cost-effective algorithm, incorporates diverse clinicopathologic and molecular variables of EC, and yields superior prognostication of the risk of nodal involvement, distant metastasis, disease progression, and overall survival as compared to other classification systems.

The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [18F]FDG PET/CT examinations.

To create the 3D convolutional neural network (CNN)-based system that can use whole-body [18F]FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC). In this study, 1224 image sets from OC patients who underwent whole-body [18F]FDG PET/CT at Kowsar Hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classification as cancerous, and non-cancerous and staging as stage III, and stage IV were determined by pathological diagnosis and specialists' interpretation. New deep neural network algorithms, the OCDAc-Net, and the OCDAs-Net were developed for diagnostic classification and staging of OC patients using [18F]FDG PET/CT images. Examinations were divided into independent training (75%), validation (10%), and testing (15%) subsets. This study included 37 women (mean age 56.3years; age range 36-83years). Data augmentation techniques were applied to the images in two phases. There were 1224 image sets for diagnostic classification and staging. For the test set, 170 image sets were considered for diagnostic classification and staging. The OCDAc-Net areas under the receiver operating characteristic curve (AUCs) and overall accuracy for diagnostic classification were 0.990 and 0.92, respectively. The OCDAs-Net achieved areas under the receiver operating characteristic curve (AUCs) of 0.995 and overall accuracy of 0.94 for staging. The proposed 3D CNN-based models provide potential tools for recurrence/post-therapy surveillance in OC. The OCDAc-Net and the OCDAs-Net model provide a new prognostic analysis method that can utilize PET images without pathological findings for diagnostic classification and staging.

Relationships between Alternative Nurse Staffing Level Measurements and Nurses’ Perceptions of Nurse Staffing Level Adequacy, Fatigue, and Care Quality

Aims. This study examined the influence of nurse staffing level, measured using various methods, on nurses’ perceived adequacy of nurse staffing level, fatigue, and nursing care quality. Background. Although previous studies have recommended various methods of measuring nurse staffing level, there is a lack of research that compares different measurement methods or considers nurses’ perceptions of staffing level on a daily basis. Methods. We conducted a cross-sectional study using work sampling and the questionnaire method in a general hospital in South Korea from July 18 to August 14, 2022. Results based on responses from 90 nurses and scores of 5,536 inpatients derived from the Korean Patient Classification System were included in the analysis. Results. The average nurse-to-patient ratio a day was 1 : 3.20, and the registered nursing hours per patient day was 2.35. Perceived insufficient nurse staffing and fatigue were higher on weekdays than on weekends ( p < 0.001 ). All variables measuring the nurse staffing level affected the nurses’ perceived inadequacy of nurse staffing level, fatigue, and nursing care quality, compared to other variables related to nurse staffing level, such as work intensity and demanding nursing hours per nurse (R2: 0.19–0.31), the nurse-to-patient ratio had the lowest explanatory power in explaining the nurses’ perceptions (R2: 0.14–0.18). Conclusions. Nurse staffing level measurement should consider the acuity of inpatients and nursing care time. Further research is needed to utilize nurses’ perceptions of the appropriate nurse staffing level. Implications for Nursing Management. Efforts are required to maintain an appropriate nurse staffing level through continuous monitoring of nurses’ perceptions and acuity of inpatients to preserve nurses’ alertness during work and improve nursing care quality.

A risk stratification model to predict chemotherapy benefit in medullary carcinoma of the breast: a population-based SEER database

Whether patients with medullary breast carcinoma (MBC) receive chemotherapy is controversial. Therefore, the aim of our study was to screen out patients with MBC who benefit from chemotherapy. We enrolled 618 consecutive patients with MBC from The Surveillance, Epidemiology, and End Results (SEER) database (2010–2018). Cox regression analysis was used to identify independent prognostic factors. Next, a nomogram was constructed and evaluated using calibration plots and the area under the curve (AUC) of receiver operating characteristic (ROC) curves. Kaplan‒Meier curves were used to evaluate the overall survival (OS) benefit of chemotherapy in different risk groups. A total of 618 MBC patients were involved in our study, and an 8:2 ratio was used to randomly split them into a training cohort (n = 545) and a validation cohort (n = 136). Next, a nomogram predicting 3- and 5-year OS rates was constructed based on the five independent factors (age at diagnosis, T stage, N status, subtype and radiation). The nomogram AUCs for 3- and 5-year OS (training set: 0.793 and 0.797; validation set: 0.781 and 0.823) and calibration plots exhibited good discriminative and predictive ability. Additionally, a novel risk classification system for MBC patients demonstrated that we do not have enough evidence to support the benefit effect of chemotherapy for the high-risk group as the result is not statistically significant (total population: p = 0.180; training set: p = 0.340) but could improve OS in the low-risk group (total population: p = 0.001; training set: p = 0.001). Our results suggested that chemotherapy should be selected more carefully for high-risk groups based on a combination of factors and that the possibility of exemption from chemotherapy should be confirmed by more clinical trials in the future.

Diagnosis Related Groups of Patients Admitted from an Urban Academic Medical Center to a Virtual Hybrid Hospital-at-Home Program.

The diagnosis related group (DRG) is used as an economic patient classification system based on clinical characteristics, hospital stay, and treatment costs. Mayo Clinic's virtual hybrid hospital-at-home program, advanced care at home (ACH), offers high-acuity home inpatient care for a variety of diagnosis. This study aimed to determine the DRGs admitted to the ACH program at an urban academic center. A retrospective study was performed on all patients discharged from the ACH program at Mayo Clinic Florida from July 6, 2020, to February 1, 2022. DRG data were extracted from the Electronic Health Record (EHR). Categorization of DRG was done by systems. The ACH program discharged 451 patients with DRGs. Categorization of the DRG demonstrated that the most frequent code assigned corresponded to respiratory infections (20.2%), followed by septicemia (12.9%), heart failure (8.9%), renal failure (4.9%), and cellulitis (4.0%). The ACH program covers a wide range of high-acuity diagnosis across multiple medical specialties at its urban academic medical campus, including respiratory infections, severe sepsis, congestive heart failure, and renal failure, all with major complications or comorbidities. The ACH model of care may be useful in taking care of patients with similar diagnosis at other urban academic medical institutions.

Tongue size matters: revisiting the Mallampati classification system in patients with obstructive sleep apnea.

The Mallampati classification system has been used to predict obstructive sleep apnea (OSA). Upper airway soft tissue structures are prone to fat deposition, and the tongue is the largest of these structures. Given that a higher Mallampati score is associated with a crowded oropharynx, we hypothesized that the Mallampati score is associated with tongue volume and an imbalance between tongue and mandible volumes. Adult males underwent clinical evaluation, polysomnography, and upper airway CT scans. Tongue and mandible volumes were calculated and compared by Mallampati class. Eighty patients were included (mean age, 46.8 years). On average, the study participants were overweight (BMI, 29.3 ± 4.0 kg/m2) and had moderate OSA (an apnea-hypopnea index of 26.2 ± 26.7 events/h). Mallampati class IV patients were older than Mallampati class II patients (53 ± 9 years vs. 40 ± 12 years; p < 0.01), had a larger neck circumference (43 ± 3 cm vs. 40 ± 3 cm; p < 0.05), had more severe OSA (51 ± 27 events/h vs. 24 ± 23 events/h; p < 0.01), and had a larger tongue volume (152 ± 19 cm3 vs. 135 ± 18 cm3; p < 0.01). Mallampati class IV patients also had a larger tongue volume than did Mallampati class III patients (152 ± 19 cm3 vs. 135 ± 13 cm3; p < 0.05), as well as having a higher tongue to mandible volume ratio (2.5 ± 0.5 cm3 vs. 2.1 ± 0.4 cm3; p < 0.05). The Mallampati score was associated with the apnea-hypopnea index (r = 0.431, p < 0.001), BMI (r = 0.405, p < 0.001), neck and waist circumference (r = 0.393, p < 0.001), tongue volume (r = 0.283, p < 0.001), and tongue/mandible volume (r = 0.280, p = 0.012). The Mallampati score appears to be influenced by obesity, tongue enlargement, and upper airway crowding.

Endoscopic Ultrasound Predicts Risk of Occult Intra-Abdominal Metastases in Localized Gastric Cancer: A Validation Study.

In gastric cancer (GC) patients without imaging evidence of distant metastasis, diagnostic staging laparoscopy (DSL) is recommended to detect radiographically occult peritoneal metastasis (M1). DSL carries a risk for morbidity and its cost-effectiveness is unclear. Use of endoscopic ultrasound (EUS) to improve patient selection for DSL has been proposed but not validated. We aimed to validate an EUS-based risk classification system predicting risk for M1 disease. We retrospectively identified all GC patients without positron emission tomography (PET)/computed tomography (CT) evidence of distant metastasis who underwent staging EUS followed by DSL between 2010 and 2020. T1-2, N0 disease was EUS "low-risk"; T3-4 and/or N+ disease was "high-risk". A total of 68 patients met inclusion criteria. DSL identified radiographically occult M1 disease in 17 patients (25%). Most patients had EUS T3 tumors (n = 59, 87%) and 48 (71%) patients were node-positive (N+). Five (7%) patients were classified EUS "low-risk" and 63 (93%) were classified "high-risk". Of 63 "high-risk" patients, 17 (27%) had M1 disease. The ability of "low-risk" EUS to predict M0 disease at laparoscopy was 100% and DSL would have been avoided in five patients (7%). This stratification algorithm showed a sensitivity of 100% (95% confidence interval (CI): 80.5-100%) and a specificity of 9.8% (95% CI: 3.3-21.4%). Use of an EUS-based risk classification system in GC patients without imaging evidence of metastasis helps identify a subset of patients at low-risk for laparoscopic M1 disease who may avoid DSL and proceed directly to neoadjuvant chemotherapy or resection with curative intent. Larger, prospective studies are needed to validate these findings.

The Determiners of Temporal Lobe Epilepsy Surgery Outcome in a Developing Country

Background: Epilepsy surgery has become a successful treatment option for patients with drug-resistant epilepsies. Objectives: This study aimed to assess temporal lobe epilepsy (TLE) surgery outcome and its association with epilepsy duration, the presence of pre-surgical generalized tonic-clonic seizure (GTCS), and brain MRI findings. Materials &amp; Methods: This retrospective study was performed from 2016 to 2020 at Loghman-Hakim Hospital, Tehran, Iran. The patients’ demographic data, epilepsy type, duration, frequency, brain image findings, the selected surgical approaches, and histopathological findings were collected. The post-operative seizure outcome was expressed one year according to the proposed International League Against Epilepsy (ILAE) classification system. The independent sample t-test, chi-square, and one-way analysis of variance were applied to analyze the differences between variables in terms of quantitative and categorical data. Results: A total number of 69 TLE surgeries were enrolled in our study. No differences were found in the surgical outcome base on the ILAE classification system of patients with lesional vs non-lesional MRIs (P=0.834). In patients with mesial temporal sclerosis, no correlation was found between the surgical approaches including selective amygdalohippocampectomy, anterior temporal lobectomy, and post-operative seizure outcome (P=0.142). Seven patients (10.1%) developed post-operative functional seizures. Conclusion: In our study, the epilepsy duration and the presence of GTCSs before surgery had no effect on the surgery outcome. In patients with mesial temporal sclerosis, amygdalohippocampectomy, and anterior temporal lobectomy did not confer different surgical outcomes. TLE patients with normal MRI showed similar surgical outcomes as lesional patients. A significantly high proportion of our patients developed functional seizures, postoperatively.

Comparison of patient classification systems for dimensioning nursing staff.

To compare nursing staff workload and dimensioning measured by two patient classification systems. Cross-sectional study, developed in a clinical inpatient unit of a large hospital in southern Brazil, between June and August 2022. Included patients (n = 260) were assessed through two different patient classification systems. The dimensioning calculation provided by the standard and descriptive statistics were applied. Of the total number of patients, 1,248 classifications were performed with each of the classification systems. One of the instruments showed a concentration of demand for minimal care (54.5%) and the other for intermediate care (63.4%). The anticipation of required nursing hours was discrepant (235.58 and 298.16 hours), as well as the projected nursing staff, which was of 53 and 67 workers, respectively. Measurement of workload and dimensioning were different when using two patient classification systems in the same sample. Additional accuracy studies shall be carried out.

Development and initial experience of a novel classification system for patients with brain stem haemorrhage.

There is no uniform classification standard for brain stem haemorrhage. On the basis of previous experience in the treatment of brainstem haemorrhage, this study explored and established a set of criteria for brainstem haemorrhage classification, risk-stratified such patients and guided the selection of treatment options so as to achieve accurate and standardized diagnosis and treatment. Thirty patients with brainstem haemorrhage from April 2019 to May 2022 were included. According to the amount and location of the brain stem bleeding, it was divided into the following types: small haemorrhage type (type 1), medium haemorrhage type (lateral type 2a, dorsal type 2b, ventral type 2c), and large haemorrhage type (type 3), and the preoperative condition and postoperative outcome within 3 months were evaluated. The included 30 patients with brainstem haemorrhage were aged 53.2 ±13.8 years old, and 80% were men. Among them, 5 patients were type 1 (16.7%), 2 patients type 2a (6.7%), 7 patients type 2b (23.3%), 5 patients type 2c (16.7%) and 11 patients type 3 (36.7%). The prognosis among these subtypes was significantly different ( p < 0.001). All type 1 patients were cured, with the highest mortality rate in type 2c patients (100%). Compared with type 2b (5.5 ±3.5 days) and type 2c (3.4 ±2.5 days), type 3 patients tend to die within fewer days (2.9 ±2.7 days). The difference in NIHSS scores was significant among surviving patients ( p < 0.001). Type 1 is the lowest at 1.8 ±2.2 points; type 3 is the highest at 35.0 ±3.5 points. Relying on the anatomical basis and treatment plan, we propose a different classification, which is conducive to quickly identifying the haemorrhage type and degree of disease, and putting forward an appropriate treatment plan, which is expected to improve the patient prognosis.

CLASSIFICAÇÃO DE PACIENTES E DIMENSIONAMENTO DE PESSOAL DE ENFERMAGEM EM UNIDADE DE INTERNAÇÃO PEDIÁTRICA

ABSTRACT Objective: to describe the implementation of the Patient Classification System and the dimensioning of the nursing staff in a pediatric inpatient unit. Method: a descriptive, cross-sectional, and retrospective study, carried out at a university hospital in the Midwest of Brazil. After the implementation of the Pediatric Patient Classification System, we compiled data from the classifications of the level of care complexity (N=4,639) among patients (n=608) admitted from January to December 2019. Descriptive statistical analysis was employed, including proprietary methodology for staff sizing. Results: there was a prevalence of intermediate care patients. According to the staff dimensioning, there was a surplus (+10) of mid-level workers, and the projected number of nurses (six) was compatible with the available number. Conclusion: the strategic implementation/employment of the Patient Classification System was indispensable for the pediatric nursing staffing forecast, considering that the staffing surplus found should be appreciated with caution.

Sudden cardiac death multiparametric classification system for Chagas heart disease's patients based on clinical data and 24-hours ECG monitoring.

About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.

The value of Coronary Artery Disease - Reporting and Data System (CAD-RADS) in Outcome Prediction of CAD Patients; a Systematic Review and Meta-analysis.

Coronary computed tomographic angiography (CCTA) reporting has traditionally been operator-dependent, and no precise classification is broadly used for reporting Coronary Artery Disease (CAD) severity. The Coronary Artery Disease Reporting and Data Systems (CAD-RADS) was introduced to address the inconsistent CCTA reports. This systematic review with meta-analysis aimed to comprehensively appraise all available studies and draw conclusions on the prognostic value of the CAD-RADS classification system in CAD patients. Online databases of PubMed, Embase, Scopus, and Web of Science were searched until September 19th, 2022, for studies on the value of CAD-RADS categorization for outcome prediction of CAD patients. 16 articles were included in this systematic review, 14 of which had assessed the value of CAD-RADS in the prediction of major adverse cardiovascular events (MACE) and 3 articles investigated the outcome of all-cause mortality. Our analysis demonstrated that all original CAD-RADS categories can be a predictor of MACE [Hazard ratios (HR) ranged from 3.39 to 8.63] and all categories, except CAD-RADS 1, can be a predictor of all-cause mortality (HRs ranged from 1.50 to 3.09). Moreover, higher CAD-RADS categories were associated with an increased hazard ratio for unfavorable outcomes among CAD patients (p for MACE = 0.007 and p for all-cause mortality = 0.018). The evidence demonstrated that the CAD-RADS classification system can be used to predict incidence of MACE and all-cause mortality. This indicates that the implementation of CAD-RADS into clinical practice, besides enhancing the communication between physicians and improving patient care, can also guide physicians in risk assessment of the patients and predicting their prognosis.

Korozif Madde Alan Çocukların Hastane Süreci: Türkiye'de Tek Merkezli Retrospektif Çalışma

Objective: This study retrospectively was aimed to determine how the treatment and care processes of children ingestion corrosive substances are maintained. Material and Methods: The population of the study is children with ingested corrosive substances, and the sample consisted of pediatric patients who ingestion corrosive substances from the pediatric surgery department of a university hospital in western Türkiye. Medical and nursing files of children with corrosive substance ingestion from the university's electronic patient file were examined. The sample of the study consisted of 70 pediatric patients who came between the years 2018-2021. The analysis of data was calculated using SPSS 24.0 program, frequency, percentage, median, interquartile range, comparison analysis and logistic regression. Results: The median age at first admission of pediatric patients was 4.0 years and 57.1% were girls. 35.7% of the pediatric patients due to oil consumption, 30.0% of them applied to the pediatric surgery department in the summer season, and the hospital was not the first application center for 61.4%. According to the Rush Medicus Patient Classification System, 40% of the children were defined as low-level dependent patients. The first arrival age of children affects the level of nursing dependency. It was determined that the rate of dilatation in pediatric patients varied according to the hospital first application center. Conclusion: As a result, although the dependency level of the children taking corrosive substances to the nurse is low, it was determined that the child's first admission age and dilatation status affect the level of addiction.

Comparison of patient classification systems for dimensioning nursing staff

RESUMO Objetivo: Comparar a carga de trabalho e o dimensionamento do pessoal de enfermagem mensurados por dois sistemas de classificação de pacientes. Método: Estudo transversal, desenvolvido em uma unidade de internação clínica de um hospital de grande porte do sul do Brasil, entre junho e agosto de 2022. Os pacientes incluídos (n = 260) foram avaliados por dois diferentes sistemas de classificação de pacientes. Aplicou-se o cálculo de dimensionamento previsto pela norma e estatística descritiva. Resultados: Sobre o total de pacientes, foram realizadas 1.248 classificações com cada um dos sistemas de classificação. Um dos instrumentos demonstrou concentração de demanda de cuidados mínimos (54,5%) e outro de cuidados intermediários (63,4%). A previsão de horas de enfermagem requeridas foi discrepante (235,58 e 298,16 horas), bem como a projeção do pessoal de enfermagem dimensionado, que foi de 53 e 67 trabalhadores, respectivamente. Conclusão: A mensuração da carga de trabalho e o dimensionamento foram distintos ao se utilizar dois sistemas de classificação de pacientes numa mesma amostra. Sugere-se que sejam realizados estudos adicionais de acurácia.

Classification(s) of Cervical Deformity.

Cervical spine deformities (CSD) are complex surgical issues with currently heterogenous management strategies. The classification of CSD is still an evolving field. Rudimentary classification schemas were initially proposed in the late 20th century but were largely informal and based on the underlying etiology (i.e. , postsurgical, traumatic, or inflammatory). The first formal classification schema was proposed by Ames et al. in 2015 who established a standard nomenclature for describing these deformities. This classification system established 5 deformity descriptors based on curve apex location (cervical, cervicothoracic, thoracic, craniovertebral junctional, and coronal deformities) and 5 deformity modifiers which helped surgeons utilize a standard language when discussing CSD patients. Koller et al. in 2019 subsequently established a classification system for patients with rigid cervical kyphosis based on regional and global sagittal alignment. Most recently, Kim et al. in 2020 proposed an updated classification system utilizing dynamic cervical spine imaging to guide surgical treatment of CSD patients. It identified 4 major groups of deformities - (1) those with "flat-neck" deformities caused by cervical lordosis T1 slope mismatch; (2) those with focal kyphotic deformities between 2 cervical vertebrae; (3) those with cervicothoracic deformities caused by large T1 slope; and (4) those with coronal deformities. Group 2 deformities most often required combined anterior-posterior approaches with short constructs, and group 3 deformities most often required posterior-only approaches with 3-column osteotomies.