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Objective: This scoping review aims to map studies that applied artificial intelligence (AI) tools to perform health technology assessment tasks in human health care. The review also aims to understand specific processes in which the AI tools were applied and to comprehend the technical characteristics of these tools., Introduction: Health technology assessment is a complex, time-consuming, and labor-intensive endeavor. The development of automation techniques using AI has opened up new avenues for accelerating such assessments in human health settings. This could potentially aid health technology assessment researchers and decision-makers to deliver higher quality evidence., Inclusion Criteria: This review will consider studies that assesses the use of AI tools in any process of health technology assessment in human health. However, publications in which AI is a means of clinical aid, such as diagnostics or surgery will be excluded., Methods: A search for relevant articles will be conducted in databases such as CINAHL (EBSCOhost), Embase (Ovid), MEDLINE (PubMed), Science Direct, Computer and Applied Sciences Complete (EBSCOhost), LILACS, Scopus, and Web of Science Core Collection. A search for gray literature will be conducted in GreyLit.Org, ProQuest Dissertations and Theses, Google Scholar, and the Google search engine. No language filters will be applied. Screening, selection, and data extraction will be performed by 2 independent reviewers. The results will be presented in graphic and tabular format, accompanied by a narrative summary., Details of This Review Can Be Found in Open Science Framework: osf.io/3rm8g., Competing Interests: The authors declare no conflicts of interest, (Copyright © 2024 JBI.)

Introduction: Health technology assessment (HTA) plays a vital role in healthcare decision-making globally, necessitating the identification of key factors impacting evaluation outcomes due to the significant workload faced by HTA agencies., Objectives: The aim of this study was to predict the approval status of evaluations conducted by the Brazilian Committee for Health Technology Incorporation (CONITEC) using natural language processing (NLP)., Methods: Data encompassing CONITEC's official report summaries from 2012 to 2022. Textual data was tokenized for NLP analysis. Least Absolute Shrinkage and Selection Operator, logistic regression, support vector machine, random forest, neural network, and extreme gradient boosting (XGBoost), were evaluated for accuracy, area under the receiver operating characteristic curve (ROC AUC) score, precision, and recall. Cluster analysis using the k-modes algorithm categorized entries into two clusters (approved, rejected)., Results: The neural network model exhibited the highest accuracy metrics (precision at 0.815, accuracy at 0.769, ROC AUC at 0.871, and recall at 0.746), followed by XGBoost model. The lexical analysis uncovered linguistic markers, like references to international HTA agencies' experiences and government as demandant, potentially influencing CONITEC's decisions. Cluster and XGBoost analyses emphasized that approved evaluations mainly concerned drug assessments, often government-initiated, while non-approved ones frequently evaluated drugs, with the industry as the requester., Conclusions: NLP model can predict health technology incorporation outcomes, opening avenues for future research using HTA reports from other agencies. This model has the potential to enhance HTA system efficiency by offering initial insights and decision-making criteria, thereby benefiting healthcare experts.

Objective: to synthesize evidence from qualitative studies on nursing experiences regarding child and adolescent mental health care in specialized services., Method: a systematic review with meta-synthesis of qualitative studies according to JBI guidelines. CINAHL, EMBASE, MEDLINE, LILACS, PSYCinfo, Scopus and Web of Science databases were used. The findings were classified according to the level of reliability and credibility and categorized by similarity between contents., Results: 229 articles were identified, and five were included in the final sample and organized into the categories: Emotional impact; and Understanding nursing role. The level of evidence found was moderate., Final Considerations: nursing experiences are permeated by emotional exhaustion, feelings of frustration and difficulty in understanding their professional role. The need for training spaces that qualify this care is highlighted.

Background: There are few predictive studies about early posttransplant outcomes taking into account baseline and posttransplant variables. The objective of this study was to create a predictive model for 30-day graft rejection using machine learning techniques., Methods: Retrospective study with 1255 patients undergoing transplant from living and deceased donors at a tertiary health service in Brazil. Recipient, donor, transplantation, and postoperative period data were collected from physical and electronic records. We split the data into derivation (training) and validation (test) datasets. Five supervised machine learning algorithms were developed with this subset of variables in the training set: Simple Logistic Regression, Lasso, Multilayer Perceptron, XGBoost, and Light GBM., Results: There were 147 (12.48%) cases of graft rejection within 30 days of transplantation. The best model was XGBoost (accuracy, 0.839; receiver operating characteristic area under the curve, 0.715; precision, 0.900). The model showed that deceased donor transplantation, glomerulopathy as an underlying disease, and donor's use of vasoactive drugs had more than 20% importance as rejection risk factors. The variables with the greatest predictive values were thymoglobulin induction and delayed graft function., Conclusions: We fitted a machine learning model to predict 30-day graft rejection after kidney transplantation that reaches a higher accuracy and precision. Machine learning models could contribute to predicting kidney survival using nontraditional approaches., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest, (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objectives: This study aimed to evaluate the impact of the COVID-19 pandemic on Brazilian health technology assessment processes based on public reports from the National Committee for Health Technology Incorporation (CONITEC)., Methods: This descriptive study analyzed CONITEC's official reports on Brazil available on its website between 2018 and 2021 that aimed to propose recommendations for technologies to be incorporated into its public healthcare system. We used descriptive statistics covering the number of technologies and number of reports about drugs per year, objective, type of technology, demanding sector, and outcome before 2018 to 2019 and during the COVID-19 pandemic (2020-2021). Furthermore, we used logistic regression to explore any association between the final decision labeled as "incorporated" and the emergence of the COVID-19 pandemic., Results: A total of 278 reports were analyzed. Approximately 85% (136 of 278), 79% (220 of 278), and 45% of the reports (125 of 278) were about drugs, for incorporation, and requested by the government, respectively. Moreover, 74 of 130 (57%) and 56 of 148 decisions (38%) were "incorporated" before and during the pandemic, respectively. No significant association was noted between incorporated decisions and the arrival of the COVID-19 pandemic for all technologies (odds ratio 1.43; 95% CI 0.84-2.46; P = .192) and for drugs (odds ratio 1.43; 95% confidence interval 0.81-2.53; P = .223) while adjusting for the type of technology and demandant., Conclusions: The COVID-19 pandemic has brought many challenges, but it does not seem to have had a significant impact on the health technology assessment approval decisions of CONITEC in Brazil., (Copyright © 2023 International Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Introduction: For the reduction of PTH levels, two classes of drugs are available in the Brazilian market: non-selective and selective vitamin D receptor activators and calcimimetics. Among the mentioned drugs, the SUS provides oral calcitriol, paricalcitol and cinacalcet., Objectives: Develop cost-effectiveness (CE) and budgetary impact (BI) analysis of cinacalcet versus paricalcitol for patients on dialysis with SHPT, from the perspective of SUS., Methodology: A decision tree model was constructed for CE analysis, which considered the outcome of avoided parathyroidectomy and a time horizon of 1 year. As for the BI analysis, two scenarios were considered, one of which was measured demand and other epidemiological, based on data from the Brazilian Society of Nephrology (BSN)., Results: The CE analysis showed that the use of cinacalcet results in one-off savings of R$1,394.64 per year and an incremental effectiveness of 0.08, in relation to avoided parathyroidectomy. The incremental CE ratio (ICER) was - R$ 17,653.67 per avoided parathyroidectomy for cinacalcet, as it was more effective and cheaper compared to paricalcitol. As for the BI analysis, it was estimated that the incremental BI with the expansion of the use of cinacalcet in the SUS will be between - R$ 1,640,864.62 and R$ 166,368.50 in the first year, considering the main and the epidemiological scenarios. At the end of 5 years after the expansion of use, an BI was estimated between - R$ 10,740,743.86 and - R$ 1,191,339.37; considering the same scenarios., Conclusion: Cinacalcet was dominant to avoid parathyroidectomies, being cost-effective.

Introduction: Hyperparathyroidism (SHPT) secondary to chronic kidney disease (CKD) is characterized by high levels of parathyroid hormone (PTH), hyperplasia of the parathyroid glands and cardiovascular disease. Selective and non-selective and selective vitamin D-receptor activators, calcimimetics, are available in the Brazilian market to reduce PTH levels., Objectives: To develop a cost-effectiveness (C/E) and budgetary impact (BI) analysis of intravenous paricalcitol vs. oral calcitriol for patients on dialysis with SHPT, from the perspective of the Brazilian Public Health Care System (SUS)., Methodology: We built a decision-tree model to analyze C/E, which considered the outcome of avoided death and a time horizon of 1 year. As for the BI analysis, two scenarios were considered, one of demand and one of epidemiological approach, based on data from the Brazilian Society of Nephrology., Results: The analysis showed that the C/E ratio was R$ 1,213.68 per year, and an incremental effectiveness of 0.032, referring to avoided death. The incremental C/E ratio was R$37,927.50 per death averted by paricalcitol. It was estimated that the incremental BI with the expansion of paricalcitol use will be between R$1,600,202.28 and R$4,128,565.65 in the first year, considering the main and epidemiological scenarios. At the end of 5 years after the expansion of its use, an incremental BI was estimated between R$ 48,596,855.50 and R$ 62,90,555.73., Conclusion: Intravenous paricalcitol has superior efficacy and similar safety to oral calcitriol, reducing the overall mortality of dialysis patients, although it implies a higher cost.

The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients' data were obtained through hospital records. Hospitals' data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (β = - 0.37; 95% CI - 0.71 to - 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (β = - 0.40; 95% CI - 0.72 to - 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (β = - 0.59; 95% CI - 0.98 to - 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (β = 0.40; 95% CI 0.11-0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality., (© 2022. The Author(s), under exclusive licence to Società Italiana di Medicina Interna (SIMI).)

Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones., Methods: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients., Results: Median (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO 2 /FiO 2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/)., Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: To provide a perspective on the current practice of randomized clinical trials (RCTs) of diagnostic strategies focusing on patient-important outcomes., Study Design and Setting: We conducted a comprehensive search of MEDLINE and included RCTs published in full-text reports that evaluated alternative diagnostic strategies., Results: Of 56,912 unique citations, we sampled 7,500 and included 103 eligible RCTs, therefore suggesting that MEDLINE includes approximately 781 diagnostic RCTs. The 103 eligible trials reported on: mortality (n = 41; 39.8%); morbidities (n = 63; 61.2%); symptoms/quality of life/functional status (n = 14; 13.6%); and on composite end points (n = 10; 9.7%). Of the studies that reported statistically significant results (n = 12; 11.6%), we judged 7 (58.3%) as at low risk of bias with respect to missing outcome data and 4 (33.3%) as at low risk of bias regarding blinding. Of the 41 RCTs that reported on mortality, only one (2.4%) reported statistically significant results. Of 63 RCTs addressing morbidity outcomes, 11 (17.5%) reported statistically significant results, all of which reported relative effects of greater than 20%., Conclusion: RCTs of diagnostic tests are not uncommon, and sometimes suggest benefits on patient-important outcomes but often suffer from limitations in sample size and conduct., (Copyright © 2017 Elsevier Inc. All rights reserved.)