Models to predict length of stay in the emergency department: a systematic literature review and appraisal.

INTRODUCTION: Prolonged Length of Stay (LOS) in ED (Emergency Department) has been associated with poor clinical outcomes. Prediction of ED LOS may help optimize resource utilization, clinical management, and benchmarking. This study aims to systematically review models for predicting ED LOS and to assess the reporting and methodological quality about these models. METHODS: The online database PubMed, Scopus, and Web of Science (10 Sep 2023) was searched for English language articles that reported prediction models of LOS in ED. Identified titles and abstracts were independently screened by two reviewers. All original papers describing either development (with or without internal validation) or external validation of a prediction model for LOS in ED were included. RESULTS: Of 12,193 uniquely identified articles, 34 studies were included (29 describe the development of new models and five describe the validation of existing models). Different statistical and machine learning methods were applied to the papers. On the 39-point reporting score and 11-point methodological quality score, the highest reporting scores for development and validation studies were 39 and 8, respectively. CONCLUSION: Various studies on prediction models for ED LOS were published but they are fairly heterogeneous and suffer from methodological and reporting issues. Model development studies were associated with a poor to a fair level of methodological quality in terms of the predictor selection approach, the sample size, reproducibility of the results, missing imputation technique, and avoiding dichotomizing continuous variables. Moreover, it is recommended that future investigators use the confirmed checklist to improve the quality of reporting.

Designing, validation and evaluation of the expert system of "Healthy Menopause" and assessing its effect on the management of menopause symptoms: an exploratory mixed method study protocol.

BACKGROUND: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women's related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the "Healthy Menopause" expert system on the management of menopausal symptoms. METHODS/DESIGN: A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women's experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts' opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves. DISCUSSION: This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system "Healthy Menopause". This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner.

Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, easy for use and affordable way for managing related problems and meeting related needs is important. Menopause is a period of women's life that has physical, psychological and social consequences. It is important to identify methods that are effective, practical and affordable. New technologies can increase women's ability to access educational information. This is the first study for designing, validating and evaluating of the expert system "Healthy Menopause". A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase (qualitative): The conventional content analysis method will be used. The second phase: Designing a healthy menopause expert system: It is based on the codes of women's challenges from the first phase, along with conducting interviews and literature review. The participatory approach (Participatory Design) through nominal group or if needed, Delphi method based on the categories of needs and solutions by considering the opinions of the participants, available experts related to this issue will be listed. It should be used to design a healthy menopause expert system at this stage. The third phase (quantitative): The evaluation of the healthy menopause expert system will be a randomized clinical trial that determine the effect of the healthy menopause expert system on the management of menopause symptoms. In the present study an expert system (ES) will be designed that can be installed on mobile phones and computers. This tool is not only educational but also interactively helps to adapt to continuous changes, so by asking questions about menopause the system will respond as if an expert (midwife or gynecologist) is giving advice.

Predicting the mortality of patients with Covid-19: A machine learning approach.

Background and Aims: Infection with Covid-19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid-19 using machine learning techniques can be effective in reducing mortality rate in Covid-19. The aim of this study is to compare four machine-learning algorithm for predicting mortality in Covid-19 disease. Methods: The data of this study were collected from hospitalized patients with COVID-19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid-19. Each record contained 38 variables. Four machine-learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling. Results: GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places. Conclusion: Considering the combination of multiple influential factors affecting death Covid-19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.

Prediction of Breast Cancer using Machine Learning Approaches.

Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data. Material and Methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network. Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.

Practical Recommendations for the Preoperative Screening and Protective Protocols in Cancer Surgeries During COVID-19: A Systematic Review.

Introduction: The new coronavirus (COVID-19) has posed many new challenges to the health care and the timing of surgical care. At the beginning of the pandemic many guidelines recommended postponing elective surgical procedures to reallocate resources. As regards, delay in cancer treatment could be effective on cancer progression. The aim of this systematic review was to outline a guideline for preoperative screening before cancer surgeries and protecting health care workers during the pandemic. Materials and Methods: This study was conducted through a search in electronic databases up to August 2020. PubMed, EMBASE, Web of Science, Scopus, Science Direct, and Google Scholar databases were searched without time limitation. The keywords were a combination of preoperative, cancer surgery, COVID-19, and their synonyms. Results: The most commonly used ways to triage preoperatively were telephone pre-assessment for suspicious symptoms and history of contact or travel, 14-day self-isolation, in- hospital queries at admission, temperature monitoring, and isolation in a single room COVID-free ward or physical distancing. Reverse transcription-polymerase chain reaction (RT-PCR) test 24-72 h before operation was recommended commonly, except in inaccessible centers, but non-contrast chest-CT scan is not routinely advised for elective surgeries to salvage medical resources. Recommended personal protective equipment (PPE) for staffs were wearing N95 mask in addition to gown, gloves, eye protection in aerosol-generating procedures (AGPs), and wearing gloves, hats, and disposable surgical masks, practice distancing, and hand hygiene for all staffs. Meanwhile team separation of hospital staffs caring for COVID-19 patients, segregated areas for COVID-19 clean and contact, restriction of visitors and family members, and personal distancing are mostly recommended. Conclusion: We hope this review would be a guidance for triage, preoperative testing, and summarizing safety principles during COVID-19 pandemic alongside with surgical reintegration.

Colorectal Cancer Registration: Data Approach to Knowledge.

For ensuring the quality of data and facilitating data exchange between healthcare providers and professional organizations, it is necessary to define a standard data set. The main aim of this study was to define a national minimum data set for colorectal cancer in Iran. To develop this data set, a combination of literature review and two rounds of a modified Delphi technique were used. An initial checklist was proposed based on a literature review and comparative studies. Based on the literature review, main categories, including: demographic information, diagnostic information, treatment information, clinical status assessment information, and clinical trial information were proposed. In this study, the national minimum data set of colorectal cancer was collected. Developing this data set through standard contents can improve effective health information exchange for both healthcare providers and health information systems.

Discovery of Hidden Patterns in Breast Cancer Patients, Using Data Mining on a Real Data Set.

The aim is to recognize the unknown atterns in a real breast cancer dataset using data mining algorithms as a new method in medicine. Due to excessive missing data in the collection only data on 665 of 809 patients were available. The other missing values were estimated using the EM algorithm in SPSS21 software. Fields have been converted into discrete fields and finally the APRIORI algorithm has been used to analyze and explore the unknown patterns. After the rule extraction, experts in the field of breast cancer eliminated redundant and meaningless relations. 100 association rules with a confidence value of more than 0.9 explored by the APRIORI algorithm and after the clinical expert feedback, 10 clinically meaningful relations have been detected and reported. Due to the high number of risk factors, the use of data mining is effective for cancer data. These patterns provide the future study hypotheses of specific clinical studies.

Development of a National Core Dataset for the Iranian ICU Patients Outcome Prediction; a Comprehensive Approach.

OBJECTIVE: To define a core dataset for ICU Patients Outcome Prediction in Iran. This core data set will lead us to design ICU outcome prediction models with the most effective parameters. METHODS: A combination of literature review, national survey and expert consensus meetings were used. First, a literature review was performed by a general search in PubMed to find the most appropriate models for intensive care mortality prediction and their parameters. Secondly, in a national survey, experts from a couple of medical centers in all parts of Iran were asked to comment on a list of items retrieved from the earlier literature review study. In the next step, a multi-disciplinary committee of experts was installed.  In 4 meetings each data item was examined separately and included/excluded by committee consensus. RESULTS: The combination of the literature review findings and experts' consensus resulted in a draft dataset including 26 data items. 92% percent of data items in the draft dataset were retrieved from the literature study and the others were suggested by the experts. The final dataset of 24 data items covers patient history and physical examination, chemistry, vital signs, oxygenations and some more specific parameters. Conclusions: This dataset was designed to develop a nationwide prognostic model for predicting ICU mortality and length of stay. This dataset opens the door for creating standardized approaches in data collection in the Iranian intensive care unit estimation of resource utility.

Intensive Care Decision Making: Using Prognostic Models for Resource Allocation.

Accurate outcome prediction by the means of available clinical contributing factors will support researchers and administrators in realistic planning, workload determination, resource optimization, and evidence-based quality control process. This study is aimed to evaluate APACHE II and SAPS II prediction models in an Iranian population. A a prospective cross-sectional study was conducted in four tertiary care referral centers located in the top two most populated cities in Iran, from August 2013 to August 2015. The Brier score, Area under the Receiver Operating Characteristics Curve (AUC), and Hosmer-Lemeshow (H-L) goodness-of-fit test were employed to quantify models' performance. A total of 1799 patients (58.5% males and 41.5% females) were included for further score calculation. The overall observed mortality (24.4%) was more than international rates due to APACHE II categories. The Brier score for APACHE II and SAPS II were 0.17 and 0.196, respectively. Both scoring systems were associated with acceptable AUCs (APACHE II = 0.745 and SAPS II = 0.751). However, none of prediction models were fitted to dataset (H-L ρ value < 0.01). With regards to poor performance measures of APACHE II and SAPS II in this study, finding recalibrated version of current prediction models is considered as an obligatory research question before applying it as a clinical prioritization or quality control instrument.

External Validation of European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) for Risk Prioritization in an Iranian Population.

INTRODUCTION: The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death concentrating on accurate stratification of candidate patients for cardiac surgery. OBJECTIVE: The objective of this study was to determine the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries in one area of Iran. METHODS: A retrospective cohort study was conducted to collect the required variables for all consecutive patients who underwent heart surgeries at Emam Reza hospital, Northeast Iran between 2014 and 2015. Univariate and multivariate analysis were performed to identify covariates which significantly contribute to higher EuroSCORE II in our population. External validation was performed by comparing the real and expected mortality using area under the receiver operating characteristic curve (AUC) for discrimination assessment. Also, Brier Score and Hosmer-Lemeshow goodness-of-fit test were used to show the overall performance and calibration level, respectively. RESULTS: Two thousand five hundred eight one (59.6% males) were included. The observed mortality rate was 3.3%, but EuroSCORE II had a prediction of 4.7%. Although the overall performance was acceptable (Brier score=0.047), the model showed poor discriminatory power by AUC=0.667 (sensitivity=61.90, and specificity=66.24) and calibration (Hosmer-Lemeshow test, P<0.01). CONCLUSION: Our study showed that the EuroSCORE II discrimination power is less than optimal for outcome prediction and less accurate for resource allocation programs. It highlights the need for recalibration of this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran.

Models to predict length of stay in the Intensive Care Unit after coronary artery bypass grafting: a systematic review.

INTRODUCTION: Intensive Care Units (ICU) length of stay (LoS) prediction models are used to compare different institutions and surgeons on their performance, and is useful as an efficiency indicator for quality control. There is little consensus about which prediction methods are most suitable to predict (ICU) length of stay. The aim of this study is to systematically review models for predicting ICU LoS after coronary artery bypass grafting and to assess the reporting and methodological quality of these models to apply them for benchmarking. EVIDENCE ACQUISITION: A general search was conducted in Medline and Embase up to 31-12-2016. Three authors classified the papers for inclusion by reading their title, abstract and full text. All original papers describing development and/or validation of a prediction model for LoS in the ICU after CABG surgery were included. We used a checklist developed for critical appraisal and data extraction for systematic reviews of prediction modeling and extended it on handling specific patients subgroups. We also defined other items and scores to assess the methodological and reporting quality of the models. EVIDENCE SYNTHESIS: Of 5181 uniquely identified articles, fifteen studies were included of which twelve on development of new models and three on validation of existing models. All studies used linear or logistic regression as method for model development, and reported various performance measures based on the difference between predicted and observed ICU LoS. Most used a prospective (46.6%) or retrospective study design (40%). We found heterogeneity in patient inclusion/exclusion criteria; sample size; reported accuracy rates; and methods of candidate predictor selection. Most (60%) studies have not mentioned the handling of missing values and none compared the model outcome measure of survivors with non-survivors. For model development and validation studies respectively, the maximum reporting (methodological) scores were 66/78 and 62/62 (14/22 and 12/22). CONCLUSIONS: There are relatively few models for predicting ICU length of stay after CABG. Several aspects of methodological and reporting quality of studies in this field should be improved. There is a need for standardizing outcome and risk factor definitions in order to develop/validate a multi-institutional and international risk scoring system.

External validation of european system for cardiac operative risk evaluation II (euroscore II) for risk prioritization in an iranian population

Abstract Introduction: The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death concentrating on accurate stratification of candidate patients for cardiac surgery. Objective: The objective of this study was to determine the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries in one area of Iran. Methods: A retrospective cohort study was conducted to collect the required variables for all consecutive patients who underwent heart surgeries at Emam Reza hospital, Northeast Iran between 2014 and 2015. Univariate and multivariate analysis were performed to identify covariates which significantly contribute to higher EuroSCORE II in our population. External validation was performed by comparing the real and expected mortality using area under the receiver operating characteristic curve (AUC) for discrimination assessment. Also, Brier Score and Hosmer-Lemeshow goodness-of-fit test were used to show the overall performance and calibration level, respectively. Results: Two thousand five hundred eight one (59.6% males) were included. The observed mortality rate was 3.3%, but EuroSCORE II had a prediction of 4.7%. Although the overall performance was acceptable (Brier score=0.047), the model showed poor discriminatory power by AUC=0.667 (sensitivity=61.90, and specificity=66.24) and calibration (Hosmer-Lemeshow test, P<0.01). Conclusion: Our study showed that the EuroSCORE II discrimination power is less than optimal for outcome prediction and less accurate for resource allocation programs. It highlights the need for recalibration of this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran.

Which Models Can I Use to Predict Adult ICU Length of Stay? A Systematic Review.

OBJECTIVE: We systematically reviewed models to predict adult ICU length of stay. DATA SOURCES: We searched the Ovid EMBASE and MEDLINE databases for studies on the development or validation of ICU length of stay prediction models. STUDY SELECTION: We identified 11 studies describing the development of 31 prediction models and three describing external validation of one of these models. DATA EXTRACTION: Clinicians use ICU length of stay predictions for planning ICU capacity, identifying unexpectedly long ICU length of stay, and benchmarking ICUs. We required the model variables to have been published and for the models to be free of organizational characteristics and to produce accurate predictions, as assessed by R across patients for planning and identifying unexpectedly long ICU length of stay and across ICUs for benchmarking, with low calibration bias. We assessed the reporting quality using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies. DATA SYNTHESIS: The number of admissions ranged from 253 to 178,503. Median ICU length of stay was between 2 and 6.9 days. Two studies had not published model variables and three included organizational characteristics. None of the models produced predictions with low bias. The R was 0.05-0.28 across patients and 0.01-0.64 across ICUs. The reporting scores ranged from 49 of 78 to 60 of 78 and the methodologic scores from 12 of 22 to 16 of 22. CONCLUSION: No models completely satisfy our requirements for planning, identifying unexpectedly long ICU length of stay, or for benchmarking purposes. Physicians using these models to predict ICU length of stay should interpret them with reservation.

User Interface Problems of a Nationwide Inpatient Information System: A Heuristic Evaluation.

INTRODUCTION: While studies have shown that usability evaluation could uncover many design problems of health information systems, the usability of health information systems in developing countries using their native language is poorly studied. The objective of this study was to evaluate the usability of a nationwide inpatient information system used in many academic hospitals in Iran. MATERIAL AND METHODS: Three trained usability evaluators independently evaluated the system using Nielsen's 10 usability heuristics. The evaluators combined identified problems in a single list and independently rated the severity of the problems. We statistically compared the number and severity of problems identified by HIS experienced and non-experienced evaluators. RESULTS: A total of 158 usability problems were identified. After removing duplications 99 unique problems were left. The highest mismatch with usability principles was related to "Consistency and standards" heuristic (25%) and the lowest related to "Flexibility and efficiency of use" (4%). The average severity of problems ranged from 2.4 (Major problem) to 3.3 (Catastrophe problem). The experienced evaluator with HIS identified significantly more problems and gave higher severities to problems (p<0.02). DISCUSSION: Heuristic Evaluation identified a high number of usability problems in a widely used inpatient information system in many academic hospitals. These problems, if remain unsolved, may waste users' and patients' time, increase errors and finally threaten patient's safety. Many of them can be fixed with simple redesign solutions such as using clear labels and better layouts. This study suggests conducting further studies to confirm the findings concerning effect of evaluator experience on the results of Heuristic Evaluation.