Performance of Electronic Prediction Rules for Prevalent Delirium at Hospital Admission

Incidence of Delirium during the Initiation Phase of Morphine and Hydromorphone Therapy in Cancer Patients: A Retrospective Comparative Study

Background

Opioids are known to induce delirium, and the incidence of delirium induced by individual opioids has been investigated. However, only a limited number of studies have examined the incidence of delirium induced by oral hydromorphone.

Objective

To investigate whether differences exist in the incidence of delirium associated with oral morphine and oral hydromorphone during the initiation phase of treatment.

Methods

The participants were opioid-naive inpatients with cancer who started oral morphine or oral hydromorphone at Shizuoka Cancer Center (in Shizuoka, Japan) between June 2017 and November 2020. The incidence of delirium in the first week of opioid use was compared between the 2 groups.

Results

A total of 90 patients met the inclusion criteria, 27 who received oral hydromorphone and 63 who received oral morphine. The incidence rate of delirium in the oral hydromorphone group tended to be higher (19%, 5/27) than in the oral morphine group (8%, 5/63), although the difference was not statistically significant (odds ratio 0.4, 95% confidence interval, 0.1-1.4, p = 0.16 by the Fisher exact test). Propensity score matching was used to control for differences in patient background as confounders in the development of delirium, following which the incidence rate of delirium remained higher, but not significantly so, in the oral hydromorphone group (11%, 2/19) than in the oral morphine group (5%, 1/19) (odds ratio 0.5, 95% confidence interval 0.04-5.7, p > 0.99 by the Fisher exact test).

Conclusions

There was no statistically significant difference in the incidence of delirium between those who received morphine and those who received hydromorphone, which suggests that for opioid-naive inpatients with cancer, oral hydromorphone can be used in a manner similar to that for oral morphine.

Multi-instance learning for identifying high-risk subregions associated with synchronous distant metastasis in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is one of the most common histological subtypes of renal tumors.

PURPOSE

To identify high-risk subregions associated with synchronous distant metastasis.

METHODS

This study enrolled a total of 277 patients with ccRCC. Voxel intensity and local entropy values were compiled within the region of interest for all patients. Unsupervised k-means clustering yielded three subregions per tumor. Radiomic features were extracted, and random forest-based feature selection was conducted. The selected features were used in a multi-instance support vector machine (mi-SVM) model for training, and predictions were made on the validation cohort. Model performance was evaluated using five-fold cross-validation. The subregion with the highest score for patients with synchronous distant metastasis was identified across all cohorts.

RESULTS

The mi-SVM model yielded an average area under the curve (AUC) of 0.812 in the training cohort and 0.805 in the validation cohort. In the entire cohort of patients with synchronous distant metastasis, subregion 2, characterized by tumor periphery and intratumoral transitional components, accounted for the highest proportion (48.57%, 30.6/63) among all subregions. It represents a high-risk subregion for synchronous distant metastasis of clear cell renal cell carcinoma.

CONCLUSION

The peripheral and intratumoral transition zones of clear cell renal cell carcinoma are high-risk subregions associated with synchronous distant metastasis.

Machine learning approach to predict tracheal necrosis after total pharyngolaryngectomy

BACKGROUND

Tracheal necrosis is a potentially severe complication of total pharyngolarynjectomy (TPL), sometimes combined with total esophagectomy. The risk factors for tracheal necrosis after TPL without total esophagectomy remain unknown.

METHODS

We retrospectively reviewed data of 395 patients who underwent TPL without total esophagectomy. Relevant factors associated with tracheal necrosis were evaluated using random forest machine learning and traditional multivariable logistic regression models.

RESULTS

Tracheal necrosis occurred in 25 (6.3%) patients. Both the models identified almost the same factors relevant to tracheal necrosis. History of radiotherapy was the most important predicting and significant risk factor in both models. Paratracheal lymph node dissection and total thyroidectomy with TPL were also relevant. Random forest model was able to predict tracheal necrosis with an accuracy of 0.927.

CONCLUSIONS

Random forest is useful in predicting tracheal necrosis. Countermeasures should be considered when creating a tracheostoma, particularly in patients with identified risk factors.

Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review

PURPOSE

The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units.

METHODS

APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis.

RESULTS

Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability.

CONCLUSIONS

Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

OBJECTIVES

Early identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature.

METHODS

We searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice.

RESULTS

Among 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance.

DISCUSSION

ML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings.

CONCLUSION

This review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.

Head-to-head comparison of 14 prediction models for postoperative delirium in elderly non-ICU patients: an external validation study

OBJECTIVES

Delirium is associated with increased morbidity, mortality, prolonged hospitalisation and increased healthcare costs. The number of clinical prediction models (CPM) to predict postoperative delirium has increased exponentially. Our goal is to perform a head-to-head comparison of CPMs predicting postoperative delirium in non-intensive care unit (non-ICU) elderly patients to identify the best performing models.

SETTING

Single-site university hospital.

DESIGN

Secondary analysis of prospective cohort study.

PARTICIPANTS AND INCLUSION

CPMs published within the timeframe of 1 January 1990 to 1 May 2020 were checked for eligibility (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). For the time period of 1 January 1990 to 1 January 2017, included CPMs were identified in systematic reviews based on prespecified inclusion and exclusion criteria. An extended literature search for original studies was performed independently by two authors, including CPMs published between 1 January 2017 and 1 May 2020. External validation was performed using a surgical cohort consisting of 292 elderly non-ICU patients.

PRIMARY OUTCOME MEASURES

Discrimination, calibration and clinical usefulness.

RESULTS

14 CPMs were eligible for analysis out of 366 full texts reviewed. External validation was previously published for 8/14 (57%) CPMs. C-indices ranged from 0.52 to 0.74, intercepts from -0.02 to 0.34, slopes from -0.74 to 1.96 and scaled Brier from -1.29 to 0.088. Based on predefined criteria, the two best performing models were those of Dai et al (c-index: 0.739; (95% CI: 0.664 to 0.813); intercept: -0.018; slope: 1.96; scaled Brier: 0.049) and Litaker et al (c-index: 0.706 (95% CI: 0.590 to 0.823); intercept: -0.015; slope: 0.995; scaled Brier: 0.088). For the remaining CPMs, model discrimination was considered poor with corresponding c-indices <0.70.

CONCLUSION

Our head-to-head analysis identified 2 out of 14 CPMs as best-performing models with a fair discrimination and acceptable calibration. Based on our findings, these models might assist physicians in postoperative delirium risk estimation and patient selection for preventive measures.

Establishing and evaluating a quality improvement collaborative to address hospital to home transitions for older people

Introduction

Hospital use increases with age. Older people and their families have reported poor experiences of care at the time of discharge home from hospital. As part of a larger project, we established and evaluated a quality improvement collaborative to address hospital to home transitions for older people.

Methods

We convened an expert panel of 34 stakeholders to identify modifiable issues in the hospital-home transition period. We established a collaborative involving health professionals across a range of agencies working to common goals. Teams were supported by a network manager, three learning sessions and quality improvement methodology to address their identified area for improvement. We used mixed methods to evaluate whether the establishment of the quality improvement collaborative built networks, built capacity in the health professionals and improved the quality of care for older people. Evaluation methods included interviews, surveys, network mapping and case studies.

Results

Nine teams (n=41 participants) formed the collaborative and attended all meetings. Mapping showed an increase in networks between participants and organisations at the conclusion of the collaborative. Interview data showed that building relationships across services was one of the most important parts of the collaborative. Survey results revealed that most (77%) believed their quality improvement skills had developed through participation. Advice and regular meetings to progress project work were considered important in ensuring teams stayed focused. In terms of improving the quality of care, some participants indicated that they achieved the stated aims of their project better than expected (21%), most (41%) felt they achieved their aim as expected, 26% got close to their aim and the rest did not know the outcome (13%).

Conclusions

Establishing a quality improvement collaborative was a positive activity in terms of building a network across organisations and progressing quality improvement projects which aimed to achieve the same overall goal.

Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health. Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist. Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved. Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation.

Delirium in Intensive Care

Purpose of Review

Delirium in the intensive care unit (ICU) has become increasingly acknowledged as a significant problem for critically ill patients affecting both the actual course of illness as well as outcomes. In this review, we focus on the current evidence and the gaps in knowledge.

Recent Findings

This review highlights several areas in which the evidence is weak and further research is needed in both pharmacological and non-pharmacological treatment. A better understanding of subtypes and their different response to therapy is needed and further studies in aetiology are warranted. Larger studies are needed to explore risk factors for developing delirium and for examining long-term consequences. Finally, a stronger focus on experienced delirium and considering the perspectives of both patients and their families is encouraged.

Summary

With the growing number of studies and a better framework for research leading to stronger evidence, the outcomes for patients suffering from delirium will most definitely improve in the years to come.

Using Concept Unique Identifiers to Filter Electronic Health Records for Delirium Cases

Delirium, an acute mental status change associated with inattention, confusion, hypervigilance, or somnolence due to a medical cause, is considered a medical emergency. Unfortunately, screening and diagnosis of delirium in acute care are often inadequate. It is estimated that 60% of delirium cases are not identified, and in claims data, they are underreported. Using information technology, we investigated whether concept unique identifiers from the Unified Language Medical System Metathesaurus could be used as a method to filter electronic health records for possible delirium cases. This article provides the reader with an overview of delirium, the Unified Language Medical System Metathesaurus, and our method for retrospectively filtering electronic health records for delirium cases from our clinical research database. Using a retrospective observational approach, we randomly selected 150 electronic health records with narrative notes containing a delirium concept unique identifier. One hundred records were used for training and 50 were used for validation and interrater reliability. Our results validate electronic health record-selected concept unique identifiers and provide insights into their use. Refinement and application of this method on a larger scale can provide an initial filter for identifying patients with delirium from the electronic health record.

A novel model to label delirium in an intensive care unit from clinician actions

BACKGROUND

In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance.

METHODS

EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation.

RESULTS

Our best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes.

CONCLUSIONS

Hurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models.

A retrospective study of factors associated with persistent delirium

BACKGROUND

It has been reported that delirium causes various problems. Many researchers have reported the risk factors associated with the onset of delirium; however, there are few reports focused on persistent delirium. This study aimed to identify the risk factors associated with persistent delirium.

METHODS

A total of 573 patients hospitalised in Nara Prefecture General Medical Centre from October 2014 through September 2017 who were referred to the psychiatry consultation service were included in this study. Persistent delirium was defined as delirium lasting for 14 days or more. A retrospective study was carried out based on the patients' records. The relationship between various background factors and persistent delirium was statistically analysed.

RESULTS

Of the 573 hospitalised patients, 295 were diagnosed as having delirium. Forty-six patients with persistent delirium and 181 patients with nonpersistent delirium were included in this study. Multivariable logistic regression analyses revealed that male gender, opioid analgesics use, non-opioid analgesics use, and low serum sodium were significantly and independently associated with persistent delirium. Ramelteon or trazodone was used significantly more in persistent delirium, although each use was not significant.

CONCLUSION

This is the first study to reveal that male gender and use of analgesics were associated with persistent delirium in general hospital. However, as this is a case-control study and may contain bias, future cohort studies and intervention studies are needed. It is also necessary to investigate the relevance of the 'degree of pain' behind the use of analgesics.

Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients

BACKGROUND

Our objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort.

METHODS

We analyzed data from an observational cohort study of 560 older adults (≥ 70 years) without dementia undergoing major elective non-cardiac surgery. Post-operative delirium was determined by the Confusion Assessment Method supplemented by a medical chart review (N = 134, 24%). Five machine learning algorithms and a standard stepwise logistic regression model were developed in a training sample (80% of participants) and evaluated in the remaining hold-out testing sample. We evaluated three overlapping feature sets, restricted to variables that are readily available or minimally burdensome to collect in clinical settings, including interview and medical record data. A large feature set included 71 potential predictors. A smaller set of 18 features was selected by an expert panel using a consensus process, and this smaller feature set was considered with and without a measure of pre-operative mental status.

RESULTS

The area under the receiver operating characteristic curve (AUC) was higher in the large feature set conditions (range of AUC, 0.62-0.71 across algorithms) versus the selected feature set conditions (AUC range, 0.53-0.57). The restricted feature set with mental status had intermediate AUC values (range, 0.53-0.68). In the full feature set condition, algorithms such as gradient boosting, cross-validated logistic regression, and neural network (AUC = 0.71, 95% CI 0.58-0.83) were comparable with a model developed using traditional stepwise logistic regression (AUC = 0.69, 95% CI 0.57-0.82). Calibration for all models and feature sets was poor.

CONCLUSIONS

We developed machine learning prediction models for post-operative delirium that performed better than chance and are comparable with traditional stepwise logistic regression. Delirium proved to be a phenotype that was difficult to predict with appreciable accuracy.

Preoperative risk factors for delirium in patients aged ≥75 years undergoing spinal surgery: a retrospective study

OBJECTIVE

The increasing number of spinal surgeries being performed in the elderly has increased the incidence of postoperative delirium. The prediction of delirium is complex, and few studies have been performed to examine the preoperative risk factors for delirium after spinal surgery in the elderly. This study was performed to clarify such risk factors in patients aged ≥75 years undergoing spinal surgery.

METHOD

This retrospective observational study included 299 patients aged ≥75 years. Comorbidities, medication history, preoperative examination findings, surgery-related characteristics, and health scale assessments, including the 36-Item Short-Form Survey (SF-36) score and prognostic nutritional index (PNI), were examined as potential risk factors for delirium.

RESULTS

Delirium occurred in 53 patients (17.7%). The preoperative risk factors for delirium were a history of stroke and mental disorders, hypnotic drug use, malnutrition, hyponatremia, anemia, respiratory dysfunction, and cervical surgery. Logistic regression analysis demonstrated that the independent predictors of delirium were a history of stroke, non-benzodiazepine hypnotic drug use, preoperative hyponatremia, the PNI, and the SF-36 physical component summary (PCS) score.

CONCLUSIONS

Independent preoperative predictors of delirium in elderly patients undergoing spinal surgery included a history of stroke, non-benzodiazepine hypnotic drug use, preoperative hyponatremia, the PNI, and the SF-36 PCS score.

Delirium risk prediction models for intensive care unit patients: A systematic review

OBJECTIVE

To systematically review the delirium risk prediction models for intensive care unit (ICU) patients.

METHODS

A systematic review was conducted. The Cochrane Library, PubMed, Ovid and Web of Science were searched to collect studies on delirium risk prediction models for ICU patients from database establishment to 31 March 2019. Two reviewers independently screened the literature according to the pre-determined inclusion and exclusion criteria, extracted the data and evaluated the risk of bias of the included studies using the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) checklist. A descriptive analysis was used to describe and summarise the data.

RESULTS

A total of six models were included. All studies reported the area under the receiver operating characteristic curve (AUROC) of the prediction models in the derivation and (or) validation datasets as over 0.7 (from 0.75 to 0.9). Five models reported calibration metrics. Decreased cognitive reserve and the Acute Physiology and Chronic Health Evaluation II (APACHE-II) score were the most commonly reported predisposing and precipitating factors, respectively, of ICU delirium among all models. The small sample size, lack of external validation and the absence of or unreported blinding method increased the risk of bias.

CONCLUSION

According to the discrimination and calibration statistics reported in the original studies, six prediction models may have moderate power in predicting ICU delirium. However, this finding should be interpreted with caution due to the risk of bias in the included studies. More clinical studies should be carried out to validate whether these tools have satisfactory predictive performance in delirium risk prediction for ICU patients.

Delirium in hospitalized older adults

Delirium is a common and underdiagnosed problem in hospitalized older adults. It is associated with an increased risk of poor cognitive and functional outcomes, institutionalization, and death. Timely diagnosis of delirium and non-pharmacological prevention and management strategies can improve patient outcomes. The Confusion Assessment Method (CAM) is the most widely used clinical assessment tool for the diagnosis of delirium. Multiple variations of the CAM have been developed for ease of administration and for the unique needs of specific patient populations, including the 3-min diagnostic CAM (3D CAM), CAM-Intensive Care Unit (CAM-ICU), Delirium Triage Screen (DTS)/Brief CAM (b-CAM), 4AT tool, and ultrabrief delirium assessment. Strong evidence supports the effectiveness of nonpharmacologic strategies as the primary intervention for the prevention of delirium. Multicomponent delirium prevention strategies can reduce the incidence of delirium by 40%. Investigation of underlying medical precipitants and optimization of non-pharmacological interventions are first line in the management of delirium. Despite a lack of evidence supporting use of antipsychotics, low dose antipsychotics remain second line for off-label treatment of distressing psychoses and/or agitated behaviors that are refractory to non-pharmacological behavioral interventions and pose an imminent risk of harm to self or others. Any antipsychotic prescription for delirium should be accompanied by an appropriate taper plan. Follow up with primary care providers on discharge from hospital for ongoing screening of cognitive impairment is important.

Machine learning analysis to identify the association between risk factors and onset of nosocomial diarrhea: a retrospective cohort study

BACKGROUND

Although several risk factors for nosocomial diarrhea have been identified, the detail of association between these factors and onset of nosocomial diarrhea, such as degree of importance or temporal pattern of influence, remains unclear. We aimed to determine the association between risk factors and onset of nosocomial diarrhea using machine learning algorithms.

METHODS

We retrospectively collected data of patients with acute cerebral infarction. Seven variables, including age, sex, modified Rankin Scale (mRS) score, and number of days of antibiotics, tube feeding, proton pump inhibitors, and histamine 2-receptor antagonist use, were used in the analysis. We split the data into a training dataset and independant test dataset. Based on the training dataset, we developed a random forest, support vector machine (SVM), and radial basis function (RBF) network model. By calculating an area under the curve (AUC) of the receiver operating characteristic curve using 5-fold cross-validation, we performed feature selection and hyperparameter optimization in each model. According to their final performances, we selected the optimal model and also validated it in the independent test dataset. Based on the selected model, we visualized the variable importance and the association between each variable and the outcome using partial dependence plots.

RESULTS

Two-hundred and eighteen patients were included. In the cross-validation within the training dataset, the random forest model achieved an AUC of 0.944, which was higher than in the SVM and RBF network models. The random forest model also achieved an AUC of 0.832 in the independent test dataset. Tube feeding use days, mRS score, antibiotic use days, age and sex were strongly associated with the onset of nosocomial diarrhea, in this order. Tube feeding use had an inverse U-shaped association with the outcome. The mRS score and age had a convex downward and increasing association, while antibiotic use had a convex upward association with the outcome.

CONCLUSION

We revealed the degree of importance and temporal pattern of the influence of several risk factors for nosocomial diarrhea, which could help clinicians manage nosocomial diarrhea.

Big Data and Discovery Sciences in Psychiatry

The modern society is a so-called era of big data. Whereas nearly everybody recognizes the "era of big data", no one can exactly define how big the data is a "big data". The reason for the ambiguity of the term big data mainly arises from the widespread of using that term. Along the widespread application of the digital technology in the everyday life, a large amount of data is generated every second in relation with every human behavior (i.e., measuring body movements through sensors, texts sent and received via social networking services). In addition, nonhuman data such as weather and Global Positioning System signals has been cumulated and analyzed in perspectives of big data (Kan et al. in Int J Environ Res Public Health 15(4), 2018 [1]). The big data has also influenced the medical science, which includes the field of psychiatry (Monteith et al. in Int J Bipolar Disord 3(1):21, 2015 [2]). In this chapter, we first introduce the definition of the term "big data". Then, we discuss researches which apply big data to solve problems in the clinical practice of psychiatry.

Prediction of Incident Delirium Using a Random Forest classifier

Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visits to train and test a model predicting delirium arising in hospital. Incident delirium was defined as the first instance of a positive CAM occurring at least 48 h into a hospital stay. A Random Forest machine learning algorithm was used with demographic data, comorbidities, medications, procedures, and physiological measures. The data set was randomly partitioned 80% / 20% for training and validating the predictive model, respectively. Of the 51,240 patients in the training set, 2774 (5.4%) experienced delirium during their hospital stay; and of the 12,798 patients in the validation set, 701 (5.5%) experienced delirium. Under-sampling of the delirium negative population was used to address the class imbalance. The Random Forest predictive model yielded an area under the receiver operating characteristic curve (ROC AUC) of 0.909 (95% CI 0.898 to 0.921). Important variables in the model included previously identified predisposing and precipitating risk factors. This machine learning approach displayed a high degree of accuracy and has the potential to provide a clinically useful predictive model for earlier intervention in those patients at greatest risk of developing delirium.