Browsing by Author "Holler, Emma"
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Item Delirium and neuropsychological recovery among emergency general surgery survivors (DANE): study protocol for a randomized controlled trial and collaborative care intervention(BMC, 2023-10-03) Mohanty, Sanjay; Holler, Emma; Ortiz, Damaris; Meagher, Ashley; Perkins, Anthony; Bylund, Peggy; Khan, Babar; Unverzagt, Frederick; Xu, Hupuing; Ingraham, Angela; Boustani, Malaz; Zarzaur, Ben; Surgery, School of MedicineBackground: Delirium is a complex neuropsychiatric syndrome which consists of acute and varying changes in cognition and consciousness. Patients who develop delirium are at increased risk for a constellation of physical, cognitive, and psychological disabilities long after the delirium has ended. Collaborative care models integrating primary and specialty care in order to address patients with complex biopsychosocial needs have been demonstrated to improve outcomes in patients with chronic diseases. The purpose of this study is to evaluate the ability of a collaborative care model on the neuropsychologic recovery of delirium survivors following emergency surgery. Methods: This protocol describes a multicenter (eight hospitals in three states) randomized controlled trial in which 528 patients who develop delirium following emergency surgery will be randomized to either a collaborative care model or usual care. The efficacy of the collaborative care model on cognitive, physical, and psychological recovery in these delirium survivors will then be evaluated over 18 months. Discussion: This will be among the first randomized clinical trials in postoperative delirium survivors evaluating an intervention designed to mitigate the downstream effects of delirium and improve the neuropsychologic recovery after surgery. We hope that the results of this study will add to and inform strategies to improve postoperative recovery in this patient group.Item Development and Temporal Validation of an Electronic Medical Record-Based Insomnia Prediction Model Using Data from a Statewide Health Information Exchange(MDPI, 2023-05-05) Holler, Emma; Chekani, Farid; Ai, Jizhou; Meng, Weilin; Khandker, Rezaul Karim; Ben Miled, Zina; Owora, Arthur; Dexter, Paul; Campbell, Noll; Solid, Craig; Boustani, Malaz; Electrical and Computer Engineering, School of Engineering and TechnologyThis study aimed to develop and temporally validate an electronic medical record (EMR)-based insomnia prediction model. In this nested case-control study, we analyzed EMR data from 2011–2018 obtained from a statewide health information exchange. The study sample included 19,843 insomnia cases and 19,843 controls matched by age, sex, and race. Models using different ML techniques were trained to predict insomnia using demographics, diagnosis, and medication order data from two surveillance periods: −1 to −365 days and −180 to −365 days before the first documentation of insomnia. Separate models were also trained with patient data from three time periods (2011–2013, 2011–2015, and 2011–2017). After selecting the best model, predictive performance was evaluated on holdout patients as well as patients from subsequent years to assess the temporal validity of the models. An extreme gradient boosting (XGBoost) model outperformed all other classifiers. XGboost models trained on 2011–2017 data from −1 to −365 and −180 to −365 days before index had AUCs of 0.80 (SD 0.005) and 0.70 (SD 0.006), respectively, on the holdout set. On patients with data from subsequent years, a drop of at most 4% in AUC is observed for all models, even when there is a five-year difference between the collection period of the training and the temporal validation data. The proposed EMR-based prediction models can be used to identify insomnia up to six months before clinical detection. These models may provide an inexpensive, scalable, and longitudinally viable method to screen for individuals at high risk of insomnia.Item Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study(JMIR, 2025-01-09) Holler, Emma; Ludema, Christina; Ben Miled, Zina; Rosenberg, Molly; Kalbaugh, Corey; Boustani, Malaz; Mohanty, Sanjay; Surgery, School of MedicineBackground: Postoperative delirium (POD) is a common complication after major surgery and is associated with poor outcomes in older adults. Early identification of patients at high risk of POD can enable targeted prevention efforts. However, existing POD prediction models require inpatient data collected during the hospital stay, which delays predictions and limits scalability. Objective: This study aimed to develop and externally validate a machine learning-based prediction model for POD using routine electronic health record (EHR) data. Methods: We identified all surgical encounters from 2014 to 2021 for patients aged 50 years and older who underwent an operation requiring general anesthesia, with a length of stay of at least 1 day at 3 Indiana hospitals. Patients with preexisting dementia or mild cognitive impairment were excluded. POD was identified using Confusion Assessment Method records and delirium International Classification of Diseases (ICD) codes. Controls without delirium or nurse-documented confusion were matched to cases by age, sex, race, and year of admission. We trained logistic regression, random forest, extreme gradient boosting (XGB), and neural network models to predict POD using 143 features derived from routine EHR data available at the time of hospital admission. Separate models were developed for each hospital using surveillance periods of 3 months, 6 months, and 1 year before admission. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Each model was internally validated using holdout data and externally validated using data from the other 2 hospitals. Calibration was assessed using calibration curves. Results: The study cohort included 7167 delirium cases and 7167 matched controls. XGB outperformed all other classifiers. AUROCs were highest for XGB models trained on 12 months of preadmission data. The best-performing XGB model achieved a mean AUROC of 0.79 (SD 0.01) on the holdout set, which decreased to 0.69-0.74 (SD 0.02) when externally validated on data from other hospitals. Conclusions: Our routine EHR-based POD prediction models demonstrated good predictive ability using a limited set of preadmission and surgical variables, though their generalizability was limited. The proposed models could be used as a scalable, automated screening tool to identify patients at high risk of POD at the time of hospital admission.Item Development of a population‐level prediction model for intensive care unit (ICU) survivorship and mortality in older adults: A population‐based cohort study(Wiley, 2023-10-19) Khan, Sikandar H.; Perkins, Anthony J.; Fuchita, Mikita; Holler, Emma; Ortiz, Damaris; Boustani, Malaz; Khan, Babar A.; Gao, Sujuan; Medicine, School of MedicineBackground and aims: Given the growing utilization of critical care services by an aging population, development of population-level risk models which predict intensive care unit (ICU) survivorship and mortality may offer advantages for researchers and health systems. Our objective was to develop a risk model for ICU survivorship and mortality among community dwelling older adults. Methods: This was a population-based cohort study of 48,127 patients who were 50 years and older with at least one primary care visit between January 1, 2017, and December 31, 2017. We used electronic health record (EHR) data to identify variables predictive of ICU survivorship. Results: ICU admission and mortality within 2 years after index primary care visit date were used to divide patients into three groups of "alive without ICU admission", "ICU survivors," and "death." Multinomial logistic regression was used to identify EHR predictive variables for the three patient outcomes. Cross-validation by randomly splitting the data into derivation and validation data sets (60:40 split) was used to identify predictor variables and validate model performance using area under the receiver operating characteristics (AUC) curve. In our overall sample, 92.2% of patients were alive without ICU admission, 6.2% were admitted to the ICU at least once and survived, and 1.6% died. Greater deciles of age over 50 years, diagnoses of chronic obstructive pulmonary disorder or chronic heart failure, and laboratory abnormalities in alkaline phosphatase, hematocrit, and albumin contributed highest risk score weights for mortality. Risk scores derived from the model discriminated between patients that died versus remained alive without ICU admission (AUC = 0.858), and between ICU survivors versus alive without ICU admission (AUC = 0.765). Conclusion: Our risk scores provide a feasible and scalable tool for researchers and health systems to identify patient cohorts at increased risk for ICU admission and survivorship. Further studies are needed to prospectively validate the risk scores in other patient populations.Item Major Surgery and Long Term Cognitive Outcomes: The Effect of Postoperative Delirium on Dementia in the Year Following Discharge(Elsevier, 2022) Mohanty, Sanjay; Gillio, Anna; Lindroth, Heidi; Ortiz, Damaris; Holler, Emma; Azar, Jose; Boustani, Malaz; Zarzaur, Ben; Surgery, School of MedicineBackground: Delirium is among the most common complications following major surgery. Delirium following medical illness is associated with the development of chronic cognitive decline. The objective of this study was to determine the association of postoperative delirium with dementia in the year following surgery. Materials and methods: This was a retrospective cohort study in a large health network (January 2013 to December 2019). All patients over age 50 undergoing surgery requiring an inpatient stay were included. Our main exposure was an episode of delirium. The primary outcome was a new dementia diagnosis in the 1 y following discharge. Secondary outcomes included hospital length of stay, non-home discharge destination, mortality and rehospitalizations in 1 y. Results: There were 39,665 patients included, with a median age of 66. There were 4156 of 39,665 emergencies (10.5%). Specialties were general surgery (12,285/39,665, 31%) and orthopedics (11,503/39,665, 29%). There were 3327 (8.4%) patients with delirium. Delirious patients were older and were more likely to have comorbid conditions and undergone complex procedures. There were 1353 of 39,665 (3.5%) patients who developed dementia in the year following their surgery; 4930 of 39,665 (12.4%) who died; and 8200 of 39,665 (20.7%) who were readmitted. Delirium was associated with a new dementia diagnosis after adjusting for baseline characteristics (Odds ratio [OR] 13.9; 95% CI, 12.2-15.7). Similarly, delirium was also associated with 1 y mortality (OR 3.1; 95% CI 2.9-3.4) and readmission (OR 1.9, 95% CI 1.7-2.0). Conclusions: Postoperative delirium is the strongest factor associated with development of dementia in the year following a major operation. Strategies to prevent, identify, and treat delirium in the postoperative setting may improve long-term cognitive recovery.Item Modeling acute care utilization: practical implications for insomnia patients(Springer Nature, 2023-02-07) Chekani, Farid; Zhu, Zitong; Khandker, Rezaul Karim; Ai, Jizhou; Meng, Weilin; Holler, Emma; Dexter, Paul; Boustani, Malaz; Ben Miled, Zina; Medicine, School of MedicineMachine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate the practical utility of different data modalities and cohort segmentation strategies when designing models for emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications. Segmentation compares a cohort of insomnia patients to a cohort of general non-insomnia patients under varying age and disease severity criteria. Transfer testing between the two cohorts is introduced to demonstrate that an insomnia-specific model is not necessary when predicting future ED visits, but may have merit when predicting IH visits especially for patients with an insomnia diagnosis. The results also indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. Based on these findings, the proposed evaluation methodologies are recommended to ascertain the utility of disease-specific models in addition to the traditional intra-cohort testing.Item Pre-Existing Anxiety and Depression in Injured Older Adults: An Under-Recognized Comorbidity With Major Health Implications(Wolters Kluwer, 2022-12-07) Ortiz, Damaris; Perkins, Anthony J.; Fuchita, Mikita; Gao, Sujuan; Holler, Emma; Meagher, Ashley D.; Mohanty, Sanjay; French, Dustin D.; Lasiter, Sue; Khan, Babar; Boustani, Malaz; Zarzaur, Ben; Surgery, School of MedicineObjective: To compare differences in baseline depression and anxiety screenings between older injured patients with pre-existing diagnoses and those without. Background: Little is known about the prevalence and impact of psychiatric comorbidities on early postinjury depression and anxiety in nonneurologically injured older adults. Methods: This was a retrospective post-hoc analysis of data from the Trauma Medical Home, a multicenter randomized controlled trial (R01AG052493-01A1) that explored the effect of a collaborative care model on postinjury recovery for older adults compared to usual care. Results: Nearly half of the patients screened positive for at least mild depressive symptoms as measured by the Patient Health Questionnaire-9. Forty-one percent of the patients screened positive for at least mild anxiety symptoms as measured by the Generalized Anxiety Disorder Scale. Female patients with a history of concurrent anxiety and depression, greater injury severity scores, and higher Charlson scores were more likely to have mild anxiety at baseline assessment. Patients with a history of depression only, a prior history of depression and concurrent anxiety, and higher Charlson scores (greater medical comorbidity) had greater odds of at least mild depression at the time of hospital discharge after traumatic injury. Conclusions: Anxiety and depression are prevalent in the older adult trauma population, and affect women disproportionately. A dual diagnosis of depression and anxiety is particularly morbid. Mental illness must be considered and addressed with the same importance as other medical diagnoses in patients with injuries.Item Preinjury Functional Independence is not Associated with Discharge Location in Older Trauma Patients(Elsevier, 2021) Holler, Emma; Meagher, Ashley D.; Ortiz, Damaris; Mohanty, Sanjay; Newnum, America; Perkins, Anthony; Gao, Sujuan; Kinnaman, Gabriel; Boustani, Malaz; Zarzaur, Ben; Surgery, School of MedicineBackground: The purpose of this study was to evaluate the association between pre-injury Katz Index of Independence in Activities of Daily Living (Katz ADL) functional status and discharge to a facility in non-neurologically injured older trauma patients. Methods: Data were obtained from 207 patients in the Trauma Medical Home study cohort. Multivariable logistic regression was performed to identify factors associated with non-home discharge. Results: Average patient age was 67.9 (SD 11.1). Patients were predominantly white (89.4%) and female (52.2%) with a median ISS of 11 (IQR 9-14). The most common mechanism of injury was fall (48.3%), followed by motor vehicle crash (41.1%). Nearly all patients (94.7%) reported independence in activities of daily living prior to hospitalization for injury. Discharge disposition varied, 51.7% of patients were discharged home, 37.7% to subacute rehabilitation, 10.1% to acute rehabilitation and 0.5% to long-term acute care. There was no relationship between pre-injury independence and likelihood of discharge home (P = 0.1331). Age (P < 0.0001), BMI (P = 0.0002), Charlson comorbidity score of 3 or greater (P = 0.0187), being single (P = 0.0077), ISS ≥ 16 (P = 0.0075) and being female with self-reported symptoms of anxiety and/or depression over the past two weeks (P = 0.0092) were associated with significantly greater odds of non-home discharge. Conclusions: Pre-injury Katz ADL is not associated with discharge disposition, though other significantly associated factors were identified. It is imperative that discussions regarding discharge disposition are initiated early during acute hospitalization. Trauma programs could potentially benefit from implementing an inpatient intervention focused on building coping skills for older patients exhibiting symptoms of anxiety or depression.Item Prognostic models for predicting insomnia treatment outcomes: A systematic review(Elsevier, 2024) Holler, Emma; Du, Yu; Barboi, Cristina; Owora, Arthur; Anesthesia, School of MedicineObjective: To identify and critically evaluate models predicting insomnia treatment response in adult populations. Methods: Pubmed, EMBASE, and PsychInfo databases were searched from January 2000 to January 2023 to identify studies reporting the development or validation of multivariable models predicting insomnia treatment outcomes in adults. Data were extracted according to CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) guidelines and study quality was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). Results: Eleven studies describing 53 prediction models were included and appraised. Treatment response was most frequently assessed using wake after sleep onset (n = 10; 18.9%), insomnia severity index (n = 10; 18.9%), and sleep onset latency (n = 9, 17%). Dysfunctional Beliefs About Sleep (DBAS) score was the most common predictor in final models (n = 33). R2 values ranged from 0.06 to 0.80 for models predicting continuous response and area under the curve (AUC) ranged from 0.73 to 0.87 for classification models. Only two models were internally validated, and none were externally validated. All models were rated as having a high risk of bias according to PROBAST, which was largely driven by the analysis domain. Conclusion: Prediction models may be a useful tool to assist clinicians in selecting the optimal treatment strategy for patients with insomnia. However, no externally validated models currently exist. These results highlight an important gap in the literature and underscore the need for the development and validation of modern, methodologically rigorous models.Item Short-term Outcomes for Patients and Providers After Elective Tracheostomy in COVID-19–Positive Patients(Elsevier, 2021-04) Murphy, Patrick; Holler, Emma; Lindroth, Heidi; Laughlin, Michelle; Simons, Clark J.; Streib, Erik W.; Boustani, Malaz; Ortiz, Damaris; Surgery, School of MedicineBackground Urgent guidance is needed on the safety for providers of percutaneous tracheostomy in patients diagnosed with COVID-19. The objective of the study was to demonstrate that percutaneous dilational tracheostomy (PDT) with a period of apnea in patients requiring prolonged mechanical ventilation due to COVID-19 is safe and can be performed for the usual indications in the intensive care unit. Methods This study involves an observational case series at a single-center medical intensive care unit at a level-1 trauma center in patients diagnosed with COVID-19 who were assessed for tracheostomy. Success of a modified technique included direct visualization of tracheal access by bronchoscopy and a blind dilation and tracheostomy insertion during a period of patient apnea to reduce aerosolization. Secondary outcomes include transmission rate of COVID-19 to providers and patient complications. Results From April 6th, 2020 to July 21st, 2020, 2030 patients were admitted to the hospital with COVID-19, 615 required intensive care unit care (30.3%), and 254 patients required mechanical ventilation (12.5%). The mortality rate for patients requiring mechanical ventilation was 29%. Eighteen patients were assessed for PDT, and 11 (61%) underwent the procedure. The majority had failed extubation at least once (72.7%), and the median duration of intubation before tracheostomy was 15 d (interquartile range 13-24). The median positive end-expiratory pressure at time of tracheostomy was 10.8. The median partial pressure of oxygen (PaO2)/FiO2 ratio on the day of tracheostomy was 142.8 (interquartile range 104.5-224.4). Two patients had bleeding complications. At 1-week follow-up, eight patients still required ventilator support (73%). At the most recent follow-up, eight patients (73%) have been liberated from the ventilator, one patient (9%) died as a result of respiratory/multiorgan failure, and two were discharged on the ventilator (18%). Average follow-up was 20 d. None of the surgeons performing PDT have symptoms of or have tested positive for COVID-19. Conclusions and relevance: PDT for patients with COVID-19 is safe for health care workers and patients despite higher positive end-expiratory pressure requirements and should be performed for the same indications as other causes of respiratory failure.