Detecting Incident Delirium within Routinely Collected Inpatient Rehabilitation Data: Validation of a Chart-Based Method

Background: Delirium is a brain condition associated with poor outcomes in rehabilitation. It is therefore important to assess delirium incidence in rehabilitation. Purpose: To develop and validate a chart-based method to identify incident delirium episodes within the electronic database of a Swiss rehabilitation clinic, and to identify a study population of validated incident delirium episodes for further research purposes. Design: Retrospective validation study. Settings: Routinely collected inpatient clinical data from ZURZACH Care. Participants: All patients undergoing rehabilitation at ZURZACH Care, Rehaklinik Bad Zurzach between 2015 and 2018 were included. Methods: Within the study population, we identified all rehabilitation stays for which ≥2 delirium-predictive key words (common terms used to describe delirious patients) were recorded in the medical charts. We excluded all prevalent delirium episodes and defined the remaining episodes to be potentially incident. At least two physicians independently confirmed or refuted each potential incident delirium episode by reviewing the patient charts. We calculated the positive predictive value (PPV) with 95% confidence interval (95% CI) for all potential incident delirium episodes and for specific subgroups. Results: Within 10,515 rehabilitation stays we identified 554 potential incident delirium episodes. Overall, 125 potential incident delirium episodes were confirmed by expert review. The PPV of the chart-based method varied from 0.23 (95% CI 0.19–0.26) overall to 0.69 (95% CI 0.56–0.79) in specific subgroups. Conclusions: Our chart-based method was able to capture incident delirium episodes with low to moderate accuracy. By conducting an additional expert review of the medical charts, we identified a study population of validated incident delirium episodes. Our chart-based method contributes towards an automated detection of potential incident delirium episodes that, supplemented with expert review, efficiently yields a validated population of incident delirium episodes for research purposes.

Download Full-text

3124 Early Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG

Journal of Clinical and Translational Science ◽

10.1017/cts.2019.93 ◽

2019 ◽

Vol 3 (s1) ◽

pp. 38-38

Author(s):

Safa Kaleem ◽

Christa B. Swisher

Keyword(s):

Positive Predictive Value ◽

Real Time ◽

Predictive Value ◽

Quantitative Eeg ◽

University Hospital ◽

Duke University ◽

Study Population ◽

Time To Detection ◽

Sensitivity Specificity ◽

Improve Patient

OBJECTIVES/SPECIFIC AIMS: 1. Determine positive predictive value, negative predictive value, sensitivity, and specificity of Neuro ICU nurse interpretation of real-time bedside qEEG. 2. Determine difference in time to detection of first seizure between Neuro ICU nurse qEEG interpretation and EEG fellow reads of cEEG. 3. Determine what qualities of seizures make detection by neuro ICU nurses more or less likely – e.g. duration of seizures, type of seizures, spatial extent of seizures. METHODS/STUDY POPULATION: Recruit neuro ICU nurses taking care of 150 patients admitted to the Neuro ICU at Duke University Hospital who are initiated on cEEG monitoring. Nurses will be consented for their participation in the study. Neuro ICU nurses will evaluate the qEE RESULTS/ANTICIPATED RESULTS: From literature estimates of a 20% seizure prevalence in critical care settings, we hope to have 30 patients with seizures and 120 without. Based on prior study in the Duke Neuro ICU, we hypothesize that Neuro ICU nurses will have sensitivity and DISCUSSION/SIGNIFICANCE OF IMPACT: This is the first prospective study of neuro ICU nurse interpretation of real-time bedside qEEG in patients with unknown NCSE/NCS presence. If nurse sensitivity, specificity, and positive predictive value are clinically useful, which we deem would be so at a sensitivity of 70% or greater, with acceptable false alarm rate, nurse readings of qEEG could significantly decrease the time to treatment of seizures in the Neuro ICU patient population, and perhaps could improve patient outcomes.

Download Full-text

A PROSPECTIVE OBSERVATIONAL STUDY OF CLINICAL PROFILE IN ETIOLOGY OF ANEMIA IN ELDERLY-A HOSPITAL BASED STUDY IN A TERTIARY CARE CENTRE IN SOUTH INDIA

International Journal of Advanced Research ◽

10.21474/ijar01/13508 ◽

2021 ◽

Vol 9 (09) ◽

pp. 961-972

Author(s):

K. Shivaraju ◽

◽

Karanam Sai Arun ◽

Mandhala Saikrishna ◽

◽

...

Keyword(s):

Tertiary Care ◽

The Elderly ◽

Moderate Degree ◽

Care Hospital ◽

Tertiary Care Centre ◽

Total Study Population ◽

Study Population ◽

Poor Outcomes ◽

One Year ◽

Severe Degree

Background: Anemia is often considered as a normal physiological process that occurs with aging. but recently since past 2 decades anemia of any degree is being recognized as significant independent contributor to morbidity mortality and frailty in elderly patients. It is easy to overlook anemia in elderly as symptoms of anemia like fatigue, SOB etc are often attributed to aging process itself. many evidences accumulated states that anemia of any degree reflects poor health and increased vulnerability to poor outcomes. Materials and methods: This study was conducted in the department of internal medicine in a tertiary care hospital over a period of one year with sample size of 362 subjects satisfying the inclusion and exclusion criteria after taking the consent form. Results: Moderate degree anaemia was the commonest around, 65.6% followed by severe degree anemia 20.1%. Easy fatigability was the commonest symptom and pallor was the commonest sign of anaemia in elderly. Anemia is not always a consequence of aging. Anemia of unexplained etiology constituted 15.97% of total study population. Conclusion: Anaemia in elderly is a challenge and has to be approached in an organized manner for appropriate diagnosis and evaluation to look into its cause and plan management to improve the quality of life of the elderly persons.An effort should always be made to reach etiological diagnosis before instituting specific therapy.

Download Full-text

Performance of the New Automated Latex Immunoturbidometric Assay in the Diagnosis of Heparin Induced Thrombocytopenia: A Single Institution Experience

Blood ◽

10.1182/blood-2019-127312 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4689-4689

Author(s):

Sriman Swarup ◽

Somedeb Ball ◽

Nimesh Adhikari ◽

Anita Sultan ◽

Khatrina Swarup ◽

...

Keyword(s):

Positive Predictive Value ◽

Negative Predictive Value ◽

Sensitivity And Specificity ◽

Predictive Value ◽

Serotonin Release ◽

Small Sample ◽

Second Phase ◽

Heparin Induced Thrombocytopenia ◽

Study Population ◽

Release Assay

Introduction: Heparin induced thrombocytopenia (HIT) is a severe prothrombotic condition, usually triggered by exposure to heparin products. It is characterized by platelet activation induced by the formation of antibodies to the platelet factor 4 (PF4)/ heparin polyanion complexes. Diagnostic algorithm includes clinical scoring (4T score) alongside serological test for detection of these antibodies (HIT-Ab), while serotonin release assay (SRA) remains the gold- standard for confirmation. The automated latex immunoturbidometric assay (LIA) has recently been FDA approved as a screening tool for HIT and is a potential alternative to the conventional particle immunofiltration assay (PIFA) for time-sensitive detection of HIT-Ab to guide treatment considerations. We recently introduced LIA in our institution. In this study, we present our experience with LIA in comparison to PIFA in the diagnosis of HIT. Methods: We retrospectively reviewed the charts of all the patients on whom a PIFA was ordered between March 2017 and March 2018 in our hospital. We collected information on the results of the PIFA and SRA (if available). We replaced PIFA with LIA for HIT screening. Then, we introduced a structured protocol for diagnosis of HIT in our institution by incorporating 4T scoring alongside LIA order in the electronic medical record (EMR), in December 2018. We reviewed the EMR of all the patients on whom HIT-Ab test (LIA) was ordered between January and June of 2019, and collected similar information as before. All the data were compiled in a single master excel sheet for calculation of performance characteristics (sensitivity, specificity, positive and negative predictive values) for both PIFA and LIA. A patient was considered to have the diagnosis of HIT if the result of SRA was available and positive. Results: In the first phase, a total of 31 orders for SRA was noted against 170 PIFA orders. Five patients had a positive SRA, of whom two were PIFA negative. Half the patients with a negative SRA result were positive for PIFA. Hence, the sensitivity and specificity of PIFA test for our study population were noted to be 60% and 50%, respectively. PIFA had a positive predictive value (PPV) of mere 18.75% for the diagnosis of HIT, whereas the negative predictive value (NPV) was found to be 86.66%. Introduction of structured protocol for HIT diagnosis substantially reduced the number of inappropriate SRA orders in the second phase. On review of data for six months with the new HIT-Ab test LIA, SRA was ordered in only eight patients, to go with 69 orders for the LIA. The result of LIA was positive in all three patients with a positive SRA, whereas it was false positive in four instances. Only one patient was negative for both LIA and SRA during this period. LIA was found to be 100% sensitive and 20% specific for the diagnosis of HIT in our sample. PPV and NPV for LIA were 42.85% and 100%, respectively. Conclusion: The sensitivity and specificity of LIA were found to be 100% and 20%, respectively, in our study population, which is different from the earlier report (Warkentin et al. 2017). The small sample size is a limitation of our study. Higher PPV and NPV for LIA, with its quick turnaround time, make it a useful alternative for the time-sensitive determination of post-test probability for HIT in patients. [HIT- Ab- Heparin Induced Thrombocytopenia Antibody, PIFA- Particle Immunofiltration Assay, LIA- Latex Immunoturbidometric Assay, SRA- Serotonin Release Assay, +ve- Positive, -ve - Negative, PPV- Positive Predictive Value, NPV- Negative Predictive Value] Disclosures No relevant conflicts of interest to declare.

Download Full-text

Abstract P384: Outcomes of Endovascular Therapy in Patients With Pre-Stroke Mobility Impairment

Stroke ◽

10.1161/str.52.suppl_1.p384 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Rachel Beekman ◽

Jie-Lena Sun ◽

Brooke Alhanti ◽

Lee H Schwamm ◽

Eric Smith ◽

...

Keyword(s):

Inpatient Rehabilitation ◽

Stroke Severity ◽

Limited Data ◽

Mobility Impairment ◽

Endovascular Thrombectomy ◽

Stroke Registry ◽

Logistic Regression Models ◽

Poor Outcome ◽

Poor Outcomes ◽

Get With The Guidelines

Background and Purpose: Patients with pre-stroke mobility impairment were excluded from endovascular clinical trials. There is limited data regarding safety and outcomes of endovascular thrombectomy (EVT) in this population. We used a large, national dataset (Get With The Guidelines (GWTG)-Stroke) to evaluate the safety and outcomes of EVT in patients with pre-stroke mobility impairment (PSMI). Methods: We included patients who underwent EVT in the GWTG-Stroke registry between 2015 and 2019. PSMI was defined as inability to ambulate independently and poor outcome was defined as in-hospital mortality or discharge to hospice. GEE logistic regression models were used to evaluate the association between PSMI and outcomes. Results: Of 56,762 patients treated with EVT, 2919 (5.14%) had PSMI. Patients with PSMI were older (median 79 [IQR 70-87] vs 70 [59-80], P<0.001), more likely to be female (63.4% vs 49.2%, P<0.001), had more medical comorbidities, presented with a higher NIHSS (19 [12-24] vs 15 [9-21], P<0.001), and were less likely to be treated with tPA (36.8% vs 45.6%, P<0.001). PSMI was not associated with intracranial hemorrhage but was associated with poor outcome (Table 1). Patients with PSMI with poor outcomes were more likely to be older (83 [74-89] vs 77 [68-86], P<0.001) and have a higher presenting NIHSS (21 [16-25] vs 16 [11-22], p<0.001). Forty-nine percent of patients with PSMI with age >80 years and NIHSS >20 had a poor outcome. Conclusions: Amongst patients with PSMI treated with EVT, two thirds survived and one third were discharged to home or to inpatient rehabilitation. Advanced age and increased stroke severity increased the likelihood of poor outcomes. EVT appears safe in patients with PSMI, yet further study of effectiveness in this population is warranted.

Download Full-text

Abstract P35: Mild or Improving Stroke is the Most Frequently Documented Reason for Not Giving rt-PA in Time-Eligible Acute Ischemic Stroke Patients: Findings From Get With the Guidelines-Stroke

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.4.suppl_1.ap35 ◽

2011 ◽

Vol 4 (suppl_1) ◽

Author(s):

Eric E Smith ◽

Gregg C Fonarow ◽

Mathew J Reeves ◽

Margueritte Cox ◽

DaiWai Olson ◽

...

Keyword(s):

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Skilled Nursing Facility ◽

Inpatient Rehabilitation ◽

National Study ◽

Stroke Patients ◽

Nursing Facility ◽

Skilled Nursing ◽

Poor Outcomes ◽

Get With The Guidelines

Introduction: Previous studies suggest that mild or improving stroke is a frequently cited reason for not giving IV rt-PA and that some of these patients have poor outcomes. Methods: We examined the frequency of rt-PA use and contraindications among acute ischemic stroke patients arriving ≤2 hrs in the Get With The Guidelines-Stroke Program. Results: Between 4/1/2003-9/29/2009 there were 98,708 patients who arrived directly to the hospital within 2 hours. Among these patients 26.4% received IV rt-PA, 30.9% did not receive rt-PA solely because of mild/improving stroke, 28.6% had other contraindications, and 14.1% had no documented contraindications. From 2003-2009 rtPA use increased, the proportions not given rtPA despite no documented contraindications decreased, and the proportions with mild/improving stroke or other contraindications were similar (Figure). The initial NIH Stroke Scale (NIHSS) was recorded in 62.1% with mild/improving stroke and 82.3% given rt-PA; 75% of mild/improving stroke patients had NIHSS <5 while 90% of IV rt-PA-treated patients had NIHSS ≥5. Short-term outcomes in patients with mild/improving stroke were not always good: 1.1% died, 0.7% were discharged to hospice, 10.3% to a skilled nursing facility and 15.1% to an inpatient rehabilitation facility. Conclusion: In this large national study, mild/improving stroke is the most common reason for not giving rt-PA to early arriving patients. More patients are excluded because of mild/improving stroke than are treated with rt-PA. When deciding whether to withhold thrombolysis in patients with mild/improving stroke, clinicians should consider the risk of poor outcomes in this population.

Download Full-text

Measuring the Value of a Practical Text Mining Approach to Identify Patients With Housing Issues in the Free-Text Notes in Electronic Health Record: Findings of a Retrospective Cohort Study

Frontiers in Public Health ◽

10.3389/fpubh.2021.697501 ◽

2021 ◽

Vol 9 ◽

Author(s):

Elham Hatef ◽

Gurmehar Singh Deol ◽

Masoud Rouhizadeh ◽

Ashley Li ◽

Katyusha Eibensteiner ◽

...

Keyword(s):

Text Mining ◽

Positive Predictive Value ◽

Pattern Matching ◽

Predictive Value ◽

Healthcare Providers ◽

Improve Patient Care ◽

Free Text ◽

Study Population ◽

Electronic Health ◽

Matching Techniques

Introduction: Despite the growing efforts to standardize coding for social determinants of health (SDOH), they are infrequently captured in electronic health records (EHRs). Most SDOH variables are still captured in the unstructured fields (i.e., free-text) of EHRs. In this study we attempt to evaluate a practical text mining approach (i.e., advanced pattern matching techniques) in identifying phrases referring to housing issues, an important SDOH domain affecting value-based healthcare providers, using EHR of a large multispecialty medical group in the New England region, United States. To present how this approach would help the health systems to address the SDOH challenges of their patients we assess the demographic and clinical characteristics of patients with and without housing issues and briefly look into the patterns of healthcare utilization among the study population and for those with and without housing challenges.Methods: We identified five categories of housing issues [i.e., homelessness current (HC), homelessness history (HH), homelessness addressed (HA), housing instability (HI), and building quality (BQ)] and developed several phrases addressing each one through collaboration with SDOH experts, consulting the literature, and reviewing existing coding standards. We developed pattern-matching algorithms (i.e., advanced regular expressions), and then applied them in the selected EHR. We assessed the text mining approach for recall (sensitivity) and precision (positive predictive value) after comparing the identified phrases with manually annotated free-text for different housing issues.Results: The study dataset included EHR structured data for a total of 20,342 patients and 2,564,344 free-text clinical notes. The mean (SD) age in the study population was 75.96 (7.51). Additionally, 58.78% of the cohort were female. BQ and HI were the most frequent housing issues documented in EHR free-text notes and HH was the least frequent one. The regular expression methodology, when compared to manual annotation, had a high level of precision (positive predictive value) at phrase, note, and patient levels (96.36, 95.00, and 94.44%, respectively) across different categories of housing issues, but the recall (sensitivity) rate was relatively low (30.11, 32.20, and 41.46%, respectively).Conclusion: Results of this study can be used to advance the research in this domain, to assess the potential value of EHR's free-text in identifying patients with a high risk of housing issues, to improve patient care and outcomes, and to eventually mitigate socioeconomic disparities across individuals and communities.

Download Full-text

A comparison of self-reported data on disability pension status with data from a nationwide administrative register

Norsk Epidemiologi ◽

10.5324/nje.v19i2.587 ◽

2010 ◽

Vol 19 (2) ◽

Author(s):

Ingeborg Hartz ◽

Aage Tverdal ◽

Svetlana Skurtveit

Keyword(s):

Positive Predictive Value ◽

Sensitivity And Specificity ◽

Disability Pension ◽

Reference Standard ◽

Total Study Population ◽

Predictive Values ◽

Study Population ◽

Validity Measures ◽

Reported Data ◽

Low Prevalence

Objectives: Validation studies of self-reported disability pension status have been scarce. The objective of this study was to estimate the sensitivity and specificity, as well as positive and negative predictive values of self-reported disability pension status using an official administrative register as reference standard.Methods: Data from Cohort of Norway (CONOR) surveys conducted in 2001 in the three Norwegian counties Oslo (HUBRO), Hedmark and Oppland (OPPHED) are included in this study, altogether 17,244 individuals. At the time of investigation, the subjects included in our study-population were aged 30-31, 40-41, 45-47 and 59-61 years. Self-reported data on disability pension status was compared with data from the nationwide population and housing census in Norway (Statistics Norway), performed November 3rd 2001. Data were linked using the unique 11-digit identification number, assigned to all individuals living in Norway.Results: Sensitivity and specificity for self-reported questions on disability pension were 97.6% (95% CI 91.1-94.1) and 96.8% (96.5-97.1). Positive and negative predictive values were 70.1% (67.9-72.3) and 99.8% (99.7-99.9). Validity measures in a subpopulation, those surveyed in October-November 2001, were almost identical as for the total study-population surveyed in 2001.Conclusion: The sensitivity of self-reported disability pension status was high. The positive predictive value was lower which may reflect a low prevalence.

Download Full-text

Personality Characteristics Determine Health-Related Quality of Life as an Outcome Indicator of Geriatric Inpatient Rehabilitation

Current Gerontology and Geriatrics Research ◽

10.1155/2008/474618 ◽

2008 ◽

Vol 2008 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Jörg Richter ◽

Martina Schwarz ◽

Barbara Bauer

Keyword(s):

Quality Of Life ◽

Regulatory System ◽

Inpatient Rehabilitation ◽

Geriatric Rehabilitation ◽

Character Inventory ◽

Related Quality ◽

Health Related ◽

One Year ◽

Rehabilitation Clinic

Background. The aim of the present study was to investigate the relationships between personality and quality of life during the course of geriatric rehabilitation, against the background of Cloninger's biosocial theory of personality.Methods. All consecutive patients of a geriatric rehabilitation clinic during one year were evaluated at admission and discharge () by means of the ‘‘Vienna List’’ (a newly developed questionnaire for the assessment of quality of life in patients with severe dementia), and two variants of the Temperament and Character Inventory.Results. Self-directedness showed the most general and highest impact on quality of life and successful rehabilitation.Conclusions. It is probable in old and very old individuals who are on their highest level of maturity that the character represents the most important regulatory system in the encounter with challenges of daily life, which necessitates rehabilitation.

Download Full-text

Predicting Dysphagia with National Institute of Health Stroke Scale: Distinction between Infra- and Supratentorial Region is Essential

Cerebrovascular Diseases ◽

10.1159/000493371 ◽

2018 ◽

Vol 46 (3-4) ◽

pp. 150-158 ◽

Cited By ~ 2

Author(s):

Bendix Labeit ◽

Hannah Mueller ◽

Paul Muhle ◽

Inga Claus ◽

Tobias Warnecke ◽

...

Keyword(s):

Positive Predictive Value ◽

Negative Predictive Value ◽

Predictive Value ◽

Haemorrhagic Stroke ◽

University Hospital ◽

Neurogenic Dysphagia ◽

Screening Parameter ◽

Study Population ◽

Moderate Sensitivity ◽

The Ideal

Background: For the early detection of post-stroke dysphagia (PSD), valid screening parameters are crucial as part of a step-wise diagnostic procedure. This study examines the role of the National Institute of Health Stroke Scale (NIH-SS) as a potential low-threshold screening parameter. Methods: During a ten-year period, 687 newly admitted patients at University Hospital Muenster were included in a retrospective analysis, if they had ischemic or haemorrhagic stroke confirmed by neuroimaging and had received NIH-SS scoring and endoscopic swallowing evaluation upon admission. The NIH-SS score was correlated with dysphagia severity as measured by the validated 6-point fiberoptic endoscopic dysphagia severity score (FEDSS), and the ideal cut-off score to predict PSD, defined as FEDSS > 1, was calculated. Supra- and infratentorial strokes were analysed separately due to their differing role in the pathophysiology of neurogenic dysphagia. Results: NIH-SS and dysphagia severity show a significant positive correlation in the whole study population (R2 = 0.745) as well as in both analysed subgroups (R2 = 0.494 for supra- and R2 = 0.646 for infratentorial strokes, p < 0.0005, respectively). For supratentorial strokes, the ideal NIH-SS cut-off is > 9 (sensitivity 68.3%, specificity 61.5%, positive predictive value 89.7%, negative predictive value 28.4%). For infratentorial strokes, a lower ideal cut-off > 5 was calculated (sensitivity 67.4%, specificity 85.0%, positive predictive value 95.1%, negative predictive value 37.8%). Conclusions: NIH-SS may be used as an adjunct to predict dysphagia in acute stroke patients with moderate sensitivity and specificity. Differentiation between supra- and infratentorial regions is essential not to miss dysphagia in infratentorial stroke.

Download Full-text

Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms

Nature Communications ◽

10.1038/s41467-021-22877-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Shinichi Goto ◽

Keitaro Mahara ◽

Lauren Beussink-Nelson ◽

Hidehiko Ikura ◽

Yoshinori Katsumata ◽

...

Keyword(s):

Artificial Intelligence ◽

Positive Predictive Value ◽

Cardiac Amyloidosis ◽

Delayed Diagnosis ◽

Model Performance ◽

Automated Detection ◽

Academic Medical Centers ◽

Cardiac Diseases ◽

Medical Centers ◽

Academic Medical

AbstractPatients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85–0.91 for ECG and 0.89–1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3–4% at 52–71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74–77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.

Download Full-text