scholarly journals 362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S251-S251
Author(s):  
Joanna S Cavalier ◽  
Benjamin Goldstein ◽  
Cara L O’Brien ◽  
Armando Bedoya
Keyword(s):  
Positive Predictive Value ◽  
Early Warning ◽  
Predictive Value ◽  
Risk Model ◽  
Medical Center ◽  
Academic Medical Center ◽  
Significant Proportion ◽  
Case Series ◽  
Early Warning Score ◽  
Severe Illness

Abstract Background The novel coronavirus disease (COVID-19) results in severe illness in a significant proportion of patients, necessitating a way to discern which patients will become critically ill and which will not. In one large case series, 5.0% of patients required an intensive care unit (ICU) and 1.4% died. Several models have been developed to assess decompensating patients. However, research examining their applicability to COVID-19 patients is limited. An accurate predictive model for patients at risk of decompensation is critical for health systems to optimally triage emergencies, care for patients, and allocate resources. Methods An early warning score (EWS) algorithm created within a large academic medical center, with methodology previously described, was applied to COVID-19 patients admitted to this institution. 122 COVID-19 patients were included. A decompensation event was defined as inpatient mortality or an unanticipated transfer to an ICU from an intermediate medical ward. The EWS was calculated at 12-hour and 24-hour intervals. Results Of 122 patients admitted with COVID-19, 28 had a decompensation event, yielding an event rate of 23.0%. 8 patients died, 13 transferred to the ICU, and 6 both transferred to the ICU and died. Decompensation within 12 and 24 hours were predicted with areas under the curve (AUC) of 0.850 and 0.817, respectively. Using a three-tiered risk model, use of the customized EWS score for patients identified as high risk of decompensation had a positive predictive value of 44.4% and 11.1% and specificity of 99.3% and 99.6% and 12- and 24-hour intervals. Amongst medium-risk patients, the score had a specificity of 85.0% and 85.4%, respectively. Conclusion This EWS allows for prediction of decompensation, defined as transfer to an ICU or death, in COVID-19 patients with excellent specificity and a high positive predictive value. Clinically, implementation of this score can help to identify patients before they decompensate in order to triage at time of presentation and allocate step-down beds, ICU beds, and treatments such as remdesivir. Disclosures All Authors: No reported disclosures

scholarly journals Comparison of early warning scores for sepsis early identification and prediction in the general ward setting

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Sean C Yu ◽  
Nirmala Shivakumar ◽  
Kevin Betthauser ◽  
Aditi Gupta ◽  
Albert M Lai ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Early Warning ◽  
Predictive Value ◽  
Early Identification ◽  
Academic Medical Center ◽  
Scoring Systems ◽  
General Ward ◽  
Early Warning Score ◽  
Patient Acuity ◽  
Early Warning Scores

Abstract The objective of this study was to directly compare the ability of commonly used early warning scores (EWS) for early identification and prediction of sepsis in the general ward setting. For general ward patients at a large, academic medical center between early-2012 and mid-2018, common EWS and patient acuity scoring systems were calculated from electronic health records (EHR) data for patients that both met and did not meet Sepsis-3 criteria. For identification of sepsis at index time, National Early Warning Score 2 (NEWS 2) had the highest performance (area under the receiver operating characteristic curve: 0.803 [95% confidence interval [CI]: 0.795–0.811], area under the precision recall curves: 0.130 [95% CI: 0.121–0.140]) followed NEWS, Modified Early Warning Score, and quick Sequential Organ Failure Assessment (qSOFA). Using validated thresholds, NEWS 2 also had the highest recall (0.758 [95% CI: 0.736–0.778]) but qSOFA had the highest specificity (0.950 [95% CI: 0.948–0.952]), positive predictive value (0.184 [95% CI: 0.169–0.198]), and F1 score (0.236 [95% CI: 0.220–0.253]). While NEWS 2 outperformed all other compared EWS and patient acuity scores, due to the low prevalence of sepsis, all scoring systems were prone to false positives (low positive predictive value without drastic sacrifices in sensitivity), thus leaving room for more computationally advanced approaches.

scholarly journals Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Vincent J. Major ◽  
Yindalon Aphinyanaphongs
Keyword(s):  
Positive Predictive Value ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Medical Center ◽  
Academic Medical Center ◽  
Model Performance ◽  
Risk Of Death ◽  
Temporal Validation ◽  
Receiver Operating

Abstract Background Automated systems that use machine learning to estimate a patient’s risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for implemented systems. Methods A prognostic study included adult admissions at a multi-site, academic medical center between 2015 and 2017. A predictive model for all-cause mortality (including initiation of hospice care) within 60 days of admission was developed. Model generalizability is assessed in temporal validation in the context of potential demographic bias. A subsequent prospective cohort study was conducted at the same sites between October 2018 and June 2019. Model performance during prospective validation was quantified with areas under the receiver operating characteristic and precision recall curves stratified by site. Prospective results include timeliness, positive predictive value, and the number of actionable predictions. Results Three years of development data included 128,941 inpatient admissions (94,733 unique patients) across sites where patients are mostly white (61%) and female (60%) and 4.2% led to death within 60 days. A random forest model incorporating 9614 predictors produced areas under the receiver operating characteristic and precision recall curves of 87.2 (95% CI, 86.1–88.2) and 28.0 (95% CI, 25.0–31.0) in temporal validation. Performance marginally diverges within sites as the patient mix shifts from development to validation (patients of one site increases from 10 to 38%). Applied prospectively for nine months, 41,728 predictions were generated in real-time (median [IQR], 1.3 [0.9, 32] minutes). An operating criterion of 75% positive predictive value identified 104 predictions at very high risk (0.25%) where 65% (50 from 77 well-timed predictions) led to death within 60 days. Conclusion Temporal validation demonstrates good model discrimination for 60-day mortality. Slight performance variations are observed across demographic subpopulations. The model was implemented prospectively and successfully produced meaningful estimates of risk within minutes of admission.

Preliminary Assessment of an Automated Surveillance System for Infection Control

2004 ◽  
Vol 25 (4) ◽  
pp. 325-332 ◽  
Author(s):  
Marc-Oliver Wright ◽  
Eli N. Perencevich ◽  
Christopher Novak ◽  
Joan N. Hebden ◽  
Harold C. Standiford ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Infection Control ◽  
Predictive Value ◽  
Surveillance System ◽  
Control Charts ◽  
Medical Center ◽  
Academic Medical Center ◽  
Kappa Score ◽  
Efficient Manner ◽  
Automated Surveillance

AbstractBackground and Objective:Rapid identification and investigation of potential outbreaks is key to limiting transmission in the healthcare setting. Manual review of laboratory results remains a cumbersome, time-consuming task for infection control practitioners (ICPs). Computer-automated techniques have shown promise for improving the efficiency and accuracy of surveillance. We examined the use of automated control charts, provided by an automated surveillance system, for detection of potential outbreaks.Setting:A 656-bed academic medical center.Methods:We retrospectively reviewed 13 months (November 2001 through November 2002) of laboratory-patient data, comparing an automated surveillance application with standard infection control practices. We evaluated positive predictive value, sensitivity, and time required to investigate the alerts. An ICP created 75 control charts. A standardized case investigation form was developed to evaluate each alert for the likelihood of nosocomial transmission based on temporal and spatial overlap and culture results.Results:The 75 control charts were created in 75 minutes and 18 alerts fired above the 3-sigma level. These were independently reviewed by an ICP and associate hospital epidemiologist. The review process required an average of 20 minutes per alert and the kappa score between the reviewers was 0.82. Eleven of the 18 alerts were determined to be potential outbreaks, yielding a positive predictive value of 0.61. Routine surveillance identified 5 of these 11 alerts during this time period.Conclusion:Automated surveillance with user-definable control charts for cluster identification was more sensitive than routine methods and is capable of operating with high specificity and positive predictive value in a time-efficient manner.

scholarly journals Score for Emergency Risk Prediction (SERP): An Interpretable Machine Learning AutoScore–Derived Triage Tool for Predicting Mortality after Emergency Admissions

2021 ◽  
Author(s):  
Feng Xie ◽  
Marcus Eng Hock Ong ◽  
Johannes Nathaniel Min Hui Liew ◽  
Kenneth Boon Kiat Tan ◽  
Andrew Fu Wah Ho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Positive Predictive Value ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Early Warning ◽  
Predictive Value ◽  
Early Warning Score ◽  
Secondary Outcomes ◽  
Clinical Tools

AbstractImportanceTriage in the emergency department (ED) for admission and appropriate level of hospital care is a complex clinical judgment based on the tacit understanding of the patient’s likely acute course, availability of medical resources, and local practices. While a scoring tool could be valuable in triage, currently available tools have demonstrated limitations.ObjectiveTo develop a tool based on a parsimonious list of predictors available early at ED triage, to provide a simple, early, and accurate estimate of short-term mortality risk, the Score for Emergency Risk Prediction (SERP), and evaluate its predictive accuracy relative to published tools.Design, Setting, and ParticipantsWe performed a single-site, retrospective study for all emergency department (ED) patients between January 2009 and December 2016 admitted in a tertiary hospital in Singapore. SERP was derived using the machine learning framework for developing predictive models, AutoScore, based on six variables easily available early in the ED care process. Using internal validation, the SERP was compared to the current triage system, Patient Acuity Category Scale (PACS), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), Cardiac Arrest Risk Triage (CART), and Charlson Comorbidity Index (CCI) in predicting both primary and secondary outcomes in the study.Main Outcomes and MeasuresThe primary outcome of interest was 30-day mortality. Secondary outcomes include 2-day mortality, inpatient mortality, 30-day post-discharge mortality, and 1-year mortality. The SERP’s predictive power was measured using the area under the curve (AUC) in the receiver operating characteristic (ROC) analysis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated under the optimal threshold, defined as the point nearest to the upper-left corner of the ROC curve.ResultsWe included 224,666 ED episodes in the model training cohort, 56,167 episodes in the validation cohort, and 42,676 episodes in the testing cohort. 18,797 (5.8%) of them died in 30 days after their ED visits. Evaluated on the testing set, SERP outperformed several benchmark scores in predicting 30-day mortality and other mortality-related outcomes. Under cut-off score of 27, SERP achieved a sensitivity of 72.6% (95% confidence interval [CI]: 70.7-74.3%), a specificity of 77.8% (95% CI: 77.5-78.2), a positive predictive value of 15.8% (15.4-16.2%) and a negative predictive value of 98% (97.9-98.1%).ConclusionsSERP showed better prediction performance than existing triage scores while maintaining easy implementation and ease of ascertainment at the ED. It has the potential to be widely applied and validated in different circumstances and healthcare settings.Key pointsQuestionHow does a tool for predicting hospital outcomes based on a machine learning-based automatic clinical score generator, AutoScore, perform in a cohort of individuals admitted to hospital from the emergency department (ED) compared to other published clinical tools?FindingsThe new tool, the Score for Emergency Risk Prediction (SERP), is parsimonious and point-based. SERP was more accurate in identifying patients who died during short or long-term care, compared with other point-based clinical tools.MeaningSERP, a tool based on AutoScore is promising for triaging patients admitted from the ED according to mortality risk.

scholarly journals Characteristics and Outcomes Among Hospitalized COVID-19-Positive Patients in a Nonurban Environment

2021 ◽  
Author(s):  
Robert P Lennon ◽  
Theodore J Demetriou ◽  
M Fahad Khalid ◽  
Lauren Jodi Van Scoy ◽  
Erin L Miller ◽  
...  
Keyword(s):  
New York ◽  
Medical Center ◽  
Academic Medical Center ◽  
Military Medicine ◽  
Case Series ◽  
Outcome Data ◽  
Urban Environments ◽  
Urban Setting ◽  
Published Data ◽  
Retrospective Case

ABSTRACT Introduction Virtually all hospitalized coronavirus disease-2019 (COVID-19) outcome data come from urban environments. The extent to which these findings are generalizable to other settings is unknown. Coronavirus disease-2019 data from large, urban settings may be particularly difficult to apply in military medicine, where practice environments are often semi-urban, rural, or austere. The purpose of this study is compare presenting characteristics and outcomes of U.S. patients with COVID-19 in a nonurban setting to similar patients in an urban setting. Materials and Methods This is a retrospective case series of adults with laboratory-confirmed COVID-19 infection who were admitted to Hershey Medical Center (HMC), a 548-bed tertiary academic medical center in central Pennsylvania serving semi-urban and rural populations, from March 23, 2020, to April 20, 2020 (the first month of COVID-19 admissions at HMC). Patients and outcomes of this cohort were compared to published data on a cohort of similar patients from the New York City (NYC) area. Results The cohorts had similar age, gender, comorbidities, need for intensive care or mechanical ventilation, and most vital sign and laboratory studies. The NYC’s cohort had shorter hospital stays (4.1 versus 7.2 days, P < .001) but more African American patients (23% versus 12%, P = .02) and higher prevalence of abnormal alanine (>60U/L; 39.0% versus 5.9%, P < .001) and aspartate (>40U/L; 58.4% versus 42.4%, P = .012) aminotransferase, oxygen saturation <90% (20.4% versus 7.2%, P = .004), and mortality (21% versus 1.4%, P < .001). Conclusions Hospitalists in nonurban environments would be prudent to use caution when considering the generalizability of results from dissimilar regions. Further investigation is needed to explore the possibility of reproducible causative systemic elements that may help improve COVID-19-related outcomes. Broader reports of these relationships across many settings will offer military medical planners greater ability to consider outcomes most relevant to their unique settings when considering COVID-19 planning.

Multi-Disciplinary Skull Base Conference and its Effects on Patient Management

2021 ◽  
pp. 000348942110212
Author(s):  
Nathan Kemper ◽  
Scott B. Shapiro ◽  
Allie Mains ◽  
Noga Lipschitz ◽  
Joseph Breen ◽  
...  
Keyword(s):  
Skull Base ◽  
Medical Center ◽  
Academic Medical Center ◽  
Significant Proportion ◽  
Internal Auditory Canal ◽  
Patient Characteristics ◽  
Management Plan ◽  
Case Review ◽  
Retrospective Case ◽  
Lateral Skull Base

Objective: Examine the effects of a multi-disciplinary skull base conference (MDSBC) on the management of patients seen for skull base pathology in a neurotology clinic. Methods: Retrospective case review of patients who were seen in a neurotology clinic at a tertiary academic medical center for pathology of the lateral skull base and were discussed at an MDSBC between July 2019 and February 2020. Patient characteristics, nature of the skull base pathology, and pre- and post-MDSBC plan of care was categorized. Results: A total of 82 patients with pathology of the lateral skull base were discussed at a MDSBC during an 8-month study period. About 54 (65.9%) had a mass in the internal auditory canal and/or cerebellopontine angle while 28 (34.1%) had other pathology of the lateral skull base. Forty-nine (59.8%) were new patients and 33 (40.2%) were established. The management plan changed in 11 (13.4%, 7.4-22.6 95% CI) patients as a result of the skull base conference discussion. The planned management changed from some form of treatment to observation in 4 patients, and changed from observation to some form of treatment in 4 patients. For 3 patients who underwent surgery, the planned approach was altered. Conclusions: For a significant proportion of patients with pathology of the lateral skull base, the management plan changed as a result of discussion at an MDSBC. Although participants of a MDSBC would agree of its importance, it is unclear how an MDSBC affects patient outcomes.

Abstract P389: External Validation of the 2CAN Score for Inpatient Stroke Detection

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
oleg otlivanchik ◽  
Jenny Lu ◽  
Natalie Cheng ◽  
Daniel L Labovitz ◽  
charles esenwa ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Medical Center ◽  
Academic Medical Center ◽  
External Validation ◽  
Acute Stroke Care ◽  
Metabolic Encephalopathy ◽  
Cardiac Procedure ◽  
Academic Medical

Introduction: Up to 15% of all strokes occur in patients who are already hospitalized for other conditions. A validated clinical tool to help rapidly discriminate between mimics and stroke among inpatients could greatly improve acute stroke care. Recently, the 2CAN score was developed and validated at a single Midwest academic medical center to identify inpatient strokes; a score of ≥2 was highly sensitive and specific for stroke. We sought to externally validate the 2CAN score at our institution. Methods: We conducted a retrospective cohort study of consecutive inpatient stroke codes at a single Northeast academic medical center from 7/1/2018 to 11/1/2019. Pre-specified variables, including patient demographics, vascular risk factors, and clinical features (neurological examination, vital signs, laboratory values, and final diagnoses), were abstracted from the electronic medical record. We determined the sensitivity, specificity, positive and negative predictive value of a 2CAN score ≥2 for stroke (ischemic stroke, hemorrhagic stroke, or TIA) in our cohort. The 2CAN score consists of clinical deficit score (0-3 points), recent cardiac procedure (1 point), atrial fibrillation (1 point), and code called within 24 hours of admission (1 point). We used multivariate logistic regression to identify additional determinants of stroke. Results: We identified 111 inpatient stroke codes on 110 patients, mean age 67 ± 1 year, 46.8% women, and 73.8% Black or Hispanic. Final diagnosis was stroke for 54 codes (48.6%) and mimic for 57 codes (51.3%), most commonly toxic-metabolic encephalopathy. 2CAN score ≥2 had 96.3% sensitivity, 45.6% specificity, 62.7% positive predictive value, and 92.3% negative predictive value for stroke. In a multivariable logistic regression model, only recent cardiac procedure (OR: 5.5; 95% CI: 1.1-27.5) and high clinical deficit score (OR: 3.9; 95% CI: 1.9-6.1) predicted stroke. Conclusion: The 2CAN score is externally valid and helps distinguish stroke from mimic in inpatients; having a score of <2 makes stroke very unlikely.

Perioperative outcomes for patients on chronic buprenorphine undergoing spine surgery: A case series

2021 ◽  
Vol 17 (7) ◽  
pp. 171-177
Author(s):  
Ashley L. Sharp, MD ◽  
Stephanie Gilbert, MD ◽  
Danielle N. Perez, MD ◽  
Kerstin Kolodzie, MD, PhD, MAS ◽  
Matthias Behrends, MD
Keyword(s):  
Pain Management ◽  
Outcome Measures ◽  
Spine Surgery ◽  
Elective Surgery ◽  
Medical Center ◽  
Academic Medical Center ◽  
Case Series ◽  
Perioperative Outcomes ◽  
Perioperative Pain ◽  
Perioperative Pain Management

Objective: Pain management following spine surgery can be challenging as patients routinely suffer from chronic pain and opioid tolerance. The increasing popularity of buprenorphine use for pain management in this population may further complicate perioperative pain management due to the limited efficacy of other opioids in the presence of buprenorphine. This study describes perioperative management and outcomes in patients on chronic buprenorphine who underwent elective inpatient spine surgery.Design: The authors performed a retrospective chart review of all patients 18 years of age taking chronic buprenorphine for any indication who had elective inpatient spine surgery at a single institution. Perioperative pain management data were analyzed for all patients who underwent spine surgery and were maintained on buprenorphine during their hospital stay.Setting: The study was performed at a single tertiary academic medical center. Main outcome measures: The primary outcome measures were post-operative pain scores and analgesic medication requirements.Results: Twelve patients on buprenorphine underwent inpatient spine surgery. Acceptable pain control was achieved in all cases. Management included preoperative dose limitation of buprenorphine when indicated and the extensive use of multimodal analgesia.Conclusion: The question whether patients presenting for painful, elective surgery should continue using buprenorphine perioperatively is an area of controversy, and the present manuscript provides more evidence for the concept of therapy continuation with buprenorphine.

Phosphaturic Mesenchymal Tumors of the Sinonasal Area and Skull Base: Experience at a Single Institution

2021 ◽  
pp. 000348942110374
Author(s):  
Davis P. Argersinger ◽  
Catherine T. Haring ◽  
John E. Hanks ◽  
Kevin J. Kovatch ◽  
S. Ahmed Ali ◽  
...  
Keyword(s):  
Skull Base ◽  
Surgical Resection ◽  
Medical Center ◽  
Fibroblast Growth Factor 23 ◽  
Academic Medical Center ◽  
Relevant Literature ◽  
Case Series ◽  
Radiological Evidence ◽  
Lateral Skull Base ◽  
Complete Surgical Resection

Objectives: Phosphaturic mesenchymal tumor (PMT) is a rare, polymorphous neoplasm with a highly variable presentation and natural history and unpredictable clinical course. The primary objective was to describe our clinical experience with and management of 4 markedly different cases of sinonasal and skull base PMT. Methods: A retrospective case series with chart review, and relevant literature review, was performed at a tertiary academic medical center between 1998 and 2020. Adult patients treated for PMTs of the sinonasal area and skull base were included. Our main outcome measures included postoperative laboratory findings and radiological evidence of disease remission. Results: Four patients (2 Males, 2 Females; Mean Age: 63.5 years) with PMTs of the skull base have been managed at our institution since 1998. Patient presentations varied, ranging from severe phosphorus wasting and osteoporosis to symptoms secondary to mass effect, including nasal obstruction and rhinorrhea. All 4 patients were eventually found to have elevated levels of fibroblast growth factor 23. Tumors were located in the sinonasal area (right frontal sinus, right ethmoid sinus, and right nasal cavity, respectively) in 3 patients and in the lateral skull base (right jugular foramen) in 1 patient. All 4 patients underwent complete surgical resection of their tumors. PMT tissue pathology was confirmed in all cases. Gross total resection was achieved in all patients. There was no chemical or radiological evidence of disease recurrence in any patients at follow-up. Conclusions: The presentation of skull base PMT is variable, and it may mimic other mass pathologies of the head and neck. Complete surgical resection with negative margins is potentially curative.

Sign in / Sign up

Export Citation Format

Share Document