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Article ; Online: Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis.

Teeple, Stephanie / Chivers, Corey / Linn, Kristin A / Halpern, Scott D / Eneanya, Nwamaka / Draugelis, Michael / Courtright, Katherine

BMJ quality & safety

2023 Volume 32, Issue 9, Page(s) 503–516

Abstract: Objective: Evaluate predictive performance of an electronic health record (EHR)-based, inpatient 6-month mortality risk model developed to trigger palliative care consultation among patient groups stratified by age, race, ethnicity, insurance and ... ...

Abstract	Objective: Evaluate predictive performance of an electronic health record (EHR)-based, inpatient 6-month mortality risk model developed to trigger palliative care consultation among patient groups stratified by age, race, ethnicity, insurance and socioeconomic status (SES), which may vary due to social forces (eg, racism) that shape health, healthcare and health data. Design: Retrospective evaluation of prediction model. Setting: Three urban hospitals within a single health system. Participants: All patients ≥18 years admitted between 1 January and 31 December 2017, excluding observation, obstetric, rehabilitation and hospice (n=58 464 encounters, 41 327 patients). Main outcome measures: General performance metrics (c-statistic, integrated calibration index (ICI), Brier Score) and additional measures relevant to health equity (accuracy, false positive rate (FPR), false negative rate (FNR)). Results: For black versus non-Hispanic white patients, the model's accuracy was higher (0.051, 95% CI 0.044 to 0.059), FPR lower (-0.060, 95% CI -0.067 to -0.052) and FNR higher (0.049, 95% CI 0.023 to 0.078). A similar pattern was observed among patients who were Hispanic, younger, with Medicaid/missing insurance, or living in low SES zip codes. No consistent differences emerged in c-statistic, ICI or Brier Score. Younger age had the second-largest effect size in the mortality prediction model, and there were large standardised group differences in age (eg, 0.32 for non-Hispanic white versus black patients), suggesting age may contribute to systematic differences in the predicted probabilities between groups. Conclusions: An EHR-based mortality risk model was less likely to identify some marginalised patients as potentially benefiting from palliative care, with younger age pinpointed as a possible mechanism. Evaluating predictive performance is a critical preliminary step in addressing algorithmic inequities in healthcare, which must also include evaluating clinical impact, and governance and regulatory structures for oversight, monitoring and accountability.
MeSH term(s)	Pregnancy ; Female ; United States ; Humans ; Palliative Care ; Retrospective Studies ; Electronic Health Records ; Ethnicity ; Referral and Consultation
Language	English
Publishing date	2023-03-31
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2592909-4
ISSN	2044-5423 ; 2044-5415
ISSN (online)	2044-5423
ISSN	2044-5415
DOI	10.1136/bmjqs-2022-015173
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Article: Wellbeing and nature connectedness for emerging adult undergraduates after a short expedition: A small pilot study.

Down, Michael J A / Chivers, Paola / Kirsch, Prudence / Picknoll, Duncan

Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals

2021 Volume 33, Issue 3, Page(s) 912–919

Abstract: Issue addressed: Emerging adult university undergraduates are a vulnerable population due to various life stressors. Previous studies have reported a range of positive outcomes from outdoor expeditions for this population. This small pilot study aimed ... ...

Abstract	Issue addressed: Emerging adult university undergraduates are a vulnerable population due to various life stressors. Previous studies have reported a range of positive outcomes from outdoor expeditions for this population. This small pilot study aimed to investigate the impacts of an outdoor expedition on wellbeing and connectedness to nature and possible confounding by gender and living environment. Methods: A sample of 54 Health and Physical Education emerging adult undergraduates in the second year of their four-year degree completed a 3-day/2-night immersion expedition. Pre-post differences and a repeated-measures analysis with confounders examined the expedition's impact on scores from the Warwick-Edinburgh Mental Well-being Scale and Connectedness to Nature Scale. Results: Involvement in a short expedition resulted in improvements to wellbeing and connectedness to nature. Females reported a stronger connectedness to nature than males, while living environments may affect well-being. CONCLUSIONS/SO WHAT?: Incorporating regular contact with nature into the structure of undergraduate programs may improve wellbeing and protect this population's mental and emotional health. In a world adjusting to the effects of a global pandemic, opportunities for teaching in non-traditional classrooms (such as outdoors) may also protect physical health.
MeSH term(s)	Adult ; Expeditions ; Female ; Humans ; Male ; Mental Health ; Pilot Projects ; Students
Language	English
Publishing date	2021-11-16
Publishing country	Australia
Document type	Journal Article
ZDB-ID	2250864-8
ISSN	2201-1617 ; 1036-1073
ISSN (online)	2201-1617
ISSN	1036-1073
DOI	10.1002/hpja.555
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Article ; Online: Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations.

MacKay, Emily J / Stubna, Michael D / Chivers, Corey / Draugelis, Michael E / Hanson, William J / Desai, Nimesh D / Groeneveld, Peter W

PloS one

2021 Volume 16, Issue 6, Page(s) e0252585

Abstract: Objective: This study aimed to develop and validate a claims-based, machine learning algorithm to predict clinical outcomes across both medical and surgical patient populations.: Methods: This retrospective, observational cohort study, used a random ... ...

Abstract	Objective: This study aimed to develop and validate a claims-based, machine learning algorithm to predict clinical outcomes across both medical and surgical patient populations. Methods: This retrospective, observational cohort study, used a random 5% sample of 770,777 fee-for-service Medicare beneficiaries with an inpatient hospitalization between 2009-2011. The machine learning algorithms tested included: support vector machine, random forest, multilayer perceptron, extreme gradient boosted tree, and logistic regression. The extreme gradient boosted tree algorithm outperformed the alternatives and was the machine learning method used for the final risk model. Primary outcome was 30-day mortality. Secondary outcomes were: rehospitalization, and any of 23 adverse clinical events occurring within 30 days of the index admission date. Results: The machine learning algorithm performance was evaluated by both the area under the receiver operating curve (AUROC) and Brier Score. The risk model demonstrated high performance for prediction of: 30-day mortality (AUROC = 0.88; Brier Score = 0.06), and 17 of the 23 adverse events (AUROC range: 0.80-0.86; Brier Score range: 0.01-0.05). The risk model demonstrated moderate performance for prediction of: rehospitalization within 30 days (AUROC = 0.73; Brier Score: = 0.07) and six of the 23 adverse events (AUROC range: 0.74-0.79; Brier Score range: 0.01-0.02). The machine learning risk model performed comparably on a second, independent validation dataset, confirming that the risk model was not overfit. Conclusions and relevance: We have developed and validated a robust, claims-based, machine learning risk model that is applicable to both medical and surgical patient populations and demonstrates comparable predictive accuracy to existing risk models.
MeSH term(s)	Area Under Curve ; Databases, Factual ; Hospitalization/statistics & numerical data ; Humans ; Logistic Models ; Machine Learning ; Medicare ; Models, Theoretical ; Mortality ; ROC Curve ; Retrospective Studies ; Risk Assessment ; Treatment Outcome ; United States
Language	English
Publishing date	2021-06-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0252585
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Article: Nuclear Magnetic Resonance and Metadynamics Simulations Reveal the Atomistic Binding of l-Serine and

Mathew, Renny / Stevensson, Baltzar / Pujari-Palmer, Michael / Wood, Christopher S / Chivers, Phillip R A / Spicer, Christopher D / Autefage, Hélène / Stevens, Molly M / Engqvist, Håkan / Edén, Mattias

Chemistry of materials : a publication of the American Chemical Society

2022 Volume 34, Issue 19, Page(s) 8815–8830

Abstract: Interactions between biomolecules and structurally disordered calcium phosphate (CaP) surfaces are crucial for the regulation of bone mineralization by noncollagenous proteins, the organization of complexes of casein and amorphous calcium phosphate (ACP) ...

Abstract	Interactions between biomolecules and structurally disordered calcium phosphate (CaP) surfaces are crucial for the regulation of bone mineralization by noncollagenous proteins, the organization of complexes of casein and amorphous calcium phosphate (ACP) in milk, as well as for structure-function relationships of hybrid organic/inorganic interfaces in biomaterials. By a combination of advanced solid-state NMR experiments and metadynamics simulations, we examine the detailed binding of
Language	English
Publishing date	2022-09-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	1500399-1
ISSN	1520-5002 ; 0897-4756
ISSN (online)	1520-5002
ISSN	0897-4756
DOI	10.1021/acs.chemmater.2c02112
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Article ; Online: Anatomy and physical examination of the knee menisci: a narrative review of the orthopedic literature.

Chivers, Michael D / Howitt, Scott D

The Journal of the Canadian Chiropractic Association

2009 Volume 53, Issue 4, Page(s) 319–333

Abstract: Objective: The objective of this study was to review the physical examination tests available to a practitioner in order to arrive at a clinical diagnosis or suspicion of a meniscal lesion.: Background: The menisci transmit weight bearing forces and ... ...

Abstract	Objective: The objective of this study was to review the physical examination tests available to a practitioner in order to arrive at a clinical diagnosis or suspicion of a meniscal lesion. Background: The menisci transmit weight bearing forces and increase stability of the knee. The menisci also facilitate nutrition, provide lubrication and shock absorption for the articular cartilage and promote knee proprioception. The combinations of torsional and axial loading appear to be the cause of most meniscal injuries. Diagnosis of acute knee injuries has long been a topic for discussion throughout the orthopedic literature. Many clinical tests and diagnostic studies have been developed to increase the clinician's ability to accurately diagnose these types of disorders of the knee. Conclusion: The accuracy of all diagnostic tests is thought to be dependant upon the skill of the examiner, and the severity and location of the injury. The multitude of tests described to assess meniscal lesions suggests that none are consistently reliable. However, recent research has focused on a composite score to accurately predict meniscus lesions. The combination of a comprehensive history, multiple physical tests and diagnostic imaging for confirmation is typical for a clinical meniscal lesion diagnosis while the gold standard remains the arthroscopic procedure itself.
Language	English
Publishing date	2009-11-29
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2093945-0
ISSN	1715-6181 ; 0008-3194
ISSN (online)	1715-6181
ISSN	0008-3194
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Article ; Online: Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations.

Emily J MacKay / Michael D Stubna / Corey Chivers / Michael E Draugelis / William J Hanson / Nimesh D Desai / Peter W Groeneveld

PLoS ONE, Vol 16, Iss 6, p e

2021 Volume 0252585

Abstract: Objective This study aimed to develop and validate a claims-based, machine learning algorithm to predict clinical outcomes across both medical and surgical patient populations. Methods This retrospective, observational cohort study, used a random 5% ... ...

Abstract	Objective This study aimed to develop and validate a claims-based, machine learning algorithm to predict clinical outcomes across both medical and surgical patient populations. Methods This retrospective, observational cohort study, used a random 5% sample of 770,777 fee-for-service Medicare beneficiaries with an inpatient hospitalization between 2009-2011. The machine learning algorithms tested included: support vector machine, random forest, multilayer perceptron, extreme gradient boosted tree, and logistic regression. The extreme gradient boosted tree algorithm outperformed the alternatives and was the machine learning method used for the final risk model. Primary outcome was 30-day mortality. Secondary outcomes were: rehospitalization, and any of 23 adverse clinical events occurring within 30 days of the index admission date. Results The machine learning algorithm performance was evaluated by both the area under the receiver operating curve (AUROC) and Brier Score. The risk model demonstrated high performance for prediction of: 30-day mortality (AUROC = 0.88; Brier Score = 0.06), and 17 of the 23 adverse events (AUROC range: 0.80-0.86; Brier Score range: 0.01-0.05). The risk model demonstrated moderate performance for prediction of: rehospitalization within 30 days (AUROC = 0.73; Brier Score: = 0.07) and six of the 23 adverse events (AUROC range: 0.74-0.79; Brier Score range: 0.01-0.02). The machine learning risk model performed comparably on a second, independent validation dataset, confirming that the risk model was not overfit. Conclusions and relevance We have developed and validated a robust, claims-based, machine learning risk model that is applicable to both medical and surgical patient populations and demonstrates comparable predictive accuracy to existing risk models.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

Weissman, Gary E / Crane-Droesch, Andrew / Chivers, Corey / Luong, ThaiBinh / Hanish, Asaf / Levy, Michael Z / Lubken, Jason / Becker, Michael / Draugelis, Michael E / Anesi, George L / Brennan, Patrick J / Christie, Jason D / Hanson, C William / Mikkelsen, Mark E / Halpern, Scott D

Annals of internal medicine

2020 Volume 173, Issue 1, Page(s) 21–28

Abstract: Background: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.: Objective: To estimate the timing of surges in clinical demand ... ...

Abstract	Background: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations. Objective: To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated. Design: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle. Setting: 3 hospitals in an academic health system. Patients: All people living in the greater Philadelphia region. Measurements: The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators. Results: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators. Limitations: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction. Conclusion: Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic. Primary funding source: University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.
MeSH term(s)	Betacoronavirus ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/therapy ; Decision Making ; Humans ; Intensive Care Units/organization & administration ; Models, Organizational ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/therapy ; SARS-CoV-2 ; United States/epidemiology
Keywords	covid19
Language	English
Publishing date	2020-04-07
Publishing country	United States
Document type	Journal Article ; Multicenter Study
ZDB-ID	336-0
ISSN	1539-3704 ; 0003-4819
ISSN (online)	1539-3704
ISSN	0003-4819
DOI	10.7326/M20-1260
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Article ; Online: A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

Giannini, Heather M / Ginestra, Jennifer C / Chivers, Corey / Draugelis, Michael / Hanish, Asaf / Schweickert, William D / Fuchs, Barry D / Meadows, Laurie / Lynch, Michael / Donnelly, Patrick J / Pavan, Kimberly / Fishman, Neil O / Hanson, C William / Umscheid, Craig A

Critical care medicine

2019 Volume 47, Issue 11, Page(s) 1485–1492

Abstract: Objectives: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.: Design: Retrospective cohort for algorithm derivation and validation, pre- ... ...

Abstract	Objectives: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes. Design: Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation. Setting: Tertiary teaching hospital system in Philadelphia, PA. Patients: All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184). Interventions: A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction. Measurement and main result: Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer. Conclusions: Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.
MeSH term(s)	Algorithms ; Cohort Studies ; Decision Support Systems, Clinical ; Diagnosis, Computer-Assisted ; Electronic Health Records ; Hospitals, Teaching ; Humans ; Machine Learning ; Retrospective Studies ; Sensitivity and Specificity ; Sepsis/diagnosis ; Shock, Septic/diagnosis ; Text Messaging
Language	English
Publishing date	2019-08-05
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	197890-1
ISSN	1530-0293 ; 0090-3493
ISSN (online)	1530-0293
ISSN	0090-3493
DOI	10.1097/CCM.0000000000003891
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Article ; Online: Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

Ginestra, Jennifer C / Giannini, Heather M / Schweickert, William D / Meadows, Laurie / Lynch, Michael J / Pavan, Kimberly / Chivers, Corey J / Draugelis, Michael / Donnelly, Patrick J / Fuchs, Barry D / Umscheid, Craig A

Critical care medicine

2019 Volume 47, Issue 11, Page(s) 1477–1484

Abstract: Objective: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).: Design: Prospective observational study.: Setting: Tertiary teaching hospital in ... ...

Abstract	Objective: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0). Design: Prospective observational study. Setting: Tertiary teaching hospital in Philadelphia, PA. Patients: Non-ICU admissions November-December 2016. Interventions: During a 6-week study period conducted 5 months after Early Warning System 2.0 alert implementation, nurses and providers were surveyed twice about their perceptions of the alert's helpfulness and impact on care, first within 6 hours of the alert, and again 48 hours after the alert. Measurements and main results: For the 362 alerts triggered, 180 nurses (50% response rate) and 107 providers (30% response rate) completed the first survey. Of these, 43 nurses (24% response rate) and 44 providers (41% response rate) completed the second survey. Few (24% nurses, 13% providers) identified new clinical findings after responding to the alert. Perceptions of the presence of sepsis at the time of alert were discrepant between nurses (13%) and providers (40%). The majority of clinicians reported no change in perception of the patient's risk for sepsis (55% nurses, 62% providers). A third of nurses (30%) but few providers (9%) reported the alert changed management. Almost half of nurses (42%) but less than a fifth of providers (16%) found the alert helpful at 6 hours. Conclusions: In general, clinical perceptions of Early Warning System 2.0 were poor. Nurses and providers differed in their perceptions of sepsis and alert benefits. These findings highlight the challenges of achieving acceptance of predictive and machine learning-based sepsis alerts.
MeSH term(s)	Algorithms ; Attitude of Health Personnel ; Decision Support Systems, Clinical ; Diagnosis, Computer-Assisted ; Electronic Health Records ; Hospitals, Teaching ; Humans ; Machine Learning ; Medical Staff, Hospital ; Nursing Staff, Hospital ; Practice Patterns, Nurses'/statistics & numerical data ; Practice Patterns, Physicians'/statistics & numerical data ; Prospective Studies ; Sepsis/diagnosis ; Shock, Septic/diagnosis ; Text Messaging
Language	English
Publishing date	2019-05-27
Publishing country	United States
Document type	Journal Article ; Observational Study ; Research Support, N.I.H., Extramural
ZDB-ID	197890-1
ISSN	1530-0293 ; 0090-3493
ISSN (online)	1530-0293
ISSN	0090-3493
DOI	10.1097/CCM.0000000000003803
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Article: Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

Ann. intern. med

Abstract: Background: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations. Objective: To estimate the timing of surges in clinical demand and the ...

Abstract	Background: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations. Objective: To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated. Design: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle. Setting: 3 hospitals in an academic health system. Patients: All people living in the greater Philadelphia region. Measurements: The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators. Results: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators. Limitations: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction. Conclusion: Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic. Primary Funding Source: University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #38773
Database	COVID19

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