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Tanuseputro P, Webber C, Downar J. Illness trajectories in the age of big data. BMJ 2024; 384:q510. [PMID: 38428967 DOI: 10.1136/bmj.q510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Affiliation(s)
| | - Colleen Webber
- Investigator, Bruyere Research Institute, Ottawa ON, Canada
- Ottawa Hospital Research Institute, Ottawa ON, Canada
| | - James Downar
- Investigator, Bruyere Research Institute, Ottawa ON, Canada
- Ottawa Hospital Research Institute, Ottawa ON, Canada
- Department of Medicine, University of Ottawa, Ottawa ON, Canada
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2
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Wang SM, Hogg HDJ, Sangvai D, Patel MR, Weissler EH, Kellogg KC, Ratliff W, Balu S, Sendak M. Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study. JMIR Form Res 2023; 7:e43963. [PMID: 37733427 PMCID: PMC10557008 DOI: 10.2196/43963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/20/2023] [Accepted: 04/30/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Machine learning (ML)-driven clinical decision support (CDS) continues to draw wide interest and investment as a means of improving care quality and value, despite mixed real-world implementation outcomes. OBJECTIVE This study aimed to explore the factors that influence the integration of a peripheral arterial disease (PAD) identification algorithm to implement timely guideline-based care. METHODS A total of 12 semistructured interviews were conducted with individuals from 3 stakeholder groups during the first 4 weeks of integration of an ML-driven CDS. The stakeholder groups included technical, administrative, and clinical members of the team interacting with the ML-driven CDS. The ML-driven CDS identified patients with a high probability of having PAD, and these patients were then reviewed by an interdisciplinary team that developed a recommended action plan and sent recommendations to the patient's primary care provider. Pseudonymized transcripts were coded, and thematic analysis was conducted by a multidisciplinary research team. RESULTS Three themes were identified: positive factors translating in silico performance to real-world efficacy, organizational factors and data structure factors affecting clinical impact, and potential challenges to advancing equity. Our study found that the factors that led to successful translation of in silico algorithm performance to real-world impact were largely nontechnical, given adequate efficacy in retrospective validation, including strong clinical leadership, trustworthy workflows, early consideration of end-user needs, and ensuring that the CDS addresses an actionable problem. Negative factors of integration included failure to incorporate the on-the-ground context, the lack of feedback loops, and data silos limiting the ML-driven CDS. The success criteria for each stakeholder group were also characterized to better understand how teams work together to integrate ML-driven CDS and to understand the varying needs across stakeholder groups. CONCLUSIONS Longitudinal and multidisciplinary stakeholder engagement in the development and integration of ML-driven CDS underpins its effective translation into real-world care. Although previous studies have focused on the technical elements of ML-driven CDS, our study demonstrates the importance of including administrative and operational leaders as well as an early consideration of clinicians' needs. Seeing how different stakeholder groups have this more holistic perspective also permits more effective detection of context-driven health care inequities, which are uncovered or exacerbated via ML-driven CDS integration through structural and organizational challenges. Many of the solutions to these inequities lie outside the scope of ML and require coordinated systematic solutions for mitigation to help reduce disparities in the care of patients with PAD.
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Affiliation(s)
- Sabrina M Wang
- Duke University School of Medicine, Durham, NC, United States
| | - H D Jeffry Hogg
- Population Health Science Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle Eye Centre, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Devdutta Sangvai
- Population Health Management, Duke Health, Durham, NC, United States
| | - Manesh R Patel
- Department of Cardiology, Duke University, Durham, NC, United States
| | - E Hope Weissler
- Department of Vascular Surgery, Duke University, Durham, NC, United States
| | | | - William Ratliff
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Mark Sendak
- Duke Institute for Health Innovation, Durham, NC, United States
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3
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Teeple S, Chivers C, Linn KA, Halpern SD, Eneanya N, Draugelis M, Courtright K. 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. BMJ Qual Saf 2023; 32:503-516. [PMID: 37001995 PMCID: PMC10898860 DOI: 10.1136/bmjqs-2022-015173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 03/08/2023] [Indexed: 04/03/2023]
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.
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Affiliation(s)
- Stephanie Teeple
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Kristin A Linn
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Scott D Halpern
- Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Nwamaka Eneanya
- Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | | | - Katherine Courtright
- Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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4
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Chi S, Kim S, Reuter M, Ponzillo K, Oliver DP, Foraker R, Heard K, Liu J, Pitzer K, White P, Moore N. Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm. JAMA Netw Open 2023; 6:e238795. [PMID: 37071421 PMCID: PMC10114011 DOI: 10.1001/jamanetworkopen.2023.8795] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 02/28/2023] [Indexed: 04/19/2023] Open
Abstract
Importance Goal-concordant care is an ongoing challenge in hospital settings. Identification of high mortality risk within 30 days may call attention to the need to have serious illness conversations, including the documentation of patient goals of care. Objective To examine goals of care discussions (GOCDs) in a community hospital setting with patients identified as having a high risk of mortality by a machine learning mortality prediction algorithm. Design, Setting, and Participants This cohort study took place at community hospitals within 1 health care system. Participants included adult patients with a high risk of 30-day mortality who were admitted to 1 of 4 hospitals between January 2 and July 15, 2021. Patient encounters of inpatients in the intervention hospital where physicians were notified of the computed high risk mortality score were compared with patient encounters of inpatients in 3 community hospitals without the intervention (ie, matched control). Intervention Physicians of patients with a high risk of mortality within 30 days received notification and were encouraged to arrange for GOCDs. Main Outcomes and Measures The primary outcome was the percentage change of documented GOCDs prior to discharge. Propensity-score matching was completed on a preintervention and postintervention period using age, sex, race, COVID-19 status, and machine learning-predicted mortality risk scores. A difference-in-difference analysis validated the results. Results Overall, 537 patients were included in this study with 201 in the preintervention period (94 in the intervention group; 104 in the control group) and 336 patients in the postintervention period. The intervention and control groups included 168 patients per group and were well-balanced in age (mean [SD], 79.3 [9.60] vs 79.6 [9.21] years; standardized mean difference [SMD], 0.03), sex (female, 85 [51%] vs 85 [51%]; SMD, 0), race (White patients, 145 [86%] vs 144 [86%]; SMD 0.006), and Charlson comorbidities (median [range], 8.00 [2.00-15.0] vs 9.00 [2.00 to 19.0]; SMD, 0.34). Patients in the intervention group from preintervention to postintervention period were associated with being 5 times more likely to have documented GOCDs (OR, 5.11 [95% CI, 1.93 to 13.42]; P = .001) by discharge compared with matched controls, and GOCD occurred significantly earlier in the hospitalization in the intervention patients as compared with matched controls (median, 4 [95% CI, 3 to 6] days vs 16 [95% CI, 15 to not applicable] days; P < .001). Similar findings were observed for Black patient and White patient subgroups. Conclusions and Relevance In this cohort study, patients whose physicians had knowledge of high-risk predictions from machine learning mortality algorithms were associated with being 5 times more likely to have documented GOCDs than matched controls. Additional external validation is needed to determine if similar interventions would be helpful at other institutions.
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Affiliation(s)
- Stephen Chi
- Division of Pulmonary and Critical Care Medicine, Washington University in St Louis, St Louis, Missouri
| | - Seunghwan Kim
- Institute for Informatics, Washington University in St Louis, St Louis, Missouri
| | | | | | - Debra Parker Oliver
- Division of Palliative Medicine, Department of Medicine, Washington University in St Louis, St Louis, Missouri
| | - Randi Foraker
- Institute for Informatics, Washington University in St Louis, St Louis, Missouri
| | | | - Jingxia Liu
- Division of Public Health Sciences, Department of Surgery, Washington University in St Louis, St Louis, Missouri
- Division of Biostatistics, Washington University in St Louis, St Louis, Missouri
| | - Kyle Pitzer
- Division of Palliative Medicine, Department of Medicine, Washington University in St Louis, St Louis, Missouri
- Division of Biostatistics, Washington University in St Louis, St Louis, Missouri
| | - Patrick White
- Division of Palliative Medicine, Department of Medicine, Washington University in St Louis, St Louis, Missouri
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5
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Williams N, Hermans K, Cohen J, Declercq A, Jakda A, Downar J, Guthrie DM, Hirdes JP. The interRAI CHESS scale is comparable to the palliative performance scale in predicting 90-day mortality in a palliative home care population. BMC Palliat Care 2022; 21:174. [PMID: 36203180 PMCID: PMC9540725 DOI: 10.1186/s12904-022-01059-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/29/2022] [Accepted: 09/09/2022] [Indexed: 11/10/2022] Open
Abstract
Background Prognostic accuracy is important throughout all stages of the illness trajectory as it has implications for the timing of important conversations and decisions around care. Physicians often tend to over-estimate prognosis and may under-recognize palliative care (PC) needs. It is therefore essential that all relevant stakeholders have as much information available to them as possible when estimating prognosis. Aims The current study examined whether the interRAI Changes in Health, End-Stage Disease, Signs and Symptoms (CHESS) Scale is a good predictor of mortality in a known PC population and to see how it compares to the Palliative Performance Scale (PPS) in predicting 90-day mortality. Methods This retrospective cohort study used data from 2011 to 2018 on 80,261 unique individuals receiving palliative home care and assessed with both the interRAI Palliative Care instrument and the PPS. Logistic regression models were used to evaluate the relationship between the main outcome, 90-day mortality and were then replicated for a secondary outcome examining the number of nursing visits. Comparison of survival time was examined using Kaplan-Meier survival curves. Results The CHESS Scale was an acceptable predictor of 90-day mortality (c-statistic = 0.68; p < 0.0001) and was associated with the number of nursing days (c = 0.61; p < 0.0001) and had comparable performance to the PPS (c = 0.69; p < 0.0001). The CHESS Scale performed slightly better than the PPS in predicting 90-day mortality when combined with other interRAI PC items (c = 0.72; p < 0.0001). Conclusion The interRAI CHESS Scale is an additional decision-support tool available to clinicians that can be used alongside the PPS when estimating prognosis. This additional information can assist with the development of care plans, discussions, and referrals to specialist PC teams.
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Affiliation(s)
- Nicole Williams
- Department of Kinesiology and Physical Education, Wilfrid Laurier University, 75 University Ave W, Waterloo, Canada.
| | - Kirsten Hermans
- LUCAS - Center for Care Research and Consultancy, KU Leuven, Minderbroedersstraat 8 box, 5310, 3000, Leuven, Belgium.,End-of-life Care Research Group, University of Brussels (VUB) and Ghent University (UGent), Laarbeeklaan 103, 1090, Brussels, Belgium
| | - Joachim Cohen
- End-of-life Care Research Group, University of Brussels (VUB) and Ghent University (UGent), Laarbeeklaan 103, 1090, Brussels, Belgium
| | - Anja Declercq
- LUCAS - Center for Care Research and Consultancy, KU Leuven, Minderbroedersstraat 8 box, 5310, 3000, Leuven, Belgium
| | - Ahmed Jakda
- Department of Family Medicine, McMaster University, 100 Main Street West, Hamilton, Canada
| | - James Downar
- Department of Medicine, Division of Palliative Care, University of Ottawa, Ottawa, Canada
| | - Dawn M Guthrie
- Department of Kinesiology and Physical Education, Wilfrid Laurier University, 75 University Ave W, Waterloo, Canada.,Department of Health Sciences, Wilfrid Laurier University, 75 University Ave W, Waterloo, Canada
| | - John P Hirdes
- School of Public Health Sciences, University of Waterloo, 200 University Ave W, Waterloo, Canada
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6
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Taseen R, Ethier JF. Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: Toward routine decision analysis before implementation. J Am Med Inform Assoc 2021; 28:2366-2378. [PMID: 34472611 PMCID: PMC8510333 DOI: 10.1093/jamia/ocab140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/15/2021] [Accepted: 06/21/2021] [Indexed: 11/22/2022] Open
Abstract
Objective The study sought to evaluate the expected clinical utility of automatable prediction models for increasing goals-of-care discussions (GOCDs) among hospitalized patients at the end of life (EOL). Materials and Methods We built a decision model from the perspective of clinicians who aim to increase GOCDs at the EOL using an automated alert system. The alternative strategies were 4 prediction models—3 random forest models and the Modified Hospital One-year Mortality Risk model—to generate alerts for patients at a high risk of 1-year mortality. They were trained on admissions from 2011 to 2016 (70 788 patients) and tested with admissions from 2017-2018 (16 490 patients). GOCDs occurring in usual care were measured with code status orders. We calculated the expected risk difference (beneficial outcomes with alerts minus beneficial outcomes without alerts among those at the EOL), the number needed to benefit (number of alerts needed to increase benefit over usual care by 1 outcome), and the net benefit (benefit minus cost) of each strategy. Results Models had a C-statistic between 0.79 and 0.86. A code status order occurred during 2599 of 3773 (69%) hospitalizations at the EOL. At a risk threshold corresponding to an alert prevalence of 10%, the expected risk difference ranged from 5.4% to 10.7% and the number needed to benefit ranged from 5.4 to 10.9 alerts. Using revealed preferences, only 2 models improved net benefit over usual care. A random forest model with diagnostic predictors had the highest expected value, including in sensitivity analyses. Discussion Prediction models with acceptable predictive validity differed meaningfully in their ability to improve over usual decision making. Conclusions An evaluation of clinical utility, such as by using decision curve analysis, is recommended after validating a prediction model because metrics of model predictiveness, such as the C-statistic, are not informative of clinical value.
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Affiliation(s)
- Ryeyan Taseen
- Respiratory Division, Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec, Canada.,Centre Interdisciplinaire de Recherche en Informatique de la Santé, University of Sherbrooke, Sherbrooke, Quebec, Canada.,Groupe de Recherche Interdisciplinaire en Informatique de la Santé, University of Sherbrooke, Sherbrooke, Quebec, Canada
| | - Jean-François Ethier
- Centre Interdisciplinaire de Recherche en Informatique de la Santé, University of Sherbrooke, Sherbrooke, Quebec, Canada.,Groupe de Recherche Interdisciplinaire en Informatique de la Santé, University of Sherbrooke, Sherbrooke, Quebec, Canada.,General Internal Medicine Division, Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec, Canada
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7
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Gensheimer MF, Aggarwal S, Benson KRK, Carter JN, Henry AS, Wood DJ, Soltys SG, Hancock S, Pollom E, Shah NH, Chang DT. Automated model versus treating physician for predicting survival time of patients with metastatic cancer. J Am Med Inform Assoc 2021; 28:1108-1116. [PMID: 33313792 DOI: 10.1093/jamia/ocaa290] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/09/2020] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model. MATERIALS AND METHODS A machine learning model was trained using 14 600 metastatic cancer patients' data to predict each patient's distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015-2016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots. RESULTS The radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63-0.81), 0.77 (0.73-0.81), and 0.68 (0.65-0.71), respectively. CONCLUSIONS The machine learning model's predictions were more accurate than those of the treating physician or a traditional model.
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Affiliation(s)
| | - Sonya Aggarwal
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Kathryn R K Benson
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Justin N Carter
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - A Solomon Henry
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Douglas J Wood
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Scott G Soltys
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Steven Hancock
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Erqi Pollom
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Nigam H Shah
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Daniel T Chang
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
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8
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Bange EM, Courtright KR, Parikh RB. Implementing automated prognostic models to inform palliative care: more than just the algorithm. BMJ Qual Saf 2021; 30:775-778. [PMID: 34001650 DOI: 10.1136/bmjqs-2021-013510] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2021] [Indexed: 12/14/2022]
Affiliation(s)
- Erin M Bange
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Penn Center for Cancer Care Innovation, Abramson Cancer Center, Philadelphia, Pennsylvania, USA
| | - Katherine R Courtright
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi B Parikh
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA .,Penn Center for Cancer Care Innovation, Abramson Cancer Center, Philadelphia, Pennsylvania, USA.,Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Corporal Michael J Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA
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9
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Wegier P, Kurahashi A, Saunders S, Lokuge B, Steinberg L, Myers J, Koo E, van Walraven C, Downar J. mHOMR: a prospective observational study of an automated mortality prediction model to identify patients with unmet palliative needs. BMJ Support Palliat Care 2021:bmjspcare-2020-002870. [PMID: 33941574 DOI: 10.1136/bmjspcare-2020-002870] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 03/30/2021] [Accepted: 04/14/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Identification of patients with shortened life expectancy is a major obstacle to delivering palliative/end-of-life care. We previously developed the modified Hospitalised-patient One-year Mortality Risk (mHOMR) model for the automated identification of patients with an elevated 1-year mortality risk. Our goal was to investigate whether patients identified by mHOMR at high risk for mortality in the next year also have unmet palliative needs. METHOD We conducted a prospective observational study at two quaternary healthcare facilities in Toronto, Canada, with patients admitted to general internal medicine service and identified by mHOMR to have an expected 1-year mortality risk of 10% or more. We measured patients' unmet palliative needs-a severe uncontrolled symptom on the Edmonton Symptom Assessment Scale or readiness to engage in advance care planning (ACP) based on Sudore's ACP Engagement Survey. RESULTS Of 518 patients identified by mHOMR, 403 (78%) patients consented to participate; 87% of those had either a severe uncontrolled symptom or readiness to engage in ACP, and 44% had both. Patients represented frailty (38%), cancer (28%) and organ failure (28%) trajectories were admitted for a median of 6 days, and 94% survived to discharge. CONCLUSIONS A large majority of hospitalised patients identified by mHOMR have unmet palliative needs, regardless of disease, and are identified early enough in their disease course that they may benefit from a palliative approach to their care. Adoption of such a model could improve the timely introduction of a palliative approach for patients, especially those with non-cancer illness.
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Affiliation(s)
- Pete Wegier
- Humber River Hospital, Toronto, Ontario, Canada
- Institute for Health Policy, Management, & Evaluation, University of Toronto, Toronto, Ontario, Canada
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Allison Kurahashi
- Temmy Latner Centre for Palliative Care, Sinai Health System, Toronto, Ontario, Canada
| | | | - Bhadra Lokuge
- Temmy Latner Centre for Palliative Care, Sinai Health System, Toronto, Ontario, Canada
| | - Leah Steinberg
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Temmy Latner Centre for Palliative Care, Sinai Health System, Toronto, Ontario, Canada
| | - Jeff Myers
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Temmy Latner Centre for Palliative Care, Sinai Health System, Toronto, Ontario, Canada
- Albert and Temmy Latner Family Palliative Care Unit, Bridgepoint Active Healthcare, Toronto, Ontario, Canada
| | - Ellen Koo
- Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Carl van Walraven
- Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - James Downar
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Division of Palliative Care, Ottawa Hospital, Ottawa, Ontario, Canada
- Bruyere Research Institute, Ottawa, Ontario, Canada
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10
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Saunders S, Downar J, Subramaniam S, Embuldeniya G, van Walraven C, Wegier P. mHOMR: the acceptability of an automated mortality prediction model for timely identification of patients for palliative care. BMJ Qual Saf 2021; 30:837-840. [PMID: 33632758 DOI: 10.1136/bmjqs-2020-012461] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/08/2021] [Accepted: 02/16/2021] [Indexed: 12/31/2022]
Affiliation(s)
- Stephanie Saunders
- Department of Rehabilitation Sciences, McMaster University, Hamilton, Ontario, Canada
| | - James Downar
- Division of Palliative Care, The Ottawa Hospital, Ottawa, Ontario, Canada.,Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada.,Bruyère Research Institute, Ottawa, Ontario, Canada.,Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | | | - Gaya Embuldeniya
- Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Carl van Walraven
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada.,Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Department of Epidemiology & Community Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Pete Wegier
- Humber River Hospital, Toronto, Ontario, Canada .,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.,Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
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11
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Yarnell CJ, Jewell LM, Astell A, Pinto R, Devine LA, Detsky ME, Downar J, Ilan R, Rawal S, Wong N, You JJ, Fowler RA. Observational study of agreement between attending and trainee physicians on the surprise question: "Would you be surprised if this patient died in the next 12 months?". PLoS One 2021; 16:e0247571. [PMID: 33630939 PMCID: PMC7906409 DOI: 10.1371/journal.pone.0247571] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 02/10/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Optimal end-of-life care requires identifying patients that are near the end of life. The extent to which attending physicians and trainee physicians agree on the prognoses of their patients is unknown. We investigated agreement between attending and trainee physician on the surprise question: "Would you be surprised if this patient died in the next 12 months?", a question intended to assess mortality risk and unmet palliative care needs. METHODS This was a multicentre prospective cohort study of general internal medicine patients at 7 tertiary academic hospitals in Ontario, Canada. General internal medicine attending and senior trainee physician dyads were asked the surprise question for each of the patients for whom they were responsible. Surprise question response agreement was quantified by Cohen's kappa using Bayesian multilevel modeling to account for clustering by physician dyad. Mortality was recorded at 12 months. RESULTS Surprise question responses encompassed 546 patients from 30 attending-trainee physician dyads on academic general internal medicine teams at 7 tertiary academic hospitals in Ontario, Canada. Patients had median age 75 years (IQR 60-85), 260 (48%) were female, and 138 (25%) were dependent for some or all activities of daily living. Trainee and attending physician responses agreed in 406 (75%) patients with adjusted Cohen's kappa of 0.54 (95% credible interval 0.41 to 0.66). Vital status was confirmed for 417 (76%) patients of whom 160 (38% of 417) had died. Using a response of "No" to predict 12-month mortality had positive likelihood ratios of 1.84 (95% CrI 1.55 to 2.22, trainee physicians) and 1.51 (95% CrI 1.30 to 1.72, attending physicians), and negative likelihood ratios of 0.31 (95% CrI 0.17 to 0.48, trainee physicians) and 0.25 (95% CrI 0.10 to 0.46, attending physicians). CONCLUSION Trainee and attending physician responses to the surprise question agreed in 54% of cases after correcting for chance agreement. Physicians had similar discriminative accuracy; both groups had better accuracy predicting which patients would survive as opposed to which patients would die. Different opinions of a patient's prognosis may contribute to confusion for patients and missed opportunities for engagement with palliative care services.
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Affiliation(s)
- Christopher J. Yarnell
- Institute of Health Management, Policy, and Evaluation, University of Toronto, Toronto, Canada
- Department of Medicine, Sinai Health System, Toronto, Canada
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada
| | - Laura M. Jewell
- Memorial University of Newfoundland, Discipline of Family Medicine, Happy Valley-Goose Bay, Canada
| | - Alex Astell
- University of Manitoba Faculty of Medicine, Section of Critical Care Medicine, Manitoba, Canada
| | - Ruxandra Pinto
- Sunnybrook Health Sciences Centre Department of Critical Care, Toronto, Canada
| | - Luke A. Devine
- Department of Medicine, Sinai Health System, Toronto, Canada
- University of Toronto Temerty Faculty of Medicine, Division of General Internal Medicine, Toronto, Canada
| | - Michael E. Detsky
- Department of Medicine, Sinai Health System, Toronto, Canada
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada
| | - James Downar
- The Ottawa Hospital, Ottawa, Canada
- University of Ottawa Faculty of Medicine, Division of Palliative Care, Ottawa, Canada
| | - Roy Ilan
- Department of Critical Care Medicine, Rambam Health Care Campus, Technion, Israel Institute of Technology, Haifa, Israel
| | - Shail Rawal
- University of Toronto Temerty Faculty of Medicine, Division of General Internal Medicine, Toronto, Canada
- University Health Network, General Internal Medicine, Toronto, Canada
| | - Natalie Wong
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada
- University of Toronto Temerty Faculty of Medicine, Division of General Internal Medicine, Toronto, Canada
- Departments of General Internal Medicine and Critical Care Medicine, St Michael’s Hospital, Toronto, Canada
| | - John J. You
- Division of General Internal and Hospitalist Medicine, Department of Medicine, Trillium Health Partners, Mississauga, Ontario, Canada
| | - Rob A. Fowler
- Institute of Health Management, Policy, and Evaluation, University of Toronto, Toronto, Canada
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada
- Sunnybrook Health Sciences Centre Department of Critical Care, Toronto, Canada
- Institute for Clinical Evaluative Sciences, Toronto, Canada
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Major VJ, Aphinyanaphongs Y. Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites. BMC Med Inform Decis Mak 2020; 20:214. [PMID: 32894128 PMCID: PMC7487547 DOI: 10.1186/s12911-020-01235-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/26/2020] [Indexed: 11/10/2022] Open
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.
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Affiliation(s)
- Vincent J Major
- Department of Population Health, NYU Langone Health, 227 East 30th St, 6th Floor, New York, NY, 10016, USA.
| | - Yindalon Aphinyanaphongs
- Department of Population Health, NYU Langone Health, 227 East 30th St, 6th Floor, New York, NY, 10016, USA
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Major VJ, Jethani N, Aphinyanaphongs Y. Estimating real-world performance of a predictive model: a case-study in predicting mortality. JAMIA Open 2020; 3:243-251. [PMID: 32734165 PMCID: PMC7382635 DOI: 10.1093/jamiaopen/ooaa008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/05/2020] [Accepted: 03/19/2020] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE One primary consideration when developing predictive models is downstream effects on future model performance. We conduct experiments to quantify the effects of experimental design choices, namely cohort selection and internal validation methods, on (estimated) real-world model performance. MATERIALS AND METHODS Four years of hospitalizations are used to develop a 1-year mortality prediction model (composite of death or initiation of hospice care). Two common methods to select appropriate patient visits from their encounter history (backwards-from-outcome and forwards-from-admission) are combined with 2 testing cohorts (random and temporal validation). Two models are trained under otherwise identical conditions, and their performances compared. Operating thresholds are selected in each test set and applied to a "real-world" cohort of labeled admissions from another, unused year. RESULTS Backwards-from-outcome cohort selection retains 25% of candidate admissions (n = 23 579), whereas forwards-from-admission selection includes many more (n = 92 148). Both selection methods produce similar performances when applied to a random test set. However, when applied to the temporally defined "real-world" set, forwards-from-admission yields higher areas under the ROC and precision recall curves (88.3% and 56.5% vs. 83.2% and 41.6%). DISCUSSION A backwards-from-outcome experiment manipulates raw training data, simplifying the experiment. This manipulated data no longer resembles real-world data, resulting in optimistic estimates of test set performance, especially at high precision. In contrast, a forwards-from-admission experiment with a temporally separated test set consistently and conservatively estimates real-world performance. CONCLUSION Experimental design choices impose bias upon selected cohorts. A forwards-from-admission experiment, validated temporally, can conservatively estimate real-world performance. LAY SUMMARY The routine care of patients stands to benefit greatly from assistive technologies, including data-driven risk assessment. Already, many different machine learning and artificial intelligence applications are being developed from complex electronic health record data. To overcome challenges that arise from such data, researchers often start with simple experimental approaches to test their work. One key component is how patients (and their healthcare visits) are selected for the study from the pool of all patients seen. Another is how the group of patients used to create the risk estimator differs from the group used to evaluate how well it works. These choices complicate how the experimental setting compares to the real-world application to patients. For example, different selection approaches that depend on each patient's future outcome can simplify the experiment but are impractical upon implementation as these data are unavailable. We show that this kind of "backwards" experiment optimistically estimates how well the model performs. Instead, our results advocate for experiments that select patients in a "forwards" manner and "temporal" validation that approximates training on past data and implementing on future data. More robust results help gauge the clinical utility of recent works and aid decision-making before implementation into practice.
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Affiliation(s)
- Vincent J Major
- Department of Population Health, NYU Langone Health, New York, New York, USA
| | - Neil Jethani
- Department of Population Health, NYU Langone Health, New York, New York, USA
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Downar J, Wegier P, Tanuseputro P. Early Identification of People Who Would Benefit From a Palliative Approach-Moving From Surprise to Routine. JAMA Netw Open 2019; 2:e1911146. [PMID: 31517959 DOI: 10.1001/jamanetworkopen.2019.11146] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- James Downar
- Division of Palliative Care, Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Palliative Care, Bruyere Continuing Care, Ottawa, Ontario, Canada
| | - Pete Wegier
- Temmy Latner Centre for Palliative Care, Sinai Health System, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Peter Tanuseputro
- Division of Palliative Care, Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Palliative Care, Bruyere Continuing Care, Ottawa, Ontario, Canada
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