The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Objectives: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. Methods: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. Results: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter “reduction in ICU length of stay.” Conclusions: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter “reduction in ICU length of stay” and potential spill-over effects

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Objectives: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. Methods: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. Results: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter “reduction in ICU length of stay.” Conclusions: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter “reduction in ICU length of stay” and potential spill-over effects

The Potential Cost-Effectiveness of a Cell-Based Bioelectronic Implantable Device Delivering Interferon-β1a Therapy Versus Injectable Interferon-β1a Treatment in Relapsing–Remitting Multiple Sclerosis

Background: Current first-line disease-modifying therapies (DMT) for multiple sclerosis (MS) patients are injectable or oral treatments. The Optogenerapy consortium is developing a novel bioelectronic cell-based implant for controlled release of beta-interferon (IFNβ1a) protein into the body. The current study estimated the potential cost effectiveness of the Optogenerapy implant (hereafter: Optoferon) compared with injectable IFNβ1a (Avonex). Methods: A Markov model simulating the costs and effects of Optoferon compared with injectable 30 mg IFNβ1a over a 9-year time horizon from a Dutch societal perspective. Costs were reported in 2019 Euros and discounted at a 4% annual rate; health effects were discounted at a 1.5% annual rate. The cohort consisted of 35-year-old, relapsing–remitting MS patients with mild disability. The device is implanted in a daycare setting, and is replaced every 3 years. In the base-case analysis, we assumed equal input parameters for Optoferon and Avonex regarding disability progression, health effects, adverse event probabilities, and acquisition costs. We assumed reduced annual relapse rates and withdrawal rates for Optoferon compared with Avonex. Sensitivity, scenario, value of information, and headroom analysis were performed. Results: Optoferon was the dominant strategy with cost reductions (− €26,966) and health gains (0.45 quality-adjusted life-years gained). A main driver of cost differences are the acquisition costs of Optoferon being 2.5 times less than the costs of Avonex. The incremental cost-effectiveness ratio was most sensitive to variations in the annual acquisition costs of Avonex, the annual withdrawal rate of Avonex and Optoferon, and the disability progression of Avonex. Conclusion: Innovative technology such as the Optoferon implant may be a cost-effective therapy for patients with MS. The novel implantable mode of therapeutic protein administration has the potential to become a new mode of treatment administration for MS patients and in other disease areas. However, trials are needed to establish safety and effectiveness

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

OBJECTIVES: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. METHODS: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. RESULTS: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter "reduction in ICU length of stay." CONCLUSIONS: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter "reduction in ICU length of stay" and potential spill-over effects

Health-related quality of life of multiple sclerosis patients: a European multi-country study

Abstract Background Inconsistent use of generic and disease-specific health-related quality of life (HRQOL) instruments in multiple sclerosis (MS) studies limits cross-country comparability. The objectives: 1) investigate real-world HRQOL of MS patients using both generic and disease-specific HRQOL instruments in the Netherlands, France, the United Kingdom, Spain, Germany and Italy; 2) compare HRQOL among these countries; 3) determine factors associated with HRQOL. Methods A cross-sectional, observational online web-based survey amongst MS patients was conducted in June–October 2019. Patient demographics, clinical characteristics, and two HRQOL instruments: the generic EuroQOL (EQ-5D-5L) and disease-related Multiple Sclerosis Quality of Life (MSQOL)-54, an extension of the generic Short Form-36 (SF-36) was collected. Health utility scores were calculated using country-specific value sets. Mean differences in HRQOL were analysed and predictors of HRQOL were explored in regression analyses. Results In total 182 patients were included (the Netherlands: n = 88; France: n = 58; the United Kingdom: n = 15; Spain: n = 10; living elsewhere: n = 11). Mean MSQOL-54 physical and mental composite scores (42.5, SD:17.2; 58.3, SD:21.5) were lower, whereas the SF-36 physical and mental composite scores (46.8, SD:22.6; 53.1, SD:22.5) were higher than reported in previous clinical trials. The mean EQ-5D utility was 0.65 (SD:0.26). Cross-country differences in HRQOL were found. A common predictor of HRQOL was disability status and primary progressive MS. Conclusions The effects of MS on HRQOL in real-world patients may be underestimated. Combined use of generic and disease-specific HRQOL instruments enhance the understanding of the health needs of MS patients. Consequent use of the same instruments in clinical trials and observational studies improves cross-country comparability of HRQOL

Health-related quality of life of multiple sclerosis patients: a European multi-country study

Abstract Background Inconsistent use of generic and disease-specific health-related quality of life (HRQOL) instruments in multiple sclerosis (MS) studies limits cross-country comparability. The objectives: 1) investigate real-world HRQOL of MS patients using both generic and disease-specific HRQOL instruments in the Netherlands, France, the United Kingdom, Spain, Germany and Italy; 2) compare HRQOL among these countries; 3) determine factors associated with HRQOL. Methods A cross-sectional, observational online web-based survey amongst MS patients was conducted in June–October 2019. Patient demographics, clinical characteristics, and two HRQOL instruments: the generic EuroQOL (EQ-5D-5L) and disease-related Multiple Sclerosis Quality of Life (MSQOL)-54, an extension of the generic Short Form-36 (SF-36) was collected. Health utility scores were calculated using country-specific value sets. Mean differences in HRQOL were analysed and predictors of HRQOL were explored in regression analyses. Results In total 182 patients were included (the Netherlands: n = 88; France: n = 58; the United Kingdom: n = 15; Spain: n = 10; living elsewhere: n = 11). Mean MSQOL-54 physical and mental composite scores (42.5, SD:17.2; 58.3, SD:21.5) were lower, whereas the SF-36 physical and mental composite scores (46.8, SD:22.6; 53.1, SD:22.5) were higher than reported in previous clinical trials. The mean EQ-5D utility was 0.65 (SD:0.26). Cross-country differences in HRQOL were found. A common predictor of HRQOL was disability status and primary progressive MS. Conclusions The effects of MS on HRQOL in real-world patients may be underestimated. Combined use of generic and disease-specific HRQOL instruments enhance the understanding of the health needs of MS patients. Consequent use of the same instruments in clinical trials and observational studies improves cross-country comparability of HRQOL