key: cord-1042751-3ngbiohz authors: Valente, R.; Di Domenico, S.; Santori, G.; Mascherini, M.; Papadia, F.; Orengo, G.; Gratarola, A.; Cafiero, F.; De Cian, F. title: A new model to prioritize and optimize access to elective surgery throughout the COVID-19 pandemic: A feasibility & pilot study. date: 2020-07-26 journal: nan DOI: 10.1101/2020.07.21.20157719 sha: e444f0e56debba4b4819402ec830558c4540ffc6 doc_id: 1042751 cord_uid: 3ngbiohz Background The COVID-19 outbreak burdens non-COVID elective surgery patients with figures similar to the SARS-Cov-2, by creating an overwhelming demand, increasing waiting times and costs. New tools are urgently needed to manage elective access. The study assesses the SWALIS-2020 model ability to prioritize and optimize access to surgery during the pandemic. Methods A 2020 March - May feasibility-pilot study, tested a software-aided, inter-hospital, multidisciplinary pathway. All specialties patients in the Genoa Departments referred for urgent elective surgery were included in a multidisciplinary pathway adopting a modified Surgical Waiting List InfoSystem (SWALIS) cumulative prioritization method (PAT-2020) based on waiting time and clinical urgency, in three subcategories: A1-15 days (certain rapid disease progression), A2-21 days (probable progression), and A3-30 days (potential progression). Results Following the feasibility study (N=55 patients), 240 referrals were evaluated in 4 weeks without major criticalities (M/F=73/167, Age=68.7 +/- 14.0). Waiting lists were prioritized and monitored, and theatres allocated based on demand. The SWALIS-2020 score (% of waited-against-maximum time) at operation was 88.7 +/- 45.2 at week 1 and then persistently over 100% (efficiency), over a controlled variation (equity), with a difference between A3 (153.29 +/- 103.52) vs. A1 (97.24 +/- 107.93) (p <0.001), and A3 vs. A2 (88.05 +/- 77.51) (p <0.001). 222 patients underwent surgery, without related complications or delayed/failed discharges. Conclusions The pathway has selected the very few patients with the greatest need, optimizing access even with +30% capacity weekly modifications. We will use the pathway to manage active, backlog, and hidden waiting lists throughout the further pandemic phases, and are looking for collaboration for multi-center research. https://www.isrctn.com/ISRCTN11384058. The pathway has selected the very few patients with the greatest need, optimizing access even with +30% capacity weekly modifications. We will use the pathway to manage active, backlog, and hidden waiting lists throughout the further pandemic phases, and are looking for collaboration for multi-center research. https://www.isrctn.com/ISRCTN11384058. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . https://doi.org/10.1101/2020.07. 21 patients, with temporary capacity changes involving anesthetists and theatre staff, who were diverted to Intensive Therapy Units (ITUs) managing severe acute respiratory syndrome from COVID-19 (SARS-Cov-2). An immediate consequence of these re-arrangements is the reduction in surgical capacity, forcing hospitals to run only emergency theatres and suspend nearly all elective complex and cancer operations 1-3 , as well as most of outpatient care. Knowledge of the problem has reached the scientific 4 and wider communities 5 , disclosing figures of patients at risk of a similar magnitude to that directly caused by the viral infection 6 , and summing COVID-19 and cancer burdens 7 . Services face the challenge to prioritize access to surgery, streamlining patient flow and managing an overwhelmingly accumulated demand for surgery, expected to nearly triplicate in the next year, with massive additional costs 8 . However, not all progressive diseases determine the same urgency. In cancer patients, tumor biology might be categorized on its speed. Several national professional efforts have delivered guidance to select patients for cancer surgery in the COVID-19 era, establishing their level of urgency 9-12 , with specific proposals 13 . The problem is growing further in complexity, costs and time, far surpassing initial figures, due to the elective surgery backlog and "hidden" waiting lists 14 , determining an unprecedented, overwhelming and longlasting demand/supply imbalance 15 . In Liguria, the metropolitan area of Genoa (840.000 inhabitants), 2,000 hospital beds are provided by an urban health and social care Trust, several hospitals, and an adult research hospital (Policlinico San Martino -PSMRH), which is also a comprehensive cancer center. As no change was proposed in patients' clinical treatment and in the existing governance, our study was set with as a single-cohort before-after service improvement research design, reported using the STROBE statement 24 . The study is registered as ISRCTN11384058. We started working in the third week of March 2020, designing our project in two phases: . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. a) The governance issues and our developments' compliance with the existing National and Regional directives. b) The applicability of the prioritization model to the procedural principles set by the Liguria Health Authority commissioners and the PSMRH. c) The willingness of clinicians to recruit patients. d) The number and characteristics of eligible patients. e) The subsequent pilot study outcome measures, based on the SWALIS performance indexes, and any further ones appropriate. f) The measures of centrality and variation of these main outcome measures. g) Availability of the data needed and development adequacy of software/database. h) The overall time needed to manage the data. To prioritize the waiting list, we have adopted a modified SWALIS model (SWALIS 2020), as follows: 1. Clinical urgency assessment, following the Italian National urgency categories 18 , with specific adaptations grading the likelihood of progressing to deterioration or emergency for urgent cases. We have re-defined the model introducing three urgent subcategories: A1-15 days (certain rapid progression), A2-21 days (probable rapid progression), and A3-30 days (potential rapid progression) ( Table 2) . The list is dynamically ordered by computing a "priority" score (SWALIS 2020 score) for each referral, based on the waited time in relation to the corresponding maximum allowed ( Figure 2a ). All patients reach the top of the list at the speed set by their clinical . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. . https://doi.org/10.1101/2020.07.21.20157719 doi: medRxiv preprint urgency, progressing through pre-admission stages by a score obtained following a cumulative linear prioritization method (Patent pending, Figure 2b ). Defining updated urgency (U 0 , U 1 , U 2 , ... U n ) at time of re-evaluation (t 0 , t 1 , t 2 , … t n ), and defining P(t n+1 + U) = 1 as an expression of 100% of the maximum allowed waiting time, the cumulative priority (P) at the time of prioritization P(t) is defined as follows: During the study, project participants were aware of the original SWALIS model general CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . https://doi.org/10.1101/2020.07.21.20157719 doi: medRxiv preprint a. the "SWALIS 2020" method. The list is ordered dynamically on the basis of the SWALIS 2020 score (i.e. the percentage of patients' maximum allowed waiting time), according to a novel method (RV, SD) adopting linear cumulative prioritization, including recording any change in the level of urgency (Figures 1, 2) b. Admission and theatre scheduling. Whilst exploring the forthcoming calendar, the waiting list order is computed by the future theatre scheduling date. Some degree of practical flexibility is allowed to each Surgical Unit, in scheduling their surgeries preventing the waste of theatre time, provided patients close to breaching their maximum time are scheduled. In case of a last-minute re-schedule or cancellation, the same Unit schedules the next closest suitable patient on the priority list. In order to allow for COVID-19 swab analysis, patients are scheduled for admission 48 hours prior to surgery based on theatre availability, expected ITU needs, length of stay, and complications of discharge. (Table 1) , all patients were discussed at the MDT videoconferences to achieve full consensus prior to scheduling. Overall, 55 patients were referred. The SWALIS 2020 outcome indexes included the SWALIS 2020 score at admission and deviation events, the number of postponements (prior to admission) and cancellations (on the day) ( Table 2) . f) The results at weeks 1 and 2 are shown in Table 3 . Waiting lists consisted of 51 and 60 patients respectively, with admission priority scores of 88.7% (±45.2) and 118% (±60.1). g) The procedure and database were defined; referrals were sent with approximate 70% overall completeness; the run-time prototype was utilized in the MDT videoconferences. h) At meetings end data was 100% complete, clearing any missing data in run-time. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. Table 3 . Two-hundred-forty patient referrals (Age=68.7±14.0, Female/Male=73/167) were recorded in four weeks with no reported major criticality from 12 Surgical Units, and 221 patients underwent surgery (Age=67.7±14.8, Female/Male=73/148). No major adverse event has occurred. No perioperative complication, hospital stay prolongation, or failed discharge was reported as caused by the allocation model. Charted data are shown in Figure 3 . Two-hundred-twenty-one patients have been scheduled as of 9 May 2020. Seven A1-urgency-patients could be scheduled in the week immediately following discussion due to major worsening conditions (progressive upper airway obstruction, near-irresectability or severe anemia). At week 1 167% of the patients discussed exceeded theatre capacity. The length of the waiting list constantly increased until week 6, when an increase in theatre availability allowed for more adequate processing. The model has since made it possible to constantly monitor for progressive priority score increase (clinical safety and effectiveness) allowing for scheduling of approximately 60% of highest priority patients, while monitoring priority ranges (vertical and horizontal equity). About 40% of patients were admitted with a priority lower than the average, due to organizational reasons (last-minute reschedules, theatre time optimization, ICU capacity constraints, surgical team reasons), and never below 75%. Deviations from planned surgeries occurred in 22 cases: positive swab (n=8), unavailable ICU bed (n=7), changed clinical conditions (n=6), and patient inability to reach the hospital (n=1). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. to achieve an efficient allocation of theatre capacity. While clinical effectiveness has not been . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . https://doi.org/10.1101/2020.07.21.20157719 doi: medRxiv preprint tested directly, accepting the waiting time breach as a surrogate marker its control has proven positive too. This is a preliminary pilot report in an extremely unusual condition, hence carrying limitations in internal validity, forced by the pressing need for an immediate clinical implementation in the unprecedented and complex context of the COVID-19 pandemic. We have initially focused on the waiting list prioritization and allowed free final patient scheduling, as such a policy was not the focus of our pilot. Additionally, our experience is limited to a small geographical area, and we have scheduled a small number of patients overall. Nonetheless, the COVID-19-GOA-Surgery MDT pathway is now established has smoothly processed dozens of patients affected by very wide diagnosis groups. There are limits of external validity too, as this model has never been tested in a controlled experimental study, on a macro-regional scale. However, our experience is multi-institutional, is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . https://doi.org/10.1101/2020.07.21.20157719 doi: medRxiv preprint pooled waiting list, accurately selecting the fewer cancer patients in most need to receive effective surgery, even with extremely scarce resources. It has been able to keep providing appropriate and audited access to surgery even in the case of rapid capacity modifications, planning, allocation and scheduling of theatre and intensive care resources, and managing backlog consistently. With adaptation to the normalizing patient flow, these findings encourage its wider use in the next phases of the pandemic which is is expected to bring about dramatic consequences on non-COVID urgent cancer and routine patients. The potential benefits of our model might be extended to all these, as a legacy model for a finely sustainable elective surgery patient flow, in surgical departments and hub-spoke networks. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. T A B L E S T a b l e 1 : N a t i o n a l H e a l t h S e r v i c e s i m p l i c i t c r i t e r i a f o r u r g e n c y c a t e g o r i e s i n s u r g e r y Italy 18 UK 20 Australia 29 30 Days from placement on waiting list. Has the potential to deteriorate quickly to the point where it may become an emergency. Category A1 15 Days from placement on waiting list. Certain (100%) rapid progression or deterioration. 21 Days from placement on waiting list. Probable (70-80%) rapid progression or deterioration. 30 Days from placement on waiting list. Possible (50%) rapid progression or deterioration. 15 -31 Days from GP referral. 31 Days from GP referral to Consultant review, 62 from GP referral to treatment start. Admission within 30 days from placement on waiting list. Has the potential to deteriorate quickly to the point where it may become an emergency. 18 Weeks from GP referral to treatment start. Admission within 90 days from placement on waiting list. Condition causing pain, dysfunction or disability. Unlikely to deteriorate quickly. Unlikely to become an emergency. Admission within 365 days from placement on waiting list. Condition causing pain, dysfunction or disability. Unlikely to deteriorate quickly. Does not have the potential to become an emergency. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. Data are mean (SD) or n. ICU=intensive care unit. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. u r e 2 : T h e p r i o r i t i z a t i o n m e t h o d . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. Given on U and t 0 , and defining P(t 0 +U) = 1, the priority (P) at the time of prioritization P(t) is defined as follows: t 1 0 = patient 1 clock start date; U 1 = patient 1 urgency category maximum allowed waiting time; t 1 0 = patient 2 clock start date; U 2 = patient 2 urgency category maximum allowed waiting time; P 1 = patient 1 priority at time of prioritization (t); P 2 = patient 2 priority at time of prioritization (t). Clinical conditions can change during the waiting time (t 0 , t 1 , t 2 , … t n ) affecting the patient's urgency (U 0 , U 1 , U 2 , ...U n ). Priority can be calculated as summation, based on urgency variations: t 0 = start waiting time; U 0 = urgency for patient at starting time t 0 ; t n = updated urgency time; U n = updated urgency for patient; t = time of prioritization. The SWALIS 2020 prioritization method assumes four priority scores stages: "Ideal" (0-50%), color code white, "optimal" (51-75%) color code green, "due" (76-100%) color code yellow, "overdue" (>100%) color code red. ሻ . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. URG=urgency category in the waiting list by using the SWALIS score. A1=15 days (certain rapid progression). A2=21 days (probable rapid progression). A3=30 days (potential rapid progression). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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