Castro, Janna C.

Importance Thirty-day hospital readmission is used to guide postdischarge care; however, a single binary 30-day estimate does not distinguish when risk occurs from when intervention is most actionable, with implications for allocation of postdischarge resources. Objective To evaluate time-structured hospital readmission risk within 30 days after discharge and implications for clinical utility. Design, Setting, and Participants Retrospective prognostic study of adults with hospital discharges from 2017 to 2024 (from 19 hospitals within a US health system for the development cohort) and 2008 to 2019 (from Medical Information Mart for Intensive Care IV for the external cohort) with 30-day follow-up. Analyses were conducted from November 2025 through June 2026. Exposure Postdischarge prediction window, including landmark intervals of 0 to 7, 8 to 14, 15 to 21, and 22 to 30 days and cumulative horizons through days 7, 14, 21, and 30 after discharge. Main Outcomes and Measures First all-cause hospital readmission before death during each landmark interval and by each cumulative horizon. Risk estimates were derived using clinical information available at hospital discharge. Performance was assessed using discrimination, calibration, decision curves, and capacity-constrained analyses. Results Among 617 841 discharges in the development cohort, the median (IQR) patient age was 65 (50-76) years, and 315 220 discharges (51.0%) occurred among female patients. The 30-day readmission rate was 16.3% (95% CI, 16.2%-16.5%), with 68 797 readmissions (68.2%) occurring within 14 days. Cumulative-horizon analyses characterized readmission risk through days 7, 14, 21, and 30, whereas landmark interval-specific analyses showed lower positive predictive values (PPVs) beyond day 7. At 5% intervention capacity, landmark PPVs were 58.3% (95% CI, 57.4%-59.1%) for days 0 to 7, 16.0% (95% CI, 15.5%-16.6%) for days 8 to 14, 14.5% (95% CI, 13.9%-15.0%) for days 15 to 21, and 12.9% (95% CI, 12.4% to 13.5%) for days 22 to 30; numbers needed to prevent 1 readmission were 8.6, 31.3, 34.6, and 38.6, respectively. Conclusions and Relevance In this prognostic study of adult hospital discharges, hospital readmission risk varied over time and was not captured by a single binary 30-day estimate. These findings suggest that time-structured readmission modeling can characterize when readmission risk occurs, but additional work is needed before such models can reliably guide interventions during specific postdischarge intervals.

Introduction Fluorescent In-Situ Hybridization (FISH) has been an indispensable tool to characterize the cytogenetic landscape of multiple myeloma (MM), mainly for identifying primary abnormalities such as heavy chain gene (IgH) translocations and odd-number chromosome trisomies. These aberrations play a crucial role in risk stratifying the disease given their correlation with disease aggressiveness, treatment response and overall prognosis. High-risk (HR) cytogenetics are identified through the presence of t(14;16), t(14;20) or loss of the p53 gene locus. Less is known about the prognostic implication of secondary abnormalities, in particular gain of 1q21. We aimed to evaluate the association between primary and secondary cytogenetic abnormalities and their impact on overall survival (OS) in MM patients. Methods We performed a retrospective analysis on a FISH records database from three Mayo Clinic campuses. Cohort included patients >18 years of age, diagnosed with multiple myeloma between 2005 and 2022, with FISH performed within two years of diagnosis. Demographics including age, date of diagnoses and OS data were retrieved from electronic medical records. Statistical analyses were done using BlueSky Statistics software. Study was conducted in accordance with the principles of the Declaration of Helsinki; informed consent was obtained from all participants. Results We analyzed a total of 2221 patients, among which 59.8% were male, with a median age of diagnosis 65 years. Hyperdiploidy was the most prevalent primary cytogenetic abnormality observed in 44.3% of patients, followed by t(11;14) as the most common translocation (23%), and t(4;14) (8.2%). Gain(1q21) was assessed in 925 patients, among which 64%, 30.3% and 5.7% had 2, 3 and ≥4 copies respectively. Furthermore, 11.4% (n=253) exhibited either mutated TP53 or del17p, and 37.8% (n=839) presented with 13q loss or monosomy 13. The median follow-up for the overall population was 102 months (8.5 years), with a median OS of 75.5 months. Patients with high-risk translocations had significantly shorter median OS of 40.01 months compared to 80.65 months for those with standard risk translocations (p <0.001). The presence of ≥4 1q copies was associated with significantly reduced overall survival (2 copies: not reached, 3 copies: 69.4 months, ≥4 copies: 40.1774; p <0.001). This was consistent even when considering the presence of high-risk translocations (Figure 1). To confirm the independent significance of these findings, we performed multivariate Cox regression models, incorporating age at diagnosis, year of diagnosis, gender, presence of hyperdiploid disease, and presence of high-risk translocations. Given that overall survival outcomes were analyzed irrespective of treatment received, we incorporated year of diagnosis as a variable to control for potential impact of evolving treatment regimens over the years. The presence of ≥4 1q copies was associated with reduced OS, with a hazard ratio (HR) of 2.12 (CI: 1.38-3.26; p <0.001). The presence of 3 copies also showed prognostic significance (HR: 1.43; p: 0.012). Similarly, both the presence of TP53mut/mon17 (HR: 1.96, CI: 1.66-2.31; p <0.001) and 13qdel/mon13 (HR: 1.21, CI: 1.08-1.38) were associated with decreased OS. On the other hand, 1p deletion did not have any significant prognostic implications (HR: 1.84, CI: 0.38-1.59; p 0.113). When incorporating all cytogenetic events into a multivariate model, variables associated with reduced OS included age at diagnosis, the presence of both of 3 and ≥4 1q copies, TP53mut/mon17, t(4;14), t(14;16) and t(14;20). However, 13qDel/mon13 lost significance when incorporated with other abnormalities (Table 1). Conclusion Our investigation sheds light on the less explored realm of secondary cytogenetic abnormalities and confirm a strong association between increasing number of 1q copies and reduced overall survival, independent of other cytogenetic abnormalities and treatment regimens. These results underscore the utility of FISH-based comprehensive cytogenetic profiling at baseline, which enables reliable identification of high-risk patients. Future prospective trials should further elucidate the pathogenic mechanism for these findings. Our analysis used OS as primary endpoint and included over 2000 MM patients, reinforcing the clinical relevance of our findings.

AbstractIntroductionInpatient hyperglycemia is an established independent risk factor among several patient cohorts for hospital readmission. This has not been studied after kidney transplantation. Nearly one‐third of patients who have undergone a kidney transplant reportedly experience 30‐day readmission.MethodsData on first‐time solitary kidney transplantations were retrieved between September 2015 and December 2018. Information was linked to the electronic health records to determine diagnosis of diabetes mellitus and extract glucometric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30‐day readmission. We report the average performance of the models on the testing set on bootstrapped partitions of the data to ensure statistical significance.ResultsThe cohort included 1036 patients who received kidney transplantation; 224 (22%) experienced 30‐day readmission. The machine learning algorithm was able to predict 30‐day readmission with an average area under the receiver operator curve (AUC) of 78% with (76.1%, 79.9%) 95% confidence interval (CI). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient‐hyperglycemia, inpatient‐hypoglycemia, minimum and maximum glucose values among those with higher 30‐day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper‐ and hypoglycemia, the recipient and donor body mass index (BMI) values, presence of delayed graft function, and African American race as the most predictive risk factors of 30‐day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed.ConclusionsSuboptimal glucose metrics during hospitalization after kidney transplantation are associated with an increased risk for 30‐day hospital readmission. Optimizing hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30‐day readmission.

Background: The use of inpatient location for the depiction of glycemic control is an alternative approach to the traditional analysis of hospital-derived glucometric data. Our aim was to develop a method of spatial representation and to test for corresponding statistical variation in inpatient glucose control data. Methods: Point-of-care blood glucose data from inpatients with diabetes mellitus were extracted. Calculations included patient-day weighted means (PDWMs) and percentage of patient hospital days with hypoglycemia. Results were overlaid onto hospital floor plans, and room numbers were used as geolocators to generate cross-sectional (2-dimensional) and perspective (3-dimensional) views of the data. Linear mixed and mixed-effects logistic regression models were used to compare the location effect and to assess statistical variation in the data after adjusting for age, sex, and severity of illness. Results: Visual inspection of cross-sectional and perspective maps demonstrated variation in glucometric outcomes across areas within the hospital. Statistical analysis confirmed significant variation between some hospital wings and floors. Conclusions: Spatial depiction of glucometric data within the hospital could yield insights into hot spots of poor glycemic control. Future studies on how to operationalize this approach, and whether this method of analysis can drive changes in glycemic management practices, need to be conducted.

Abstract Background Myeloid neoplasms (MN) occurring secondary to treatment of non-malignant disease, especially autoimmune diseases (AID), and the overlap between them, is an increasing clinically important observation. Confounding variables for myeloid transformation include the nature of autoimmune disease as an incessant, chronically activated inflammatory cascade that has been associated with an increased risk of myeloid malignancy, and disease modifying therapies, often with cytotoxic and immunosuppressive drugs. Thus, the addition of therapeutic agents for treatment of AID theoretically amplifies the opportunity for MN to develop in genetically susceptible individuals. To our knowledge, there is no large study evaluating a comprehensive primary AID population that developed MDS or AML with detailed assessment of therapeutic interventions for AID. The presented study queries the association of cytotoxic, anti-inflammatory, and immune-modulating agents to treat AID as risk factors for developing myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML). Methods In a 10-year period from 2004-2014, a total of 40,011 patients with an ICD-9 coded diagnosis of AID were seen at Mayo Clinic Arizona and Florida campuses; 311 patients from this population had a concomitant coded diagnosis of MDS and/or AML. In an IRB approved, retrospective chart review, we confirmed 86 cases meeting strict inclusion criteria for AID and confirmed MDS or AML. We performed a case-control match from the same primary autoimmune population for age, gender, and autoimmune specific diagnosis at a 2:1 ratio querying for therapeutic interventions. Results Myeloid neoplasm specifics in our case cohort of 86 patients were 55 MDS, 21 de novo AML, and 10 AML with history of MDS. Average age was 72 years with a slight male predominance (57%). Rheumatoid arthritis (26.4%), psoriasis (20.7%), systemic lupus (13.8%), was the most common autoimmune profiles. Median onset of autoimmune disease to diagnosis of myeloid neoplasm was 6 years (range 1-54 years). Up to 26% controls documented no systemic agent for treatment of autoimmune disease and similarly 12.8% of the case cohort (p=0.142). Case and control populations treated with systemic therapies had similar exposures by category for immune-modulating agents (33.7% and 37.8% respectively) and cytotoxic therapies (47.7 and 44.8% respectively). A total of 57/86 cases (66.3%) received either a cytotoxic or immune-modulating agent. In comparison, 105/172 (61.1%) controls received either agent; p=0.495. Azathioprine was the only statistically significant agent exposure observed more frequently in the study cohort than controls with an OR of 7.05 (p= < 0.001). There was no increased incidence of MDS or AML in TNF-antagonist treated patients. No significant correlation for duration of agent exposure by drug category was observed. Discussion In a large primary AID population, azathioprine exposure was associated with a 7 fold risk of MDS or AML (p=< 0.001). Notable, but not statistically significant case cohort trends among cytotoxic agents was exposure to cyclophosphamide (OR 3.58) followed by mitoxantrone (OR 2.73). Methotrexate, mercaptopurine, and mycophenolate had favorable odd ratios, but these were not statistically significant. No association was found for TNF-inhibitory agents. Finally, there was no association of drug exposure time to the incidence in development of MDS or AML. Disclosures Dueck: Bayer: Honoraria. Mesa:Gilead: Research Funding; Novartis: Consultancy; Celgene: Research Funding; Incyte Corporation: Research Funding; Galena: Consultancy; Ariad: Consultancy; CTI: Research Funding; Promedior: Research Funding. Al-Kali:Onconova Therapeutics, Inc.: Research Funding; Celgene: Research Funding. Foran:Millennium Pharmaceuticals, Inc.: Research Funding; novartis: Honoraria; karyopharm: Honoraria; medscape: Honoraria; pfizer: Honoraria; boehringer ingelheim: Research Funding; agios: Research Funding; Cellerant: Research Funding.

Abstract Background Cytotoxic agents are a well-established risk factor for therapy related myeloid neoplasms (t-MN). The incidence of t-MN in patients (pts) with autoimmune diseases (AID) treated with cytotoxic and immuno-modulatory agents are not well described in the literature. Most case series assess an MDS/AML population and co-occurrence with an AID. To our knowledge, there exists no large case-control studies evaluating a primary AID population that developed therapy-related MDS or AML, and the relationship to detailed therapeutic intervention for AID. Our study queries the association of cytotoxic and immune-modulating agents to treat AID as risk factor for developing MDS or AML. Methods Between 2004-2014, 23,671 pts with an ICD-9 coded AID were seen at Mayo Clinic Arizona; 148 pts from this population had a concomitant coded diagnosis of MDS/AML. In an IRB approved, retrospective chart review we confirmed 61 cases meeting strict inclusion criteria. We performed a case-control match from the same primary AID population for age, gender, and autoimmune specific diagnosis at a 2:1 ratio. AML/MDS cases were compared to controls by use of chi-square test for frequency data. Odds ratios with 95% confidence interval were constructed using conditional logistic regression. Case incidence was compared to SEER data for the general population Results Of 61 cases 33 were MDS, 20 de-novo AML, and 8 MDS transformed to AML. Psoriasis 16 (26.2%) and rheumatoid arthritis 14 (23.0%) were the most common autoimmune diagnoses, followed by systemic lupus 11 (18%), Crohn's disease 7 (11.5%), and ulcerative colitis 5 (8.2%). 39 out of 61 cases (63.9%) received either a cytotoxic or immune modulating agent for AID, compared to 64/122 (52.5%) in the control group; p=0.140. 7/61 cases (11.5%) received both a cytotoxic and immune modulating agent as compared to 16/122 (13.1%) in controls; p=0.753. Overall, the case population received more agents than controls. No cytotoxic or immune-modulating treatment was documented in 16% of cases and 36% controls. Exposures to cytotoxic and immune-modulating agents were similar in both case and control cohorts. Exposure time was not statistically significant. Azathioprine was the only statistically significant agent exposure observed more often in the case cohort than control. Per SEER data, over a 10-year period in a general age-matched population that is comparable to our AID population cohort, we would expect 54 MDS and 35 AML cases. Table 1. Exposures and Timeline Cases (n=61) Controls (n=122) P value * Number of Systemic Agents, Total None 1 2 3 4+ - 10 (16.4%) 26 (42.6%) 12 (19.7%) 7 (11.5%) 6 (9.8%) - 44 (36.1%) 32 (26.2%) 31 (25.4%) 9 (7.4%) 6 (4.9%) 0.020 Exposure to Cytotoxic agent < 1 year 2-5 years 6-10 years 11 years or more Unknown (n=26) 7 (26.9%) 6 (23.1%) 5 (19.2%) 3 (11.5%) 5 (19.2%) (n=43) 5 (11.6%) 8 (18.6%) 6 (13.9%) 12 (27.9%) 12 (27.9%) 0.276 Exposure to Immune modulating agent < 1 year 2-5 years 6-10 years 11 years or more Unknown (n=20) 6 (30%) 6 (30%) 3 (15%) 1 (5%) 4 (20%) (n=37) 6 (16.2%) 10 (27.0%) 5 (13.5%) 3 (8.1%) 13 (35.1%) 0.661 Table 2. Treatments and Odds Ratio Odds Ratio 95% CI for OR P value * Cytotoxic OVERALLAzathioprine Cyclophosphamide Methotrexate Mitoxantrone 6-MP Mycophenolate 1.39 4.16 1.28 0.74 2.73 1.00 1.21 0.72, 2.67 1.29, 13.44 0.24, 6.89 0.29, 1.88 0.23, 33.0 0.23, 4.35 0.28, 5.21 0.322 0.017 0.773 0.528 0.429 0.999 0.803 Immune modulating agent OVERALLAnti-TNF Leflunomide Plaquenil 1.19 0.73 2.00 2.73 0.52, 2.73 0.24, 2.24 0.40, 9.91 0.80, 8.33 0.672 0.585 0.396 0.077 Discussion Treatment of AID does not seem to increase the incidence of MDS or AML. Furthermore, the incidence of myeloid neoplasm occurring in association with treatment of AID is not higher than expected in the general SEER population. However, the case cohort was exposed to more agents overall and more likely to have exposure to azathioprine than controls. Limitations of our study are associated with its retrospective nature, possible associated underrepresentation of t-MN, and autoimmune population profile. Nevertheless, this is the largest case study with detailed drug exposures with timelines for pts with AID, contributing to a better understanding regarding possible risk of developing MDS/AML in pts treated for AID. Confirmation of the same study at Mayo Clinic Rochester and Florida are ongoing. Disclosures Al-Kali: Novartis: Research Funding.