Pancreaticobiliary obstruction is a rare but life-threatening complication. Plastic biliary stents are a temporary utility to maintain the patency of the common bile ducts, typically lasting about four months. Biliary stents can rarely have complications, with the most common being migration through the gastrointestinal tract. We present a case of a patient with a plastic stent placed over five years, which was complicated by severe hematochezia due to the retention of the stent in a diverticulum. Given the increased risk of life-threatening complications post-stent life expectancy, there should be systems in place to prevent patients from being lost to follow-up. In the United States, over 20 million Americans are affected by gallbladder disease . Of thosThis article was previously presented as a meeting abstract at the 2020 American College of Gastroenterology (ACG) Annual Scientific Meeting on October 25, 2021.A 72-year-old veteran  with a known history of hypertension, peripheral vascular disease, cholecystitis status after laparoscopic cholecystectomy complicated by biliary leak requiring the placement of two biliary stents, prior polysubstance use, and post-traumatic stress disorder presented with a two-month history of functional decline and weight loss failure to thrive. His labs were significant for the following: WBC 10.3, aspartate transaminase (AST)/alanine transaminase (ALT) 153/94, alkaline phosphatase (ALP) 703, total bilirubin 1.5, procalcitonin 2.24, C-reactive protein (CRP) 190.3, erythrocyte sedimentation rate (ESR) 64, gamma-glutamyl transpeptidase (γ-GTP) 609, fractionated alkaline phosphatase 87% liver isozyme, and CA 19-9 of 494. The patient was subsequently admitted for further management, given the concern for malignancy. He was started on broad-spectrum antibiotics and intravenous fluid resuscitation; however, he developed three episodes of hematochezia with a drop in hemoglobin from 14.6 to 9.0 with associated symptoms. He underwent a colonoscopy, which revealed a dislodged stent with its pigtail entrapped within a sigmoid diverticulum. Bleeding stigmata were visualized adjacent to the stent Figures -1D. The There are 40 published cases involving foreign bodies and sigmoid diverticular disease . To our We speculate this patient had recent stent dislodgement, leading to common bile duct occlusion and resultant cholangitis. After localization of the biliary stent in the sigmoid colon on CT, the patient was initially managed conservatively for spontaneous passage through the ileocecal valve. However, once the patient developed a hemodynamically significant GI bleed, endoscopic retrieval of the displaced stent became paramount. After endoscopic stent retrieval, intravenous fluids, and blood transfusion, the patient became hemodynamically stable. This case underscores the importance of creating a robust standardized post-stenting follow-up schedule. As time elapses post-stenting, the risk and severity of complication increase and can have deleterious consequences. Thus, every effort should be made to avoid loss of follow-up to prevent life-threatening complications of biliary stent complications. This can be accomplished by creating an internal hospital registry where each stent placed has a follow-up assigned to ensure the removal of the stent. This may allow for the prevention of adverse outcomes like those present in this patient. Patients with pancreaticobiliary obstructions are at high risk for morbidity and mortality. The gold standard in treatment is the placement of temporary stents to bypass the blockage and provide a conduit for easy bile flow. Stent migration can occur in a small percentage of patients with the risk increasing from the time since placement, and though most pass spontaneously, some do not. It is important to ensure that close follow-up is established with patients who receive this intervention as a rare but life-threatening gastrointestinal bleed, as in our patient, may occur.


This cross-sectional study analyzes trends in Medicare Part D use and spending on oral-targeted drugs for chronic lymphocytic leukemia from 2014 to 2020. Among the top-selling drugs in the US, ibrutinib accounted for more than $2.8 billion in annual net Medicare spending by 2020.2The treatment of chronic lymphocytic leukemia (CLL), the most common adult leukemia, has changed considerably in recent years. In 2013, the US Food and Drug Administration approved ibrutinib (Imbruvica), an oral inhibitor of Bruton tyrosine kinase (BTKi) that is both more effective and safer than previous standard-of-care chemotherapy.3 In 2014, phosphatidylinositol-3 kinase inhibitors were also approved for CLL, although they are less effective and more toxic and, thus, usually reserved for multiply relapsed patients.1Subsequently, BTKis with fewer adverse effects, such as acalabrutinib, were approved for CLL, as was venetoclax, the first-in-class B-cell lymphoma-2 inhibitor, which offers additional clinical advantages such as time-limited therapy and the potential for complete remission.We hypothesized that the emergence of multiple oral-targeted drugs for CLL may lower costs due to competition and affect overall spending on ibrutinib. In this cross-sectional study, we analyzed trends in Medicare Part D use and spending on these drugs from 2014 to 2020.4 We estimated net spending after rebates using 4-quarter rolling average non-Medicaid rebates from SSR Health and adjusted spending to 2020 US dollars according to the consumer price index for all urban consumers. A sensitivity analysis was performed to evaluate net price trajectories beyond Medicare to assess whether mandatory coverage requirements of oncology products in Part D might be associated with trends. Microsoft Excel version 16.70 (Microsoft) was used to conduct the data analysis from March 2022 to October 2022. This study was not submitted for institutional review board approval and did not require informed consent because it used public, nonidentifiable data and did not constitute human participants research, in accordance with 45 CFR §46.We used publicly available Medicare Part D data to determine annual spending on oral CLL drugs, the number of beneficiaries who received these drugs, and the average spending per 30-day fill.Six oral medications were included in the study: 3 BTKis , 2 phosphatidylinositol-3 kinase inhibitors , and 1 B-cell lymphoma-2 inhibitor (venetoclax). From 2014 to 2020, annual net Medicare spending on these 6 drugs across all indications increased from $254 million to $3.7 billion . In 2014Estimated net spending per 30-day supply of ibrutinib increased 46%, from $8206 in 2014 to $11 980 in 2020, despite market entry of venetoclax in 2016 , acalabrutinib in 2017 ($11 428) and zanubrutinib in 2020 ($12 521) . A sensi6 Brand to brand competition may have been ineffective at lowering Medicare costs due to lags between approval and change in prescriber practices, constraints on payers’ ability to effectively use formularies to negotiate prices, and financial incentives that can encourage intermediaries such as pharmacy benefit managers to accept high prices.5As shown in this cross-sectional study, Medicare costs for a 30-day supply of ibrutinib, the first oral CLL drug, increased markedly from 2014 to 2020, despite competition from other drugs, some of which were less expensive than ibrutinib. The inability of brand to brand competition to lower prices has been observed in other contexts, particularly among cancer drugs.Despite increased within-class and between-class competition from drugs with better clinical profiles, both the cost and the rate of prescribing ibrutinib increased markedly. One limitation of this study is that Medicare does not report spending by indication, so we cannot know what proportion of this spending is for CLL vs for other B-cell lymphomas. Further research is needed to understand why oncologists have not embraced clinically superior options for CLL being sold at prices similar to, if not lower than, ibrutinib.


What is the relative burden of oncologic and nononcologic mortality among long-term survivors of cancer in the US?In this cohort study of 627 702 patients surviving 5 years or more from an initial diagnosis of early-stage breast, prostate, or colorectal cancer, the risk of dying from the index cancer varied widely relative to noncancer-specific causes of death. Patients with low oncologic risk at the time of diagnosis had at least 3-fold higher risk of noncancer death compared with death from the index cancer.This study suggests that risk-stratified care may help quantify the relative importance of oncologic and primary care surveillance for long-term survivors of cancer. Improvements in cancer outcomes have led to a need to better understand long-term oncologic and nononcologic outcomes and quantify cancer-specific vs noncancer-specific mortality risks among long-term survivors.To assess absolute and relative cancer-specific vs noncancer-specific mortality rates among long-term survivors of cancer, as well as associated risk factors.This cohort study included 627 702 patients in the Surveillance, Epidemiology, and End Results cancer registry with breast, prostate, or colorectal cancer who received a diagnosis between January 1, 2003, and December 31, 2014, who received definitive treatment for localized disease and who were alive 5 years after their initial diagnosis . Statistical analysis was conducted from November 2022 to January 2023.Survival time ratios (TRs) were calculated using accelerated failure time models, and the primary outcome of interest examined was death from index cancer vs alternative (nonindex cancer) mortality across breast, prostate, colon, and rectal cancer cohorts. Secondary outcomes included subgroup mortality in cancer-specific risk groups, categorized based on prognostic factors, and proportion of deaths due to cancer-specific vs noncancer-specific causes. Independent variables included age, sex, race and ethnicity, income, residence, stage, grade, estrogen receptor status, progesterone receptor status, prostate-specific antigen level, and Gleason score. Follow-up ended in 2019.The study included 627 702 patients : 364 230 with breast cancer, 118 839 with prostate cancer, and 144 633 with colorectal cancer who survived 5 years or more from an initial diagnosis of early-stage cancer. Factors associated with shorter median cancer-specific survival included stage III disease for breast cancer  and colorectal cancer , as well as a Gleason score of 8 or higher for prostate cancer . For all cancer cohorts, patients at low risk had at least a 3-fold higher noncancer-specific mortality compared with cancer-specific mortality at 10 years of diagnosis. Patients at high risk had a higher cumulative incidence of cancer-specific mortality than noncancer-specific mortality in all cancer cohorts except prostate.This study is the first to date to examine competing oncologic and nononcologic risks focusing on long-term adult survivors of cancer. Knowledge of the relative risks facing long-term survivors may help provide pragmatic guidance to patients and clinicians regarding the importance of ongoing primary and oncologic-focused care. This cohort study assesses absolute and relative cancer-specific vs noncancer-specific mortality rates among long-term survivors of cancer, as well as associated risk factors. As such, relevant empirical data are lacking to inform long-term survivors of these common cancers of their relative risk of cancer-specific vs noncancer-specific mortality, which could be used to help inform models of survivorship care that are tailored to patients’ unique risk profiles.As such, there is a critical need to provide quantitative risk estimates of oncologic and nononcologic outcomes among long-term survivors of cancer in representative US cohorts. Given that breast, prostate, and colorectal cancers account for half of all diagnoses for survivors of cancer, individuals with these cancers provide the ideal study population for survivorship risk stratification research.In this study, our objective was to assess the absolute and relative risks of cancer-specific vs noncancer-specific mortality among long-term (≥5 years) survivors of breast, prostate, colon, and rectal cancers within the US, to ultimately promote risk assessments to inform the implementation of risk-stratified survivorship pathways.27 The SEER program collects population-based cancer incidence and survival data across 18 registries in the US covering approximately 48.0% of the US population. The Yale institutional review board approved this study as exempt because SEER-Medicare data were deidentified, and informed consent was not required. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.We used data from the most recent data set (2021) of the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program.Patients who received a diagnosis of breast, prostate, or colorectal cancer from January 1, 2003, through December 31, 2014, with documented receipt of definitive treatment and who had survived at least 5 years from diagnosis were included. Definitive treatment was defined by site-directed surgery and, among patients with prostate cancer, also included radiotherapy. Patients with stage IV disease and those diagnosed at autopsy or on a death certificate were excluded. In addition, patients with missing information on demographic characteristics, clinical factors, or duration of follow-up were excluded .28 Mortality due to the primary cancer was classified as cancer-specific mortality. Mortality due to causes other than the primary cancer was classified as noncancer-specific mortality. Hence, for a patient whose index cancer was breast cancer, a death from any other cancer, as well as any noncancer condition, would be classified as noncancer-specific. The clinical motivation for the study was to prioritize the need for cancer-specific vs noncancer-specific  clinician surveillance at 5 years after the diagnosis of a definitively treated, early-stage cancer. Although the 5-year benchmark is somewhat debatable, it remains a practical window after which nononcologic follow-up is considered for many cancers. For example, the National Institutes of Health have used a 5-year horizon in multiple requests for applications to investigate long-term survivors of cancer.29 Cancer-specific events were defined as death from the incident cancer, which would presumably be most effectively detected and/or managed by a patient’s initial oncologist.The outcomes of interest were cancer-specific mortality and noncancer-specific mortality. The outcomes were defined based on SEER cause-of-death classification variables.All demographic and clinical factors were collected at the time of diagnosis, including patients’ age, sex , race and ethnicity , year of diagnosis, median household income (county level), and area of residency (metropolitan vs nonmetropolitan) and were included in the analysis. Race and ethnicity were classified by the SEER program. The SEER program determines race and ethnicity through medical record abstraction or by using a computer algorithm that searches surnames of the reported cases to determine Hispanic origin. Although certain cancer characteristics do vary across racial and ethnic groups, race and ethnicity were included in the study not as a biological construct, but as a proxy for structural racism. Age at diagnosis was categorized into 5 categories: younger than 50 years, 50 to 54 years, 55 to 59 years, 60 to 64 years, 65 years or older. All patients lived at least 5 years after diagnosis. Therefore, patients aged 65 years or older at the time of diagnosis were 70 years or older at their entry into the study.Tumor-related factors at the time of diagnosis included stage, grade, nodal status, hormone receptor (estrogen and progesterone) status (breast cancer only), laterality (breast cancer only), prostate-specific antigen (PSA) level (prostate cancer only), and Gleason score (prostate cancer only). We used American Joint Committee on Cancer staging reported in SEER for cancer stage. Grade IV disease was infrequently reported and therefore grouped with grade III. Prostate-specific antigen level was categorized as low (<10 ng/mL), intermediate (10-20 ng/mL), or high (>20 ng/mL) . Gleason score was also categorized as low (≤6), intermediate (7), or high (≥8). Treatment-related factors included chemotherapy and radiotherapy.Statistical analysis was conducted from November 2022 to January 2023. A least absolute shrinkage and selection operator (LASSO) was used to select the factors associated with cancer-specific and noncancer-specific mortality separately. Variables with a regression coefficient equal to zero after the shrinking process were excluded from the model, and variables with nonzero coefficients were included in survival analysis (eTable 3 in 32 Parameter estimates from AFT models were converted to time ratio (TR) estimates to interpret the effect of a covariate on the time scale. A TR greater than 1 indicates that the covariate is associated with accelerated survival time , whereas a TR less than 1 indicates that the covariate is associated with decelerated survival time .Because the proportional hazards assumption was not satisfied for several covariates, we used accelerated failure time (AFT) models, which do not rely on the proportional hazards assumption.37 For patients with breast cancer, risk groups were categorized as (1) low risk (≥65 years and stage I), (2) high risk (<65 years and stages II-III), or (3) intermediate risk (everyone else). Patients with prostate cancer were classified as (1) low risk (≥65 years and Gleason score of 6), (2) high risk (<65 years and Gleason score >6), or (3) intermediate risk (everyone else). Patients with colorectal cancer were classified as (1) low risk (≥65 years and stage I), (2) high risk (<65 years and stages II-III), or (3) intermediate risk (everyone else).Based on established risk factors of cancer-specific mortality, patients with cancer were grouped into 3 risk groups of oncologic mortality: low risk, intermediate risk, or high risk.P values were from 2-sided tests and results were deemed statistically significant at P < .05. The package grpreg was used to perform LASSO regularization in R.The cumulative incidence function curves of cancer-specific and noncancer-specific mortality by risk groups were generated for all cancer sites. Statistical analyses were conducted using SAS, version 9.4 (SAS Institute Inc), Stata, version 17 (StataCorp LP), and R, version 4.0.4 . All We identified 627 702 patients : 364 230 with breast cancer, 118 839 with prostate cancer, 104 488 with colon cancer, and 40 145 patients with rectal cancer with stage I, II, or III disease diagnosed between 2003 and 2014 and treated with definitive intent surgery and/or radiotherapy.A total of 123 701 patients with breast cancer (34.0%), 57 958 patients with prostate cancer (48.8%), 56 839 patients with colon cancer (54.4%), and 15 464 patients with rectal cancer (38.5%) were aged 65 years or older . A totalAmong patients with breast cancer, 301 897 (82.9%) were estrogen receptor (ER) positive and 264 768 (72.7%) were progesterone receptor positive . A totalAcross all cancer cohorts, most patients died of noncancer-related causes. Heart disease was the leading cause of noncancer-specific deaths, followed by Alzheimer disease, chronic obstructive pulmonary disease (COPD), and cerebrovascular disease  were associated with heart disease . Likewise, patients with grade III breast cancer had a 24% reduction in median survival time for breast cancer–specific mortality than those with grade I disease  . PatientIn the prostate cancer cohort, patients with a PSA level higher than 20 ng/mL had a 22% reduction in median survival time for prostate cancer–specific mortality , and those with a Gleason score of 8 or higher had almost a 40% reduction in median survival time for prostate cancer–specific mortality  . PatientBased on established risk factors of cancer-specific mortality, which were also confirmed by our analysis, patients were categorized into 3 risk groups. After 10 years of cancer diagnosis, there was a substantially different risk of cancer-specific vs noncancer-specific mortality between the low-risk and high-risk groups . For patAmong patients with prostate cancer, the low-risk group had an almost 9 times higher cumulative incidence of nonprostate cancer–specific mortality compared with the cumulative incidence of prostate–specific mortality . The cum38 to our knowledge, this study is the first to focus on long-term (≥5 years) survivors. We found that the risk of oncologic and nononcologic mortality among long-term survivors of cancer varied widely by risk group. Ten years after cancer diagnosis, the noncancer-specific mortality was substantially higher than the cancer-specific mortality among patients with low oncologic risk, as assessed using standard prognosticating markers including stage or Gleason score. Conversely, cancer-specific mortality was high among those with adverse prognostic factors for their cancer , except patients with prostate cancer. By quantifying the relative long-term risks of oncologic vs nononcologic mortality among these patients, we hope to help patients and clinicians place the relative importance of oncologic and nononcologic care into perspective. Although defining risk-stratified management is beyond the scope of this study, our findings suggest that patient groups with relatively high risks of nononcologic mortality, such as those 65 years or older with lower-stage disease, may particularly benefit from higher-intensity primary care surveillance. Given that the benefit associated with preventive care takes years to manifest, increased intensity of primary care may be most effective if initiated shortly after diagnosis, which could take place concurrently with oncologic management.Estimating the relative risk of cancer-specific vs noncancer-specific mortality among long-term survivors of cancer is a critical first step in the development of risk-stratified models of care. Although many studies have previously examined competing oncologic vs nononcologic risks of common cancers,The factors associated with mortality among long-term survivors varied depending on patients’ age, tumor biology, and stage at diagnosis. Patients with low oncologic risk—defined as those aged 65 years or older, with a low tumor stage, and with a low Gleason score (for prostate cancer)—had substantially higher mortality associated with causes other than their initial cancer. Heart disease, Alzheimer disease, COPD, cerebrovascular disease, and lung disease were the dominant causes of death among patients with low oncologic risk across all cancer sites. In the low oncologic risk group, the ratio of cumulative mortality of noncancer-specific vs cancer-specific causes of death between 5 and 10 years after diagnosis was highest in the prostate cancer cohort (9-fold) followed by the colon (7-fold), breast (7-fold), and rectal (3-fold) cancer cohorts.42 For example, a recent study using SEER data found a greater risk of breast cancer–specific mortality among younger women with more advanced and aggressive disease than older women with hormone receptor–positive and low-grade breast cancer, but that age was not independently associated with an increased risk of mortality for other tumor subtypes.43 However, such studies were not restricted to long-term survivors. In our cohort of long-term survivors, we found that older age  was associated with poor cancer-specific survival. This finding is consistent with longer-term studies of breast cancer, which have reported an increased risk of cancer-specific mortality among older women compared with younger women.47 Our study used 5 years as the definition of long-term survivors. Other definitions of survivorship windows have been reported,48 and one could consider using different definitions for different cancers based on differences in the natural history of different cancers. We chose this milestone because it represents a highly pragmatic time point at which many survivors of cancer and their managing clinicians reexamine plans for cancer surveillance and general health maintenance. In support of the 5-year milestone, the longest running study of long-term survivors of cancer, to our knowledge, the Childhood Cancer Survivor Study, is limited entirely to patients who have survived 5 years from their cancer diagnosis.52 Last, although not the focus of this study, there can be a difference in risk profiles of patients before vs after 5 years from diagnosis. For example, we found that patients with ER-negative tumors were associated with higher cancer-specific mortality during the initial 5 years after diagnosis but with lower risk of cancer-specific mortality after 5 years, presumably because most ER-negative recurrences take place within 5 years.Previous studies have suggested that high-risk biology and stage at diagnosis increase the risk of cancer-related mortality among young women with breast cancer.There are several limitations to this study. First, validation of the models was performed through internal data validation only. Second, there were only a few clinical- and treatment-related factors in the SEER database. We included information about cancer stage and treatment at the time of diagnosis; however, we did not have information on the entire course of treatment, disease recurrence, or progression, which are crucial in estimating mortality for populations with cancer. Most important, data on patient comorbidities were not available, and therefore comorbidities could not be examined in our study. Variations in treatment and access to care can be significantly associated with cancer outcomes, particularly in racial and ethnic minority groups that often experience disparities in accessing quality health care services  of mortality based on cancer-specific prognostic factors that are associated with mortality. We found that the risk of cancer-specific vs noncancer-specific mortality varied substantially by cancer risk group, further informing the need for a personalized, risk-stratified approach to care that would eliminate unnecessary extended oncologic follow-up by optimizing the coordination between treating oncologists and PCPs. Future studies should include more follow-up information regarding treatment and disease recurrence.


Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs. Despite the potential for image-based phenotypic quantification of organoids, their complex 3D structure, and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges. In this work, we developed a virtual painting system, PhaseFIT (phase-fluorescent image transformation) utilizing customized and morphologically rich 2.5D intestinal organoids, which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images. This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects. The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images. The virtual painting results of nuclei, secretory cell markers, and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content. We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation, cell population, and cell stemness. PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids, enabling large-scale, informative, and efficient organoid phenotypic quantification. PhaseFIT would enable the use of organoids in high-throughput drug screening applications. PhaseFIT is a cutting-edge, segmentation-informed generative AI which effortlessly transforms one dimensional phase contrast images into rich, multi-channel fluorescent visuals for rapid, annotation-free phenotypic quantification, setting a new standard for high content drug screening applications. Moreover, deep neural networks (DNNs) have been trained to facilitate digital staining on phase images, such as, digital histological hematoxylin and eosin (H&E) staining and Masson’s trichrome staining on the phase images of tissue sections4, and digital fluorescent staining of specific proteins, such as Tau/Map2, on the phase images of neurons5. Yet, deep learning-enabled phase-contrast imaging analysis has not been utilized to quantify the phenotypes of organoid system, which contains high-dimensional and complex cellular composition with heterogeneous distributions.Phase-contrast imaging is a low-cost and readily available technique commonly employed to observe the spreading, growth, and morphology of cells in vitro. On the other hand, immunofluorescent (IF) imaging remains the gold standard for discerning high-contrast molecular specificity . However, IF-based imaging is time-consuming, labor-intensive, and detrimental to cells, as it requires sample preparation and staining with dyes and typically only works for fixed, non-viable cells. Recently, quantitative phase imaging (QPI) has emerged as a powerful, label-free, and live cell-based approach to provide quantitative information about cell number, morphology, and cell cycles7. Moreover, gut organoids have been used to model specific gastrointestinal diseases, such as colorectal carcinoma (CRC)9 and inflammatory bowel diseases (IBD)10. While organoid culture is a rapidly emerging platform with broad applications in both basic science and translational medicine, due to the intractable nature of the IF process for the complex 3D structures of the organoid system, the image-based phenotypic quantification of 3D organoids is usually limited to organoid number, size, and viability based on phase/brightfield imaging. Even though 2D and 2.5D organoid culture models that have recently been developed are more compatible with multiplex immunofluorescence (mpIF)12, mpIF is still an expensive and time-consuming process with large batch-to-batch heterogeneity. Thus, there is a significant need for the development of strategies for affordable, efficient, and informative organoid phenotypic quantification for large-scale applications, such as organoid-based drug screening.An organoid is a mini organ in in vitro culture, which can mimic the diverse cell populations, complex anatomy, and physiological functions of in vivo organs. For example, same with the native intestinal epithelium, intestinal organoids exhibit distinguished crypt–villus structures wherein stem cells and Paneth cells reside in the crypt regions, and the absorptive cells , and the secretory cells  are in the villus regions13. Recent investigations showed the feasibility of using deep learning to bridge quantitative phase imaging and fluorescent stains for understanding cell viability14. However, fine-grained transformation of complex and high-content organoid phase-contrast images into multiplex fluorescent staining images remains technically challenging, especially with no assistance of manual annotations. This is because DNNs are required to be trained for a high level of accuracy in simulating the complex optical relationship, unclear morphological boundaries, and high-content heterogeneities in organoid phase images while locating the implicit and even sparse protein expressions in the phase images to be able to generate the virtual fluorescent painting.Previous applications in cross-modality image transformations, such as phase-H&E virtual staining or H&E to mpIF translation, typically leverage deep generative adversarial networks and use one specific type of microscopic image as an input to generate various other staining typesIn the present study, we developed phase-fluorescent image transformation (PhaseFIT) for deep generative model-enabled virtual multiplex fluorescent painting on annotation-free phase-contrast live-organoid images. PhaseFIT is specially designed based on the characteristics of the image transformation task, taking into consideration the challenges posed by the complexities associated with a phase image (which is comprised of a broad spectrum of black & white intensity associated with different cell types and intracellular organelles) and highly heterogeneous composition of intestinal epithelial cells . To overcome these challenges, PhaseFIT incorporates a segmentation algorithm to perform the image translation task with precision. To the best of our knowledge, this is the first algorithm that applies a segmentation-informed model to the phase-fluorescent image transformation task.To enhance image generation, PhaseFIT incorporates channel-wise attention to focus on the most influential feature maps, as well as spatial-wise attention to suppress redundant feature regions. These design choices ensure that PhaseFIT effectively captures and utilizes the critical information present in the images for improved cross-modality image-to-image translation. This segmentation-informed model reaches nearly real-time inference for transforming images from live organoid phase images to multiplex fluorescent staining. Moreover, the proposed algorithm outperforms the state-of-the-art generative adversarial networks-based stain transformation methods when generating virtual painting from heterogeneous and artifact-lean organoid phase images. To validate the sensitivity and usefulness of PhaseFIT, we quantified the effects of three compounds on the phenotypes of the treated organoids, which exhibited that PhaseFIT is a potent tool for image-based organoid phenotypic quantification in biomedical and biopharmaceutical studies.7 not only accurately mimics the key features of in vivo gut epithelium (such as the crypt-villus structure and the diverse cell populations), but it also bypasses the tedious culture and staining protocols that are required for the conventional 3D organoids. Moreover, the 2.5D organoid model is readily amenable to being integrated into high-throughput screening platforms that would be necessary for large-scale drug testing and validation. However, the standard fluorescence imaging-based phenotypic analysis is not conducive to high-throughput systems owing to some of its limitations, including the time-consuming staining process, high cost of antibodies, complex system for multiplex imaging acquisition, and artificial variations in both staining and imaging across different batches  can efficiently identify the crypt-villus structure  and the protein LGR5-EGFP were chosen to exhibit the high-physiologically relevant features of the 2.5D organoids Fig. 7. Hoechsure Fig. b and c. ure Fig. a and b. ure Fig. 7. LGR5-Eons Fig. . UEA-I+ s18 Fig. . We have21. By contrast, CycleGAN improved the robustness in unsupervised learning and cross-domain translation for transforming H&E staining images into special stains26. We trained all three models and tested them using an identical dataset . Four quantitative measures of Dice, pixel-wise Recall (sensitivity) scores, structural similarity index (SSIM), and mean squared error29 were tested for comparing the qualities of the generated images on both local pixel level and global cell morphological level .We trained the PhaseFIT with ground truth images from the real Hoechst and UEA-I staining Figs.  and 5 anCompared to the ground truth from real Hoechst nucleus staining, the virtual nucleus staining images generated by PhaseFIT showed superiority compared to the GAN methods with respect to identifying the crypt-villus structure. All the crypt regions identified from ground truth images  between phase-fluorescent pairs using segmentation, delivering a more precise and efficient virtual painting compared to the other two GAN models. PhaseFIT accurately recapitulated the LGR5 signals in the ground truth images Fig. .+ cells in the live phase images  signaling pathway32. After administering these compounds, we collected live organoid phase images as input for the PhaseFIT workflow to generate virtual multiplex images for DAPI, LGR5, and UEA-I , and Dorsomorphin (DOR). These compounds have been used to manipulate the ISC differentiation in previous studies: DAPT induces secretory cell differentiation by inhibiting the Notch signaling pathwaylls Fig. . We confAs an emerging technique, in vitro organoid models, which faithfully serve as surrogates of in vivo organs, are the next generation of in vitro culture systems quickly replacing the conventional cell culture models. Organoids also provide a complementary alternative to animals as in vitro models of disease and for toxicological testing. Considering the labor-intensive and time-consuming nature of acquiring fluorescent images, extracting cell-level information using the standard phase contrast images would significantly broaden the large-scale applications of organoids.34. Specifically, the GAN-based methods, such as, Conditional-GAN21 and Cycle-GAN26, are used for transforming between different stains, such as H&E, IHC, or phase images of tissue section samples. However, transferring phase-contrast images to fluorescent images using deep learning techniques presents several challenges that GAN-based methods cannot easily overcome. That includes the sensitivity to imperfections in the optical path, which may be a result of misaligned mirrors or lenses and consequently result in blurred images. Analyzing organoids using phase-contrastive imaging are much more challenging than cells, even with expert annotations. Meaningful unbiased analysis, interpretation, and manipulation of these highly complex images require even more complex levels of computation, and this is where PhaseFIT excels.During the past 10 years, deep learning models have been trained to recognize biological and clinical images for biomedical and therapeutic applications7 and trained it to generate virtual DAPI nuclear staining, LGR5-EGFP signals, and UEA-I staining. These multiplex fluorescent images provided comprehensive interpretable phenotypic profiling, including distinguishing crypt-villus structure based on nuclear distribution and quantifying the proportion of the LGR5+ stem cells and UEA-I+ secretory cells. These phenotypes are highly physiologically relevant. The changes in crypt structure and stem cell populations are involved in the development of both IBD and cancer19. We confirmed that PhaseFIT significantly outperformed the two previously developed GAN-based methods in generating fluorescent representations of the crypt regions and stem cell populations. Our primary objective is to identify the overall regions that are populated by the cells and accentuate contrasts between different cell types. This aids in facilitating scientific exploration and analysis and does not necessitate the generation of realistic nucleus contours. It is particularly challenging given the complexities associated with a phase image, which is comprised of a broad spectrum of black & white intensity associated with different cell types and intracellular organelles and highly heterogeneous composition of intestinal epithelial cells, which include several different cell types with different sizes and cellular densities. Indeed, even an expert eye is unable to distinguish between the different cell populations solely from a phase image. It is notable that despite these challenges, PhaseFIT, as underscored by both qualitative and quantitative measures, exhibits at least a two-fold improvement in sensitivity compared to the existing state-of-the-art methodologies. Of note, PhaseFIT is not limited to the three virtual stains that were developed here. Following the same workflow, PhaseFIT can be expanded to generate virtual painting for other signals  once these signals can be visualized to train the model.PhaseFIT is specifically designed for live-organoid phenotypic profiling via generating informative virtual fluorescent stains based on phase images of live organoids. PhaseFIT leverages the advancement of segmentation by identifying and separating cells and structures within a phase-contrast image, especially when there is an overlap or close proximity between objects. We applied PhaseFIT on 2.5D gut organoidsPhaseFIT has been specifically designed to address the challenges that are associated with background noise in fluorescent images. The segmentation-informed module included in PhaseFIT helps to overcome interference and accurately distinguishes between genuine signals and background noise. Additionally, PhaseFIT employs an attention-based approach to tackle the challenges related to staining variability between or within cells. This approach is particularly beneficial when working with multi-channel stains, as it ensures accurate alignment and improves overall accuracy of virtual painting.Label-free PhaseFIT provides an efficient, accurate, and unbiased workflow for quantifying organoid phenotypes by bypassing the time-consuming, expensive, and complex processes of fixation, staining, and multiplex imaging required for fluorescence imaging. In addition, the virtual painting generated from PhaseFIT is based on the live organoid phase images, which preserve the organoid morphology in live status without any artificial perturbation and can be processed in real-time (necessary for assessing time-dependent responses).32.As a proof-of-concept in the application of drug screening, we demonstrated that PhaseFIT can reliably quantify the impacts of three chemical compounds on the organoid phenotypes, underscored by both qualitative and quantitative measures, exemplifies the successful implementation of PhaseFIT and attests to its ability to fulfill the requisite requirements. Specifically, DAPT increased the crypt size and increased the populations of ISCs and secretory cells. VPA and DOR showed impacts that were opposite to DAPT. These phenotypes were confirmed with the ground-truth images and are consistent with previous reportsAfter the FDA announced the removal of the mandatory animal testing requirement before commencing human trials for all drugs, we envision the future of in vitro research and acknowledge the immense scalability of organoid cultures. Herein, we present PhaseFIT, the first generative AI system designed to facilitate the transition from phase-contrast images to multipixel Immunofluorescence (IF) staining images. This development addresses the rapidly growing need for organoids in high-throughput analyses and enhances the utility of the extensive repositories of phase-contrast images available in the scientific community. The introduction of PhaseFIT could amplify and accelerate the application of organoids in the domain of drug discovery. Hence, it is poised to significantly propel advancement in these crucial research fields.35 for isolation of the intestinal crypts . In these mice, the LGR5 knock-in allele has mosaic expression in the intestine and expresses a fusion protein of EGFP and CreERT2. Thus, this model allows for visualization of ISCs and progenitor cells based on GFP expression. Animal studies were conducted in accordance with the National Institutes of Health “Guide for the Care and Use of Laboratory Animals”  and approved by the Institutional Animal Care and Use Committees (IACUC) of the Massachusetts General Hospital.We used Lgr5-EGFP-IRES-CreERT reporter miceg. The crypt pellets collected here were then used for seeding on the hydrogel.The proximal 12–15 cm small intestines were collected from 10 to 14 weeks old mice from either sex. The intestinal lumen was opened longitudinally. The mucous was removed using the blunt side of the blades. Then, the intestine was washed with ice-cold PBS without calcium and magnesium , and cut into 5 mm–1 cm fragments, and placed into 50 ml conical tubes that were filled with ice-cold 50 ml of PBS/EDTA . The fragments were incubated and shaken on ice for 40 min and washed with ice-cold 50 ml of PBS. Then, the fragments were vigorously shaken in 25 ml PBS and filtered twice through a 70 μm mesh  into a 50 ml conical tube to remove the villi and tissue pieces. The crypts were mainly in the suspension which were centrifuged for 5 min at 100×36. Briefly, the recipe for different Young’s modulus was 7.5% acrylamide and 0.034% bisacrylamide for 2.4 kPa. 0.1% ammonia persulfate  and 0.05% TEMED  were added to start the polymerization process. The polymerization required 40 min to 1 h. Then, sulfo-SANPAH  was used to activate the gel surface under a 15 W 365 nm UV  for 10 min. After the activation, 0.1 mg/ml type I collagen  was added onto the gel overnight to covalently attach to the gel surface for the organoid culture.The crypt pellets were suspended in the seeding media and counted using a cytometer to control the crypt density as 10,000/ml. 30 μl crypt suspension was added to a 96-well plate coated with a 2.4 kPa polyacrylamide (PA) hydrogel matrix. PA hydrogel was fabricated as previously describedFour hours after seeding the crypts, the floating cells/clusters were washed with PBS . 0.3 ml ENR  media/well was added and changed every other day. To make the ENR media, advanced DMEM/F12  was supplemented with 50 ng/ml EGF , 100 ng/ml Noggin , 10% R-spondin conditional media (iLab in Harvard digestive center), 1% Glutamax , 1% HEPES , 0.2% Primocin , 0.2% Normocin , 1% B27 , 0.5% N2 , and 1.25 mM N-Acetyl-Cystein . To make the seeding media, the ENR media was supplemented with 3 μM Chir-99021  and 10 μM Y-27632 . 10 µM DAPT in DMSO, 1 mM Valproic acid , and 5 µM Dorsomorphin  in DMSO were supplied as needed.+ ISCs, and UEA+ secretory cells  and Ulex Europaeus Agglutinin I  for 30 min. Immediately after the staining, the living cells were imaged using ×20 objective of EVOS M5000 with the phase channel and the DAPI, GFP, and TexRed light cubes . For training purposes, 50 wells (10 per mouse) were prepared for each channel, and 889 images (10 per well) were acquired. For the drug testing experiments, six wells (three per mouse) were prepared, and 18 images (three per well) were acquired for each channel. Crypt regions are featured with more dense nuclei than villus regions Fig. . In addills Fig. , Image-JTo maintain precise segmentation, the original images (source) and the corresponding masks (target) with a size of 1536*1024 pixels are cropped into smaller patches (256*256 pixels) with 50% overlapping  deep generative models for comparisons with the proposed method. The model families can be represented as conditional-GAN-based DeepLIIF37. To address this issue, we designed a novel deep generative network to output three individual modalities, DAPI (in blue), Lgr5 (in green), and UEA-I (in red), given a set of phase-contrast images  as inputs. As seen in Fig. 22). Additionally, the challenges of the noises of artifacts and implicitly sparse distributions of the stains, such as UEA-I, need to be considered. Therefore, we deployed a semantic segmentation strategy to operate the image-to-image generation task, which helps localize these key cell-related regions and generate the targets precisely.Although deep generative networks have shown remarkable success in generating high-quality data that is difficult to distinguish from real data, one of their key challenges is the ability to capture the complex structure and patterns of the data in a compact and efficient manner. Current deep generative networks can deal with the tasks of translating one possible representation of a scene into another. If the given scene contains additional noise information, the network could generate undesired output38. The SE model has two main components: the squeeze operation and the excitation operation. The squeeze operation applies a global pooling layer to the input feature map, reducing the spatial dimensions to a single value per channel. This captures channel-wise statistics and provides a global view of the feature distribution. In the excitation operation, the output of the squeeze operation passes through two fully connected layers. These layers learn a set of channel-wise weighting coefficients that rescale the original feature map. This amplifies the saliency of the most informative channels while suppressing the less relevant ones.This segmentation model is constructed using an improved U-Net framework by adding channel-wise attention to focus on these most contributing feature maps and spatial-wise attention to suppress redundant feature regions . Specifically, the encoder part uses the SE-ResNeXt50, which is obtained by incorporating the SE module into the ResNeXt50 architecture, enabling the network to learn adaptive channel-wise feature dependencies. The SE module is inserted between two convolutional layers in each ResNeXt block. ResNeXt50 is an extension of the ResNet architecture that utilizes grouped convolutions to enhance its representational capacity. SE-ResNeXt50 comprises five down-sampling stages to obtain hierarchical features, where each stage includes three convolution operations, three batch normalization modules, two ReLU operations, and a squeeze-and-excitation (SE) module38. The scSE module extends the SE module by incorporating both channel-wise and spatial-wise feature dependencies using two parallel branches. The first branch performs the original SE module’s channel-wise squeeze and excitation operations, while the second branch performs spatial squeeze and excitation operations. In the spatial squeeze operation, a global average pooling layer is applied along the channel axis to reduce the channel-wise feature maps to a single spatial value. This captures the spatial-wise statistics of the feature map, providing a global view of the spatial distribution. In the spatial excitation operation, weights are learned for each spatial location by applying two fully connected layers to the squeezed feature descriptor. These weights are then used to rescale the original feature map, emphasizing important spatial features and suppressing less important ones.The encoder generates high-level features and increases the feature diversity by reducing the spatial size of the feature matrix and increasing the number of feature channels/maps. These feature maps can be regarded as different views obtained from different perspectives of the image representation. The SE module is used to impose more weight on the feature maps that contribute the most. The decoder part consists of five up-sampling stages to recover image resolution, where each network block includes two sequential convolution, batch normalization, and ReLU operations, as well as a concurrent spatial and channel squeeze & excitation (scSE) moduleAt each decoder stage, it combines these features at the corresponding encoder level using skip connection (feature concatenation). Then, the features eventually used for prediction are concatenated from each decoder stage to form the fused features as shown in Fig. Therefore, a feature map selection strategy is also necessary. In addition, the goal of the decoder is to gradually increase the feature size to obtain the final segmented image (target image). Thus, feature selection in the spatial view is also an important strategy. Based on the above considerations, we use the scSE module to combine channel-wise and spatial-wise concerns to focus on these regions with distinct appearances, which helps better segment the details.39. The focal loss applies larger weights to these hard-to-distinguish samples and conversely, smaller weights to these easy-to-distinguish samples. The Dice loss is used to measure the degree of overlap between two sets/regions. The two loss functions are calculated as follows:The segmentation loss function combines the focal loss (1) and Dice loss (2), which have been shown to better handle data imbalance problemsith pixels in the training image, yi represents its ground truth label and For the 15.The model predicts the probability for each pixel of the tile image. These predicted tiles are then merged to generate segmented images. To obtain the target color images , the segmented images are in-painted by multiplying each pixel probability by 255. After the model outputs the inferences on each tile image, a context-aggregated image inpainting module is applied to smooth the tile concatenation using a binary mask, where non-zero pixels correspond to the division line between each tile pair. We use the aggregated contextual transformation (AOT) block to handle the spatial merging process−1 with a GPU to complete the virtual painting on a 96-well assay.All experiments in this study were constructed using a 5-fold cross-validation strategy, where four folds of the data were used for model training and one held-out fold was reserved for validation purposes. PhaseFIT model was trained using the Adam optimizer with an initial learning rate of 0.0003. The learning rate was then reduced by a factor of 10 at the 10th and 20th epochs. To increase sample diversity and mitigate overfitting, a data augmentation strategy was implemented. This strategy included random cropping, horizontal flip, vertical flip, random scaling, brightness change, contrast change, and Gauss noise change. Furthermore, a dropout layer with a probability of 0.5 was incorporated into the model architecture. The mini-batch size and the number of epochs were set to 24 and 30, respectively. All experiments were implemented in PyTorch and performed on a workstation equipped with four 32 GB NVIDIA Tesla v100 GPU cards. It takes <1 s mmThe Cycle-GAN and Conditional GAN (cGAN) used in this study as a comparison, were trained using their official implementations. Both the generator and discriminator in the cGAN and Cycle-GAN models were trained using the Adam optimizer with an initial learning rate of 0.0002 for a total of 200 epochs. The mini-batch size is set as 32. Unlike the PhaseFIT model, the GAN model takes the entire images as inputs (without clipping) to ensure better global image translation. To achieve this, we filled the input images to make them square by using the edge pixels. These filled edges were subsequently removed in the final prediction results.29 were evaluated for comparing the similarity between the virtual painting images and the ground truth images . The score ‘1’ in the Dice and Recall test means the highest similarity between the ground truth group and the virtual stain group. The lower is the score, the poorer is the similarity. Since most of the pixels in single-channel IF images are in black, these two metrics can robustly reflect both pixel-level and microenvironment-level virtual painting fidelity. We classified each pixel in the generated images into categories of true positive (TP), false positive (FP), and false negative (FN) based on the overlap with the original fluorescent image (GT). A pixel was considered TP if it was present in the GT and its grayscale color intensity differed by no more than 2 levels (0–255) from the GT. As all three models were trained and tested on the same dataset, we conducted a paired Wilcoxon rank-sum test to calculate the p-value for the statistical analysis. All the evaluations were implemented in the Python Programming Language. We used scikit-image/skimage for SSIM, SciPy for Dice coefficient, and scikit-learn/sklearn for mean squared error, respectively.To create a fair comparison, we adopted cross-validation and partitioned the dataset into five folds. In each repetition, four folds were used for training PhaseFIT and the other two GAN models, and one fold served as the held-out test set that was never seen by the models. Four quantitative measures i.e., Dice, Recall (sensitivity), SSIM scores, and pixel-wise mean squared error


The average age of the surgical population continues to increase, as does prevalence of long-term diseases. However, outcomes amongst multi-morbid surgical patients are not well described.We included adults undergoing non-obstetric surgical procedures in the English National Health Service between January 2010 and December 2015. Patients could be included multiple times in sequential 90-day procedure spells. Multi-morbidity was defined as presence of two or more long-term diseases identified using a modified Charlson comorbidity index. The primary outcome was 90-day postoperative death. Secondary outcomes included emergency hospital readmission within 90 days. We calculated age- and sex-adjusted odds ratios (OR) with 95% confidence intervals (CI) using logistic regression. We compared the outcomes associated with different disease combinations.We identified 20 193 659 procedure spells among 13 062 715 individuals aged 57 (standard deviation 19) yr. Multi-morbidity was present among 2 577 049 (12.8%) spells with 195 965 deaths (7.6%), compared with 17 616 610 (88.2%) spells without multi-morbidity with 163 529 deaths (0.9%). Multi-morbidity was present in 1 902 859/16 946 808 (11.2%) elective spells, with 57 663 deaths , and 674 190/3 246 851 (20.7%) non-elective spells, with 138 302 deaths . Emergency readmission followed 547 399 (22.0%) spells with multi-morbidity compared with 1 255 526 (7.2%) without. Multi-morbid patients accounted for 57 663/114 783 (50.2%) deaths after elective spells, and 138 302/244 711 (56.5%) after non-elective spells. The rate of death varied five-fold from lowest to highest risk disease pairs.One in eight patients undergoing surgery have multi-morbidity, accounting for more than half of all postoperative deaths. Disease interactions amongst multi-morbid patients is an important determinant of patient outcome. HES captures data related to hospital admissions with linkage infrastructure enabling longitudinal mapping.,We included adults (aged ≥18 yr) undergoing surgical procedures defined according to previously described OPCS 4.7 procedure code lists which we previously refined to remove minor and non-surgical procedures.We classified each procedure spell as elective or non-elective (including emergency admissions and transfers) using admission method codes for the first hospital episode. Procedure spells were categorised as in-patient or day-case using patient class. Age is reported in completed years at the start of spell. We grouped OPCS 4.7 codes based on anatomical location as described previously.Multi-morbidity was identified using an algorithm to categorise different combinations of diseases. Individual diseases were defined using an adapted version of the Charlson comorbidity index based on ICD-10 diagnostic codes.The primary outcome was death within 90 days after the date of surgery. Date of death was captured from Office for National Statistics civil registration death records. This was linked by NHS Digital to civil registration death data using a patient level identifier. Secondary outcome measures were length of hospital stay, and emergency hospital readmission within 90 days of surgery.Continuous variables are presented as mean (standard deviation) or median (inter-quartile range). Categorical variables are presented as number (percentage). We present crude and adjusted rates of death, 90-day hospital readmission, and length of hospital stay. The crude rate of death used the number of spells as the denominator. The crude rate of hospital readmission used the number of spells where the patient was discharged alive after index surgery as the denominator. Adjustment was performed for age and sex using logistic regression models. We adjusted length of stay using multivariable negative binomial models. We describe disease combinations as dyads (two diseases), triads (three diseases), and quads (four diseases). We extracted all combinations of diseases for all patients, stratified by admission grouping (elective or non-elective). We included only disease combinations with a prevalence of more than one per 2000 procedure spells, which equates to around one spell per month for each acute NHS trust in England.The relative risk of death for each individual disease present for a multi-morbid patient was calculated by comparing the age-standardised rate of death for each disease combination, to the single disease in combination with any other disease. For example, for chronic kidney disease and diabetes, the risk ratio compares this with the age-adjusted rate of death associated with chronic kidney disease when at least one other disease of any type is also present. We standardised by age directly using ONS mid-year estimates of the population in 2012.post hoc sensitivity analysis. We calculated the age-standardised rate of 90-day death associated with each procedure for all patients in the cohort, stratified by elective or non-elective surgery. Each procedure was identified by three-character OPCS codes. We identified some rare (n<50) procedures so used the rate of 90-day death associated with the two-character version of the code. High-risk procedures were defined as those with a crude rate of 90-day death of ≥5%. We present the prevalence of high-risk procedures amongst multi-morbid and non-multi-morbid patients. We included procedure-related rate of death as a term in the overall model of the association between multi-morbidity and death at 90 days. We added the procedure-related rate of death to the logistic regression model as a continuous variable for each dyad comparison to measure how this altered the association with death. To explore potential cluster effects, we created a mixed-effects model in a random sample of 10% of the cohort. Hospital providers were included as a random intercept, and the presence of multi-morbidity as a slope varying with hospital providers , sex, surgical setting (elective or non-elective), procedure-associated harm (modelled as a restricted cubic spline with three knots), and the presence of multi-morbidity. We estimated the population attributable fraction of death associated with multi-morbidity by dividing the number of deaths expected if multi-morbidity were not present in the model by the number of actual deaths.We identified 20 193 659 surgical procedure spells amongst 13 062 715 patients with a mean age of 57.3 (19.2) yr , Fig. 1.n=114 783), and 56.5% of all deaths after non-elective surgery (n=244 711).Outcomes data are presented in The most frequent dyad of diseases across both elective and non-elective surgery was respiratory disease and diabetes mellitus. Among elective spells, the prevalence of this dyad was 2.2% (372 587 of 16 946 808) and among non-elective spells the prevalence was 3.0% (97 179 of 3 246 851). The prevalence of all disease combinations was greater amongst non-elective than elective spells, and for identical disease combinations, patients undergoing non-elective surgery were older . Common The dyad with the highest rate of death within 90 days of elective surgery was cardiac failure and liver disease . The dyad with the highest rate of death within 90 days of non-elective surgery was cardiac failure and cancer  . Among eAmong elective spells, we identified 92 combinations of between two and four diseases with a rate of death exceeding 1 in 20. These are listed in To identify which diseases promoted poor outcomes in combination with other diseases, we compared all dyads. We present the risk of death associated with each dyad, stratified by elective or non-elective surgery, in There was a significant interaction between multi-morbidity and age which was non-linear . We examWhen we included provider codes as random intercepts, there was a reduction in the strength of association between multi-morbidity and outcomes . For exaAround one in five patients had more than one surgical spell. We randomly selected one surgical spell per patient; the association with 90-day death was unchanged (OR: 4.96 [95% CI: 4.95–5.03]).When accounting for the presence of multi-morbidity, age, sex, procedural risk, and surgical admission type, the OR for death was 5.58 [5.44–5.73] . Using tThe principal finding of this study is that one in eight patients presenting for surgery have multi-morbidity, of whom one in 13 dies within 90 days . A high-Our findings align with previous studies of multi-morbidity among older surgical patients using large administrative healthcare datasets in other countries. Coagulopathy, electrolyte disturbances, and liver disease were identified in a study of 280 000 patients in the USA.We found it challenging to identify consistent patterns of multi-morbidity that were associated with a high burden of death and disability. This difficulty has been encountered by researchers exploring multi-morbidity in other domains.Earlier studies focussed on emergency surgery, we included both elective and non-elective patients.,,One potential confounder of the association between multi-morbidity and death after surgery is the nature of the required procedure. For example, patients with a history of cancer may require a more major procedure (such as bowel resection) than those without cancer. We found procedures performed among multi-morbid patients were more often high risk than those performed on non-multi-morbid patients. Some patients with multi-morbidity may not be offered low-risk surgeries given the perceived adverse balance in risk of perioperative complications and patient benefit.,This analysis has strengths and limitations. We included all patients undergoing surgery performed in, or funded by, the English NHS over a 5-yr period. By using distinct surgical spells, we were able to use information about multiple procedures for each patient which increased the available data size. Some patients may have had repeat procedures beyond our 90-day window for the same indication, however including these is reasonable as they represent new operations. Most prior studies include the first procedure for each patient, which overlooks outcomes of subsequent procedures. Our approach facilitated exploration of relatively rare disease combinations. Some patients may be considered ‘surgically multi-morbid’ if they have had multiple procedures. These patients may have extra considerations, such as the risk of allergic reaction from repeat exposure to neuromuscular blockade agents.One in eight patients presenting for surgery have multi-morbidity. These patients account for more than half of all deaths within 90 days of surgery. More than one in five multi-morbid patients require hospital readmission within 90 days of surgery. Patients requiring non-elective surgery have a higher prevalence of multi-morbidity, and experience worse outcomes. However, we could not identify simple rules that define high-risk multi-morbidity, but counting diseases overlooks important interactions between them. Strategies are required to improve care for multi-morbid patients around the time of surgery. Enhanced recovery after surgery pathways may represent one approach. However, multi-morbid patients may undergo surgeries outside the limited settings where this has demonstrated benefit. Identification of patients with high-risk disease combinations may facilitate stratification to high-risk pathways or enrich randomised trials of perioperative interventions to improve their value.AF affirms the manuscript is an accurate and transparent account of the study being reported. No important aspects of the study have been omitted.HES data were made available by NHS Digital . Approvals for the use of anonymised HES data were obtained as part of the standard approval process. It is not possible to share raw patient-level data without approvals from NHS Digital.A patient with lived experience of major surgery was consulted in the design and interpretation of this study.Study design: AJF, JP, RPData collection and analysis: AJF, HW, DCInterpretation: all authorsWriting the first draft of the manuscript: AJFRevision for important intellectual content and approved the final version: all authorsAccess to the data and act as guarantors: AJF, DCThis study uses data provided by patients and collected by the NHS as part of their care. We would like to acknowledge and thank all involved in the generation and collection of these data.British Journal of Anaesthesia and the British Journal of Surgery. TEFA holds a National Institute for Health Research Clinical Lectureship, is a member of the editorial board of the British Journal of Anaesthesia, has received research grants from the National Institute for Academic Anaesthesia and Barts Charity, and has received consultancy fees from Merck Sharp & Dohme (MSD) within the past 5 yr unrelated to this work. DC is an associate editor of the Journal of Health Services Research & Policy.AJF holds a National Institute for Health Research Doctoral Research fellowship (DRF-2018-11-ST2-062). MAHW and JP report no relevant conflicts of interest. RP has received honoraria, research grants, or both from Edwards Lifesciences, Intersurgical, and GlaxoSmithKline within the past 5 yr and holds editorial roles with the National Institute for Health and Care Research (NIHR) Doctoral Research Fellowship (DRF-2018-11-ST2-062) to AJF. The funding source had no role in the study design, data collection, analysis, interpretation, or writing the report.


Currently, the main pharmaceutical intervention for COVID-19 is vaccination. While antidepressant (AD) drugs have shown some efficacy in treatment of symptomatic COVID-19, their preventative potential remains largely unexplored. Analysis of association between prescription of ADs and COVID-19 incidence in the population would be beneficial for assessing the utility of ADs in COVID-19 prevention.Retrospective study of association between AD prescription and COVID-19 diagnosis was performed in a cohort of community-dwelling adult mental health outpatients during the 1st wave of COVID-19 pandemic in the UK. Clinical record interactive search (CRIS) was performed for mentions of ADs within 3 months preceding admission to inpatient care of the South London and Maudsley (SLaM) NHS Foundation Trust. Incidence of positive COVID-19 tests upon admission and during inpatient treatment was the primary outcome measure.AD mention was associated with approximately 40% lower incidence of positive COVID-19 test results when adjusted for socioeconomic parameters and physical health. This association was also observed for prescription of ADs of the selective serotonin reuptake inhibitor (SSRI) class.This preliminary study suggests that ADs, and SSRIs in particular, may be of benefit for preventing COVID-19 infection spread in the community. The key limitations of the study are its retrospective nature and the focus on a mental health patient cohort. A more definitive assessment of AD and SSRI preventative potential warrants prospective studies in the wider demographic. More than 3 years since the declaration of the global pandemic, COVID-19 remains a major public health concern across the world. In the beginning of the pandemic, the main strategy for limiting COVID-19 spread in the population necessarily relied on non-pharmaceutical interventions of variable effectiveness, including individual measures such as personal protective equipment and social distancing, as well as society-wide restrictions such as lockdowns , level oOne potentially promising approach involves repurposing of previously characterised drugs –8. NotwiStudies in cell-based models indicate that ADs may target cell biological mechanisms implicated in early stages of SARS-CoV-2 infection, hinting at the potential prophylactic effect of ADs –24. To iWe conducted an observational, retrospective, matched cohort study of individuals admitted to the 4 inpatient care units  affiliated with the South London and Maudsley (SLaM) NHS Foundation Trust during the 1st wave of the COVID-19 pandemic of 2020. SLaM provides near-monopoly comprehensive mental health services to a geographic catchment of 1.3 million residents in four boroughs of south London. SLaM has used electronic health records across all its services since 2006 and its Clinical Record Interactive Search (CRIS) platform was set up in 2008 to provide researcher access to de-identified data from these records within a robust governance infrastructure. CRIS has been subsequently developed through a range of data linkages and natural language processing (NLP) algorithms , and theFor exposure, we used a natural language processing (NLP) algorithm to identify the medications mentioned in the patient’s record in a 6-month window before or after referral, which provides a validated proxy measure for drug receipt . The lisThe primary outcome measure was the result of the laboratory test for COVID-19 (antigen or PCR). Any incidence of a positive COVID-19 test result during the inpatient stay was categorised as ‘positive’. For categorisation as ‘negative,’ all COVID test results during inpatient stay were required to be negative.We ascertained age at the time of admission, gender, and ethnicity (dichotomised to white and non-white) as recorded at the time of hospital admission. We identified the diagnosis given closest to hospital admission in structured fields. According to the International Statistical Classification of Diseases and Related Health Problems (WHO ICD-10)  criteriaF01-F09: Mental disorders due to known physiological conditions;F10-F19: Mental and behavioural disorders due to psychoactive substance use;F20-F29: Schizophrenia, schizotypal, delusional, and other non-mood psychotic disorders;F30-F39: Mood [affective] disorders;F40-F48: Anxiety, dissociative, stress-related, somatoform and other nonpsychotic mental disorders;F50-F59: Behavioural syndromes associated with physiological disturbances and physical factors;F60-F69: Disorders of adult personality and behaviour;F70-F79: Intellectual disabilities;F80-F89: Pervasive and specific developmental disorders;F90-F98: Behavioural and emotional disorders with onset usually occurring in childhood and adolescence.Mental and physical health problems as well as functional difficulties were scored using the Health of the Nation Outcome Scales (HoNOS) . This isχ2 tests and Z-score statistics were used to analyse COVID-19 test results for each covariate. Logistic regression models were then assembled to quantify odds ratios (ORs) for the associations between antidepressant medication receipt and incidence of COVID-19 positive test result, applying the above sub-categorisation of antidepressants and timing as secondary analyses. 95% confidence intervals (CI) and P-values for ORs were calculated, and P-values < 0.05 were considered statistically significant. Initially unadjusted logistic regression analyses were carried out, followed by adjustments for sociodemographic factors  and then further adjustments for those significant HoNoS subscales and primary mental health diagnosis. Finally, primary analyses (only patients receiving any antidepressant medication) were stratified by primary mental health diagnosis measured using ICD-10 diagnosis at the time of index hospital admission. All statistical analyses were conducted with STATA version15 [Initially, ersion15 .18. During the study period (1 April–31 December 2020), 5664 cases of mental health inpatient care admission at SLaM facilities had been tested for COVID-19, with 202 (3.56%) testing positive. Characteristics of the study cohort are presented in Table We leveraged data from the CRIS platform, which provides research access to deidentified electronic clinical records for SLaMp = 0.000663, χ2 test). Most prescribed ADs belonged to the SSRI class, in line with their prevalence in treatment of major depressive disorder [p = 0.002853, χ2 test). Associations with other AD classes were not statistically significant.We then queried CRIS for mentions of ADs in the clinical records of patients within the time period of 90 days preceding the date of admission. The list of drugs included in the query is presented in Table disorder : two thiTo further investigate the relationship between AD/SSRI and COVID-19 test results, we performed multiple logistic regression analysis Table . We founThe results of this study indicate that prior receipt of ADs and specifically SSRIs in community-dwelling mental health outpatients was associated with a decreased likelihood of COVID-19 incidence, suggesting that these drugs may have a protective effect against COVID-19 in this population. A key strength of this study is the large number of participants. Furthermore, the focus on the 1st wave of COVID-19 avoids the confounding effects of the ‘herd immunity’ in the population due to mass vaccination and/or previous exposure to COVID-19 in the following time period . ImportaSSRI treatment is associated with a dropout rate of 28% over the standard 6-month treatment course . Given tThe mechanisms underlying the putative protective effects of SSRIs in COVID-19 remain unclear. Some of the proposed cell biology mechanisms include blockade of viral replication , 23, modThe study has a number of important limitations, largely due to its retrospective nature and focus on a cohort of mental health patients. It cannot demonstrate a causal relationship between medications and COVID-19 test results; indeed, the protective effect is consistent with either decreased infection rate or increased recovery rate from COVID-19.The time period of the study was limited to the first wave of the COVID-19 pandemic, which presented novel adverse effects on mental health ; also, dFinally, although the association of interest appeared to be present across a range of diagnostic groups, the possibility cannot be ruled out that antidepressant use may have been a marker of personal or behavioural factors conferring protection, e.g. compliance with societal restrictions and/or personal protection measures in place at the time. To address the above limitations and to further corroborate the findings of this study, randomised prospective clinical studies for selected ADs in the general population will be of essence.Our results suggest that ADs, and SSRIs in particular, may provide a degree of protection against COVID-19, specifically SARS-CoV-2 infection. This evidence lends some support to further investigation of drug repurposing as an complementary strategy to vaccination in appropriate contexts, especially considering the key advantages of ADs viz. well-characterised safety profile, low price, and ready availability. Identification of affordable and safe drugs that reduce the risk of COVID-19 is likely to be relevant for the global pandemic response, particularly in cases and situations where mass vaccination may be problematic.In the longer term, there may be merit in investigating the utility of ADs/SSRIs for treatment of other respiratory infections using similar cell biological mechanisms to COVID-19, e.g. influenza . For now