Explore the vast range of titles available.

Coronavirus Disease 2019 is a pandemic that is straining healthcare resources, mainly hospital beds. Multiple risk factors of disease progression requiring hospitalization have been identified, but medical decision-making remains complex. To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. The final model was displayed as a nomogram and programmed into an online risk calculator. One healthcare system in Ohio and Florida. All patients infected with SARS-CoV-2 between March 8, 2020 and June 5, 2020. Those tested before May 1 were included in the development cohort, while those tested May 1 and later comprised the validation cohort. Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. 4,536 patients tested positive for SARS-CoV-2 during the study period. Of those, 958 (21.1%) required hospitalization. By day 3 of hospitalization, 24% of patients were transferred to the intensive care unit, and around half of the remaining patients were discharged home. Ten patients died. Hospitalization risk was increased with older age, black race, male sex, former smoking history, diabetes, hypertension, chronic lung disease, poor socioeconomic status, shortness of breath, diarrhea, and certain medications (NSAIDs, immunosuppressive treatment). Hospitalization risk was reduced with prior flu vaccination. Model discrimination was excellent with an area under the curve of 0.900 (95% confidence interval of 0.886-0.914) in the development cohort, and 0.813 (0.786, 0.839) in the validation cohort. The scaled Brier score was 42.6% (95% CI 37.8%, 47.4%) in the development cohort and 25.6% (19.9%, 31.3%) in the validation cohort. Calibration was very good. The online risk calculator is freely available and found at https://riskcalc.org/COVID19Hospitalization/. Retrospective cohort design. Our study crystallizes published risk factors of COVID-19 progression, but also provides new data on the role of social influencers of health, race, and influenza vaccination. In a context of a pandemic and limited healthcare resources, individualized outcome prediction through this nomogram or online risk calculator can facilitate complex medical decision-making.

The eighth edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) Staging Manual has been updated and improved to ensure the highest degree of clinical relevance and to improve its utility for patient evaluation and clinical research. Major changes include: 1) pathologically organ-confined disease is now considered pT2 and is no longer subclassified by extent of involvement or laterality, 2) tumor grading now includes both the Gleason score (as in the seventh edition criteria) and the grade group (introduced in the eighth edition criteria), 3) prognostic stage group III includes select, organ-confined disease based on prostate-specific antigen and Gleason/grade group status, and 4) 2 statistical prediction models are included in the staging manual. The AJCC will continue to critically analyze emerging prostate cancer biomarkers and tools for their ability to prognosticate and guide treatment decision making with the highest level of accuracy and confidence for patients and physicians.

AimPredicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers.MethodsThis is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net reclassification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of ≥5 ml/min per year, ≥40% sustained decline, or kidney failure within 5 years.ResultsIn 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min−1 [1.73 m]−2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol, follow-up was 4.3 years and 21% had the composite endpoint. On cross-validation in derivation (n = 686), KidneyIntelX had an AUC of 0.77 (95% CI 0.74, 0.79). In validation (n = 460), the AUC was 0.77 (95% CI 0.76, 0.79). By comparison, the AUC for the clinical model was 0.62 (95% CI 0.61, 0.63) in derivation and 0.61 (95% CI 0.60, 0.63) in validation. Using derivation cut-offs, KidneyIntelX stratified 46%, 37% and 17% of the validation cohort into low-, intermediate- and high-risk groups for the composite kidney endpoint, respectively. The PPV for progressive decline in kidney function in the high-risk group was 61% for KidneyIntelX vs 40% for the highest risk strata by KDIGO categorisation (p < 0.001). Only 10% of those scored as low risk by KidneyIntelX experienced progression (i.e., NPV of 90%). The NRIevent for the high-risk group was 41% (p < 0.05).ConclusionsKidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD.

Despite advances in cancer genomics, radiotherapy is still prescribed on the basis of an empirical one-size-fits-all paradigm. Previously, we proposed a novel algorithm using the genomic-adjusted radiation dose (GARD) model to personalise prescription of radiation dose on the basis of the biological effect of a given physical dose of radiation, calculated using individual tumour genomics. We hypothesise that GARD will reveal interpatient heterogeneity associated with opportunities to improve outcomes compared with physical dose of radiotherapy alone. We aimed to test this hypothesis and investigate the GARD-based radiotherapy dosing paradigm. We did a pooled, pan-cancer analysis of 11 previously published clinical cohorts of unique patients with seven different types of cancer, which are all available cohorts with the data required to calculate GARD, together with clinical outcome. The included cancers were breast cancer, head and neck cancer, non-small-cell lung cancer, pancreatic cancer, endometrial cancer, melanoma, and glioma. Our dataset comprised 1615 unique patients, of whom 1298 (982 with radiotherapy, 316 without radiotherapy) were assessed for time to first recurrence and 677 patients (424 with radiotherapy and 253 without radiotherapy) were assessed for overall survival. We analysed two clinical outcomes of interest: time to first recurrence and overall survival. We used Cox regression, stratified by cohort, to test the association between GARD and outcome with separate models using dose of radiation and sham-GARD (ie, patients treated without radiotherapy, but modelled as having a standard-of-care dose of radiotherapy) for comparison. We did interaction tests between GARD and treatment (with or without radiotherapy) using the Wald statistic. Pooled analysis of all available data showed that GARD as a continuous variable is associated with time to first recurrence (hazard ratio [HR] 0·98 [95% CI 0·97–0·99]; p=0·0017) and overall survival (0·97 [0·95–0·99]; p=0·0007). The interaction test showed the effect of GARD on overall survival depends on whether or not that patient received radiotherapy (Wald statistic p=0·011). The interaction test for GARD and radiotherapy was not significant for time to first recurrence (Wald statistic p=0·22). The HR for physical dose of radiation was 0·99 (95% CI 0·97–1·01; p=0·53) for time to first recurrence and 1·00 (0·96–1·04; p=0·95) for overall survival. The HR for sham-GARD was 1·00 (0·97–1·03; p=1·00) for time to first recurrence and 1·00 (0·98–1·02; p=0·87) for overall survival. The biological effect of radiotherapy, as quantified by GARD, is significantly associated with time to first recurrence and overall survival for patients with cancer treated with radiation. It is predictive of radiotherapy benefit, and physical dose of radiation is not. We propose integration of genomics into radiation dosing decisions, using a GARD-based framework, as the new paradigm for personalising radiotherapy prescription dose. None. [Display omitted]

We derive and test a CT-based biological age model for predicting longevity, using an automated pipeline of explainable AI algorithms that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. We apply these AI tools to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women; median follow-up, 5.3 years). The final weighted CT biomarker selection was based on the index of prediction accuracy. The CT model significantly outperforms standard demographic data for predicting longevity (IPA = 29.2 vs. 21.7; 10-year AUC = 0.880 vs. 0.779; p  < 0.001). Age- and sex-corrected survival hazard ratio for the highest-vs-lowest risk quartile was 8.73 (95% CI,8.14-9.36) for the CT biological age model, and increased to 24.79 after excluding cancer diagnoses within 5 years of CT. Muscle density, aortic plaque burden, visceral fat density, and bone density contributed the most. Here we show a personalized phenotypic CT biological age model that can be opportunistically-derived, regardless of clinical indication, to better inform risk assessment. The authors develop a model derived from an automated pipeline of explainable AI body composition tools applied to abdominal CT. They provide a tool for the personalized phenotypic assessment of biological aging that can be opportunistically derived, regardless of clinical indication.

The American Joint Committee on Cancer (AJCC) has increasingly recognized the need for more personalized probabilistic predictions than those delivered by ordinal staging systems, particularly through the use of accurate risk models or calculators. However, judging the quality and acceptability of a risk model is complex. The AJCC Precision Medicine Core conducted a 2-day meeting to discuss characteristics necessary for a quality risk model in cancer patients. More specifically, the committee established inclusion and exclusion criteria necessary for a risk model to potentially be endorsed by the AJCC. This committee reviewed and discussed relevant literature before creating a checklist unique to this need of AJCC risk model endorsement. The committee identified 13 inclusion and 3 exclusion criteria for AJCC risk model endorsement in cancer. The emphasis centered on performance metrics, implementation clarity, and clinical relevance. The facilitation of personalized probabilistic predictions for cancer patients holds tremendous promise, and these criteria will hopefully greatly accelerate this process. Moreover, these criteria might be useful for a general audience when trying to judge the potential applicability of a published risk model in any clinical domain.

Abstract Background and Aims Crohn’s disease (CD) is a chronic illness that affects both the pediatric and adult populations with an increasing worldwide prevalence. We aim to identify a large, single-center cohort of patients with CD using natural language processing (NLP) in combination with codified data and extract surgical rates and medication usage from the electronic medical record (EMR). Methods Patients with CD were identified from the entire Cleveland Clinic EMR using ICD codes and CD-specific terms identified by NLP to fit a logistic regression model. Cohorts were developed for pediatric-onset (younger than 18 years) and adult-onset (18 years and older) CD. Surgeries were identified using current procedural terminology (CPT) codes and NLP. Crohn’s disease–related medications were extracted using physician orders in the EMR. Results Patients with pediatric-onset (n = 2060) and adult-onset (n = 4973) CD were identified from 2000 to 2017 with a positive predictive value of 98.5%. Rate of CD-related abdominal surgery over time was significantly higher in adult-onset compared with pediatric-onset CD (10-year surgery rate 49.9% vs 37.7%, respectively; P < 0.001). Treatment with biologics was significantly higher in pediatric vs adult-onset CD cohorts (63.6% vs 49.2%; P < 0.001). The overall rate of CD-related abdominal surgery was significantly higher in those who received <6 months of a biologic compared with ≥6 months of a biologic for both cohorts (pediatric 64.1% vs 39.1%, P ≤ 0.001; adult 69.3% vs 56.5%, P ≤ 0.001). Additionally, 60.9% in pediatric-onset CD and 43.5% in adult-onset CD treated with ≥6 months of biologic therapy have not required abdominal surgery. On multivariable analysis, perianal surgery was a significant risk factor for abdominal surgery in both cohorts. Conclusion We used a combination of codified and NLP data to establish the largest, North American, single-center EMR cohort of pediatric- and adult-onset CD patients and determined that biologics are associated with lower rates of surgery over time, potentially altering the natural history of the disease.

Half of patients who have resective brain surgery for drug-resistant epilepsy have recurrent postoperative seizures. Although several single predictors of seizure outcome have been identified, no validated method incorporates a patient's complex clinical characteristics into an instrument to predict an individual's post-surgery seizure outcome. We developed nomograms to predict complete freedom from seizures and Engel score of 1 (eventual freedom from seizures allowing for some initial postoperative seizures, or seizures occurring only with physiological stress such as drug withdrawal) at 2 years and 5 years after surgery on the basis of sex, seizure frequency, secondary seizure generalisation, type of surgery, pathological cause, age at epilepsy onset, age at surgery, epilepsy duration at time of surgery, and surgical side. We designed the models from a development cohort of patients who had resective surgery at the Cleveland Clinic (Cleveland, OH, USA) between 1996 and 2011. We then tested the nomograms in an external validation cohort operated on over a similar period in four epilepsy surgery centres, in Brazil, France, Italy, and the USA. We assessed performance of the nomogram by calculating concordance statistics and assessing the calibration of predicted freedom from seizures with the reported freedom from seizures and Engel score of 1. The development cohort included 846 patients and the validation cohort included 604 patients. Variables included in the nomograms were sex, seizure frequency, secondary seizure generalisation, type of surgery, and pathological cause. In the development cohort, the baseline risk of complete freedom from seizures was 0·57 at 2 years and 0·40 at 5 years. The baseline risk of Engel score of 1 was 0·69 at 2 years and 0·62 at 5 years. In the validation cohort, the models had a concordance statistic of 0·60 for complete freedom from seizures and 0·61 for Engel score of 1. Calibration curves showed adequate calibration (judged by eye) of predicted and reported freedom from seizures, throughout the range of seizure outcomes. If validated in prospective cohorts, these nomograms could be used to predict seizure outcomes in patients who have been judged eligible for epilepsy surgery. Cleveland Clinic Epilepsy Center.

To understand the consequences of functional cardiac stress testing among patients considering noncardiac nonophthalmologic surgery. A retrospective cohort study of 118,552 patients who made 159,795 visits to a dedicated preoperative risk assessment and optimization clinic between 2008 and 2018. A large integrated health system. Patients who visited a dedicated preoperative risk assessment and optimization clinic before noncardiac nonophthalmologic surgery. To assess changes to care delivered, we measured the probability of completing additional cardiac testing, cardiac surgery, or noncardiac surgery. To assess outcomes, we measured time-to-mortality and total one-year mortality. In causal inference models, preoperative stress testing was associated with increased likelihood of coronary angiography (relative risk: 8.6, 95% CI 6.1–12.1), increased likelihood of percutaneous coronary intervention (RR: 4.1, 95% CI: 1.8–9.2), increased likelihood of cardiac surgery (RR: 6.8, 95% CI 4.9–9.4), decreased likelihood of noncardiac surgery (RR: 0.77, 95% CI 0.75–0.79), and delayed noncardiac surgery for patients completing noncardiac surgery (mean 28.3 days, 95% CI: 23.1–33.6). The base rate of downstream cardiac testing was low, and absolute risk increases were small. Stress testing was associated with higher mortality in unadjusted analysis but was not associated with mortality in causal inference analyses. Preoperative cardiac stress testing likely induces coronary angiography and cardiac interventions while decreasing use of noncardiac surgery and delaying surgery for patients who ultimately proceed to noncardiac surgery. Despite changes to processes of care, our results do not support a causal relationship between stress testing and postoperative mortality. Analyses of care cascades should consider care that is avoided or substituted in addition to care that is induced. •Cardiac stress testing probably leads to higher rates of cardiac testing and interventions (a “care cascade”)•However, stress testing also decreases the likelihood that patients will complete surgery•Stress testing probably does not change mortality in the short- or long-term•Health datasets rarely capture care that is considered but not completed; many care cascades might instead be substitutions

Background Lymphedema (LE) after axillary lymph node dissection (ALND) is a multifactorial, chronic, and disabling condition that currently affects an estimated 4 million people worldwide. Although several risk factors have been described, it is difficult to estimate the risk in individual patients. We therefore developed nomograms based on a large data set. Methods Clinicopathologic features were collected from a prospective cohort comprising 1,054 women with unilateral breast cancer undergoing ALND as part of their surgical treatment from August 2001 to November 2002. LE was defined as a volume difference of at least 200 ml between arms at 6 months or more after surgery. The cumulative incidence of LE was ascertained by the Kaplan–Meier method, and Cox proportional hazard models were used to predict the risk of developing LE on the basis of the available data at each time point: model 1, preoperatively; model 2, within 6 months from surgery; and model 3, at 6 months or later after surgery. Results The 5 year cumulative incidence of LE was 30.3%. Independent risk factors for LE were age, body mass index, ipsilateral arm chemotherapy infusions, level of ALND, location of radiotherapy field, development of postoperative seroma, infection, and early edema. When applied to the validation set, the concordance indices were 0.706, 0.729, and 0.736 for models 1, 2, and 3, respectively. Conclusions The proposed nomograms can help physicians and patients predict the 5 year probability of LE after ALND for breast cancer. Free online versions of the nomograms are available at http://www.lymphedemarisk.com/ .