OBJECTIVE: To demonstrate applying American Association for Thoracic Surgery Quality Gateway (AQG) outcomes models to a Surgeon Case Study of quality assurance in adult cardiac surgery.
METHODS: The case study includes 6,989 cardiac and thoracic aorta operations performed in adults at Cleveland Clinic by one surgeon from 2001 to 2023. AQG models were used to predict expected probabilities for operative mortality and major morbidity, and to compare hospital outcomes, surgery type, risk profile, and individual risk-factor levels using virtual (digital) twin causal inference. These models were based on postoperative procedural outcomes after 52,792 cardiac operations performed in 19 hospitals of 3 high-performing hospital systems with overall hospital mortality of 2.0%, analyzed by advanced machine learning for rare events.
RESULTS: For individual surgeons, their patients, hospitals, and hospital systems, the Surgeon Case Study demonstrated that AQG provides expected outcomes across the entire spectrum of cardiac surgery, from single-component primary operations to complex multi-component reoperations. Actionable opportunities for quality improvement based on virtual twins is illustrated for patients, surgeons, hospitals, risk profile groups, operations, and risk factors vis-à-vis other hospitals.
CONCLUSIONS: Using minimal data collection and models developed using advanced machine learning, this case study shows that probabilities can be generated for operative mortality and major morbidity after virtually all adult cardiac operations. It demonstrates the utility of 21st century causal inference (virtual [digital] twin) tools for assessing quality for surgeons asking \"How am I doing?\" their patients asking \"What are my chances?\" and the profession asking \"How can we get better?\"

OBJECTIVE: The multifactorial dynamic perfusion index was recently introduced as a predictor of cardiac surgery-associated acute kidney injury. The multifactorial dynamic perfusion index was developed based on retrospective data retrieved from the patient files. The present study aims to prospectively validate this index in an external series of patients, through an on-line measure of its various components.
METHODS: Inclusion criteria were adult patients undergoing cardiac surgery with cardiopulmonary bypass. Data collection included preoperative factors and cardiopulmonary bypass-related factors. These were collected on-line using a dedicated monitor. Factors composing the multifactorial dynamic perfusion index are the nadir haematocrit, the nadir oxygen delivery, the time of exposure to a low oxygen delivery, the nadir mean arterial pressure, cardiopulmonary bypass duration, the use of red blood cell transfusions and the peak arterial lactates.
RESULTS: Two hundred adult patients were investigated. The multifactorial dynamic perfusion index had a good (c-statistics 0.81) discrimination for cardiac surgery-associated acute kidney injury (any stage) and an excellent (c-statistics 0.93) discrimination for severe patterns (stage 2-3). Calibration was modest for cardiac surgery-associated acute kidney injury (any stage) and good for stage 2-3. The use of vasoconstrictors was an additional factor associated with cardiac surgery-associated acute kidney injury.
CONCLUSIONS: The multifactorial dynamic perfusion index is validated for discrimination of cardiac surgery-associated acute kidney injury risk. It incorporates modifiable risk factors, and may help in reducing the occurrence of cardiac surgery-associated acute kidney injury.

BACKGROUND: Pleural effusion (PE) is common in advanced-stage lung cancer patients and is related to poor prognosis. Identification of cancer cells is the standard method for the diagnosis of a malignant PE (MPE). However, it only has moderate sensitivity. Thus, more sensitive diagnostic tools are urgently needed.
METHODS: The present study aimed to discover potential protein targets to distinguish malignant pleural effusion (MPE) from other non-malignant pathologies. We have collected PE from 97 patients to explore PE proteomes by applying state-of-the-art liquid chromatography-mass spectrometry (LC-MS) to identify potential biomarkers that correlate with immunohistochemistry assessment of tumor biopsy or with survival data. Functional analyses were performed to elucidate functional differences in PE proteins in malignant and benign samples. Results were integrated into a clinical risk prediction model to identify likely malignant cases. Sensitivity, specificity, and negative predictive value were calculated.
RESULTS: In total, 1689 individual proteins were identified by MS-based proteomics analysis of the 97 PE samples, of which 35 were diagnosed as malignant. A comparison between MPE and benign PE (BPE) identified 58 differential regulated proteins after correction of the p-values for multiple testing. Furthermore, functional analysis revealed an up-regulation of matrix intermediate filaments and cellular movement-related proteins. Additionally, gene ontology analysis identified the involvement of metabolic pathways such as glycolysis/gluconeogenesis, pyruvate metabolism and cysteine and methionine metabolism.
CONCLUSIONS: This study demonstrated a partial least squares regression model with an area under the curve of 98 and an accuracy of 0.92 when evaluated on the holdout test data set. Furthermore, highly significant survival markers were identified (e.g., PSME1 with a log-rank of 1.68 × 10-6).

Cancer incidence risks, lifetime effects and radiation detriments are determined for the whole population and various subpopulations as a result of acute and chronic exposure to low-LET radiation, taking into account the risk models, procedures and representative populations provided by ICRP. The results are given for solid cancers in different organs, as well as for soft tissue cancer in bone marrow. For most cancer sites a good agreement is obtained between the results of this study and the values published by the ICRP. The agreement with ICRP values is better for the whole population than for the working age population, where the results are systematically elevated. For chronic exposure, the years of life lost per radiation-induced cancer incidence are generally higher. In particular, this results in a radiation detriment for the whole population that is 30% higher than for acute exposure. The study reveals that risk quantities show a pronounced age dependence. The highest radiation risks are attributed to young persons; the lowest to persons in advanced ages. The total detriment imposed on people in different ages varies by a factor of about 30. The average values provided by the ICRP mask these variations and considerably underestimate radiation risks in childhood and adolescence. This also concerns the determination of the effective dose for persons in these age groups. Unlike the ICRP, which provides different nominal detriments for the whole population and the working age population, the results of this study do not support the use of different detriments for these populations.

The West London lung screening pilot aimed to identify early-stage lung cancer by targeting low-dose CT (LDCT) to high risk participants. Successful implementation of screening requires maximising participant uptake and identifying those at highest risk. As well as reporting pre-specified baseline screening metrics, additional objectives were to 1) compare participant uptake between a mobile and hospital-based CT scanner and 2) evaluate the impact on cancer detection using two lung cancer risk models.
From primary care records, ever-smokers aged 60-75 were invited to a lung health check at a hospital or mobile site. Participants with PLCOM2012 6-yr risk ≥1.51 % and/or LLPv2 5-yr risk ≥2.0 % were offered a LDCT. Lung cancer detection rate, stage, and recall rates are reported. Participant uptake was compared at both sites (chi-squared test). LDCT eligibility and cancer detection rate were compared between those recruited under each risk model.
Of 8366 potential participants invited, 1047/5135 (20.4 %) invitees responded to an invitation to the hospital site, and 702/3231 (21.7 %) to the mobile site (p = 0.14). The median distance travelled to the hospital site was less than to the mobile site (3.3 km vs 6.4 km, p < 0.01). Of 1159 participants eligible for a scan, 451/1159 (38.9 %) had a LLPv2 ≥2.0 % only, 71/1159 (6.1 %) had a PLCOM2012 ≥1.5 % only; 637/1159 (55.0 %) met both risk thresholds. Recall rate was 15.9 %. Lung cancer was detected in 29/1145 (2.5 %) participants scanned (stage 1, 58.6 %); 5/29 participants with lung cancer did not meet a PLCOM2012 threshold of ≥1.51 %; all had a LLPv2 ≥2.0 %.
Targeted screening is effective in detecting early-stage lung cancer. Similar levels of participant uptake at a mobile and fixed site scanner were demonstrated, indicating that uptake was driven by factors in addition to scanner location. The LLPv2 model was more permissive; recruitment with PLCOM2012 alone would have missed several cancers.

The objective of this study was to assess family history links between breast cancer and prostate cancer in a prospectively collected cohort from the Prostate, Lung, Colorectal, and Ovary (PLCO) trial.
This is a secondary analysis of the PLCO trial datasets. Eligible patients included female participants with complete information about prostate cancer family history and male participants with complete information about breast cancer family history. Multivariate Cox regression analysis was used to assess the impact of prostate cancer family history on breast cancer incidence and mortality. Likewise, multivariate Cox regression analysis was used to assess the impact of breast cancer family history on prostate cancer incidence and mortality.
A total of 45,807 female participants were eligible and included in the current study, and a total of 43,009 male participants were eligible and included in the current analysis. Within a multivariate Cox regression analysis, prostate cancer family history in a first-degree relative (FDR) was associated with a higher probability of diagnosis of breast cancer (hazard ratio [HR], 1.128; 95% confidence interval [CI], 1.007-1.265; P = .038); whereas it was not associated with a higher risk of death from breast cancer (HR, 1.065; 95% CI, 0.668-1.698; P = .791). Similarly, within a multivariate Cox regression analysis, breast cancer family history in an FDR was associated with a higher probability of diagnosis of prostate cancer (HR, 1.092; 95% CI, 1.011-1.180; P = .026) but not death from prostate cancer (HR, 0.442; 95% CI, 0.137-1.432; P = .173).
Female participants with a family history of prostate cancer in an FDR were more likely to develop postmenopausal breast cancer. Likewise, male participants with a family history of breast cancer in an FDR are at a higher probability of prostate cancer development. Risk correlation for premenopausal breast cancer could not be assessed owing to the nature of the PLCO study.

Lung nodules are a diagnostic challenge, with an estimated yearly incidence of 1.6 million in the United States. This study evaluated the accuracy of an integrated proteomic classifier in identifying benign nodules in patients with a pretest probability of cancer (pCA) ≤ 50%.
A prospective, multicenter observational trial of 685 patients with 8- to 30-mm lung nodules was conducted. Multiple reaction monitoring mass spectrometry was used to measure the relative abundance of two plasma proteins, LG3BP and C163A. Results were integrated with a clinical risk prediction model to identify likely benign nodules. Sensitivity, specificity, and negative predictive value were calculated. Estimates of potential changes in invasive testing had the integrated classifier results been available and acted on were made.
A subgroup of 178 patients with a clinician-assessed pCA ≤ 50% had a 16% prevalence of lung cancer. The integrated classifier demonstrated a sensitivity of 97% (CI, 82-100), a specificity of 44% (CI, 36-52), and a negative predictive value of 98% (CI, 92-100) in distinguishing benign from malignant nodules. The classifier performed better than PET, validated lung nodule risk models, and physician cancer probability estimates (P < .001). If the integrated classifier results were used to direct care, 40% fewer procedures would be performed on benign nodules, and 3% of malignant nodules would be misclassified.
When used in patients with lung nodules with a pCA ≤ 50%, the integrated classifier accurately identifies benign lung nodules with good performance characteristics. If used in clinical practice, invasive procedures could be reduced by diverting benign nodules to surveillance.
ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov).