The role of three-dimensional printed cardiac models in the management of complex congenital heart diseases

BackgroundThree-dimensional printing is a process enabling computer-assisted conversion of imaging data from patients into physical “printed” replicas. This has been extrapolated to reconstructing patient-specific cardiac models in congenital heart diseases. The aim of this study was to analyze the impact of three-dimensional printing in surgical decision making in selected cases of complex congenital heart disease by creating patient-specific printed models.MethodsPatients with complex congenital heart diseases with unresolved management decisions after evaluation by echocardiography, cardiac catheterization, and cardiac computed tomography were included with intent to aid in surgical decision making. Three-dimensional models were created from computed tomographic images by an outsourced firm using computer applications. All cases were reviewed by the same team before and after the cardiac models were prepared. The management decisions were grouped as either “corrective surgery” or “no surgery or palliation” The impact of the surgical decision pre and post three-dimensional cardiac model was analyzed by applying Cohen’s kappa test of agreement.ResultsTen patients were included, of which five were of increased pulmonary blood flow, and five were of decreased pulmonary flow. The commonest indication for three-dimensional printed models was to establish the routability of the aorta and pulmonary artery to their respective ventricles (in five patients). The nonagreement between the decision taken before and after the cardiac model was 80%, with kappa −0.37 and P value 0.98.ConclusionsThree-dimensional printed cardiac models contribute to better decision making in complex congenital heart diseases enabling safer execution of any complex congenital heart surgery.     

Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience

ObjectiveLarge clinical databases are increasingly used for research and quality improvement. We describe an approach to data quality assessment from the General Medicine Inpatient Initiative (GEMINI), which collects and standardizes administrative and clinical data from hospitals.MethodsThe GEMINI database contained 245 559 patient admissions at 7 hospitals in Ontario, Canada from 2010 to 2017. We performed 7 computational data quality checks and iteratively re-extracted data from hospitals to correct problems. Thereafter, GEMINI data were compared to data that were manually abstracted from the hospital’s electronic medical record for 23 419 selected data points on a sample of 7488 patients.ResultsComputational checks flagged 103 potential data quality issues, which were either corrected or documented to inform future analysis. For example, we identified the inclusion of canceled radiology tests, a time shift of transfusion data, and mistakenly processing the chemical symbol for sodium (“Na”) as a missing value. Manual validation identified 1 important data quality issue that was not detected by computational checks: transfusion dates and times at 1 site were unreliable. Apart from that single issue, across all data tables, GEMINI data had high overall accuracy (ranging from 98%–100%), sensitivity (95%–100%), specificity (99%–100%), positive predictive value (93%–100%), and negative predictive value (99%–100%) compared to the gold standard.Discussion and ConclusionComputational data quality checks with iterative re-extraction facilitated reliable data collection from hospitals but missed 1 critical quality issue. Combining computational and manual approaches may be optimal for assessing the quality of large multisite clinical databases.     

Family history of esophageal cancer modifies the association of serum lipids and malignant esophageal lesions: a nested case-control study from the “Endoscopic Screening for Esophageal Cancer in China” trial

Background:The association of lipids and cancer has varied greatly among different cancer types, lipid components and study populations. This study is aimed to investigate the association of serum lipids and the risk of malignant lesions in esophageal squamous epithelium.Methods:In the “Endoscopic Screening for Esophageal Cancer in China” (ESECC) trial, serum samples were collected and tested for total cholesterol (TC), triglycerides, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol at the time of subject enrollment. Cases were defined as malignant esophageal lesions identified by baseline endoscopic examination or by follow-up to May 31, 2018. Controls were randomly selected using incidence density sampling in the same cohort. Conditional logistic models were applied to identify the association of serum lipids and the risk of malignant esophageal lesions. Effect modification was evaluated by testing interaction terms of the factor under assessment and these serum lipid indicators.Results:No consistent association between serum lipid levels and esophageal malignant lesions were found in a pooled analysis of 211 cases and 2101 controls. For individuals with a family history of esophageal cancer (EC), high TC, and LDL-C were associated with a significantly increased risk of having malignant lesions (odds ratio [OR]_{High vs. Low TC} = 2.22, 95% confidence interval [CI]: 1.14–4.35; OR_{High vs. Low LDL-C} = 1.93, 95% CI: 1.01–3.65). However, a negative association was observed in participants without an EC family history (OR_{High vs. Low TC} = 0.69, 95% CI: 0.48–0.98, P_interaction = 0.002; OR_{High vs. Low LDL-C} = 0.50, 95% CI: 0.34–0.76, P_interaction < 0.001).Conclusions:In this study, we found that the association of serum lipids and malignant esophageal lesions might be modified by EC family history. The stratified analysis would be crucial for population-based studies investigating the association of serum lipids and cancer. The mechanism by which a family history of EC modifies this association warrants further investigation.     

Pregnancy outcomes of the first thawing cycle in “freeze-all” strategy of infertility patients with fever during oocyte recruitment: a matched-pair study

Background:It is currently unknown whether patients with a fever after controlled ovulation during egg retrieval could increase the risk of pelvic infection or not, and fever itself may affect endometrial receptivity or embryo quality with poor pregnancy outcomes. The aim of this study was to analyze the outcomes of patients with fever during oocyte retrieval after the first frozen-thawed embryo transfer (FET) cycle.Methods:This was a 1:3 retrospective paired study matched for age. In this study, 58 infertility patients (Group 1) had a fever during the control ovulation, and the time of the oocyte retrieval was within 72 hours, they underwent ovum pick up and whole embryo freezing (“freeze-all” strategy). The control subjects (Group 2) are 174 patients matched for age who underwent whole embryo freezing for other reasons. The baseline characteristics, clinical data of ovarian stimulation, and outcomes, such as the clinical pregnancy rate, ongoing clinical pregnancy rate were compared between the two groups in the subsequent FET cycle.Results:All patients had no pelvic inflammatory disease after oocyte retrieval. Anti-Mullerian hormone (AMH) levels (4.2 vs. 2.2, P <0.001) were higher in group 2, and the number of oocytes retrieved, and fertilization rate were lower in group 1 (P < 0.001), but the endometrial thickness, the number of embryo transfers, and the type of luteal support supplementation were similar between the two groups. Regarding pregnancy outcomes in the subsequent FET cycle, the implantation rate, clinical pregnancy rate, early spontaneous rate, ectopic pregnancy rate, and ongoing pregnancy rate were all not significantly different. Further regression analyses showed that the clinical pregnancy rate and ongoing pregnancy rate were also not significantly different.Conclusions:Transvaginal ultrasound-guided follicular puncture for oocyte retrieval is a safe and minimally invasive method for patients with fever. Moreover, the fever had almost no effect on embryo quality.     

Psychiatric assessment of patients with “20th-century disease” (“total allergy syndrome”)

“Twentieth-century disease”, or “total allergy syndrome”, is a condition attributed to hypersensitivity to the environment that may sometimes be seen as so serious that the patient is incapable of living in the modern world. Although the popular media frequently carry stories about it, there is little scientific literature. It is diagnosed by clinical ecologists, who maintain, among other theories, that susceptible individuals experience an overload in assaults by artificial materials in the environment. The patients usually have multiple ill defined symptoms for which no organic cause can be found, but they vigorously resist psychiatric referral, as they attribute their symptoms to allergy. A group of 18 patients who were purportedly suffering from 20th-century disease were referred to a university psychiatric consultation liaison service. They virtually all had a long history of visits to physicians, and their symptoms were characteristic of several well known psychiatric disorders. The case histories and management of three of them are presented. Although this group of patients may have been atypical in that they had more severe psychologic symptoms, the experience indicates that a psychiatric diagnosis ought to be considered. The symptoms of 20th-century disease have much in common with other conditions known to physicians for centuries.     

Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility

ObjectiveThe US Preventive Services Task Force (USPSTF) requires the estimation of lifetime pack-years to determine lung cancer screening eligibility. Leading electronic health record (EHR) vendors calculate pack-years using only the most recently recorded smoking data. The objective was to characterize EHR smoking data issues and to propose an approach to addressing these issues using longitudinal smoking data.Materials and MethodsIn this cross-sectional study, we evaluated 16 874 current or former smokers who met USPSTF age criteria for screening (50–80 years old), had no prior lung cancer diagnosis, and were seen in 2020 at an academic health system using the Epic^® EHR. We described and quantified issues in the smoking data. We then estimated how many additional potentially eligible patients could be identified using longitudinal data. The approach was verified through manual review of records from 100 subjects.ResultsOver 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%). Addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screening (P < .001).DiscussionMissing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening. Data collection and analysis strategies that reflect changes in smoking habits over time could improve the identification of patients eligible for screening.ConclusionThe use of longitudinal EHR smoking data could improve lung cancer screening.     

Management of “psychosomatic” problems in clinical practice

Skilled interviewing and investigation are essential in the diagnosis and treatment of “psychosomatic” illness, the term “psychosomatic” being used in its more colloquial sense to refer to illness characterized by somatic symptoms and related psychopathologic disorders but without organic disease.     

Commentary on the “Family Rule”

The “family rule” paper by Dr Foreman proposes a way of resolving the present uncertainty about medical law on children's consent and refusal. This commentary reviews how doctors' decisions are already well protected by English law and respected by the courts. The “family rule” appears to be likely only to complicate the already diffuse law on parental consent, and to weaken further the competent minor's position in cases of uncertainty and disagreement. It leaves the difficult questions about defining and assessing children's competence unanswered. This commentary suggests that these questions would be better resolved through professionally determined standards of good practice that respect children and parents, rather than through rules or laws.     

Renal medication-related clinical decision support (CDS) alerts and overrides in the inpatient setting following implementation of a commercial electronic health record: implications for designing more effective alerts

ObjectiveTo assess the appropriateness of medication-related clinical decision support (CDS) alerts associated with renal insufficiency and the potential/actual harm from overriding the alerts.Materials and MethodsOverride rate frequency was recorded for all inpatients who had a renal CDS alert trigger between 05/2017 and 04/2018. Two random samples of 300 for each of 2 types of medication-related CDS alerts associated with renal insufficiency—“dose change” and “avoid medication”—were evaluated by 2 independent reviewers using predetermined criteria for appropriateness of alert trigger, appropriateness of override, and patient harm.ResultsWe identified 37 100 “dose change” and 5095 “avoid medication” alerts in the population evaluated, and 100% of each were overridden. Dose change triggers were classified as 12.5% appropriate and overrides of these alerts classified as 90.5% appropriate. Avoid medication triggers were classified as 29.6% appropriate and overrides 76.5% appropriate. We identified 5 adverse drug events, and, of these, 4 of the 5 were due to inappropriately overridden alerts.ConclusionAlerts were nearly always presented inappropriately and were all overridden during the 1-year period studied. Alert fatigue resulting from receiving too many poor-quality alerts may result in failure to recognize errors that could lead to patient harm. Although medication-related CDS alerts associated with renal insufficiency had previously been found to be the most clinically beneficial alerts in a legacy system, in this system they were ineffective. These findings underscore the need for improvements in alert design, implementation, and monitoring of alert performance to make alerts more patient-specific and clinically appropriate.     

Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2

ObjectiveRisk prediction models are widely used to inform evidence-based clinical decision making. However, few models developed from single cohorts can perform consistently well at population level where diverse prognoses exist (such as the SARS-CoV-2 [severe acute respiratory syndrome coronavirus 2] pandemic). This study aims at tackling this challenge by synergizing prediction models from the literature using ensemble learning.Materials and MethodsIn this study, we selected and reimplemented 7 prediction models for COVID-19 (coronavirus disease 2019) that were derived from diverse cohorts and used different implementation techniques. A novel ensemble learning framework was proposed to synergize them for realizing personalized predictions for individual patients. Four diverse international cohorts (2 from the United Kingdom and 2 from China; N = 5394) were used to validate all 8 models on discrimination, calibration, and clinical usefulness.ResultsResults showed that individual prediction models could perform well on some cohorts while poorly on others. Conversely, the ensemble model achieved the best performances consistently on all metrics quantifying discrimination, calibration, and clinical usefulness. Performance disparities were observed in cohorts from the 2 countries: all models achieved better performances on the China cohorts.DiscussionWhen individual models were learned from complementary cohorts, the synergized model had the potential to achieve better performances than any individual model. Results indicate that blood parameters and physiological measurements might have better predictive powers when collected early, which remains to be confirmed by further studies.ConclusionsCombining a diverse set of individual prediction models, the ensemble method can synergize a robust and well-performing model by choosing the most competent ones for individual patients.     

“A catalyst for action”: Factors for implementing clinical risk prediction models of infection in home care settings

ObjectiveThe study sought to outline how a clinical risk prediction model for identifying patients at risk of infection is perceived by home care nurses, and to inform how the output of the model could be integrated into a clinical workflow.Materials and MethodsThis was a qualitative study using semi-structured interviews with 50 home care nurses. Interviews explored nurses’ perceptions of clinical risk prediction models, their experiences using them in practice, and what elements are important for the implementation of a clinical risk prediction model focusing on infection. Interviews were audio-taped and transcribed, with data evaluated using thematic analysis.ResultsTwo themes were derived from the data: (1) informing nursing practice, which outlined how a clinical risk prediction model could inform nurse clinical judgment and be used to modify their care plan interventions, and (2) operationalizing the score, which summarized how the clinical risk prediction model could be incorporated in home care settings.DiscussionThe findings indicate that home care nurses would find a clinical risk prediction model for infection useful, as long as it provided both context around the reasons why a patient was deemed to be at high risk and provided some guidance for action.ConclusionsIt is important to evaluate the potential feasibility and acceptability of a clinical risk prediction model, to inform the intervention design and implementation strategy. The results of this study can provide guidance for the development of the clinical risk prediction tool as an intervention for integration in home care settings.     

“Complications” of the Landry-Guillain-Barré-Strohl syndrome

Prognosis for life in the Landry-Guillain-Barré-Strohl syndrome is dependent upon the development of respiratory and non-respiratory “complications” and their successful management. Review of the literature, a case history, and a study of 14 patients with this syndrome at the University Hospital, Edmonton, indicate that “complications” can be anticipated in virtually all areas of acute care management, including respiratory, gastrointestinal, urinary tract, central and autonomic nervous systems, metabolic, cardiovascular, and infectious disease. The proper management of patients with the Landry-Guillain-Barré-Strohl syndrome demands an awareness of the totality of care required and the presence of a hospital system that provides for vital system monitoring and support, and for ready interdisciplinary consultation.     

“Skid Row” Syndrome: A Medical Profile of the Chronic Drunkenness Offender

From skid row, 227 chronic “drunks”, inmates of the Toronto Jail, were studied to determine the physical features and illnesses of this group. Complete physical examinations, liver function tests, routine hematology, urinalysis, chest radiographs were carried out and previous hospital records were obtained for each man. The data were analyzed by IBM computer and reported in terms of body systems. Items that occurred in sufficient frequency were separated out and listed to compile a “skid row” syndrome. The men averaged 45 years of age, had been drinking heavily for 20 years and had four drunken convictions a year. Tuberculosis was found in 8.8%. Epilepsy was confirmed in 8%. Cirrhosis of the liver was definite in 3% of the group; 75% were under the Canadian average weight, and 25% had significant body deformities. It was estimated that, if necessary therapy was carried out, 90% of the men would be able to perform useful labour.     

Improving the delivery of palliative care through predictive modeling and healthcare informatics

ObjectiveAccess to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team.Materials and MethodsElectronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient’s corresponding care team.ResultsTraining performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an “in-production” AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes.ConclusionsA machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.     

Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model

Background:The basis of individualized treatment should be individualized mortality risk predictive information. The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure (ACLF) patients based on a random survival forest (RSF) algorithm.Methods:The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People''s Hospital of Foshan, Shunde Hospital of Southern Medical University, and Jiangmen Central Hospital. Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort (n = 276). Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method (n = 276). The RSF algorithm was used to develop an individual prognostic model for ACLF patients. The Brier score was used to evaluate the diagnostic accuracy of prognostic models. The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves (AUROCs) of prognostic models.Results:Multivariate Cox regression identified hepatic encephalopathy (HE), age, serum sodium level, acute kidney injury (AKI), red cell distribution width (RDW), and international normalization index (INR) as independent risk factors for ACLF patients. A simplified RSF model was developed based on these previous risk factors. The AUROCs for predicting 3-, 6-, and 12-month mortality were 0.916, 0.916, and 0.905 for the RSF model and 0.872, 0.866, and 0.848 for the Cox model in the model cohort, respectively. The Brier scores were 0.119, 0.119, and 0.128 for the RSF model and 0.138, 0.146, and 0.156 for the Cox model, respectively. The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients.Conclusions:The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients. Additionally, the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95% confidence intervals at user-defined time points.     

Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees

ObjectiveAfter deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks of over-updating/fitting, we study online methods with performance guarantees. Materials and MethodsWe introduce 2 procedures for continual recalibration or revision of an underlying prediction model: Bayesian logistic regression (BLR) and a Markov variant that explicitly models distribution shifts (MarBLR). We perform empirical evaluation via simulations and a real-world study predicting Chronic Obstructive Pulmonary Disease (COPD) risk. We derive “Type I and II” regret bounds, which guarantee the procedures are noninferior to a static model and competitive with an oracle logistic reviser in terms of the average loss.ResultsBoth procedures consistently outperformed the static model and other online logistic revision methods. In simulations, the average estimated calibration index (aECI) of the original model was 0.828 (95%CI, 0.818–0.938). Online recalibration using BLR and MarBLR improved the aECI towards the ideal value of zero, attaining 0.265 (95%CI, 0.230–0.300) and 0.241 (95%CI, 0.216–0.266), respectively. When performing more extensive logistic model revisions, BLR and MarBLR increased the average area under the receiver-operating characteristic curve (aAUC) from 0.767 (95%CI, 0.765–0.769) to 0.800 (95%CI, 0.798–0.802) and 0.799 (95%CI, 0.797–0.801), respectively, in stationary settings and protected against substantial model decay. In the COPD study, BLR and MarBLR dynamically combined the original model with a continually refitted gradient boosted tree to achieve aAUCs of 0.924 (95%CI, 0.913–0.935) and 0.925 (95%CI, 0.914–0.935), compared to the static model’s aAUC of 0.904 (95%CI, 0.892–0.916).DiscussionDespite its simplicity, BLR is highly competitive with MarBLR. MarBLR outperforms BLR when its prior better reflects the data.ConclusionsBLR and MarBLR can improve the transportability of clinical prediction models and maintain their performance over time.     

Feeling better on hemodialysis: user-centered design requirements for promoting patient involvement in the prevention of treatment complications

ObjectiveHemodialysis patients frequently experience dialysis therapy sessions complicated by intradialytic hypotension (IDH), a major patient safety concern. We investigate user-centered design requirements for a theory-informed, peer mentoring-based, informatics intervention to activate patients toward IDH prevention.MethodsWe conducted observations (156 hours) and interviews (n = 28) with patients in 3 hemodialysis clinics, followed by 9 focus groups (including participatory design activities) with patients (n = 17). Inductive and deductive analyses resulted in themes and design principles linked to constructs from social, cognitive, and self-determination theories.ResultsHemodialysis patients want an informatics intervention for IDH prevention that collapses distance between patients, peers, and family; harnesses patients’ strength of character and resolve in all parts of their life; respects and supports patients’ individual needs, preferences, and choices; and links “feeling better on dialysis” to becoming more involved in IDH prevention. Related design principles included designing for: depth of interpersonal connections; positivity; individual choice and initiative; and comprehension of connections and possible actions.DiscussionFindings advance the design of informatics interventions by presenting design requirements for outpatient safety and addressing key design opportunities for informatics to support patient involvement; these include incorporation of behavior change theories. Results also demonstrate the meaning of design choices for hemodialysis patients in the context of their experiences; this may have applicability to other populations with serious illnesses.ConclusionThe resulting patient-facing informatics intervention will be evaluated in a pragmatic cluster-randomized controlled trial in 28 hemodialysis facilities in 4 US regions.     

Experience with a Diabetic Service in an “Open” Hospital

A survey of the management of diabetes mellitus in an “open” hospital, Calgary General Hospital, was conducted in 1954 by reviewing the records of 100 consecutive diabetic admissions and by interviewing medical, nursing and dietetic staff members. The diabetic state was controlled satisfactorily by diet and insulin, but early diabetic complications and patient education tended to be overlooked by physicians. Diabetic management from the nursing, administrative and dietetic standpoints was considered to be inefficient, unpredictable and incomplete.

In 1955 a comprehensive diabetic service was instituted which co-ordinated the activities of medical, nursing and dietetic staffs and provided for patient education. A repeat survey conducted in 1961, in which the records of 87 consecutive diabetic admissions were reviewed, showed marked improvement in all areas of diabetic patient care.

Objections to voluntary conformity by staff members were surprisingly absent. The institution of a diabetic service is recommended for all hospitals as a means of improving diabetic care.