Clinical Interviewing: An Essential but Neglected Method of Medicine.

Clinical interviewing is the basic method to understand how a person feels and what are the presenting complaints, obtain medical history, evaluate personal attitudes and behavior related to health and disease, give the patient information about diagnosis, prognosis, and treatment, and establish a bond between patient and physician that is crucial for shared decision making and self-management. However, the value of this basic skill is threatened by time pressures and emphasis on technology. Current health care trends privilege expensive tests and procedures and tag the time devoted to interaction with the patient as lacking cost-effectiveness. Instead, the time spent to inquire about problems and life setting may actually help to avoid further testing, procedures, and referrals. Moreover, the dialogue between patient and physician is an essential instrument to increase patient's motivation to engage in healthy behavior. The aim of this paper was to provide an overview of clinical interviewing and its optimal use in relation to style, flow and hypothesis testing, clinical domains, modifications according to settings and goals, and teaching. This review points to the primacy of interviewing in the clinical process. The quality of interviewing determines the quality of data that are collected and, eventually, of assessment and treatment. Thus, interviewing deserves more attention in educational training and more space in clinical encounters than it is currently receiving.

Clinimetric Criteria for Patient-Reported Outcome Measures.

Patient-reported outcome measures (PROMs) are self-rated scales and indices developed to improve the detection of the patients' subjective experience. Given that a considerable number of PROMs are available, it is important to evaluate their validity and usefulness in a specific research or clinical setting. Published guidelines, based on psychometric criteria, do not fit in with the complexity of clinical challenges, because of their quest for homogeneity of components and inadequate attention to sensitivity. Psychometric theory has stifled the field and led to the routine use of scales widely accepted yet with a history of poor performance. Clinimetrics, the science of clinical measurements, may provide a more suitable conceptual and methodological framework. The aims of this paper are to outline the major limitations of the psychometric model and to provide criteria for clinimetric patient-reported outcome measures (CLIPROMs). The characteristics related to reliability, sensitivity, validity, and clinical utility of instruments are critically reviewed, with particular reference to the differences between clinimetric and psychometric approaches. Of note is the fact that PROMs, rating scales, and indices developed according to psychometric criteria may display relevant clinimetric properties. The present paper underpins the importance of the clini-metric methodology in choosing the appropriate PROMs. CLIPROM criteria may also guide the development of new indices and the validation of existing PROMs to be employed in clinical settings.

The Biological and Biographical Basis of Precision Medicine.

Construction of a patient narrative (case history) is a core strategy in the care of patients. Recent advances in biomarker identification and digital sensors to monitor physiological and behavioral features have made constructing a case history more complex. Notably, however, although a biological profile is increasingly a part of the patient's profile, an analogous patient-based biographical (life experience) profile is typically overlooked. Evolving concepts such as allostasis and allostatic load refer to processes promoting stability of physiological systems in the presence of diverse life experiences. Integrating details of both biology and biography is a goal of "precision medicine." In this review, we describe how complex interactions between biology and biography affect disease risk and treatment response and highlight a strategy to develop narratives that establish the integration of biology and biography as the scientific basis for precision medicine.

Medicine based evidence and personalized care of patients.

BACKGROUND: For the past 70- years patient care has been dominated by Evidence Based Medicine (EBM) with its emphasis on Randomized Controlled Trials (RCTs) and clinical guidelines to standardize medical decision-making. METHODS: Critical assessment of the literature and analyses of the arguments that favor patient care based primarily on individual variability in disease risk or treatment response versus emphasis on group standardization. RESULTS: Medicine Based Evidence (MBE) is used to guide decision making for an individual patient at hand by profiling the clinical features (biology) and life experience (biography) of the patient and then finding approximate matches to the patient in a clinical library of patients assembled from diverse sources (RCTs, cohorts, registries, electronic health records and more). CONCLUSION: Medicine is transitioning from population based model of clinical care that relies on average results from RCTs to an individual-based model of "personalized" medicine. For individualized care of the patient at hand, MBE is the preferred scientific strategy to generate evidence for patient care.

Phase angle, frailty and mortality in older adults.

BACKGROUND: Frailty is a multidimensional phenotype that describes declining physical function and a vulnerability to adverse outcomes in the setting of physical stress such as illness or hospitalization. Phase angle is a composite measure of tissue resistance and reactance measured via bioelectrical impedance analysis (BIA). Whether phase angle is associated with frailty and mortality in the general population is unknown. OBJECTIVE: To evaluate associations among phase angle, frailty and mortality. DESIGN: Population-based survey. SETTING: Third National Health and Nutritional Examination Survey (1988-1994). PARTICIPANTS: In all, 4,667 persons aged 60 and older. MEASUREMENTS: Frailty was defined according to a set of criteria derived from a definition previously described and validated. RESULTS: Narrow phase angle (the lowest quintile) was associated with a four-fold higher odds of frailty among women and a three-fold higher odds of frailty among men, adjusted for age, sex, race-ethnicity and comorbidity. Over a 12-year follow-up period, the adjusted relative hazard for mortality associated with narrow phase angle was 2.4 (95 % confidence interval [95 % CI] 1.8 to 3.1) in women and 2.2 (95 % CI 1.7 to 2.9) in men. Narrow phase angle was significantly associated with mortality even among participants with little or no comorbidity. LIMITATIONS: Analyses of BIA and frailty were cross-sectional; BIA was not measured serially and incident frailty during follow-up was not assessed. Participants examined at home were excluded from analysis because they did not undergo BIA. CONCLUSIONS: Narrow phase angle is associated with frailty and mortality independent of age and comorbidity.

Short inter-pregnancy intervals, parity, excessive pregnancy weight gain and risk of maternal obesity.

To investigate the relationship among parity, length of the inter-pregnancy intervals and excessive pregnancy weight gain in the first pregnancy and the risk of obesity. Using a prospective cohort study of 3,422 non-obese, non-pregnant US women aged 14-22 years at baseline, adjusted Cox models were used to estimate the association among parity, inter-pregnancy intervals, and excessive pregnancy weight gain in the first pregnancy and the relative hazard rate (HR) of obesity. Compared to nulliparous women, primiparous women with excessive pregnancy weight gain in the first pregnancy had a HR of obesity of 1.79 (95% CI 1.40, 2.29); no significant difference was seen between primiparous without excessive pregnancy weight gain in the first pregnancy and nulliparous women. Among women with the same pregnancy weight gain in the first pregnancy and the same number of inter-pregnancy intervals (12 and 18 months or ≥18 months), the HR of obesity increased 2.43-fold (95% CI 1.21, 4.89; p = 0.01) for every additional inter-pregnancy interval of <12 months; no significant association was seen for longer inter-pregnancy intervals. Among women with the same parity and inter-pregnancy interval pattern, women with excessive pregnancy weight gain in the first pregnancy had an HR of obesity 2.41 times higher (95% CI 1.81, 3.21; p < 0.001) than women without. Primiparous and nulliparous women had similar obesity risk unless the primiparous women had excessive pregnancy weight gain in the first pregnancy, then their risk of obesity was greater. Multiparous women with the same excessive pregnancy weight gain in the first pregnancy and at least one additional short inter-pregnancy interval had a significant risk of obesity after childbirth. Perinatal interventions that prevent excessive pregnancy weight gain in the first pregnancy or lengthen the inter-pregnancy interval are necessary for reducing maternal obesity.

Development of a Novel Clinical Risk Score for COVID-19 Infections.

OBJECTIVE: The ongoing emergence of novel severe acute respiratory syndrome coronavirus 2 strains such as the Omicron variant amplifies the need for precision in predicting severe COVID-19 outcomes. This study presents a machine learning model, tailored to the evolving COVID-19 landscape, emphasizing novel risk factors and refining the definition of severe outcomes to predict the risk of a patient experiencing severe disease more accurately. METHODS: Utilizing electronic health records from the Healthjump database, this retrospective study examined over 1 million US COVID-19 diagnoses from March 2020 to September 2022. Our model predicts severe outcomes, including acute respiratory failure, intensive care unit admission, or ventilator use, circumventing biases associated with hospitalization, which exhibited ∼4× geographical variance of the new outcome. RESULTS: The model exceeded similar predictors with an area under the curve of 0.83 without lab data to predict patient risk. It identifies new risk factors, including acute care history, health care encounters, and distinct medication use. An increase in severe outcomes, typically 2-3× higher than subsequent months, was observed at the onset of each new strain era, followed by a plateau phase, but the risk factors remain consistent across strain eras. CONCLUSION: We offer an improved machine learning model and risk score for predicting severe outcomes during changing COVID-19 strain eras. By emphasizing a more clinically precise definition of severe outcomes, the study provides insights for resource allocation and intervention strategies, aiming to better patient outcomes and reduce health care strain. The necessity for regular model updates is highlighted to maintain relevance amidst the rapidly evolving COVID-19 epidemic.

Childbearing, stress and obesity disparities in women: a public health perspective.

The perinatal period, from early in the first trimester to 1 year postpartum, provides opportunities for novel public health interventions to reduce obesity disparities. We present a unifying socio-biological framework to suggest opportunities for multidisciplinary research and public health approaches to elucidate and target the mechanisms for the development of maternal obesity and related disparities. The framework illustrates the interplay of the social, cultural and physical environment; stress appraisal and response; and coping behaviors on short-term outcomes (e.g. allostatic load and gestational weight gain), the intermediate outcomes of persistent insulin resistance and post-partum weight retention, and longer term outcomes of obesity and its disease consequences. Testing the proposed relationships may provide insights into how childbearing risk factors such as gestational weight gain, postpartum weight retention and parity contribute to obesity, which are needed to inform public health policies and clinical care guidelines aimed at reducing obesity and improving the health of women.

The bedside evaluation: ritual and reason.

The bedside evaluation, consisting of the history and physical examination, was once the primary means of diagnosis and clinical monitoring. The recent explosion of imaging and laboratory testing has inverted the diagnostic paradigm. Physicians often bypass the bedside evaluation for immediate testing and therefore encounter an image of the patient before seeing the patient in the flesh. In addition to risking delayed or missed diagnosis of readily recognizable disease, physicians who forgo or circumvent the bedside evaluation risk the loss of an important ritual that can enhance the physician-patient relationship. Patients expect that some form of bedside evaluation will take place when they visit a physician. When physicians complete this evaluation in an expert manner, it can have a salutary effect. If done poorly or not at all, in contrast, it can undermine the physician-patient relationship. Studies suggest that the context, locale, and quality of the bedside evaluation are associated with neurobiological changes in the patient. Recognizing the importance of the bedside evaluation as a healing ritual and a powerful diagnostic tool when paired with judicious use of technology could be a stimulus for the recovery of an ebbing skill set among physicians.

Empirical evaluation of very large treatment effects of medical interventions.

CONTEXT: Most medical interventions have modest effects, but occasionally some clinical trials may find very large effects for benefits or harms. OBJECTIVE: To evaluate the frequency and features of very large effects in medicine. DATA SOURCES: Cochrane Database of Systematic Reviews (CDSR, 2010, issue 7). STUDY SELECTION: We separated all binary-outcome CDSR forest plots with comparisons of interventions according to whether the first published trial, a subsequent trial (not the first), or no trial had a nominally statistically significant (P < .05) very large effect (odds ratio [OR], ≥5). We also sampled randomly 250 topics from each group for further in-depth evaluation. DATA EXTRACTION: We assessed the types of treatments and outcomes in trials with very large effects, examined how often large-effect trials were followed up by other trials on the same topic, and how these effects compared against the effects of the respective meta-analyses. RESULTS: Among 85,002 forest plots (from 3082 reviews), 8239 (9.7%) had a significant very large effect in the first published trial, 5158 (6.1%) only after the first published trial, and 71,605 (84.2%) had no trials with significant very large effects. Nominally significant very large effects typically appeared in small trials with median number of events: 18 in first trials and 15 in subsequent trials. Topics with very large effects were less likely than other topics to address mortality (3.6% in first trials, 3.2% in subsequent trials, and 11.6% in no trials with significant very large effects) and were more likely to address laboratory-defined efficacy (10% in first trials,10.8% in subsequent, and 3.2% in no trials with significant very large effects). First trials with very large effects were as likely as trials with no very large effects to have subsequent published trials. Ninety percent and 98% of the very large effects observed in first and subsequently published trials, respectively, became smaller in meta-analyses that included other trials; the median odds ratio decreased from 11.88 to 4.20 for first trials, and from 10.02 to 2.60 for subsequent trials. For 46 of the 500 selected topics (9.2%; first and subsequent trials) with a very large-effect trial, the meta-analysis maintained very large effects with P < .001 when additional trials were included, but none pertained to mortality-related outcomes. Across the whole CDSR, there was only 1 intervention with large beneficial effects on mortality, P < .001, and no major concerns about the quality of the evidence (for a trial on extracorporeal oxygenation for severe respiratory failure in newborns). CONCLUSIONS: Most large treatment effects emerge from small studies, and when additional trials are performed, the effect sizes become typically much smaller. Well-validated large effects are uncommon and pertain to nonfatal outcomes.

Population health science as a unifying foundation for translational clinical and public health research.

Separated both in academics and practice since the Rockefeller Foundation effort to "liberate" public health from perceived subservience to clinical medicine a century ago, research in public health and clinical medicine have evolved separately. Today, translational research in population health science offers a means of fostering their convergence, with potentially great benefit to both domains. Although evidence that the two fields need not and should not be entirely distinct in their methods and goals has been accumulating for over a decade, the prodigious efforts of biomedical and social sciences over the past year to address the COVID-19 pandemic has placed this unifying approach to translational research in both fields in a new light. Specifically, the coalescence of clinical and population-level strategies to control disease and novel uses of population-level data and tools in research relating to the pandemic have illuminated a promising future for translational research. We exploit this unique window to re-examine how translational research is conducted and where it may be going. We first discuss the transformation that has transpired in the research firmament over the past two decades and the opportunities these changes afford. Next, we present some of the challenges-technical, cultural, legal, and ethical- that need attention if these opportunities are to be successfully exploited. Finally, we present some recommendations for addressing these challenges.

Long COVID and Medicine's Two Cultures.

Medicine has separated the two cultures of biological science and social science in research, even though they are intimately connected in the lives of our patients. To understand the cause, progression, and treatment of long COVID , biology and biography, the patient's lived experience, must be studied together.