Quest for Biomarkers of Positive Health: A Review.

The positive health of a person can be defined as the ability to live long in good health, possibly with no activity limitation. No method is yet available for its objective assessment in individuals, and we propose a framework in this communication that can operationalize this concept. Instead of distal factors, such as diet and lifestyle because these are subjective and difficult to measure, we concentrate on the objectively measurable biomarkers such as immunity level, endorphins, and handgrip strength. The focus is on the major parameters that may protect from diseases and infirmity and can be assessed by noninvasive methods. A combination of such parameters may signify positive health. This may be a novel way to measure positive health at the individual level. In this communication, we briefly review the literature and identify a few major biomarkers that provide a protective shield and could determine the status of positive health at the individual level. This exercise demonstrates that the assessment of the positive health of a person is feasible. A scale based on these and other relevant parameters can be developed later that could quantitatively measure the exact level of positive health. As the exact combination of the parameters that protects from ailments is not fully known yet, a framework such as this may help in identifying the data gaps that require attention in this context. The proposed framework may initiate a discussion on indicators of positive health and characterize the parameters for intervention that could increase a healthy life.

Assessing agreement between two methods of measuring blood glucose level without Bland-Altman limits.

OBJECTIVE: To describe a new method of assessing agreement between two methods of measuring blood glucose level in view of several problems with the currently used Bland-Altman method. METHODS: Study the deficiencies in the Bland-Altman method and propose a simple method that overcomes these deficiencies. The simple method does not require the calculation of Bland-Altman limits. RESULTS: Clinical tolerance limits, which are needed anyway, can be directly used to obtain the agreement by the percentage of differences within these limits. This method is simple, robust, and nonparametric. It is more flexible also as the clinical tolerance limits can be varied for different values of the measurement such as to have close agreement at critical values and relaxed agreement at other values. Non-symmetric limits can also be set up under the simple method. CONCLUSION: The method of assessing agreement between two methods of measuring blood glucose level can be greatly improved by directly using clinical tolerance limits without calculating the Bland-Altman limits.

Direct Use of Clinical Tolerance Limits for Assessing the Degree of Agreement between Two Methods of Measuring Blood Pressure.

A large number of new methods of measuring blood pressure (BP) have recently emerged with advances in technology. Different methods of measuring BP generally provide varying readings compared with one another. Clinicians must decide how to respond to these variations and how to assess the degree of agreement. Clinical agreement between two quantitative measurements on a group of subjects is generally assessed with the Bland-Altman method. This method requires a comparison of Bland-Altman limits with the prespecified clinical tolerance limits. This review describes an alternative simple and robust method that directly uses clinical tolerance limits for assessing agreement without calculating Bland-Altman limits. The many advantages of this method are illustrated with the help of real-life examples of BP measurements.

The Palliative Care Knowledge Questionnaire-Basic (PCKQ-B): Development and Validation of a Tool to Measure Knowledge of Health Professionals about Palliative Care in India.

Objectives: A well-educated and aware staff are the needed to overcome barriers to palliative care provision in the face of ever-increasing needs of palliative care in developing countries like India. Lack of a standardised and locally validated need assessment tool is felt as one of the important requirements for any successful educational program. This project is to develop and validate a basic palliative care knowledge needs assessment questionnaire. Materials and Methods: Development of Palliative Care Knowledge Questionnaire-Basic (PCKQ-B) for Indian nurses working in a tertiary care hospital in Delhi-NCR, India was undertaken with four basic processes, that is, item generation, content validity, pilot testing and field testing. Literature search helped us to find appropriate domains and items; content validity obtained with a panel of seven experts, pilot testing and finally field testing was done on 102 nurses working in 6 branches of Max Super Specialty Hospital, Delhi-NCR, India. The field study data were used to do item analyses including item difficulty, item discrimination and calculate reliability indicators including test-retest correlation and KR-20. Results: Content Validity index (CVI) of all the items was >0.78 (except 1 item) and Scale-CVI was 0.98 (excellent content validity). Items were selected on the basis of item difficulty index of 0.1 to 0.9 and an item discrimination index greater than 0.2. A total of 25 questions were selected that elicited KR-20 value of 0.65 and intra class correlation (test-retest reliability) of 0.52. Conclusion: Overall, PCKQ-B is a 25 item questionnaire with a moderate reliability. Although validated for nurses in tertiary care hospitals, this tool might find utility in education of primary health workers, community health workers, social workers, or physiotherapists in palliative care teams.

Secondary infections modify the overall course of hospitalized patients with COVID-19: a retrospective study from a network of hospitals across North India.

Objective: To gain better insight into the extent of secondary bacterial and fungal infections in hospitalized patients in India, and to assess how these alter the course of coronavirus disease 2019 (COVID-19) so that control measures can be suggested. Methods: In this retrospective, multicentre study, the data of all patients who tested positive for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) on reverse transcriptase polymerase chain reaction (RT-PCR), admitted to hospital between March 2020 and July 2021, were accessed from the electronic health records of a network of 10 hospitals across five states in North India. Results: Of 19,852 patients testing positive for SARS-CoV-2 on RT-PCR and admitted to the study hospitals during the study period, 1940 (9.8%) patients developed secondary infections (SIs). Patients with SIs were, on average, 8 years older than patients without SIs (median age 62.6 vs 54.3 years; P<0.001). The risk of SIs was significantly (P<0.001) associated with age, severity of disease at admission, diabetes, admission to the intensive care unit (ICU), and ventilator use. The most common site of infection was urine (41.7%), followed by blood (30.8%) and sputum/bronchoalveolar lavage/endotracheal fluid (24.8%); the least common was pus/wound discharge (2.6%). Gram-negative bacilli (GNB) were the most common organisms (63.2%), followed by Gram-positive cocci (GPC) (19.6%) and fungi (17.3%). Most patients with SIs were on multiple antimicrobials. The most commonly used antibiotics against GNB were beta-lactam/beta-lactamase inhibitors (76.9%), carbapenems (57.7%), cephalosporins (53.9%), and antibiotics against carbapenem-resistant Enterobacteriaceae (47.1%). Empirical use of antibiotics against GPC was seen in 58.9% of patients with SIs, and empirical use of antifungals was observed in 56.9% of patients with SIs. The average length of hospital stay for patients with SIs was almost twice as long as that of patients without SIs (median 13 vs 7 days). Overall mortality among patients with SIs (40.3%) was more than eight times higher than that among patients without SIs (4.6%). Only 1.2% of patients with SIs with mild COVID-19 at admission died, compared with 17.5% of those with moderate COVID-19 at admission and 58.5% of those with severe COVID-19 at admission (P<0.001). The mortality rate was highest in patients with bloodstream infections (49.8%), followed by those with hospital-acquired pneumonia (47.9%), urinary tract infections (29.4%), and skin and soft tissue infections (29.4%). The mortality rate in patients with diabetes with SIs was 45.2%, compared with 34.3% in those without diabetes (P<0.001). Conclusions: SIs complicate the course of patients hospitalized with COVID-19. These patients tend to have a much longer hospital stay, a higher requirement for oxygen and ICU care, and a significantly higher mortality rate compared with those without SIs. The groups most vulnerable to SIs are patients with more severe COVID-19, elderly patients and patients with diabetes. Judicious empirical use of combination antimicrobials in these groups of vulnerable patients can save lives. It is desirable to have region- or country-specific guidelines for appropriate use of antibiotics and antifungals to prevent their overuse.

Survival Analysis: Where, Why, What and How?

Durations of any event, such as duration of hospitalization, is usually found to have a highly skewed distribution and incomplete values due to dropouts and limited follow-up. The usual methods of statistical analysis are, therefore, not applicable. The method of survival analysis is a nonparametric method and is designed to overcome these problems. Survival is a generic term and is used for any time-to-event data. The entire survival pattern at different points in time is studied by the Kaplan-Meier method under certain conditions. Log-rank method is used to compare survival pattern in two or more groups. Hazard is the rate of occurrence of an event per unit of time and studied by Cox method. The concept of survival and all these methods of survival analysis are briefly discussed in this short note in a non-mathematical format for medical audience.

Normative cutoffs of muscle mass, muscle strength, and frailty for healthy Indian population.

BACKGROUND: Population-derived cutoffs for low skeletal muscle mass, skeletal muscle strength, and frailty among Indians are lacking. Studies describing sarcopenia and frailty among patients with chronic liver diseases have used cutoffs derived from Caucasian populations giving erroneous results. AIMS: We aimed to derive gender-specific cutoffs for low skeletal muscle mass and skeletal muscle strength from healthy Indians. METHODS: Healthy Indian population consisted of two groups. Group 1 (Gp I) included 242 healthy liver donors and group 2 (Gp II) 272 healthy health care workers. Skeletal muscle index (SMI) was calculated from computed tomography (CT) abdomen performed prior to donor hepatectomy only in Gp I. Liver frailty index (LFI) was computed using the online calculator, after recording hand grip strength (HGS), chair stand-up test (CSUT), and balance test in both groups. HGS was measured using the Smedley handgrip dynamometer. CSUT was noted as time to complete 5 chair stand-ups with subjects' arms folded across the chest. Gender-specific cutoffs of SMI and HGS were derived as <5th percentile of the distribution values and as >95th percentile for CSUT and LFI values. RESULTS: The SMI was measured from Gp I subjects (n=242; 120 males [mean age 31.13] and 122 females [mean age 36.60]). The HGS, CSUT, and LFI were measured in Gp I and Gp II subjects (n=514; 272 males [mean age 34.30] and 242 females [mean age 37.52]). The cutoffs for SMI, HGS, CSUT, and LFI were <27.72 cm2/m2, <25.63 kg, >10 s, and >3.49, respectively for healthy males. The corresponding cutoffs for healthy females were <24.4 cm2/m2, <16.7 kg, > 10 s, and >3.68, respectively. CONCLUSIONS: We derived gender-specific cutoffs for SMI, HGS, CSUT, and LFI from healthy adult Indian population, which can be used to detect sarcopenia and frailty among patients with liver diseases, as well as other conditions.

Medical Biostatistics as a Science of Managing Medical Uncertainties.

Biostatistics is generally understood as the branch of statistics that deals with data relating to biological processes. While this remains the core of biostatistics activities, medical biostatistics has additional features. It is rarely realized that the ultimate function of medical biostatistics is to manage medical uncertainties, particularly those that are data based. We propose to define medical biostatistics as the science of managing empirical uncertainties in health and medicine. This definition describes the subject more appropriately and has the potential to put it on a pedestal it deserves because of its focus on medical aspects. This note provides the rationale for this proposal.

Effectiveness of convalescent plasma in Indian patients with COVID-19.

BACKGROUND: Convalescent plasma (CP) is being used as a treatment option in hospitalized patients with COVID-19. Till date, there is conflicting evidence on efficacy of CP in reducing COVID-19 related mortality. OBJECTIVE: To evaluate the effect of CP on 28-day mortality reduction in patients with COVID-19. METHODS: We did a multi-centre, retrospective case control observational study from 1st May 2020 to 31st August 2020. A total of 1079 adult patients with moderate and severe COVID-19 requiring oxygen, were reviewed. Of these, 694 patients were admitted to ICU. Out of these, 333 were given CP along with best supportive care and remaining 361 received best supportive care only. RESULTS: In the overall group of 1079 patients, mortality in plasma vs no plasma group was statistically not significant (22.4% vs 18.5%; p = 0.125; OR = 1.27, 95% CI: 0.94--1.72). However, in patients with COVID-19 admitted to ICU, mortality was significantly lower in plasma group (25.5% vs 33.2%; p = 0.026; OR = 0.69, 95%CI: 0.50-0.96). This benefit of reduced mortality was most seen in age group 60 to 74 years (26.7% vs 43.0%; p = 0.004; OR = 0.48, 95% CI: 0.29-0.80), driven mostly by females of this age group (23.1% vs 53.5%; p = 0.013; OR = 0.26, 95% CI: 0.09-0.78). Significant difference in mortality was observed in patients with one comorbidity (22.3% vs 36.5%; p = 0.004; OR = 0.50, 95% CI: 0.31-0.80). Moreover, patients on ventilator had significantly lower mortality in the plasma arm (37.2% vs 49.3%; p = 0.009; OR = 0.61, 95% CI: 0.42-0.89); particularly so for patients on invasive mechanical ventilation (63.9% vs 82.9%; p = 0.014; OR = 0.37, 95% CI: 0.16-0.83). CONCLUSION: The use of CP was associated with reduced mortality in COVID-19 elderly patients admitted in ICU, above 60 years of age, particularly females, those with comorbidities and especially those who required some form of ventilation.

Reporting of Basic Statistical Methods in Biomedical Journals: Improved SAMPL Guidelines.

Statistical methods have become an essential component of all empirical biomedical research. Science requires that these methods are fully reported with complete accuracy so that the evidence base could be fully appraised for validity, reliability, and generalizability. To meet this objective, Statistical Analyses and Methods in Published Literature (SAMPL) guidelines have been prepared for statistical reporting in biomedical publications. This communication proposes substantial improvement of these guidelines to make them more comprehensive, organized, compact, and easier to adopt.

Evaluation of quality of multivariable logistic regression in Indian medical journals using multilevel modeling approach.

BACKGROUND: Availability of user-friendly statistical software has increased the application of multivariable logistic regression (MLR) in the medical journal many fold. The reporting quality in terms of checking assumptions, model building strategies, proper coding, and report format need proper care and attention to communicate correct and reliable model results. OBJECTIVE: The objective of this article is to evaluate the quality of MLR article based on 10-point well establish criteria and to study the factors that may influence the quality. METHODS: Study included PubMed indexed Indian medical journals as on March 2010 and published at least ten original articles that applied MLR during 10 years was included in the study. Multilevel modeling was applied to assess the role of journal and article attributes on MLR quality. RESULTS: Twelve out of 39 Indian PubMed indexed journals fulfilled the inclusion criterion. Of a total 5599 original articles in these journals, 262 (4.68%) applied MLR in their study. Conformity of linear gradient assumption for continuous covariate was the least fulfilled criterion. One-third of the MLR articles involved statistician or epidemiologist as co-author, and almost same number of MLR articles' first author was from outside India. The trend of 10-point criteria remained consistent although the number of MLR articles increased over the period. The average quality score was 3.78 (95% confidence interval: 2.97-4.60) out of a possible 10. Larger sample size, involvement of statistician as co-author, non-Indian as the first author, and use of SAS/STATA software increased the quality of MLR articles. CONCLUSIONS: The quality of MLR articles in Indian medical journals is lagging behind as compared to the quality of MLR articles published from the United States and Europe medical journals. Joint effort of editors, reviewers, and authors are required to improve the quality of MLR in Indian journals so that the reader gets the correct results.

Demystifying LMS and BCPE methods of centile estimation for growth and other health parameters.

Lambda-Mu-Sigma and Box-Cox Power Exponential are popular methods for constructing centile curves but are difficult to understand for medical professionals. As a result, the methods are used by experts only. Non-experts use software as a blackbox that can lead to wrong curves. This article explains these methods in a simple non-mathematical language so that medical professionals can use them correctly and confidently.

Receiver operating characteristic (ROC) curve for medical researchers.

Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous or on ordinal scale (minimum 5 categories). This is an effective method for assessing the performance of a diagnostic test. The aim of this article is to provide basic conceptual framework and interpretation of ROC analysis to help medical researchers to use it effectively. ROC curve and its important components like area under the curve, sensitivity at specified specificity and vice versa, and partial area under the curve are discussed. Various other issues such as choice between parametric and non-parametric methods, biases that affect the performance of a diagnostic test, sample size for estimating the sensitivity, specificity, and area under ROC curve, and details of commonly used softwares in ROC analysis are also presented.