Community-Acquired Pneumonia, Endotypes and Phenotypes (NACef): Prospective, observational cohort study of Translational Medicine

Abstract

Community-Acquired Pneumonia (CAP) remains a prominent infectious process associated with elevated in-hospital morbidity and mortality rates. Through the exploration of phenotypes, endotypes, and biomarkers, it becomes feasible to identify individuals at a heightened risk of adverse outcomes from CAP. The dataset encompasses clinical information from 768 patients diagnosed with CAP at Clinica Universidad de La Sabana, Colombia. Clinical data encompasses Baseline Clinical Data, In-hospital follow-up and data after hospital discharge. This repository has been utilized within the framework of a prospective research study, involving an observational cohort conducted in the domain of translational medicine. The dataset offers the opportunity for diverse statistical analyses, contributing to an enhanced comprehension of CAP, Endotypes, and Phenotypes. Moreover, this dataset proves valuable in the context of educational initiatives.

Background

Community-acquired pneumonia (CAP) is an infectious process of the lower respiratory tract that currently continues to be the main cause of hospitalization and in-hospital mortality worldwide [1]. Advances in microbiological isolation techniques have facilitated a more precise characterization of the frequently implicated microorganisms in pneumonia development. Simultaneously, there is an increased awareness of a broader spectrum of microorganisms worldwide, exhibiting resistance patterns to multiple medications. This limitation complicates the selection of an effective antibiotic therapy for individual patients. Nonetheless, ongoing studies explore biochemical modifications of existing antibiotics, seeking effects beyond bactericidal action, aiming for an immunomodulatory impact [2]. Antibiotic treatment has classically been based on the severity of presentation and the area of attention required. Within the process that leads to the development of pneumonia, whether community-acquired pneumonia (CAP) or another type; It has been established that even from the moment of birth through vertical transmission through the birth canal, the upper airway is colonized and this balance that exists between the colonization of commensal germs, their growth, destruction and predominance will determine the development of pathologies. infectious and even allergic as has been demonstrated in asthma [3].

The role played by the bacterial composition in the intestine-lung axis has been shown to be decisive in pulmonary infectious processes, and particularly the dominance of some type of germ may even determine the risk of exacerbations, as has been seen with colonization by Streptococcus in nasopharynx and exacerbation of asthmatic pathology. Similarly, in pneumococcus infections, it has been shown that the conformation of the bacterial flora, and the dominance, for example, of the pneumococcus in the gastrointestinal tract in a murine model decreases levels of Tumor Necrosis Factor (TNF) and interleukin 10 (IL-10) causing a decrease in the phagocytic capacity of alveolar macrophages [4].

The importance of analyzing phenotypes and biomarkers arises from the need to comprehend the underlying mechanisms of pneumonia. Despite advancements in knowledge, patients hospitalized for pneumonia still face mortality despite receiving adequate treatment and optimal general medical care. Consequently, identifying endotypes, phenotypes, and biomarkers becomes crucial for identifying individuals at the highest risk of CAP-related mortality. This database aims to collect clinical and paraclinical information from patients with community-acquired pneumonia and to delineate the most common outcomes in this population, ultimately contributing to the enhancement of clinical outcomes.

The role played by the bacterial composition in the intestine-lung axis has been shown to be decisive in pulmonary infectious processes, and particularly the dominance of some type of germ may even determine the risk of exacerbations, as has been seen with colonization by Streptococcus in nasopharynx and exacerbation of asthmatic pathology. Similarly, in pneumococcus infections, it has been shown that the conformation of the bacterial flora, and the dominance, for example, of the pneumococcus in the gastrointestinal tract in a murine model decreases levels of Tumor Necrosis Factor (TNF) and interleukin 10 (IL-10) causing a decrease in the phagocytic capacity of alveolar macrophages [4].

The importance of analyzing phenotypes and biomarkers arises from the need to comprehend the underlying mechanisms of pneumonia. Despite advancements in knowledge, patients hospitalized for pneumonia still face mortality despite receiving adequate treatment and optimal general medical care. Consequently, identifying endotypes, phenotypes, and biomarkers becomes crucial for identifying individuals at the highest risk of CAP-related mortality. This database aims to collect clinical and paraclinical information from patients with community-acquired pneumonia and to delineate the most common outcomes in this population, ultimately contributing to the enhancement of clinical outcomes.

Methods

This is a prospective, translational (T0-T2), observational cohort study focused on patients diagnosed with community-acquired pneumonia admitted to a third-level hospital in Bogotá, Colombia. The inclusion criteria were the following: Patients older than 18 years of age with a clinical picture compatible with pneumonia according to the ATS/IDSA definition [5] and who had been admitted in the last 24 hours. Exclusion criteria for participation were inability to give consent, patients with active tuberculosis, diagnostic doubt, or greater likelihood of a differential diagnosis. 768 patients were included in which sputum/bronchoalveolar lavage, urine, and blood samples were drawn at admission, the next 72 hours after admission, and at discharge.

After sample recollection, patients were characterized based on clinical severity scores (CURB-65, PSI, and SOFA). Samples were microbiological and immunologically described using western blot, ELISA, and real-time PCR.

To ensure data privacy, a script was created to anonymize the dataset. The script converts all specified date columns to datetime objects and computes the difference in days for each date relative to the admission date. To further anonymize the admission date, a base date far in the future is selected, and a significant offset is added to simulate future dates, effectively masking the actual admission dates. As a result, the final dataset, now containing relative days and anonymized dates, preserves the temporal structure of the data while ensuring confidentiality.

Data Description

The database "NACef_2024.csv" was recorded in REDCap (Research Electronic Data Capture) Software [6] created by Vanderbilt University and it contains patient data, including demographic and clinical data. The database consists of 768 records that met the inclusion criteria, also including a list of identification variables, which were anonymized, as well as the admission date to the hospital, COVID-19 diagnostic status, and whether the patient was discharged or deceased during hospitalization. The following is a comprehensive list of variables contained in the database, each of which is further explained in the "NACef_Data_Dictionary.csv" document. This data is provided in a CSV format, which makes it compatible with a variety of data analysis tools and software packages.

Identification/ Baseline

• record_id: Patient ID number, unique identifier of the patient
• age: Patient age in years
• gender: Gender
• height: Height in cm
• weight: Weight in kg
• bmi: Body mass index
• admission_date: Admission date
• health_work: Indicates whether the patient works in the health-care field
• rural_work: Indicates whether the patient works in livestock or meat industry
• geriatric_home: Indicates whether the patient lives in a geriatric home
• postramiento: Indicates whether the patient lives in a state of prostration
• living_space: Indicates whether the patient lives in overcrowding
• vac_influenza: Patient has history of previous influenza vaccination
• date_vac_influenza: Date of previous influenza vaccination
• vac_neumococo: Patient has history of previous pneumococcus vaccination
• vac_neumo_type: Name of pneumococcus vaccination
• date_vac_neumo_2: Date of second pneumococcus vaccination
• prev_infec: Indicates whether the patient has history of respiratory tract infections in the previous12 months (including pneumonia and/or COPD exacerbation)
• num_prev_infec: Number of respiratory tract infections in the previous 12 months
• date_infec: Date of previous respiratory tract infections
• urg_12_month: Indicates whether the patient has visited the emergency department in the previous 12 months
• num_urg: Number of emergency department visits in the previous 12 months
• date_hosp: Date of previous hospitalization in the emergency department
• ab_12month: Indicates whether the patient has received antibiotics in the previous 12 months
• prev_ab_num: Number of occasions the patient has received antibiotics in the last 12 months
• prev_ab_date: Date of previous antibiotics cycle
• prev_ab_days: Number of days using antibiotics
• ab_prev: Name of the antibiotic that the patient received
• comorbid: Comorbidities at the moment of admission
• copd: Indicates whether the Patient has a history of Chronic Obstructive Pulmonary Disease
• gold: Current patient's GOLD classification
• ant_taba: Indicates whether the Patient has History of smoking
• icu: Indicates Whether the Patient was admitted to the ICU at admission
• int_24h: Interventions received in the first 24 hours after hospital admission
• ant_mdrd: Indicates whether the patient has had an Infection by MRSA, Pseudomonas, BLEE in the previous year
• mdr: Type of infection or colonization in the previous year
• sosp_covid: Indicates whether the patient has been diagnosed with COVID-19 in the current hospital visit
• vac_covid: indicates whether the patient has received any COVID vaccine
• ab_24h: Name of the antibiotics that were administered in the first 24 hours after admission
• criteria: Indicates whether the patient has any severity criteria according to IDSA/ATS
• minor_criteria: Minor criteria for pneumonia that the patient meets
• mayor_criteria: Major criteria for pneumonia that the patient meets
• sev_criteria: Indicates whether the patient meets severity criteria according to IDSA/ATS (1 major or 3 minor)
• admission_sofa: Patient's SOFA scale at admission
• admission_curb: Patient's CURB 65 at admission
• admission_psi: Patient's PSI at admission

In-hospital follow-up

• hosp_stay: Hospital setting in which the patient is currently hospitalized
• ab_empiric: Indicates whether the patient received empiric antibiotic
• ab_empiric_2: Name of the empiric antibiotic that the patient received
• ab_conjug: Indicates whether the patient received more than one empiric antibiotic
• ab_conjug2: Name of the second empiric antibiotic
• isolated_micro: Isolated microorganism from patient's samples
• coinfection: Indicates whether the patient presented coinfection by another microorganism
• coinfection_microorg: Name of the microorganism that generated co-infection
• res_pattern: Antibiotic resistance pattern
• res_pattern_2: Resistant Pattern of an additional microorganism
• sofa_72: Patient's SOFA scale after 72 hours of admission

Hospital discharge

• etio_pneumo: Indicates whether an ethiological pathogen was identified
• etio_pneumo_patogen: Name of the pathogen identified
• 2_rt_pcr_covid: Indicates whether a second RT-PCR test was performed to confirm the diagnosis of COVID-19.
• extub: Indicates whether the patient was extubated
• date_extub: Date of extubation
• traqueost: Indicates wheter the patient required tracheostomy
• date_traqueost: Date of tracheostomy
• icu_discharge: Indicates if the patient was discharged from ICU
• discharge_date: Date of ICU discharge
• live_discharge: Indicates wheter the patient was discharged alive
• icu_death: Patient died in the ICU
• gen_hosp_death: Patient died during general hospitalization
• main_diagnosis: Patient's main diagnosis
• days_ab: Number of days with antibiotics
• ventilation: Indicates whether the patient required mechanical ventilation during ICU
• ventilation_type: Type of mechanical ventilation that the patient received
• clinical_resp: Clinical response to mechanical ventilation
• failure_cause: In case the clinical response was "Failure", what was the cause of the failure?
• treatm_fail_criteria: In case the clinical response was "Failure", which criteria of treatment failure did the patient meet?
• In case the clinical response was "Failure", which criteria of Treatment failure did the patient meet?
• secund_infec: Indicates which secondary infection the patient acquired at the same hospital visit
• date_non_pulmonar: Date of the non-pulmonary infection diagnosis
• cv_comp: Patient had any cardiovascular complication
• cv: Name of the cardiovascular complication
• another_study: Patient was participating in another study
• which_study: Indicates at which study the patient was participating

Usage Notes

Please carefully review the annotation guidelines before using this dataset. Users interested in replicating or extending the analysis of the collected data are encouraged to consult the GitHub repository associated with this project [5]. The code used in the analysis is available in this repository, allowing researchers to review, modify, and utilize the techniques and methods employed in the study. To access the code, simply visit the link provided in the GitHub Repository [5] section and explore the available files and scripts. If you have any questions or need more information on how to use the code, feel free to contact the project team.

Given the observational, translational, and prospective design, a repository of valuable data has been established for future investigations. The amassed dataset encompasses both initial clinical information and molecular analyses, thereby augmenting the caliber and breadth of data accessible for subsequent studies. It is important to acknowledge that the single-center nature of this data collection may pose a limitation for research endeavors aiming to encompass a broader population.

Release Notes

1.0.0. Initial release of the database.

Ethics

Regarding the ethics considerations, this study is thought to have minimum risk given the sample collection process with the requirement of informed consent from every patient in order to be included in the study. The protocol was reviewed and approved by the IRB: "Subcomisión de Investigación y Ética en Investigación sobre Calidad Científica e Integridad Ética" (Clínica Universidad de La Sabana's Ethics Committee) during session Number 021 held on February 4th, 2020.

Acknowledgements

Universidad de la Sabana and Clinica Universidad de La Sabana.

Conflicts of Interest

The authors have no conflicts of interest to declare.

References

1. Postma DF, Van Werkhoven CH, Van Elden LJR, Thijsen SFT, Hoepelman AIM, Kluytmans JAJW, et al. Antibiotic treatment strategies for community-acquired pneumonia in adults. N Engl J Med. 2015;372(14):1312–23. doi:10.1056/NEJMoa1406330.
2. Altenburg J, De Graaff CS, Van Der Werf TS, Boersma WG. Immunomodulatory effects of macrolide antibiotics - Part 1: Biological mechanisms. Respiration. 2010;81(1):67–74. doi:10.1159/000320319.
3. Pulvirenti G, Parisi GF, Giallongo A, Papale M, Manti S, Savasta S, et al. Lower airway microbiota. Front Pediatr. 2019;7:393. doi:10.3389/fped.2019.00393.
4. McAleer JP, Kolls JK. Contributions of the intestinal microbiome in lung immunity. Eur J Immunol. 2018;48(1):39–49. doi:10.1002/eji.201646721.
5. Metlay JP, Waterer GW, Long AC, Anzueto A, Brozek J, Crothers K, et al. Diagnosis and treatment of adults with community-acquired pneumonia. An official clinical practice guideline of the American Thoracic Society and Infectious Diseases Society of America. Am J Respir Crit Care Med. 2019 Oct 1;200(7):e45–67.
6. Vanderbilt University, National Institutes of Health. Research electronic data capture (REDCap) [Internet]. Available from: https://projectredcap.org/
7. TS-ID-CCM/NACef: NACef (Community-Acquired Pneumonia, Endotypes and Phenotypes) is a single-center dataset that includes clinical information from 768 patients diagnosed with Community-Acquired Pneumonia (CAP) at Clinica Universidad de La Sabana, Colombia. [Internet]. [cited 2024 Mar 5]. Available from: https://github.com/TS-ID-CCM/NACef